Real-time Data Analysis Research Articles

ATC-CNN-Based IoT Framework for Real-Time Cardiovascular Monitoring: A Comparative Analysis with Deep Learning Models

Background: Cardiovascular diseases (CVDs), including hypertension, coronary heart disease, and ventricular fibrillation, remain the leading cause of death worldwide. The COVID-19 pandemic accelerated the adoption of telecardiology to minimize in-person interactions, enhancing access to cardiovascular care, especially in remote areas. However, traditional real-time monitoring (RTM) systems often require patient visits, creating a need for cost-effective, user-friendly alternatives that leverage IoT and AI for improved patient management. Methods: This study developed an IoT-enabled RTM system integrated with an Adaptive Thresholding Classifier-Convolutional Neural Network (ATC-CNN) framework for managing cardiovascular patients (CP). Data from a heart failure dataset in the UCI Machine Learning Archive was used to train and evaluate the model. Multiple deep learning (DL) algorithms, including MLP, CNN, LSTM, RNN, and the proposed ATC-CNN, were assessed based on accuracy, precision, recall, and F1 scores. Results: The ATC-CNN model achieved an accuracy of 0.9831, outperforming other DL models, including MLP, CNN, LSTM, and RNN. It demonstrated superior capabilities in handling complex cardiovascular data, offering reliable and timely diagnosis with high precision and recall. The integration of IoT and AI facilitated continuous monitoring and real-time data analysis, addressing key limitations of traditional RTM systems. Conclusion: The ATC-CNN framework shows great potential for revolutionizing cardiovascular care by enhancing remote monitoring capabilities and clinical decision-making. It provides an effective, real-time solution for early diagnosis and intervention, particularly in remote and underserved areas. Future research should focus on expanding datasets and refining the model for broader adaptability in diverse healthcare settings.

Detecting and Preventing ARP Spoofing Attacks Using Real-Time Data Analysis and Machine Learning

Detecting and Preventing ARP Spoofing Attacks Using Real-Time Data Analysis and Machine Learning

Looking beyond disruption to build agile and resilient supply chains for the future

The events of recent years, ranging from geopolitical tensions and global health events to labour shortages and economic crises, have highlighted significant vulnerabilities within global supply chains and emphasised the need for agility and resilience. Recent studies referenced in this paper, including the ‘Global Business Optimism Insights’ report, underscore that while large businesses tend to prioritise strengthening their supply chains, smaller businesses are often more focused on customer base expansion, potentially risking operational robustness. This approach could jeopardise customer satisfaction due to failures in meeting on-time, in-full (OTIF) delivery metrics, leading to potential financial penalties and customer dissatisfaction. With that in mind, this paper aims to help supply chain organisations understand how they can obtain the agility and resilience needed to weather ongoing disruption and futureproof their operations. It provides recommendations on how this can be achieved, pointing to access to trusted, real-time data across the supply chain network as a critical component. The paper also identifies, however, that many supply chain businesses struggle to obtain this access to data for rapid and informed decision making. Research from InterSystems finds that this data challenge is further complicated by a lack of end-to-end supply chain visibility, which is often exacerbated by data silos created by disparate systems and manual processes. These visibility challenges negatively affect key performance indicators such as OTIF metrics and restrict an organisation’s ability to adapt to disruptions swiftly. The paper explores how supply chain organisations can overcome these difficulties, identifying investment in digital supply chain technologies such as decision intelligence platforms as a way in which they can bridge the real-time execution gap and enable supply chain leaders to optimise processes. These platforms integrate real-time data analytics, artificial intelligence (AI) and machine learning (ML) to enhance visibility, decision making and operational efficiency. The paper draws the conclusion that building resilient, future-proof supply chains is crucial for organisations to manage disruptions and capitalise on opportunities effectively. In the wake of ongoing disruption, the paper aims to demonstrate that by adopting the right technological solutions and embracing a cultural shift to data-driven operations, supply chains can transform to better withstand future challenges and move their organisation forward, thus ensuring sustained growth and enhanced customer loyalty.

