Search Results (6,408)

Background: Insect-derived proteins constitute an underutilized biological resource requiring urgent exploration to address global food protein shortages. However, their widespread application is hindered by the allergenic potential, particularly phospholipase A2 (PLA2), a highly immunoreactive allergen prevalent in edible insects such as ants and honeybees. Objective: This study systematically investigated the molecular mechanism underlying quercetin-mediated reduction in PLA2 allergenicity, aiming to establish a novel strategy for developing hypoallergenic insect protein resources. Methods and Results: Through integrated computational and experimental approaches, we identified quercetin’s dual non-covalent and covalent binding capabilities with PLA2. Molecular docking revealed robust interactions (the binding energy of −6.49 kcal/mol) within the catalytic pocket. Meanwhile, mass spectrometry specifically identified Cys37 as the covalent modification site, which can bind to quercetin and increase the gyration radius (Rg) of PLA2 within 75–125 ns. Molecular dynamics simulations illustrated quercetin-induced conformational changes affecting critical antigenic epitopes. Murine experiments further confirmed that quercetin-modified PLA2 exhibited significantly reduced IgE reactivity and allergic responses compared to native PLA2, as demonstrated by assessments of anaphylactic behavior, histopathological changes, and measurements of serum IgE antibody and biogenic amine levels. Conclusions: Collectively, these findings provide a transformative approach to safely utilize insect-derived proteins for sustainable nutrition solutions. Full article

Accurate observations of Ocean Surface Wind Speed (OSWS) are vital for predicting extreme weather and understanding ocean–atmosphere interactions. However, spaceborne sensors (e.g., ASCAT, SMAP) often experience data loss due to harsh weather and instrument malfunctions. Existing inpainting methods often rely on reanalysis data that is released with delays, which restricts their real-time capability. Additionally, deep-learning-based methods, such as Transformers, face challenges due to their high computational complexity. To address these challenges, we present the Multimodal Wind Speed Inpainting Dataset (MWSID), which integrates 12 auxiliary forecasting variables to support real-time OSWS inpainting. Based on MWSID, we propose the MMamba framework, combining the Multimodal Feature Extraction module, which uses mutual information (MI) theory to optimize feature selection, and the OSWS Reconstruction module, which employs Attention-Mamba-2 within a Residual-in-Residual-Dense architecture for efficient OSWS inpainting. Experiments show that MMamba outperforms MambaIR (state-of-the-art) with an RMSE of 0.5481 m/s and an SSIM of 0.9820, significantly reducing RMSE by 21.10% over Kriging and 8.22% over MambaIR in high-winds (>15 m/s). We further introduce MMamba-L, a lightweight 0.22M-parameter variant suitable for resource-limited devices. These contributions make MMamba and MWSID powerful tools for OSWS inpainting, benefiting extreme weather prediction and oceanographic research. Full article

This study analyzes the factors that affect the technical efficiency (TE) of firms in the supporting industry in the context of Vietnam’s digitalized economy. Stochastic frontier analysis (SFA), Fixed Effect Models, and System-GMM methods are applied to reach the findings that the quality of human resources, capital intensity, and firm size have positive effects on TE. Furthermore, exogenous environmental factors, such as the domestic demand of an industry impacting all upstream businesses, which use inputs that are products of that industry (BSpill-ratio), and the FDI backward effect (BFSpill), also exhibit positive effects. These confirm that the linkage between domestic supporting industry suppliers and FDI assembly enterprises plays an important role in improving TE. Vietnam’s digital transformation since 2020 has also created some interesting changes in the correlation coefficient. Location, sectors, competitiveness, and investment environment are also considered, and the results suggest that they are all determinants to be considered in management policies at both the firm level and the government level. Our contribution in this study is new policies aligned with many major changes in the world economic context, such as the tough tariff policy implemented by recent presidential administrations and a series of reforms of the Vietnamese Government, as well as strong digital transformation in Vietnam. The key findings of this research are important as they confirm which factors are really determinants for the Vietnamese government to implement investment policies for this industry effectively. Full article

