Search Results (99,066)

Active packaging systems have emerged as a promising strategy to control microbial spoilage without direct incorporation of preservatives into food matrices. In this context, this study evaluated bacterial nanocellulose (BNC) as a nanostructured carrier for vapor-phase delivery of natural antifungal compounds in bread preservation. Cinnamaldehyde (CIN), cinnamon extract and clove extract were screened against Aspergillus niger, Penicillium chrysogenum, and Rhizopus microsporus using minimum inhibitory concentration (MIC) and inverted halo assays. CIN demonstrated complete fungal inhibition at 0.19% (v/v), corresponding to approximately 2.0 mg/mL, outperforming plant extracts, which exhibited limited and concentration-dependent activity. When incorporated into BNC at a 1:1 ratio (50% reduced loading), CIN maintained inhibition halos comparable to the free compound, indicating effective release and preserved bioavailability. The performance of the system was further evaluated in a bread model using a non-contact active packaging approach. Fungal growth in control samples was detected by day 6 (>10⁵ CFU/g), while incorporation of plant extracts into BNC delayed spoilage to day 9 (≈50% shelf-life extension). In contrast, breads treated with CIN, either free or BNC-incorporated, showed no detectable fungal growth throughout 21 days of storage, corresponding to a shelf-life extension of at least 15 days. These results demonstrate that antifungal efficacy in vapor-phase systems depends primarily on the intrinsic potency of the active compound, while BNC acts as an effective carrier matrix that promotes sustained retention and functional availability of CIN. The use of BNC-based active packaging for cinnamaldehyde delivery represents a promising clean-label strategy to control fungal spoilage and extend the shelf life of bread without direct incorporation into the food matrix. Full article

To enhance drive performance, this paper introduces an advanced speed controller architecture intended for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). This newly developed controller integrates decision tree theory (DTT) with sliding mode theory (SMT). Initially, the regression algorithm from the classification and regression tree (CART) framework is applied to partition the deviation between the actual motor speed and the target command into 10 distinct error zones. These intervals serve as the basis for configuring three critical parameters of a standard exponential reaching law sliding mode controller (ERLSMC): namely, the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain. Following each split, the mean squared error (MSE) of the respective nodes is evaluated to determine the root node. The dataset is recursively bifurcated into dual subsets using the chosen split variables and thresholds, establishing a structured decision pathway through each successive child node. As a result, the sliding mode speed controller receives dynamically optimized modifications for its three key gains in real time during BLDCM operation. In addition, the controller continuously computes an updated sliding mode dynamic trajectory control gain by tracking the derivative of the speed error. Tuning these three operational gains effectively mitigates the transient overshoot typically induced by the conventional exponential reaching law (ERL) across diverse running states. This mechanism ensures that the speed response of the BLDCM drive system dynamically and accurately follows target commands under fluctuating conditions. Advantageously, the introduced control strategy avoids intensive computational routines and eliminates the need for extensive training datasets, ensuring straightforward implementation. To validate this approach, the proposed methodology is applied to the BLDCM drive system using the Matlab/Simulink environment. Its execution is benchmarked against conventional sliding mode controllers (SMCs) configured with three distinct control strategies: the constant speed reaching law (CSRL), the standard ERL, and the extension theory combined with exponential reaching law (ETERL). The resulting simulation data confirms that the proposed adaptive controller delivers superior performance over the alternative three reaching laws regarding both transient command tracking and robustness in load regulation. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Obesity is a major global health challenge strongly associated with increased cardiometabolic morbidity and mortality. In men, obesity is characterized by a predominance of visceral adiposity, which is metabolically active and closely linked to systemic inflammation, hormonal dysregulation, and adverse cardiovascular outcomes. Despite its clinical relevance, male obesity remains underrecognized as a distinct pathophysiological condition. This study aimed to analyze the inflammatory mechanisms underlying male obesity and their relationship with cardiometabolic risk. A structured narrative review was conducted based on a PICo-guided research question, with literature searches performed in PubMed/MEDLINE, Scopus, Web of Science, Embase, and ScienceDirect, covering publications from 2015 to 2026. Studies focusing on male obesity, inflammatory pathways, and cardiometabolic outcomes were included. Evidence indicates that visceral adipose tissue acts as an active endocrine organ, releasing pro-inflammatory cytokines such as TNF-α and IL-6, contributing to chronic low-grade inflammation. This inflammatory state is associated with insulin resistance (IR), endothelial dysfunction, and oxidative stress, mediated by intracellular pathways including NF-κB and JNK. Additionally, adipokine imbalance, characterized by reduced adiponectin and increased leptin levels, further exacerbates metabolic and vascular impairment. Hormonal alterations, particularly reduced testosterone levels, play a key role in amplifying visceral fat accumulation and inflammation, creating a bidirectional relationship between hypogonadism and metabolic dysfunction. Clinically, these mechanisms highlight the importance of integrating inflammatory biomarkers, body composition assessment, and hormonal evaluation into the management of male obesity. Emerging therapies, including GLP-1 receptor agonists and immunometabolic interventions, offer promising strategies for reducing cardiometabolic risk. In conclusion, male obesity represents a complex, inflammation-driven condition requiring a comprehensive and mechanism-based approach to improve clinical outcomes and guide future therapeutic developments. Full article

