Search Results (1,072)

This paper presents a distributed measurement system intended to effectively monitor the health status of switchgears under varying temperature conditions. In particular, thermocouples are deployed as temperature sensors for the continuous monitoring of a medium-voltage (MV) switchgear. Then, by integrating a low-cost microcontroller unit, the proposed system can implement previously trained unsupervised learning techniques for health status evaluation. This approach enables the early detection of potential faults by identifying anomalous temperature patterns, thus supporting predictive maintenance and extending the lifespan of switchgears. The results show strong clustering performance with low execution times, highlighting the suitability of the method for resource-constrained hardware. Furthermore, onboard temperature processing eliminates the need for data transmission to remote servers, reducing latency and communication overhead while improving system responsiveness. The paper includes a numerical analysis on synthetic data as well as a validation on real measurements. Overall, the presented distributed measurement system offers a scalable and cost-effective solution to enhance the reliability and safety of MV switchgears. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

Fly-ash-based bubble lightweight soil is widely used due to its environmental friendliness, load reduction, ease of construction, and low costs. In this study, 41 sets of 28 d compressive strength data on lightweight soils with different water–cement ratios, blowing agent dosages, and fly ash dosages were collected through a literature search and indoor tests. Using the compressive strength index and SEM tests, the correlation between the mix ratio design and the microscopic particle components was investigated. The findings were as follows: carbonation reactions occurred in lightweight soil during the maintenance process, and the particles were spherical; increasing the dosage of blowing agent increased the soil’s porosity and pore diameter, leading to the formation of through-holes and reducing the compressive strength and mobility; increasing the fly ash dosage and water–cement ratio increased the soil’s mobility but reduced its compressive strength; and the strength decreased significantly when the fly ash dosage was more than 16% (e.g., the strength at a 20% dosage was 17.8% lower than that at a 15% dosage). Feature importance analysis showed that the water–cement ratio (57.7%), fly ash dosage (30.9%), and blowing agent dosage (11.1%) had a significant effect on strength. ExtraTrees, LightGBM, and Bayesian-optimized Random Forest models were used for 28d strength prediction with coefficients of determination (R²) of 0.695, 0.731, and 0.794, respectively. The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. The accuracy of the model is expected to be further improved with expansions in the database. A 28 d compressive strength prediction platform for fly-ash-based bubble lightweight soil was ultimately developed, providing a convenient tool for researchers and engineers to predict material properties and mix ratios. Full article

Traditional 2D optical markers degrade quickly in underwater applications due to sediment accumulation and marine biofouling, becoming undetectable within weeks. This paper presents a Three-Dimensional Binary Marker, a novel passive fiducial marker designed for underwater Long-Term Deployment. The Three-Dimensional Binary Marker addresses the 2D-markers limitation through a 3D design that enhances resilience and maintains contrast for computer vision detection over extended periods. The proposed solution has been validated through simulation, water tank testing, and long-term sea trials for 5 months. In each stage, the marker was compared based on detection per visible frame and the detection distance. In conclusion, the design demonstrated superior performance compared to standard 2D markers. The proposed Three-Dimensional Binary Marker provides compatibility with widely used fiducial markers, such as ArUco and AprilTag, allowing quick adaptation for users. In terms of fabrication, the Three-Dimensional Binary Marker uses additive manufacturing, offering a low-cost and scalable solution for underwater localization tasks. The proposed marker improved the deployment time of fiducial markers from a couple of days to sixty days and with a range up to seven meters, providing robustness and reliability. As the marker survivability and detection range depend on its size, it is still a valuable innovation for Autonomous Underwater Vehicles, as well as for inspection, maintenance, and monitoring tasks in marine robotics and offshore infrastructure applications. Full article

Electric submersible pumps (ESPs) are critical for artificial lift operations; however, they are prone to frequent failures, often resulting in high operational costs and production downtime. Traditional ESP failure classifications are limited by lack of standardization and the conflation of failure modes with root causes. To address these limitations, this study proposes a new two-step integrated failure modes and root cause (IFMRC) classification system. The new framework clearly distinguishes between failure modes and root causes, providing a systematic, structured approach that enhances fault diagnosis and failure analysis and can lead to better failure prevention strategies. This methodology was validated using a case study of over 4000 ESP installations. The data came from Egypt’s Western Desert, covering a decade of operational data. The sources included ESP databases, workover records, and detailed failure investigation (DIFA) reports. The failure modes were categorized into electrical, mechanical, hydraulic, chemical, and operational types, while root causes were linked to environmental, design, operational, and equipment factors. Statistical analysis, in this case study, revealed that motor short circuits, low flow conditions, and cable short circuits were the most frequent failure modes, with excessive heat, scale deposition, and electrical grounding faults being the dominant root causes. This study underscores the importance of accurate root cause failure classification, robust data acquisition, and expanded failure diagnostics to improve ESP reliability. The proposed IFMRC framework addresses limitations in conventional taxonomies and facilitates ongoing enhancement of ESP design, operation, and maintenance in complex field conditions. Full article