Enhancing CI/CD Pipelines with Azure Pipelines

This paper has provided in-depth insight on the optimisation of continuous deployment and continuous integration pipelines by using Azure pipelines in the Microsoft’s Azure DevOps suite. It effectively showed different challenges such as scalability issues, automation complexities, toolchain incompatibility. It showed the importance of cloud-based services, security measures and containerization. Through leveraging the effective resource utilization and dynamic testing, the Azure pipeline increase the software reliability and efficiency. This study outlines the benefits for different industries such as sports in which the continuous updates and real-time data analytics is vital.

Enhancing security and risk management with predictive analytics: A proactive approach

and interconnected world. This paper explores the transformative potential of predictive analytics in enhancing security and risk management by shifting from reactive to proactive strategies. Examining the theoretical foundations and application areas of predictive analytics, the paper highlights how organizations can anticipate and mitigate risks across various domains, including cybersecurity, fraud detection, and supply chain management. The benefits of predictive analytics, such as early threat detection and optimized resource allocation, are discussed alongside data quality, privacy, and model interpretability challenges. Additionally, emerging trends, such as artificial intelligence, real-time data analytics, and blockchain technology, are key drivers shaping the future of predictive analytics in risk management. The paper concludes by emphasizing organizations' need to integrate predictive analytics into their risk management frameworks to enhance resilience and ensure long-term success in a rapidly evolving risk landscape.

E-commerce on Hardware sales: A case study of Computer technologies Union in Owerri, Nigeria.

The incorporation of computers into contemporary company operations has significantly altered productivity and communication, essentially making paper-based file systems outdated. The advancement of technology has made it possible to manage data more accurately, streamline procedures, and obtain information more quickly. Better managerial skills are now a trademark of the digital age, allowing companies to accurately and quickly respond to information requests. Innovative business models have also been made possible by the transition to digital technology, especially in the areas of customer engagement and operational management. A more dynamic and responsive business environment has been made possible by the automation of repetitive processes, real-time data analysis, and enhanced communication channels. This has allowed organisations to respond to shifting market conditions with accuracy and agility. A significant advancement in this digital shift is the emergence of e-commerce platforms. These platforms have facilitated a smooth and mutually beneficial relationship between businesses and customers. Online shopping provides customers with an unparalleled level of ease, enabling them to peruse, evaluate, and buy things from the comfort of their own homes. These websites give businesses access to a wider market, an extra source of income, and insightful data analytics on consumer behaviour. By providing easy navigation, safe payment choices, and personalised experiences, the e-commerce model improves client engagement. The transition to e-commerce has increased revenues as well as customer happiness and brand loyalty by offering a convenient and effective purchasing experience. For Computer Technologies Union (CTU) in Owerri, the project's goal was to create an e-commerce website that would improve customer interaction and business operations in response to current trends. To enable simple online purchases, the website was built with a feature-rich catalogue of store merchandise and an intuitive shopping cart. The system was designed to be reliable, scalable, and effective by utilising a three-tier design. Although the middle layer, which used PHP, handled business logic with ease, the backend MySQL database ensured dependable data management. Shopping was simple and easy with the client interface, which could be accessed via a web browser. The speed and precision of service delivery at CTU Owerri were greatly enhanced by the new system after it was fully implemented. While the firm benefited from increased operational efficiency and the ability to better meet customer expectations, customers had a convenient and pleasurable shopping experience. In addition to modernising CTU Owerri's operations, this calculated decision gave it a competitive edge in the online market.

Enhanced Particle Classification in Water Cherenkov Detectors Using Machine Learning: Modeling and Validation with Monte Carlo Simulation Datasets

The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis.

Development of an IoT-Based Air Conditioning Meter for Real-Time Operational Status

Air conditioning systems have become a staple in households and businesses, particularly in regions with tropical climates, where maintaining indoor comfort is crucial. However, the widespread use of air conditioning units has led to a significant increase in energy consumption, contributing to higher electricity bills and environmental concerns, particularly in terms of carbon emissions. Traditional air conditioning units often operate without real-time monitoring, leading to inefficiencies and unnecessary energy use. In response to these challenges, there has been a growing interest in the development of smart, energy-efficient solutions that can monitor and control air conditioning systems remotely. The advent of the Internet of Things (IoT) has opened up new possibilities for integrating sensors and microcontrollers with cloud-based platforms, enabling real-time data collection and analysis. This research builds on these advancements by developing an IoT-based meter designed to monitor the operational status of air conditioning units, providing users with immediate feedback and enabling more efficient energy management.

