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by Massimo Sacchi, Barbara Amadesi, Adriano De Faveri, Gilles Faggio, Camilla Gotti, Arnaud Ledru, Sergio Nissardi, Bernard Recorbet, Marco Zenatello and Nicola Baccetti

Diversity 2025, 17(8), 526; https://doi.org/10.3390/d17080526 (registering DOI) - 28 Jul 2025

Abstract

We investigated the spatial structure and colony itinerancy of Audouin’s gull (Ichthyaetus audouinii) adult breeders across multiple breeding sites in the central Mediterranean Sea during 25 years of fieldwork. Using cluster analysis of marked individuals from different years and sites, we identified five spatial breeding units of increasing hierarchical scale—Breeding Sites, Colonies, Districts, Regions and Marine Sectors—which reflect biologically meaningful boundaries beyond simple geographic proximity. To determine the most appropriate scale for monitoring local populations, we applied multievent capture–recapture models and examined variation in survival and site fidelity across these units. Audouin’s gulls frequently change their location at the Breeding Site and Colony levels from one year to another, without apparent survival costs. In contrast, dispersal beyond Districts boundaries was found to be rare and associated with reduced survival rates, indicating that breeding Districts represent the most relevant biological unit for identifying local populations. The survival disadvantage observed in individuals leaving their District likely reflects increased extrinsic mortality in unfamiliar environments and the selective dispersal of lower-quality individuals. Within breeding Districts, birds may benefit from local knowledge and social information, supporting demographic stability and higher fitness. Our findings highlight the value of adopting a District-based framework for long-term monitoring and conservation of this endangered species. At this scale, demographic trends such as population growth or decline emerge more clearly than when assessed at the level of singular colonies. This approach can enhance our understanding of population dynamics in other mobile species and support more effective conservation strategies aligned with natural population structure. Full article

(This article belongs to the Special Issue Ecology, Diversity and Conservation of Seabirds—2nd Edition)

16 pages, 828 KiB

Open AccessReview

The Role of Prophylactic HIPEC in High-Risk Gastric Cancer Patients: Where Do We Stand?

by Alexandros Diamantis, Athina A. Samara, Anastasios Lafioniatis, Michel B. Janho, Theodoros Floros and Konstantinos Tepetes

Cancers 2025, 17(15), 2492; https://doi.org/10.3390/cancers17152492 - 28 Jul 2025

Abstract

For patients diagnosed with a malignancy at high risk of developing peritoneal metastases, the concept of prophylactic hyperthermic intraperitoneal chemotherapy (HIPEC) has emerged. The aim of the present study is to assess the role of prophylactic HIPEC in gastric cancer patients at high risk of PC, based on the currently available data in the literature. In total, 14 RCTs and 16 non-RCTs were identified and included in the present review, with 1383 patients included in the RCTs (627 of whom underwent HIPEC) and 1647 patients included in the non-RCTs (with 609 undergoing HIPEC). Prophylactic HIPEC appears to be useful and effective in treating patients with high-risk gastric cancer, improving both overall and disease-free survival. The heterogeneity of data regarding treatment protocols and complication rates suggests that further research is necessary to develop optimal therapeutic approaches and personalized treatment options; in particular, large-scale randomized control trials are needed in order to elucidate the potential benefits associated with the use of prophylactic HIPEC. Full article

(This article belongs to the Special Issue Surgical Treatment of Abdominal Tumors)

29 pages, 1987 KiB

Open AccessArticle

Structural Heterogeneity of Biochar Modulates’ Soil Hydraulic Properties and Nutrient Migration

by Guohui Li, Yayong Chen, Xiaopeng Chen, Beibei Zhou, Manli Duan, Hongyan Zhu and Guomin Shao

Agronomy 2025, 15(8), 1830; https://doi.org/10.3390/agronomy15081830 - 28 Jul 2025

