Real-time Data Collection Research Articles

A Review of the Application and Prospect of Medical Diagnosis System in the Context of Artificial Intelligence

Artificial intelligence (AI) has become a major force for change in the healthcare industry, especially in diagnostic systems, where AI plays a key role in improving the accuracy, speed, and efficiency of diagnosis. The deep integration of AI with internet-based platforms has revolutionized the way healthcare is delivered, especially in low-resource settings, where AI provides a scalable solution for healthcare systems. This article explores the application of artificial intelligence in medical diagnosis, focusing on how AI technologies such as machine learning and deep learning can be integrated into areas such as medical imaging, disease prediction, and telemedicine. The article also discusses the rapid development of the internet-based healthcare system, emphasizing the role of AI in improving diagnostic models through real-time data collection and analysis. At the same time, the article also analyzes current challenges, such as data privacy, ethical issues, and regulatory challenges, which limit the widespread application of AI in clinical practice. Through a comprehensive review of existing research, this article outlines the potential and limitations of AI in medical diagnostics and provides insights into its future development trends. The findings show that although AI has great potential to improve the quality and accessibility of healthcare, its application in clinical practice still needs to carefully consider many factors such as ethics, technology, and regulation.

Efficient and accurate identification of maize rust disease using deep learning model

Common corn rust and southern corn rust, two typical maize diseases during growth stages, require accurate differentiation to understand their occurrence patterns and pathogenic risks. To address this, a specialized Maize-Rust model integrating a SimAM module in the YOLOv8s backbone and a BiFPN for scale fusion, along with a DWConv for streamlined detection, was developed. The model achieved an accuracy of 94.6%, average accuracy of 91.6%, recall rate of 85.4%, and F1 value of 0.823, outperforming Faster-RCNN and SSD models by 16.35% and 12.49% in classification accuracy, respectively, and detecting a single rust image at 16.18 frames per second. Deployed on mobile phones, the model enables real-time data collection and analysis, supporting effective detection and management of large-scale outbreaks of rust in the field.

The Role of IoT and Analytics in Shaping Smart Cities: A Case Study Approach

The paper titled "The Role of IoT and Analytics in Shaping Smart Cities: A Case Study Approach" explores the transformative impact of the Internet of Things (IoT) and data analytics on urban environments, emphasizing their role in the development of smart cities. As municipalities increasingly adopt technology to enhance quality of life, this research identifies key components of smart city infrastructure, including urban planning, basic services, and policy frameworks. The study highlights how IoT facilitates real-time data collection and analysis, which are crucial for effective urban management and decision-making. By examining various case studies, particularly focusing on Barcelona, the paper illustrates practical applications of IoT in traffic management, waste reduction, and citizen engagement. The findings underscore the necessity for policymakers and urban planners to embrace these technologies to foster sustainable urban development while addressing challenges such as data security and integration. This comprehensive literature review serves as a foundational resource for stakeholders aiming to leverage technological synergies in creating resilient and efficient urban spaces.

Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach

As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around data systems and IoT technologies, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the application of technologies, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on machine learning and IoT applications. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on smart technologies and systems. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on digital transformation. Topic 6 focuses on technical aspects such as modeling, system performance and prediction algorithms. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out.

A Predictive Maintenance Framework for Offshore Industrial Equipment: Digital Transformation for Enhanced Reliability

