Pomegranate is widely cultivated across China, and the phenolics in its peel are principal components associated with health benefits. Ultra-high performance liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (UHPLC-QTOF-MS) and ultra-performance liquid chromatography coupled to a triple quadrupole mass spectrometer (UPLC-QQQ-MS) were used in this study, aiming at profiling the total phenolic composition in pomegranate peel from nine selected cultivars in 7 production areas. Sixty-four phenolic compounds were identified or annotated, and 23 of them were firstly reported in pomegranate peel. Principal component analysis (PCA) plots show differences and similarities of phenolics among nine cultivars. Furthermore, 15 phenolic compounds were quantified with the standards, and punicalagin, ellagic acid, gallocatechin, punicalin, catechin, and corilagin were found to be dominant. Punicalagin weighed the highest content (28.03–104.14 mg/g). This study can provide a deeper and more detailed insight into the phenolic composition in pomegranate peel and facilitate the health-promoting utilization of phenolics. Punica granatum L.) has been cultivated with a wide geographical global distribution, namely, China, India, Russia, Iran, Uzbekistan, Afghanistan, Spain, Italy, Greece, Morocco, and America  experiments  with optimized conditions under MRM mode. With nine cultivars to be investigated and the use of UHPLC-QTOF-MS, more phenolic compounds are expected to be discovered or understood in this study.Methanol , ethanol , acetonitrile , and formic acid (LC/MS grade) were all purchased from Thermo Fisher Scientific Technology Co., Ltd. . Standard compounds, namely, punicalagin, punicalin, corilagin, ellagic acid, gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, epigallocatechin gallate, kaempferol-3-O-glucoside, isoquercitrin, luteolin-7-O-glucoside, naringenin-7-O-glucoside, and rutin, were all purchased from Yuanye Biotechnology Co., Ltd. .There were seven main production areas in China and we selected the cultivars from all seven production areas with the most representativeness. Nine cultivars of pomegranate fruit, Red agate (RA) from Huaiyuan County , Tunisian Soft Seed (TSS) from Yingyang city , Sweet With Green Seed (SGS) from Mengzi County , Green Peel Soft Seed (GPSS) from Huili County , Green Peel (GP) from Zaozhuang city , Net Skin Sweet (NSS) from Lintong district , Piyaman (PYM) from Hetian prefecture , Acidic Pomegranate (AP) from Kashgar prefecture , Sweet Pomegranate (SP) from Kashgar prefecture  were collected. The analysis of phenolic composition among nine cultivars was performed using the UHPLC-QTOF-MS  and the method of Xu et al.  was refePreliminary separation was performed by UHPLC at the flow rate of 0.4 ml/min with the column  maintained at 40°C and samples maintained at 4°C. Mobile phase A was 0.2% formic acid aqueous solution and B was acetonitrile. The solvent gradient was as followed: 0–11.50 min, 5–30% B; 11.50–11.51 min, 30–100%; 11.51–15.00 min, 100–100% B; 15.00–15.01 min, 100–5%; and 15.01–18.00 min, 5–5% B. The injection volume was 2 μl for all samples.m/z range of 50–1,200 and the acquisition rate of 2 spectra/s. The quality control (QC) sample, a mixture of aliquots from every sample, was inserted into the queue every 5 samples to ensure the stability and repeatability of the system. Reference ions were 121.050873 and 922.009798 for positive ion mode, and 112.9855 and 1033.9881 for negative ion mode.The MS conditions for positive and negative ion modes were the same in gas temperature (325°C), drying gas flow (7 L/min), spray voltage (35 psi), sheath gas temperature (350°C), sheath gas flow (11 L/min), and fragmentation voltage (380 V). The capillary voltage for positive ion mode was 3,500 V and for negative ion mode was 3,000 V, while the nozzle voltage for positive ion mode was 0 and positive ion mode was 1,500 V, respectively. Data acquisition was performed by TOF MS mode with the The MS/MS was performed under the auto MS/MS mode with QTOF only. The mass acquisition range was 30–1,200 with an acquisition rate of 4 spectra/s. The four highest responding parent ions were selected to be fragmented in every acquisition cycle. Separately, the samples of TSS cultivar were acquired three times under the collision energy of 10, 20, and 40 eV with other conditions the same as those under MS conditions.The quantification of main phenolic compounds was performed using a UPLC-QQQ-MS .Chromatographic separation was firstly carried out using a UPLC at the flow rate of 0.3 ml/min with the column  maintained at 40°C and samples maintained at 8°C. Mobile phase A was 0.3% formic acid aqueous solution and B was acetonitrile. The solvent gradient was as followed: 0–10.00 min, 5–27.5% B; 10.00–12.50 min, 27.5–55% B; 12.50–13.50 min, 55–100% B; 13.50–16.50 min, 100–100% B; 16.50–16.51 min, 100–5% B; and 16.51–20.00 min, 5–5% B. The injection volume was 2 μl for all samples.R2 > 0.999.Under both MRM mode and negative ion mode, the optimization for quantification, especially the parameters of cone voltage and collision energy, was performed by both Intellistart and manual tuning with desolvation gas temperature of 500°C, desolvation gas flow of 1,000 L/h, and capillary voltage of 2.0 kV. All standard compounds were dissolved in appropriate solvents shown in  and dilum/z.Based on the reference of Xu et al.  with somhttps://www.metaboanalyst.ca/)   and SIMCStatistical analysis was performed by SPSS Statistics 25.0 for the one-way ANOVA, and the bubble plot and the waterfall plot were performed by Origin 2019b.m/z, and every colored bubble, graded by intensity value with log transformed, represented one feature of m/z appearing at a specific time. All the colored bubbles distribution in the figure reflected the case of acquired data. Most bubbles were yellow and orange under the positive mode, while they were orange and green or even blue under the negative mode, indicating that the samples had a better response intensity under the negative mode.m/z and retention time were imported for further identification. Finally, a total of 64 compounds were annotated by UHPLC-QTOF-MS  and 5 other phenolic compounds  were identical to this study. Ambigaipalan et al. , increase the accuracy of m/z and fragments (compared with LC-MS/MS with low resolution), improve the range of detected compounds, strengthen the confirmation of main phenolic compounds with the standards, and save the time and solvents.The phenolic composition was one of the popular fields in pomegranate peel investigations, and differences were shown in references for compounds annotation or identification. El-Hadary and Ramadan  identifin et al.  analyzedn et al. . Abdullan et al.  did the It is noteworthy to have an insight into these compounds. Punicalagin and punicalin are ellagitannins . For ellTwo reasons could explain the differences of phenolic compounds in classes from pomegranate peel. One reason was mainly dependent on the characteristics of cultivars cultivated under different natural conditions. The cultivars in this study covered all the main production areas in China, which are various and different from the cultivars in references. Apart from thymol and olivetonide, the other 21 compounds annotated for the first time were all flavonoids in this study. The various derivatives of the initial phenylpropanoid scaffold play important roles in the plant, such as structural integrity, reproduction, UV photoprotection, and internal regulation of plant cell physiology and signaling . Nine sePeak areas of the compounds  were extIn the PCA score plot, the points in the same color represented the repeated samples for one cultivar. The separations were observed among the cultivars, and the distance among them symbolized the degree of similarity. AP (from Kashgar prefecture) and PYM (from Hetian prefecture) were very close to each other. However, SP, also from Kashgar prefecture, was far from AP or PYM. The three cultivars were all from Xinjiang Uygur Autonomous Region. The results showed that AP and PYM were more similar to each other in phenolic composition, while SP was significantly different from the other two cultivars. RA, GP, GPSS, NSS, and TS were in a big cluster, indicating that these five cultivars were relatively similar in phenolic composition. The unique one was SGS, from Mengzi County, Yunnan Province. It was very far from any other cultivars in the scores plot, showing its specificity in phenolic composition. The relation between cultivars and phenolic compounds was displayed by the combination of the loading plot and the PCA score plot. AP and PYM showed high positive scores along PC1 and the observations in the loading plot indicated that the two cultivars could be positively associated with maritimetin-6-O-glucoside, eriodictyol-7-O-glucoside, pelargonidin-3-O-glucoside, eriodictyol, pelargonidin-3,5-di-beta-D-glucoside, ellagic acid, β-punicalagin, cyanidin-3,5-di-O-glucoside, α-punicalagin, gallic acid, granatin B, and dihydrokaempferol. SGS was highly associated with kaempferol-3-glucoside-3”-rhamnoside, fisetin, kaempferol-3-O-glucoside, kaempferol-3-O-arabinoside, cyanidin-3-O-alpha-arabinoside, luteolin 4'-O-glucoside, hyperoside, bracteatin, quercetin-3-O-xyloside, isoquercitrin, delphinidin 3-galactoside, and rutin. All these phenolic compounds related to SGS were flavonoids and this fact could account for the unique separation in PCA score plot of SGS. In addition, it could be deduced that the cultivar from Yunnan Province had the potential for the use of flavonoids. Li et al.  investigFifteen phenolic compounds are quantified by UPLC-QQQ-MS under negative ion mode with the optimized condition of cone voltage and collision energy, as shown in supporting information As shown in Li et al.  investigSixty-four phenolic compounds in pomegranate peel from nine selected cultivars were identified or putatively annotated, and 23 of them were firstly annotated. Thymol and undulatoside A were not detected in all cultivars. Cultivars were well-separated by PCA. For quantification, punicalagin, ellagic acid, gallocatechin, punicalin, catechin, and corilagin dominated among all the phenolic compounds. Punicalagin possessed the highest content with the range from 28.03 to 104.14 mg/g. These results confirmed that the nine cultivars and the use of UHPLC-QTOF-MS were helpful for the discovery and understanding of more phenolic