Search Results (56)

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

This study investigated the mechanical performance and durability of concretes produced with varying proportions of recycled coarse aggregate from construction and demolition waste (CDW), ranging from 0% to 100% replacement of natural coarse aggregate, using recycled aggregates derived from crushed concrete and mortar debris, characterized by lower density and high water absorption (~9%) compared to natural aggregates. A key contribution of this research lies in the inclusion of intermediate replacement levels (20%, 25%, 45%, 50%, and 65%), which are less explored in the literature and allow a more refined identification of performance thresholds. Fresh-state parameters (slump), axial compressive strength (7 and 28 days), total immersion water absorption, sorptivity, and chloride ion penetration depth (after 90 days of immersion in a 3.5% NaCl solution) were evaluated. The results indicate that, up to 50% CDW content, the concrete maintains slump (≥94 mm), characteristic strength (≥37.2 MPa at 28 days), and chloride penetration (≤14.1 mm) within the limits for moderate exposure conditions, in accordance with ABNT: NBR 6118. Water absorption doubled from 4.5% (0% CDW) to 9.5% (100% CDW), reflecting the higher porosity and adhered mortar on the recycled aggregate, which necessitates adjustments to the water–cement ratio and SSD pre-conditioning to preserve workability and minimize sorptivity. Concretes with more than 65% CDW exhibited chloride penetration depths exceeding 15 mm, potentially compromising durability without additional mitigation. The judicious incorporation of CDW, combined with optimized mix design practices and the use of supplementary cementitious materials (SCMs), demonstrates technical viability for reducing environmental impacts without significantly impairing the structural performance or service life of the concrete. Full article

With the increasing penetration of renewable energy, the stochastic and intermittent nature of its generation increases operational uncertainty and vulnerability, posing significant challenges for grid stability. However, traditional algorithms typically identify critical nodes by focusing solely on the network topology or power flow, or by combining the two, which leads to the inaccurate and incomplete identification of essential nodes. To address this, we propose the Three-Dimensional Value-Based Gravity Model (3V-GM), which integrates structural and electrical–physical attributes across three layers. In the plane layer, we combine each node’s global topological position with its real-time supply–demand voltage state. In the line layer, we introduce an electrical coupling distance to quantify the strength of electromagnetic interactions between nodes. In the point layer, we apply eigenvector centrality to detect latent hub nodes whose influence is not immediately apparent. The performance of our proposed method was evaluated by examining the change in the load loss rate as nodes were sequentially removed. To assess the effectiveness of the 3V-GM approach, simulations were conducted on the IEEE 39 system, as well as six other benchmark networks. The simulations were performed using Python scripts, with operational parameters such as bus voltages, active and reactive power flows, and branch impedances obtained from standard test cases provided by MATPOWER v7.1. The results consistently show that removing the same number of nodes identified by 3V-GM leads to a greater load loss compared to the six baseline methods. This demonstrates the superior accuracy and stability of our approach. Additionally, an ablation experiment, which decomposed and recombined the three layers, further highlights the unique contribution of each component to the overall performance. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

About 10% of all forms of melanoma occur in a familial context and may be due to germline predisposing mutations transmitted as autosomal dominant traits within the affected families. CDKN2A is a highly penetrant gene associated to familial melanomas, being responsible of up to 40% of the cases. Other high, moderate, and low penetrance genes are being discovered, even if their own contribution to melanoma risk is still under debate. Indeed, next generation sequencing-based strategies enable large genomic regions to be analyzed, thus identifying novel candidate genes. These strategies, in diagnostic settings, may also improve the identification of the hereditary cases between all melanomas. The identification of the at-risk subjects gives an important opportunity for cancer surveillance in order to reduce the risk of onset and/or make early diagnosis. In addition, the identification of molecular biomarkers may drive the future development of specific targeted therapies, as already done for other inherited cancer syndromes. Here, we summarize the state of the art regarding the molecular basis of the hereditary susceptibility to develop melanoma. Full article

