Search Results (54,204)

Photovoltaic power conversion systems require maximum power point tracking (MPPT) strategies capable of fast dynamic response with low computational burden, while remaining reliable under variable environmental conditions. While neural-network-based methods have been widely investigated, their practical deployment is often limited by the availability of representative training data and by the gap between offline algorithm development and real-time converter-level validation. This paper presents a reproducible hardware-in-the-loop workflow for the development and assessment of a lightweight neural MPPT controller applied to a photovoltaic system with a two-phase interleaved boost converter. The proposed approach generates a large synthetic training dataset using the single-diode photovoltaic model, leveraging only measurable quantities (PV voltage, PV current, and module temperature) as neural network inputs. The trained network estimates the voltage and current corresponding to the maximum power point, while a proportional-integral controller drives the converter toward the predicted operating point. The trained network is deployed on an STM32 microcontroller interfaced with the Typhoon HIL platform, allowing its real-time behavior to be tested against the emulated system. The measured neural MPPT execution time on the microcontroller is around 65 $\mathsf{\mu }$s, with an overall CPU occupancy of nearly 4%, considering the PI controller stage. The implemented setup reproduces the photovoltaic generator, converter dynamics, switching behavior, and realistic irradiance and temperature profiles under repeatable real-time conditions. The interleaved boost architecture also reduces input current ripple and distributes current stress, making the setup suitable for medium-power photovoltaic applications. The main contribution of this work lies in the integrated modeling, training, control, and hardware-in-the-loop validation procedure, supporting the implementation of neural MPPT strategies. Full article

Invasive non-indigenous species (NIS) are impacting Mediterranean coastal ecosystems, presenting ecological and economic management challenges. This study evaluates the economic viability of processing biomass from wild removals of the NIS pearl oyster, Pinctada radiata (Leach, 1814), into value-added seafood products in Greece. The study explored four product formats under two funding scenarios (Scenario A, full private financing; and Scenario B, 60% public co-financing): frozen, vacuum-packed raw oyster meat; frozen, vacuum-packed, breaded raw oyster meat; steamed, brine-smoked, vacuum-packed oyster meat; and steamed oyster meat preserved in olive oil and Mediterranean herbs. In Scenario A, with full private financing, the cumulative net cash flow (NCF) over five years was €165,402.78, with a profitability index (PI) of 2.93 and a payback period (PP) of 1.71 years. In Scenario B, with 60% public co-financing, the owner’s contribution reduced to €34,320.00. At the same time, the annual after-tax cash flow reached €50,240.56, resulting in a five-year cumulative NCF of €216,882.78, a PI of 7.32, and a PP of 0.68 years. These findings indicate that utilizing biomass from wild removals can be a financially viable strategy for managing NIS. This approach focuses on removal rather than aquaculture or translocation of P. radiata and emphasizes the need for adaptive governance and biodiversity protection to ensure effective population control. The study proposes a replicable bioeconomic model for integrating invasive biomass removal with value-added seafood processing in Mediterranean coastal regions. Full article

The rapid evolution of sophisticated cyber threats has drastically increased the cybersecurity risks in Industrial Internet of Things environments due to the massive interconnection of industrial devices, sensors, programmable logic controllers, gateways, and edge computing infrastructures. Traditional intrusion detection systems are insufficient for modern IIoT networks due to challenges with dynamic attack behaviors, class imbalance, concept drift, and computational limitations of resource-constrained edge devices. Although machine learning and deep learning have significantly improved intrusion detection performance, current studies usually deal with these challenges separately and do not provide a holistic view of adaptive and real-time industrial internet of things (IIoT) cybersecurity. In this survey, we provide a structured narrative review of adaptive intrusion detection techniques, focusing on three emerging research directions, including GAN-based data augmentation, drift-aware learning, and Edge Intelligence. It provides a structured narrative review of machine learning, deep learning, and hybrid IDS models, benchmark IIoT datasets, and representative techniques addressing data imbalance, concept drift, and low-latency edge deployment. The survey further provides a comparative study of existing approaches in terms of detection capability, adaptability, computational efficiency, scalability, and deployment suitability. To fill the gap between those complementary research directions, the survey combines the literature into a unified reference architecture that merges GAN-based data augmentation, drift-aware learning, and Edge Intelligence to enable adaptive, real-time IIoT intrusion detection. Finally, we discuss key research challenges and future opportunities in autonomous, collaborative, and trustworthy IIoT cybersecurity, thus providing a practical roadmap for the development of next-generation intelligent intrusion detection systems. Full article