Abstract

Biochar application is a well-recognized strategy to enhance agricultural soil fertility, but its structural heterogeneity leads to inconsistent outcomes in soil improvement, particularly in water and nutrient transport dynamics. In order to ensure the beneficial effects of biochar-amended agricultural soils in terms of water retention and fertilizer fixation, in this paper, we aim to elucidate the effect of the structural heterogeneity of biochar on the hydraulic properties and nutrient transport of agricultural soils. This study compares biochars at millimeter (BMP), micrometer (BUP), and nanometer (BNP) scales using CT scanning, and investigates the effects of different application rates (0.0–2.0%) on soil’s hydraulic properties and nutrient transport using soil column experiments and CDE analyses. The results show that biochar generally decreased soil saturated hydraulic conductivity (SSHC), except for the application of 2.0% BMP, which increased it. Biochar enhanced soil saturated water content (SSWC) and water holding capacity (WHC), with the 2.0% BMP treatment achieving the highest values (SSHC: 49.34 cm/d; SSWC: 0.40 g/g; WHC: 0.25 g/g). BUPs and BNPs inhibited water infiltration due to pore-blocking, while 2.0% BMP promoted infiltration. Convective dispersion equation analysis (CDE) indicated that BUPs and BNPs reduced water and nutrient transport, with 2.0% BMP showing optimal performance. Statistical analyses revealed that biochar’s structural heterogeneity significantly affected soil water repellency, its hydraulic properties, and solute transport (p < 0.05). Smaller particles enhanced water retention and nutrient fixation, while larger particles improved WHC at appropriate rates. These findings provide valuable insights for optimizing biochar application to improve soil functions and support sustainable agriculture. Full article

(This article belongs to the Section Soil and Plant Nutrition)

15 pages, 482 KiB

Open AccessArticle

The Correlation Between Body Pain Indicators and the Facial Expression Scale in Sows During Farrowing and Pre-Weaning: The Effects of Parity, the Farrowing Moment, and Suckling Events

by Elena Navarro, Raúl David Guevara, Eva Mainau, Ricardo de Miguel and Xavier Manteca

Animals 2025, 15(15), 2225; https://doi.org/10.3390/ani15152225 - 28 Jul 2025

Abstract

Parturition is accepted as a painful situation. Few studies explore pain-specific behaviours during farrowing in sows. The objectives of this study were, first, to assess if behavioural pain indicators (BPIs) are affected by the farrowing moment, parity, and suckling events, and second, to determine the relationship between the Facial Action Units (FAUs) and BPIs during farrowing. Ten Danbred sows were recorded throughout farrowing and on day 19 post-farrowing. Continuous observations of five BPIs and five FAUs were obtained across the three moments studied: (i) at the expulsion of the piglets, (ii) the time interval between the delivery of each piglet, and (iii) 19 days after farrowing, used as a control. Primiparous sows had more BPIs but fewer postural changes than multiparous sows. The BPIs were more frequent during suckling events in the pre-weaning moment. All the FAUs and BPIs were rare or absent post-farrowing (p < 0.05), and almost all of them were more frequent during farrowing (especially at the moment of delivery). Back arching showed the highest correlation with all the FAUs, and tension above the eyes showed the highest correlation with four of the BPIs. The BPIs and FAUs indicate that sows experience more pain during farrowing than during the third week post-farrowing, and piglet expulsion is the most painful moment in farrowing. Full article

(This article belongs to the Section Animal Welfare)

30 pages, 7259 KiB

Open AccessArticle

Multimodal Data-Driven Hourly Dynamic Assessment of Walkability on Urban Streets and Exploration of Regulatory Mechanisms for Diurnal Changes: A Case Study of Wuhan City

by Xingyao Wang, Ziyi Peng and Xue Yang

Land 2025, 14(8), 1551; https://doi.org/10.3390/land14081551 - 28 Jul 2025

Abstract

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

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18 pages, 651 KiB

Open AccessArticle

Price Impacts of Energy Transition on the Interconnected Wholesale Electricity Markets in the Northeast United States

by Jay W. Zarnikau, Chi-Keung Woo, Kang Hua Cao and Han Steffan Qi

Energies 2025, 18(15), 4019; https://doi.org/10.3390/en18154019 - 28 Jul 2025

Abstract

Our regression analysis documents that energy policies to promote renewable energy development, as well as hydroelectric imports from Canada, lead to short-run reductions in average electricity prices (also known as merit-order effects) throughout the Northeast United States. Changes in the reliance upon renewable energy in one of the Northeast’s three interconnected electricity markets will impact wholesale prices in the other two. The retirement of a 1000 MW nuclear plant can increase prices by about 9% in the Independent System Operator of New England market and 7% in the New York Independent System Operator market in the short run at reference hubs, while also raising prices in neighboring markets. Some proposed large-scale off-shore wind farms would not only lower prices in local markets at the reference hubs modeled but would also lower prices in neighboring markets. Full article

(This article belongs to the Section A: Sustainable Energy)

