Search Results (15,630)

As the core refrigeration equipment in cooling systems, the operational state of chiller units is crucial for ship support, equipment cooling, and mission stability. However, because of their sensitivity and the complexity of operating environments, obtaining large volumes of complete, fault-labeled data is difficult in practical engineering appli-cations. This limitation makes it challenging for traditional data-driven approaches to deliver accurate fault diagnoses. Furthermore, data collected from different devices or under varying operating conditions often differ significantly in both feature dimensions and distributions, i.e., data heterogeneity, which further complicates model transfer. To address these challenges, this study proposes a deep transfer learning–based fault di-agnosis method designed to leverage abundant knowledge from the source domain while adaptively learning features of the target domain. Given the persistent difficulties in collecting sufficient high-quality labeled fault data, traditional data-driven models continue to face restricted diagnostic performance on target equipment. At the same time, data heterogeneity across devices or operating conditions intensifies the challenge of cross-domain knowledge transfer. To overcome these issues, this study develops a heterogeneous transfer learning method that integrates a dual-channel autoencoder, domain adversarial training, and pseudo-label self-training. This combination enables precise small-sample knowledge transfer from the source to the target domain. Specifi-cally, the dual-channel autoencoder is first applied to align heterogeneous feature di-mensions. Then, a Gradient Reversal Layer (GRL) and a domain discriminator are in-troduced to extract domain-invariant features. In parallel, high-confidence pseu-do-labeled samples from the target domain are incorporated into joint training to im-prove generalization and robustness. Experimental results confirm that the method achieves high fault diagnosis accuracy in typical industrial application scenarios, ena-bling effective identification of common faults in various types of chiller units under conventional operating conditions, the proposed method achieves higher accuracy and F1-scores in multi-class fault diagnosis tasks compared with both traditional approaches and existing transfer learning methods. These findings provide a novel perspective for advancing the intelligent operation and maintenance of chiller units. Full article

TiAl alloys are ideal candidates to replace nickel-based superalloys in aero-engines due to their low density and high specific strength, yet their industrial application is hindered by narrow heat treatment windows and unbalanced mechanical performance. To address this, this study investigates the microstructure and mechanical properties of Ti-44.9Al-4.1Nb-1.0Mo-0.1B-0.05Y-0.05Si (TNM-derived) alloys hot-rolled in the (α₂ + γ) two-phase region. The research employs varying heat treatment temperatures (1150–1280 °C) and cooling rates (0.1–2.5 °C/s), combined with XRD, SEM, EBSD characterization, and 800 °C high-temperature tensile tests. Key findings: Discontinuous dynamic recrystallization (DDRX) of γ grains is the primary mechanism refining lamellar colonies during deformation. Higher heat treatment temperatures reduce γ/β phases (which constrain colony growth), increasing the volume fraction of lamellar colonies but exerting minimal impact on interlamellar spacing. Faster cooling shifts γ lamella nucleation from confined to grain boundaries to multi-sites (grain boundaries, γ lamella peripheries, α grains) and changes grain boundaries from jagged and interlocking to smooth and straight, which boosts nucleation sites and refines interlamellar spacing. Fine lamellar colonies and narrow interlamellar spacing enhance tensile strength, while eliminating brittle βo phases and promoting interlocking boundaries with uniform equiaxed γ grains improve plasticity. Full article

Defect detection in textile manufacturing is critically hampered by the inefficiency of manual inspection and the dual constraints of deep learning (DL) approaches. Specifically, DL models suffer from poor generalization, as the rapid iteration of fabric types makes acquiring sufficient training data impractical. Furthermore, their high computational costs impede real-time industrial deployment. To address these challenges, this paper proposes a texture-adaptive fabric defect detection method. Our approach begins with a Dynamic Subspace Feature Extraction (DSFE) technique to extract spatial luminance features of the fabric. Subsequently, a Light Field Offset-Aware Reconstruction Model (LFOA) is introduced to reconstruct the luminance distribution, effectively compensating for environmental lighting variations. Finally, we develop a texture-adaptive defect detection system to identify potential defective regions, alongside a probabilistic ‘OutlierIndex’ to quantify their likelihood of being true defects. This system is engineered to rapidly adapt to new fabric types with a small number of labeled samples, demonstrating strong generalization and suitability for dynamic industrial conditions. Experimental validation confirms that our method achieves 70.74% accuracy, decisively outperforming existing models by over 30%. Full article