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness. Full article

The spatiotemporal uneven distribution of water resources conflicts sharply with human demands, with pumping stations facing efficiency decline due to aging infrastructure and complex hydraulic interactions. This study employs numerical simulation to investigate operational mechanisms in a parallel pump system at the Yanhuanding Yanghuang Cascade Pumping Station. Using ANSYS Fluent 2024 R1 and the SST k-ω turbulence model, we demonstrate that variable-speed control expands the adjustable flow range to 1.17–1.26 m³/s while maintaining system efficiency at 83–84% under head differences of 77.8–79.8 m. Critically, energy losses (δH) at the 90° outlet pipe junction escalate from 3.8% to 18.2% of total energy with increasing flow, while Q-criterion vortex analysis reveals a 63% vortex area reduction at lower speeds. Furthermore, a dual-mode energy dissipation mechanism was identified: at 0.90n₀ speed, turbulent kinetic energy surges by 115% with minimal dissipation change, indicating large-scale vortex dominance, whereas at 0.80n₀, turbulent dissipation rate increases drastically by 39%, signifying a shift to small-scale viscous dissipation. The novelty of this work lies in the first systematic quantification of junction energy losses and the revelation of turbulent energy transformation mechanisms in parallel pump systems. These findings provide a physics-based foundation for optimizing energy efficiency in high-lift cascade pumping stations. Full article

This entry presents a comprehensive overview of the computational study of Old English that surveys the evolution from early digital corpora to recent artificial intelligence applications. Six interconnected domains are examined: textual resources (including the Helsinki Corpus, the Dictionary of Old English Corpus, and the York-Toronto-Helsinki Parsed Corpus), lexicographical resources (analysing approaches from Bosworth–Toller to the Dictionary of Old English), corpus lemmatisation (covering both prose and poetic texts), treebanks (particularly Universal Dependencies frameworks), and artificial intelligence applications. The paper shows that computational methodologies have transformed Old English studies because they facilitate large-scale analyses of morphology, syntax, and semantics previously impossible through traditional philological methods. Recent innovations are highlighted, including the development of lexical databases like Nerthusv5, dependency parsing methods, and the application of transformer models and NLP libraries to historical language processing. In spite of these remarkable advances, problems persist, including limited corpus size, orthographic inconsistency, and methodological difficulties in applying modern computational techniques to historical languages. The conclusion is reached that the future of computational Old English studies lies in the integration of AI capabilities with traditional philological expertise, an approach that enhances traditional scholarship and opens new avenues for understanding Anglo-Saxon language and culture. Full article

The increasing complexity of managing maintenance activities across geographically dispersed hospitality facilities necessitates advanced digital solutions capable of effectively balancing operational costs and service quality. This study addresses this challenge by designing and validating an intelligent Prescriptive Maintenance module, leveraging advanced Reinforcement Learning (RL) techniques within a Digital Twin (DT) infrastructure, specifically tailored for luxury hospitality networks characterized by high standards and demanding operational constraints. The proposed framework is based on an RL agent trained through Proximal Policy Optimization (PPO), which allows the system to dynamically prescribe preventive and corrective maintenance interventions. By adopting such an AI-driven approach, platforms are the enablers to minimize service disruptions, optimize operational efficiency, and proactively manage resources in dynamic and extended operational contexts. Experimental validation highlights the potential of the developed solution to significantly enhance resource allocation strategies and operational planning compared to traditional preventive approaches, particularly under varying resource availability conditions. By providing a comprehensive and generalizable representation model of maintenance management, this study delivers valuable insights for both researchers and industry practitioners aiming to leverage digital transformation and AI for sustainable and resilient hospitality operations. Full article

The rapid development of wireless communication technology is leading to increasingly scarce spectrum resources, making efficient utilization a critical challenge. This paper proposes a Convolutional Neural Network–Long Short-Term Memory-Integrated Gradient-Weighted Class Activation Mapping (GC-CNN-LSTM) model, aimed at enhancing the accuracy of long-term spectrum prediction across multiple frequency bands and improving model interpretability. First, we achieve multi-frequency long-term spectrum prediction using a CNN-LSTM and compare its performance against models including LSTM, GRU, CNN, Transformer, and CNN-LSTM-Attention. Next, we use an improved Grad-CAM method to explain the model and obtain global heatmaps in the time–frequency domain. Finally, based on these interpretable results, we optimize the input data by selecting high-importance frequency points and removing low-importance time segments, thereby enhancing prediction accuracy. The simulation results show that the Grad-CAM-based approach achieves good interpretability, reducing RMSE and MAPE by 6.22% and 4.25%, respectively, compared to CNN-LSTM, while a similar optimization using SHapley Additive exPlanations (SHAP) achieves reductions of 0.86% and 3.55%. Full article