24 pages, 8476 KiB

Open AccessArticle

A Weakly Supervised Network for Coarse-to-Fine Change Detection in Hyperspectral Images

by Yadong Zhao and Zhao Chen

Remote Sens. 2025, 17(15), 2624; https://doi.org/10.3390/rs17152624 - 28 Jul 2025

Abstract

Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear feature learning capabilities, yet they face several challenges: mixed-pixel phenomenon affecting pixel-level detection accuracy; heterogeneous spatial scales of change targets where coarse-grained features fail to preserve fine-grained details; and dependence on high-quality labels. To address these challenges, this paper introduces WSCDNet, a weakly supervised HSI-CD network employing coarse-to-fine feature learning, with key innovations including: (1) A dual-branch detection framework integrating binary and multiclass change detection at the sub-pixel level that enhances collaborative optimization through a cross-feature coupling module; (2) introduction of multi-granularity aggregation and difference feature enhancement module for detecting easily confused regions, which effectively improves the model’s detection accuracy; and (3) proposal of a weakly supervised learning strategy, reducing model sensitivity to noisy pseudo-labels through decision-level consistency measurement and sample filtering mechanisms. Experimental results demonstrate that WSCDNet effectively enhances the accuracy and robustness of HSI-CD tasks, exhibiting superior performance under complex scenarios and weakly supervised conditions. Full article

(This article belongs to the Section Remote Sensing Image Processing)

22 pages, 5844 KiB

Open AccessArticle

Scaling, Leakage Current Suppression, and Simulation of Carbon Nanotube Field-Effect Transistors

by Weixu Gong, Zhengyang Cai, Shengcheng Geng, Zhi Gan, Junqiao Li, Tian Qiang, Yanfeng Jiang and Mengye Cai

Nanomaterials 2025, 15(15), 1168; https://doi.org/10.3390/nano15151168 - 28 Jul 2025

Abstract

Carbon nanotube field-effect transistors (CNTFETs) are becoming a strong competitor for the next generation of high-performance, energy-efficient integrated circuits due to their near-ballistic carrier transport characteristics and excellent suppression of short-channel effects. However, CNT FETs with large diameters and small band gaps exhibit obvious bipolarity, and gate-induced drain leakage (GIDL) contributes significantly to the off-state leakage current. Although the asymmetric gate strategy and feedback gate (FBG) structures proposed so far have shown the potential to suppress CNT FET leakage currents, the devices still lack scalability. Based on the analysis of the conduction mechanism of existing self-aligned gate structures, this study innovatively proposed a design strategy to extend the length of the source–drain epitaxial region (L_ext) under a vertically stacked architecture. While maintaining a high drive current, this structure effectively suppresses the quantum tunneling effect on the drain side, thereby reducing the off-state leakage current (I_off = 10⁻¹⁰ A), and has good scaling characteristics and leakage current suppression characteristics between gate lengths of 200 nm and 25 nm. For the sidewall gate architecture, this work also uses single-walled carbon nanotubes (SWCNTs) as the channel material and uses metal source and drain electrodes with good work function matching to achieve low-resistance ohmic contact. This solution has significant advantages in structural adjustability and contact quality and can significantly reduce the off-state current (I_off = 10⁻¹⁴ A). At the same time, it can solve the problem of off-state current suppression failure when the gate length of the vertical stacking structure is 10 nm (the total channel length is 30 nm) and has good scalability. Full article

(This article belongs to the Special Issue Advanced Nanoscale Materials and (Flexible) Devices)

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14 pages, 1767 KiB

Open AccessArticle

An Adaptive Overcurrent Protection Method for Distribution Networks Based on Dynamic Multi-Objective Optimization Algorithm

by Biao Xu, Fan Ouyang, Yangyang Li, Kun Yu, Fei Ao, Hui Li and Liming Tan

Algorithms 2025, 18(8), 472; https://doi.org/10.3390/a18080472 - 28 Jul 2025

Abstract

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

25 pages, 1866 KiB

Open AccessArticle

A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering

by Chunju Zhang, Chaoqun Chu, Kang Zhou, Shu Wang, Yunqiang Zhu, Jianwei Huang, Zhaofu Wu and Fei Gao

ISPRS Int. J. Geo-Inf. 2025, 14(8), 295; https://doi.org/10.3390/ijgi14080295 - 28 Jul 2025