This review presents a novel predictive maintenance framework designed to enhance the reliability of offshore industrial equipment by integrating artificial intelligence (AI), the Internet of Things (IoT), and 3D modeling. In offshore energy operations, maintaining equipment reliability is paramount due to the harsh environmental conditions and the critical nature of uninterrupted service. Traditional maintenance approaches, such as reactive or preventive methods, often fall short in addressing the complexity and unpredictability of offshore environments. The proposed framework leverages real-time monitoring, lifecycle management, and predictive analytics to anticipate equipment failures before they occur, optimizing operational uptime and minimizing downtime costs. The framework focuses on three key components: AI-driven predictive analytics, IoT-based real-time data collection, and 3D modeling for virtual equipment monitoring. AI algorithms analyze vast datasets from sensors to detect patterns and predict potential failures, allowing for proactive maintenance scheduling. IoT sensors continuously monitor equipment health, providing real-time insights into operational conditions, such as vibrations, temperature, and pressure. Furthermore, 3D modeling offers a visual representation of offshore equipment, helping to forecast potential failures and visualize maintenance needs more effectively. This integrated approach addresses the unique challenges of offshore operations by providing more accurate predictions, reducing risks associated with equipment failure, and enhancing the overall efficiency of offshore energy operations. The framework's novelty lies in its fusion of cutting-edge technologies, which together form a comprehensive solution to redefine reliability engineering in offshore industries. The model aims to drive the digital transformation of maintenance practices, improving safety, reducing costs, and ensuring the continued performance of critical offshore infrastructure.

Integrasi Teknologi IoT pada Sistem CRM untuk Meningkatkan Kepuasan dan Retensi Pelanggan

The Internet of Things (IoT) has become one of the leading technologies in digital transformation, including in customer relationship management (CRM). This article discusses how IoT integration in CRM systems can improve customer satisfaction and retention through real-time data collection, service personalization, and data-driven decision making. With a literature study and implementation analysis, this research shows that IoT technology can provide deeper insight into customer behavior, improve operational efficiency, and create a more satisfying customer experience.

Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems

This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.

Construction of an Intelligent Medical Monitoring System Network for Exercise Rehabilitation Based on the Internet of Things

Exercise rehabilitation refers to a rehabilitation method that restores or improves human motor function through scientific, systematic, and regular exercise training, and it has a very wide range of applications. This paper explored the construction of an intelligent medical monitoring system network for sports rehabilitation based on the Internet of Things (IoT) technology. The system consists of a wireless sensor acquisition module for collecting sports rehabilitation data, a data storage and analysis module for cloud management and deep learning analysis, and a medical monitoring module for remote supervision and patient interaction. The system aimed to achieve personalized exercise rehabilitation plans through real-time monitoring and data collection of patients, and provide remote medical monitoring services to improve the rehabilitation effect and quality of life of patients. The system architecture adopted the IoT technology and cloud storage technology, and combined the deep learning convolutional neural network (CNN) model to achieve remote monitoring and data analysis. The monitoring center of the system can monitor patients’ physiological indicators, exercise status, and rehabilitation effects in real time through IoT wireless sensors, and upload the data to the cloud platform for storage and analysis. Doctors and patients can access data through mobile phones or computers and communicate online. The data showed that when measuring a patient’s heart rate using wireless sensors, the measurement accuracy mostly exceeded 99.8%, and some samples had detection accuracy up to 100%. In addition, traditional sensors can detect heart rates up to 5.1[Formula: see text]s, while wireless sensors can detect heart rates up to 1.28[Formula: see text]s. The CNN had a maximum recall rate of 99.51%, a precision of 99.68%, and an accuracy rate of 99.8% in the classification of psychological data of rehabilitation patients. The experimental results indicated that the system can achieve comprehensive monitoring and effective management of the patient’s rehabilitation process, and its operation is stable and has good practical value.

Water Quality Monitoring Using Arduino and Turbidity Sensor

Water quality monitoring is essential for ensuring safe and clean water for various applications, including drinking, agriculture, and industrial use. Traditional methods of water quality assessment are time-consuming and require laboratory testing, making real-time monitoring challenging. This project proposes an Arduino-based water quality monitoring system that provides a cost-effective and efficient solution for realtime data collection and analysis. The system utilizes multiple sensors to measure key parameters such as pH level, turbidity, temperature, dissolved oxygen, and conductivity. These sensors are interfaced with an Arduino microcontroller, which processes the collected data and transmits it to a display module or a cloud-based server for remote monitoring. The real-time data acquisition enables users to take immediate corrective actions when water quality deviates from acceptable standards. The proposed system is ideal for applications in rural and urban water supply systems, aquaculture, and environmental monitoring. Its low cost, ease of deployment, and real-time functionality make it a promising alternative to traditional water quality testing methods. Future enhancements can include IoT integration for remote monitoring and AI-based analytics for predictive water quality assessment. Keywords: Arduino, water quality monitoring, pH sensor, turbidity sensor, IoT, real-time monitoring