Public spaces are important components of urban life, supporting social interaction, recreation, and environmental outcomes. Their success, however, depends not only on their physical provision but also on governance structures that guide their daily operation and maintenance routines. This study examines how operational governance and management practices influence user perception of public spaces by comparing two urban parks in Kathmandu: Ratna Park, a major city-level space, and Nandi Keshwor Bagaicha Park, a neighborhood-scale park. Using a mixed-method approach, the research employed a user survey (n = 191), interviews, and field observations. Survey data were used to develop composite indices for maintenance, safety, amenities, and user comfort. Descriptive statistics, Pearson correlations, independent-samples t-tests, and multiple regression models were used to examine the influence of governance on user perception. The findings reveal notable differences between the two parks. Nandi Keshwor Bagaicha Park scored higher on perceived safety (mean = 4.30) and comfort (mean = 4.01), while Ratna Park showed stronger performance in amenities (mean = 3.91). Although correlations between governance indicators and comfort were weak, regression analyses showed that maintenance, safety, and amenities accounted for only a small portion of the comfort variance (r² = 0.03). These findings indicate that operational variables alone do not fully explain user perception and suggest that broader management practices and patterns of use may also influence perceptions of comfort. This study provides exploratory empirical insight into public space governance and highlights the importance of strengthening operational systems and management practices in contemporary cities. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

The increasing penetration of inverter-based resources has significantly reduced system inertia, motivating the emergence of Fast Frequency Response (FFR) as a dedicated ancillary service. Existing methods for enabling wind power systems to deliver FFR universally treat the wind farm as a single equivalent turbine under uniform wind conditions, an assumption that is invalid in real large-scale wind farms where heterogeneous turbine types, rated capacities, inertia constants, and spatially non-uniform wind speed distributions render uniform allocation strategies suboptimal or operationally unsafe. This paper proposes a centralized wind farm-level FFR control framework that coordinates heterogeneous wind turbine generators (WTGs) and supercapacitor energy storage systems (SCESSs) through a prioritized two-tier dispatch hierarchy, in which SCESSs are assigned the highest dispatch priority and WTGs are engaged only when aggregate storage capacity is insufficient. A constrained optimization problem is formulated to allocate the individual FFR contribution of each WTG by minimizing the total kinetic energy extracted from the wind farm, while enforcing torque, electrical power, and rotor speed constraints for every unit with respect to turbine type, inertia constant, and prevailing wind condition. A coordinated rotor speed recovery strategy further eliminates secondary frequency disturbances during the post-FFR transition. The proposed framework is validated on a 138 MW heterogeneous wind farm simulation model comprising both Doubly-Fed Induction Generator and Permanent Magnet Synchronous Generator units interconnected to a modified IEEE 14-bus test system. The proposed method achieves a 38.85% improvement in frequency nadir relative to a baseline with no FFR provision, outperforming all investigated state-of-the-art approaches, while reducing total kinetic energy extraction from the wind turbine generators and eliminating secondary frequency disturbances during the post-FFR recovery phase. Full article