Developing a digital data platform for surveillance of food and water-borne pathogens in North East India: insight for public health advocacy.

Robust digital infrastructure is vital and the need of the hour, especially in the healthcare sector, for real-time data generation, analysis, and quick decision-making. Food- and water-borne illnesses represent a prominent cause of morbidity and mortality worldwide. India, a developing nation with diverse cultures and food practices, poses a high risk of food-borne diseases and outbreaks, yet is often underreported and ineffectively researched. Also, the unique socio-economic and environmental factors of the Northeast (NE) region contribute to the high burden of food-borne diseases. To address these trepidations, the Indian Council of Medical Research (ICMR) has undertaken a study for the surveillance of food-borne pathogens in NE India. The present study focuses on the development of a digital database system for the systematic surveillance of foodborne disease outbreaks, aiming to address the gaps in traditional surveillance methods and improve disease detection and response capabilities. The digital system integrates mobile applications, web-based platforms, and advanced analytics tools to enable real-time data collection, dissemination, and analysis of food-borne illness data. Additionally, the secure and scalable nature of the system enhances data accuracy and accessibility, making it a valuable tool for enhancing food-borne disease surveillance efforts in resource-constrained settings.

Advancements in Soft Robotic Implants for Long-Term Drug Delivery and Tissue Monitoring

Soft robotic implants are a major upgrade in medical devices providing novel means for long-term drug administration and tissue tracking. The eco-friendly biodegradable body-prosthetic will not only be compatible with the human body, but can also mingle completely without inducing foreign-body responses and discomfort. Since these implants provide programmed and localized drug delivery; hence the significance of these sutures could be found in administering the drugs at a specific site and specific interval so as to diminish the side effects and improved therapeutic outcomes. Modern developments of soft robotics implants have improved their design, performance, interaction with biological and other natural materials. Material developments are becoming smarter, where responsive materials in the form of: smart materials, which can react to environmental stimuli and adjust drug delivery rates immediately (and); etc. Moreover, the evolution of microfabrication and nanotechnology also created high precision and less invasive devices, rendering more efficient and well-accepted to patients. These implants are especially valuable for long-term afflictions like diabetes and cancer as they streamline the ongoing, precise delivery of medications. They also have a crucial role in pain management and postoperative care, effectively providing prolonged release of analgesics, and ultimately enhance patient comfort thus contributing to smoother recovery. For tissue monitoring, implantable flexible soft robotic devices with integrated sensors and actuators are able to provide real-time data collection and analysis for the continuous monitoring of vital physiological information, such as cardiovascular health, neural activity, skin conditions (wound healing) etc. This is important, particularly for early detection of complications and receiving timely interventions to optimize patient outcomes. In the future there are a number of intriguing applications that could be developed with these soft robotic implants like personalized medicine and integration with other state-of-the-art medical technologies. Further research and development should/hopefully will address outstanding challenges e.g. technical, regulatory etc to allow more wide-ranging clinical application with consequent transformative impacts on healthcare. Finally, soft robotic implants show great potential to improve drug delivery and tissue monitoring, with continuous development bringing the field closer to better performing medical solutions.

Enhancing Safety Management in the Ghanaian Construction Industry: Evaluating the Role of UAV Implementation and Construction Managers Understanding