Zinc is an essential trace element for human and plant growth and development. Exploiting naturally Zn-rich soil resources represents an important pathway to develop green characteristic agriculture, improve the nutritional quality of agricultural products, and advance agricultural sustainability aligned with the United Nations Sustainable Development Goals (SDGs2, 3 and 15). To clarify the geochemical characteristics and bioavailability of soil zinc in cultivated lands of the mid-low hilly region in southwestern Henan, and to scientifically evaluate the exploitation potential of Zn-rich land resources, this study selected the farming area of Fangcheng County, Henan Province, as its research object. Based on 1:50,000 land-quality geochemical survey data, 1602 topsoil samples and 61 paired wheat-grain–root-zone soil samples were collected. Multiple statistical approaches, including descriptive statistics, Universal Kriging spatial interpolation, Pearson correlation analysis and segmented regression, were adopted to systematically investigate Zn concentrations, spatial-distribution patterns, element paragenesis relationships and wheat Zn-accumulation characteristics in cultivated soils. Furthermore, the resource potential of Zn-rich land was assessed according to the Specification for Delineation and Identification of Natural Zn-Rich Land (DD2025-04). The results showed the following: (1) The mean Zn concentration of topsoil was 72.3 mg/kg (N = 1602, after removing 3σ outliers), which was higher than the national soil background value (67 mg/kg). With a coefficient of variation of 23.0%, soil Zn exhibited moderate enrichment and relatively uniform distribution. Its spatial pattern was dominated by geomorphic types, showing a north-high–south-low trend. Significant Zn-enrichment advantages were found in hilly areas, yellow-cinnamon soils, and regions covered by alluvial–proluvial and residual-slope parent materials, whereas Zn concentrations were markedly low in strongly alkaline soils. (2) Soil Zn was significantly positively correlated with Cd (r = 0.62), Cu (r = 0.51), V (r = 0.54) and Fe₂O₃ (r = 0.57), and moderately positively correlated with Se (r = 0.41) and Mo (r = 0.43). These correlations indicate homologous soil-forming parent-material sources and Zn-adsorption–enrichment mechanisms mediated by Fe-Mn oxides. The paragenetic relationship between soil Zn and Cd implies that heavy-metal ecological risks should be considered simultaneously during Zn-rich-land exploitation to guarantee ecological sustainability of cultivated land. (3) The average Zn concentration in wheat grains was 28.48 mg/kg, and the mean bioconcentration factor (BCF) was 0.42 (N = 61, averaged from individual sample-pair ratios), demonstrating a moderate Zn-accumulation capacity of wheat. Only a moderate correlation existed between total soil Zn and wheat-grain Zn, suggesting that high total soil Zn concentrations do not necessarily produce high-Zn crops. (4) Soil pH regulated Zn bioavailability: BCF was positively correlated with pH for samples with pH < 6.5 (R² = 0.116, p = 0.013), while a non-significant negative trend was observed for the subgroup pH ≥ 6.5 (R² = 0.269, p = 0.153). Wheat presented relatively high Zn-accumulation potential under neutral-pH conditions. Nevertheless, the observational dataset cannot confirm pH = 6.5 as a strict optimum threshold, which needs further validation through field-controlled experiments. Either extremely acidic or alkaline conditions may reduce available Zn supply via Zn leaching loss or hydroxide precipitation. (5) In accordance with DD2025-04, Zn-rich soils accounted for 21.85% of the study area, and Zn-enriched wheat grains occupied 21.31%, indicating abundant reserves of Zn-rich land resources. These findings can provide geochemical support for the scientific utilization of Zn-rich cultivated land, industrial layout of Zn-enriched wheat, and precise cultivated-land quality management in the hilly areas of southwestern Henan, promoting sustainable utilization of soil resources and nutrition-sensitive agricultural development. Full article