compounds in pomegranate peel. The variety of phenolic compounds revealed the potential of these valuable compounds and the results could be used as the database for pomegranate peel. For absolutely quantified compounds with relatively high contents, more attention should be paid, and further investigations and developments of them are still needed such as extraction, bioactivity, or function.The original contributions presented in the study are included in the article/GM and LX contributed to methodology, investigation, and formal analysis. LX, XL, and ZX contributed to reviewing and editing. GM did data curation and original draft writing. XL helped in supervision and resources. All authors contributed conceptualization to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Cancer and its treatment pose challenges that affect not only patients but also their significant others, including intimate partners. Accumulating evidence suggests that couples’ ability to communicate effectively plays a major role in the psychological adjustment of both individuals and the quality of their relationship. Two key conceptual models have been proposed to account for how couple communication impacts psychological and relationship adjustment: the social-cognitive processing (SCP) model and the relationship intimacy (RI) model. These models posit different mechanisms and outcomes, and thus have different implications for intervention. The purpose of this project is to test and compare the utility of these models using comprehensive and methodologically rigorous methods. Aims are: (1) to examine the overall fit of the SCP and RI models in explaining patient and partner psychological and relationship adjustment as they occur on a day-to-day basis and over the course of 1 year; (2) to examine the fit of the models for different subgroups ; and (3) to examine the utility of various methods of assessing communication by examining the degree to which baseline indices from different measurement strategies predict self-reported adjustment at 1-year follow up. The study employs a longitudinal, multi-method approach to examining communication processes including: standard self-report questionnaires assessing process and outcome variables collected quarterly over the course of 1 year; smartphone-based ecological momentary assessments to sample participant reports in real time; and laboratory-based couple conversations from which we derive observational measures of communicative behavior and affective expression, as well as vocal indices of emotional arousal. Participants are patients with stage II-IV breast, colon, rectal, or lung cancer and their spouses/partners, recruited from two NCI-designated comprehensive cancer centers. Results will be published in scientific journals, presented at scientific conferences, and conveyed to a larger audience through infographics and social media outlets. Findings will inform theory, measurement, and the design and implementation of efficacious interventions aimed at optimizing both patient and partner well-being. Patients with cancer often report disease- and treatment-related side effects including fatigue, pain, and cognitive impairment . Emotionand partners to the illness experience  model, lack of cognitive processing is the primary mechanism linking communication difficulties to psychological distress. Individuals who perceive their partner as unreceptive to discussing cancer-related concerns talk less about them with their partner, reducing cognitive processing needed to assimilate and accommodate the cancer experience into their world view, leading to increased psychological distress . The prohave demonstrated mediating paths, for example, that mutual avoidance is associated with greater psychological distress through decreased intimacy . Similar to facial expressions, features of the voice contain significant information about internal emotional experiences  women with a higher f0 overall displayed more behaviors likely to elicit positive support (such as stating their needs clearly), and (b) women displayed fewer adaptive support-eliciting behaviors when their partners exhibited higher overall f0. These findings suggest that it may be adaptive for female patients to experience and express emotional arousal during conversations of cancer-related concerns, but that high emotional arousal on the part of their partners may interfere with this process. The findings, while preliminary, suggest that this is a promising approach with the potential to inform measurement and theory of the processes through which communication affects adaptation to cancer. In addition to examining overall levels of emotional arousal, this approach lends itself to a more detailed examination of how arousal evolves across the interaction, providing further insights into communication-related processes.Another promising objective approach to studying couples’ communication is the assessment of expressed emotional arousal during couples’ interactions eriences . f0 is ted pitch , and is ed pitch . It is aed pitch . To dateThe purpose of the present project is to provide a comprehensive and methodologically rigorous evaluation of both the SCP and RI models, including delineation of mediators (how they work) and moderators (for whom they work). Specific aims are: (1) To examine the overall fit of the SCP and RI models in explaining patient and partner psychological and relationship adjustment as they occur on a day-to-day basis and over the course of 1 year. (2) To examine the fit of the models for different subgroups: males vs. females, and patients vs. partners. (3) To examine the utility of the various methods of assessing communication  by examining the degree to which each of these baseline indices predicts self-reported psychological and relationship adjustment at 1-year follow up.via actor effects, and that the RI model will predict individual psychological adjustment via actor effects and relationship adjustment via both actor and partner effects (aim 1). It is also likely that the strength of these associations will vary between individuals or subgroups (aim 2). While there are numerous variables that could potentially moderate these mediating effects, based on the literature and potential clinical relevance, we chose to examine gender and role (patient vs. spouse). With regard to gender, stronger positive associations between perceived social constraints and distress have been found among male vs. female cancer patients  a dThe study design is longitudinal, with repeated questionnaire-based assessments at baseline and 4, 8, and 12 months post-baseline to capture quarterly reports across a 1-year follow-up period without undue participant burden. Patients are recruited from the Duke Cancer Institute in Durham, NC and the Seattle Cancer Care Alliance in Seattle, WA, two NCI-designated comprehensive cancer care centers. Inclusion criteria for patients are:•age 18 or older•stage II-IV breast, colon, rectal, or lung cancer•currently receiving or having received a form of systemic therapy (or hormone therapy for breast cancer) within 2 years of diagnosis of current stage•life expectancy of at least 6 months per primary oncologist•ability to speak and comprehend English•being married or in a committed, cohabiting relationship with a same- or opposite-sex partnerWe chose to focus on breast, colorectal, and lung cancers because they are relatively common solid tumor cancers and because, with the exception of breast cancer, occur frequently in both males and females.Inclusion criteria for partners are:•age 18 or older•ability to speak and comprehend English•being married to or in a committed, cohabiting relationship with the patientExclusion criteria are provider non-approval (patients), cognitive impairment prohibiting completion of study assessments (patients and partners), and logistical constraints preventing participation (patients and partners).Patients identified as meeting initial medical inclusion criteria per medical records are sent a study brochure and letter signed by their primary oncologist introducing the study and informing them that they will be contacted by a research team member by phone (with opt-out instructions in case they do not want to be contacted). Those contacted and deemed fully eligible based on further screening confirmation during the recruitment phone call  are provided a detailed description of the study aims, procedures, risks and benefits, and probed for understanding. If the patient decides that they would like to participate, the research team member obtains permission to speak with the partner. If permission is granted, the research team member describes the study aims, procedures, risks and benefits to the partner. If a given patient declines, their partner is not contacted. If both dyad members agree to participate, the in-person baseline assessment visit is scheduled. This visit commences with a formal face-to-face consent process  and signing of consent documents.via REDCap at 4, 8, and 12 months post-baseline. Each activity is described in turn below. Participation is estimated at approximately 8 h total: a 2-h baseline assessment visit, twice-daily EMA surveys 5–10 min each for 14 days, and 3 follow-up questionnaires (30–45 min each).Study participation lasts for 12 months and is depicted in Data are extracted from medical record summary, oncology, and clinic visit notes to obtain screening and follow-up information: cancer site, current cancer stage, confirmation of current cancer stage within 2 years, current or previous therapies (systemic and/or hormone) and surgeries, primary oncologist, gender, primary language, year of birth, and marital/partner status.via email for all follow-up questionnaire administrations.All questionnaires are administered using Research Electronic Data Capture (REDCap), a secure web-based tracking and on-line data acquisition system . Our proSupplemental measures added after the commencement of enrollment and designed to answer ancillary questions include the Revised Adult Attachment Scale , the ParWe developed a study-specific smartphone application using the LifeData platform. The app is free to use and compatible with both iOS and Android devices. Participants are asked to download the app during the baseline laboratory session, with guidance from an experimenter. They are also given a user instruction and FAQ document to take home and refer