Search Results (35,854)

Rolling bearings represent one of the core functional components of rotating machinery, with their application scope continuously expanding into various sectors of modern social production and life, making the research on fault diagnosis of rolling bearings increasingly significant. Effective vibration feature extraction and improved classification models are crucial to achieving accurate and automated fault diagnosis of rolling bearings. We proposed a fault diagnosis approach based on a Swin Transformer–Improved ResNet module. In the data preprocessing stage, the frequency-domain features and time-domain multi-scale features of fault signals are extracted using FFT and VMD methods, respectively. And then, dual-channel feature extraction is employed using both the Swin Transformer and Improved ResNet module, followed by feature fusion through an ECA module, thereby enhancing diagnostic accuracy and model robustness. The architecture retains shallow-level feature details while incorporating global contextual information, improving feature representation and detection precision. Extensive experiments were carried out on data collected from an SEU bearing dataset, including model validation, ablation analysis, comparative evaluation and simulated noise testing. An average classification accuracy of 99.41% was achieved by the proposed model under uniform experimental conditions, as evidenced by the obtained experimental results, outperforming other models by at least 0.96%. Even under severe noise interference with a signal-to-noise ratio of -4, the model maintained an average accuracy of 91.92%, exceeding that of noise-resistant counterparts. Moreover, generalization experiments on the CWRU bearing dataset under varying load conditions revealed an average fault diagnosis accuracy exceeding 98%, confirming the model’s strong cross-domain adaptability. Full article

Modern applications are increasingly built around networking, collaboration, and automation. Drones, or Unmanned Aerial Vehicles (UAVs), are a key part of this shift. Many complex missions require multiple UAVs to work together as a team, which means deciding how to group them efficiently is a real optimization challenge. This paper introduces EPICEAg (Enhanced Preference-Inspired Co-Evolutionary Algorithm with goal vectors), a new algorithm for forming optimal UAV teams, called coalitions. EPICEAg builds on an existing algorithm called PICEAg but adds three important improvements: it uses k-medoids clustering through the Partitioning Around Medoids (PAM) algorithm for more reliable team leader selection, and applies two advanced sorting methods—shift-based density estimation and epsilon-ranking—to manage the complexity of the search. The algorithm optimizes seven goals at once: how well tasks are completed, how efficiently resources are used, how reliable the team and its communications are, how trustworthy the individual drones are, and how much energy they have left. Tests across several mission scenarios show that EPICEAg consistently performs better than PICEAg, NSGA-II, and MOPSO. Full article

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

This study examines the human–nature relationship in the thoughts of Yunus Emre (d. ca. 1320) and addresses the Qur’anic positioning of humanity as laden with responsibility through the idea of amanah (entrustment), while focusing on Yunus Emre’s reflections on this concept as both a mystical stance and a moral state. His poems place humanity not as an absolute claim of ownership over the world and other beings, but rather within a relationship based on testimony, decency, and equality. He presents nature not as an object requiring protection or an area needing transformation but as a framework for contemplation and reflection in which the divine order is visible. In this context, humans’ established relationship with the world reflects a stance determined not by domination or interference but by a consciousness of limitation and a sense of moderation. By revealing the aspects of his understanding of humanity and nature that overlap with the concept of amanah in Islamic thought, this study argues that this overlap should be evaluated not as conceptual equivalence but rather in terms of mystical and moral affinity. This approach aims to demonstrate how Yunus Emre’s ideas, while not offering direct solutions to modern environmental debates, provide a historical mystical perspective that allows for a rethinking of the human–nature relationship. Full article

Phenolic compounds are vital bioactive constituents in fruits, yet modern strawberry breeding has often reduced their diversity. Here, we employed a multi-omics approach integrating UPLC-MS/MS-based metabolomics and RNA-seq transcriptomics to investigate the divergence in phenolic profiles and their transcriptional regulation between a wild strawberry (Fragaria nilgerrensis, HM) and three cultivated varieties (white ‘Danxue’ (DX), pink ‘Fenyu’ (FY), and red ‘Red Face 99’ (RF)). The wild HM genotype exhibited higher antioxidant activity and a significantly more complex phenolic profile, dominated by high-abundance galloylated and benzoylated glucosides (e.g., digallic acid methyl ester, salicylic acid-2-O-glucoside) that were largely absent or depleted in cultivated fruits. In contrast, the cultivated varieties displayed specialized yet simplified profiles: DX accumulated hydroxycinnamoyl galactonic acids, FY was enriched in feruloylated glucosides, and RF was characterized by coumaroyl-glucose derivatives. Transcriptomic analysis identified a set of MYB transcription factors (e.g., FxaYL_531g0581170, FxaYL_642g0175720) significantly upregulated in wild HM, with strong correlations to key bioactive phenolics such as 4-hydroxybenzoate and salicylic acid derivatives. These findings illustrate how selective breeding has reshaped phenolic composition through alterations in MYB regulatory networks. The wild strawberry germplasm thus represents a valuable natural reservoir for biofortification strategies aimed at restoring the nutritional and functional quality of modern strawberry cultivars. Full article

