Search Results (3,527)

This study addresses the critical problem of barriers hindering Green Supply Chain Management (GSCM) adoption in Egypt’s petrochemical sector, a major economic driver that produces approximately 4.5 million tons annually but generates significant GHG emissions and hazardous waste. The objective is to identify, rank, and analyze the hierarchical relationships among internal and external barriers using a mixed-methods approach. This study focuses on the full petrochemical supply chain in Egypt, encompassing upstream (raw material sourcing), midstream (manufacturing/refining processes), and downstream (distribution, waste management, reverse logistics), with an emphasis on emission/waste reduction practices. Data were collected via a structured questionnaire from 400 employees in Egyptian petrochemical firms and analyzed using Interpretive Structural Modeling (ISM). The findings showed that internal impediments, such as a lack of corporate leadership and support (IB1), a critical shortage of resources (IB6), and the absence of green initiatives (IB5), serve as driving forces that exert a cascading influence over the external barriers, which include insufficient government support (EB1), a lack of markets for recycled materials (EB5), and human resources or expertise shortages (EB7). The study contributes to the existing literature on GSCM by incorporating international trends and specifically addressing Egyptian issues, including weak policies, difficult supply chains, high energy-intensive operations, and costly operations. The study suggests that sending clear messages from the top and providing financial incentives can help push the obstacles aside and guide the industry down the path of environmentally responsible operations. Full article

Models for dual cascades in power-spectra for fully three-dimensional (3D) incompressible turbulence are reviewed and summarized. Special attention is given to analyses where the basic equations for 3D incompressible flows are expanded in terms of the eigenfunctions for the curl-operator. The possibilities for forward and inverse cascades in 3D fluid turbulence are illustrated and quantified. Conditions for dual- and forward-energy cascades in wavenumber space are presented. The forward or unidirectional cascade is found to dominate, a result consistent with the basic physical arguments formulated by vortex-line stretching. The analysis gives additional details to quantify the cascade conditions including dual cascades. Selected initial or boundary value conditions can give transient space or time intervals, where a dual cascade is dominating. Full article

In intelligent unmanned agricultural machinery research, navigation line extraction in natural field/orchard environments is critical for autonomous operation. Existing methods still face two prominent challenges: (1) Dynamic shooting perspective shifts caused by natural environmental interference lead to geometric distortion of image features, making it difficult to acquire high-precision navigation features; (2) Symmetric distribution of crop row boundaries hinders traditional algorithms from accurately extracting effective navigation trajectories, resulting in insufficient accuracy and reliability. To address these issues, this paper proposes an environment-adaptive navigation path extraction method for multi-type agricultural scenarios, consisting of two core components: an Attention-Feature-Enhanced U-Net (AFU-Net) for semantic segmentation of navigation feature regions, and a key-point constraint-based adaptive navigation line extraction algorithm. AFU-Net improves the U-Net framework by embedding Efficient Channel Attention (ECA) modules at the ends of Encoders 1–3 to enhance feature expression, and replacing Encoder 4 with a cascaded Semantic Aware Multi-scale Enhancement (SAME) module. Trained and tested on both our KVW dataset and Yu’s field dataset, our method achieves outstanding performance: On the KVW dataset, AFU-Net attains a Mean Intersection over Union (MIoU) of 97.55% and a real-time inference speed of 32.60 FPS with only 3.95 M Params, outperforming state-of-the-art models. On Yu’s field dataset, it maintains an MIoU of 95.20% and 16.30 FPS. Additionally, compared with traditional navigation line extraction algorithms, the proposed adaptive algorithm reduces the mean absolute yaw angle error (mAYAE) to 2.06° in complex scenarios. This research exhibits strong adaptability and robustness, providing reliable technical support for the precise navigation of intelligent agricultural machinery across multiple agricultural scenarios. Full article

Wind power plants (WPPs), as large-scale cyber–physical systems (CPSs), have become essential to renewable energy generation but are increasingly exposed to cyber threats. Attacks on supervisory control and data acquisition (SCADA) networks can cause cascading physical and economic impacts. The systematic synthesis of cyber risk analysis methods specific to WPPs and cyber–physical energy systems (CPESs) is a need of the hour to identify research gaps and guide the development of resilient protection frameworks. This study employs a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to review the state of the art in this area. Peer-reviewed studies published between January 2010 and January 2025 were taken from four major journals using a structured set of nine search queries. After removing duplicates, applying inclusion and exclusion criteria, and screening titles and abstracts, 62 studies were examined for analysis on the basis of a synthesis framework. The studies were classified along three methodological dimensions, qualitative vs. quantitative, model-based vs. data-driven, and informal vs. formal, giving us a unified taxonomy of cyber risk analysis approaches. Among the included studies, 45% appeared to be qualitative or semi-quantitative frameworks such as STRIDE, DREAD, or MITRE ATT&CK; 35% were classified as quantitative or model-based techniques such as Bayesian networks, Markov decision processes, and Petri nets; and 20% adopted data-driven or hybrid AI/ML methods. Only 28% implemented formal verification, and fewer than 10% explicitly linked cyber vulnerabilities to safety consequences. Key research gaps include limited integration of safety–security interdependencies, scarce operational datasets, and inadequate modelling of environmental factors in WPPs. This systematic review highlights a predominance of qualitative approaches and a shortage of data-driven and formally verified frameworks for WPP cybersecurity. Future research should prioritise hybrid methods that integrate formal modelling, synthetic data generation, and machine learning-based risk prioritisation to enhance resilience and operational safety of renewable-energy infrastructures. Full article