Water scarcity in Peru is increasingly shaped by competing sectoral demands, particularly between large-scale mining and agriculture. Both sectors rely heavily on limited freshwater resources in arid coastal and Andean basins, generating complex trade-offs between economic productivity, environmental sustainability, and social equity. This review synthesizes and critically evaluates current knowledge on water footprint (WF) dynamics within mining–agriculture systems, integrating hydrosocial theory, water–energy–food nexus thinking, and sustainability transition frameworks. Mining activities in Peru are characterized by high blue and grey water footprints, associated with intensive extraction processes and contamination risks, while agriculture exhibits diverse water footprints depending on crop type, irrigation efficiency, and climatic conditions. The interaction of these sectors creates hydrosocial conflicts driven by unequal water allocation, environmental degradation, and institutional fragmentation. This paper identifies key drivers of conflict and evaluates emerging pathways for sustainability transitions, including technological innovation, nature-based solutions, and participatory governance mechanisms. An integrative conceptual framework derived from a thematic synthesis of the reviewed literature is proposed. The findings provide actionable insights for policymakers and researchers seeking to reconcile economic development with water sustainability in resource-constrained environments. Full article

To address the safety concerns of incapacitated patients caused by changes in vehicle pose during the operation of an autonomous rescue vehicle on an unstructured road surface, this paper proposes an active leveling control scheme based on the Stewart platform. First, a complete kinematic and dynamic model of the Stewart platform and a double-layer platform leveling control model were established. Subsequently, a non-singular terminal sliding-mode control (NTSMC) algorithm based on a radial basis function (RBF) neural network was designed. By using the neural network to approximate aggregate uncertainties online, high-precision control of the Stewart platform was achieved. Additionally, to enhance perception capabilities in dynamic environments, an ORB-SLAM3 algorithm was proposed that integrates the YOLO11n-Seg instance segmentation algorithm. This approach effectively filters out dynamic feature points, enabling robust vehicle pose estimation. Finally, a physical double-layer Stewart platform experimental system was constructed to comprehensively validate the proposed control and vision algorithms. Full article

The increasing complexity and volume of technical documentation, including requirements specifications, patents, and engineering reports, create significant challenges for manual analysis and knowledge extraction. This paper includes a systematic review of methods for semantic content analysis of technical documents, with a particular focus on Natural Language Processing (NLP) techniques and Transformer-based models. The study formalizes the task of structured information extraction and provides a mathematical description of Named Entity Recognition (NER) as a core subtask. A practical case study demonstrates an end-to-end NER pipeline for Russian-language technical requirements, leveraging ruRoberta-large via spaCy-transformers. The results highlight both the potential and limitations of current approaches, emphasizing the critical role of annotation consistency and document format normalization. This work contributes to the development of intelligent systems for engineering documentation analysis and outlines key directions for future research. Full article

Hypoxia is a prevalent characteristic of neurological diseases, including ischemic injury, neurodegeneration and infectious disease complications. Concurrently, hypoxia shapes both protective and pathological responses within the central nervous system (CNS). Central to this process is hypoxia-inducible factor 1α (HIF1α), a transcription factor that regulates cellular adaptation to reduced oxygen availability through coordinated glycolytic, inflammatory and cell survival pathways. Under hypoxic conditions, HIF1α transcriptional activity influences microglial activation, mitochondrial quality control, and cytokine production, thereby modulating neuroinflammation and neuroprotection. Preclinical evidence points toward hypoxia preconditioning being neuroprotective through HIF1α-dependent mechanisms in a context-dependent matter. This review synthesizes the current understanding of the role of HIF1α across neurological disease contexts, highlighting the intersection of hypoxia, neuroinflammation and neuronal survival. Ultimately, defining the cell-specific and context-dependent involvement of HIF1α will be critical for targeted therapeutic approaches to alleviate neuronal death and slow disease progression. Full article