The accurate and efficient placement of traffic cones is a critical safety and logistical requirement in diverse industrial environments. This study introduces a novel dataset specifically designed for the near-overhead detection of traffic cones, containing both bounding box annotations and apex keypoints. Leveraging this dataset, we systematically evaluated whether classical object detection methods or keypoint-based detection methods are more effective for the task of cone apex localization. Several state-of-the-art YOLO-based architectures (YOLOv8, YOLOv11, YOLOv12) were trained and tested under identical conditions. The comparative experiments showed that both approaches can achieve high accuracy, but they differ in their trade-offs between robustness, computational cost, and suitability for real-time embedded deployment. These findings highlight the importance of dataset design for specialized viewpoints and confirm that lightweight YOLO models are particularly well-suited for resource-constrained robotic platforms. The key contributions of this work are the introduction of a new annotated dataset for overhead cone detection and a systematic comparison of object detection and keypoint detection paradigms for apex localization in real-world robotic applications. Full article

Effluents from the textile industry, particularly those containing synthetic azo dyes, poses a significant environmental threat, necessitating the development of more effective and sustainable pollutant removal methods. Traditional dye removal techniques often fall short in efficiency and environmental impact, prompting the exploration of enzymatic degradation as a promising alternative. This study focuses on chloroperoxidase, a natural biocatalyst recognized for its ability to oxidize synthetic dyes into less harmful products. By exploring the mechanistic distinction between chlorination and oxidative processes, we investigate the enzyme’s specific degradation pathways for azo dyes and the resulting by-products. Utilizing analytical techniques, including liquid chromatography/mass spectrometry (LC/MS), and density functional theory (DFT), we gain insights into the decolorization mechanism, revealing that the enzyme preferentially generates oxidative products through C–N bond cleavage as its initial degradation step. These findings underscore not only the unique mechanistic properties of chloroperoxidase but also its potential as a biocatalyst for industrial applications. This study advocates further research into the optimization of enzyme-based systems, highlighting their relevance in advancing greener chemical practices in the textile industry, thus contributing to more sustainable manufacturing processes. Full article

Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, this paper presents a benchmark evaluation of anomaly detection algorithms for PV cybersecurity applications. We evaluate three state-of-the-art algorithms (One-Class SVM, Isolation Forest, and Local Outlier Factor) across 12 attack scenarios, establishing performance baselines and identifying algorithm-specific strengths and limitations. Our experimental results reveal a clear detectability hierarchy. This work proposes a standardized benchmark for PV cybersecurity research and provides the industry with evidence-based guidance for security system deployment. Full article

The diversification of cultivars and sustainable production in banana and plantain cultivation, with a view to reducing losses and differentiating derivative products, are of great importance for productive advances linked to sustainable development. In this study, the morphological, physicochemical, and functional characteristics of starches isolated from four dessert cultivars of Prata subgroup (BRS Platina, Gorutuba Biocell, Prata-Anã, BRS Gerais) and plantain cultivars (Tipo Velhaca, Mongolo, and BRS Terra-Anã) were evaluated. All starches exhibited a B-type crystalline pattern, with variations in granule shape and in amylose and resistant starch contents, which particularly differentiated plantains. Differences in viscosity and gelatinization properties highlighted the potential of certain cultivars for specific industrial applications. Multivariate analysis emphasized the diversity among starches, reinforcing their importance as versatile and sustainable raw materials for industry, with the potential to add value and reduce losses in the production chain. Full article

The NewEcoSmart project addresses the need to foster inclusive green and digital transitions in rural habitat sectors by systematically assessing local socio-technical readiness and tailoring capacity-building interventions. We adapted the validated SCIROCCO Exchange Maturity Self-Assessment Tool—selecting eight dimensions relevant to environmental, technological and social innovation—and conducted a two-phase evaluation across three pilot sites in Italy, Portugal and Spain. Phase 1 mapped stakeholder evidence against predefined criteria; Phase 2 engaged local actors (45+ adults, SMEs and micro-firms) in a self-assessment to determine digital, green and entrepreneurial skill gaps. For each domain of the SCIROCCO Tool, local actors can assign a minimum of 0 to a maximum of 5. The final score of the SCIROCCO tool can be a minimum of 0 to a maximum of 40. Quantitative maturity scores revealed heterogeneous profiles (Pacentro and Majella Madre = 5; Yecla = 10; Adelo Area = 23), underscoring diverse ecosystem strengths and limitations. A qualitative analysis, framed by Smart Healthy Age-Friendly Environments (SHAFE) domains, identified emergent training needs that are clustered at three levels: MACRO (community-wide awareness and engagement), MESO (decision-maker capacity for strategic planning and governance) and MICRO (industry-specific practical skills). The adapted SCIROCCO tool effectively proposes the assessment of socio-technical maturity in rural contexts and guides the design of a modular, multi-layered training framework. These findings support the need for scalable deployment of interventions that are targeted to the maturity of the local ecosystems to accelerate innovations through equitable green and digital transformations in complex socio-cultural settings. Full article

