Search Results (12)

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource constraints. Full article

Studying nonlinear oscillations in mechanical systems is fundamental to understanding complex dynamic behavior in engineering applications. While classical analytical methods remain valuable for systems with limited complexity, they become increasingly inadequate when nonlinearities are strong and geometrically induced, as in the case of large-amplitude oscillations. This paper presents a combined numerical and experimental investigation of a mechanical system composed of two coupled pendulums, exhibiting significant nonlinear behavior due to elastic deformation throughout their motion. A mathematical model of the system was developed using the MatLab/Simulink ver.6.1 environment, considering gravitational, inertial, and nonlinear elastic restoring forces. One of the major challenges in accurately modeling such systems is accurately representing damping, particularly in the absence of dedicated dampers. In this work, damping coefficients were experimentally identified through decrement measurements and incorporated into the simulation model to improve predictive accuracy. The simulation outputs, including angular displacements, velocities, accelerations, and phase trajectories over time, were validated against experimental results obtained via high-precision inertial sensors. The comparison shows a strong correlation between numerical and experimental data, with minimal relative errors in amplitude and frequency. This research represents the first stage of a broader study aimed at analyzing forced and parametrically excited oscillations. Beyond validating the model, the study contributes to the design of a robust experimental framework suitable for further exploration of nonlinear dynamics. The findings have practical implications for the development and control of mechanical systems subject to dynamic loads, with potential applications in automation, vibration analysis, and system diagnostics. Full article

In this paper, a very fast and reliable strategy for load restoration utilizing optimal distribution feeder reconfiguration (DFR) is developed. The automated network configuration switches can improve the resilience of a microgrid (MG) equipped with a centralized and coordinated energy management system (EMS). The EMS has the authority to reconfigure the distribution network to fulfil high priority loads in the entire network, at the lowest cost, while maintaining the voltage at desirable bounds. In the case of islanded operation, the EMS is responsible for serving the high priority loads, including the establishment of new MGs, if necessary. This paper discusses the main functionality of the EMS in both grid-connected and islanded operation modes of MGs. The proposed model is developed based on a mixed-integer quadratically constrained program (MIQCP), including an optimal power flow (OPF) problem to minimize the power losses in normal operation and the load shedding in islanded operation, while keeping voltage and capacity constraints. The proposed framework is implemented on a modified IEEE 33-bus test system and the results show that the model is fast and accurate enough to be utilized in real-life situations without a loss of accuracy. Full article

Prescribed burns have recently become a widespread environmental management practice for biodiversity restoration to reduce fuel load, to provide forest fire suppression operational opportunities, to favor plant recruitment or to manage wild species. Prescribed fires were again applied in Doñana National Park (southern Spain) after decades of non-intervention regarding fire use. Here, we assessed their impacts on the soil CO₂ effluxes over two years after burning to test the hypothesis that if the ecosystem is resilient, soil respiration will have a rapid recovery to the conditions previous to the fire. Using soil automated CO₂ flux chambers to continuously measure respiration in burned and unburned sites, we showed that soil respiration varies among seasons but only showed significant differences between burned and unburned plots in the fall season one year after fire, which corresponded with the end of the dry season. Comparing soil respiration values from the burned plots in the three fall seasons studied, soil respiration increased significantly in the fall one year after fire, but decreased in the following fall to the values of the control plots. This study highlights the resilience of soil respiration after prescribed fire, showing the potential benefits of prescribed fire to reduce catastrophic wildfires, especially in protected areas subjected to non-intervention. Full article

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. Full article

In the conservation of monumental heritage, the collection and utilization of information are of primary importance. The Heritage Building Information Modeling (HBIM) procedure harnesses the potential of three-dimensional models, offering significant advantages in accessing documentation, interoperability, multidimensionality of intervention design, cost evaluation, and maintenance management. Our attention here is focused on the Certosa di Pisa (Italy), a large historical complex built in the 14th century as a monastery of the Carthusian Order, currently in a state of deterioration and in need of restoration and re-functionalization. The multifaceted nature of this monumental complex, with its intricate interplay of architectural elements spanning different historical periods and featuring diverse techniques, poses a significant challenge for structural safety assessment. This case study presents an opportunity to explore an HBIM approach to streamline the diagnostic process and facilitate the intervention design phase. The goal is achieved by utilizing an accurate 3D model enriched with data from multiple sources and automating certain operations for a simplified safety assessment of masonry structures under both gravity and seismic loads. The usefulness of the HBIM methodology is highlighted as a valuable tool in the realm of cultural heritage structures for both practitioners and scholars alike. Full article

Failure of resource in automated manufacturing systems could cause a complete system shutdown. This paper addresses the issue of unreliable resource failure in manufacturing cells through the use of load-sharing redundant resources (LSRRs). The aim is to use more than one type of a failure-prone resource to share tasks between a failure-prone resource, called a target resource, and reliable ones called load-sharing redundant resources (LSRRs). Both an unreliable resource and its LSRR perform the same tasks, and there is normally a system that assigns tasks to them. If the target resource fails, all the tasks will be performed by the LSRRs. After the faulty target resource is fixed and restored, its assigned tasks are automatically returned to it. This way the system can continue to produce or process parts. Thus, a total system shutdown due to unreliable resource failures is eliminated. The proposed method is tested using real examples. The results, compared with those obtained by the studies in the literature, show that the proposed method has an outstanding performance and outperforms some of the existing studies. Full article

