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Accurate weld defect detection is critical for ensuring structural safety and evaluating welding quality in industrial applications. Manual inspection methods have inherent limitations, including inefficiency and inadequate sensitivity to subtle defects. Existing detection models, primarily designed for natural images, struggle to adapt to the characteristic challenges of weld X-ray images, such as high noise, low contrast, and inter-defect similarity, particularly leading to missed detections and false positives for small defects. To address these challenges, a multi-dimensional feature fusion model (MADet), which is a multi-branch deep fusion network for weld defect detection, was proposed. The framework incorporates two key innovations: (1) A multi-scale feature fusion network integrated with lightweight attention residual modules to enhance the perception of fine-grained defect features by leveraging low-level texture information. (2) An anchor-based feature-selective detection head was used to improve the discrimination and localization accuracy for five typical defect categories. Extensive experiments on both public and proprietary weld defect datasets demonstrated that MADet achieved significant improvements over the state-of-the-art YOLO variants. Specifically, it surpassed the suboptimal model by 7.41% in mAP@0.5, indicating strong industrial applicability. Full article

(This article belongs to the Section Manufacturing Processes and Systems)

11 pages, 1174 KiB

Open AccessArticle

385 nm AlGaN Near-Ultraviolet Micro Light-Emitting Diode Arrays with WPE 30.18% Realized Using an AlN-Inserted Hole Spreading Enhancement Superlattice Electron Blocking Layer

by Qi Nan, Shuhan Zhang, Jiahao Yao, Yun Zhang, Hui Ding, Qian Fan, Xianfeng Ni and Xing Gu

Coatings 2025, 15(8), 910; https://doi.org/10.3390/coatings15080910 (registering DOI) - 3 Aug 2025

Abstract

In this work, we demonstrate high-efficiency 385 nm AlGaN-based near-ultraviolet micro light emitting diode (NUV-Micro LED) arrays. The epi structure is prepared using a novel AlN-inserted superlattice electrical blocking layer which enhances hole spreading in the p-type region significantly. The NUV-Micro LED arrays in this work comprise 228 chips in parallel with wavelengths at 385 nm, and each single chip size is 15 × 30 μm². Compared with conventional bulk AlGaN-based EBL structures, the NUV-Micro LED arrays that implemented the new hole spreading enhanced superlattice electrical blocking layer (HSESL-EBL) structure proposed in this work had a remarkable increase in light output power (LOP) at current density, increasing the range down from 0.02 A/cm² to as high as 97 A/cm². The array’s light output power is increased up to 1540% at the lowest current density 0.02 A/cm², and up to 58% at the highest current density 97 A/cm², measured under room temperature (RT); consequently, the WPE is increased from 13.4% to a maximum of 30.18%. This AlN-inserted HESEL-EBL design significantly enhances both the lateral expansion efficiency and the hole injection efficiency into the multi quantum well (MQW) in the arrays, improving the concentration distribution of the holes in MQW while maintaining good suppression of electron leakage. The array’s efficiency droop has also been greatly reduced. Full article

(This article belongs to the Section Surface Engineering for Energy Harvesting, Conversion, and Storage)

18 pages, 6388 KiB

Open AccessArticle

Intermittent and Adaptive Control Strategies for Chaos Suppression in a Cancer Model

by Rugilė Jonuškaitė and Inga Telksnienė

Math. Comput. Appl. 2025, 30(4), 81; https://doi.org/10.3390/mca30040081 (registering DOI) - 3 Aug 2025

Abstract

The chaotic dynamics observed in mathematical models of cancer can correspond to the unpredictable tumor growth and treatment responses seen in clinical settings. Suppressing this chaos is a significant challenge in theoretical oncology. This paper investigates and compares four distinct control strategies designed to stabilize a chaotic three-dimensional tumor-immune interaction model. The objective is to steer the system from its chaotic attractor to a target unstable periodic orbit, representing a transition to a more regular and predictable dynamic. The strategies, all based on the external force control paradigm, include continuous control, a simple state-dependent intermittent control, an improved intermittent control with a minimum activation duration to suppress chattering, and an adaptive intermittent control with a time-varying feedback gain. The performance of each strategy is quantitatively evaluated based on tracking accuracy and the required control effort. Full article