The integration of unmanned aerial vehicles (UAVs) and other rising technologies has become paramount in construction safety monitoring and management. The study aimed to gain insight into the use of drones in construction safety practices and management in the Ghanaian construction industry. Through a literature review, five key domains (aerial surveillance and real-time monitoring, data analysis and risk assessment, emergency response and search-and-rescue operations, information management and visualisation, and safety training and education) were identified as the areas where UAVs or drones could enhance safety practices and management. The study included construction professionals who had a minimum of ten (10) years of experience in project management across different positions. A convenience sampling technique was used for the selection of participants from construction companies within the city of Accra. A structured questionnaire was designed and distributed to these professionals. The collected data was analysed using descriptive statistics and correlation analysis. The correlation analysis highlights the association among the variables used. The study underscores the pivotal role of UAVs in transforming safety practices and management. The diverse applications and potential benefits of UAV technology provide insights for policymakers, educators, and industry stakeholders to optimise safety management strategies and enhance efficiency on construction sites. In addition, the findings emphasise the importance of education and training initiatives to bridge the knowledge gap among construction professionals, thereby promoting a more sustainable and secure construction environment in Ghana.

AI-Powered Anomaly Detection for Kubernetes Security: A Systematic Approach to Identifying Threats

This study delves into the intricacies of AI-based threat detection in Kubernetes security, with a specific focus on its role in identifying anomalous behavior. By harnessing the power of AI algorithms, vast amounts of telemetry data generated by Kubernetes clusters can be analyzed in real-time, enabling the identification of patterns and anomalies that may signify potential security threats or system malfunctions. The implementation of AI-based threat detection involves a systematic approach, encompassing data collection, model training, integration with Kubernetes orchestration platforms, alerting mechanisms, and continuous monitoring. AI-powered threat detection offers numerous advantages, including predictive threat detection, increased accuracy and scalability, shorter response times, and the ability to adapt to evolving threats. However, it also presents challenges, such as ensuring data quality, managing model complexity, mitigating false positives, addressing resource requirements, and maintaining security and privacy standards. The proposed AI-powered anomaly detection framework for Kubernetes security demonstrated significant improvements in threat identification and mitigation. Through real-time analysis of telemetry data and leveraging advanced AI algorithms, the system accurately identified over 92% of simulated security threats and anomalies across various Kubernetes clusters. Additionally, the integration of automated alerting mechanisms and response protocols reduced the average response time by 67%, enabling rapid containment of potential breaches.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Artificial intelligence enabled biodegradable all-textile sensor for smart monitoring and recognition

Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

Transforming Agriculture with Drones: Applications, Challenges and Implementation Strategies

The integration of drone technology, or Unmanned Aerial Vehicles (UAVs), has revolutionized the agricultural sector, offering a new era of precision and efficiency. This paper examines the diverse applications of drones in agriculture, including crop health assessment, precision crop spraying, soil analysis, irrigation management, and livestock monitoring. Equipped with advanced sensors and cameras, drones provide real-time data collection and analysis, enabling farmers to make informed decisions, optimize resource use, and swiftly respond to environmental changes. This technological advancement enhances productivity, conserves resources, and promotes sustainable farming practices. However, the adoption of drones in agriculture faces challenges such as high costs, regulatory uncertainties, and the need for specialized skills, along with concerns over privacy and weather dependency. Despite these obstacles, the potential benefits like improved resource utilization and reduced environmental impact underscore the importance of drones in meeting the growing global food demand. This paper aims to provide an extensive overview of the role and potential of drone technology in reshaping the future of agriculture, empowering farmers to optimize yields, conserve resources and embrace a more sustainable approach in the face of global challenges