Strong earthquake-induced geological hazards are characterized by extensive affected areas, severe consequences, and significant long-term cascading effects, posing serious threats to human life, property safety, and sustainable socio-economic development in earthquake-affected regions. Existing landslide susceptibility assessments mainly focus on regional-scale predictions, while slope-scale co-seismic responses and dynamic hazard management remain insufficiently studied. This study focuses on Hejiapo, Longchi Town, Dujiangyan City, located in the strong earthquake-affected area of the Wenchuan earthquake. A comprehensive approach integrating multi-temporal remote sensing interpretation, field geological surveys, topographic mapping, statistical modeling, and numerical simulation was employed to identify major controlling factors and evaluate landslide susceptibility. Six conditioning factors were selected, and an area-based Information Value (IV) model was applied for landslide susceptibility assessment. Based on the susceptibility assessment results and field investigation, potential unstable zones were identified. Numerical simulations were further conducted to analyze the potential failure modes, movement processes, and affected areas of these unstable zones. The results indicate that: (1) A total of 28 post-earthquake landslides were identified, with a cumulative area of 364,932 m², accounting for 19.64% of the study area. These landslides exhibit characteristics of being spatially clustered and having relatively small individual scales and a single dominant failure type. (2) The susceptibility assessment results show that very-high- and high-susceptibility zones are negatively correlated with road distance and fault distance, and are mainly distributed along both sides of the roads and steep ridge areas. (3) Two potential unstable zones were delineated, covering a total area of approximately 0.051 km², mainly distributed along the southwestern ridges of Hejiapo. (4) The identified unstable zones have the potential to generate high-elevation landslide debris flow hazards. Numerical simulations reveal that the debris materials would mainly migrate downward along slope channels. The channel–road intersections and channel outlets are identified as the principal potential impact zones, which should be considered a priority area for geological hazard prevention. This study provides a methodological framework beyond the traditional static approach of “hazard investigation” by establishing a dynamic risk management concept (susceptibility zones–risk points–potential disaster chains). The proposed framework enhances the understanding of the entire disaster prevention and mitigation process and provides scientific support for precise regional hazard mitigation planning and territorial spatial management. Full article

This study proposes and implements a DCHC-based CRM/CCM hybrid control with a switching-period limit condition to improve light-load efficiency in an AC-coupled ESS power converter. In grid-connected ESSs, the operating point continuously varies depending on load and grid conditions, and particularly in the light-load region, switching losses become relatively dominant, which can degrade overall system efficiency. Furthermore, in the medium-to-high-load regions, increased current ripple and peak current can lead to greater device stress and conduction losses, making it difficult to simultaneously ensure both efficiency and reliability across the entire load range with a single operating mode. To address this issue, this study proposes an approach that generates the current reference based on a PR controller and combines it with a DCHC-based current control structure to flexibly switch the control mode according to operating conditions. The proposed method is designed to balance the loss factors by reducing switching losses through CRM operation under light-load conditions, while concurrently employing CCM operation as the load increases to limit current ripple and device current stress. Consequently, this study demonstrates the potential for high-efficiency operation of the power converter system by improving the control strategy at the single-phase inverter stage in an AC-coupled ESS environment, and the validity of the proposed method is confirmed through simulation and experiments. Full article

Although the world is rich in deepwater oil and gas resources, deepwater drilling faces the challenge of a narrow safety density window. Dual-gradient drilling is an effective approach to address this narrow safety density window, and subsea pump-lift dual-gradient drilling is currently one of the most widely used methods. To improve handling capacity and effectiveness under well kick conditions, we established a simplified hydrodynamic model for subsea pump-lift dual-gradient drilling that accounts for the subsea pump’s head-flow performance curve. Based on this hydrodynamic model, we proposed automatic control methods based on throttling regulation and variable-speed regulation and conducted simulation analyses. The results indicate that both control methods can safely, efficiently, and automatically handle well kicks; throttle control has shorter response times and higher precision, while variable-speed control regulates bottom hole pressure by adjusting the subsea pump’s rotational speed and offers better energy-saving performance. These findings provide new insights into handling well kicks in subsea pump-lift dual-gradient drilling. Full article