Study on the Optimization of Logistics Procurement Mode in the Background of Supply Chain Internet of Things

In the traditional logistics procurement model, there are problems such as incomplete information acquisition and information lag. The information between suppliers and purchasers cannot be exchanged in a timely manner, which leads to certain risks and increased costs when both parties formulate strategies. The application of Internet of Things (IoT) technology in supply chain management, through real-time data collection, intelligent processing, and optimized decision-making, significantly enhances the transparency, efficiency, and response speed of the supply chain. It is conducive to promoting internal management innovation and supplier collaborative innovation, reducing the cost of information acquisition and communication with suppliers during the procurement process, and thereby promoting the improvement of total factor productivity. This paper analyzes the problems existing in the logistics procurement model in the traditional environment, compares and analyzes the role and innovation of the procurement model under the background of supply chain IoT, and finally compares and demonstrates the logistics service levels of two companies before and after the optimization of the procurement model under the background of supply chain IoT. The optimization of the procurement model under the background of supply chain IoT can effectively improve the service quality of enterprises, enhance the overall management level, assist enterprises in sharing risks, effectively reduce the inventory costs of enterprises, and effectively strengthen the integration effect of logistics information. Therefore, the research on the optimization of the procurement model under the background of supply chain IoT is very necessary.

Computational Nuclear Oncology Toward Precision Radiopharmaceutical Therapies: Ethical, Regulatory, and Socioeconomic Dimensions of Theranostic Digital Twins.

Computational nuclear oncology for precision radiopharmaceutical therapy (RPT) is a new frontier for theranostic treatment personalization. A key strategy relies on the possibility to incorporate clinical, biomarker, image-based, and dosimetric information in theranostic digital twins (TDTs) of patients to move beyond a one-size-fits-all approach. The TDT framework enables treatment optimization by real-time monitoring of the real-world system, simulation of different treatment scenarios, and prediction of resulting treatment outcomes, as well as facilitating collaboration and knowledge sharing among health care professionals adopting a harmonized TDT. To this aim, the major social, ethical, and regulatory challenges related to TDT implementation and adoption have been analyzed. Methods: The artificial intelligence-dosimetry working group of the Society of Nuclear Medicine and Molecular Imaging is actively proposing, motivating, and developing the field of computational nuclear oncology, a unified set of scientific principles and mathematic models that describe the hierarchy of etiologic mechanisms involved in RPT dose response. The major social, ethical, and regulatory challenges to realize TDTs have been highlighted from the literature and discussed within the working group, and possible solutions have been identified. Results: This technology demands the implementation of advanced computational tools, harmonized and standardized collection of large real-time data, and modeling protocols to enable interoperability across institutions. However, current legislations limit data sharing despite TDTs' benefiting from such data. Although anonymizing data is often sufficient, ethical concerns may prevent sharing without patient consent. Approaches such as seeking ethical approval, adopting federated learning, and following guidelines can address this issue. Accurate and updated data input is crucial for reliable TDT output. Lack of reimbursement for data processing in treatment planning and verification poses an economic barrier. Ownership of TDTs, especially in interconnected systems, requires clear contracts to allocate liability. Complex contracts may hinder TDT implementation. Robust security measures are necessary to protect against data breaches. Cross-border data sharing complicates risk management without a global framework. Conclusion: A mechanism-based TDT framework can guide the community toward personalized dosimetry-driven RPT by facilitating the information exchange necessary to identify robust prognostic or predictive dosimetry and biomarkers. Although the future is bright, we caution that care must be taken to ensure that TDT technology is implemented in a socially responsible manner.

Using space technology approach to improve quality in emergency departments in India: a quality improvement program.