Enhancing Agriculture through AloT and Data Analytics Middleware

Middleware in Agriculture delves into the data analytics role of systematic Artificial Intelligence of Things (AIoT) in convergence agriculture with smart farming. AIoT devices, remote diagnostic data analytics platforms, data analytics, formal recognition, and vision learning have generated both the amount and nature of work in rural areas. The reason for the developing changes in the global population by age is the bias in the distribution of food resources, as well as changes in climate change and the soil condition of compost. Data scientists are soon attempting to dive convergence Internet of Things (IoT) advances in savvy cultivating to support ranchers in producing better seeds, crop assurance, and manures utilizing AIoT convergence technology[1]. This distinguishes consumer-provider-administrator as a service subscriber roleon the platform, which goes for earning the nation's economy and the profitability of ranchers. The central regions where AIoT begins to emerge are agricultural robots, scrutiny, and soil and yield observation. While middleware technology for data analysis is applied, there is a great advantage of analyzing regional observation data at a real-time level. Based on this analysis method, it will spread to high-tech industries connected to production, processing, and consumption, which are subdivided areas of each precision agriculture. In this sense, ranchers are utilizing sensors for data analysis and soil information in a detailed way to gather a model that the executives dynamic platforms might use for further examination. Through an outline of real-time data analytics AIoT applications in the farming convergence industry, this paper tries a trust data basement forecast to the farming protocols[2]. It starts with a roadmap to the AIoT and an investigation of real-time data strategies utilized in the horticultural industry data analytics using Universal Middleware, service platforms, vision learning, and dynamic statistics. This study advances a dynamic platform examination of the literature on data resources and how AIoT is utilized in farming convergence. This study looked at the unstructured systems of modern agriculture, divided these processes into devices and services, and presented a systematic formalization model. It should contribute to the integration of segmented data connectivity in the AIoT field. The enhanced research model contributes to the discovery of numerous untapped additional services between devices and services.

Design of an integrated network order system for main distribution network considering power dispatch efficiency

This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system’s prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management.

(Invited) Aiot-Integrated Digital Imaging Sensor for Molecular-Fingerprint Profiling of Extracellular Vesicles in Liquid Biopsy

The advancement of liquid biopsy techniques for non-invasive tumor analysis holds immense potential in continuously tracking tumor status—recurrence, progression, and treatment response—in real-time. To comprehensively assess tumor evolution, cellular diversity, and drug resistance mechanisms, a pivotal step involves scrutinizing proteins and nucleic acids within extracellular vesicles (EVs) present in biofluids. Our focus lies in crafting an advanced digital bead-based sensor system that seamlessly evaluates the molecular profile of EVs in glioblastoma cases. Our innovative approach enriches tumor-relevant genetic data within EVs using antibody-specific magnetic beads, subsequently amplifying it via reverse transcription recombinase polymerase amplification system across forty thousand droplets. These results are scrutinized using a compact imaging device. The core strengths of our methodology include: (a) Streamlined Sample Processing: Our system facilitates ultra-sensitive miRNA detection in EVs within a rapid ninety-minute window, owing to optimized sample preparation and a compact detection mechanism; (b) Enhanced Signal Clarity: Employing a straightforward isothermal assay significantly boosts the fluorescent signal-to-noise ratio within each 70-µm droplet, achieving over 104 turnovers of fluorescent reporters on a chip; (c) Real-Time Data Analysis: Our system seamlessly captures real-time imaging data for the edge computing, utilizing trained YOLOv5 models for classification, and presents genetic results on the smartphone. Its user-friendly design enables utilization by healthcare workers with varying skill levels. This pioneering approach has the potential to offer affordable, rapid, and informative diagnostics particularly beneficial for underserved regions and healthcare systems facing financial constraints. Furthermore, its adaptability for diverse cancers could revolutionize the pace of diagnosis and treatment decision-making through liquid biopsy, potentially establishing its widespread application in global healthcare.

Developing a system for the real-time collection and analysis of mobile vehicle emission data

In this study, an integrated system for remotely monitoring air pollution data was developed. This system combines vehicle exhaust emission data with driving information. It comprises an exhaust extraction device (EED), an improved On-Board Diagnostic II (OBDII) device, a mobile app, and a backend server database. The EED detects exhaust gases such as NOx, COx, and PM2.5 and transmits this information to the OBDII device through 2.4G communication. The OBDII device gathers vehicle driving information through a CAN 2.0 interface, integrates this information with vehicle exhaust emission data, and transmits the integrated information to the app through Bluetooth. The app forwards the integrated data to the backend database through 4G/5G communication for storage. Overall, this system provides a platform for the comprehensive collection, analysis, and application of big data pertaining to vehicle pollution sources and driving behavior. Analysis of actual driving experiments involving three vehicles revealed a correlation of over 80 % between vehicles’ revolutions per minute and CO2 and PM2.5 emissions, indicating that driving behaviors affect vehicle exhaust emissions. In summary, the proposed system not only stores relevant driving information in real time but also provides a platform for the future analysis of driving behavior and vehicle exhaust emissions.