Search Results (8,971)

In this study, we designed three promising platforms based on metal–organic frameworks (MOFs) to develop paper-based analytical devices (PADs) for biosensing applications. PADs have become increasingly popular in field sensing in recent years due to their portability, low cost, simplicity, efficiency, fast detection capability, excellent sensitivity, and selectivity. In addition, MOFs are excellent choices for developing highly sensitive and selective sensors due their versatility for functionalizing, structural stability, and capability to adsorb and desorb specific molecules by reversible interactions. These materials also offer the possibility to modify their structure and properties, making them highly versatile and adaptable to different environments and sensing needs. In this research, we synthesized and characterized three different amino-functionalized MOFs: UiO-66-NH₂ (Zr), MIL-125-NH₂ (Ti), and MIL-101-NH₂ (Fe). These MOFs were used to fabricate PADs capable of sensitive and portable monitoring of alkaline phosphatase (ALP) enzyme activity by laser-induced fluorescence (LIF). Overall, amino-derivated MOF platforms demonstrate significant potential for integration into biosensor PADs, offering key properties that enhance their performance and applicability in analytical chemistry and diagnostics. Full article

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time. Full article

Recent years have seen outstanding developments in research and technology, highlighting the importance of railway transportation, especially the implementation of high-speed trains, which is becoming more and more challenging. This facilitates extensive research into the science and technology of the electrical interaction between the components of pantograph–catenary systems (PCSs). Problems regarding the PCS can result in infrastructure incidents, potentially stopping train operations. A common cause of failure in electrified railway PCS is a contact wire’s zigzag length that exceeds the prescribed technical limit, which can be caused by missing droppers or faults in the mounting mechanism. This work proposes a video camera-based monitoring technique for zigzag geometry measurement that additionally employs a Global Positioning System (GPS) sensor to detect the current geographical position of the point of zigzag length measurement. There are two proposed techniques for measuring the length of the zigzag based on image processing. In the first technique, using previously recorded data, the images are analyzed in the laboratory, and in the second, the images are analyzed in real time. Based on the results, we suggest a model and prediction of zigzag length employing hybrid deep neural networks. Full article

The modern healthcare system strives to provide patients with more comfortable and less invasive experiences, focusing on noninvasive and painless diagnostic and treatment methods. A key priority is the early diagnosis of life-threatening diseases, which can significantly improve patient outcomes by enabling treatment at earlier stages. While most patients must undergo diagnostic procedures before beginning treatment, many existing methods are invasive, time-consuming, and inconvenient. To address these challenges, electrochemical-based wearable and point-of-care (PoC) sensing devices have emerged, playing a crucial role in the noninvasive, continuous, periodic, and remote monitoring of key biomarkers. Due to their numerous advantages, several wearable and PoC devices have been developed. In this focused review, we explore the advancements in metal–organic frameworks (MOFs)-based wearable and PoC devices. MOFs are porous crystalline materials that are cost-effective, biocompatible, and can be synthesized sustainably on a large scale, making them promising candidates for sensor development. However, research on MOF-based wearable and PoC sensors remains limited, and no comprehensive review has yet to synthesize the existing knowledge in this area. This review aims to fill that gap by emphasizing the design of materials, fabrication methodologies, sensing mechanisms, device construction, and real-world applicability of these sensors. Additionally, we underscore the importance and potential of MOF-based wearable and PoC sensors for advancing healthcare technologies. In conclusion, this review sheds light on the current state of the art, the challenges faced, and the opportunities ahead in MOF-based wearable and PoC sensing technologies. Full article

Falls among older adults represent a critical global public health problem, as they are one of the main causes of disability in this age group. We have developed an automated approach to identifying fall risk using low-cost, accessible technology. Trunk movement patterns were collected from 181 older people, with and without a history of falls, during the execution of the Mini-BESTest. Data were captured using smartphone sensors (an accelerometer, a gyroscope, and a magnetometer) and classified based on fall history using deep learning algorithms (LSTM). The classification model achieved an overall accuracy of 88.55% a precision of 90.14%, a recall of 87.93%, and an F1 score of 89.02% by combining all signals from the Mini-BESTest tasks. The performance outperformed the metrics we obtained from individual tasks, demonstrating that aggregating all cues provides a more complete and robust assessment of fall risk in older adults. The results suggest that combining signals from multiple tasks allowed the model to better capture the complexities of postural control and dynamic gait, leading to better prediction of falls. This highlights the potential of integrating multiple assessment modalities for more effective fall risk monitoring. Full article

