The new Industry 5.0 paradigm complements the well-known Industry 4.0 approach by specifically driving research and innovation to facilitate the transition to sustainable, human-centric and resilient industry. In the manufacturing context, workers' diversity in terms of experience, productivity and physical capacity represents a significant challenge for companies, especially those characterized by high staff turnover and manual processes with high workload and poor ergonomics. In seeking to address such challenges, this research adopts a human-centric perspective to define new flexible job arrangements by developing a new multi-objective job rotation scheduling model. The proposed model is unique in that it aims to achieve multiple job assignment objectives by simultaneously considering different socio-technical factors: workers' experience, physical capacity and limitations, postural ergonomic risks, noise and vibration exposure, and workers' boredom. The model's implementation in real environments can be supported by new sensor-based technologies that collect data on workers' efficiency, ergonomic scores and task performance and enable workers to participate in measuring perceived fatigue and boredom. The primary goal of our model is to find the most appropriate assignment of job and individual-flexible rest-break plan for each worker. The authors test the model application in an industrial setting. Useful managerial insights emerge and prescriptive recommendations are provided.
Motivated by the mechanistic model of the resting energy expenditure, we present a new multiple hypothesis testing approach to evaluate organ/tissue-specific resting metabolic rates. The approach is based on generalized marginal regression estimates for a subset of coefficients along with a stepwise multiple testing procedure with a minimization–maximization of the normalized estimates (maximization over all its components and minimization over all possible choices of the subset). The approach offers a valid way to address challenges in multiple hypothesis testing on regression coefficients in linear regression analysis especially when covariates are highly correlated. Importantly, the approach yields estimates that are conditionally unbiased. In addition, the approach controls a family-wise error rate in the strong sense. The approach was used to analyze a real study on resting energy expenditure in 131 healthy adults, which yielded an interesting and surprising result of age-related decrease in resting metabolic rate of kidneys. Simulation studies were also presented with various strengths of multi-collinearity induced by pre-specified correlation in covariates.
This paper incorporates insights from organizational identity and identification, social network research and post-merger integration to explore factors influencing employees' identification with a merged nonprofit organization. We propose that nonprofit employees' identification with the merged nonprofit organization is associated with their network size, relational heterogeneity, and perceived effectiveness of integration processes. Empirical results suggest that employees with larger mentoring and socioemotional support networks exhibit strong post-merger identification. Relational heterogeneity within the workflow network has an inverted U-shape relationship with post-merger identification. Employees' perceived effectiveness of integration processes significantly influences their sense of identity with the new organization. Implications for better managing post-merger identification are discussed.
We find that earnings forecasts by analysts with more local peers, defined as analysts working in the same brokerage office who cover different firms headquartered in the same area, are more accurate. These heightened accuracy effects are concentrated in settings where local peers are particularly valuable, such as when analysts have less access to corporate management, when earnings are harder to forecast, and when analysts have stronger incentives to work hard. In examining the nature of the information transmitted by local peers, we find that earnings forecasts by analysts with more local peers better reflect negative geographic shocks in firm earnings. In addition, geographic momentum in stock returns is attenuated for firms that are followed by more local peers, especially when area returns are negative. These findings suggest that social interactions among local peer analysts facilitate the transmission of complex, soft information about geographic factors to investors.
We construct via forcing models for the level by level equivalence between strong compactness and supercompactness in which \(\square _\delta \) holds for \(\delta \) in a stationary subset A of the least supercompact cardinal. We may write \(A = A_0 \cup A_1\), where both \(A_0\) and \(A_1\) are stationary, \(A_0\) is composed of regular cardinals, and \(A_1\) is composed of singular cardinals. In our models, a weak version of \(\square \) holds for every infinite cardinal, various versions of the combinatorial principle \(\diamondsuit \) hold at every regular cardinal, and the versions of \(\diamondsuit \) holding at relevant successor cardinals are controlled by a suitably defined ground model function. In the first model constructed, GCH holds everywhere, and there are no restrictions on the class of supercompact cardinals. In the second model constructed, GCH holds except at inaccessible cardinals, and no cardinal is supercompact up to an inaccessible cardinal.
Genotype–phenotype causal modeling has evolved significantly since Johannsen’s and Wright’s original designs were published. The development of genomewide assays to interrogate and detect possible causal variants associated with complex traits has expanded the scope of genotype–phenotype research considerably. Clusters of causal variants discovered by genomewide assays and associated with complex traits have been used to develop polygenic risk scores to predict clinical diagnoses of multidimensional human disorders. However, genomewide investigations have met with many challenges to their research designs and statistical complexities which have hindered the reliability and validity of their predictions. Findings linked to differences in heritability estimates between causal clusters and complex traits among unrelated individuals remain a research area of some controversy. Causal models developed from case–control studies as opposed to experiments, as well as other issues concerning the genotype–phenotype causal model and the extent to which various forms of pleiotropy and the concept of the endophenotype add to its complexity, will be reviewed.