Abstract

In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits their effectiveness in downstream reasoning tasks. To address this, we propose a spatio-temporal evolutionary knowledge embedding approach (ST-EKA) that enhances entity representations by modeling their evolution through type-aware encoding, temporal and spatial decay mechanisms, and context aggregation. ST-EKA integrates four core components, including an entity encoder constrained by relational type consistency, a temporal encoder capable of handling both time points and intervals through unified sampling and feedforward encoding, a multi-scale spatial encoder that combines geometric coordinates with semantic attributes, and an evolutionary knowledge encoder that employs attention-based spatio-temporal weighting to capture contextual dynamics. We evaluate ST-EKA on three representative GeoKG datasets—GDELT, ICEWS, and HAD. The results demonstrate that ST-EKA achieves an average improvement of 6.5774% in AUC and 5.0992% in APR on representation learning tasks. In question answering tasks, it yields a maximum average increase of 1.7907% in AUC and 0.5843% in APR. Notably, it exhibits superior performance in chain queries and complex spatio-temporal reasoning, validating its strong robustness, good interpretability, and practical application value. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

49 pages, 2471 KiB

Open AccessReview

Drought Analysis Methods: A Multidisciplinary Review with Insights on Key Decision-Making Factors in Method Selection

by Abdul Baqi Ahady, Elena-Maria Klopries, Holger Schüttrumpf and Stefanie Wolf

Water 2025, 17(15), 2248; https://doi.org/10.3390/w17152248 - 28 Jul 2025

Abstract

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

(This article belongs to the Section Hydrology)

13 pages, 2441 KiB

Open AccessArticle

Enzymatic Stoichiometry and Driving Factors Under Different Land-Use Types in the Qinghai–Tibet Plateau Region

by Yonggang Zhu, Feng Xiong, Derong Wu, Baoguo Zhao, Wenwu Wang, Biao Bi, Yihang Liu, Meng Liang and Sha Xue

Land 2025, 14(8), 1550; https://doi.org/10.3390/land14081550 - 28 Jul 2025

Abstract

Eco-enzymatic stoichiometry provides a basis for understanding soil ecosystem functions, with implications for land management and ecological protection. Long-term climatic factors and human interferences have caused significant land-use transformations in the Qinghai–Tibet Plateau region, affecting various ecological functions, such as soil nutrient cycling and chemical element balance. It is currently unclear how large-scale land-use conversion affects soil ecological stoichiometry. In this study, 763 soil samples were collected across three land-use types: farmland, grassland, and forest land. In addition, changes in soil physicochemical properties and enzyme activity and stoichiometry were determined. The soil available phosphorus (SAP) and total phosphorus (TP) concentrations were the highest in farmland soil. Bulk density, pH, SAP, TP, and NO₃⁻-N were lower in forest soil, whereas NH₄⁺-N, available nitrogen, soil organic carbon (SOC), available potassium, and the soil nutrient ratio increased. Land-use conversion promoted soil β-1,4-glucosidase, N-acetyl-β-glucosaminidase, and alkaline phosphatase activities, mostly in forest soil. The eco-enzymatic C:N ratio was higher in farmland soils but grassland soils had a higher enzymatic C:P and N:P. Soil microorganisms were limited by P nutrients in all land-use patterns. C limitation was the highest in farmland soil. The redundancy analysis indicated that the ecological stoichiometry in farmland was influenced by TN, whereas grass and forest soils were influenced by SOC. Overall, the conversion of cropland or grassland to complex land-use types can effectively enhance soil nutrients, enzyme activities, and ecosystem functions, providing valuable insights for ecological restoration and sustainable land management in alpine regions. Full article

(This article belongs to the Section Land Use, Impact Assessment and Sustainability)

13 pages, 600 KiB

Open AccessArticle

Validating the Arabic Adolescent Nutrition Literacy Scale (ANLS): A Reliable Tool for Measuring Nutrition Literacy

by Sahar Obeid, Souheil Hallit, Feten Fekih-Romdhane, Yonna Sacre, Marie Hokayem, Ayoub Saeidi, Lamya Sabbah, Nikolaos Tzenios and Maha Hoteit