With the increasing penetration of renewable energy, doubly-fed variable speed pumped storage units (DFVSPSUs) are playing an increasingly critical role in grid frequency regulation. However, traditional PI control struggles to address the control challenges posed by the strong nonlinearity of the units and abrupt operational condition changes. This paper proposes an improved β-incremental generalized predictive controller (β-GPC), which achieves precise rotor-side current control through instantaneous linearization combined with parameter identification featuring a forgetting factor. Simulation results demonstrate that under different power command step changes, the traditional PI controller requires up to approximately 0.48 s to reach a steady state while exhibiting a certain degree of oscillations. In contrast, the enhanced β-GPC controller can stabilize the unit in just 0.2 s without any overshoot or subsequent oscillations. It is evident that the proposed controller delivers a superior regulation performance, characterized by a shorter settling time, reduced overshoot, and minimized oscillations. Full article

Anthropogenic activities on soil layers contribute to reworking and eventual modification, which, in most cases, are detrimental to the soil. Going by the significance of soil to life in many ramifications, it is imperative that its consistent assessment enhances and guides management practices. This study focuses on delineating soil structure alterations using ground-penetrating radar (GPR), a geophysical survey method. The principle of operation and the simplicity of the technique have attracted the choice of the non-destructive testing (NDT) method with a view that it could circumvent the drawbacks that characterized the conventional methods hitherto used for such evaluation. Furthermore, the technique allows for the spatial investigation of the concealing sub-layer of the soil and, thus, informs its choice. A test site was selected on a plain farmland in Kraków, Poland, where some parts of the soil structure distortions were induced using tractor movement, which exerted normal stress from the soil surface layer. Subsequently, GPR measurements were acquired via pre-established profiles on the test site, and soil samples were taken for the laboratory evaluation of some of the soil’s physical properties. An analysis of the field data revealed that zones of distorted soil structures have lower attenuation effects on the GPR signal, with corresponding lower amplitude values compared with the unaltered soil structure zones. Evaluated physical properties such as bulk density and state variables like moisture water contents also show a declining trend from the unaltered soil structure zone to the altered zones. The results have revealed characteristic signatures of the zone of soil structure alterations from GPR scanning that can enhance its identification and characterization in the field and, thus, promote decision making toward the effective utilization and management of soil. Full article

Microwave nondestructive testing (MNDT) has shown great potential in detecting defects in various materials. This is due to it being safe and noninvasive. Safety is essential for the operators as well as the specimens being tested. Being noninvasive is important in maintaining the health of critical structures and components across various industries. In this paper, a review of MNDT methods is given with a comparison against other NDT techniques. First, the latter techniques are described, namely testing using a dye penetrant, ultrasound, eddy currents, magnetic particles, or radiography. Next, an overview of various microwave NDT methods is provided through a review of the applications, advantages, and limitations of each technique. Further, a detailed review of emerging MNDT techniques like microwave microscopy, active microwave thermography, and chipless radio frequency identification is presented. Next, a brief description of current and emerging algorithms employed in MNDT is discussed, with emphasis on those using artificial intelligence. By providing a comprehensive review, this article aims to shed light on the current state of MNDT, thus serving as a reference for subsequent innovations in this rapidly evolving domain. Full article

Background: Understanding the extent of the disease penetration and assessing its impact is critical during a pandemic. However, laboratory-based COVID-19 estimation can be resource-intensive and may not be feasible during an emergency, particularly in low-resource settings. Aim: To investigate whether self-reported symptoms can be used for COVID-19 screening to estimate the burden among individuals aged 18 years and above in a rural setting. Methods: A community-based cross-section study was conducted in a rural district of Haryana, a state in north India, using a self-reported semi-structured questionnaire developed on a digital platform. Information on COVID-19 manifestations as essential and non-essential, confirmed laboratory tests, and disability data using Washington Groups of Short Set were obtained. The sensitivity of the COVID-19 symptoms was estimated against laboratory-confirmed true positives. A chi-square or Fisher exact test for association and a multivariable regression to determine the predictors of the prevalence was carried out. Results: In total, 2954 respondents (79.8%), out of 3700 enumerated, were interviewed. The mean age of respondents was 42 years (SD 17.2), with 54.8% female respondents. The prevalence of COVID-19 based on self-reported symptoms was 6.2% (95%CI: 5.3–7.1). The age-adjusted prevalence was 6.04% (95%CI: 5.9–6.1). Of the total COVID-19 cases, 170 (5.7%, 95%CI: 4.9–6.5) revealed a laboratory-confirmed test. Given three essential symptoms to declare provisionally COVID-19 cases, the sensitivity was 82.9% (141/170), but considering two or more essential symptoms along with two or more non-essential, the sensitivity reached up to 91.8% (156/170). The multivariable analysis showed that increased age, higher education attainment, students, entrepreneurs, persons working in private sectors, and participants with poor hygiene were predictors. Conclusions: A symptoms-based identification of COVID-19 cases can give a reliable estimate and valuable insight into the extent of the penetration, especially in low-middle-income countries, and can be a supplement, not a replacement, to a laboratory test. Full article

