Search Results (397)

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

This study presents pedagogical innovations in the undergraduate course Postharvest Technology and Quality Management at Sultan Qaboos University (SQU), where project-based learning (PBL) is used to integrate academic quality assurance and sustainability education, aligning with the United Nations Sustainable Development Goals (SDGs). This study adopts a descriptive multiple-case approach to analyze five representative student projects and their alignment with the SDGs. The projects address real-world postharvest challenges, including quality preservation, renewable energy use, and food loss reduction. A qualitative cross-case analysis based on SDGs mapping criteria was used to evaluate project alignment and societal outcomes. Representative student projects demonstrate how inquiry-driven learning enhances technical competence and research skills. Quantitative outcomes include a reduction in weight loss from 27.1% to 18.8% in coated tomatoes, increased weight loss up to 46.37% under severe mechanical damage in zucchini, and significant firmness reduction in bruised apples (53.23 N to 21.64 N). Hybrid infrared–hot air drying improved drying efficiency by reducing drying time and enhancing moisture removal, while banana coating experiments showed reduced moisture loss and delayed ripening. The analysis shows that all five projects align with at least two SDGs, with SDG 12 addressed in 100% of the cases. The curriculum is explicitly aligned with SDG 2 (Zero Hunger), 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), 12 (Responsible Consumption and Production), and 13 (Climate Action). The study highlights the societal relevance of course-based projects through their contribution to SDG-related challenges and emphasizes the role of mentorship, teamwork, and experiential learning infrastructure in sustaining effective PBL implementation. Cross-case comparison highlights common sustainability contributions, including a reduction in postharvest losses, adoption of natural preservation methods, and improvements in energy-efficient processing. The findings highlight the potential of course-based PBL as a context-specific approach for integrating sustainability into undergraduate education. Full article

Scope-3 emissions from construction supply chains (CSCs) account for the majority of the construction sector’s greenhouse gas (GHG) footprint. However, procurement-driven decarbonization (PDD) remains constrained by persistent data quality (DQ) deficits, including boundary divergence, limited verification, incomplete information, and fragmented interoperability. This PRISMA-guided systematic literature review (SLR) synthesizes 68 studies to examine how governance mechanisms (GMs) and digital technologies (DTs) can be co-designed within procurement workflows to improve the reliability of carbon data. By integrating quantitative matrix-based analysis, qualitative thematic coding, and a governance–technology pairing logic, the review identifies a division of labor across DQ dimensions. Standard-based governance and boundary rules strengthen completeness, consistency, and interpretability. At the same time, DTs enhance accessibility and timeliness and provide targeted improvements in accuracy and logical coherence when embedded within structured schemas. Assurance emerges as the most reliable mechanism for accuracy, information-management standards for timeliness, and early stakeholder involvement for accessibility. These insights translate into procurement-oriented measures, including European Standard (EN)-aligned scope definitions; ISO 14083-aligned logistics accounting; Industry Foundation Classes (IFC)/Level of Information Need (LOIN)-based information requirements; selective assurance; uncertainty-aware disclosure; and integrated digital measurement, reporting, and verification (MRV) systems combining Environmental Product Declaration (EPD) platforms, Artificial Intelligence (AI) validation, and blockchain. Collectively, these measures enable comparable, verifiable data and support scalable decarbonization. Full article

Varistors are critical overvoltage protection components in modern power electronic systems. They effectively absorb and dissipate surge energy to ensure the safe and stable operation of electrical equipment. However, surface defects can lead to substandard performance or even trigger equipment failure, compromising overall system stability. Therefore, high-precision surface defect detection is essential for quality assurance. To address these challenges, we propose a lightweight model termed Defect-Aware and Edge-Reconstruction Enhanced YOLO (DAER-YOLO) for efficient varistor inspection. First, we construct a C3k2-based defect-aware enhancement module (C3k2-iEMA). This module tackles the difficulty of extracting features from small or morphologically complex defects. By integrating multi-scale feature extraction, an attention mechanism, and efficient nonlinear mapping, it strengthens the perception of defect details. Second, to enhance the reconstruction capability for edge damage and small-object defects, we introduce the Efficient Up-Convolution Block (EUCB). This block improves multi-level feature fusion and generates clearer enhanced feature maps. Based on these improvements, DAER-YOLO outperforms the YOLOv11n baseline on a custom varistor dataset, with mAP@50 and mAP@50:95 increasing by 1.6% and 2.3%, respectively. Experimental results demonstrate that the model effectively improves detection accuracy while exhibiting significant potential for real-time industrial applications. Full article

