Real-time Data Research Articles

Societal Adaptation to Pandemic Challenges: Analysing the Role of Local Governance and Healthcare Systems in Madhya Pradesh

The COVID-19 pandemic posed unprecedented challenges, compelling communities globally to adapt rapidly to protect lives and maintain societal functions. This study examines the role of local governance and healthcare systems in Madhya Pradesh in addressing these challenges and fostering societal resilience. Drawing on quantitative data from government health agencies and qualitative insights from community feedback, this research explores how local governance structures facilitated public health response, resource allocation, and pandemic preparedness. Findings indicate that decentralized decision-making and robust healthcare infrastructure were instrumental in managing health crises, providing essential services, and supporting economic resilience at the grassroots level. Key results show that regions with stronger local governance exhibited a 30% lower infection rate compared to less governed areas, while areas with well-resourced healthcare systems saw a 20% higher recovery rate. Resource distribution, however, varied significantly, with urban areas receiving 60% more medical supplies than rural regions, creating disparities in healthcare access. Data analysis revealed that healthcare facilities that integrated digital solutions, such as telemedicine, were able to serve 45% more patients remotely, mitigating the risk of virus spread and reducing hospital overload. The study identifies the need for capacity-building initiatives, infrastructure strengthening, and data-driven decision-making to enhance future pandemic preparedness. Future prospects include continuous training programs for healthcare workers, increasing community awareness of health protocols, and leveraging technology for real-time data monitoring. These insights contribute to a comprehensive framework for building resilient healthcare systems in Madhya Pradesh, better prepared for future public health crises.

Use of Nanotechnology in Cosmetics and Cosmeceuticals

Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted delivery and prolonged effects. The paper explores the potential benefits and safety concerns associated with these nano-based cosmetic products. It also provides a comprehensive analysis of recent advancements, regulatory considerations, and future trends in the field, emphasizing the need for further research to fully understand the long-term implications of nanotechnology in cosmetics and cosmeceuticals. This review aims to provide a critical overview for researchers and industry professionals interested in the evolving landscape of nanotechnology in beauty and skincare. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework

The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

Optimizing Cloud Data Storage: Evaluating File Formats for Efficient Data Warehousing

This paper presents a detailed analysis of three widely-used data storage formats—Parquet, Avro, and ORC— evaluating their performance across key metrics such as query execution, compression efficiency, data skipping, schema evolution, and throughput. Each format offers distinct advantages depending on the nature of the workload. Parquet is optimized for read-heavy analytical queries, providing excellent compression and efficient query performance through its columnar structure. Avro excels in write-heavy, real-time data streaming scenarios, where schema flexibility and backward compatibility are crucial. ORC balances the two, offering strong support for analytical and transactional workloads, especially in handling complex queries and nested data structures. This comparative study highlights the contexts in which each format performs best, providing valuable insights into the trade-offs associated with their use in cloud data warehouses and large-scale data processing environments.

Research on Bionic Robot Motion Control Based on Reinforcement Learning

This research explores the application of reinforcement learning (RL) to enhance the motion control of bio-inspired robots across various environments. Focusing on underwater, terrestrial, and aerial robotic models, the study integrates model-free Q-learning, Deep Q Network (DQN), State-Action-Reward-State-Action (SARSA), and Double Deep Q Network (DDQN) algorithms. These methods are employed to achieve adaptive and efficient movement strategies without relying on pre-defined environmental models. The methodologies range from real-time data capture using live camera feeds for terrestrial robots to simulating aquatic and flight dynamics in controlled environments. Experimental results confirm the efficacy of these RL methods, demonstrating significant improvements in the robots' ability to adapt to dynamic and unknown environments, optimize movement efficiency, and navigate complex scenarios. The findings suggest a promising direction for future robotic applications, emphasizing the need for further research on optimizing these algorithms and expanding their real-world applicability. This study highlights the potential of RL to revolutionize robotic motion control, making robots more versatile and capable in varied and unpredictable settings.

The Effect of ERP System Utilization, Inventory Management, and Interdivisional Collaboration on Supply Chain Operational Efficiency in the Automotive Industry

This study examines the impact of ERP system utilization, inventory management practices, and interdivisional collaboration on supply chain operational efficiency in Indonesia’s automotive industry. Using a quantitative approach, data were collected from 120 respondents and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that ERP system utilization, inventory management, and interdivisional collaboration contribute to supply chain efficiency, with a combined effect explaining 67% of the variance in operational efficiency. Findings highlight ERP systems’ role in enhancing real-time data sharing, inventory management’s impact on stock optimization, and collaboration’s ability to streamline cross-functional processes. This study provides insights for industry practitioners seeking to optimize supply chain performance through technology adoption, robust inventory practices, and cohesive interdepartmental collaboration.

Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

PicPatrolling: Smart Surveillance with ESP32CAM

Robotics is an exciting and developing branch of science. Robots are now common in society because of their high efficiency and durability, and they have many users. Security and surveillance robots, especially those with wheeled movements and agility, are important for multiplexes in security and surveillance domains, including public security, military, facilities, and home security. These robots carry built-in high-end sensors, cameras, and communication equipment that simplify real-time data collection in indoor and outdoor environments. "PicPatrolling: Smart Surveillance with ESP32CAM” is a technique that attempts to improve the current concept of security and surveillance. The proposed system incorporates ESP32CAM for image capture and transmission without delay and GPS for location tracking. The system includes sending photo e-mail alerts, making immediate reports, and guaranteeing adequate area coverage. The robot can move during operation in a complex environment using computer vision, machine learning, and sensor fusion to recognize objects, faces, and changes and monitor important regions.. Application areas for surveillance robots include commercial and populated regions, transport points and architectural structures, other soft regions that people cannot access, and other dangerous areas that humans cannot monitor for various reasons.

Potential and Challenges of Using Sensing Technology to Support the Eradication of Armed Criminal Groups in Papua

Papua, a resource-rich region in Indonesia, faces significant security challenges due to the presence of armed criminal groups (KKB). The eradication of KKB in this area requires advanced tools, among which sensing technology shows considerable promise. Sensing technologies, including satellites and drones, can offer precise and real-time data crucial for monitoring and responding to KKB activities. However, the effective deployment of these technologies is impeded by Papua's complex geographical terrain, extreme weather conditions, and inadequate infrastructure. This study aims to explore the potential of sensing technology in combating KKB by addressing these specific challenges and proposing strategies to optimize its use. Through a systematic literature review, the research identifies effective sensing methods and highlights the obstacles encountered in similar contexts. Findings suggest that adaptive technology development, increased resilience to weather conditions, and improvements in infrastructure are essential for maximizing the benefits of sensing technology. This research contributes to the development of effective strategies for eradicating KKB and enhancing security in Papua, emphasizing the need for coordinated efforts between government, research institutions, and the private sector to address the region's unique challenges.

Optimization Analysis of Tennis Player Training Mode Based on 3D Simulation Model

This paper is based on 3D simulation technology and deeply analyzes the optimization strategy of tennis player training mode. The research focuses on two core systems: one is a remote motion monitoring system based on architecture, which captures and analyzes athletes’ motion data in real time, providing coaches with accurate motion feedback and training effectiveness evaluation; The second is a 3D-based safety monitoring system, which uses 3D simulation technology to simulate the training environment, monitor and warn potential safety risks in real time, and ensure the safety of athletes during training. These two systems are designed for different demand scenarios in tennis training, with complementary functions, and together constitute an intelligent solution for optimizing the training mode of tennis players. By continuously improving algorithms, optimizing data storage and computation methods, and improving the accuracy of data analysis and mapping, this study aims to enhance the scientific, efficient, and safe training of tennis players, providing strong support for the comprehensive improvement of their skills and physical fitness.

Multi-Source Monitoring and Numerical Simulation Deformation on Highway Steep Slopes Under Rainfall Effects

Rainfall is one of the most important factors affecting slope stability. This study employed multi-source monitoring devices to observe the slope displacements in real time under rainfall infiltration and performed numerical simulations to investigate the effects of different rainfall conditions and anti-slip pile configurations on slope stability. Specifically, multi-source monitoring operations were conducted on the high and steep slopes along the Yunmao Expressway. Real-time data on slope deformation, rainfall, and displacement at the tops of anti-slip piles were collected and analyzed, and numerical simulations were conducted using Geo Studio finite-element software. The findings indicated that abrupt deformation of slopes occurs once a threshold rainfall amount is surpassed and sustained over a specific duration. Slope displacement decreased with increasing slope depth above the potential slip fracture surface, with a more rapid reduction in deformation rates observed in slopes reinforced with anti-slip piles. For equivalent rainfall amounts, short-duration, intense rainfalls led to a rapid decrease in the slope safety factor, which also recovered rapidly once the rainfall ceased, in contrast to long-duration, mild rainfalls. The presence and location of anti-slip piles significantly influenced slope stability; therefore, project implementation should carefully consider factors such as cost and duration for optimal decision making.

Towards an integrated model for predictive well control using real-time drilling fluid data

This paper explores the development of an integrated, real-time predictive model for well control that leverages data from drilling fluids to enhance operational safety and efficiency. Traditional well control methods, often reactive in nature, present significant limitations in managing the risks associated with well control issues, such as blowouts and kicks. By utilizing real-time data from drilling fluids—such as pressure, temperature, flow rate, and mud weight—predictive models enable early detection of potential well control problems, allowing for faster decision-making and proactive management of well stability. Key components of the model include data acquisition systems, real-time analytics, and decision-making algorithms that work together to minimize risks and reduce non-productive time (NPT). The paper also highlights the advantages of predictive well control in improving safety, mitigating operational risks, and its potential future applications in broader drilling operations.