Nutrients 2025, 17(15), 2457; https://doi.org/10.3390/nu17152457 - 28 Jul 2025

Abstract

Introduction: Nutrition literacy has garnered growing research attention worldwide, yet only a few instruments have been developed to specifically measure this construct among adolescents. Accordingly, the present research sought to examine the validity and reliability of the Adolescent Nutrition Literacy Scale (ANLS) within a group of Lebanese adolescents. Methods: A cross-sectional study was carried out from December 2022 to March 2023, targeting a nationally representative sample. Results: Fit indices of the three-factor structure were good. Internal reliability was adequate for the following three subscales: Functional Nutrition Literacy (FNL) (ω = 0.88/α = 0.88), Interactive Nutrition Literacy (INL) (ω = 0.87/α = 0.86) and Critical Nutrition Literacy (CNL) (ω = 0.89/α = 0.89). Invariance was established across genders at configural, metric, and scalar levels. A significantly higher mean FNL and INL scores were found in males compared to females, with no significant difference between the two genders in terms of CNL. Higher FNL, but not CNL and INL scores were significantly associated with lower child food security. Conclusions: The Arabic ANLS has exhibited robust psychometric reliability, validity, and cost-effectiveness as a tool for assessing nutrition literacy. By utilizing the Arabic version of the ANLS, we can more efficiently and accurately assess the nutritional literacy of adolescents. Full article

(This article belongs to the Topic Transforming Childhood Nutrition: New Perspectives on Food Literacy and Healthy Diets: 2nd Edition)

21 pages, 5108 KiB

Open AccessArticle

tDCS and Cognitive Training for Fatigued and Cognitively Impaired People with Multiple Sclerosis: An SCED Study

by Teresa L’Abbate, Nefeli K. Dimitriou, George Dimakopoulos, Franca Tecchio and Grigorios Nasios

Brain Sci. 2025, 15(8), 807; https://doi.org/10.3390/brainsci15080807 - 28 Jul 2025

Abstract

Background/Objectives: Fatigue and cognitive impairment are common issues for People with Multiple Sclerosis (PwMS), affecting over 80% and 40–65%, respectively. The relationship between these two debilitating conditions is complex, with cognitive deficits exacerbating fatigue and vice versa. This study investigates the effects of a multimodal intervention combining cognitive rehabilitation and neuromodulation to alleviate fatigue and enhance cognitive performance in PwMS. Methods: The research employed multiple baselines across the subjects in a Single-Case Experimental Design (mbSCED) with a cohort of three PwMS diagnosed with Relapsing–Remitting MS. The intervention protocol consisted of a baseline phase followed by a four-week treatment involving transcranial direct current stimulation (tDCS) and cognitive training using RehaCom^® software (version 6.9.0). Fatigue levels were measured using the modified Fatigue Impact Scale (mFIS), while cognitive performance was evaluated through standardized neuropsychological assessments. Results: The multimodal protocol exhibited high feasibility and acceptability, with no dropouts. Individual responsiveness outcomes varied, with two PwMS showing significant decreases in fatigue and improvements in cognitive performance, particularly in the trained domains. Their motor performance and quality of life also improved, suggesting that the treatment had indirect beneficial effects. Conclusions: This study provides preliminary evidence for the potential benefits of integrating neuromodulation and cognitive rehabilitation as a personalized therapeutic strategy for managing fatigue and cognitive impairments in MS. Further research is needed to delineate the specific contributions of each intervention component and establish standardized protocols for clinical implementation. The insights gained may lead to more effective, tailored treatment options for PwMS. Full article

(This article belongs to the Special Issue Recent Advances in Assessment and Rehabilitation of Communication and Language Disorders: Enhancing Innovations, Neuromodulation and AI)

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19 pages, 2466 KiB

Open AccessArticle

Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection

by Qingqing Xiang, Gang Wu, Zhiqiang Liu and Xudong Zeng

Metals 2025, 15(8), 843; https://doi.org/10.3390/met15080843 - 28 Jul 2025

Abstract

To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion and optimized network architecture. Firstly, an adaptive weighted down-sampling (ADSConv) module was proposed, which improves detector adaptability to diverse defects via the weighted fusion of down-sampled feature maps. Next, the C2f_DWR module was proposed, integrating optimized C2F architecture with a streamlined DWR design to enhance feature extraction efficiency while reducing computational complexity. Then, a Multi-Scale-Focus Diffusion Pyramid was designed to adaptively handle multi-scale object detection by dynamically adjusting feature fusion, thus reducing feature redundancy and information loss while maintaining a balance between detailed and global information. Experiments demonstrate that the proposed ADP-YOLOv8-n detection algorithm achieves superior performance, effectively balancing detection accuracy, inference speed, and model compactness. Full article

(This article belongs to the Special Issue Nondestructive Testing Methods for Metallic Material)

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