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Previous studies have acknowledged the impact of weather changes on retail uncertainty. They primarily focus on understanding how weather conditions affect offline consumer behavior and aim to develop effective marketing strategies. However, there is little research on the complex impact of weather on online shopping behavior. To bridge this gap, we conduct a study with a sample of 261 consumers from China with shopping experience in community retail shops (CRSs). We utilize the S-O-R model and theories, including meteorological emotional effect theory, emotional coherence, and meteorological psychology, to model and elucidate the relationship between weather and consumers’ online shopping behavior in CRS. Our findings reveal that weather conditions affect consumers’ spending patterns and purchase diversity, mediated by consumers’ emotions and risk aversion when they comfortably shop online at home. Furthermore, employing the fsQCA model, we identify the critical path through which weather conditions and consumer types influence risk-aversion awareness. The results provide management implications for retailers to develop online marketing strategies for different consumer types.

To effectively combat emerging infectious diseases like COVID-19, it is crucial to adopt strict prevention and control measures promptly to effectively contain the spread of the epidemic. In this paper, we propose a transmission model to investigate the influence of two control strategies: reducing contact numbers and improving medical resources. We examine these strategies in terms of constant control and time-varying control. Through sensitivity analysis on two reproduction numbers of the model with constant control, we demonstrate that reducing contact numbers is more effective than improving medical resources. Furthermore, these two constant controls significantly influence the peak values and timing of infections. Specifically, intensifying control measures can reduce peak values, albeit at the expense of delaying the peak time. In the model with time-varying control, we initially explore the corresponding optimal control problem and derive the characteristic expression of optimal control. Subsequently, we utilize real data from January 10th to April 12th, 2020, in Wuhan city as a case study to perform parameter estimation by using our proposed improved algorithm. Our findings illustrate that implementing optimal control measures can effectively reduce infections and deaths, and shorten the duration of the epidemic. Then, we numerically explore that implementing control measures promptly and increasing intensity to reduce contact numbers can make actual control be more closer to optimized control. Finally, we utilize the real data from October 31st to November 18th, 2021, in Hebei province as a second case study to validate the feasibility of our proposed suggestions.

Taking the perspective of innovative knowledge management, this study aimed to investigate the stimulation mechanism of continuous innovative knowledge contribution (CIKC). Through a quantitative study conducted in an open innovation community, we modeled a stimulus–organism–response framework to conduct a path analysis from the external environment to internal cognition, and then to knowledge contribution, and filled in the gaps in the mediating and moderating mechanisms. We focused on the stimuli of knowledge contribution, in view of both quantity and quality. Panel data from six periods in one year was collected for dynamic analysis, and we used the fixed effect model to test our hypotheses of mediation effect, moderation effect, and mediated-moderation effect. There were some interesting findings, showing that user’s self-efficacy plays a partial mediating role in the quantity, rather than the quality, of CIKC; meanwhile, the users’ knowledge level plays a moderating role, and there is a negative moderating mechanism of knowledge level in the process from participation value to the quality of CIKC.

Abstract User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information for recommendation tasks. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a neural inference method (NIGraphNet) by mining user social interest for item recommendation. It can unearth user latent topics combined with user relation learning. Specifically, we exploit a neural variational inference approach to learn the distributions between user interests and hidden topics. (We denote it as interest-topic distributions in the following.) Then, we adopt a unified graph-based training loss that jointly learns the hidden topics and user relations for item recommendation. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations is conducive to the user’s automatic language understanding.

