Search Results (578)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Agricultural production in southern Angola faces challenges due to unsustainable practices, including inefficient use of water, fertilizers, and machinery, resulting in low yields and environmental degradation. Therefore, clear and measurable indicators are needed to guide farmers toward more sustainable practices. The scientific literature insufficiently addresses this issue, leaving a significant gap in the evaluation of key performance indicators (KPIs) that can guide good agricultural practices (GAPs) adapted to the context of southern Angola, with the goal of promoting a more resilient and sustainable agricultural sector. So, the objective of this study is to identify and assess KPIs capable of supporting the selection of GAPs suitable for maize, potato, and tomato cultivation in the context of southern Angolan agriculture. A systematic literature review (SLR) was conducted, screening 2720 articles and selecting 14 studies that met defined inclusion criteria. Five KPIs were identified as the most relevant: gross margin, net profit, water use efficiency, nitrogen use efficiency, and machine energy. These indicators were analyzed and standardized to evaluate their contribution to sustainability across different GAPs. Results show that organic fertilizers are the most sustainable option for maize, drip irrigation for potatoes, and crop rotation for tomatoes in southern Angola because of their efficiency in low-resource environments. A clear, simple, and effective representation of the KPIs was developed to be useful in communicating to farmers and policy makers on the selection of the best GAPs in the cultivation of different crops. The study proposes a validated KPI-based methodology for assessing sustainable agricultural practices in developing regions such as southern Angola, aiming to lead to greater self-sufficiency and economic stability in this sector. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors, with a five-year overall survival rate below 10%. While the introduction of multi-agent chemotherapy regimens has improved outcomes marginally, most patients with advanced disease continue to have limited therapeutic options. Molecular profiling has uncovered actionable genomic alterations in select subgroups of PDAC, yet the clinical impact of targeted therapies remains modest. This review aims to provide a clinically oriented synthesis of emerging molecular targets in PDAC, their therapeutic relevance, and practical considerations for biomarker testing, including current FDA and EMA indications. Methods: A narrative review was conducted using data from PubMed, Embase, Scopus, and international guidelines (NCCN, ESMO, ASCO). The selection focused on evidence published between 2020 and 2025, highlighting molecularly defined PDAC subsets and the current status of targeted therapies. Results: Actionable genomic alterations in PDAC include KRAS G12C mutations, BRCA1/2 and PALB2-associated homologous recombination deficiency, MSI-H/dMMR status, and rare gene fusions involving NTRK, RET, and NRG1. While only a minority of patients are eligible for targeted treatments, early-phase trials and real-world data have shown promising results in these subgroups. Testing molecular profiling is increasingly standard in advanced PDAC. Conclusions: Despite the rarity of targetable mutations, systematic molecular profiling is critical in advanced PDAC to guide off-label therapy or clinical trial enrollment. A practical framework for identifying and acting on molecular targets is essential to bridge the gap between precision oncology and clinical management. Full article

This study explores digital inequality as a socio-spatial phenomenon within the context of smart inclusion, focusing on the Regional Unit of Xanthi, Greece—a region marked by ethno-cultural diversity and pronounced urban–rural contrasts. Using a mixed-methods design, this research integrates secondary quantitative data with qualitative insights from semi-structured interviews, aiming to uncover how spatial, demographic, and cultural variables shape digital engagement. Geographic Information System (GIS) tools are employed to map disparities in internet access and ICT infrastructure, revealing significant gaps linked to geography, education, and economic status. The findings demonstrate that digital inequality is particularly acute in rural, minority, and economically marginalized communities, where limited infrastructure intersects with low digital literacy and socio-economic disadvantage. Interview data further illuminate how residents navigate exclusion, emphasizing generational divides, perceptions of technology, and place-based constraints. By bridging spatial analysis with lived experience, this study advances the conceptualization of digitally inclusive smart regions. It offers policy-relevant insights into how territorial inequality undermines the goals of smart development and proposes context-sensitive interventions to promote equitable digital participation. The case of Xanthi underscores the importance of integrating spatial justice into smart city and regional planning agendas. Full article

