Amgad S. D. Khaled

BACKGROUND Comparative evaluation of machine-learning models for health surveillance data can be distorted by asymmetric random-seed evaluation, single holdout splits, weak hyperparameter tuning, inappropriate missing-data handling, duplicate predictor profiles, and temporal leakage. OBJECTIVE We compared 11 classical, neural, and feature-token Transformer classifiers for diphtheria and dengue surveillance data from Yemen using symmetric repeated-seed evaluation, group-aware validation, temporal testing, calibration assessment, decision-curve analysis, and a selection-bias audit. METHODS : The diphtheria analytic cohort included 566 records with definitive culture results from a source line list of 2,035 suspected or notified cases. Models were evaluated using identical 5-fold outer and 3-fold inner StratifiedGroupKFold cross-validation and the same 5 random seeds. For dengue, 118 records from 27 predictor-identical groups with conflicting labels were excluded from the primary analysis. Data from 2017-2018 were used for development (n=3,672), and 2019 was reserved as a locked temporal test set. One 2019 record with an exact predictor profile observed during development was excluded from the strict primary test (n=2,314). Hyperparameters for final temporal models were selected using group cross-validation within 2017-2018 only. Macro-F1 was the primary metric. Additional measures assessed classification performance, discrimination, calibration, and decision-curve net benefit. For the primary confidence-interval and paired-comparison analyses, probabilities from the 5 seed-specific fits were averaged at the observation level to form a 5-model probability ensemble. Group-bootstrap 95% CIs and paired group-bootstrap differences were then calculated from these ensemble predictions. RESULTS For diphtheria, Deep MLP had the highest 5-seed ensemble Macro-F1 (0.6372, 95% CI 0.5962-0.6757), followed by random forest (0.6263, 95% CI 0.5879-0.6665) and the feature-token Transformer (0.5897, 95% CI 0.5468-0.6322). The Deep MLP-minus-Transformer difference was 0.0475 (95% CI 0.0078-0.0871). Random forest had the highest ROC-AUC (0.6718), the lowest Brier score (0.2278), and a calibration slope closest to 1 (0.8499) among the leading models. The diphtheria selection audit showed substantial differences between retained and excluded records. For dengue, positive prevalence increased from 46.8% during development to 79.8% in the strict 2019 test. Internal performance was high, but all models had lower Macro-F1 on the temporal test. XGBoost performed best in 2019, with a 5-seed ensemble Macro-F1 of 0.5109 (95% CI 0.4886-0.5328), compared with 0.4421 (95% CI 0.4204-0.4639) for the Transformer; the paired difference was 0.0688 (95% CI 0.0523-0.0852). Both models were poorly calibrated after temporal shift. CONCLUSIONS The feature-token Transformer did not outperform the strongest comparator on either task. Strong internal dengue performance did not translate to the subsequent calendar period, and the diphtheria selection audit limits inference to cases with classifiable culture results. Prospective recalibration, external validation, and setting-specific utility assessment are required before deployment. CLINICALTRIAL na

Activity-based costing (ABC) is a way to figure out actual costs. It has become an essential part of both manufacturing and service organizations. This study aims to figure out the effect of the ABC on the manufacturing system. The data was collected from different countries. The method of this study is the systematic Literature Review of the last ten years, 2012-2022. Activities-based costing (ABC) is a good system because it was adopted by many manufacturing businesses to achieve several benefits, including cost ascertainment, which helped them make wise price decisions, cost reduction, which increased profitability, increased productivity, time and resource savings, and improved production quality. Lack of information and inadequate understanding of how to use the ABC system in their company were to blame for companies that did not accelerate its use. Using activity-based costing is effective.

A huge body of research analyzed panic buying during the pandemic; however, there is a dearth of studies scrutinizing social media triggering panic buying of drugs and medical supplies. This study assesses the impact of social media on panic buying of drugs and medical supplies during COVID-19. An online survey was conducted in the Delhi-NCR region (India) using a 5-point Likert scale questionnaire. The data were collected from the respondents (N = 250) who were youngsters considering their pivotal role in the battle against COVID-19. Regression analysis in SPSS was used to process the data. The results manifested a strong impact of social media on buying behavior during COVID-19. Perceived scarcity (p = .000), perceived quality (p = .000), perceived cost (p = .000) of medical supplies, and fear-of-missing-out (p = .000) were found to strongly influence panic buying. Further, perceived scarcity was found to have a significant impact on FOMO (p = .0400). At the same time, perceived cost also had a substantial effect on perceived quality (p = .0100). The results indicated that perceived scarcity did not affect perceived quality (p = .0600). People indulged in hoarding during COVID-19 to remove their fear of missing out. The perception of scarcity of medicines, the quality degradation that may happen later, or the likelihood that costs may increase in the future contributed fairly to people stockpiling. Perceived scarcity also induced fear of missing out, while perception about the quality was dependent on perceived cost.

