Search Results (1,594)

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately ${10}^{-7}$, and the difference in data transmission capacity was approximately ${1.99\times 10}^{-7}\pm {9.97\times 10}^{-6}$ with a 95% confidence interval of ${-1.16\times 10}^{-7}$ to ${5.13\times 10}^{-7}$ among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care. Full article

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users’ requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi’s production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. Full article

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO₂, CO, O₂), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations. Full article

Vehicle classification technology is one of the foundations in the field of automatic driving. With the development of deep learning technology, visual transformer structures based on attention mechanisms can represent global information quickly and effectively. However, due to direct image segmentation, local feature details and information will be lost. To solve this problem, we propose an improved vision transformer vehicle classification network (IND-ViT). Specifically, we first design a CNN-In D branch module to extract local features before image segmentation to make up for the loss of detail information in the vision transformer. Then, in order to solve the problem of misdetection caused by the large similarity of some vehicles, we propose a sparse attention module, which can screen out the discernible regions in the image and further improve the detailed feature representation ability of the model. Finally, this paper uses the contrast loss function to further increase the intra-class consistency and inter-class difference of classification features and improve the accuracy of vehicle classification recognition. Experimental results show that the accuracy of the proposed model on the datasets of vehicle classification BIT-Vehicles, CIFAR-10, Oxford Flower-102, and Caltech-101 is higher than that of the original vision transformer model. Respectively, it increased by 1.3%, 1.21%, 7.54%, and 3.60%; at the same time, it also met a certain real-time requirement to achieve a balance of accuracy and real time. Full article

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs. Full article

In recent years, the rapid development of wireless technologies has led to the widespread adoption of the Internet of Things (IoT) in various fields. One of the fastest-growing segments of IoT is the “smart” wearables sector. In the next few years, the development of flexible plant-wearable devices that can provide vital information about the physiological characteristics of plants will be essential to support the faster growth of precision agriculture. We propose a small (overall size Ø35 mm × 0.8 mm), ultra-lightweight (0.4 g), and elegant-shaped antenna for unobtrusive integration on a plant surface for application in IoT-based precision agriculture at ISM 2.45 GHz band. The radiating element has a design that resembles a dragonfly, making the antenna visually unnoticeable. We used ZZ Plant leaves as the substrate for the antenna and transparent polymer foil for encapsulating the conductive parts, achieving a highly flexible, waterproof, and chemically resistant antenna for application in harsh environments. The obtained results indicate that the antenna is resilient to changes in substrate relative permittivity up to ±20%. It exhibits high radiation efficiency (between 26% and 40%) and omnidirectional patterns across the ISM 2.45 GHz band. Moreover, the measured results align reasonably well with the simulated ones. Full article

Facility Management (FM) has increasingly focused on integrating Building Information Modeling (BIM) with the Internet of Things (IoT), known as digital twins, in large-scale development projects. Effective BIM integration in FM requires improved cooperation among participants across various project stages. This digital revolution aims to enhance planning, construction, and asset management efficiency, benefiting all parties. However, BIM utilization in FM is limited by incomplete owner understanding, insufficient data accessibility, and stakeholders’ unfamiliarity with BIM procedures and standards. Despite recognizing BIM’s significance, the FM industry faces significant implementation challenges. Facility managers often lack a comprehensive understanding of BIM’s benefits in streamlining operations and enhancing cost efficiency, as well as the necessary skills for its use. Addressing these barriers requires developing an Employer’s Information Requirements (EIR) document at a project’s outset, providing a strategic plan and vision for all involved parties. BIM and IoT are pivotal technologies for transitioning to efficient building operations and crucial for reducing time, costs, and operational challenges throughout any project. This research aims to establish a digital trio workflow, integrating BIM, EIR, and IoT to maximize stakeholder benefits. It explores how preparing the EIR through stakeholder communication can improve design processes, sustainability, efficiency, cost, and time, especially for megaprojects. Full article

Amid the increasingly severe challenges faced by traditional agricultural development, it has become necessary for farms to undergo operational transformations. In considering the direction of this transformation, the growing proportion of older adults in the population and the maturation of modern smart technologies applied to industries must be taken into account. By integrating intelligent Internet of Things (IoT) solutions to aid business operations, leisure farms are expected to provide significant benefits to both operators and visitors. Taiwan, which has long been a leader in precision agriculture, serves as a benchmark in Asia for the successful transformation of traditional farms into leisure farms, becoming a model for neighboring countries. This study investigates the transformative potential of intelligent IoT technology solutions on leisure farms, highlighting their capacity to attract senior citizens and create sustainable business models in competitive, homogeneous markets. The primary objective of this research is to uncover the advantageous factors associated with the adoption of intelligent IoT technology solutions in leisure farms. Employing a grounded theory approach, this research conducted face-to-face semi-structured interviews with 40 leisure farm operators to gain insights into the innovative and sustainable value propositions of leisure farms. This study identifies six key advantageous factors and six constraint factors. This research provides forward-looking insights into the application of intelligent IoT technology solutions in leisure farms, emphasizing strategic directions for operators. The integration of these solutions presents a unique opportunity for leisure farms to meet the demands of elderly individuals seeking safe, natural environments without compromising their interests. By offering tailored leisure activities and entertainment, these solutions enhance the quality of life of seniors and promote rural lifestyles, positioning leisure farms as innovative and competitive players in the market. The insights provided in this study can also inform government policymakers and serve as a foundation for future researchers to extend related studies from a customer perspective. Full article