High-silica–alumina coal gangue is rich in kaolinite, quartz, and other mineral components. The potential for resource utilization is huge, but the silica–aluminate structure is highly stable, and it is difficult to achieve efficient dissociation and elemental enrichment using traditional extraction processes. This study selects typical high-silica–alumina coal gangue as the research object and systematically studies the rules of the physical phase transformation mechanism and ion migration behavior in the activation process of the sodium-based additives stage. In addition, a graded leaching and separation processing route is established, realizing the effective separation and extraction of silica–alumina. The key parameters were optimized using response surface methodology (RSM), obtaining the optimal activation conditions of 800 °C, 30 min, and an additives ratio of 0.8. Under these conditions, the highest dissolution rates of silica and alumina are 82.1% and 92.36%, respectively. Characterization techniques such as XRD, FTIR, and SEM reveal that the activation mechanism of coal gangue involves the decomposition of the aluminosilicate framework and the erosion of sodium ions. At the same time, the chemical bonding reorganization contributes to forming water-soluble sodium silicate (Na₂SiO₃) and insoluble nepheline (NaAlSiO₄), which significantly promotes the release of Si and Al. When the activation temperature is too high, the nepheline phase is transformed into amorphous glassy sodium aluminate and precipitated on the surface, which gradually encapsulates the sodium silicate. This encapsulation restricts dissolution pathways, thereby leading to system densification. Moreover, enhanced resistance to acid attack leads to a decrease in the dissolution rates of Si and Al. This study elucidates the mineral phase reconstruction and element migration mechanisms involved in sodium-based activation and presents a viable approach for the high-value utilization of coal gangue. Full article

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection. Full article

(1) Background: The development of Generative Artificial Intelligence (GenAI) is transforming knowledge-intensive domains such as Education. However, Large Language Models (LLMs), which serve as the foundational components for GenAI tools, are trained on static datasets, often producing misleading, factually incorrect, or outdated responses. Our study explores the performance gains of Retrieval-Augmented LLMs over baseline LLMs while also identifying the trade-off opportunity between smaller-parameter LLMs augmented with user-specific data to larger parameter LLMs. (2) Methods: We experimented with four different LLMs, each with a different number of parameters, to generate outputs. These outputs were then evaluated across seven lexical and semantic metrics to identify performance trends in Retrieval-Augmented Generation (RAG)-Augmented LLMs and analyze the impact of parameter size on LLM performance. (3) Results and Discussions: We have synthesized 968 different combinations to identify this trend with the help of different LLM sizes/parameters: TinyLlama 1.1B, Mistral 7B, Llama 3.1 8B, and Llama 1 13 B. These studies were grouped into two themes: RAG-Augmented LLM percentage improvements to baseline LLMs and compelling trade-off possibilities of RAG-Augmented smaller-parameter LLMs to larger-parameter LLMs. Our experiments show that RAG-Augmented LLMs demonstrate high lexical and semantic scores relative to baseline LLMs. This offers RAG-Augmented LLMs as a compelling trade-off for reducing the number of parameters in LLMs and lowering overall resource demands. (4) Conclusions: The findings outline that by leveraging RAG-Augmented LLMs, smaller-parameter LLMs can perform better or equivalently to large-parameter LLMs, particularly demonstrating strong lexical improvements. They reduce the risks of hallucination and keep the output more contextualized, making them a better choice for knowledge-intensive content in academic and research sectors. Full article

In the era of rapid digital transformation, efficient network bandwidth allocation is vital for ensuring high-quality service and seamless network operations, particularly in environments with dynamic traffic patterns. This study proposes a novel approach to optimize bandwidth allocation using a Backpropagation Neural Network (BPNN) algorithm. The research utilizes a dataset sourced from Kaggle that has been modified to focus on prioritization during resource allocation and employs a preprocessing pipeline for consistency across network parameters. The BPNN model is trained with normalized data and evaluated using metrics such as Mean Squared Error (MSE) and R² score. The results, with an MSE of 0.1637 and an R² score of 0.9920, demonstrate high accuracy and minimal error in predicting bandwidth allocation. Furthermore, training and validation loss trends confirm the model’s convergence and effectiveness in real-time applications. The study underscores the integration of machine learning with network optimization principles, offering a robust framework for dynamic bandwidth management and resource-efficient network operations. Full article