Artificial Intelligence (AI) and Generative AI are transformative yet double-edged technologies with evolving risks. While research emphasises trustworthy, fair, and responsible AI by focusing on its “what” and “why,” it overlooks practical “how.” To bridge this gap in financial services, an industry at the forefront of AI adoption, this study employs a qualitative approach grounded in existing Responsible AI and Corporate Digital Responsibility (CDR) frameworks. Through thematic analysis of 15 semi-structured interviews conducted with professionals working in finance, we illuminate nine non-technical barriers that practitioners face, such as sustainability challenges, trade-off balancing, stakeholder management, and human interaction, noting that GenAI concerns now eclipse general AI issues. CDR practitioners adopt a more human-centric stance, emphasising consensus-building and “no margin for error.” Our findings offer actionable guidance for more responsible AI strategies and enrich academic debates on Responsible AI and AI-CDR symbiosis. Full article

Reliable adaptation in Class II resin-based composite (RBC) restorations with margins on cementum remains challenging. This study compared the internal adaptation (IA) and closed porosity (CP) of three restorative strategies for such cavities, using either total-etch or self-etch adhesive approaches. Standardized box-only cavities were prepared on both proximal surfaces of 30 extracted molars, applying self-etch on mesial and total-etch on distal cavities. Group 1 used a layered microhybrid RBC; Group 2 used a flowable RBC base beneath a layered microhybrid RBC; and Group 3 used a thermoviscous RBC in a 4 mm bulk increment. Micro-computed tomography was employed to assess IA and CP. ANOVA, Tukey post hoc, and univariate analyses were used to evaluate group differences and the effects of adhesive/restorative strategies. Group 2 demonstrated the best adaptation (0.10%), whereas Group 3 exhibited the highest internal gap ratio (0.63%) and the lowest CP (p = 0.006). Total-etch adhesive significantly improved IA compared to self-etch (p < 0.001). These findings emphasize the impact of material selection and adhesive technique on the quality of restorations in cementum-located Class II cavities. Full article

Animal welfare assessments have raised farmers’ awareness of their management practices, contributing to measurable improvements. However, these protocols often highlight critical points without providing clear guidance on the prioritization of corrective actions. To address this gap, qualified advisory support may play a pivotal role in translating assessments into effective and sustainable interventions. This study evaluates the impact of direct and continuous expert support on improving animal welfare in dairy farms. Data were collected from 21 dairy farms in southeastern Tuscany (Italy) using the Classyfarm (CReNBA) protocol. Each farm underwent two assessments at a three-month interval (T0 and T1), during which tailored support was provided to address specific criticalities. At T0, over 60% of the farms obtained only marginally acceptable welfare scores (mean 67.48 ± 4.75), with major deficiencies in farm management practices, particularly regarding hygiene and space management. At T1, all farms showed substantial improvements, with an average increase of 22% in total welfare scores (mean 82.05 ± 5.71) and a mean of nine improved parameters per farm. These findings underscore the effectiveness of structured, continuous consultancy in not only resolving critical issues but also in fostering more informed, proactive, and sustainable farm management. The direct involvement of experts appears to be a key driver in enhancing both animal welfare and operational outcomes in dairy farming. Full article

The transition to clean energy is not inherently positive or negative, and its impacts depend on the social context, power relations, and mechanisms to include marginalized voices. India, with its ambitious climate targets and commitment to the UN SDG Agenda, is a key country for ensuring an inclusive and sustainable transition. This paper aims to understand whether India’s commitment to the SDG Agenda’s overarching principle of ‘leaving no one behind’ is reflected in the design of its domestic solar policies. It analyzes how energy justice concerns are addressed in state-level solar policies. To that end, a pragmatic framework was developed to identify key justice dimensions and indicators, linked to the SDG targets, that are essential for an inclusive transition. This research conducted a qualitative interpretive content analysis of 29 solar energy policies, using the three identified framework dimensions—income growth, enhancing inclusion, and equal opportunities. We found that the themes around energy access, employment, and skill development are reflected in policies, while those around the inclusion of the poor, women, and community remain limited. The findings indicate that the policies have focused on low-impact justice concerns, lacking structural transformation. To address these gaps, the study proposes targeted subsidies, community ownership, and gender-inclusive mechanisms. The framework offers a pragmatic tool for the evaluation of clean energy policies in the Global South, and the empirical results provide insights for the synergistic implementation of the climate and sustainable development agenda. Full article

