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This paper develops a methodological approach to assessing the supply chain management in small- and medium-sized businesses with further testing it in the companies in the post-Soviet economies. The research objectives were achieved in terms of a sophisticated multi-country project resulting in assessment of the supply chain management for small- and medium-sized businesses in Russia, Kazakhstan, and Azerbaijan (256 businesses in total). The study was implemented through a phased application of online survey methods using closed-ended single-choice questions, including a 5-point Likert scale, mean score ranking, and ABC analysis using the Pareto principle. The assessment showed that, regardless of the surveyed country, almost all respondent businesses use a reactive supply chain design. Since the reactive design does not allow for the implementation of resilient supply chain development, the recommended practices, which have been developed during the pilot phase, deal with improving the supply chain management and gradual transformation of the reactive design into a cooperative management model to ensure resilient supply chain development under uncertainty.

Aedes mosquitoes, known as vectors of mosquito-borne diseases, pose significant risks to public health and safety. Modeling the population dynamics of Aedes mosquitoes requires comprehensive approaches due to the complex interplay between biological mechanisms and environmental factors. This study developed a model that couples differential equations with a neural network to simulate the dynamics of mosquito population, and explore the relationships between oviposition rate, temperature, and precipitation. Data from nine cities in Guangdong Province spanning four years were used for model training and parameter estimation, while data from the remaining three cities were reserved for model validation. The trained model successfully simulated the mosquito population dynamics across all twelve cities using the same set of parameters. Correlation coefficients between simulated results and observed data exceeded 0.7 across all cities, with some cities surpassing 0.85, demonstrating high model performance. The coupled neural network in the model effectively revealed the relationships among oviposition rate, temperature, and precipitation, aligning with biological patterns. Furthermore, symbolic regression was used to identify the optimal functional expression for these relationships. By integrating the traditional dynamic model with machine learning, our model can adhere to specific biological mechanisms while extracting patterns from data, thus enhancing its interpretability in biology. Our approach provides both accurate modeling and an avenue for uncovering potential unknown biological mechanisms. Our conclusions can provide valuable insights into designing strategies for controlling mosquito-borne diseases and developing related prediction and early warning systems. Author summary: When simulating population dynamics, scholars often rely on predefined functions to describe processes such as growth, development, and mortality. However, these functions may not be able to accurately capture the underlying biological mechanisms, posing challenges for related research. In this study, we adopted a data-driven approach integrated with mechanistic principles to simulate mosquito dynamics. Given the unknown functional relationships between oviposition rate, temperature, and precipitation, we employed a neural network to model these relationships, resulting in a model that combines the neural network with traditional mechanistic approaches. This method can successfully simulate the changing trends of mosquitoes, infer unknown parameters, train the neural network, and capture the relationships between oviposition rate, temperature, and precipitation. Data from nine cities in Guangdong Province were used for model training, while data from the remaining three cities were used for model validation. Our model successfully fitted data from all cities using the same set of parameters, demonstrating satisfactory performance. On this basis, we successfully identified the functional form between oviposition rate, temperature, and precipitation through the symbolic regression algorithm, which was consistent with the results obtained from the neural network. These findings show that our method can accurately simulate population dynamics while exploring unknown biological mechanisms. [ABSTRACT FROM AUTHOR]

The modern academic world can hardly boast of substantial research on how people become loyal to businesses thanks to TikTok. The current research seeks to add some knowledge to the field by exploring the impact that TikTok services' quality and satisfaction from using it have on Russian and Chinese users' loyalty to opinion leaders in this social network. The analysis of research data collected from the survey of 400 TikTok users was done by means of the partial least squares structural equation modeling method. This allowed obtaining results that confirmed that e-service quality significantly and positively affects users' e-satisfaction and loyalty to opinion leaders. E-satisfaction was found to affect loyalty and mediate the relationship between loyalty and e-service quality significantly and positively. This study provides a number of practical recommendations for brands and businesses interested in promoting their products or services through TikTok. [ABSTRACT FROM AUTHOR]