For aquaculture systems, maximizing feed efficiency is a major challenge since it directly affects growth rates and economic sustainability. Feed is one of the largest costs in aquaculture, and feed waste is a significant environmental issue that requires effective management strategies. This paper suggests a novel approach for optimal fish feeding in pond aquaculture systems that integrates vision language models (VLMs), optical flow, and advanced image processing techniques to enhance feed management strategies. The system allows for the precise assessment of fish needs in connection to their feeding habits by integrating real-time data on biomass estimates and water quality conditions. By combining these data sources, the system makes informed decisions about when to activate automated feeders, optimizing feed distribution and cutting waste. A case study was conducted at a profit-driven tilapia farm where the system had been operational for over half a year. The results indicate significant improvements in feed conversion ratios (FCR) and a 28% reduction in feed waste. Our study found that, under controlled conditions, an average of 135 kg of feed was saved daily, resulting in a cost savings of approximately $1800 over the course of the study. The VLM-based fish feeding behavior recognition system proved effective in recognizing a range of feeding behaviors within a complex dataset in a series of tests conducted in a controlled pond aquaculture setting, with an F1-score of 0.95, accuracy of 92%, precision of 0.90, and recall of 0.85. Because it offers a scalable framework for enhancing aquaculture resource use and promoting sustainable practices, this study has significant implications. Our study demonstrates how combining language models and image processing could transform feeding practices, ultimately improving aquaculture’s environmental stewardship and profitability. Full article

Semantic segmentation of ultra-high-resolution remote sensing (UHR-RS) imagery plays a critical role in land use and land cover analysis, yet it remains computationally intensive due to the enormous input size and high spatial complexity. Existing studies have commonly employed strategies such as patch-wise processing, multi-scale model architectures, lightweight networks, and representation sparsification to reduce resource demands, but they have often struggled to maintain long-range contextual awareness and scalability for inputs of arbitrary size. To address this, we propose RingFormer-Seg, a scalable Vision Transformer framework that enables long-range context learning through multi-device parallelism in UHR-RS image segmentation. RingFormer-Seg decomposes the input into spatial subregions and processes them through a distributed three-stage pipeline. First, the Saliency-Aware Token Filter (STF) selects informative tokens to reduce redundancy. Next, the Efficient Local Context Module (ELCM) enhances intra-region features via memory-efficient attention. Finally, the Cross-Device Context Router (CDCR) exchanges token-level information across devices to capture global dependencies. Fine-grained detail is preserved through the residual integration of unselected tokens, and a hierarchical decoder generates high-resolution segmentation outputs. We conducted extensive experiments on three benchmarks covering UHR-RS images from 2048 × 2048 to 8192 × 8192 pixels. Results show that our framework achieves top segmentation accuracy while significantly improving computational efficiency across the DeepGlobe, Wuhan, and Guangdong datasets. RingFormer-Seg offers a versatile solution for UHR-RS image segmentation and demonstrates potential for practical deployment in nationwide land cover mapping, supporting informed decision-making in land resource management, environmental policy planning, and sustainable development. Full article

Nanostores—micro, independent grocery retailers—are often defined overlooking their socioeconomic roles and relational significance in favour of their primary functional aspects. To close this gap, this study adopts a systemic perspective to examine how multiple stakeholders (owners, customers, and suppliers) shape nanostore identity. Accordingly, this study proposes a framework of X-Y-Z identity statements, along with the use of the TASCOI tool, to examine nanostore descriptions and map their roles, expectations, and transformation processes. This systemic framework, rooted in management cybernetics, enabled the collection and analysis of 168 survey responses from 34 stores in Mexico City. The results show that nanostore identities are varied and context-dependent, operating as grocery stores, family projects, community anchors, economic lifelines, and competitors. This diversity influences stakeholder engagement, resource utilisation, and operational decisions. Overall, this study provides a transferable framework for analysing micro-business identity and transformation, with implications for problem-solving, decision-making, and policy development. Future research should address the current limitations of this study, including its geographical cross-sectional design, limited sampling method, reliance on self-reported perceptions, and lack of generalisability to other populations. Future work will involve exploring other urban contexts, utilising longitudinal data, expanding the sample, and adopting a participatory research approach to gain a deeper understanding of identity dynamics and their implications for nanostore resilience and survivability. Full article