Depression represents a leading cause of global disability, yet its pathogenesis remains incompletely understood. This review synthesizes emerging evidence highlighting the multifaceted role of Aquaporin-4 (AQP4), the central nervous system’s predominant water channel, in the pathophysiology of depression. Preclinical studies frequently report AQP4 dysregulation in depression models, characterized by reduced perivascular expression and impaired polarization in mood-relevant brain circuits. We delineate how AQP4 impairment is implicated in depression through several interconnected mechanistic pathways: (1) exacerbating glutamate excitotoxicity by disrupting astrocytic glutamate clearance; (2) impairing monoaminergic neurotransmission and synaptic plasticity; (3) potentiating neuroinflammatory cascades; (4) inducing mitochondrial functional impairment and oxidative stress; and (5) participating in hypothalamic–pituitary–adrenal (HPA) axis dysregulation by disrupting perineuronal osmotic and ionic homeostasis in response to arginine vasopressin (AVP) signaling. Furthermore, we explore the therapeutic relevance of AQP4, noting that diverse antidepressant treatments appear to partly exert their effects by modulating AQP4 expression and function. Collectively, the evidence positions AQP4 not as a solitary causative factor, but as a critical contributing component within the broader astrocyte–neuron–immune network. We therefore propose AQP4 as a promising node for therapeutic intervention, whose modulation may help counteract core pathophysiological processes in depression, offering a potential avenue for novel treatment development. Full article

Tree pollen allergies are influenced by regional atmospheric pollen concentrations and flora distribution. Climate change and urban landscaping have altered airborne pollen profiles in Ukraine, potentially affecting sensitization patterns. We examined 7518 patients (57.63% children) sensitized to at least one of 26 molecular components from 19 tree species using ALEX testing (2020–2022). Atmospheric pollen data from Ukrainian aerobiology stations were integrated with clinical data. Regional sensitization was mapped using the Geographic Information System, and Bayesian network modeling determined hierarchical relationships. Sensitization to Cry j 1 (46.01%), Bet v 1 (41.67%), and Fag s 1 (34.38%) dominated across age groups. High Fagales sensitization correlated with elevated atmospheric Betula, Alnus, and Corylus pollen concentrations, confirming environmental exposure-sensitization relationships. Bayesian modeling identified Bet v 1 as the root allergen (89.43% accuracy) driving cascading sensitization to other Fagales and non-Fagales allergens. Unexpectedly high Cry j 1 sensitization despite minimal atmospheric Cryptomeria presence suggests Thuja and Ambrosia cross-reactivity. Fagales sensitization dominated 10 of 17 regions, correlating with forest geography and urban landscaping. This study validates aerobiological monitoring’s clinical relevance. Diagnostic protocols should prioritize Bet v 1 while interpreting Cry j 1 positivity as potential cross-reactivity. Climate-driven shifts in atmospheric pollen patterns require ongoing coordinated aerobiological and clinical surveillance. Full article

Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water-level fluctuations, and aqueous surface extent, influence the distribution and size of waterbird colonies (Ardeidae, Threskiornithidae, and Phalacrocoracidae) in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote-sensing-derived habitat metrics, in situ meteorological and hydrological measurements to model colony abundance dynamics. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of numerical population dynamics. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting an adaptive spatial response that was consistent with behavioral flexibility. We propose an adaptive management framework prioritizing sustainable solutions for maintaining minimum lacustrine water levels to preserve critical foraging zones. This integrative framework highlights the pivotal role of remote sensing in transitioning from reactive monitoring to predictive conservation of deltaic ecosystems. Full article