Sustainability challenges and solutions in medical laboratory equipment manufacturing

Background: Ultra-low temperature (ULT) freezers are vital for storing biological samples in medical labs, but they have high energy demands and significant environmental impacts. This study investigates the energy efficiency, environmental footprint, and performance of different ULT freezer models. Methodology: A lifecycle assessment (LCA) was performed on five ULT freezers to evaluate global warming potential (GWP), ozone depletion potential (ODP), and energy consumption. Key performance metrics, including compressor efficiency, thermal stability, and material recyclability, were assessed under various operating conditions. Real-time data was collected over 30 days. Results: Freezers with variable-speed compressors exhibited up to 25% lower energy consumption than those with fixed-speed systems. Freezers using low-GWP refrigerants, such as CO₂, reduced total CO₂ emissions by up to 30% over a 10-year lifespan. Models incorporating advanced insulation materials, like vacuum-insulated panels (VIPs), showed improved thermal stability and reduced heat rejection, leading to lower cooling loads. Conclusion: Adopting advanced compressor technologies, low-GWP refrigerants, and high-performance insulation materials in ULT freezers can significantly reduce energy use and environmental impact. These innovations are crucial for sustainable medical laboratory operations, balancing performance with environmental responsibility.

IMPLEMENTING FLOOD-RESISTANT DESIGN STRATEGIES IN COASTAL REGIONS

This systematic review synthesizes findings from 85 articles to examine flood-resistant design strategies in coastal regions, emphasizing an integrated approach that combines traditional engineering solutions, nature-based strategies, green infrastructure, adaptive design, and predictive modeling. The review identifies a continuing reliance on engineered defenses, such as seawalls and automated floodgates, which have proven effective but are limited in addressing the escalating impact of climate change. Findings highlight the growing importance of nature-based solutions—wetlands, mangroves, and coral reefs—that not only mitigate flood risks but also enhance biodiversity and long-term sustainability. Green infrastructure, including permeable pavements and rain gardens, emerges as a valuable urban solution, although it faces implementation challenges in densely populated areas. The integration of predictive modeling and real-time data is transforming flood management by enabling proactive and adaptive responses, though these technologies remain financially inaccessible for many regions. Comparative analysis underscores that flood-resilient design is highly context-dependent, with successful practices varying based on local geography, resources, and socio-economic factors. This review concludes that future flood-resilient infrastructure must prioritize adaptable, sustainable, and region-specific solutions, highlighting the need for cross-disciplinary collaboration and continued policy support to enhance flood resilience and protect vulnerable coastal communities against the effects of climate change.

Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment.

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions.

Composite Hedges Nanopores codec system for rapid and portable DNA data readout with high INDEL-Correction

Reading digital information from highly dense but lightweight DNA medium nowadays relies on time-consuming next-generation sequencing. Nanopore sequencing holds the promise to overcome the efficiency problem, but high indel error rates lead to the requirement of large amount of high quality data for accurate readout. Here we introduce Composite Hedges Nanopores, capable of handling indel rates up to 15.9% and substitution rates up to 7.8%. The overall information density can be doubled from 0.59 to 1.17 by utilizing a degenerated eight-letter alphabet. We demonstrate that sequencing times of 20 and 120 minutes are sufficient for processing representative text and image files, respectively. Moreover, to achieve complete data recovery, it is estimated that text and image data require 4× and 8× physical redundancy of composite strands, respectively. Our codec system excels on both molecular design and equalized dictionary usage, laying a solid foundation approaching to real-time DNA data retrieval and encoding.

The future of wearable health technology: From monitoring to preventive healthcare

Wearable health technology has undergone rapid development, evolving from simple fitness trackers to sophisticated medical devices that provide real-time monitoring and data collection. As these technologies become increasingly integrated into healthcare systems, their potential to shift from reactive treatment to proactive preventive care is gaining attention. This review paper explores the current state of wearable health devices, including the key technologies that enable monitoring and data analysis, and discusses the latest advancements such as AI integration, smart fabrics, and implantable wearables. The paper also delves into the significant role these technologies play in preventive healthcare by enabling continuous monitoring, early disease detection, and personalized health interventions. Furthermore, it addresses the challenges facing wearables, including issues of data accuracy, privacy, and user compliance. Finally, the paper explores future directions and ethical considerations, emphasizing the potential for wearables to reshape healthcare by promoting more accessible, equitable, and preventive healthcare solutions.

Adaptive FPGA-Based Accelerators for Human-Robot Interaction in Indoor Environments.

This study addresses the challenges of human-robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human-robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.