Quality of service is a major determinant of customer commitment to the organization. Therefore, it is important to understand the importance of service quality for the corporate image as well. In this study, the predicting roles of quality of service and customer experience have been unveiled in customer commitment through the mediating effect of corporate image. The population frame used in this study is the customers of logistic services providers in China. Total data from the 366 customers have been used to analyze the hypotheses formulated. The sample has been selected using convenience sampling and the software used for data analysis is Smart-PLS. The analytical technique used is partial least square structural equation modeling. Results of the study show that service quality and customer experience have a significant role in the customer commitment to the suppliers. In addition, it has also been found that service quality and customer experience have a major contribution to building the corporate image of the services suppliers. Further, corporate image played a significant mediating role in the relationship between service quality and customer commitment. The study has theoretically contributed to the body of literature by finding the importance of service quality for predicting customer commitment to the suppliers.

The link between infrastructure and green logistics performance in the services sector has been studied in this study. The mediating function of business performance and service quality are also discussed in this study as this is a deductive and cross-sectional study, the hypothesis was first formulated based on the literature gap and then tested. The unit of analysis is management personnel, entry-level economists, and financial analysts. The structural equation modeling-partial least-squares analysis (PLS-SEM) approach was employed in this investigation. The authors validated the reliability and validity of constructs and indicators used to measure constructs by assessing measurement models. The measurement methodology has then utilized to confirm the study hypothesis. The study results demonstrate that infrastructure and green logistics performance have a beneficial influence on services trade and environment; besides that, service quality and company performance are also important factors in improving services trade in China. Furthermore, firm performance and service quality have been identified as strong positive mediators between Green logistic performance, infrastructure, and services trade & environment. This study has a theoretical contribution by introducing, verifying, and proposing a new measurement for three variables: infrastructure, green logistic performance, and services trade & environment. This study has numerous relevant and helpful implications for government agencies and departments. It will provide a pathway for the upcoming industry practitioners in terms of green logistic performance and service trade & environment.

Background: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). Purpose: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. Study Type: Prospective. Population: Post‐surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). Field Strength/Sequence: 3.0 T/Turbo spin echo T1/T2‐weighted, T2*‐weighted multiecho gradient echo and dMRI. Assessment: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single‐factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed‐effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. Statistical Tests: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single‐factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. Results: The single‐factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10–1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27–6.04), which was stronger than the other variables. Data Conclusion: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. Evidence Level: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

This paper selected microplastic polyethylene terephthalate (PET), commonly found in water/wastewater plant effluent, to investigate the changes of PET oxidized under ozonation (designated as ozonized PET), followed by sodium hypochlorite oxidation (designated as ozonized-chlorinated PET) and studied their influence on the adsorption of the disinfection by-product bromoform (TBM). Fragmentation and cracks appeared on the oxidized PET surface. As the oxidation degree increased, the contact angle decreased from 137° to 128.90° and 128.50°, suggesting hydrophilicity was enhanced. FTIR and XPS analyses suggested that carbonyl groups increased on the surface of ozonized PET and ozonized-chlorinated PET, while the formation of intermolecular halogen bonds was possible when PET experienced dual oxidation. These physiochemical changes enhanced the adsorption of TBM. The adsorption capacity of TBM followed the order of ozonized-chlorinated PET (2.64 × 10−6 μg/μg) > ozonized PET (2.58 × 10−6 μg/μg) > pristine PET (2.43 × 10−6 μg/μg). The impact of raw water characteristics on the adsorption of TBM onto PETs, such as the pH, and the coexistence of inorganic ions and macromolecules (humic acid, surfactant, and bovine serum albumin) were studied. A different predominant adsorption mechanism between TBM and pristine PET or oxidized PETs was proposed. [ABSTRACT FROM AUTHOR]