Background/Objectives: Coupled with the already-problematic background rates of traditional cigarette consumption during pregnancy, the surging epidemic of electronic cigarette usage among pregnant women redoubles the importance of understanding the impacts of nicotine exposure during critical periods of development. To date, a burgeoning body of human epidemiological and animal model research indicates that not only the children but also the grandchildren of maternal smokers are at higher risk for neurodevelopmental disorders such as ADHD, autism, and schizophrenia and are predisposed to neurodevelopmental abnormalities which transcend these diagnoses. However, the roles of discrete cellular sub-populations in these and other intergenerational consequences of smoking during pregnancy remain indeterminate. Methods: Toward the resolution of this void in the literature, the present study characterized alterations in the gene expression profiles of dopamine receptor D1-expressing striatal cells from the first- and second-generation male progeny of female mice that were continuously exposed to nicotine beginning prior to conception, continuing throughout pregnancy, and concluding upon weaning of offspring. Results: Dopamine receptor D1-expressing striatal cells from our mouse models of the children and grandchildren of maternal smokers exhibit differential expression patterns for a multitude of genes that are (1) individually associated with neurodevelopmental disorders, (2) collectively overrepresented in gene set annotations related to brain, behavioral, neurobiological, and epigenomic phenotypes shared among neurodevelopmental disorders, and (3) orthologous to human genes that exhibit differential DNA methylation signatures in the newborns of maternal smokers. Conclusions: Together with our and others’ previous findings, the results of this study support the emerging theory that, by inducing extensive alterations in gene expression that in turn elicit cascading neurobiological changes which ultimately confer widespread neurobehavioral abnormalities, nicotine-induced epigenomic dysregulation may be a primary driver of neurodevelopmental deficits and disorders in the children and grandchildren of maternal smokers. Full article

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article

Bt Cry toxins remain the cornerstone of transgenic crop protection against Lepidopteran pests, yet field-evolved resistance, particularly in invasive species such as Spodoptera frugiperda and Helicoverpa armigera, can threaten their long-term efficacy. This review presents a comprehensive and unified mechanistic framework that synthesizes current understanding of Bt Cry toxin modes of action and the complex, multilayered regulatory mechanisms of field-evolved resistance. Beyond the classical pore-formation model, emerging evidence highlights signal transduction cascades, immune evasion via suppression of Toll/IMD pathways, and tripartite toxin–host–microbiota interactions that can dynamically modulate protoxin activation and receptor accessibility. Resistance arises from target-site alterations (e.g., ABCC2/ABCC3, Cadherin mutations), altered midgut protease profiles, enhanced immune regeneration, and microbiota-mediated detoxification, orchestrated by transcription factor networks (GATA, FoxA, FTZ-F1), constitutive MAPK hyperactivation (especially MAP4K4-driven cascades), along with preliminary emerging findings on non-coding RNA involvement. Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants. Future sustainability hinges on system-level integration of single-cell transcriptomics, midgut-specific CRISPR screens, microbiome engineering, and AI-accelerated protein design to preempt resistance trajectories and secure Bt biotechnology within integrated resistance and pest management frameworks. Full article

Peripheral inflammation is increasingly recognized as a critical driver of sustained neuroinflammation and cognitive dysfunction in neurodegenerative and inflammation-associated disorders. Systemic inflammatory mediators can compromise blood–brain barrier integrity, activate glial cells, and initiate maladaptive neuroimmune cascades that disrupt hippocampal–prefrontal circuits underlying learning and memory. Here, we investigated whether early systemic administration of human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) mitigates inflammation-driven cognitive deficits in a chronic lipopolysaccharide (LPS) mouse model. Adult mice received daily LPS injections for seven days to induce persistent systemic and central inflammation, which was confirmed by serum and hippocampal cytokine analyses in a separate cohort at the time of MSC administration, followed by intravenous MSC treatment immediately after cessation of the inflammatory insult. Behavioral testing revealed significant impairments in spatial working memory, recognition memory, and associative learning. These deficits were accompanied by pronounced microglial activation, immune cell accumulation, astrocytosis, and a shift toward a pro-inflammatory cytokine milieu with suppression of IL-10 in the hippocampal CA1 region and medial prefrontal cortex. Early MSC treatment attenuated glial reactivity, reduced pro-inflammatory cytokines, restored IL-10 expression, and partially rescued cognitive performance. Collectively, these findings identify a post-inflammatory therapeutic window in which early MSC-based immunomodulation can rebalance neuroimmune signaling and limit inflammation-induced hippocampal–prefrontal circuit dysfunction, highlighting a clinically relevant strategy for targeting cognitive impairment associated with chronic systemic inflammation. Full article