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition. Full article

Background: In recent years, there has been an increasing effort to take advantage of the potential use of low magnetic induction devices with less than 1 T, referred to as Low-Field MRI (LF MRI). LF MRI systems were used, especially in the early days of magnetic resonance technology. Over time, magnetic induction values of 1.5 and 3 T have become the standard for clinical devices, mainly because LF MRI systems were suffering from significantly lower quality of the images, e.g., signal–noise ratio. In recent years, due to advances in image processing with artificial intelligence, there has been an increasing effort to take advantage of the potential use of LF MRI with induction of less than 1 T. This overview article focuses on the analysis of the evidence concerning the diagnostic efficacy of modern LF MRI systems and the clinical comparison of LF MRI with 1.5 T systems in imaging the nervous system, musculoskeletal system, and organs of the chest, abdomen, and pelvis. Methodology: A systematic literature review of MEDLINE, PubMed, Scopus, Web of Science, and CENTRAL databases for the period 2018–2023 was performed according to the recommended PRISMA protocol. Data were analysed to identify studies comparing the accuracy, reliability and diagnostic performance of LF MRI technology compared to available 1.5 T MRI. RESULTS: A total of 1275 publications were retrieved from the selected databases. Only two articles meeting all predefined inclusion criteria were selected for detailed assessment. Conclusions: A limited number of robust studies on the accuracy and diagnostic performance of LF MRI compared with 1.5 T MRI was available. The current evidence is not sufficient to draw any definitive insights. More scientific research is needed to make informed conclusions regarding the effectiveness of LF MRI technology. Full article

The leading cause of cancer deaths worldwide is attributed to non-small cell lung cancer (NSCLC), necessitating a continual focus on improving the diagnosis and treatment of this disease. In this review, the latest breakthroughs and emerging trends in managing NSCLC are highlighted. Major advancements in diagnostic methods, including better imaging technologies and the utilization of molecular biomarkers, are discussed. These advancements have greatly enhanced early detection and personalized treatment plans. Significant improvements in patient outcomes have been achieved by new targeted therapies and immunotherapies, providing new hope for individuals with advanced NSCLC. This review discusses the persistent challenges in accessing advanced treatments and their associated costs despite recent progress. Promising research into new therapies, such as CAR-T cell therapy and oncolytic viruses, which could further revolutionize NSCLC treatment, is also highlighted. This review aims to inform and inspire continued efforts to improve outcomes for NSCLC patients globally, by offering a comprehensive overview of the current state of NSCLC treatment and future possibilities. Full article

This study conducts a literature review to analyse the incorporation of Industry 4.0 in the construction sector, known as Construction 4.0, in developing countries. This study utilises an effective technique, encompassing academic databases, journals, and conference proceedings, to carefully examine relevant studies published with respect to developing countries. The primary areas of emphasis involve the definition of Construction 4.0. The technologies of execution include six cutting-edge technologies such as Building Information Modelling (BIM), Internet of Things (IoT), robotics, 3D printing, UAVs, and artificial intelligence in construction procedures. This analysis also explores the awareness and understanding of Industry 4.0 in the construction sector (Construction 4.0) in developing countries before identifying where it is being applied therein. Furthermore, obstacles that impede the mainstream adoption in developing countries are identified, including but not limited to such things as insufficient technological infrastructure, skill deficiencies, and budgetary limitations. This review consolidates various studies to provide a thorough comprehension of the present condition of Construction 4.0 in developing nations. As such, this paper aims to provide a guide for future research, policy making, and industry practices in order to promote sustainable and technologically advanced construction methods in these settings. Full article

Trichomonas vaginalis infection represents the most prevalent non-viral, curable parasitic sexually transmitted infection (STI) worldwide. The demand for precise and cost-effective point-of-care (POC) tests is paramount in the pursuit of STI epidemic control, ensuring expeditious patient diagnosis and therapeutic interventions. In the present study, we searched academic databases, including PubMed (US National Library of Medicine and the National Institutes of Health), Scopus, and Web of Science, employing the following keywords: “Trichomonas vaginalis”, “diagnosis”, “point-of-care tests”, and “rapid diagnosis”, to provide information about the development and effectiveness of POC tests to identify T. vaginalis. Present assays for T. vaginalis exhibit suboptimal performance, and the integration of advanced technologies, notably nanotechnologies, emerges as a formidable instrumentality for augmenting diagnostic precision while curtailing expenditure. In this review, we provide an encompassing survey of cutting-edge POC tests for T. vaginalis diagnosis and offer an outlook on future prospects in this domain. Full article

This study introduces a system that merges AI with low-power IoT (Internet Of Things) technology to enhance environmental monitoring, with a specific focus on accurately predicting forest fires through time series analysis. Utilizing affordable sensors and wireless communication technologies like LoRa (Long Range), environmental data have been gathered. One of the key features of this approach is the comparison of the real-time local environmental data with meteorological service environmental data to ensure accuracy. This comparison informs a feedback loop that improves the model’s predictive accuracy. The research also delves into detailed time series analysis, incorporating the Autoregressive Integrated Moving Average (ARIMA) model to identify the best windows of opportunity for communication and to provide future forecasting. Finally, a decision tree model serves as the last step, providing a comprehensive assessment of fire risk due to its straightforward application and clarity. Validation of the fire detection component remains a critical future task to confirm its effectiveness and reliability. Full article

Introduction: Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components. Method: A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy. Results: The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31–60 min in 34%, 61–90 min in 11%, 91–120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%). Conclusion: The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance. Full article