The escalating issue of water pollution driven by rapid industrialization necessitates the development of advanced remediation technologies. In this study, a novel method for producing chromium (Cr(VI)) ion-imprinted biochar (Cr(VI)-IIP-PEI@NBC) from wheat residue was proposed. After acid-oxidative modifications, polyethyleneimine (PEI) and glutaraldehyde (GA) were employed as the functional monomer and crosslinker, respectively, to enhance the biochar’s selectivity and adsorption capacity. Under optimized conditions (pH 2.0, 55 °C), the adsorbent achieved a maximum Cr(VI) uptake of 212.63 mg/g, which was 2.3 times higher than that of the non-imprinted biochar. The material exhibited exceptional specificity (99.64%) for Cr(VI) and maintained >80% adsorption efficiency after five regeneration cycles, demonstrating excellent reusability. Comprehensive structural characterization via Fourier transform infrared spectroscopy (FT-IR), thermal gravimetric analysis (TGA), Brunner–Emmet–Teller measurements (BET), and Scanning Electron Microscopy (SEM) confirmed successful Cr(VI) imprinting in the biochar and its high thermal stability and mesoporous architecture, elucidating the mechanisms behind its superior performance. This study presents a sustainable and high-performance adsorbent for the efficient treatment of chromium-contaminated wastewater, with significant potential for industrial applications. Full article

Postbiotics, as a novel class of functional components, have garnered considerable scholarly and industrial interest due to their distinctive advantages in food processing applications and their positive impact on human health. Although postbiotics have demonstrated potential in alleviating constipation, their specific mechanism of action and bioactive components remain unclear. This study aimed to investigate the ameliorative effects and potential mechanisms of postbiotics derived from Bifidobacterium animalis subsp. lactis BL-99 (BL-99) on FC using both in vivo and in vitro models. The findings revealed that both BL-99 and its postbiotics significantly mitigated FC symptoms, as evidenced by enhanced intestinal motility, and elevated fecal water content. Additionally, treatment with BL-99 postbiotics was associated with an increase in the thickness of the intestinal muscular layer and a reduction in apoptosis of intestinal smooth muscle cells (SMCs). Mechanistically, BL-99 postbiotics were found to enhance the contractile response and promote the proliferation of intestinal SMCs. Furthermore, untargeted metabolomics analysis identified two key bioactive peptides, Glu-Val and Glu-Leu, as the active components in BL-99 responsible for regulating SMC function. Collectively, these findings highlight the potential of BL-99 postbiotics as a promising functional food ingredient for alleviating FC, providing a novel and effective strategy for the developing dietary interventions targeting this condition. Full article

Corona treatment is commonly used in industry to enhance the surface-free energy of plastic films. However, corona treatment may cause some undesirable effects affecting further processing, such as sealing. In this paper, we deeply analyze the corona treatment effect on selected properties of various polymer films commonly used in packaging applications. The films were treated at two power levels (100 W and 300 W), and the experimental design included surface characterization and mechanical testing to assess changes in wettability, chemical structure, and seal strength. The Owens–Wendt approach confirmed the corona treatment effect by static contact angle measurement and surface free energy calculation. Next, their seal strength was evaluated in relation to surface energy and chemical structure changes. FTIR spectroscopy was used to identify functional groups potentially affected by corona treatment. The results indicate that the impact of corona treatment is material-dependent. In general, corona treatment at a lower level increases the seal strength, while corona treatment at a higher power level is related to a decrease in seal strength. The study highlights the importance of optimizing corona treatment parameters for specific materials to enhance seal performance without compromising surface integrity. Full article