Shot peening is a critical surface treatment for improving the fatigue resistance of aircraft propeller blades operating under complex cyclic loads. While accurate coverage evaluation is essential for quality assurance, its development is severely hindered by a fundamental bottleneck: the extreme scarcity of annotated datasets in this niche aerospace domain, where data collection is costly and low-frequency, as each acquisition requires the actual peening of high-value components. Consequently, existing practices are restricted to subjective manual inspection or conventional segmentation methods that lack robustness under complex textures. To bridge this gap, this study develops an integrated automated surface evaluation framework, termed DT-ZSAM (Dual-Threshold Zero-shot Assessment Model), which circumvents the data-dependency bottleneck by leveraging the zero-shot capabilities of the Segment Anything Model (SAM) within a custom-designed prompt-generation pipeline. To ensure end-to-end automation without manual intervention, the framework identifies candidate regions via a dual-threshold scheme in grayscale and brightness domains and extracts representative prompt points through density-based analysis refined by DBSCAN clustering. Experimental results demonstrate that the proposed framework achieves precise segmentation without requiring any pixel-level annotated training data. Notably, the proposed framework yielded a coverage rate of 30.57%, aligning closely with the expert visual consensus (25–35%), whereas the standard commercial instrument (TCV-2A) significantly overestimated the coverage at 62.33% due to its sensitivity to surface textures and fixed calibration logic. This framework provides a robust and pragmatic solution for high-stakes industrial quality control, offering a reliable path for automating inspection in domains where large-scale data acquisition is practically unfeasible. Full article

The Maceió Metropolitan Region (MMR) has undergone significant changes due to public policies that promote urban growth. This has intensified environmental impacts, adversely affecting local communities. The Remote Sensing Ecological Index (RSEI), a remote sensing-based metric, was used to evaluate ecosystem quality. The study assessed annual ecosystem quality in the MMR, Alagoas, using RSEI values from MODIS data spanning 2000 to March 2024/2025. To ensure data quality and reliable results, all MODIS data underwent rigorous quality control, including the exclusion of pixels affected by cloud cover, shadows, and missing values. Only data points meeting established MODIS quality assurance standards were used. Annual RSEI values varied considerably, from 0.449 in 2005 to 0.636 in 2014. Most areas in the MMR are classified as moderate quality (0.4 < RSEI < 0.6), particularly in central and eastern sectors. The lowest-quality regions (0 < RSEI < 0.4) are concentrated in the east—including Maceió, the hub city—and the west, largely due to high population density. The Sen-Slope Estimator and trend analysis revealed significant trends in the hub city, with positive trends in the northeast. Urban expansion has led to the loss of native vegetation, including sugarcane fields and remnants of the Atlantic Forest. The Pettitt test identified a structural change in 2018, likely linked to environmental violations related to the Braskem petrochemical industry and salt extraction in Maceió. Full article

Moisture content is a key quality attribute in dried areca nuts, affecting subsequent processing performance and storage stability, yet routine measurement by oven-drying is time-consuming and destructive. This study developed a rapid and non-destructive method for determining moisture content in dried areca nuts by integrating near-infrared spectroscopy with chemometric and machine learning-assisted methodologies. Various spectral preprocessing methods, feature wavelength selection algorithms, and modeling approaches were compared. The results indicated that Multiplicative Scatter Correction (MSC) most effectively eliminated physical scattering interference. The Partial Least Squares Regression (PLSR) model established using full-wavelength spectra demonstrated optimal predictive performance. It achieved a coefficient of determination for the prediction set (Rp²), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.9639, 0.1960, and 10.3461, respectively, indicating excellent predictive accuracy and robustness. Feature wavelength selection did not enhance model performance in this study, which can be attributed to the broad absorption bands of water in the near-infrared spectrum and its complex interactions with the sample matrix where the full spectrum data retains essential information more comprehensively. This research provides a reliable and practical technical means for moisture management in areca nuts, offering important support for quality assurance and standardized production practices within the areca industry. Full article

This study investigates the relevant literature on applications of Artificial Intelligence (AI) for wood as a material using a systematic review and screening process. The Web of Science (WoS) database identified 50 peer-reviewed publications dealing with AI applications for wood as a material. Bibliometrix and VOSviewer software were used to evaluate publication trends, country contributions, keyword co-occurrences, and AI application areas. Based on these analyses, an annual growth rate of 23.28% between 2014 and 2025 (November) in publications published per year was measured and an average of 6.92 citations per publication was observed as of November 2025. Most notably, a considerable increase in AI-focused research after 2023 was identified. Before 2022, work done using AI tools (such as neural networks, deep learning, and others) did not necessarily use the term AI and hence were not found by our search. China, Canada, and Poland were the countries with the highest number of publications. The leading journals with publications on AI applications for wood as a material were Forests and Wood Material Science and Engineering. The most frequently occurring keywords in the publications reviewed were “AI,” “machine learning,” and “deep learning.” In general, according to the publications reviewed, AI applications for wooden materials improved productivity, material evaluation, and quality assurance. The findings highlighted the impact of AI on the sector and show that AI will change the industry. Full article

