EFFECT OF COMBINING LOSARTAN AND PERINDOPRIL IN DIABETIC PATIENTS WITH PROTEINURIA

  Objective: This retrospective study compared the efficacy and safety of losartan monotherapy versus losartan and perindopril combination therapy in diabetic patients with hypertension and albuminuria. Factors affecting the change in albuminuria were identified.Methods: Through quota sampling, the study recruited patients from a tertiary care hospital. Outcome parameters included urinary albumin creatinine ratio (UACR), blood pressure (BP), serum creatinine, and estimated glomerular filtration rate (eGFR). Both pre- and post-treatment measurements were traced from patient's medical records.Results: No significant differences (p=0.615) were detected in the pre- and post-treatment change in UACR between the monotherapy group (−38.3 mg/g, interquartile range [IQR]:−618.8-0) and combination therapy group (−88.4 mg/g, IQR: −729.3-+375.7). There was a considerably higher percentage of patients attaining the target BP of <125/75 mmHg in the combination group (66.7%; p<0.001). In terms of safety, combined losartan and perindopril caused more hypotension (p=0.003), higher rise in serum creatinine (p=0.481), and greater drop in eGFR (p=0.861). Body mass index was shown to have significant negative correlation with UACR reduction (r=−0.449; p=0.036).Conclusion: The main finding of this study was that losartan alone was as equally efficacious as combined losartan and perindopril in lowering UACR and BP among diabetic patients.Â

Harder, Better, Faster, Stronger: Interactive Visualization for Human-Centered AI Tools

Human-centered AI (HCAI), rather than replacing the human, puts the human user in the driver's seat of so-called human-centered AI-infused tools (HCAI tools): interactive software tools that amplify, augment, empower, and enhance human performance using AI models; often novel generative or foundation AI ones. In this paper, we discuss how interactive visualization can be a key enabling technology for creating such human-centered AI tools. Visualization has already been shown to be a fundamental component in explainable AI models, and coupling this with data-driven, semantic, and unified interaction feedback loops will enable a human-centered approach to integrating AI models in the loop with human users. We present several examples of our past and current work on such HCAI tools, including for creative writing, temporal prediction, and user experience analysis. We then draw parallels between these tools to suggest common themes on how interactive visualization can support the design of future HCAI tools

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management

Exploring Internet of Things Adoption Challenges in Manufacturing Firms: A Fuzzy Analytical Hierarchy Process Approach

Innovation is crucial for sustainable success in today's fiercely competitive global manufacturing landscape. Bangladesh's manufacturing sector must embrace transformative technologies like the Internet of Things (IoT) to thrive in this environment. This article addresses the vital task of identifying and evaluating barriers to IoT adoption in Bangladesh's manufacturing industry. Through synthesizing expert insights and carefully reviewing contemporary literature, we explore the intricate landscape of IoT adoption challenges. Our methodology combines the Delphi and Fuzzy Analytical Hierarchy Process, systematically analyzing and prioritizing these challenges. This approach harnesses expert knowledge and uses fuzzy logic to handle uncertainties. Our findings highlight key obstacles, with "Lack of top management commitment to new technology" (B10), "High initial implementation costs" (B9), and "Risks in adopting a new business model" (B7) standing out as significant challenges that demand immediate attention. These insights extend beyond academia, offering practical guidance to industry leaders. With the knowledge gained from this study, managers can develop tailored strategies, set informed priorities, and embark on a transformative journey toward leveraging IoT's potential in Bangladesh's industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and equips industry leaders to navigate them effectively. This strategic navigation, in turn, enhances the competitiveness and sustainability of Bangladesh's manufacturing sector in the IoT era

The influence of emotional labour strategies on customer satisfaction and word of mouth recommendations in group tours

Service employees are required to create and maintain a positive emotional disposition during a service encounter to influence the customer’s impressions and overall satisfaction. Different emotional labour strategies performed by employees will yield different effects because customers may perceive them as sincere, fake or robotic. This study aims to link the customer’s perception of the emotional labour strategies performed by service employees and the potential outcomes of these strategies on the customers. A correlation analysis is used to identify the relationship between the strategies and the customer outcome whereas a multiple regression is used to identify which of the strategies has the strongest effect on the customer outcome. Overall, the findings offer strategic marketing implications for practitioners

Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research