Search Results (271)

Given the inadequacies in the management of reactive power compensation equipment in distribution networks and insufficient power data mining, existing studies pay little attention to asymmetric reactive power compensation equipment and face pain points such as difficult quantification of nonlinear relationships and challenging evaluation of mechanical switches. First, this paper proposes a data mining-based diagnostic method for the operating status of asymmetric reactive power compensation equipment: it preprocesses data via singular value decomposition and matrix approximation. Second, it classifies load types with K-means clustering, defines “health degree” by introducing mutual information and a reliability coefficient, constructs dual switching criteria, and defines the switching qualification rate. Third, the TOPSIS method is employed for dual-index comprehensive evaluation, and equipment status levels are classified with statistical analysis. Finally, the case analysis demonstrates that the proposed method is accurate, applicable, and easy to implement, which can serve as a basis for equipment troubleshooting and maintenance, thereby filling the relevant research gap. Full article

The successful establishment of battery electric vehicles (BEVs) is strongly linked to criteria such as cost and range. In particular, the need for air conditioning strains battery capacities and limits the availability of BEVs. Thermal energy storage systems (TESs) open up alternative paths for heat and cold supply with excellent scalability and cost efficiency. Previous TES concepts have largely focused on heat during cold seasons, but storage-based air conditioning systems for all seasons are still missing. To fill this gap, a concept based on a Brayton cycle allowing heat and cold supply and, simultaneously, an output of electrical energy at times when no air conditioning is needed was investigated. Central thermal components include water-based cold storage and electrically heated, high-temperature, solid-medium storage, both with innovative TPMS structures and flexible operation managements. With transient simulation studies a system was identified with effective storage densities of up to 100 Wh/kg, reaching a constant heat and cold supply of 5 kW and 2.5 kW, respectively, over 41 min. In addition, the underlying cycle allows an electrical output of up to 1.7 kW during times of inactive air conditioning requirements. Compared to a reference system designed only for winter operation, the moderately lower storage densities are compensated by proportionately longer discharging times. By combining a compact and dynamic Brayton cycle with a TES in BEVs, a storage-based air conditioning system with high utilization potential and high operational flexibility was developed. In addition to further optimizations, the knowledge for TES solutions can also be transferred to today’s air conditioning systems, extending the solution space for storage-supported thermomanagement options in BEVs. Full article

Background/Objectives: Skeletal Class III malocclusion in growing patients presents therapeutic challenges. While traditional tooth-anchored facemask (FM) therapy is widely used, it may induce undesired dental effects. Bone-anchored maxillary protraction (BAMP), using either miniscrews (MSs) or miniplates (MPs), has been proposed to enhance skeletal outcomes and minimize dental compensation. The objective is to compare the efficacy of MS and MP as skeletal anchorage in the orthopedic treatment of the Class III growing patients. Methods: This systematic review and meta-analysis followed PRISMA guidelines. Five databases and manual searches were conducted without restrictions. Inclusion criteria encompassed randomized and non-randomized controlled trials assessing cephalometric outcomes in growing patients treated with MS or MP. Risk of bias was assessed with RoB 2 and ROBINS-I tools, and evidence certainty was evaluated using GRADE. A meta-analysis was performed, collecting all the statistically significant results that emerged in the 11 articles between skeletal anchorage and controls, comparing the values of the MP group with the MS group. Results: Eleven studies (seven MP, four MS) met the inclusion criteria. Both MS and MP groups showed significant maxillary advancement and improved maxillo–mandibular relationships compared to controls. Regarding vertical values, studies have reported contrasting outcomes. Soft tissue improvements were consistent in both MS and MP devices. Statistical analysis has highlighted how MP devices demonstrated more pronounced skeletal effects, while MS systems were associated with more dental effects. Conclusions: MP may be preferable when the aim is to maximize skeletal correction with fewer dental side effects, while MS can be considered in cases favoring less invasive approaches; long-term follow-up and high-quality clinical studies are needed to confirm these clinical assessments. Full article