The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of ±5–50 N·m. Within this range, the device achieved a mean absolute error of 0.559 N·m. It also maintained precision better than ±2.5 N·m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings. Full article

This study presents the design and implementation of a novel, sensor-based falling-sphere viscometer specifically tailored for measuring the viscosity of engine oil. The equipment utilizes a metallic sphere and two strategically placed sensors to determine the travel time over a predetermined distance within an oil-filled tube. By applying fundamental principles of fluid dynamics, including Stokes’ law, the system accurately calculates the dynamic viscosity based on the sphere’s velocity and the oil’s density. Experimental validation at particular temperature demonstrates the device’s sensitivity and reliability, which are critical for assessing oil degradation and engine performance. The simplicity and low cost of the design make it an attractive alternative to conventional, more complex viscometers. Furthermore, the automated data acquisition system reduces human error and enhances reproducibility of results. Overall, the developed instrument shows great promise for both laboratory research and practical maintenance applications in the automotive industry. Full article

Background: Mepolizumab is an effective treatment for severe eosinophilic asthma, leading to a depletion of blood eosinophil levels, the clinical relevance of which remains unclear. Objective: The aim of this study was to assess the relationship between mepolizumab-induced blood eosinophil reduction and clinical outcome in patients with severe eosinophilic asthma, in particular, whether the magnitude of blood eosinophil reduction was associated with clinical remission. Methods: We conducted a real-world retrospective analysis of 58 adult patients with severe eosinophilic asthma treated with mepolizumab. Clinical and respiratory functional parameters were evaluated at the start of mepolizumab treatment (T0) and after two years of treatment (T2; mean follow-up: 22.8 ± 7.5 months). Blood eosinophil counts were recorded at T0 and during the first year of treatment (T1; mean follow-up: 7.7 ± 4.1 months). Results: After two years of mepolizumab treatment, 58 severe asthmatic patients showed significant improvements in ACT score, FVC, and FEV₁ and a reduction in acute exacerbations and the use of maintenance therapies. Clinical remission was achieved in 55.1% of patients. Lower blood eosinophil counts during the first year (T1) were associated with greater improvements in lung function and fewer exacerbations. A greater relative decrease in eosinophils from baseline to T1 (ΔEOS%) was significantly associated with remission, reductions in exacerbations, and no maintenance OCS use. ΔEOS% was the only independent predictor of remission in the multivariate analysis. A ≥90% reduction predicted remission with 80% specificity (AUC = 0.726). Conclusions: Monitoring blood eosinophils after mepolizumab initiation could be a useful tool for predicting long-term response to treatment. In particular, a reduction by over 90% of peripheral blood eosinophils during the first year of mepolizumab treatment predicts clinical remission with a specificity of 80%. Considering the accessibility and the low cost of this biomarker, it may help to optimize long-term asthma management. Full article

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings. Full article

A solar air heater (SAH) converts solar energy into heated air without causing environmental pollution. It features a low initial cost and easy maintenance due to its simple design. However, owing to air’s poor thermal conductivity, its thermal efficiency is relatively low compared to that of other solar systems. To improve its thermal performance, previous studies have aimed at either enlarging the heat transfer surface or increasing the convective heat transfer coefficient. In this study, a novel SAH with fins and sequentially placed obstacles on the fin surface—designed to achieve both surface extension through a finned channel and enhancement of the heat transfer coefficient via the obstacles—was investigated using computational fluid dynamics analysis. The results confirmed that the obstacles enhanced heat transfer performance by up to 2.602 times in the finned channel. However, the obstacles also caused a pressure loss. Therefore, the thermo-hydraulic performance was discussed, and it was concluded that the obstacles with a relative height of 0.12 and a relative pitch of 10 yielded the maximum THP values among the investigated conditions. Additionally, correlations for the Nusselt number and friction factor were derived and predicted the simulation values with good agreement. Full article

Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management. Full article

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article