This paper critically examines the persistent limitations of spatial planning reforms in China in addressing urban–rural integration, despite significant and successive legislative and planning reforms. Through a historically grounded and institutionally informed analysis, the study traces the evolution of China’s planning regimes across three key phases—urban planning, urban–rural planning, and territorial spatial planning (TSP)—highlighting shifting policy logics and the enduring structural challenges that shape rural marginalization. Drawing on national planning documents and authors’ empirical insights from planning practice, the paper identifies four interrelated and persistent constraints: (1) cross-scalar and interdepartmental fragmentation in governance, (2) contradictions in the land system that restrict rural development rights, (3) fiscal dependence on land conversion that distorts planning priorities, and (4) technical and conceptual gaps that reduce rural planning to physicalist and exogenous interventions. The paper contributes by offering a periodized account of China’s rural planning reforms, situating these within global debates on rural marginalization, and evaluating the transformative potential of the TSP framework. It argues that achieving meaningful urban–rural integration requires a fundamental rethinking of planning as a developmental, rather than solely regulatory, practice—one that is territorially embedded, socially responsive, and functionally aligned with endogenous rural revitalization. Full article

The control of following motion under mesoscale gap flow fields has important applications. The flexible characteristics of the plant, wideband time-varying disturbances caused by the flow field, and requirements of high precision and low overshoot make achieving submicron level accuracy a significant challenge for traditional control methods. This study adopts the control concept of Disturbance Observer Control (DOBC) and uses H_∞ mixed-sensitivity shaping technology to design a Q-filter. Simultaneously, multiple control techniques, such as high-order reference trajectory planning, Proportional-Integral-Derivative (PID) control, low-pass filtering, notch filtering, lead lag correction, and disturbance rejection filtering, are applied to obtain a control system with a high open-loop gain, sufficient phase margin, and stable closed-loop system. Compared to traditional control methods, the new method can increase the open-loop gain by 15 times and the open-loop bandwidth by 8%. We even observed a 150-time increase of the open-loop gain at the peak frequency. Ultimately, the method achieves submicron level accuracy, making important advances in solving the control problem of semiconductor equipment. Full article

The Qiubudong silver deposit on the western margin of the Fuping ore cluster in the central North China Craton is a representative breccia-type deposit characterized by relatively high-grade ores, thick mineralized zones, and extensive alteration, indicating considerable potential for economic resource development and further exploration. Previous studies on this deposit have not addressed its genetic mineralogical characteristics. This study focuses on pyrite and quartz to investigate their typomorphic features, such as crystal morphology, trace element composition, thermoelectric properties, and luminescence characteristics, and their implications for ore-forming processes. Pyrite crystals are predominantly cubic in early stages, while pentagonal dodecahedral and cubic–dodecahedral combinations peak during the main mineralization stage. The pyrite is sulfur-deficient and iron-rich, enriched in Au, and relatively high in Ag, Cu, Pb, and Bi contents during the main ore-forming stage. Rare earth element (REE) concentrations are low, with weak LREE-HREE fractionation and a strong negative Eu anomaly. The thermoelectric coefficient of pyrite ranges from −328.9 to +335.6 μV/°C, with a mean of +197.63 μV/°C; P-type conduction dominates, with an occurrence rate of 58%–100% and an average of 88.78%. A weak–low temperature and a strong–high temperature peak characterize quartz thermoluminescence during the main mineralization stage. Fluid inclusions in quartz include liquid-rich, vapor-rich, and two-phase types, with salinities ranging from 10.11% to 12.62% NaCl equiv. (average 11.16%) and densities from 0.91 to 0.95 g/cm³ (average 0.90 g/cm³). The ore-forming fluids are interpreted as F-rich, low-salinity, low-density hydrothermal fluids of volcanic origin at medium–low temperatures. The abundance of pentagonal dodecahedral pyrite, low Co/Ni ratios, high Cu contents, and complex quartz thermoluminescence signatures are key mineralogical indicators for deep prospecting. Combined with thermoelectric data and morphological analysis, the depth interval around 800 m between drill holes ZK3204 and ZK3201 has high mineralization potential. This study fills a research gap on the genetic mineralogy of the Qiubudong deposit and provides a scientific basis for deep exploration. Full article