15 pages, 604 KiB

Open AccessArticle

Brief Repeated Attention Training for Psychological Distress: Findings from Two Experiments

by David Skvarc, Shannon Hyder, Laetitia Leary, Shahni Watts, Marcus Seecamp, Lewis Burns and Alexa Hayley

Behav. Sci. 2025, 15(8), 1052; https://doi.org/10.3390/bs15081052 (registering DOI) - 3 Aug 2025

Abstract

Psychological distress is understood to be maintained by attention. We performed two experiments examining the impact of attention training (AT) on psychological distress symptoms. Experiment one (N = 336) investigated what effects might be detected in a simple experimental design with longitudinal measurements, while experiment two (N = 214) examined whether using a different emotional stimulus could induce an immediate anxiolytic effect in response to AT. Attentional biases were operationalized as the target search latency correlated with mood and psychological distress scores. While limited evidence of attentional biases was found in participants with higher mood distress, correlations emerged in the experimental conditions at day thirty, indicating a relationship between task latency, stress, and changes in depression (experimental one). We found no immediate between–within-group differences in outcome when including different emotional stimuli (experiment two). Despite attentional biases being less apparent in community samples, attentional training for bias modification was effective in eliciting positive biases, leading to improved mood. Notably, participants in the control condition reported the greatest mood and psychological distress improvements, whereas changes in the experimental condition primarily pertained to attentional biases. Taken together, these findings suggest that AT tasks can improve distress, but not through changes in attentional biases. Full article

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14 pages, 4475 KiB

Open AccessArticle

DFT Investigation into Adsorption–Desorption Properties of Mg/Ni-Doped Calcium-Based Materials

by Wei Shi, Renwei Li, Xin Bao, Haifeng Yang and Dehao Kong

Crystals 2025, 15(8), 711; https://doi.org/10.3390/cryst15080711 (registering DOI) - 3 Aug 2025

Abstract

Although concentrated solar power (CSP) coupled with calcium looping (CaL) offers a promising avenue for efficient thermal chemical energy storage, calcium-based sorbents suffer from accelerated structural degradation and decreased CO₂ capture capacity during multiple cycles. This study used Density Functional Theory (DFT) calculations to investigate the mechanism by which Mg and Ni doping improves the adsorption/desorption performance of CaO. The DFT results indicate that Mg and Ni doping can effectively reduce the formation energy of oxygen vacancies on the CaO surface. Mg–Ni co-doping exhibits a significant synergistic effect, with the formation energy of oxygen vacancies reduced to 5.072 eV. Meanwhile, the O²⁻ diffusion energy barrier in the co-doped system was reduced to 2.692 eV, significantly improving the ion transport efficiency. In terms of CO₂ adsorption, Mg and Ni co-doping enhances the interaction between surface O atoms and CO₂, increasing the adsorption energy to −1.703 eV and forming a more stable CO₃²⁻ structure. For the desorption process, Mg and Ni co-doping restructured the CaCO₃ surface structure, reducing the CO₂ desorption energy barrier to 3.922 eV and significantly promoting carbonate decomposition. This work reveals, at the molecular level, how Mg and Ni doping optimizes adsorption–desorption in calcium-based materials, providing theoretical guidance for designing high-performance sorbents. Full article

(This article belongs to the Special Issue Performance and Processing of Metal Materials)

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17 pages, 4672 KiB

Open AccessArticle

Oscillation Mechanism of SRF-PLL in Wind Power Systems Under Voltage Sags and Improper Control Parameters

by Guoqing Wang, Zhiyong Dai, Qitao Sun, Shuaishuai Lv, Nana Lu and Jinke Ma

Electronics 2025, 14(15), 3100; https://doi.org/10.3390/electronics14153100 (registering DOI) - 3 Aug 2025