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process. Full article

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge–discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge–discharge area can be summarized into seven representative profiles. Full article

Vision-based laser penetration control has become an important research area in the field of welding quality control. Due to the complexity and large number of parameters in the monitoring model, control of the welding process based on deep learning and the reliance on long-term information for penetration identification are challenges. In this study, a penetration recognition method based on a two-stage temporal convolutional network is proposed to realize the online process control of laser welding. In this paper, a coaxial vision welding monitoring system is built. A lightweight segmentation model, based on channel pruning, is proposed to extract the key features of the molten pool and the keyhole from the clear molten pool keyhole image. Using these molten pool and keyhole features, a temporal convolutional network based on attention mechanism is established. The recognition method can effectively predict the laser welding penetration state, which depends on long-term information. In addition, the penetration identification experiment and closed-loop control experiment of unequal thickness plates are designed. The proposed method in this study has an accuracy of 98.96% and an average inference speed of 20.4 ms. The experimental results demonstrate that the proposed method exhibits significant performance in recognizing the penetration state from long sequences of welding image signals, adjusting welding power, and stabilizing welding quality. Full article

Commercially available crowdsourced connected vehicle (CV) trajectory data have recently been used to provide stakeholders with actionable and scalable roadway mobility infrastructure performance measures. Transportation agencies and automotive original equipment manufacturers (OEMs) share a common vision of ensuring the privacy of motorists that anonymously provide their journey information. As this market has evolved, the fleet mix has changed, and some OEMs have introduced additional fuzzification of CV data around 0.5 miles of frequently visited locations. This study compared the estimated Indiana market penetration rates (MPRs) between historic non-fuzzified CV datasets from 2020 to 2023 and a 5–11 May 2024, CV dataset with fuzzified records and a reduced fleet. At selected permanent interstate and non-interstate count stations, overall CV MPRs decreased by 0.5% and 0.3% compared to 2023, respectively. However, the trend in previous years was upward. Additionally, this paper evaluated the impact on data characteristics at freeways and intersections between the 5–11 May 2024, fuzzified CV dataset and a non-fuzzified 7–13 May 2023, CV dataset. The analysis found that the total number of GPS samples decreased 10% statewide. Of the evaluated 54,284 0.1-mile Indiana freeway, US Route, and State Route segments, the number of CV samples increased for 33.8% and decreased for 65.9%. This study also evaluated 26,291 movements at 3289 intersections and found that the number of available trajectories increased for 28.3% and decreased for 70.4%. This paper concludes that data representativeness is enough to derive most relevant mobility performance measures. However, since the change in available trajectories is not uniformly distributed among intersection movements, an unintended sample bias may be introduced when computing performance measures. This may affect signal retiming or capital investment opportunity identification algorithms. Full article

The detection of defects in aluminium alloys using eddy current testing (ECT) can be restricted by higher electrical conductivity. Considering the occurrence of discontinuities during the selective laser melting (SLM) process, checking the ability of the ECT method for the mentioned purpose could bring simple and fast material identification. The research described here is focused on the application of three ECT probes with different frequency ranges (0.3–100 kHz overall) for the identification of artificial defects in SLM aluminium alloy AlSi10Mg. Standard penetration depth for the mentioned frequency range and identification abilities of used probes expressed through lift-off diagrams precede the main part of the research. Experimental specimens were designed in four groups to check the signal sensitivity to variations in the size and depth of cavities. The signal behavior was evaluated according to notch-type and hole-type artificial defects’ presence on the surface of the material and spherical cavities in subsurface layers, filled and unfilled by unmolten powder. The maximal penetration depth of the identified defect, the smallest detectable notch-type and hole-type artificial defect, the main characteristics of signal curves based on defect properties and circumstances for distinguishing between the application of measurement regime were stated. These conclusions represent baselines for the creation of ECT methodology for the defectoscopy of evaluated material. Full article