Phytophthora blight, caused by Phytophthora capsici, is difficult to control after symptoms appear, motivating rapid, non-destructive detection during the asymptomatic stage. This study evaluated hyperspectral imaging (HSI) for early disease detection in chili pepper seedlings. Stem and leaf spectra were monitored during disease progression, and the basal stem was selected for its disease-related spectral changes before visible symptoms appeared. A genetic algorithm coupled with partial least squares and correlation analysis identified five characteristic wavelengths (550, 670, 722, 760, and 800 nm). Among the six machine-learning algorithms evaluated on day 5 using five-fold cross-validation, linear discriminant analysis (LDA) and support vector machine (SVM) achieved classification accuracies of 90.7% and 90.1%, respectively. Detection before symptom onset was feasible by day 5. Healthy and asymptomatic infected samples collected on that day were therefore evaluated using 20 vegetation indices to identify simpler diagnostic predictors. Single-index support vector machine (SVM) prediction accuracies ranged from 60.00% to 91.25%. The photochemical reflectance index (PRI) achieved the highest accuracy (91.25%), followed by the enhanced vegetation index (EVI) and triangular vegetation index (TVI) (90.00% each). These findings support basal-stem HSI and selected vegetation indices as candidate approaches for early P. capsici detection, subject to independent validation. Full article

Immune checkpoint inhibitors (ICIs) have transformed cancer treatment but may cause immune-related kidney injury, and their association with glomerular disease remains incompletely understood. We evaluated the risk of incident glomerular disease following ICI therapy using the TriNetX U.S. Collaborative Network. Adult patients with cancer treated between January 2014 and March 2025 who received ICIs were compared with those receiving conventional antineoplastic therapies after 1:1 propensity score matching for demographics, cancer type, comorbidities, medications, and baseline kidney function. The index date was constrained to an eligibility window ending 1 March 2024, and identical exposure requirements were applied to both cohorts. The primary outcome was incident glomerular disease (ICD-10-CM codes N00, N01, N02, N04, N05 and N08), and hazard ratios (HRs) were estimated using Cox proportional hazards models. The proportional hazards assumption was tested for every outcome, death was additionally modelled as a competing event, and subgroup heterogeneity was assessed by formal tests of interaction. Among 71,354 matched patient pairs, glomerular disease occurred in 2.5% of ICI-treated patients and in 2.4% of controls. ICI therapy was associated with a higher hazard of glomerular disease (HR 1.492, 95% CI 1.392–1.599). The excess hazard was concentrated in the first 500 days after initiation (interval HR 1.68); beyond approximately 1000 days, the interval point estimates approached unity. In a supporting analysis in which death was modelled as a competing event, the estimated 5-year cumulative incidence was 4.5% with ICI therapy and 3.2% with comparator therapy, an absolute difference of approximately 1.2 percentage points, and the corresponding difference in restricted mean time free of glomerular disease was about 20 days. These absolute estimates were derived from the survival functions of the matched cohort rather than from patient-level competing-risk regression and should be read as approximations. In exploratory analyses, heterogeneity was demonstrated by race, cancer type and ICI agent. Renal biopsy and urine protein testing were performed at indistinguishable rates in the two cohorts, arguing against differential detection. ICI therapy is associated with a modest absolute increase in the risk of coded incident glomerular disease, concentrated in the first 18 months, which may support targeted rather than indefinite renal surveillance. Full article

Early identification of oral potentially malignant disorders (OPMD) remains a major clinical challenge. Point-of-care (PoC) salivary testing may provide a rapid, non-invasive adjunctive approach to support clinical decision-making. This case–control study included 42 participants: 21 patients with clinically and histologically confirmed OPMD and 21 healthy controls. Salivary p16 and EGFR were assessed using a point-of-care device, and their measurements were integrated with predefined clinical variables by the OraFusion software to generate a device-generated risk classification of low, moderate, or high risk. Among patients with OPMD, 57.14% were classified as high risk, 4.76% as moderate risk, and 38.10% as low risk, whereas 95.24% of controls were classified as low risk and 4.76% as moderate risk. Considering moderate and high device-generated risk classifications as positive test results, the assay achieved a sensitivity of 61.90% (95% CI, 38.44–81.89%) and a specificity of 95.24% (95% CI, 76.18–99.88%). These findings suggest that PoC salivary testing may provide valuable adjunctive information through a device-generated risk classification. However, its moderate sensitivity and the small, single-centre sample preclude its use as a substitute for histopathological diagnosis. Larger prospective, multicentre studies are needed to validate its clinical utility. Full article