to as needed; study contact information is included for assistance.Participants use their own smartphones to complete the EMA unless they either do not own one or own a device with a different operating system. Those without an appropriate smartphone borrow a study iPod Touch device for this activity. The app interface across these systems is comparable.EMA is to commence following the laboratory-based visit. Participants receive a push notification to complete EMA twice daily over the course of 14 days: once at 12:00 p.m. and once at 8:00 pm, both times within a 2-h active window. Push notifications begin immediately after app download, so depending upon the time of day of the baseline assessment, participants begin receiving notifications either that afternoon or that evening. This timing, frequency, and duration was based on pilot work conducted with a non-medical sample . If partAt each EMA, patients and partners are asked to answer a series of questions. Branching logic minimizes participant burden. First, participants are asked if they talked to their partner during a given time frame (since awakening for the 12:00 p.m. time point or since the last assessment for the 8:00 p.m. time point). If no, a single item designed to assess reasons for not talking is posed . If yes, questions designed to assess perceptions of the conversation follow. These are listed in Couples are asked to participate in a 15-min cancer-related conversation that is video-recorded for later coding. Separate audio-recordings afford analysis of vocally encoded emotional arousal as described below. To assist participants in selecting topics of discussion for these conversations, we provide a list of cancer-related issues known to be relevant based on past research . These tCouples are asked to sit in chairs placed approximately two feet apart and facing one another at a slight angle. They are instructed to speak to one another vs. the camera, and to allow the conversation to flow as it normally would outside of the research setting.0 baseline for analyses of vocally encoded emotional arousal. In this task, one dyad member is handed a picture and asked to describe what is depicted in it  to their partner. After 1 min, the experimenter hands three pictures to the partner and asks them to select the one that was described. Roles are then reversed with a different set of pictures.Prior to picking the topic and the conversation, couples engage in a modified DiapixUK task  designedTwo independent yet complementary coding systems are used to characterize participants’ communicative behaviors and affective expressions captured during the 15-min couple conversations: (1) the Asymmetric Behavior Coding System  and (2) Coding is being conducted in two waves with separate coding teams for the two systems, each consisting of 4–6 trained raters. For both systems, ratings are made independently by each coder and for each 3-min segment of the 15-min conversation. Separate passes are made for patients and partners. Order of viewing is randomized across coders, with approximately 30% of cases rated by all coding team members to assess inter-rater reliability. Coding teams meet weekly by videoconference to address problematic (highly discrepant) codes or cases.The Asymmetric Behavior Coding System (ABCS) was adapted for this project to measure communicative behavior in the context of conversations regarding cancer. The ABCS consists of 24 behavior codes that load onto four higher-level factors as shown in the upper half of While the ABCS focuses on the content and function of specific communicative behaviors, the Relational Affective Topography System (RATS) focuses on the affective quality of expressions. The RATS involves three steps. First, coders record whether positive emotions, negative emotions, and flat emotions as defined in the lower half of 0) mean from the couple conversations as a marker of expressed emotional arousal during the interaction for each dyad member. Audio recordings are made using Lavalier microphones (Shure BLX88 and Tascam DR-40 Linear PCM recorder), with each microphone targeting one dyad member and recording onto a separate channel, resulting in relative differences in volume depending on who is speaking at a given time. We then extract f0 as a continuous measure across the 15-min conversation in estimated values  for every quarter-second using Praat ; plots are visually inspected for outliers, and observations are removed as needed . All data management procedures are conducted using Stata 16 , the proportion of patient-partner dyads agreeing to participate (50%), disease-specific survival rates, and drop-out over time (30%). Based on power calculations for indirect effects (via measures of cognitive processing and intimacy (Ms) comprising small-to-moderate constituent  associations of r ≥ 0.25, assuming moderate confounding of X→M and M→Y associations (R2 = 0.15).We project a complete-case sample of approximately 264 dyads for the questionnaire portion of the study, in other words, 264 dyads for which  effects  conducteAssociations hypothesized under the SCP and RI models will be examined with structural equation models (SEMs) separately for questionnaire data from T1-T4 and data from the EMA phase, primarily in a longitudinal actor-partner interdependence model (APIM) framework, with patients and partners treated as distinguishable dyad members . To examAll SEMs will be estimated in Mplus 8.7. Bootstrap standard errors for direct and indirect effects will be estimated, except for multilevel SEMs, where a Monte Carlo simulation approach will be used  within an APIM framework, in which measures from both dyad members  are included in each analysis. In these SEMs, a proposed latent factor with 6 indicators  reflecting overall communication will prospectively predict either a cognitive processing (avoidance and intrusive thoughts) composite (under the SCP model) or intimacy (under the RI model), which will, in turn, prospectively predict measures of psychological distress  and relationship adjustment . Prior to estimating the full SEMs for the questionnaire data, a measurement model for the proposed communication factor will be developed and refined using a confirmatory factor analysis approach. The degree of measurement invariance  for this proposed latent factor between patients and partners (and between men and women) will be evaluated in a sequential fashion. Non-invariance at each step in the sequence will be assessed using likelihood ratio tests (with α = 0.05) and changes in CFI (target ≤ 0.01). Potential sources of non-invariance  will be investigated and the measurement model will be modified accordingly .patient communication predicting patient intimacy) and prospective partner  effects  will be estimated along with autoregressive  associations. Mediators and outcomes will be treated as manifest variables in these models. Latent person-level intercept factors will be included to account for random between-person variability in mean levels of model constructs. Within-couple indirect associations of communication and outcomes  via mediators  will be estimated using a product-of-coefficients approach  effects  nested within couples (level 2 units). Each study day observation will have values for patient and partner reports of afternoon communication  as exogenous predictors, afternoon reports of cognitive processing  or intimacy as mediators, and evening reports of psychological distress (POMS TMD composite) or relationship satisfaction as outcomes, all from the same day’s afternoon and evening assessments. In addition, autoregressive associations are included such that afternoon reports of mediators are predicted from the preceding evening’s reports of the same variables  and each evening outcome variable is predicted by the immediately preceding observation on the same variable. See To examine potential differential functioning of the SCP and RI models across gender  and role (patient vs. partner), we will employ two different modeling approaches within an APIM framework. For gender, we will add gender × communication and gender × mediator  terms to the standard “main effects” APIM models described above. For example, we would examine patient gender × patient communication, partner gender × partner communication, patient gender × patient intimacy, and partner gender × partner intimacy terms in a single model. To examine differential functioning by role, we will examine how individual cross-role equality constraints for parallel APIM actor and partner effects  affect model fit and test between-role differences in indirect effects. These models will be supplemented by exploratory analyses examining purely within-person  models to examine indirect effects and model fit in gender and role group subsamples.Finally, to examine how communication measured early in the study predicts questionnaire measures of psychological and relationship adjustment at follow-up, we will estimate separate SEMs (again in an APIM framework), each of which uses a measure derived from each of the three different assessment methods  used in the study. First, we will estimate dyadic SEMs with actor and partner direct effects of early communication on each measure of adjustment at 12-month follow-up, adjusting for the person’s baseline assessment on the corresponding adjustment construct. In models based on questionnaire-measured communication, the general communication latent variable described above will be used. Objectively assessed communication, treated as a manifest variable, will be used in a parallel fashion in dyadic SEMs. Models based on EMA-measured communication will be estimated using two-level dyadic SEMs, with (the between-dyad variability in) repeated assessments of each patient’s and partner’s communication measure predicting psychological or relationship adjustment at follow-up, adjusting for the person’s baseline level on the outcome variable.0 relates to psychological and relationship adjustment will include examination of how the trajectory of change in f0 over the course of the 15-min conversations predicts changes in psychological and relationship adjustment across the 1-year follow-up. First, trajectories of f0 across the conversation will be estimated for each partner by estimating a three-level  multilevel growth curve model where f0 is regressed onto time (within conversation) and time2. Estimates will be generated