Modern organizations increasingly operate under a structural condition shaping their environment: resource scarcity. Firms experience supply disruptions, volatility, and growing uncertainty when access to energy, raw materials, water, and other critical inputs becomes limited. Sustainability-oriented approaches encourage responsible resource use, but they do not fully explain how organizations achieve stability and adapt under persistent resource constraints. In this context, organizational resilience—the ability of firms to absorb shocks, adapt, and ultimately transform in response to sustained pressures—has emerged as an important complementary perspective. This paper develops an integrated conceptual framework explaining how circular management practices contribute to organizational resilience in resource-constrained environments. Drawing on the circular economy, organizational resilience, and dynamic capabilities literature, the framework establishes links between circular practices and resilience outcomes mediated by organizational enablers. To complement the conceptual development, an exploratory expert-based evaluation was conducted as a form of exploratory content validation focused on face validity, conceptual coherence, and perceived relevance of the proposed framework. The results indicate strong expert agreement regarding the coherence and applicability of the model. The study integrates circular economy and resilience theories into a unified framework and provides a conceptual foundation for future empirical research on circular management and organizational resilience. Full article

In 1952, Harry Markowitz established the foundations of Modern Portfolio Theory by introducing a mean–variance framework for constructing investment portfolios that optimize the trade-off between risk and expected return. Expanding upon this classical framework, this paper develops an analytical algorithm to determine optimal asset weights for long-only portfolios that minimize total risk. We derive the necessary and sufficient conditions through rigorous determinant-based identities, providing a closed-form solution for achieving the global minimum variance. The theoretical findings are demonstrated through three numerical examples: the first examines a standard six-asset portfolio using an admissible covariance matrix; the second serves as an algebraic cautionary case by identifying negative variance when the input matrix fails to satisfy the criteria for positive definiteness; and the third example validates the practical application of the proposed identities using empirical market data. Full article

Nowadays, Global Value Chains (GVCs) play a vital role in job creation, income generation, knowledge diffusion, and productivity growth. However, significant disparities exist across countries in terms of their integration into GVCs, and Tunisia is no exception to this pattern. In this regard, the question about factors that influence GVCs’ participation is yet to be discussed, to formulate and implement appropriate strategies and reforms. Thus, using firm-level data from the 2025 World Bank Enterprise Survey, this paper examines the role of female leadership and innovation in determining Tunisian firms’ participation in GVCs. Participation in GVCs is captured by a dummy variable indicating the firm’s export and import status. Estimation results from the logit model show that female representation in decision-making positions significantly increases the likelihood of firms’ participation in GVCs. The results also highlight the importance of process innovation in GVC participation, while product innovation appears to have no significant effect. Notably, when firms combine both types of innovation, their likelihood of joining GVCs increases further. Regarding control variables, firm size appears to be an important determinant, as larger firms display a greater tendency to participate in GVCs. The findings further indicate that firm certification and foreign equity participation significantly promote integration into GVCs, while corruption constitutes a major constraint on the integration of Tunisian firms. From a policy perspective, these findings highlight the need to rethink industrial policies, with a stronger focus on process innovation as a key lever of productive sector modernization. Achieving this transformation also requires the development of an inclusive policy ecosystem that supports meaningful and sustainable progress in female’s leadership representation. Full article

Modern enterprise breaches are no longer isolated events but coordinated, multi-stage campaigns whose success depends on the defender’s inability to translate detection into timely containment. While existing frameworks—such as attack-lifecycle models, Zero Trust architectures, and detection-driven systems—provide valuable capabilities, they lack a formal mechanism for coupling inferred adversarial state with coordinated, cross-layer enforcement. This paper presents AMNDA, an Adaptive Multi-layer, stage-aware Network Defense Architecture that operationalizes lifecycle-aware defense through explicit state-to-control mapping and executable orchestration. Adversarial progression is modeled as a probabilistic state-transition process, and inferred states are systematically mapped to synchronized controls across edge protection, identity governance, internal segmentation, and behavioral detection. A formally defined orchestration function transforms detection outputs into stage-conditioned policy updates, enforcing monotonic tightening of containment as adversarial capability escalates. AMNDA is implemented and validated in a reproducible Microsoft Azure environment. Empirical results show that stage-aligned enforcement actions execute within 1.0–3.1 s, while detection latency remains the dominant constraint, with a median of 1034 s across the validation corpus. This separation reveals a critical operational insight: in modern cloud environments, the limiting factor in lifecycle defense is not enforcement capability but detection timing. The contribution of AMNDA is therefore not a new detection technique but a formal, deployable architecture that converts attack-stage inference into coordinated, low-latency containment. By bridging lifecycle modeling, Zero Trust principles, and automated orchestration, the proposed approach establishes a practical foundation for state-aware, adaptive cyber defense. Full article