Background: Segmenting nerve fibres in histological images is a tricky job because of how much the tissue looks can change. Modern neural network architectures, including U-Net and transformers, demonstrate varying degrees of effectiveness in this area. The aim of this study is to conduct a comparative analysis of the SegFormer, VGG-UNet, and FabE-Net models in terms of segmentation quality and speed. Methods: The training sample consisted of more than 75,000 pairs of images of different tissues (original slice and corresponding mask), scaled from 1024 × 1024 to 224 × 224 pixels to optimise computations. Three neural network architectures were used: the classic VGG-UNet, FabE-Net with attention and global context perception blocks, and the SegFormer transformer model. For an objective assessment of the quality of the models, expert validation was carried out with the participation of four independent pathologists, who evaluated the quality of segmentation according to specified criteria. Quality metrics (precision, recall, F1-score, accuracy) were calculated as averages based on the assessments of all experts, which made it possible to take into account variability in interpretation and increase the reliability of the results. Results: SegFormer achieved stable stabilisation of the loss function faster than the other models—by the 20–30th epoch, compared to 45–60 epochs for VGG-UNet and FabE-Net. Despite taking longer to train per epoch, SegFormer produced the best segmentation quality, with the following metrics: precision 0.84, recall 0.99, F1-score 0.91 and accuracy 0.89. It also annotated a complete histological section in the fastest time. Visual analysis revealed that, compared to other models, which tended to produce incomplete or excessive segmentation, SegFormer more accurately and completely highlights nerve structures. Conclusions: Using attention mechanisms in SegFormer compensates for morphological variability in tissues, resulting in faster and higher-quality segmentation. Image scaling does not impair training quality while significantly accelerating computational processes. These results confirm the potential of SegFormer for practical use in digital pathology, while also highlighting the need for high-precision, immunohistochemistry-informed labelling to improve segmentation accuracy. Full article

First-principles density-functional theory (PBE, Quantum ESPRESSO) was employed to quantify how Fe substitution modulates the structural, elastic, electronic, and optical behaviour of cubic fluorite Fe_xZr_1−xO₂ (x = 0.00–1.00). The fluorite FeO₂ end member was treated as a hypothetical ambient-pressure limit to trace trends across the solid solution (experimental FeO₂ being stabilized in the high-pressure pyrite phase). Mechanical stability was verified via the cubic Born criteria, and composition-dependent stiffness and anisotropy were assessed through Voigt–Reuss–Hill moduli, Pugh ratio, and elastic indices. A strong band-gap narrowing was found—from 3.41 eV (x = 0) to ≈0.02 eV (x = 0.50)—which was accompanied by a visible–NIR red-shift, large absorption (α ≈ 10⁵ cm⁻¹ at higher x), and enhanced refractive index and permittivity; metallic-like response was indicated at high Fe content. Spin-polarized calculations converged to zero total and absolute magnetization, indicating a non-magnetic ground state at 0 K within PBE. The effect of oxygen vacancies (V₀)—expected under Fe³⁺ charge compensation—was explicitly considered: V₀ is anticipated to influence lattice metrics, elastic moduli (B, G, G/B), and sub-gap optical activity, potentially modifying stability and optical figures of merit. Stoichiometric (formal Fe⁴⁺) predictions were distinguished from V₀-rich scenarios. Absolute band gaps may be underestimated at the PBE level. Full article

New-onset vertigo, dizziness and gait imbalance are amongst the most common symptoms presenting to the emergency department, accounting for 2.1–4.4% of all patients. The broad spectrum of underlying causes in these patients cuts across many specialties, which often results in diagnostic challenges. For patients meeting the diagnostic criteria for acute vestibular syndrome (AVS, i.e., presenting with acute-onset prolonged vertigo/dizziness with accompanying gait imbalance, motion intolerance, nausea/vomiting, with or without nystagmus), the typical differential diagnosis is vertebrobasilar stroke and acute unilateral vestibulopathy. However, other disorders may also present with AVS. These include non-neurological causes such as drug side-effects or intoxication, electrolyte disturbances, cardiac disease, severe anemia, carbon monoxide poisoning, endocrine disorders and others. Other non-stroke neurological disorders may also present with AVS or episodic vertigo/dizziness, including demyelinating CNS diseases, posterior fossa mass lesions, acute thiamine deficiency and vestibular migraine. Furthermore, acute physiological abnormalities (e.g., hypotension, fever, severe anemia) may unmask previous vestibular impairments that had been well-compensated. Here, we review the diagnostic approach to patients with acute-onset dizziness in the emergency room and discuss the most important differential diagnoses beyond stroke and acute unilateral vestibulopathy. Full article