This study proposes EvoDeep-Quality, a closed-loop hybrid framework integrating deep learning-based perception with multi-objective evolutionary optimization for adaptive quality control in smart manufacturing. The architecture combines a CNN-LSTM network for real-time spatiotemporal quality prediction with an NSGA-III-based optimization unit to balance conflicting objectives of quality, cost, and energy efficiency. A continuous adaptive learning loop addresses concept drift and process variability. Evaluated on an industrial-inspired synthetic dataset of textile blends (N = 5000) and validated on the real-world SECOM semiconductor manufacturing dataset, the framework demonstrates strong predictive capability (R² = 0.947 ± 0.012, MAE = 0.035 ± 0.003) and significant manufacturing performance improvements, including a 23.5% quality enhancement and an 8.7–12.3% operational cost reduction compared to traditional and standalone AI models. Statistical significance testing (paired t-test, p < 0.01) confirms the superiority of the proposed approach. This deep-evolutionary framework advances proactive quality assurance and adaptive process control, offering a scalable solution aligned with Industry 4.0 and 5.0 paradigms. Full article

The construction industry in South Africa faces challenges with the current payment system used to manage progress payments. Contractors often experience delays in progress payments for completed works. These late payments stem from the improper management of progress payment procedures, namely, information, communication, and collaboration, as well as corruption. This study proposes the integration of common data environment (CDE) as it has emerged central in managing information, improving communication and collaboration in a transparent manner. However, the implementation of CDE is facing challenges in the industry. Therefore, the study aimed at developing a model based on the implementation of CDE to uphold efficiency in the management of payment systems for progress payments. A systematic review was conducted to examine the enabling factors, characteristics of CDE in managing progress payment challenges, and benefits of integrating a payment system in a CDE platform. Furthermore, the study utilised questionnaire surveys to purposively collect data from construction professionals who implemented CDE in their projects. From 201 valid questionnaire responses, a structural equation model was developed; testing for the reliability, validity, model fit, and hypotheses was conducted using AMOS and ADANCO. The findings revealed that enabling factors such as quality technology and quality assurance team are the strongest enablers, followed by training and policy. The findings further predict that CDE integration will improve the management of the payment system by 0.589. The study provides theoretical and practical guidance for researchers, policy makers, and construction professionals seeking to strengthen CDE-based payment system frameworks in South Africa. Furthermore, it is recommended to adopt the method of questionnaire surveys and SEM to validate variables and establish their influence on one another to improve generalisation. Full article

Amid the wave of digital transformation and customer service automation, traditional chatbots are increasingly challenged by their inability to handle unstructured data and complex queries. This issue is particularly critical in the import trade industry, where customer service representatives must respond promptly to diverse inquiries involving quality anomalies, order tracking, and product substitution. Existing rule-based or keyword-driven chatbots often fail to provide accurate responses, resulting in reduced customer satisfaction and increased operational burdens. This study proposes and implements a “Retrieval-Augmented Generation (RAG)-based Customer Service Chatbot,” integrating the RAG framework with a Neo4j-based knowledge graph, specifically tailored for the import trade domain. The system constructs a dedicated QA dataset, knowledge graph, and dynamic learning mechanism. It semantically vectorizes internal documents, meeting records, quality assurance procedures, and historical dialogues, establishing interrelated knowledge nodes to enhance the chatbot’s comprehension and response accuracy. The study also incorporates GPT-based response evaluation and a high-score caching strategy, enabling dynamic learning and knowledge enhancement. Experiments were conducted using 101 representative enterprise-level queries across six categories, reflecting real-world operational scenarios and inquiry needs. The results demonstrate that the combination of knowledge graphs and RAG technology effectively reduces AI hallucinations and improves response coverage and accuracy, thereby addressing complex problems in customer service applications. This paper not only presents a feasible AI implementation model for the import trading industry but also offers a practical architectural reference for domain-specific knowledge management in the import trade and allied sectors. Full article

Friction stir welding (FSW) is a critical solid-state joining process for lightweight and high-performance metallic structures, particularly in aerospace and automotive manufacturing, yet conventional implementations remain largely dependent on offline parameter optimization and open-loop control. The purpose of this review is to examine how Industry 4.0 technologies enable the transition of FSW from a parameter-driven process into an intelligent, adaptive, and increasingly autonomous manufacturing capability. A structured review methodology was employed, including systematic literature selection and synthesis of recent research on smart sensing, industrial internet of things (IIoT), data analytics, machine learning, digital twins, automation, robotics, and human–machine interaction in FSW. The review reveals that Industry 4.0 integration enables real-time process monitoring, predictive quality assurance, closed-loop control, and virtual process optimization, resulting in improved weld quality, reliability, productivity, and scalability. Significant benefits are observed for safety-critical aerospace components and high-throughput automotive production, where adaptability and consistency are essential. However, persistent challenges remain in data standardization, model generalization, real-time digital twin integration, interoperability, cybersecurity, and workforce readiness. This review concludes that addressing these challenges through interdisciplinary research, standardization efforts, and human-centered system design is essential for enabling adaptive and data-driven FSW systems. The findings position intelligent FSW as a foundational technology for smart, resilient, and sustainable metal manufacturing in the Industry 4.0 era. Full article

