Automation seems widespread today, yet it is not implemented in daily life. However, most home automation systems are expensive, object-dependent, and lacking in crucial features. The Internet of Things was enabled by this paper’s low-cost home automation system. For development of the IoT, the system used Node-RED, an open-source platform that uses nodes to visualize tasks. This innovation could operate home devices, including plugs, from anywhere. Wireless sensor network (WSN) technology would record and upload data to the web server from each room. Using the publish-and-subscribe Message Queuing Telemetry Transport (MQTT) protocol, these WSN technologies would communicate. The third feature can modify notifications. In situations of doubt, the house member would be notified by email. This proposal promotes home automation through the IoT.

Lifestyle diseases such as diabetes manifest through subtle and non-stationary clinical patterns, posing significant challenges for real-time prediction and monitoring. Conventional machine learning models often struggle to maintain performance under evolving data distributions due to concept drift. This study proposes an adaptive deep learning framework designed to handle concept drift through incremental learning in clinical data streams. The investigation centers on evaluating the effectiveness of various buffering strategies namely, adaptive buffering, FIFO buffering, and streaming without buffering combined with drift detection mechanisms for healthcare prediction. A balanced clinical dataset exhibiting evolving patterns was used to benchmark model performance. Deep learning architectures, including BiLSTM, GRU, LSTM, and Bayesian Neural Networks were incrementally trained, and their drift(Abrupt, Gradual and Recurring) responsiveness was assessed using three categories of detectors: 1) statistical test-based, 2) error-rate-based, and 3) uncertainty-based approaches leveraging Monte Carlo Dropout. Results indicated that adaptive buffering strategies consistently outperformed FIFO and no-buffer strategies, yielding higher accuracy, precision, and recall, especially under abrupt and large drift magnitudes. The hybrid drift detection method, combined with Bi-LSTM, demonstrated the best performance in maintaining retention and minimizing forgetting, even as the drift magnitude increased. Additionally, the drift magnitude study highlighted that larger drifts had a significant impact on model performance, with adaptive buffering and uncertainty-based drift detection proving to be more resilient to high drift intensities. This research underscores the importance of combining robust drift detection methods and adaptive buffering strategies to enhance the robustness of models dealing with concept drift in real-world applications.

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems.

Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built... | Find, read and cite all the research you need on Tech Science Press

Most of the challenges to sustainable development are interconnected and systemic in nature, which makes achieving this goal particularly challenging. Research on these barriers and their solutions revealed that neither technological nor social innovation hinders the adoption of this development model. The main reason lies in the specific mechanisms for overcoming inertia, resistance to change, path dependency, and entering a new trajectory. One of the key spaces where the widest circles of the population are in close contact with new technologies is the financial sector. Fintech has significant potential to overcome these limitations, change behavioral patterns, reduce path dependence, and launch development on a new trajectory. The article provides a comprehensive analysis of these processes using the example of the expanded BRICS countries. It compares heterogeneous socioeconomic landscapes and assesses the readiness of the countries in question to master more complex development models, using digital banking as an example. The study identifies the blocking factors and suggests ways to overcome them. An interdisciplinary synthesis of the theories of narrative persuasion, evolutionary governance, and path dependence provides a new understanding of the interaction of financial systems, governance structures, and social behavior, upon which financial inclusiveness depends as a cornerstone for achieving balanced economic growth. Appendices