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids. Full article

Background: Patellofemoral instability (PFI) leads to chronic knee pain, reduced performance and chondromalacia patellae with consecutive osteoarthritis. Therefore, determining the exact patellofemoral contact mechanism, as well as the factors leading to PFI, is of great importance. The present study compares in vivo patellofemoral kinematic parameters and the contact mechanism of volunteers with healthy knees and patients with low flexion patellofemoral instability (PFI). The study was performed with a high-resolution dynamic MRI. Material/Methods: In a prospective cohort study, the patellar shift, patella rotation and the patellofemoral cartilage contact areas (CCA) of 17 patients with low flexion PFI were analyzed and compared with 17 healthy volunteers, matched via the TEA distance and sex, in unloaded and loaded conditions. MRI scans were carried out for 0°, 15° and 30° knee flexion in a custom-designed knee loading device. To suppress motion artifacts, motion correction was performed using a moiré phase tracking system with a tracking marker attached to the patella. The patellofemoral kinematic parameters and the CCA was calculated on the basis of semi-automated cartilage and bone segmentation and registrations. Results: Patients with low flexion PFI showed a significant reduction in patellofemoral CCA for 0° (unloaded: p = 0.002, loaded: p = 0.004), 15° (unloaded: p = 0.014, loaded: p = 0.001) and 30° (unloaded: p = 0.008; loaded: p = 0.001) flexion compared to healthy subjects. Additionally, patients with PFI revealed a significantly increased patellar shift when compared to volunteers with healthy knees at 0° (unloaded: p = 0.033; loaded: p = 0.031), 15° (unloaded: p = 0.025; loaded: p = 0.014) and 30° flexion (unloaded: p = 0.030; loaded: p = 0.034) There were no significant differences for patella rotation between patients with PFI and the volunteers, except when, under load at 0° flexion, PFI patients showed increased patellar rotation (p = 0.005. The influence of quadriceps activation on the patellofemoral CCA is reduced in patients with low flexion PFI. Conclusion: Patients with PFI showed different patellofemoral kinematics at low flexion angles in both unloaded and loaded conditions compared to volunteers with healthy knees. Increased patellar shifts and decreased patellofemoral CCAs were observed in low flexion angles. The influence of the quadriceps muscle is diminished in patients with low flexion PFI. Therefore, the goal of patellofemoral stabilizing therapy should be to restore a physiologic contact mechanism and improve patellofemoral congruity for low flexion angles. Full article

The electricity distribution system is the coupling point between the utility and the end-user. Typically, these systems have unbalanced feeders due to the variety of customers’ behaviors. Some significant problems occur; the unbalanced loads increase the operational cost and system investment. In radial distribution systems, swapping loads between the three phases is the most effective method for phase balancing. It is performed manually and subjected to load flow equations, capacity, and voltage constraints. Recently, due to smart grids and automated networks, dynamic phase balancing received more attention, thus swapping the loads between the three phases automatically when unbalance exceeds permissible limits by using a remote-controlled phase switch selector/controller. Automatic feeder reconfiguration and phase balancing eliminates the service interruption, enhances energy restoration, and minimize losses. In this paper, a case study from the Irbid district electricity company (IDECO) is presented. Optimal reconfiguration of phase balancing using three techniques: feed-forward back-propagation neural network (FFBPNN), radial basis function neural network (RBFNN), and a hybrid are proposed to control the switching sequence for each connected load. The comparison shows that the hybrid technique yields the best performance. This work is simulated using MATLAB and C programming language. Full article

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters. Full article

Most domestic modern buildings from the early 1900s have been constructed as heavy mass, and for many years have relied on passive measures for climate control. Since effective passive measures eventually reduce the heating and cooling loads, thus also reducing the system size, passive and hybrid measures are the most preferred Energy Conservation Measures (ECMs). In addition, the domestic situation and climate are additional constraints in energy retrofit decision making, such as a shorter budget and time, poor maintenance history, and uncertainties in vernacular lifestyle. For this reason, the performance improvement and side-effects prior to installing ECMs should be predictable, particularly in case the originality can be damaged. This complexity confirms that simulation-based Measurement and Verification (M&V) would better suit the energy retrofit of domestic historic buildings. However, many domestic investors still believe re-construction has a larger economic value than restoration. Therefore, they are even unwilling to invest in more time than a preset audit period—typically less than a week. Although simulation-based M&V is theoretically favored for retrofit decision making, its process including collecting data, modeling and analysis, and evaluating and designing ECMs could still be too demanding to domestic practitioners. While some manual, repetitive, error-prone works exist in the conventional retrofit process and simulation-based M&V, it is proposed here that enhanced Building Information Technology (BIT) is able to simplify, automate, and objectify, at least the critical steps of the retrofit project. The aim of this study is to find an efficient and effective energy retrofit strategy for domestic historic buildings that appeals to both domestic investors and practitioners by testing selective BIT tools on an actual historic building. This study concludes with the suggestion that software vendors are asked to develop enhanced features to resolve users’ pending demands. It is also suggested that, in the domestic context, how the current practice for each process of the energy retrofit of historic buildings needs to shift to take a full advantage of BIT. Full article