Health literacy is a critical determinant of health outcomes, yet it is often overlooked, particularly among marginalized groups. This paper explores the significance of health literacy, with a particular focus on low-income African American and Black (AA/B) men, a population that faces unique challenges due to intersecting factors such as race, gender, socioeconomic status, and educational disparities. We examine how these factors contribute to health literacy gaps, highlighting adverse effects on health outcomes for AA/B men compared to the general population. Additionally, we stress the growing importance of digital literacy in an increasingly technology-driven world. Not actively addressing digital health literacy, especially within chronic disease self-management programs (CDSMPs), further exacerbates health disparities within this group. Recommendations are provided for improving health literacy, with specific strategies to also enhance general literacy and digital literacy, among low-income AA/B men. The paper also advocates for a systematic review of the existing literature on health literacy among this group, emphasizing the need for tailored interventions that account for the unique challenges faced by low-income AA/B men. In conclusion, the paper underscores the critical need for targeted research and practical approaches to improve health literacy and ultimately health outcomes for AA/B men in the digital age, particularly through CDSMPs. Full article

The study of capital structure is one of the most relevant topics in finance because, despite the various theories that seek to explain it, there is still no consensus on the determining factors or the behaviors of financing decisions in companies. This study empirically analyzes the capital structure decisions of Latin American companies during the period of 2013–2023, in light of trade-off and pecking order theories. A panel data methodology was applied to 62 companies, using fixed and random effects models. The results show that, on average, companies correct around 5.80% of the gap between their current and optimal level of indebtedness per period, partially supporting the trade-off theory. However, the effects of the financial deficit on indebtedness are heterogeneous and, in most cases, inconsistent with the pecking order theory, especially in countries such as Colombia. It is concluded that country risk has a marginal influence on debt decisions, and the need to consider each country’s institutional and market particularities when analyzing the dynamics of capital structure in emerging economies is emphasized. Full article

(1) Background: This study aimed to evaluate the marginal and internal fit of ceramic crowns produced by various digital methods using microcomputed tomography (MCT) imaging. (2) Methods: The ceramic crown preparation was performed on typodont maxillary first premolar. The crown preparation was scanned with an intraoral scanner and a model scanner, and cone-beam computed tomography (CBCT) scans were performed with three different voxel sizes (0.075 mm, 0.1 mm, and 0.15 mm). The space between the crown and prepared teeth was measured at nine different points in both coronal and sagittal sections. Three different digital model acquisition techniques, namely, intraoral scanning, model scanning, and CBCT-based standard tessellation language (STL) reconstruction, were compared in terms of marginal and internal fit. (3) Results: Quantitative analyses revealed that model scanners exhibited the lowest marginal and internal gap values, indicating superior fit compared to intraoral scanners and CBCT-based models. The highest gap values were observed in the CBCT group with a voxel size of 0.15 mm. Overall, crowns obtained from model scanners demonstrated the highest success rates in both marginal and internal fit. (4) Conclusions: In conclusion, this study highlights the critical role of digital scanning accuracy in achieving clinically acceptable prosthetic fits and emphasizes the need for continued technological advancement. Full article

This study investigates the impact of data distribution and bootstrap resampling on the anomaly detection performance of the Isolation Forest (iForest) algorithm in statistical process control. Although iForest has received attention for its multivariate and ensemble-based nature, its performance under non-normal data distributions and varying bootstrap settings remains underexplored. To address this gap, a comprehensive simulation was performed across 18 scenarios involving log-normal, gamma, and t-distributions with different mean shift levels and bootstrap configurations. The results show that iForest substantially outperforms the conventional Hotelling’s T² control chart, especially in non-Gaussian settings and under small-to-medium process shifts. Enabling bootstrap resampling led to marginal improvements across classification metrics, including accuracy, precision, recall, F1-score, and average run length (ARL)₁. However, a key limitation of iForest was its reduced sensitivity to subtle process changes, such as a 1σ mean shift, highlighting an area for future enhancement. Full article