Vulvar lichen sclerosus (VLS) involves chronic inflammation, immune dysregulation, and abnormal extracellular matrix remodeling, involving extracellular matrix protein 1 (ECM1) and non-coding RNAs, particularly miR-155. Platelet-rich plasma (PRP) and adipose-derived mesenchymal stem cells (ADSCs) offer regenerative potential through the release of growth factors and cytokines that promote angiogenesis, fibroblast proliferation, collagen synthesis, and tissue repair, which could potentially compensate for the disordered matrix in VLS. This systematic review aimed to evaluate the current evidence on the efficacy and safety of PRP, ADSCs, and active substances administered through mesotherapy to adult women with VLS. A search of the PubMed, Scopus, and Web of Science databases identified 251 records, of which 13 studies met the inclusion criteria (RCTs and cohort studies involving women aged ≥ 18 years who were treated with PRP, ADSCs, or mesotherapy). The reviewed studies suggest that these therapies may improve clinical symptoms, quality of life, sexual function, and tissue quality. However, their application may be constrained by procedural invasiveness and potential immunologic risks. Moreover, the current evidence base is limited by small sample sizes, a lack of control groups, and short follow-up periods. Larger, well-designed randomized controlled trials with long-term follow-up are needed to confirm their therapeutic value and establish clear clinical guidelines. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

The instability of the energy supply, growing demand and the need to reduce carbon emissions are priority challenges in developing countries such as Ecuador, where power outages affect productivity and generate economic losses. Therefore, solar energy is positioned as a sustainable alternative. The objective of this study is to evaluate a pilot photovoltaic (PV) system for residential housing in coastal areas in the Santa Elena province, Ecuador. The methodology included the following: (i) criteria for the selection of three representative residential housings; (ii) design of a distributed generation system using PVsyst software; and (iii) proposal of strategic guidelines for the design of PV systems. This proposed system proved to be environmentally friendly, achieving reductions of between 16.4 and 32 tonnes of CO₂ in the first 10 years. A return on investment (ROI) of 16 years was achieved for the low-demand (L) scenario, with 4 years for the medium-demand (M) scenario and 2 years for the high-demand (H) scenario. The sensitivity analysis showed that the Levelized Cost of Energy (LCOE) is more variable in the L scenario, requiring more efficient designs. It is proposed to diversify the Ecuadorian energy matrix through self-supply PV systems, which would reduce electricity costs by 6% of consumption (L scenario), 30% (M scenario), and 100% (H scenario). Although generation is concentrated during the day, the net metering scheme enables compensation for nighttime consumption without the need for batteries, thereby improving the system’s profitability. The high solar potential and high tariffs make the adoption of sustainable energy solutions a justifiable choice. Full article

This paper presents an innovative multi-criteria visual quality control algorithm designed for deployment on cost-effective Edge devices within the Industrial Internet of Things environment. Traditional industrial vision systems are typically associated with high acquisition, implementation, and maintenance costs. The proposed solution addresses the need to reduce these costs while maintaining high defect detection efficiency. The developed algorithm largely eliminates the need for time- and energy-intensive neural network training or retraining, though these capabilities remain optional. Consequently, the reliance on human labor, particularly for tasks such as manual data labeling, has been significantly reduced. The algorithm is optimized to run on low-power computing units typical of budget industrial computers, making it a viable alternative to server- or cloud-based solutions. The system supports flexible integration with existing industrial automation infrastructure, but it can also be deployed at manual workstations. The algorithm’s primary application is to assess the spread quality of thick liquid mold filling; however, its effectiveness has also been demonstrated for 3D printing processes. The proposed hybrid algorithm combines three approaches: (1) the classical SSIM image quality metric, (2) depth image measurement using Intel MiDaS technology combined with analysis of depth map visualizations and histogram analysis, and (3) feature extraction using selected artificial intelligence models based on the OpenCLIP framework and publicly available pretrained models. This combination allows the individual methods to compensate for each other’s limitations, resulting in improved defect detection performance. The use of hybrid metrics in defective sample selection has been shown to yield superior algorithmic performance compared to the application of individual methods independently. Experimental tests confirmed the high effectiveness and practical applicability of the proposed solution, preserving low hardware requirements. Full article