Abstract

The synchronous reference frame phase-locked loop (SRF-PLL) is widely employed for grid synchronization in wind farms. However, it may exhibit oscillations under voltage sags or improper parameter settings. These oscillations may compromise the secure integration of large-scale wind power. Therefore, mitigating the oscillations of the SRF-PLL is crucial for ensuring stable and reliable operation. To this end, this paper investigates the underlying oscillation mechanism of the SRF-PLL from local and global perspectives. By taking into account the grid voltage and control parameters, it is revealed that oscillations of the SRF-PLL can be triggered by grid voltage sags and/or the improper control parameters. More specifically, from the local perspective, the SRF-PLL exhibits distinct qualitative behaviors around its stable equilibrium points under different grid voltage amplitudes. As a result, when grid voltage sags occur, the SRF-PLL may exhibit multiple oscillation modes and experience a prolonged transient response. Furthermore, from the global viewpoint, the large-signal analysis reveals that the SRF-PLL has infinitely many asymmetrical convergence regions. However, the sizes of these asymmetrical convergence regions shrink significantly under low grid voltage amplitude and/or small control parameters. In this case, even if the parameters in the small-signal model of the SRF-PLL are well-designed, a small disturbance can shift the operating point into other regions, resulting in undesirable oscillations and a sluggish dynamic response. The validity of the theoretical analysis is further supported by experimental verification. Full article

(This article belongs to the Special Issue New Trends in Power Electronics for Renewable Energy and Power Systems)

20 pages, 51475 KiB

Open AccessArticle

Mechanism-Driven Strength–Conductivity Synergy in Hypereutectic Al-Si Alloys Reinforced with Interface-Engineered Ni-Coated CNTs

by Xuexuan Yang, Yulong Ren, Peng Tang and Jun Tan

Materials 2025, 18(15), 3647; https://doi.org/10.3390/ma18153647 (registering DOI) - 3 Aug 2025

Abstract

Secondary hypereutectic Al-Si alloys are attractive for sustainable manufacturing, yet their application is often limited by low strength and electrical conductivity due to impurity-induced microstructural defects. Achieving a balance between mechanical and conductive performance remains a significant challenge. In this work, nickel-coated carbon nanotubes (Ni-CNTs) were introduced into secondary Al-20Si alloys to tailor the microstructure and enhance properties through interfacial engineering. Composites containing 0 to 0.4 wt.% Ni-CNTs were fabricated by conventional casting and systematically characterized. The addition of 0.1 wt.% Ni-CNTs resulted in the best combination of properties, with a tensile strength of 170.13 MPa and electrical conductivity of 27.60% IACS. These improvements stem from refined α-Al dendrites, uniform eutectic Si distribution, and strong interfacial bonding. Strengthening was achieved through grain refinement, Orowan looping, dislocation generation from thermal mismatch, and the formation of reinforcing interfacial phases such as AlNi₃C₀.₉ and Al₄SiC₄. At higher Ni-CNT contents, property degradation occurred due to agglomeration and phase coarsening. This study presents an effective and scalable strategy for achieving strength–conductivity synergy in secondary aluminum alloys via nanoscale interfacial design, offering guidance for the development of multifunctional lightweight materials. Full article

(This article belongs to the Special Issue Evaluation of Mechanical Properties and Microstructure of Lightweight Alloys)

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33 pages, 7296 KiB

Open AccessArticle

Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery

by Gabija Veličkaitė, Ignas Daugėla and Ivan Suzdalev

Appl. Sci. 2025, 15(15), 8608; https://doi.org/10.3390/app15158608 (registering DOI) - 3 Aug 2025

Abstract

This research presents the development of a novel object detection algorithm designed to identify humans in natural outdoor environments using minimal computational resources. The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations for camera stabilization. A secondary CNN refines detections and reduces false positives. Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. However, the overall accuracy reached 65%, due to a higher rate of false positives—an expected trade-off when maximizing recall. This bias is intentional, as missing a human target in search and rescue applications carries a higher cost than producing additional false detections. While supervised models, such as YOLOv5, perform well on data resembling their training sets, they exhibit significant performance degradation on previously unseen footage. In contrast, SARGAS generalizes more effectively, making it a promising candidate for real-world deployment in environments where labeled training data is limited or unavailable. The results establish a solid foundation for further improvements and suggest that unsupervised CNN-based approaches hold strong potential in object detection tasks. Full article