As a natural refrigerant, CO₂ shows significant potential in sustainable thermal engineering due to its environmental safety and economic viability. While the transcritical CO₂ cycle demonstrates strong performance in heating, low-temperature applications, and integration with renewable energy sources, its widespread adoption is hindered by key challenges at the application level. These include: high sensitivity of system efficiency to operating conditions, which creates an “efficiency hump” and narrows the optimal operating window; increased component costs and technical challenges for key devices such as multi-channel valves due to high-pressure requirements; and complex system control with limited intelligent solutions currently integrated. Despite these challenges, the transcritical CO₂ cycle holds unique value in enabling synergistic energy conversion. Its ability to efficiently match and cascade different energy grades makes it particularly suitable for data center cooling, industrial combined cooling and heating, and solar–thermal hybrid systems, positioning it as an indispensable technology in future low-carbon energy systems. To fully realize its potential, development efforts must focus on high-value applications and key technological breakthroughs. Priority should be given to demonstrating its use in fields where it holds a distinct advantage, such as low-temperature refrigeration and high-temperature industrial heat pumps, to establish commercially viable models. Concurrently, core technologies—including adaptive intelligent control algorithms, high-efficiency expanders, and cost-effective pressure-resistant components—must be advanced. Supportive policies, encompassing energy efficiency standards, safety regulations, and fiscal incentives, will be essential to facilitate the transition from demonstration projects to widespread industrial adoption. Full article

Facial information carries key personal privacy, and it is crucial to ensure its security through encryption. Traditional encryption for portrait images typically processes the entire image, despite the fact that most regions lack sensitive facial information. This approach is notably inefficient and imposes unnecessary computational burdens. To address this inefficiency while maintaining security, we propose a novel dual-region encryption model for portrait images. Firstly, a Multi-task Cascaded Convolutional Network (MTCNN) was adopted to efficiently segment facial images into two regions: facial and non-facial. Subsequently, given the high sensitivity of facial regions, a robust encryption scheme was designed by integrating a CNN-based key generator, the proposed three-dimensional Multi-module Nonlinear Feedback-coupled Chaotic System (3D-MNFC), DNA encoding, and bit reversal. The 3D-MNFC incorporating time-varying parameters, nonlinear terms and state feedback terms and coupling mechanisms has been proven to exhibit excellent chaotic performance. As for non-facial regions, the Logistic map combined with XOR operations is used to balance efficiency and basic security. Finally, the encrypted image is obtained by restoring the two ciphertext images to their original positions. Comprehensive security analyses confirm the exceptional performance of the regional model: large key space ($2^{536}$) and near-ideal information entropy (7.9995), NPCR and UACI values of 99.6055% and 33.4599%. It is worth noting that the model has been verified to improve efficiency by at least 37.82%. Full article

Due to the significant nonlinear characteristics of the magnetic bearing, it is difficult to establish an accurate mathematical model, and it is susceptible to external disturbances. Traditional control methods struggle to meet the control requirements. Active disturbance rejection control (ADRC) does not rely on accurate models and has outstanding anti-interference ability. In order to improve the anti-disturbance ability and control stability of the system, a cascaded linear–nonlinear active disturbance rejection control method (CL-NLADRC) based on the improved artificial jellyfish algorithm is proposed and applied to the magnetic levitation ball system. Firstly, the mathematical model of the magnetic levitation ball system is established, and based on this model, a cascaded linear–nonlinear extended state observer is constructed to estimate and compensate for the system state, thereby enhancing the dynamic response capability of the system. Subsequently, the tangent spiral motion and the lens reversal learning strategy are introduced to improve the artificial jellyfish algorithm to further improve the global optimization performance of the algorithm. Finally, the improved artificial jellyfish algorithm is used to optimize the CL-NLADRC controller parameters. The simulation and experimental results show that compared with the traditional LADRC and PID controllers, the proposed CL-NLADRC has a significant improvement in the steady-state error, response speed, and anti-disturbance performance of the system. Among them, the root mean square error decreased by 14% and 47%, respectively, which verified the effectiveness and stability of the method in the magnetic levitation ball system. Full article

The development of large-scale cascade hydropower complexes has improved the navigation conditions of mountainous rivers but creates unique “continuous dam zones,” presenting complex challenges for port site selection due to hydrological variability and geological risks. To address the lack of specialized evaluation tools for this specific context, this paper constructs a comprehensive evaluation indicator system tailored for mountainous reservoir areas. The proposed system explicitly integrates critical engineering and physical constraints—specifically fluctuating backwater zones, geological hazards, and dam-bypass mileage—alongside ecological and social requirements. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are integrated using a Game Theory model to determine combined weights, and the Evaluation based on Distance from Average Solution (EDAS) model is applied to rank the alternatives. An empirical analysis of the Xiluodu Reservoir area on the Jinsha River demonstrates that operational efficiency, geological safety, and environmental feasibility constitute the critical decision-making factors. The results indicate that Option C (Majiaheba site) offers the optimal solution (ASi = 0.9695), effectively balancing engineering utility with environmental protection. Sensitivity analysis further validates the consistency and stability of this ranking under different decision-making scenarios. The findings provide quantitative decision support for project implementation and offer a replicable reference for infrastructure planning in similar complex mountainous river basins. Full article