Introduction Human errors are a leading cause of disability and death among hospitalized patients. Globally, various strategies have been employed to reduce errors and to improve the quality of patient care. One such novel effort never attempted before is the Health-QUEST (Quality Upgradation Enabled by Space Technology) initiative which aims at translating the best quality and safety practices of the Indian Space Research Organization (ISRO) into the realm of emergency care. The objective of this quality improvement initiative was to understand the performance of ED across India using key performance indicators (KPI) specified in HQ (Health Quest) document, adoptability of HQ based practices in hospitals and their effect on KPI. Methods A pre- and post-intervention study design was used where each hospital served as its own control. Preintervention phase captured the time-based key performance indicators based on their existing practice, followed by implementation and training on QUEST recommendations and the post intervention phase assessed any improvement in the KPIs. Data was collected in real-time using REDCap mobile application by scanning the patient wrist bands. The time based KPIs include Door to triage time, Door to doctor time, Door to needle time, Door to pain assessment time, Door to ordering of investigation time, Time from ordering to first review of investigation, Door to discharge/disposition time. Results Seventeen hospitals were invited to participate in this quality improvement initiative, with 14 hospitals completing pilot training for real time data collection. Among them, 10 hospitals participated in the pre and post intervention data collection contributing to 10,332 patients (5296 patients during the pre-intervention and 5036 during the post intervention phase). All the hospitals had a median triage time of less than 5 minutes. Nine out of the ten hospitals recorded a baseline median discharge time of around 2 hours. The most significant reductions in time (mean difference between pre- and post-intervention) were observed in triage time (11 minutes, p < 0.05), door-to-pain assessment time (23 minutes, p < 0.05), time from ordering to first review of investigation (26 hours, p < 0.05), and disposition time (1 hour and 30 minutes, p < 0.05) Conclusion This study evaluated real-time, time-based KPIs in patient management across emergency departments in India. The Health-QUEST program proved to be a straightforward and effective model, achieving significant improvements in multiple time-based KPIs across participating EDs.

Implementation of an Internet of Things (IoT)-Based Air Quality Monitoring System for Enhancing Indoor Environments

This research investigates the development and implementation of an IoT-based air quality monitoring system designed to improve indoor environmental conditions. The primary objective of this study is to develop a comprehensive system that continuously monitors air quality parameters, including smoke, LPG gas, carbon monoxide (CO), temperature, and humidity. The system integrates real-time data collection from various sensors, which is then processed and transmitted to a cloud platform for secure storage and detailed analysis. The user-friendly interface of the software allows for intuitive monitoring and reporting, while built-in notification and alert features ensure timely responses to significant air quality changes. Testing results demonstrate that the system operates with high reliability, providing accurate data and stable performance. The findings confirm that the system effectively addresses indoor air quality concerns and offers valuable insights for maintaining a healthy and safe environment. This research contributes to the field by showcasing a practical application of IoT technology in environmental monitoring.

Data-Driven Civil Engineering: Applications of Artificial Intelligence, Machine Learning, and Deep Learning

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are a great advantage that is coming to civil engineering in ways that detail accuracy can be enhanced, many tasks automated, and predictive modeling improved. Across some of the significant subdomains, these technologies allow for eminent progress in structural health monitoring, geotechnical engineering, hydraulic systems, construction management. Currently, AI-powered models such as Artificial Neural Networks (ANNs), fuzzy logic, and evolution-based algorithms allow engineers to predict failure, optimize design, and better resource management of infrastructures. Yet, despite the potential, the adoption of AI, ML, and DL into civil engineering faces a host of challenges including data availability, computational complexity, model interpretability, integration with traditional systems, etc. High-quality, real-time data collection remains expensive and the resource-intensive nature of DL models limits their application to a large scale. In addition, the "black-box" nature of these models raises ethical and regulatory issues especially in decisions related to safety. Against this backdrop, this paper reviews current and potential applications of AI, ML, and DL in civil engineering within the framework of benefits and limitations of AI, ML, and DL, focusing on comparisons. Besides that, the paper outlines future directions regarding cloud computing, explainable AI, and regulatory frameworks. With all these changes within the scope of the discipline, AI-driven technologies will be major in safe, efficient, and sustainable infrastructure systems, provided that success is specifically dependent on addressing these key challenges.

Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems

BackgroundIn prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows.MethodsWe conducted semi-structured interviews with 20 prehospital providers recruited from four Emergency Medical Services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes.ResultsOur qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols using hands-free methods (e.g., voice commands). Key considerations for successful CDSS adoption included balancing the frequency and urgency of alerts to reduce alarm fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS.ConclusionThis study provides empirical insights into the challenges and user needs in prehospital decision-making and offers practical and system design implications for addressing these issues.

Leveraging the Internet of Things, Remote Sensing, and Artificial Intelligence for Sustainable Forest Management

Sustainable forest management is vital for addressing climate change, biodiversity loss, and deforestation. Human-induced stresses on forest ecosystems demand innovative approaches to ensure long-term health and productivity. This study explores how cutting-edge technologies, including the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), enhance sustainable forest management practices. Researchers reviewed 196 studies published between 2021 and 2024 from IEEE Xplore Digital Library, MDPI, Taylor &amp; Francis, ScienceDirect, Frontiers, Springer, SAGE, Hindawi, Nature, Wiley Online Library, and Google Scholar. The findings highlight IoT devices like drones, enabling real-time data collection on temperature, humidity, soil moisture, and tree growth, facilitating continuous forest monitoring. Remote sensing technologies, utilizing satellite imagery and aerial surveys, deliver high-resolution data for large-scale forest assessments, including forest cover changes, biomass estimation, and early detection of illegal logging. When integrated with AI, these tools enhance predictive modeling, data analysis, and decision-making, leading to more effective forest management strategies. The study also identifies challenges such as data security concerns, bandwidth limitations, interoperability issues, and high costs. Despite these barriers, IoT, remote sensing, and AI present transformative potential for improving forest resilience, carbon sequestration, and biodiversity conservation. These technologies are crucial in preserving forest ecosystems and mitigating climate change impacts by advancing real-time monitoring, optimizing resource allocation, and enabling data-driven decisions.

Analytical Examination of Commonly Utilized Biometric Equipment in Educational Institutions: Features, Applications, Practical Usage, and Role in Fostering Organizational Discipline

Biometric technology has become a vital component in modern educational institutions, facilitating the management of attendance, security, and overall discipline. This study explores the features, utilities, and effectiveness of commonly used biometric equipment in educational settings, with a focus on how these systems contribute to building a disciplined and organized institution. The analysis primarily focuses on three types of biometric technologies: fingerprint scanners, facial recognition systems, and iris recognition devices. Each technology is evaluated based on key features such as accuracy, speed, cost-effectiveness, and user-friendliness. For instance, fingerprint scanners are widely used for their affordability and ease of use, though they may suffer from wear and tear over time. Facial recognition systems, while offering contactless use and greater convenience, raise privacy concerns. Iris recognition is highly accurate but expensive, limiting its widespread adoption in educational institutions. The utilities of biometric systems are diverse, extending beyond simple attendance tracking. They are instrumental in restricting unauthorized access to sensitive areas, monitoring punctuality, and even controlling student movement within the campus. These systems ensure real-time data collection and can be integrated with existing management software for seamless reporting and analysis. The actual use of these technologies varies between institutions, with some using them merely for attendance purposes and others incorporating them into broader security protocols. However, their common purpose remains the same: to promote accountability and enhance the discipline within the institution. By minimizing human error and ensuring consistent monitoring, biometric systems play a pivotal role in fostering a disciplined environment. This study concludes that while biometric technology offers significant advantages in terms of efficiency and security, institutions must balance these benefits with considerations of cost, privacy, and ethical concerns to maximize their utility in promoting a disciplined educational environment.