for each person’s f0 intercept  and effect of time and time2 . These values will be decomposed into within- and between-couple components. STATA will be used to analyze the primary models of interest in which longitudinal psychological and relationship adjustment separately are regressed on gender (as a covariate), role (patient vs. partner), f0 parameters within- and between-couple, wave (from baseline to 12 month), and their interactions. Model influence diagnostics and sensitivity analyses  will be conducted to ensure stability of model results.Analyses examining how fAnalytic plans for the supplemental measures  will be used to examine associations between patient and partner parenting concerns and measures of communication, psychological distress, and relationship adjustment. This will build on findings from prior studies indicating that, among patients, parenting concerns are associated with higher levels of psychological distress .This measure will be used to characterize the sample with regard to financial distress, both overall and as a function of sociodemographic and medical variables (cancer type and stage). We will also examine concordance or lack thereof in financial distress within couples and the trajectory of financial distress over time, from baseline to 1-year follow-up.This measure contains items designed to assess the extent to which the pandemic has caused change in multiple life domains: routines, family income/employment, food access, medical health care access, mental health treatment access, and access to extended family and non-family social supports. Additional items assess personal and family diagnoses of COVID, and perceived severity of pandemic-related stress and stress and discord in the family . We will utilize descriptive statistics to characterize the sample with regard to COVID diagnoses and impacts on the life domains listed above. We will also conduct APIM analyses to examine concurrent intra- and inter-personal associations between pandemic-related stress and self-reported measures of communication (such as holding back) and well-being as measured by the FACT-GP.From the Revised Adult Attachment Scale, we will derive subscales of anxious and avoidant attachment styles for each participant. For analyses involving this measure, we will apply an Actor Partner Interdependence Mediation Model to APIM couples’ self-reported attachment, communication (disclosure and holding back), and physical well-being as measured by the FACT-GP. Indirect associations between a dyad member’s attachment (either anxious or avoidant) and their partner’s physical well-being are proposed as mediated by the dyad member’s own communicative behavior. We will examine model fit as described in the analytic overview above. Direct and indirect associations will be evaluated using 95% bootstrap confidence intervals.To our knowledge, this is the first multi-method longitudinal study of couples’ communication in the context of cancer. Results will provide evidence to enable testing models of how couples’ communication is associated with adjustment to the cancer experience. Our long-term goal is to use these results to design and refine interventions that will improve couples’ communication and relationship functioning, alleviate cancer-related distress, reduce caregiver burden, and optimize patient and partner recovery from the rigors of cancer. Empirical support for the SCP model would indicate that interventions should focus on strategies that enhance cognitive processing. While these could include couple-based interventions that facilitate couples’ discussion of cancer-related issues, they may also include individual interventions that provide the opportunity for cognitive processing through disclosure to supportive others  or in non-social forms such as expressive writing. In contrast, support for the RI model would indicate that couple-based interventions are needed and should focus on enhancing intimacy through strategies to increase partners’ feelings of closeness and caring . Moderation analyses will examine whether these models may differentially apply depending on sex or role (patient or partner), or on the outcome targeted. Identifying differences in associations for these different subgroups will inform clinical application of the findings.Use of technology in this study, specifically for EMA, will yield information regarding patients’ and partners’ smartphone access and their willingness to use the devices for research-based information and intervention delivery. Delivery of intervention content could occur using ecological momentary intervention or other types of real-time, mhealth methods.Study limitations include a focus on patients with a subset of cancer diagnoses and the exclusion of patients either in the earliest stage of disease or facing death within 6 months. In addition, couples electing to participate may be unrepresentative of the larger group of patients with cancer and their spouses, as patients experiencing significant effects of disease or treatment might decline participation on that basis or for other reasons. Couples might also be hesitant to engage in the video-recorded conversation, or be reluctant to discuss topics related to cancer and its treatment, as these might result in emotional discomfort.While the multi-modal nature of the assessments used in the study are a strength, as they provide multiple sources of information from both patient and partner, it is possible that some of the study procedures could be reactive and potentially affect responses to others. For example, engagement in the EMAs or the conversation could conceivably affect responses to other assessments by heightening perceptions or awareness of certain issues. We attempt to mitigate this in part by collecting the baseline self-report measures prior to the conversation and EMA. It is also possible that couples might discuss their responses to the EMA or other portions of the study, reducing the independence of their responses. We attempt to mitigate this by asking couples not to discuss their responses with each other. We believe this potential threat to internal validity is relatively low, and is offset by the benefits of collecting data in real time and in naturalistic settings.Results will be published in scientific journals, presented at scientific conferences, and conveyed to a larger audience through infographics and social media outlets. We will also share findings with key stakeholders  to inform intervention planning and implementation.The studies involving human participants were reviewed and approved by the Arizona State University Institutional Review Board and Duke Health Institutional Review Board. The patients/participants provided their written informed consent to participate in this study. Reliance agreements were established to cover human subjects activities at the University of Washington and the Fred Hutchinson Cancer Research Center, with reliance on Arizona State University’s Institutional Review Board.LP, SL, FK, JR, TS, KS, JBu, DB, JBr, NB, JG, VS, KW, and SZ obtained funding for the study, LP and SL as Multiple Principal Investigators. LP and SL were as Multiple Principal Investigators. LP, SL, FK, JR, TS, KS, JBu, DB, JBr, and NB contributed to study conception. LP, SL, FK, JR, TS, KS, JBu, DB, JBr, NB, JG, VS, MT, BB, MF, DW, and KL contributed to study design. SL wrote the first full draft of the manuscript. LP, NG, JR, MT, MF, DW, DB, BB, KR, and KL wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design. One of the important techniques for improving manufactured product quality and for reducing the manufacturing costs is statistical quality control (SQC). Since the pioneer work by Shewhart A. Walter during 1920s in Bell Telephone Laboratories, wide varieties of control chart techniques have been constructed and extensively implemented in SQC. The main feature of control charting is to identify the amount of assignable cause(s) and hence rectify it by taking necessary action on the production process before sending the outcome of the products into the market. This control charting helps to avoid nonconforming products from being manufactured by the company. More details about Shewhart control charts can be seen in Montgomery [t-chart.Usually, control charts are being designed and operating under the assumption of the normality for the variable of interest. Nevertheless, these assumptions may not be true for various realistic situations and other distributions away from normality had been considered and discussed by many authors in the literature . The Several researchers have developed diversified sampling designs to obtain more efficient control charts. Recently, researchers focused on multiple dependent state (MDS) sampling in the creation of a control chart. Wortham and Baker  proposedMore recently, Raza and Aslam , Rao et X be a random variable from a gamma distribution with shape parameter α and scale parameter β. The cumulative distribution function (cdf) of the gamma distribution is given byThe proposed control chart for a gamma distribution using gamma to normal transformation is discussed. Let X follows a gamma distribution with specific parameters, then the transformed variable X∗ = X1/3 can be distributed approximately as normal with mean μX∗ and variance σX∗2, whereWilson and Hilferty  recommenk1 and k2 are the chart constants to be found when the in-control ARL is approximately equal to preassigned value r0. The convenient form of the above control limits is given as follows: UCL1 = β1/3UL1, UCL2 = β1/3UL2, LCL1 = β1/3LL1, and LCL2 = β1/3LL2, whereThe proposed gamma control chart using GMDS sampling comprises the two pairs of control chart limits. The inner lower control limit (LCL) and upper control limit (UCL) are denoted by subscript 1, and the outer lower control limit (LCL) and upper control limit (UCL) are denoted by subscript 2. The four control limits are given byX. Compute the transformed variable X∗ as X∗ = X1/3Obtain quality measurement from the manufacturing process, and denote the quality characteristic by 2 ≤ X∗ ≤ UCL2, and the process can be considered out-of-control if X∗ ≥ UCL1 or X∗ ≤ LCL1. Or else, go to Step 3The process can be considered under control if LCLk out of m proceeding subgroups have been declared as under control, that is, LCL2 ≤ X∗ ≤ UCL2; otherwise, the output of the product can be considered out-of-control and