Grapevine black-foot disease is a destructive trunk disease with a complex pathogen composition that often involves mixed and latent infections, making timely field diagnosis challenging. To improve rapid field detection, we developed a rapid, sensitive, and low instrument-dependent nucleic acid assay. The assay integrates recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats (CRISPR)–Cas12a for the detection of Ilyonectria and Dactylonectria, two genera associated with grapevine black-foot disease. Conserved regions of the histone H3 and β-tubulin genes were selected for the design of specific RPA primers and corresponding CRISPR RNAs (crRNAs) for Ilyonectria and Dactylonectria, respectively. A workflow integrating RPA, Cas12a-mediated recognition, and lateral flow assay (LFA)-based visualization was established. The reaction conditions were optimized to enhance amplification efficiency and Cas12a recognition stability. Specificity was evaluated using DNA from target and non-target fungi, and sensitivity was determined using serially diluted templates. Under optimized conditions, the assay detected Ilyonectria DNA at concentrations as low as 3.6 ng/μL within 1 h at 39 °C. For Dactylonectria, the detection limit reached 80 fg/μL within 50 min at 41 °C. No cross-reactivity was observed. The LFA strips exhibited positive and negative bands within minutes, enabling rapid visual interpretation. This RPA-CRISPR/Cas12a-LFA system provides a rapid, visually interpretable approach for detecting selected grapevine black-foot disease-associated species in China. The workflow reduces the requirement for specialized thermocycling and fluorescence detection equipment during amplification and readout, following DNA extraction. Full article

Data-driven models can be used to understand basin-wide hydrological processes and generate predictions for future conditions, particularly in cases of scarce data availability related to basin characteristics. Although they have long been applied in hydrological modeling, there is still limited information regarding their ability to produce reliable long-term projections under climate change conditions. This study evaluates the long-term predictive performance of data-driven models by employing a hybrid deep learning architecture combining Wavelet Transform (WT) and Deep Neural Network (DNN). The dataset used in this study was obtained from the Yamula Reservoir Basin, a semi-arid agricultural basin in Türkiye. Monthly streamflow was simulated based on climate projection data from the HadGEM2-ES model under the RCP4.5 and RCP8.5 scenarios. Results showed that the WT–DNN framework was successful in learning the system dynamics and reproducing observed streamflow behavior. The model produced continuous projections for the future period; however, these projections should be interpreted with caution due to the increasing uncertainty associated with long-term climate forcing and the sensitivity of data-driven approaches to shifts in climatic and hydrological regimes. Full article

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article

Avian influenza remains a major threat to poultry production, wildlife conservation, and public health in Asia, where migratory birds, wetlands, rice paddies, domestic ducks, and live poultry trade often intersect. This Asia-focused review synthesizes ecological, epidemiological, surveillance, tracking, phylogenetic, and environmental evidence from 1996 to 2025, with particular emphasis on China, to clarify how risk develops at the wild bird–domestic poultry interface. The reviewed evidence suggests three broad epidemic phases: early Goose/Guangdong-lineage H5N1 outbreaks before 2014, recurrent clade 2.3.4.4 H5Nx expansions during 2014–2019, and the widespread clade 2.3.4.4b H5N1 period since 2020. Spatial risk is concentrated around major stopover wetlands and rice-paddy–duck landscapes, including Qinghai Lake, Poyang Lake, Sanmenxia, the Sanjiang Plain, and peri-urban market belts. Wetlands and paddies can maintain viruses environmentally, free-grazing ducks and bridge hosts can facilitate introduction, and live poultry markets and trade networks can amplify and export risk. By organizing these processes through an Interface–Amplifier–Conduit evidence-mapping approach, this review highlights setting-specific priorities, including seasonal wetland surveillance, closed farm-water systems, improved market hygiene, and better integration of ecological and genomic data for early warning and control. Full article