As informal workers struggle to survive the current crisis, there is reason to believe that more strain would also be exerted on the already fragile sector in the post-crisis era. The implications of the COVID-19 outbreak for the informal economy will continue. Faced with a long crisis, the global economy would likely shrink demand for informal goods and services. The primary goal of this paper is to study consumer behavior during the pandemic, investigate government-implemented Standard Operating Procedures (SOPs) for the unorganized retail sector, and determine if consumers prefer to have goods delivered to their homes rather than visit retail stores. This paper collected information from a number of Indian customers who made unorganized retail transactions in New Delhi and NCR Region. The sample was taken from 700 citizens of New Delhi, India. The study found that product variety, digital payment, scheduling, free delivery and lower speed have a significant effect on customer behavior. In addition, SOPs do not influence consumer behavior. The main reasons for choosing a specific channel are simple availability, security, less hassle, and compliance with all laws. The pandemic led to a renewed trust in the local Kirana shop, with new clients visiting metro and non-metro shops locally. The system in Kirana has changed from physical sales to digital aviation because of the pandemic.

This article aims to empirically examine corporate governance features and their association with Indian listed companies’ profitability. Thirty-three listed firms are selected from the top 100 companies in India. Corporate governance is defined by two parts: board of directors (size, structure, diligence) and audit committee (size, structure, diligence). In contrast, the profitability of Indian listed firms is calculated by two indicators: return on assets (ROA) and earnings per share (EPS). The outcomes concerning ROA reveal that board diligence, size of audit committee, audit committee composition, diligence of audit committee, and size of a company has a significant relationship with ROA. In contrast, board size and board composition have an insignificant association with ROA. Concerning earnings per share (EPS) model, the results show that size of audit committee, audit committee composition, diligence of audit committee, and firm size have a significant relationship with EPS. In contrast, board size, board composition, and board diligence have an insignificant association with EPS. The results may be of benefit to those scholarly researchers, practitioners, and governors who are interested in exploring the quality of corporate governance practices in an emerging market such as India and its effect on firms’ profitability.

While innovation affects company efficiency, research into service innovation is scarce and lacks consensus. In retailing, in latest years, innovation has aroused significant interest in corporate and academic world. This research analyses retail experience innovation from the perspective of marketing innovation and technological innovation perspectives to comprehend its effect on satisfaction. The main goal of this research is to explore the relationship between technological and marketing innovation, word-of-mouth and satisfaction through three key constructs: brand store equity, customer value and store image. A total of 315 retail clients from grocery, apparel, furnishings and electronics shops were covered for the study on which multiple regression was used. The study found that technological innovation was important for shaping image, value and satisfaction compared to marketing innovation. Simultaneously, shop image had the biggest effect on customer satisfaction and satisfaction is a very essential ingredient for word-of-mouth behaviour (WOM-Referral and activity).

The study aims to synthesize the challenges that retailers are facing during the COVID-19 emergency. The research is definitive, informative, and based on a single design of cross-sectional research. Quantitative data based on the research instrument were produced (a questionnaire). Five hundred responses were collected from employees of major retail stores in India. Retailer performance is considered a dependent variable, whereas employee well-being, customer and brand protection, use of technology, government policies, and supply chain are used as independent variables. The current study results indicated that employee well-being and government policies have a significant positive impact on retailer performance, while customer and brand protection, use of technology, and supply chain have a significant positive impact on retailers’ performance. This study will help retailers develop strategies for their employees to protect them and understand that technology is needed in the new normal times. This study highlights the need to be flexible in executing strategic strategies, but retailers need to develop comprehensive action plans, including selecting managers of initiative and defining goals and deadlines. Provided that retailers’ current reality is different from the old normal, no time is lost in taking audacious action.