The COVID‐19 pandemic exacerbates challenges faced by human immunodeficiency virus patients, who are at heightened risk for infection due to compromised immune systems. This study aims to develop a reliable, home‐based point‐of‐care testing (POCT) tool for early screening of acquired immunodeficiency syndrome (AIDS) coinfected with Talaromyces marneffei infection. Employing a “From weak to strong” deduction strategy for feature selection, data from 464 AIDS patients across four cohorts between February 5th, 2014, and January 8th, 2022, are analyzed. The top three features consistently observed are D‐dimer, cluster of differentiation 4+, and aspartate transaminase. Based on these features, the simplest risk‐scoring model is constructed, with the area under the receiver operating characteristic curve values of 0.91, 0.80, and 0.69 in the hold‐out cohort, external cohort 1, and external cohort 2, respectively. This “From weak to strong” deduction strategy identifies advantageous clinical features, enabling the development of simplified clinical risk scores with multiple biomarkers. To facilitate practical implementation, enhanced POCT tools are introduced, specifically a strip with segmented testing capabilities that demonstrates sensitivity and strong correlation with clinical scoring models. Furthermore, an open‐access website and a free Android mobile app are created to support community utilization. The findings underscore the effectiveness of the innovative deduction strategy and enhanced test strips, which enable bedside measurements without laboratory dependency.

This study empirically examines the impact of individual motivation on innovation performance, where innovative behavior serves as a mediating construct, and discusses whether the innovation climate has a moderating effect. This study combines the Cognitive Evaluation Theory and behavioral performance to develop a new theoretical method for explaining the relationships between individual motivation, innovative behavior, and innovation performance. Data collected via a field study of 250 subjects who participated in a science and technology competition were analyzed using a PLS-SEM model. Barron and Kenny's mediation test and the bootstrapping method were used to evaluate the model and confirm the mediation and moderation effects, respectively. Robustness checks were conducted to exclude nonlinearity and heterogeneity in the model. The results show that individual motivation positively promotes innovation performance, in which innovative behavior plays an intermediary role, and that innovation climate moderates the relationship between innovative behavior and innovation performance. According to the results of this field study, stimulators concerning motivation and climate should be primarily considered by managers to boost innovation performance.

IntroductionReal-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD.MethodsWe proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients’ facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients.ResultsWe found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales.DiscussionThe BDD can effectively measure the current state of patients’ depression and its changing trend according to the patient’s expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.

Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the Big Data era, business analytics techniques have been changing and evolving rapidly. Therefore, this paper reviews the latest techniques and applications of business analytics based on the existing literature. Meanwhile, many problems and challenges are inevitable in the progress of business analytics. Therefore, this review also presents the current challenges faced by business analytics and open research directions that need further consideration. All the research papers were obtained from the Web of Science and Google Scholar databases and were filtered with several selection rules. This paper will help to provide important insights for researchers in the field of business analytics, as it presents the latest techniques, various applications and several directions for future research.

The joint replenishment problem has been extensively studied and the joint replenishment strategy has been adopted by a large variety of retailers in recent years. However, the joint replenishment problem under minimum order quantity and other constraints does not receive sufficient attention. This paper analyzes a retailing supply chain involving a supplier that provides quantity discount schedules and limits the order quantity. The order quantity constraints include minimum order requirements for each item and as to the total quantity; additionally, the latter cannot exceed the transport capacity constraint. These are common constraints in the retail industry today and create greater complexity and difficulty in the retailer’s decision-making. To analyze the problem, an integer nonlinear programming model is set up to maximize retailers’ profit with all practical constraints. A two-layer efficient algorithm named the Marginal and Cumulative Profit-Based Algorithm (MCPB) is then proposed to find whether to order and the optimal order quantity for each item. The results of computational experiments show that the proposed algorithm can find near-optimal solutions to the problem efficiently and is a reference for retailers to solve practical joint replenishment problems.