go back to Step 1The process can be considered under control whenever The operation of the proposed control chart using GMDS scheme is described as follows:The probability of declaring as in-control for the proposed control chart when the process is actually in-control is given as follows:Therefore, the in-control average run length (ARL) when the process is under control is given byβ = β0 to β = β1 = sβ0, where s is the shift value.Assume the gamma scale parameter has been changed from The probability of process is declared as in-control while the scale parameter which has been changed can be obtained as follows:The out-of-control average run length (ARL) when the process is out-of-control is given ask1 and k2 along with ARL1 are obtained using the following algorithm:r0Decide the predetermined in-control ARL as m and kFix the known values for 0 using Equation  valuesUsing the best parametric values of Equation  and hencThe proposed control chart parameters The R codes to find the design parameters of the control chart are given in the appendix.0 and ARL1. These ARL values are used to know the effectiveness of the developed control chart. The developed chart is said to be efficient if it shows larger in-control ARL and smaller out-of-control ARL. Using the aforementioned algorithm in k1 and k2 are obtained. The out-of-control ARLs and SDRL are computed for a choice of shift values, s from 1.0 to 2.0 with an interval of 0.1 and 2.0 to 4.0 with an interval of 0.5. The values of m considered are 4, 5, and 6 and α0 = 5, 10, and 20. r0 = 370 and α0 = 5, r0 = 370 and α0 = 10, r0 = 370 and α0 = 20, r0 = 500 and α0 = 5, r0 = 500 and α0 = 10, and r0 = 500 and α0 = 20.The performance of the proposed gamma control chart using GMDS sampling is considered based on ARL, such as ARLs) of the manufacturing process increasesThe out-of-control ARL and SDRL values decline speedily when the shift . It also observed the same inclination over the other parametric combinations and ARL0 = 370 and 500From the tables, it is noticed that ARL1 and SDRL are small for k = m-2 and these values are increasing from k = m-2 to k = m for fixed values of m. In addition, noticed that ARL1 and SDRL values are large at k = m as compared to the values at k = m-1 and k = m-2 . Hence, it is concluded from the results that gamma control chart using GMDS sampling is an enormous amount of accurate than gamma control chart using MDS samplingIt is interesting to observe from the results that the values of ARLWe pointed out the following several noteworthy comments from Tables 10 = 370 and ARL0 = 500; the shape parameter of gamma distribution is given as α0 = 5, 10, and 20 to compare the developed gamma control chart under GMDS with the existing MDS and Shewhart type control chart at various shift values. These comparisons are presented in 0 = 370 and m = 4 and in 0 = 500 and m = 5 at various shape parameters of the gamma distribution.In this part, a comparison is made between the developed control chart and the existing Shewhart type control chat and MDS control chart for gamma distribution. Also, the application of developed control chart and its dominance over available control chart schemes studied using real data set is presented. In addition, through a simulation study, the supremacy of the developed control chart when compared with the existing control charts is examined. The performance of the developed control chart is studied through ARL values and we know that a control chart with smaller ARL values is more desirable. In this investigation, we studied when ARL1 values as compared with the MDS and Shewhart type control charts at various shifts (s) values and various parametric values studied in this article. At a glance, when ARL0 = 370, α0 = 5 and s = 1.4 from 1 = 27.17 whereas ARL1 = 31.10 for MDS scheme and ARL1 = 38.44 from the Shewhart type control chart. Similarly, for ARL0 = 500, α0 = 10, and s = 1.5 from 1 = 5.67 while ARL1 = 7.22 for the MDS control chart and ARL1 = 14.28 from the Shewhart type control chart. The graphical presentation is given to show the performance of developed control chat over the existing MDS and Shewhart type control charts along with various shift values . The data is reported in Xi∗ = Xi1/3. The control chart coefficients at m = 5, α0 = 5, and ARL0 = 500 are available in m = 5 and k = 5 is provided in m = 5 and k = 3 is depicted in m = 5) Xi∗ values are displayed between the inner control limits, then the process is considered to be under control while for proposed gamma control chart under GMDS, the process is said to be declared as under control if no less than 3 out of 5 previous (since k = 3 and m = 5) Xi∗ values are within the interior control limits.In order to investigate the implementation of the planned control chart over the available control charts, a simulation study is conducted. In this investigation, 30 samples are generated from the gamma distribution with shape parameter From Figures p value is 0.7322. In the present section, the developed gamma control chart using GMDS sampling is applied to the monitoring of Coronavirus (COVID-19) outbreak in China. The data set is borrowed from Li et al. , and theα0 = 2 and m = 4 are k1 = 3.1035 and k2 = 1.4645 for a developed control chart, k1 = 3.7525 and k2 = 2.1935 for MDS control chart, and L = 2.8828 for Shewhart type control chart. The chart limits of the developed control chart, MDS, and Shewhart type control charts for days from ICU intake to death data are given in Figures 77X∗. Using days from ICU intake to death data, the developed control chart can be exemplified as follows: declare the process as in-control when 4 earlier values of X∗ fall in the inner control limits of the developed gamma control chart using the MDS plan. While in the case of the developed gamma control chart using GMDS scheme, the process can be expected as in-control when at least 2 out of 4 earlier values of X∗ fall in the inner control limits.The chart constants at the estimated shape parameter of 2 are obtained using a simulation procedure given in It is clearly noticeable from 0) and out-of-control ARL (ARL1) of the proposed gamma control chart using GMDS sampling. The performance of the proposed control chart is investigated using simulation for the various shifts in the scale parameter. Tables of chart parameters alongside out-of-control ARL (ARL1) for various shift values for specified shape parameters are displayed. Furthermore, a comparative study is also carried out with the developed gamma control chart based on GMDS sampling over the existing MDS and Shewhart type gamma control charts using the ARLs. The results display that the developed gamma control chart based on GMDS sampling shows reduced ARL1 values as compared with the existing two gamma control chart discussed in this article.We developed the gamma control chart based on GMDS sampling. The computational methodology is also discussed for ARL and SDRL when the process is in-control and out-of-control. The control chart parameters for the proposed control chart are obtained using in-control average run length (ARLThe implementation of the developed control chart is demonstrated using simulation study as well as tangible data from ICU intake to death cause by COVID-19 and it is shown that designed gamma control chart using GMDS sampling detected out-of-control samples whereas the MDS and Shewhart type gamma control charts failed to detect the out-of-control signal. Hence, we conclude that the gamma control chart based on GMDS is a superior methodology as compared to the existing control charts considered in this study to detect a shift in the parameter. The developed control chart method in this paper can be used in different industrial and medical situations specifically when the researcher would like to discover a small and moderate shift in quality characteristics. Future research maybe considered as control charts for some nonnormal distributions and cost consideration using GMDS sampling design.


Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions. It has served as a key measure to ease traffic congestion and solve traffic problems. For this reason, advanced traffic signal control systems such as SCOOT2, SCATS3, and series of excellent traffic control algorithms including model-driven algorithm4, data-driven algorithm5, and artificial intelligence6 have emerged throughout nearly a century of development, which functioned as staunch supporters of the rapid development of urban traffic control.As a longstanding academic concern in this field, intelligent urban transportation has witnessed plenty of theoretical and technological innovations and applications8. Control algorithm verification with tools such as Matlab at a given input and output is common in a class of research on traffic control using the control theory9. HILS was developed by combining commercial traffic simulation software with traffic control hardware10.Using simulation to analyze and verify traffic control algorithms is an important element of traffic control research. Related research in China started in the 1980s, which marked the earliest introduction and use of commercial traffic simulation software for the main, followed by the secondary development based on commercial software such as VISSIM and Paramics to verify control algorithmsExisting traffic control simulation fails to realize the real-time connection between simulation and actual traffic control on-site, making real-time simulation, verification, and evaluation unavailable.Currently, most traffic control simulation adopts traditional passive traffic control theory as the foundation for the underlying architecture design, which provides insufficient support for advanced control models and algorithms, resulting in the lack of effective and practical simulation results. It presents deficiencies in supporting the simulation of multi-variable and multi-dimensional active traffic control for future ICVs and autonomous driving environments.Although existing research have yielded fruitful results, the following two issues remained to be addressed:The construction of control models, strategies, and optimization methods faces ideal assumptions and broad constrains as various possible problems in the actual scenario are not considered.The simulation should serve as a link between theoretical research and practical application, bridging the gap between the two, which has yet