The detection of surface defects on smartphone components is a critical step in quality assurance for industrial manufacturing. However, existing deep learning-based methods struggle with the extreme variations in defect morphology and scale, while labeled training data remains scarce due to the high cost of expert annotation. To address these challenges, we propose a twofold solution. First, we introduce MSAdaNet, a Multi-Scale Adaptive Defect Detection Network, which integrates three novel modules: a Parallel Multi-Scale Feature Aggregation (PMSFA) backbone, a Focusing Diffusion Pyramid Network (FDPN) neck, and a Scale-Adaptive Shared Detection (SASD) head. Second, to combat data scarcity, we propose a novel data generation pipeline, creating the synthetic Smartphone Camera Bezel Dataset (SCBD) of 4936 images. Extensive experiments on both real-world and synthetic datasets validate our approach. On the challenging public SSGD, MSAdaNet achieves a state-of-the-art mAP@0.5 of 54.8%, outperforming prominent frameworks and improving upon the strong YOLOv11m baseline by +10.6 points in mAP@0.5 and +18.3 points in recall. Furthermore, on our synthetic SCBD, the model achieves an impressive 94.0% mAP@0.5, confirming the quality of our data generation pipeline and the robustness of our architecture across different data distributions. Ablation studies systematically confirm the significant contribution of each proposed module, validating MSAdaNet as an effective and efficient solution for industrial defect detection. Full article

Software defect prediction is becoming key for software quality assurance. Traditional software defect prediction approaches have predominantly focused on analyzing code-level metrics, often overlooking valuable information available during the requirements phase. However, when a requirement change request (RCR) is issued, usually during the maintenance and evolution phase, predicting software defects provides an important preventative measure. Work in requirement-based software defect prediction methods typically focus on identifying requirement flaws, such as ambiguity or incompleteness, and fail to adequately predict defects that may manifest later in the operational software system. This paper proposes a context-driven representation model, named FORESIGHT, that predicts software defect types from requirements change requests using machine learning methods. The proposed model uses binary indicators to represent contextual metrics derived from change-request characteristics and supports multi-class prediction from both primary defect types and defect manifestation types. To build its representation model, three datasets were created from real-world industrial projects in different software domains (Web, Mobile, and ASRS). FORESIGHT was evaluated using Random Forest, XGBoost, and Gradient Boosting classifiers. Results show certain software defect types can be reliability predicted with Random Forest achieving the highest macro-F1 (0.815–0.873 for primary defect type prediction; 0.683–0.833 for defect manifestation prediction) across all three datasets, outperforming XGBoost and Gradient Boosting on every dataset–task combination. Findings show that contextual metrics from requirements change requests, structured within the FORESIGHT representation model, enable reliable pre-implementation prediction of specific defect types in deployed software systems. Full article

The development of functional foods has historically centered on the identification and enhancement of bioactive compounds; however, bioactivity alone does not guarantee successful translation into safe, stable, and regulatory-compliant products. A substantial proportion of functional ingredients fail during commercialization due to inadequate consideration of processing stability, food safety risks, and regulatory constraints at early stages of product design. This narrative review presents an integrated, application-oriented framework for functional food development that systematically links processing technologies, safety assurance, and regulatory readiness. Conventional and emerging processing approaches, including fermentation, thermal treatments, high-pressure processing, and non-thermal technologies, are critically examined with respect to their effects on the stability, functionality, and bioavailability of bioactive constituents within complex food matrices. Key safety challenges, including microbiological hazards, process-induced chemical contaminants, and quality degradation during storage, are discussed in the context of industrial scalability. In parallel, regulatory considerations related to ingredient classification, substantiation of functional claims, and market authorization across major jurisdictions are reviewed to identify common translational bottlenecks. To bridge the gap between laboratory research and real-world application, a Functional Food Readiness Framework is proposed to support early-stage evaluation of technological feasibility, safety compliance, and regulatory alignment. This holistic perspective aims to guide the design of functional foods that are not only biologically effective, but also robust, safe, and commercially viable. The proposed framework can assist researchers, product developers, and food industry stakeholders in making informed decisions during functional food formulation, process optimization, and regulatory strategy development. Full article