Tuta absoluta is an invasive species that has managed to expand beyond its native geographical area and causes severe yield losses in tomato. Despite the application of synthetic insecticides in getting rid of the pest, farmers have reported reduced efficacy over the course of time due to resistance. With regard to ecological pest management approaches, studies regarding the use of companion plants in the management of T. absoluta are scarce. Therefore, we screened fourteen potential companion plants for repellency using a Y-tube olfactometer followed by cage and screenhouse experiments. Data were analyzed using R software (version 4.6.0; R Core Team, 2026). The screening results showed that Allium schoenoprasum and Aeollanthus fruticosus significantly repelled T. absoluta compared to other potential companion plants at p < 0.001. In a dual-choice cage experiment, tomato plants placed with A. schoenopraum and A. fruticosus were recorded to have lower leaf damage percentages of 47.27% and 33.94%, respectively, compared to the negative control (66.34%). Similarly, tomato plants intercropped with A. schoenopraum and A. fruticosus showed lower leaf damage percentages of 13.92% and 25.00%, respectively, compared to the negative control (63.38%) under the screenhouse experiment. Therefore, A. schoenopraum and A. fruticosus exhibit significant potential in the management of T. absoluta in tomato. Full article

Metal-halide perovskite light-emitting diodes (PeLEDs) have rapidly achieved external quantum efficiencies comparable to established organic and quantum-dot LEDs. Their narrow emission spectra, tunable bandgaps, high photoluminescence efficiencies, and low-temperature processing make them promising for displays, lighting, optical communication, and flexible electronics, although their commercialization remains limited by short operational lifetime, efficiency roll-off, unstable blue emission, ion migration, interfacial degradation, and poor large-area uniformity. This review provides a device-engineering-centered analysis connecting perovskite materials and processing conditions with charge injection, radiative recombination, optical extraction, and stability. It introduces the essential characteristics of 3D, 2D/quasi-2D, and nanocrystal perovskites and evaluates major engineering approaches, including composition and dimensionality control, crystallization regulation, defect passivation, transport-layer and interface modification, charge balancing, and optical outcoupling. An emphasis of this review is its comparison of the problems addressed by these approaches and their trade-offs among efficiency, spectral stability, lifetime, and manufacturing compatibility. Degradation under electrical operation is examined with attention to Joule heating, ion migration, charge accumulation, and interfacial reactions. Emerging patterned, reconfigurable, flexible, transparent, and communication devices are also discussed. Finally, the review identifies operational stability, efficient blue emission, scalable fabrication, high-resolution patterning, lead toxicity, and competition with OLEDs as the principal challenges for PeLED commercialization. Full article

Breast cancer accounted for approximately 2.4 million new cases and 694,000 deaths worldwide in 2024. Deep learning approaches, particularly encoder–decoder architectures, have been investigated for mammographic mass segmentation. However, evaluations are commonly centered on benchmarked datasets, which include only annotated abnormal cases and therefore do not support assessment of false-positive pixel predictions on mammograms without annotated masses. Segmentation performance on the Digital Mammography Imaging Dataset (DMID) remains comparatively underexplored. DMID is particularly relevant for this task because it combines native full-field digital mammography in DICOM format with pixel-level mass annotations, complementary clinical information, and mammograms without annotated masses, enabling evaluation of both mass delineation and false-positive behavior. In this study, BCSeg-IRUNet is presented for binary mammographic mass segmentation on DMID. The model combines Inception-style multi-scale feature extraction, residual pathways, and UNet-style skip connections. Candidate architectures, including attention-based variants, were compared, followed by sequential evaluation of loss functions and batch sizes under stratified five-fold cross-validation. BCSeg-IRUNet achieved mean Dice coefficients of 0.7015 on the Mass Dataset and 0.6762 on the Full Dataset. In controlled fold-paired comparisons, BCSeg-IRUNet achieved higher Dice coefficients than the retrained AttentionUNet and MultiResUNet baselines in all five held-out test folds, with mean paired improvements of 0.0572 and 0.0499, respectively. Stratified analysis showed that smaller masses were more challenging to segment. For cross-study context, the Full Dataset performance exceeded the previously published Full Dataset Dice coefficient of 0.558, although differences in data partitioning and experimental protocols preclude a direct comparison. These findings support further validation on larger, diverse, and independent mammography datasets. Full article