to be achieved by the current simulation methods for traffic control.Therefore, in traffic control research adopting the above traditional simulation methods, the constructed control models and optimization algorithms often result in problems such as model mismatch and control strategy failure in practice, despite their evident effects in theoretical simulation. Consequently, many excellent traffic control algorithms are impractical as they fail to follow the requirements for the practical application of traffic control.To address these issues, an "Online Application—HILS—Practice" simulation integration platform for real-time testing and verification of active traffic control has been designed and developed. It established its field traffic controller and detector and online simulation to enable effective connection by directly building a data channel in the field, and the real-time verification for the advanced traffic control algorithm was implemented to ensure security and validity. It enabled the transition from traditional passive traffic control to active traffic control, increased the dimension of control variables via resource definition, and improved the flexibility and control capabilities of traffic control models and strategies.The following is how the rest of the paper is organized: a literature review of relevant work is presented in “11, intersection control is the most important issue in the field of urban traffic, even in the future vehicle–road collaboration and popular autonomous driving, making it a great concern of many scholars. Traffic simulation functions as an important means of traffic control research as it encourages the rapid development of the field. Two main traffic control simulation methods are currently available:As an age-old challenge that dates back to the 1950s12. As a result, researchers often collect historical data to perform repeated tests with the simulation software. Once the effectiveness of the control strategy is recognized, the traffic engineer goes through a series of procedures necessary to load it into an operational traffic control system or traffic controller13, to avoid possible safety problems. Secondary development modules are available in commonly used commercial traffic simulation software, including VISSIM, CORSIM, TransModeler, and SimTraffic17. Then an advanced control strategy can be developed to send the detector data generated by the simulation software to the simulator, which outputs the signal state and evaluates the traffic condition based on this information. Some traffic control studies adopted classical control theory, and usually used development tools such as Matlab to verify the rigor of theoretical proof by simple examples9.Microtraffic simulation software is often employed to evaluate the effectiveness of traffic control strategies as it is unsafe and impractical to test them directly in the field18. The basic idea of this concept was to create a link between traffic controllers and simulation software with a controller interface device (CID), which sent detector data obtained from the simulation software to the physical traffic signal controller. At the same time, the traffic signal controller determined the signal state according to the predetermined signal time control strategy and detector data from the simulation software. The device also transmitted real-time signal state from the physical traffic signal controller to the simulation software23. Although HILS brought the simulation closer to reality, previous studies have shown that each controller in HILS corresponded to a dedicated CID, and the control strategy in the simulation could not be directly loaded into the field traffic controller through CID.The concept of HILS was firstly proposed by Sisle in the 1980s to evaluate the performance of an active missile from prelaunch to interception26. However, the abundance of traffic control data benefited from the development of information technology, and the theory of traffic control may therefore be changed. Therefore, some studies have proposed active traffic control. At present, there are two main types of research on active traffic control at intersections: One is the active control based on traffic flow prediction, which is realized by building a traffic flow prediction model combined with traditional traffic control30. The other is active control based on cooperative vehicle infrastructure, which combines vehicle speed guidance and traffic control to achieve active control through the use of cooperative vehicle infrastructure information35.Traditional traffic control has developed over many yearsHowever, to the best of our knowledge, the various traffic simulation methods used in current traffic control research have not effectively linked theoretical research with practical application, with the gap between the two remaining the primary impediment to the practical availability of traffic control algorithms. Considering other practical factors, the original control methods such as time control are still applied in the field.The simulation integration platform based on resource description, taking the three levels of simulation, control, and calculation into consideration, defines and encapsulates the three resources to form a resource pool. It can flexibly schedule simulation, control, and computing resources based on demand during simulation verification, as shown in Fig. The simulation integration platform was driven by scene, simulation, and control engines. During simulation verification, the scene engine applied to the message queue service to call traffic detector and traffic controller state data. It encapsulated the data according to the input data requirements of the scene recognition algorithm before sending it to the scene recognition module to identify the current traffic scene, and then delivered the identification results to the control strategy module as input. If only the basic control strategy was required for the current traffic scene, the basic control strategy generated control parameters and transmitted them to the control engine, which merged and encapsulated the control parameters and basic parameters before sending them to the traffic controller for execution. If advanced control strategies were required for the current traffic scene, the simulation engine applied to the message queue service to invocate traffic detector and traffic controller state data. It encapsulated the data according to the input data requirements for the simulation before sending it to the simulation engine. At the same time, the advanced control strategy transmitted the generated control parameters to the simulation engine to form a simulation subroutine and obtained a definite control through simulation. The parameters were sent to the control engine, which merged and encapsulated the control parameters and basic parameters before sending them to the traffic controller for execution, as shown in Fig. It built a resource-based basic library that allowed for flexible scheduling of three resources, i.e., simulation, control, and calculation. The nature of changes in road network traffic flow lies in matching traffic demand and the resources of time and space. Traffic control is used to allocate time and space resources on the premise of traffic safety. The resource description-based simulation integration platform took the three levels of simulation, control, and calculation into consideration in a unified manner, defined and encapsulated the three as resources to form a resource pool. It could flexibly schedule simulation, control, and compute resources based on demand during simulation verification. Among them, the control resource mainly referred to the resource definition of control variables in traffic control. Computing resources were applied to meet the requirements including (1) computing power for larger-scale simulation calculations; (2) computing power for advanced control and optimized algorithms; (3) computing power for modeling more complex traffic scenarios; (4) computing power for simultaneous online simulation for multiple users; and (5) requirements for real-time/historical data collection, transmission, and storage.It enables the dual integration of model-driven and data-driven approaches, with the support of scene-driven approach. The simulation integration platform can be used to construct complex traffic scenarios and encapsulate basic simulation parameters. It supports both model-driven and data-driven simulations, with the former including car-following models, queuing models, and traffic flow distribution models . The data-driven simulation drives the traffic flow through real-time or historical data, which comes from on-site traffic detection data, and dynamic simulation parameters are calibrated through real data.It upgraded passive traffic control to active traffic control by increasing the dimension of control variables. The fundamental problem for urban traffic control lies in the allocation of road time and space resources as it starts from the 'physical resource problem' of ' cars occupying physical space' on the timeline of road traffic. Therefore, the concept of generalized traffic control was taken into account. It transformed the traditional passive traffic control with cycle and green-signal ratio adjustment as the core into a section with controllable vehicle speed, variable lanes, adjustable phase and sequence, and chain-like connection characteristics. Active traffic control was achieved by increasing the dimension of control variables. Due to its diverse control variables, active traffic control could meet current and future traffic control needs in fully automated driving, unmanned driving, vehicle–road coordination, and other complex and special scenarios to a greater extent.It enabled the high-efficient reachability from control model to control strategy then to controller by bridging the gap between simulation and reality. (1) Scene setting and parameter calibration were synchronously loaded into the traffic controller for parameter protection in accordance with the requirements of the protection mechanism. (2) The traffic control engine shared the same system with the on-site traffic control system, which could abstract the control variables/language of the traffic controller to form a mapping relationship with those of the simulation control engine, directly driving the traffic