17 pages, 4783 KiB

Open AccessArticle

Empirical Investigation of the Structural Response of Super-Span Soil–Steel Arches During Backfilling

by Bartłomiej Kunecki

Materials 2025, 18(15), 3650; https://doi.org/10.3390/ma18153650 (registering DOI) - 3 Aug 2025

Abstract

This paper presents field investigations of a corrugated steel soil–steel arch structure with a span of 25.7 m and a rise of 9.0 m—currently the largest single-span structure of its kind in Europe. The structure, serving as a wildlife crossing along the DK16 expressway in northeastern Poland, was constructed using deep corrugated steel plates (500 mm× 237 mm) made from S315MC steel, without additional reinforcements such as stiffening ribs or geosynthetics. The study focused on monitoring the structural behavior during the critical backfilling phase. Displacements and strains were recorded using 34 electro-resistant strain gauges and a geodetic laser system at successive backfill levels, with particular attention to the loading stage at the crown. The measured results were compared with predictions based on the Swedish Design Method (SDM). The SDM equations did not accurately predict internal forces during backfilling. At the crown level, bending moments and axial forces were overestimated by approximately 69% and 152%, respectively. At the final backfill level, the SDM underestimated bending moments by 55% and overestimated axial forces by 90%. These findings highlight limitations of current design standards and emphasize the need for revised analytical models and long-term monitoring of large-span soil–steel structures. Full article

(This article belongs to the Section Construction and Building Materials)

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25 pages, 4386 KiB

Open AccessArticle

Design and Testing of a Pneumatic Jujube Harvester

by Huaming Hou, Wei Niu, Qixian Wen, Hairui Yang, Jianming Zhang, Rui Zhang, Bing Xv and Qingliang Cui

Agronomy 2025, 15(8), 1881; https://doi.org/10.3390/agronomy15081881 (registering DOI) - 3 Aug 2025

Abstract

Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our research group conducted a study on mechanical harvesting technology for fresh jujubes. A pneumatic jujube harvester was designed. This harvester is composed of a self-regulating picking mechanism, a telescopic conveying pipe, a negative pressure generator, a cleaning mechanism, a double-chamber collection box, a single-door shell, a control assembly, a generator, a towing mobile chassis, etc. During the harvest, the fresh jujubes on the branches are picked under the combined effect of the flexible squeezing of the picking roller and the suction force of the negative pressure air flow. They then enter the cleaning mechanism through the telescopic conveying pipe. Under the combined effect of the upper and lower baffles of the cleaning mechanism and the negative-pressure air flow, the fresh jujubes are separated from impurities such as jujube leaves and branches. The clean fresh jujubes fall into the collection box. We considered the damage rate of fresh jujubes, impurity rate, leakage rate, and harvesting efficiency as the indexes, and the negative-pressure suction wind speed, picking roller rotational speed, and the inclination angle of the upper and lower baffles of the cleaning and selection machinery as the test factors, and carried out the harvesting test of fresh jujubes. The test results show that when the negative-pressure suction wind speed was 25 m/s, the picking roller rotational speed was 31 r/min, and the inclination angles of the upper and lower baffle plates for cleaning and selecting were −19° and 19.5°, respectively, the breakage rate of fresh jujube harvesting was 0.90%, the rate of impurity was 1.54%, the rate of leakage was 2.59%, and the efficiency of harvesting was 73.37 kg/h, realizing the high-efficiency and low-loss harvesting of fresh jujubes. This study provides a reference for the research and development of fresh jujube mechanical harvesting technology and equipment. Full article

(This article belongs to the Section Precision and Digital Agriculture)