Design and Implementation of a Virtual Hoisting System: A Digital Twin Approach for Early Warning and Fault Detection

Mine hoists are vital for efficiently transporting materials and personnel between surface and underground mining operations, making their safety and reliability paramount. However, conventional monitoring approaches, which rely on distributed sensors or video surveillance, are limited in their ability to capture the global dynamics of the system, leading to suboptimal decision-making and potential safety risks. This research introduces the integration of digital twin technology into the monitoring and management of mine hoists to address these challenges. This study uses Unity-based modeling to create a virtual system capable of real-time synchronization with its physical counterpart. The digital twin simulates dynamic load changes, predicts potential faults, and optimizes operational parameters through intelligent algorithms. A robust monitoring framework was developed, consisting of a multi-layered architecture that integrates physical systems, real-time data collection, and virtual simulations. Field tests on a multi-rope friction hoist verified the system’s performance, with results demonstrating improved stability, accuracy, and predictive capabilities. A health evaluation model further enhances safety by categorizing the hoist’s operational state into health levels such as ‘healthy,’ ‘sub-healthy,’ ‘warning,’ and ‘fault.’ This model identifies critical risks, such as wire rope tension anomalies, and provides early warnings, ensuring timely interventions.

Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture

Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This study introduces a novel hybrid plant growth model integrating stochastic, empirical, and optimization approaches, using Internet of Things sensors for real-time data collection. Unlike traditional methods, the hybrid model systematically captures environmental variability, simulates plant growth dynamics, and optimizes resource inputs. The prototype growth chamber, equipped with IoT sensors for monitoring environmental parameters such as light intensity, temperature, CO2, humidity, and water intake, was primarily used to provide accurate input data for the model and specifically light intensity, water intake and nutrient intake. While experimental tests on lettuce were conducted to validate initial environmental conditions, this study was focused on simulation-based analysis. Specific tests simulated plant responses to varying levels of light, water, and nutrients, enabling the validation of the proposed hybrid model. We varied light durations between 6 and 14 h/day, watering levels between 5 and 10 L/day, and nutrient concentrations between 3 and 11 g/day. Additional simulations modeled different sowing intervals to capture internal plant variability. The results demonstrated that the optimal growth conditions were 14 h/day of light, 9 L/day of water, and 5 g/day of nutrients; maximized plant biomass (200 g), leaf area (800 cm2), and height (90 cm). Key novel metrics developed in this study, the Growth Efficiency Ratio (GER) and Plant Growth Index (PGI), provided solid tools for evaluating plant performance and resource efficiency. Simulations showed that GER peaked at 0.6 for approximately 200 units of combined inputs, beyond which diminishing returns were observed. PGI increased to 0.8 to day 20 and saturated to 1 by day 30. The role of IoT sensors was critical in enhancing model accuracy and replicability by supplying real-time data on environmental variability. The hybrid model’s adaptability in the future may offer scalability to diverse crop types and environmental settings, establishing a foundation for its integration into decision-support systems for large-scale indoor farming.

Mechatronics Application and Performance Analysis in Intelligent Transportation Systems

This paper presents a comprehensive analysis of the integration of mechatronics in intelligent transportation systems (ITS), elucidating its functional capabilities and limitations. As urbanization accelerates and traffic demand rises, the ITS has emerged as vital solutions for addressing traffic congestion and safety issues. In this regard, mechatronics technology plays a key role by enhancing the intelligence of traffic management, thereby improving the overall efficiency and safety of transportation networks. Thus, the paper provides a detailed analysis of the mechatronics application in intelligent traffic signal control, road condition monitoring and management, and vehicle positioning and navigation. In the domain of intelligent traffic signal control, mechatronics technology facilitates adaptive signal adjustments through real-time data collection and analysis, effectively alleviating traffic congestion and enhancing road capacity. In the field of road condition monitoring and management, this technology combines advanced sensors and monitoring equipment to realize accurate identification and dynamic monitoring of vehicles, providing powerful support for traffic management and law enforcement. In addition, the use of mechatronics technology in critical areas such as vehicle positioning and navigation markedly enhances road utilization, effectively mitigates traffic congestion, as well as supports sustainable urban development initiatives. Meanwhile, the performance of mechatronics in ITS, including real-time, intelligence, safety and reliability, is analyzed, highlighting its remarkable effect in enhancing the overall performance of ITS.