through the simulation control engine. By doing so, the designer of the control strategy could be free of focusing on the difference between the simulation controller and the on-site traffic controller. (3) It supported traffic controllers by abstracting and decoupling the relationship between basic protection parameters and control variables, as well as a variety of advanced traffic controllers. (4) The platform leveraged real data to calibrate simulation parameters, allowing the control strategy to be trained and implemented in an environment close to the scene. It could effectively solve the problem that the existing control strategy simulation is effective in theory but cannot be applied in the field. (5) It also freed traffic control engineers by providing control strategy programmer and API, allowing them to focus on the design of the control strategy rather than programming skills.The four main characteristics of the simulation integration platform adopting the "Online Application—HILS—Practice" fusion architecture are as follows:36 to visualize traffic flow, calibrate dynamic parameters of traffic flow, and basic road network parameters, as illustrated in Fig. Being designed to quickly analyze and verify new control strategies, the "Online Application" simulation module took the traffic flow from the meso perspective as the main body and the intersections and road sections from the microscopic perspective as the control core, with a focus on the shape of simulated traffic flow under the influence of control strategy, to encourage iterative control strategy optimization. The simulation engine was designed with reference to SUMOAuto-calibration of simulation parameters: It is important to note that the dynamic characteristics of a single vehicle, as well as the manual calibration of simulation parameters, were intentionally ignored in the design of the simulation system. The system adopted detector data and GIS data to automatically calibrate static data , dynamic data , velocity distribution, steering ratio, headway time, etc. At the same time, traffic flow and car-following characteristics were based on the accumulation of historical detection data, and real-time detection data were corrected.In the data preparation part, the test data was encapsulated as standard input and output variables, and the signal control, vehicle, and variable sign were stored as control variables in the database.In the control strategy part, various virtual scenes were constructed based on different traffic demands, and control strategies were designed for different scenes. The set of control strategies was called agency, and control strategy was called the agent. That is to say, the new control strategy designed by researchers was defined as an agent, and the corresponding agent run under given conditions in the simulation.The device driver connected the simulation and the scene, through which a new control strategy could be directly pushed to the on-site control equipment once it presented to be reliable, allowing the implementation in the real environment at the right time. The virtual controller was used to set the basic parameters of traffic control, which were then loaded into the entity of the traffic controller for basic control. The designed complex traffic control strategy was written by the control strategy programmer, and the traffic controller was driven for execution through API, as shown in Fig. Architecture of control strategy and API design: The architecture of the simulation integration platform supported the development of advanced control strategies, which consisted of three parts: data preparation, control strategy, and device driver.As demonstrated in Fig. Figure The simulation integration platform covered over 30 complex scenarios and the application of numerous advanced control strategies, including intersection control, area control, vehicle–road coordinated control, special vehicle control, and other trending research directions in the field. The paper presents six complex traffic scenarios in Fig. 37 and traditional traffic control38 is presented in Fig. The comparative analysis of active traffic control34 had a significantly better control effect than the paper35.According to the comparative analysis of simulation under undersaturated and oversaturated scenarios as shown in Fig. The application of traditional traffic simulation methods in traffic control research often results in the problem that the constructed traffic control models and optimization algorithms face model mismatch and control strategy failure in the actual application, despite their obvious effects in theoretical simulations. This can be explained by: (1) ideal conditions and broad constraints. In the traffic control models, strategies, and optimization methods established in many studies, various possible problems in actual application were not considered; and (2) The existing simulation methods failed to connect theory and practice, with the gap between the two remaining the greatest obstacle to the practicability of the control algorithm, which, along with other basic problems, caused most primitive control methods such as time control and induction control to be unavailable in practice.39 pointed out that ‘In fact, simulation refers to the construction of a system similar to the real world. A controller that is feasible for simulation calculations on this simulation system should have the same effect after being connected to the real control object.’ This paper describes the design and development of an "Online Application—HILS—Practice" simulation integration platform for urban traffic control, which combines practical engineering experience and in-depth consideration of the problems in traffic control simulation. It is based on resource nation and describes simulation, control, and calculation as resources, allowing them to be flexible and schedulable. The dimension of the control variables has been increased, which enabled the output of multiple control variables, including green light, phase, phase sequence, lane, and vehicle speed. On this basis, various functions of "Online" simulation, "Online-HILS" parallel simulation, and "Online-HILS-Practice" integration were studied in detail. The virtual signal machine and standard protocol converter made the traffic controller a bridge connecting the "Online" simulation, "HILS" simulation, and practical applications. Finally, six simulation examples for complex scenarios were listed. The two methods of active control and passive control were simulated, compared, and analyzed with the simulation integration platform under two typical undersaturated and oversaturated scenario. The research results showed that the simulation integration platform can not only perform large-scale road network simulation, but also realize a variety of complex control strategies from vehicle speed control and lane control to phase and phase sequence control, with stable, reliable, and real-time simulation.Paper


AIM: The ERAS protocol consists of multiple items that aim to improve the outcomes of patients receiving surgery. Adhering to the protocol is difficult. We wondered whether surgeons practicing the ERAS protocol in a group would improve patient outcomes. Methods: All patients who underwent colorectal resection for benign disease or malignancy from November 2017 to December 2018 were collected and reviewed retrospectively. According to the physician’s ward round strategy, the patients were categorized into two groups, either by solo practice or group practice. Results: This study enrolled 724 patients and divided them into two groups according to the practice method: group practice (n = 256) and solo practice (n = 468). The group practice cohort had less postoperative morbidity  and shorter postoperative hospital stays  than the solo practice cohort. Group practice (p < 0.001), natural orifice specimen extraction (NOSE) procedure (p < 0.001), and blood loss >50 mL (p = 0.039) significantly affected discharge within 5 days postoperatively in multivariate analyses. Conclusions: Group practice based on a modified ERAS protocol shortens postoperative hospital stays with fewer morbidities compared with solo practice in which patients receive elective minimally invasive colorectal surgery. ERAS checklists for minimally invasive surgery have recently been shown to have a distinct impact on recovery in patients with CRC. However, the influence of medical practice on ERAS compliance is unknown. Here, we show that group practice based on a modified ERAS protocol shortens hospital stays compared to solo practice.®) is a protocol to improve the results of patients who receive all aspect of colorectal surgery. It is based on published evidence and has been found to shorten the length of hospital stay and decrease postoperative morbidities, costs, and readmissions following colorectal surgeries [Enhanced Recovery After Surgery . There are two common types of medical practice: solo practice and group practice. Solo practice is a standard and effective method led by an attending surgeon, including outpatient diagnosis and treatment, surgery, and related care after surgery . A groupAfter the Mayo brothers started their first group in the mid-1880s, physicians worldwide formed various groups that desired to provide the best services for their patients. Although these groups are constructed differently, group practices share similar characteristics. First, group practice improves patient satisfaction and experience by reducing wait times and increasing access to care. Second, group practice also increases the quality of care by increasing adherence to guidelines through easier knowledge sharing and access to information among group members . In addiThis study aimed to clarify whether the type of medical practice affects the adherence rate of the ERAS protocol for minimally invasive colorectal surgery. We evaluated the short-term outcomes of patients who underwent colorectal surgery following a modified ERAS protocol by group-practice surgeons compared with single-practice surgeons at a single tertiary care centre.Detailed information on patients who underwent elective colorectal resection for benign disease or malignancy in a single medical institute, Chang Gung Memorial Hospital, from November 2017 to December 2018 was collected prospectively and reviewed retrospectively. All the data came from patients’ medical records, and we obtained informed consent from all patients for use of their data in this study. The institutional review board approved this study (IRB No.202201164B0).The inclusion criteria were (1) a segment of bowel resection, including colon and rectum resection, (2) minimally invasive surgery, and (3) an American Society of Anaesthesiologists (ASA) physical status score less than or equal to 3. Patients who received laparotomy and conversion of laparoscopic to open surgery were excluded.Solo practice is a traditional ward round led by a single attending surgeon with fellows, residents, or nurse practitioners. The single surgeon supervises and is responsible for all aspects of a patient’s care, including clinics, admission, surgical intervention, and postoperative care.Group practice in this study refers to multiple physicians with single specialties in colorectal disease, including four attending surgeons.The difference between solo and group practice is primarily regarding the ward round and decision making. The single attending surgeon would provide one ward round per day, including weekdays and weekends, to the patients in the solo practice. In group practice, four attending surgeons would take turns providing one ward round per day when his or her clinical workload permitted. Each group practice surgeon would see his or her patients and those of the other three physician’s patients. More information about the solo and group practice is provided in We did not entirely apply all the ERAS protocol components in our institute because no standardized multidisciplinary consensus has yet been achieved about implementing the whole ERAS protocol . The comMeasurement outcomes included short-term postoperative complications, recovery, pain score, and readmission. Postoperative complications were defined as morbidity or mortality occurring within 30 days and were graded according to the Clavien–Dindo classification.Postoperative recovery evaluation was based on blood test reports, pain intensity, and the length of hospital stay. We also collected postoperative 30-day hospital readmission data. Pain intensity was estimated using a numeric rating scale (NRS) from 0 to 10, with 10 indicating the worst unbearable pain. The mean postoperative pain scores of patients were used for further evaluation.t test. Univariate and multivariate analyses were performed using binary logistic regression. A two-tailed p value < 0.05 was considered statistically significant.All data analyses were performed using IBM SPSS Statistics, Version 24.0 . Clinicopathological characteristics with categorical variables are shown as frequencies and proportions and were compared using the chi-square test. Continuous variables are presented as the means and standard deviations and were analysed using Student’s n = 256) and solo practice (n = 468) (p = 0.037). The preoperative blood tests for white blood cell (WBC) count, CRP and CEA were similar in both groups, but haemoglobin and albumin levels were slightly higher in the solo practice patients. There was no difference in previous abdominal surgery and neoadjuvant therapy in either group.From November 2017 to December 2018, a total of 928 patients received major colorectal surgery at Chang Gung Memorial Hospital. There were 194 patients who underwent laparotomy and 10 patients who tried laparoscopic surgery first but then converted to laparotomy. This study enrolled 724 patients and divided them into two groups according to practice method: group practice (n = 468) . The demp = 0.043), a higher robotic-assisted method , less postoperative morbidity , and a shorter postoperative hospital stay  than the solo practice patients. Regarding the postoperative blood test, the group practice patients had a higher WBC count  and haemoglobin level . The pain score was also lower among the group practice patients than the solo practice patients postoperatively .p < 0.001), NOSE procedure , operative method, especially low anterior resection , neoadjuvant therapy , tumour > 4 cm , blood loss > 50 mL , preoperative CEA > 5 , and preoperative CRP > 5  were associated with discharge within 5 days postoperatively in univariate analysis. After multivariate adjustment, group practice , NOSE procedure , and blood loss > 50 mL  were still significant factors affecting discharge within 5 days postoperatively.The results of univariate and multivariate analyses of discharge within 5 days postoperatively are shown in The discharge day distributions of the group practice and solo practice patients are shown in This study assessed the short-term outcomes of group practice patients based on a modified ERAS protocol compared with solo practice patients. Our data showed that group practice patients had shorter hospital stays and fewer surgical complications than solo practice patients.Group practice is one of several kinds of medical practices that include solo practice, employed physician practice, and direct primary care . This stWe started our group practice, with multiple physicians in a single specialty in 2017. The group initially consisted of four attending surgeons, and later, several younger attending physicians joined. Each day, one of four staff members takes turns conducting ward rounds, giving orders in the morning and setting daily goals for patients. The original attending physicians would later visit the patient whenever possible. In this way, staff avoided splitting their time among ward rounds, clinics, operating rooms, and examinations and delaying patient care. In addition, the team built an excellent electronic medical record platform for shift handovers that rotating residents updated. Based on the medical records and documentation, the staff can exchange various opinions with team members through this platform. Overall, team members were satisfied with this system of group practice. Based on our real-world experiences, this group practice is safe and effective in performing the modified ERAS protocol for minimally invasive colorectal surgery. It is up to team members, not necessarily the individual patient’s surgeon, to make objective decisions based on daily ERAS goals that prevent individual subjective limitations. The results may align with the tenets of objective and protocol-based patient care. However, we did not assess patient satisfaction during this period.Enhanced recovery after surgery (ERAS) is an evidence-based multispecialty and multidisciplinary approach to surgical patient care that must involve multiple professionals ,2. DanisThe ERAS society was then formed to develop perioperative care and improve recovery through research, education, auditing, and implementation of this evidence-based program . CurrentThe latest guidelines for the ERAS program released in 2018 have a total of 24 items, which are categorized into preadmission, preoperative, perioperative, and postoperative periods [However, the real-world application of ERAS is bound to have modifications. Our institution also has certain limitations, so we applied and modified several program elements at various phases, as shown in Brady et al.  reviewedIn this study, the mean postoperative hospital stay was 6.6 days in the group practice arm and 8.6 days in the solo practice arm. Compared to other investigations, this study’s hospital stay duration seems to be delayed by approximately 1 to 2 days ,34. We sSecond, our pain control strategies are also different from the guidelines. According to the guidelines, multimodal analgesia combined with epidural analgesia and avoiding opioids are suggested ,36. In oThird, we usually use surgical drains and Foley catheters. The Jackson-Pratt drain is generally placed over the pelvis for left-sided colectomy, and the Morrison pouch is used for right-sided colectomy. The primary purpose is to evacuate postoperative collections, bleeding, and residual accumulation of gas by laparoscopic pneumoperitoneum. The drain is removed approximately 2 to 3 days after surgery. Once the patient can get out of bed and move well, the Foley catheter can be removed. According to the essence of the standard ERAS protocol, routine drainage is not recommended .A fundamental basis for the success of ERAS implementation is the compliance rate of implementers. The tendency of traditional physicians to give medical orders during ward rounds seems to be related to habit and experience. Some ERAS programs were challenging for traditionally trained surgeons to follow, which may account for the slower recovery of patients in the solo group in this study. The attending surgeons commonly work with patients operated on by other members of the group practice. They give medical orders more often based on the ERAS checklist, which can increase adherence to the ERAS guidelines.Natural orifice specimen extraction (NOSE) is the opening of a hollow viscus that is already communicating with the outside world, such as the vagina or distal gastrointestinal tract, to remove the surgical specimen ,39. In oIn the future, we may set clear discharge criteria and inform patients before admission . After tThis study has several limitations. First, the retrospective design was the principal weakness of the current research, and the decision to adopt the modified ERAS protocol was not random. Second, we could not evaluate the adherence rate of each component of the modified ERAS protocol. Third, there is a high variation in physician decisions, especially in the solo group, because our institute employs more than ten attending surgeons. Each physician has his or her personal preferences in decision-making, which causes modified ERAS components to be inconsistent. Fourth, there are still some differences between the modified ERAS protocol and the actual ERAS protocol, especially concerning carbohydrate loading before surgery. Fifth, the surgical techniques and strategies adopted by surgeons, such as robotic surgery and the NOSE procedure, might have some influence on the outcomes and the length of the hospital stay for patients.In conclusion, a group practice based on a modified ERAS protocol reduces hospitalization length of stay and improves outcomes without increasing morbidity in patients undergoing minimally invasive colorectal surgery. According to our experiences, group practice can enhance ERAS performance adherence and prevent old habits and empirical tendencies of traditional surgical doctrines that occur in solo practice.