23 pages, 20324 KiB

Open AccessArticle

Hyperparameter Tuning of Artificial Neural Network-Based Machine Learning to Optimize Number of Hidden Layers and Neurons in Metal Forming

by Ebrahim Seidi, Farnaz Kaviari and Scott F. Miller

J. Manuf. Mater. Process. 2025, 9(8), 260; https://doi.org/10.3390/jmmp9080260 (registering DOI) - 3 Aug 2025

Abstract

Cold rolling is widely recognized as a key industrial process for enhancing the mechanical properties of materials, particularly hardness, through strain hardening. Despite its importance, accurately predicting the final hardness remains a challenge due to the inherently nonlinear nature of the deformation. While several studies have employed artificial neural networks to predict mechanical properties, architectural parameters still need to be investigated to understand their effects on network behavior and model performance, ultimately supporting the design of more effective architectures. This study investigates hyperparameter tuning in artificial neural networks trained using Resilient Backpropagation by evaluating the impact of varying number of hidden layers and neurons on the prediction accuracy of hardness in 70-30 brass specimens subjected to cold rolling. A dataset of 1000 input–output pairs, containing dimensional and hardness measurements from multiple rolling passes, was used to train and evaluate 819 artificial neural network architectures, each with a different configuration of 1 to 3 hidden layers and 4 to 12 neurons per layer. Each configuration was tested over 50 runs to reduce the influence of randomness and enhance result consistency. Enhancing the network depth from one to two hidden layers improved predictive performance. Architectures with two hidden layers achieved better performance metrics, faster convergence, and lower variation than single-layer networks. Introducing a third hidden layer did not yield meaningful improvements over two-hidden-layer architectures in terms of performance metrics. While the top three-layer model converged in fewer epochs, it required more computational time due to increased model complexity and weight elements. Full article

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21 pages, 8671 KiB

Open AccessArticle

Characterization of Spatial Variability in Rock Mass Mechanical Parameters for Slope Stability Assessment: A Comprehensive Case Study

by Xin Dong, Tianhong Yang, Yuan Gao, Feiyue Liu, Zirui Zhang, Peng Niu, Yang Liu and Yong Zhao

Appl. Sci. 2025, 15(15), 8609; https://doi.org/10.3390/app15158609 (registering DOI) - 3 Aug 2025

Abstract

The spatial variability in rock mass mechanical parameters critically affects slope stability assessments. This study investigated the southern slope of the Bayan Obo open-pit mine. A representative elementary volume (REV) with a side length of 14 m was determined through discrete fracture network (DFN) simulations. Based on the rock quality designation (RQD) data from 40 boreholes, a three-dimensional spatial distribution model of the RQD was constructed using Ordinary Kriging interpolation. The RQD values were converted into geological strength index (GSI) values through an empirical correlation, and the generalized Hoek–Brown criterion was applied to develop a spatially heterogeneous equivalent mechanical parameter field. Numerical simulations were performed using FLAC3D, with the slope stability evaluated using the point safety factor (PSF) method. For comparison, three homogeneous benchmark models based on the 5th, 25th, and 50th percentiles produced profile-scale safety factors of 0.96–1.92 and failed to replicate the observed failure geometry. By contrast, the heterogeneous model yielded safety factors of approximately 1.03–1.08 and accurately reproduced the mapped sliding surface. These findings demonstrate that incorporating spatial heterogeneity significantly improves the accuracy of slope stability assessments, providing a robust theoretical basis for targeted monitoring and reinforcement design. Full article

22 pages, 4267 KiB

Open AccessArticle

MCA-GAN: A Multi-Scale Contextual Attention GAN for Satellite Remote-Sensing Image Dehazing

by Sufen Zhang, Yongcheng Zhang, Zhaofeng Yu, Shaohua Yang, Huifeng Kang and Jingman Xu

Electronics 2025, 14(15), 3099; https://doi.org/10.3390/electronics14153099 (registering DOI) - 3 Aug 2025