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network’s lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads’ load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs. Full article

Accurate and efficient congested link identification is crucial in wireless sensor networks (WSNs). However, in some networks with a centralized management architecture, it is often not feasible to monitor large numbers of internal links directly or even impossible in some heterogeneous networks. Network tomography, the science of inferring the performance characteristics of a network’s interior by correlating sets of end-to-end measurements, was put forward to solve this problem. Nevertheless, a network always contains more links than end-to-end paths, making it problematic to find a determined solution. To solve this problem, most of the current methods try to use some additional prerequisites, such as the link congestion probability. However, most existing studies have not considered the congestion caused by node factors and the case of multiple congested links on one path. In this paper, we initially model the issue of link congestion as a Bayesian network model (BNM). Subsequently, we introduce a congestion link identification method based on graph convolutional networks (GCNs), novelly converting the intricate Bayesian network solving problem into a graph node classification task. The simulation results validate the feasibility of our proposed algorithm in identifying congested links and underscore its advantages in scenarios involving node congestion and multiple congested links. Full article

Traditionally, acoustic methods for leak inspection are based on the measurement of the acceleration of the external pipe wall or of the acoustic pressure in the pipe. This work presents an alternative inspection methodology based on measuring the acoustic velocity vector in the fluid filling the pipe. Unlike the acoustic pressure, the acoustic quantity is very sensitive to the presence of a pipe wall defect. Such defects are important to detect before they develop into leaks, which can lead to the loss of water, environmental pollution and service disruption. A new sensor design is proposed to measure the acoustic velocity vector in a pipe. A model is presented to demonstrate the underpinning theory behind this new sensor technology. The results of this model are compared with experimental data based on measurements of the acoustic velocity in an exhumed section of ductile iron pipe. These sensors can be deployed on robots to autonomously monitor the deterioration of buried pipes to support proactive asset management at a low operational cost. Full article

Continuous vital sign monitoring (CVSM) with wireless sensors in general hospital wards can enhance patient care. An artificial intelligence (AI) layer is crucial to allow sensor data to be managed by clinical staff without over alerting from the sensors. With the aim of summarizing peer-reviewed evidence for AI support in CVSM sensors, we searched PubMed and Embase for studies on adult patients monitored with CVSM sensors in general wards. Peer-reviewed evidence and white papers on the official websites of CVSM solutions were also included. AI classification was based on standard definitions of simple AI, as systems with no memory or learning capabilities, and advanced AI, as systems with the ability to learn from past data to make decisions. Only studies evaluating CVSM algorithms for improving or predicting clinical outcomes (e.g., adverse events, intensive care unit admission, mortality) or optimizing alarm thresholds were included. We assessed the promised level of AI for each CVSM solution based on statements from the official product websites. In total, 467 studies were assessed; 113 were retrieved for full-text review, and 26 studies on four different CVSM solutions were included. Advanced AI levels were indicated on the websites of all four CVSM solutions. Five studies assessed algorithms with potential for applications as advanced AI algorithms in two of the CVSM solutions (50%), while 21 studies assessed algorithms with potential as simple AI in all four CVSM solutions (100%). Evidence on algorithms for advanced AI in CVSM is limited, revealing a discrepancy between promised AI levels and current algorithm capabilities. Full article

In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of an on-board unit buried in the roadway and a roadside unit. The on-board unit comprises a microcontroller, an accelerometer and a Zigbee module that transfers acceleration data wirelessly to the roadside unit. The roadside unit consists of a Raspberry Pi, a Zigbee module and a USB Zigbee adapter. Laboratory tests were conducted using a vibration table and with three different accelerometers, to assess the system capability. A typical displacement signal from a five-axle truck was applied to the vibration table with two different displacement peaks, allowing for two different vehicle speeds. The prototyped system was then encapsulated in PVC packaging, deployed and tested in a real-life road situation with a fatigue carousel featuring rotating truck axles. The laboratory and on-road measurements show that displacements can be estimated with an accuracy equivalent to that of a reference sensor. Full article