Electric vehicle (EV) wireless power transfer (WPT) systems face two fundamental challenges that hinder their widespread adoption: sensitivity to coil misalignment and the need for battery-friendly fast charging protocols. This paper presents a comprehensive and integrated framework that addresses both challenges through a three-pronged approach combining advanced coil geometry optimization, multi-stage current method (MSCM) charging, and reinforcement learning (RL)-based adaptive control. First, a memetic algorithm hybridizing global exploration and local refinement is employed to design coil geometries that are inherently resilient to misalignment. The optimized coils maintain strong magnetic coupling under lateral displacements up to ±75 mm by strategically sizing the secondary coil’s outer diameter to be smaller than the primary coil’s, ensuring it remains within the optimal magnetic flux region. Second, an MSCM charging protocol is developed and optimized with the objective of balancing charging speed against battery thermal stability and state of health (SoH). The proposed strategy determines optimal current levels for each charging stage, reducing temperature rise compared to conventional CC-CV charging. Third, a novel Deep Q-Network (DQN) RL agent is implemented for real-time adaptive control of the WPT system. The RL controller dynamically adjusts phase shift in response to varying coupling conditions, load disturbances, and battery state, outperforming traditional PI controllers with 23% faster settling time and improved efficiency under dynamic misalignment scenarios. Finite element analysis (FEA) simulations validate the electromagnetic performance of the optimized coils, while an experimental prototype demonstrates the integrated system’s performance. Results show that the combined approach achieves 91.2% DC-DC efficiency under nominal conditions and maintains over 83% efficiency under lateral misalignments up to ±75 mm, fully complying with SAE J2954 alignment tolerance requirements. The MSCM charging protocol, guided by the memetic algorithm, limits battery temperature rise during a full charge cycle, while the RL controller ensures stable power delivery under real-world dynamic conditions. This work establishes a new paradigm for holistic WPT system design, demonstrating that synergistic optimization of magnetic structures, charging protocols, and intelligent control can simultaneously achieve misalignment resilience, fast charging, and adaptive robustness. Full article

Sustainable building projects require quality management approaches beyond inspection, documentation, and technical compliance. Although Total Quality Management (TQM) is widely studied in construction and organizational performance contexts, its contribution to sustainable building delivery remains fragmented. This study synthesizes literature on TQM implementation in sustainable building projects and proposes a conceptual lifecycle framework. Following PRISMA guidelines, a systematic search was conducted in Web of Science and ScienceDirect. From 441 initially identified records, 105 studies were retained after duplicate removal, title and abstract screening, and full-text eligibility assessment. The studies were analyzed through descriptive analysis, bibliometric mapping, and critical thematic synthesis. Results show that empirical studies dominate the corpus (62.9%), with TQM performance (21.9%), barriers (14.3%), and processes/procedures (12.4%) as the most explicit categories; Industry 4.0 (4.8%) and Six Sigma (3.8%) remain comparatively limited. The synthesis identifies five interpretations of TQM: compliance and quality assurance system, project performance system, cultural and behavioral capability, sustainability-enabling system, and digital/data-driven quality system. The review clarifies the conceptual boundary of TQM in sustainable building projects and proposes a lifecycle TQM-sustainability architecture linking leadership, quality culture, stakeholder coordination, supplier integration, process control, digital traceability, continuous improvement, and lifecycle feedback with sustainable building performance. Bibliometric maps are interpreted descriptively and exploratorily rather than as inferential evidence; the proposed architecture and propositions are review-derived conceptual outputs that require empirical validation. Full article