Quality control represents a critical function in industrial environments, ensuring that manufactured products meet strict standards and remain free from defects. In highly regulated sectors such as the pharmaceutical industry, traditional manual inspection methods remain widely used. However, these are time-consuming and prone to human error, and they lack the reliability required for large-scale operations, highlighting the urgent need for automated solutions. This is crucial for industrial applications, where environments evolve and new defect types can arise unpredictably. This work proposes an automated visual defect detection system specifically designed for pharmaceutical bottles, with potential applicability in other manufacturing domains. Various methods were integrated to create robust tools capable of real-world deployment. A key strategy is the use of incremental learning, which enables machine learning models to incorporate new, unseen data without full retraining, thus enabling adaptation to new defects as they appear, allowing models to handle rare cases while maintaining stability and performance. The proposed solution incorporates a multi-view inspection setup to capture images from multiple angles, enhancing accuracy and robustness. Evaluations in real-world industrial conditions demonstrated high defect detection rates, confirming the effectiveness of the proposed approach. Full article

This study investigated the spatial distribution of HMs in agricultural and urban soils near the largest oil refining complex in Serbia, identified pollution sources, and assessed ecological and human health risks. A large fraction of soil samples showed elevated Hg (40% of samples), Pb (53%), Cd (90%), and As (93%) concentrations compared to the background levels. Hotspots for Pb, As, Hg, Cd, and Zn were observed in the industrial area, indicating significant anthropogenic input. Multivariate analysis, including PMF, revealed four contamination sources: emissions from the oil refining industry, agricultural activities, traffic emissions, and natural background. The pollution indices mostly fell into the moderate pollution range, with As, Hg, and Cd showing the highest enrichment. The potential ecological risk index (RI) indicated that about one-third of the samples had moderate ecological risk and determined a major RI hotspot near the refinery. The health risk assessment identified As and Cr as the largest contributors to non-carcinogenic risk, although the average HI was below one. Monte Carlo simulation confirmed that adults and children had negligible health risks at the 95th percentile and highlighted exposure frequency and body weight as the most influential exposure parameters. Based on source-specific risk, the oil refining industry emissions had the highest impact on HI and TCR values. Full article

Sugar beet pulp (SBP), a byproduct of the sugar industry, presents significant potential for enhancing economic benefits and promoting sustainable development through its comprehensive utilization. SBP is rich in fiber, with its soluble dietary fiber (SDF) constituting a high-value component. The initial step in the preparation of SDF involves the drying of fresh SBP. This study compares the effects of hot air and microwave drying on the composition, structure, and physicochemical properties of SDF in SBP. Technologies such as gel permeation chromatography, gas chromatography–mass spectrometry, Fourier transform infrared spectroscopy, scanning electron microscopy, and Zeta potential analysis were employed. Results indicated that microwave drying enhanced sugar components in SDF, reduced polysaccharide molecular weight, and formed a uniform and porous microstructure. This resulted in a higher Zeta potential (−24.76 mV) and increased water holding capacity (5.01 g/g). Hot air-dried samples preserved a more intact cell wall network, exhibiting higher swelling capacity (5.18 mL/g). The study demonstrated how both drying methods differentially regulated SDF quality from sugar beet pulp, suggesting that drying process selection should be based on specific application needs. Full article

Sustainability reporting has emerged as a pivotal tool for corporate accountability, integrating environmental, social, and economic performance into transparent disclosures that align with global frameworks such as the Global Reporting Initiative (GRI) Standards and the United Nations Sustainable Development Goals (SDGs). This study evaluates the environmental sustainability performance of Turkey’s automotive manufacturing sector by analyzing the extent and depth of GRI-based disclosures and their alignment with SDG targets. A mixed-method approach, combining quantitative Key Performance Indicator (KPI) coverage analysis with qualitative content assessment, was applied to sustainability reports from 12 major manufacturers. By identifying the most frequently reported indicators, assessing their coverage of economic, environmental, and social dimensions, and evaluating their direct relevance to specific SDGs, this research fills a critical gap and provides actionable insights for policymakers, industry leaders, and sustainability practitioners. The results indicate that while social indicators achieve the highest average disclosure rate (77.3%), environmental themes dominate narrative emphasis, reflecting sectoral materiality and regulatory pressures rather than proportional (KPI) coverage. Key gaps include underreporting of governance-related SDGs (e.g., SDG 5, SDG 8, SDG 16), limited target-level mapping, and a lack of measurable, outcome-based indicators. The study proposes a structured methodology for linking GRI metrics to SDG targets, enabling more consistent benchmarking and highlighting opportunities for balanced integration across all sustainability pillars. The findings contribute to both academic discourse and industry practice by demonstrating the need to bridge the gap between quantitative breadth and qualitative depth in sustainability reporting, ensuring more robust alignment with the 2030 Agenda. Full article