This article presents a systematic literature review of technologies and solutions for cattle tracking and monitoring based on a comprehensive analysis of scientific articles published since 2017. The main objective of this review is to identify the current state of the art and the trends in this field, as well as to provide a guide for selecting the most suitable solution according to the user’s needs and preferences. This review covers various aspects of cattle tracking, such as the devices, sensors, power supply, wireless communication protocols, and software used to collect, process, and visualize the data. The review also compares the advantages and disadvantages of different solutions, such as collars, cameras, and drones, in terms of cost, scalability, precision, and invasiveness. The results show that there is a growing interest and innovation in livestock localization and tracking, with a focus on integrating and adapting various technologies for effective and reliable monitoring in real-world environments. Full article

Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment. Full article

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. Full article

Centrifugal pumps (CPs) are widely utilized in many different industries, and their operations are maintained by their reliable performance. CPs’ most common faults can be categorized as mechanical or flow-related faults: the first ones are often associated with damage at the impeller, while the second ones are associated with cavitation. It is possible to use computational algorithms to monitor both failures in CPs. In this study, two different problems in pumps, the defective impeller and cavitation, have been considered. When a CP is working in a faulty condition, it generates vibrations that can be measured using piezoelectric sensors. Collected data can be analyzed to extract time- and frequency-domain data. Interpreting the time-domain data showed that distinguishing the type of defect is not possible. However, indicators like kurtosis, skewness, mean, and variance can be used as input for the multi-layer perceptron (MLP) algorithm to classify pump faults. This study presents a detailed discussion of the vibration-based method outcomes, emphasizing the benefits and drawbacks of the multi-layer perceptron method. The results show that the suggested algorithm can identify the occurrence of different faults and quantify their severity during pump operation in real time. Full article

Microbial fuel cells (MFCs) represent a promising technology for sustainable energy generation, which leverages the metabolic activities of microorganisms to convert organic substrates into electrical energy. In oil spill scenarios, hydrocarbonoclastic biofilms naturally form at the water–oil interface, creating a distinct environment for microbial activity. In this work, we engineered a novel MFC that harnesses these biofilms by strategically positioning the positive electrode at this critical junction, integrating the biofilm’s natural properties into the MFC design. These biofilms, composed of specialized hydrocarbon-degrading bacteria, are vital in supporting electron transfer, significantly enhancing the system’s power generation. Next-generation sequencing and scanning electron microscopy were used to characterize the microbial community, revealing a significant enrichment of hydrocarbonoclastic Gammaproteobacteria within the biofilm. Notably, key genera such as Paenalcaligenes, Providencia, and Pseudomonas were identified as dominant members, each contributing to the degradation of complex hydrocarbons and supporting the electrogenic activity of the MFCs. An electrochemical analysis demonstrated that the MFC achieved a stable power output of 51.5 μW under static conditions, with an internal resistance of about 1.05 kΩ. The system showed remarkable long-term stability, which maintained consistent performance over a 5-day testing period, with an average daily energy storage of approximately 216 mJ. Additionally, the MFC effectively recovered after deep discharge cycles, sustaining power output for up to 7.5 h before requiring a recovery period. Overall, the study indicates that MFCs based on hydrocarbonoclastic biofilms provide a dual-functionality system, combining renewable energy generation with environmental remediation, particularly in wastewater treatment. Despite lower power output compared to other hydrocarbon-degrading MFCs, the results highlight the potential of this technology for autonomous sensor networks and other low-power applications, which required sustainable energy sources. Moreover, the hydrocarbonoclastic biofilm-based MFC presented here offer significant potential as a biosensor for real-time monitoring of hydrocarbons and other contaminants in water. The biofilm’s electrogenic properties enable the detection of organic compound degradation, positioning this system as ideal for environmental biosensing applications. Full article