Modern agriculture generates large amounts of straw, thus posing significant residue management challenges. In this context, traditional disposal methods such as residue incineration can cause severe environmental harm. From a circular economy perspective, rice and wheat straw are valuable lignocellulosic resources from which high-value bioproducts can be derived, including xylooligosaccharides (XOS). Efficient conversion of this biomass depends on the enzymatic degradation of xylan, the main hemicellulose in cereal straw. In this study, four GH10 endoxylanases were evaluated, of which X11 and its hybrid variant X11C2 showed the best performance, particularly at pH 9.0. X11 showed robustness under harsh conditions and a tendency to release short sugars such as xylose and xylobiose. Both rice and wheat straw exhibited partial saccharification, but wheat straw released higher amounts of soluble sugars, indicating a higher susceptibility to enzymatic hydrolysis. Given the growing interest in XOS as prebiotics with multiple health benefits, the enzymatic hydrolysis of low-cost agricultural residues—supported by appropriate pretreatment—represents a promising and sustainable strategy for XOS production. Full article

The ongoing academic debate on interpreting the disenchantment and re-enchantment of modern society remains unresolved. This study traces the theoretical genealogies of enchantment, disenchantment, and re-enchantment, proposing that enchantment is not a fixed concept but a dynamically evolving and reconstructed process. Focusing on sacred mountain circumambulation—a traditional pilgrimage ritual deeply entrenched in Tibetan cultural contexts—this study employs qualitative methods, conducting semi-structured interviews with 33 contemporary Tibetan youth to examine the manifestations of enchantment within this practice. Findings reveal that, against the backdrop of globalization and China’s social transformation, Tibetan youths’ circumambulation practices exhibit several emerging characteristics in organizational patterns, material preparation, modes of action, degree of ritual participation, and intergenerational differences. Within this pilgrimage activity, the process of disenchantment is evident as Tibetan youth attain higher levels of cultural and educational literacy. Traditional foundations of enchantment, such as taboos associated with sacred mountains and utilitarian motivations, persist. Simultaneously, new forms of enchantment with distinctly modern features—including topophilia and emotional value—are steadily emerging. The results suggest that disenchantment does not entail the demise of enchantment, nor does re-enchantment signify a return to traditional enchantment. Instead, sacred mountain circumambulation embodies the cognitive and perceptual process through which Tibetan youth engage with, understand, and negotiate enchantment via their individual lived experiences. This research not only uncovers the evolving significance of circumambulation in modern society but also offers a fresh perspective on how enchantment adapts and endures within contemporary contexts. Full article

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R² of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming. 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

Depression is a complex mental and neurological disorder and has become one of the most serious public health issues in modern society. Trihexyphenidyl (THY) is a traditional drug used to treat Parkinson’s disease. Recent studies have suggested that it may play a role in regulating neurotransmitters and protecting neurons, but its potential for treating depression has not been fully explored, and how it works remains unclear. Therefore, we examined the effects of THY on depression-like behaviors in zebrafish caused by chronic unpredictable stress (CUS). Our results showed that THY significantly attenuated the CUS-induced decrease in exploratory behavior and shortened the CUS-induced increase in latency time. At the tissue level, THY effectively attenuated the thinning of the optic tectum and the loss of Nissl bodies caused by CUS. In addition, THY reversed the CUS-induced increase in stress hormone levels and reduction in neurotransmitter content. Through network pharmacology and transcriptome sequencing analysis, we found that the mechanisms underlying depression-like behaviors and the antidepressant effects of THY might be related to the MAPK signaling pathway. Further experiments showed that THY regulated the CUS-induced activation of the MAPK signaling pathway, improved the abnormal activation of microglia and damage to astrocytes, and reduced the expression of pro-inflammatory factors, thereby easing neuroinflammation and improving depression-like behaviors. In summary, this study explored the potential mechanism of THY ameliorating depressive-like behaviors and provided basic theoretical evidence. Full article

Cells contain an unexpectedly large diversity of lipid molecules. Modern lipidomics studies have revealed that even a single cell type can harbor hundreds to thousands of distinct lipid species that differ in headgroup structure, acyl chain length, and degree of unsaturation. While this remarkable diversity is now well established, its biological significance remains incompletely understood. Why do cells maintain such complex lipidomes? In this review, we examine several conceptual frameworks that may help explain the origin and functional significance of lipid diversity. First, the physical properties of biological membranes impose constraints on lipid composition, as variations in lipid structure influence membrane fluidity, curvature, thickness, and phase behavior. Second, lipids can regulate membrane protein function through specific interactions and through the physical environment of the lipid bilayer. Third, lipid metabolism generates signaling molecules that participate in diverse regulatory pathways. Fourth, lipid metabolic networks continuously remodel membrane composition, producing dynamic lipidomes that can adapt to physiological conditions. Finally, evolutionary processes have shaped membrane lipid composition across different domains of life, suggesting that lipid diversity may reflect long-term adaptation to functional and environmental constraints. Taken together, these perspectives suggest that lipid diversity is unlikely to be a simple byproduct of metabolism. Instead, the cellular lipidome may emerge from the interplay of membrane biophysics, metabolic network architecture, protein regulation, and evolutionary pressures. Understanding why cells contain thousands of lipid species therefore represents an important challenge for modern cell biology and may reveal fundamental principles governing the organization of biological membranes. Full article