EHBP-1 (Ehbp1) is a conserved regulator of endocytic recycling, acting as an effector of small GTPases including RAB-10 (Rab10). Here we present evidence that EHBP-1 associates with tubular endosomal phosphatidylinositol-4,5-bisphosphate [PI(4,5)P2] enriched membranes through an N-terminal C2-like (NT-C2) domain, and define residues within the NT-C2 domain that mediate membrane interaction. Furthermore, our results indicate that the EHBP-1 central calponin homology (CH) domain binds to actin microfilaments in a reaction that is stimulated by RAB-10(GTP). Loss of any aspect of this RAB-10/EHBP-1 system in the C. elegans intestinal epithelium leads to retention of basolateral recycling cargo in endosomes that have lost their normal tubular endosomal network (TEN) organization. We propose a mechanism whereby RAB-10 promotes the ability of endosome-bound EHBP-1 to also bind to the actin cytoskeleton, thereby promoting endosomal tubulation.

The small GTPase RAB-5/Rab5 is a master regulator of the early endosome, required for a myriad of coordinated activities, including the degradation and recycling of internalized cargo. Here we focused on the recycling function of the early endosome and the regulation of RAB-5 by GAP protein TBC-2 in the basolateral C. elegans intestine. We demonstrate that downstream basolateral recycling regulators, GTPase RAB-10/Rab10 and BAR domain protein AMPH-1/Amphiphysin, bind to TBC-2 and help to recruit it to endosomes. In the absence of RAB-10 or AMPH-1 binding to TBC-2, RAB-5 membrane association is abnormally high and recycling cargo is trapped in early endosomes. Furthermore, the loss of TBC-2 or AMPH-1 leads to abnormally high spatial overlap of RAB-5 and RAB-10. Taken together our results indicate that RAB-10 and AMPH-1 mediated down-regulation of RAB-5 is an important step in recycling, required for cargo exit from early endosomes and regulation of early endosome-recycling endosome interactions.

Medial degeneration and inflammation are features of abdominal aortic aneurysms (AAAs). However, the early inflammatory event initiating aneurysm formation remains to be identified. Activated platelets release abundant proinflammatory cytokines and are involved in initial inflammation in various vascular diseases. We investigated the role of platelets in progression of AAA in vivo and in vitro. Histological studies of tissues of patients with AAA revealed that the number of platelets was increased in aneurysm sites along with the increased infiltration of T lymphocytes and augmented angiogenesis. In a murine model of AAA, apolipoprotein E-knockout mice infused with 1,000 ng/kg/min angiotensin II, treatment with clopidogrel, an inhibitor of platelets, significantly suppressed aneurysm formation (47% decrease, P

Rac1 is a founding member of the Rho-GTPase family and a key regulator of membrane remodeling. In the context of apoptotic cell corpse engulfment, CED-10/Rac1 acts with its bipartite guanine nucleotide exchange factor, CED-5/Dock180-CED-12/ELMO, in an evolutionarily conserved pathway to promote phagocytosis. Here we show that in the context of the Caenorhabditis elegans intestinal epithelium CED-10/Rac1, CED-5/Dock180, and CED-12/ELMO promote basolateral recycling. Furthermore, we show that CED-10 binds to the RAB-5 GTPase activating protein TBC-2, that CED-10 contributes to recruitment of TBC-2 to endosomes, and that recycling cargo is trapped in recycling endosomes in ced-12, ced-10, and tbc-2 mutants. Expression of GTPase defective RAB-5(Q78L) also traps recycling cargo. Our results indicate that down-regulation of early endosome regulator RAB-5/Rab5 by a CED-5, CED-12, CED-10, TBC-2 cascade is an important step in the transport of cargo through the basolateral recycling endosome for delivery to the plasma membrane.

With the rapid growth of e-commerce platforms, enterprises face intense competition and must focus on existing customers to explore their value. Customer segmentation aids in formulating marketing strategies by identifying and categorizing customers with similar traits. The widely used RFM model characterizes customer value but solely considers current customer value, neglecting potential value. Hence, this study incorporates behavioral indicators of e-commerce users for segmentation. The unsupervised k-means++ algorithm is employed for clustering, with excellent results indicated by the silhouette coefficient. This analysis identifies a customer segment with high potential value, prompting enterprises to strengthen marketing strategies and convert these customers into loyal customers. [ABSTRACT FROM AUTHOR]