Abstract

With the growing demand for ecological monitoring and geological exploration, high‑quality satellite remote‑sensing imagery has become indispensable for accurate information extraction and automated analysis. However, haze reduces image contrast and sharpness, significantly impairing quality. Existing dehazing methods, primarily designed for natural images, struggle with remote-sensing images due to their complex imaging conditions and scale diversity. Given this, we propose a novel Multi-Scale Contextual Attention Generative Adversarial Network (MCA-GAN), specifically designed for satellite image dehazing. Our method integrates multi‑scale feature extraction with global contextual guidance to enhance the network’s comprehension of complex remote‑sensing scenes and its sensitivity to fine details. MCA‑GAN incorporates two self-designed key modules: (1) a Multi‑Scale Feature Aggregation Block, which employs multi‑directional global pooling and multi‑scale convolutional branches to bolster the model’s ability to capture land‑cover details across varying spatial scales; (2) a Dynamic Contextual Attention Block, which uses a gated mechanism to fuse three‑dimensional attention weights with contextual cues, thereby preserving global structural and chromatic consistency while retaining intricate local textures. Extensive qualitative and quantitative experiments on public benchmarks demonstrate that MCA‑GAN outperforms other existing methods in both visual fidelity and objective metrics, offering a robust and practical solution for remote‑sensing image dehazing. Full article

40 pages, 8621 KiB

Open AccessArticle

Cosmic Evolution Optimization: A Novel Metaheuristic Algorithm for Numerical Optimization and Engineering Design

by Rui Wang, Zhengxuan Jiang and Guowen Ding

Mathematics 2025, 13(15), 2499; https://doi.org/10.3390/math13152499 (registering DOI) - 3 Aug 2025

Abstract

This study proposes a novel metaheuristic algorithm, Cosmic Evolution Optimization (CEO), for numerical optimization and engineering design. Inspired by the cosmic evolution process, CEO simulates physical phenomena including cosmic expansion, universal gravitation, stellar system interactions, and celestial orbital resonance.The algorithm introduces a multi-stellar framework system, which incorporates search agents into distinct subsystems to perform simultaneous exploration or exploitation behaviors, thereby enhancing diversity and parallel exploration capabilities. Specifically, the CEO algorithm was compared against ten state-of-the-art metaheuristic algorithms on 29 typical unconstrained benchmark problems from CEC2017 across different dimensions and 13 constrained real-world optimization problems from CEC2020. Statistical validations through the Friedman test, the Wilcoxon rank-sum test, and other statistical methods have confirmed the competitiveness and effectiveness of the CEO algorithm. Notably, it achieved a comprehensive Friedman rank of 1.28/11, and the winning rate in the Wilcoxon rank-sum tests exceeded 80% in CEC2017. Furthermore, CEO demonstrated outstanding performance in practical engineering applications such as robot path planning and photovoltaic system parameter extraction, further verifying its efficiency and broad application potential in solving real-world engineering challenges. Full article

25 pages, 34115 KiB

Open AccessArticle

New Belgrade’s Thermal Mosaic: Investigating Climate Performance in Urban Heritage Blocks Beyond Coverage Ratios

by Saja Kosanović, Đurica Marković and Marija Stamenković

Atmosphere 2025, 16(8), 935; https://doi.org/10.3390/atmos16080935 (registering DOI) - 3 Aug 2025

Abstract

This study investigated the nuanced influence of urban morphology on the thermal performance of nine mass housing blocks (21–26, 28–30) in New Belgrade’s Central Zone. These blocks, showcasing diverse structures, provided a robust basis for evaluating the design parameters. ENVI-met simulations were used to assess two scenarios: an “asphalt-only” environment, isolating the urban structure’s impact, and a “real-world” scenario, including green infrastructure (GI). Overall, the findings emphasize that while GI offers mitigation, the inherent urban built structure fundamentally determines thermal outcomes. An urban block’s thermal performance, it turns out, is a complex interplay between morphological factors and local climate. Crucially, simple metrics like Green Area Percentage (GAP) and Building Coverage Ratio (BCR) proved unreliable predictors of thermal performance. This highlights the critical need for urban planning regulations to evolve beyond basic surface indicators and embrace sophisticated, context-sensitive design principles for effective heat mitigation. Optimal performance arises from morphologies that actively manage heat accumulation and facilitate its dissipation, a characteristic exemplified by Block 22’s integrated design. However, even the best-performing Block 22 remains warmer compared to denser central areas, suggesting that urban densification can be a strategy for heat mitigation. Given New Belgrade’s blocks are protected heritage, targeted GI reinforcements remain the only viable approach for improving the outdoor thermal comfort. Full article

(This article belongs to the Special Issue The Potential of the Built Environment in Climate-Related Challenges (2nd Edition))

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