Importance: Given the prevalence of obesity, accessible and effective treatment options are needed to manage obesity and its comorbid conditions. Commercial weight management programs are a potential solution to the lack of available treatment, providing greater access at lower cost than clinic-based approaches, but few commercial programs have been rigorously evaluated. Objective: To compare the differences in weight change between individuals randomly assigned to a commercial weight management program and those randomly assigned to a do-it-yourself (DIY) approach. Design, setting, and participants: This 1-year, randomized clinical trial conducted in the United States, Canada, and United Kingdom between June 19, 2018, and November 30, 2019, enrolled 373 adults aged 18 to 75 years with a body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 25 to 45. Assessors were blinded to treatment conditions. Interventions: A widely available commercial weight management program that included reduced requirements for dietary self-monitoring and recommendations for a variety of DIY approaches to weight loss. Main outcomes and measures: The primary outcomes were the difference in weight change between the 2 groups at 3 and 12 months. The a priori hypothesis was that the commercial program would result in greater weight loss than the DIY approach at 3 and 12 months. Analyses were performed on an intention-to-treat basis. Results: The study include 373 participants (272 women [72.9%]; mean [SD] BMI, 33.8 [5.2]; 77 [20.6%] aged 18-34 years, 74 [19.8%] aged 35-43 years, 82 [22.0%] aged 44-52 years, and 140 [37.5%] aged 53-75 years). At 12 months, retention rates were 88.8% (166 of 187) for the commercial weight management program group and 95.7% (178 of 186) for the DIY group. At 3 months, participants in the commercial program had a mean (SD) weight loss of -3.8 (4.1) kg vs -1.8 (3.7) kg among those in the DIY group. At 12 months, participants in the commercial program had a mean (SD) weight loss of -4.4 (7.3) kg vs -1.7 (7.3) kg among those in the DIY group. The mean difference between groups was -2.0 kg (97.5% CI, -2.9 to -1.1 kg) at 3 months (P < .001) and -2.6 kg (97.5% CI, -4.3 to -0.8 kg) at 12 months (P < .001). A greater percentage of participants in the commercial program group than participants in the DIY group achieved loss of 5% of body weight at both 3 months (40.7% [72 of 177] vs 18.6% [34 of 183]) and 12 months (42.8% [71 of 166] vs 24.7% [44 of 178]). Conclusions and relevance: Adults randomly assigned to a commercial weight management program with reduced requirements for dietary self-monitoring lost more weight and were more likely to achieve weight loss of 5% at 3 and 12 months than adults following a DIY approach. This study contributes data on the efficacy of commercial weight management programs and DIY weight management approaches. Trial registration: ClinicalTrials.gov Identifier: NCT03571893.
We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester. We find that mRNA vaccine coverage at 100% combined with weekly screening testing of 25% of the campus population make it possible to safely reopen to in-person instruction. Our simulations exhibit a right-skew for total infections over the semester that becomes more pronounced with less vaccine coverage, less vaccine effectiveness and no additional preventative measures. This suggests that high levels of infection are not exceedingly rare with campus social connections the main transmission route. Finally, we find that if vaccine coverage is 100% and vaccine effectiveness is above 80%, then a safe reopening is possible even without facemask use. This models possible future scenarios with high coverage of additional “booster” doses of COVID-19 vaccines.
We consider a program that, by bringing additional liquidity to the equity markets, would benefit market participants, listed companies, an exchange, and the broader economy. Established by an issuer, managed by a third party broker-dealer intermediary, formally structured and maximally transparent, the program involves corporate share repurchase in a falling market and issuance in a rising market. We use simulation analysis to assess the procedure for 30 Dow and 30 DAX stocks for the five year span, 2008–2012; our findings indicate that the program can generate profits for firms that institute it, and we suggest that additional steps be taken to refine, further test, and implement the procedure.
Despite the documented benefits of Artificial Intelligence (AI) to the service industry, the service employees’ fear of being replaced by AI continues to be a major concern as we transition to the Feeling Economy. This paper builds upon the Feeling Economy framework and the social comparison theory to examine how different service-related tasks (thinking vs feeling) distinctively impact the service employees’ feelings and behavior. Five studies reveal that the presence of AI increases negative outcomes for employees engaging in thinking (vs. feeling) tasks due to its adverse effects on their perceived ability (i.e., relative performance). Findings further indicate that these detrimental effects only happen when service employees compare their abilities with those of AI. This research provides important theoretical and managerial implications, helping to mitigate AI’s negative outcomes on employees’ fear of replacement and reduced job performance.
Background School choice models assume that parents and children have varied educational preferences and that catering to those preferences will result in superior educational experiences for all. Yet, we still know relatively little about how parents identify the best school “fit” for their children and families. This question is particularly important in the context of gentrifying urban districts, where school choice often serves to broaden and intensify school segregation at precisely the moment in which racial, ethnic, and socioeconomic school integration could be a real possibility. Objective The purpose of this study was to better understand how parents determined the best school fit for their children within a competitive system of public school choice. Setting The study was conducted in a diverse school district in Brooklyn, New York, in which families were required to rank their preferred middle schools and schools were required to rank interested students. This competitive choice model has the potential to match students and schools by educational fit when top choices align, but can also lead to greater academic stratification and racial, ethnic, and socioeconomic segregation. Participants and Research Design Twenty-six parents from a range of social, economic, racial, ethnic, linguistic, and geographic locations and identities were interviewed over a 15-month period before, during, and after applying to middle schools for their children. The study’s longitudinal design captured parents’ shifting priorities, perceptions of school options, and notions of fit across time. Findings Study participants’ school preferences shifted through time and were often constructed collectively. Parents sought out and accessed information about their options but, faced with too many uncertainties, also turned to others for guidance and assurance. Race, ethnicity, and class often informed parents’ perceptions of schools, but not uniformly. Notions of fit varied both within and across groups, and at times parents with similar backgrounds and stated educational priorities arrived at disparate school preferences based largely on perceptions constructed within their social networks. Conclusion/Recommendations These findings suggest that offering families expanded access to and better quality information about their school options will do little to reduce the segregating effects of choice without also ensuring that schools are organized to address the diverse needs of students and families. Seeking to make all schools inclusive and pedagogically responsive to diverse learners will decrease some of the market efficiencies school choice has promised to deliver, but may offer an even greater social good by advancing the goals of integrated and equitable schooling.
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