Neural networks (NNs) are increasingly used in embedded systems driven by artificial intelligence (AI) features. Such systems are now developed for many critical applications with strong dependability constraints and trustworthiness requirements. The effect of bit-flips occurring during inferences in the field has therefore become a major concern. Dedicated design methods have been proposed to increase the robustness of NNs and enforce trustworthiness, while minimizing implementation overheads in the context of Edge AI. Such approaches are usually based on criticality criteria characterizing the most impactful bit-flips. In this work, the two main criteria are revisited in the case of bit-flips in data that is independent of the hardware/software implementation. Extensive statistical fault injection results are analyzed for a case study based on a quantized version of LeNet. They first demonstrate that any criterion is only held in some perturbation conditions, depending on the type of perturbed data and on the perturbed layer. The data representation format has also a significant impact. Surprising outcomes of the case study allow identifying a new parameter overlooked in the literature, i.e., the definition of the activation functions. Another important finding, mostly neglected in the literature, is the large number of bit-flips having a positive impact on inferences by correcting misclassifications of the nominal NN. In the presented case study, almost 40% of the misclassifications due to bit-flips are compensated for by the positive effects of other bit-flips, leading to only 0.06% global accuracy loss when bit-flips occur. These outcomes indicate that mitigations must be carefully tailored to each layer and data subset of the NN in order to accurately limit the effect of critical bit-flips. Moreover, at the same time they must avoid suppressing the advantage taken from bit-flips with positive effects. Although derived from a specific case study, these findings have a wide significance in design practice. Full article

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems. Full article

This paper presents a non-fragile observer-based dissipative control strategy for the suspension systems of electric vehicles equipped with in-wheel motors, accounting for input delays, actuator faults, and observer gain uncertainty. Traditional control approaches—such as $H_{\infty }$, passive control, and robust feedback schemes, often address these challenges in isolation and with increased conservatism. In contrast, this work introduces a unified framework that integrates fault-tolerant control, delay compensation, and robust state estimation within a dissipativity-based setting. A novel dissipativity analysis tailored to Electric Vehicle Active Suspension Systems (EV-ASSs) is developed, with nonzero delay bounds explicitly incorporated into the stability conditions. The observer is designed to ensure accurate state estimation under gain perturbations, enabling robust full-state feedback control. Stability and performance criteria are formulated via Linear Matrix Inequalities (LMIs) using advanced integral inequalities to reduce conservatism. Numerical simulations validate the proposed method, demonstrating effective fault-tolerant performance, disturbance rejection, and precise state reconstruction, thereby extending beyond the capabilities of traditional control frameworks. Full article

The forestry sector is increasingly dealing with a significant lack of labor and faces the difficult task of securing a professional, stable and sustainable manpower. In this study, different strategic directions for strengthening forestry workforce sustainability are presented and evaluated. The considered strategic directions were developed with respect to forestry employees’ views on necessary measures for making the forestry occupation more appealing. Those measures were observed in three categories: (I) stronger recruiting, (II) stronger retention and (III) higher work commitment. The findings of the survey and other performed analyses resulted in the creation of four different strategic directions: (1) the direct financial strategy, implying increased direct monetary compensation as the main instrument and putting focus on labor productivity; (2) the indirect financial strategy, stressing worker wellbeing through indirect material benefits and aiming at performance quality; (3) the educational strategy, focusing on worker training and education and (4) the technical–technological strategy, aiming at the increased utilization of modern machinery and advanced technologies in forest operations. The results of the study include a comparison of the defined strategies by SWOT analysis and the construction of An analytic Hierarchy Process (AHP) model as the multi-criteria tool for strategy evaluation. Considering the possibility and conditions of its implementation in the national forestry sector, the technical–technological strategy has been evaluated as best option to pursue. The objective of the study is to contribute to enhancing the sustainability of forestry workforce by defining critical issues and pointing to specific cornerstones that can assist in formulating effective future policies and strategies in the forestry sector. Full article