Search Results (2,708)

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

With increasing exposure to natural hazards and anthropogenic risks, the robustness of transportation networks must be enhanced to ensure national security and long-term sustainability. However, robustness-optimization research has mainly focused on single-layer networks, while the systematic exploration of multilayer networks that better reflect real-world transportation system characteristics remains insufficient. This study establishes a multilayer integrated transportation network for China, encompassing road, railway, and waterway systems, based on complex network theory. The robustness of single-layer, integrated networks and the integrated transportation networks of the seven major regions is evaluated under various attack strategies. The results indicate that the integrated network exhibits superior robustness to single-layer networks, with the road sub-network proving pivotal for maintaining structural stability. Under the same edge addition ratio, the robustness improvement achieved by the low-importance node enhancement strategy is, on average, about 80% higher than that of the high-importance node strategy, with the effect becoming more significant as the edge addition ratio increases. These findings provide theoretical support for the vulnerability identification and structural optimization of transportation networks, offering practical guidance for constructing efficient, safe, and sustainable transportation systems. Full article

Climate change threatens the sustainability of historic buildings with increasing extreme weather events, making energy resilience critical. However, studies on energy resilience often lack forward-looking, holistic approaches. This study aims to develop a conceptual framework that includes how Artificial Intelligence (AI) technologies can support energy resilience in historical buildings with data-driven prediction and analysis to increase energy resilience against climate change. This study applied a methodology with four-stage qualitative research techniques, including a systematic literature review (PRISMA method), content analysis, AI integration, and conceptual framework development processes, in the intersections of historical building, energy resilience, and climate change. The findings reveal a significant research gap in the predictive analysis of the resilience of historic buildings and the integration of AI-based tools in the context of climate change. The proposed framework outlines a multi-layered system that includes data collection, performance analysis, scenario-based prediction, and AI-assisted decision-making, aiming to enhance the resilience of the building (including building envelope, thermal, and lifecycle analysis). Consequently, this study provides a theoretical and methodological perspective and proposes a scientifically based and applicable roadmap. It also highlights the potential of AI as a bridge between energy resilience and historical buildings in the face of a rapidly changing climate. Full article

Bulk chalcogenides from the system (GeTe₄)_1−xIn_x, where x = 0; 5 and 10 mol%, were synthesized by a two-step melt quenching technique. New layered composite materials based on them and the azo polymer [1-4-(3-carboxy-4-hydrophenylazo) benzensulfonamido]-1,2-ethanediyl, sodium salt] has been prepared through spin coating, electrospray deposition and via vacuum-thermal evaporation of the chalcogenide and spin coating of the azo polymer onto it. Using the latter technology, a material consisting of one chalcogenide and one azo polymer film and three chalcogenide and three azo polymer films has been fabricated. The carried-out SEM analysis shows that in the materials, initially prepared as a bilayer and multilayer structure, diffusion at the chalcogenide/polymer interface occurs leading to the formation of a homogenous composite environment. Birefringence was induced at 444 nm in all the fabricated thin film materials. The highest value of the maximal induced birefringence has been measured for the material fabricated as a stack, Δn_max = 0.118. For the material prepared as a bilayer structure and the composite material obtained through electrospray deposition, the maximal induced birefringence takes values of Δn_max = 0.101 and Δn_max = 0.095, respectively. The sample prepared via spin coating of the chalcogenide/PAZO dispersion has the lowest value of the maximal induced birefringence (Δn_max = 0.066) in comparison to the pure PAZO polymer film (Δn_max = 0.083). Full article

The integrity analysis of a combustion chamber that uses Ablative Thermal Protection Systems (ATPSs) is a process that requires the analysis of the thermal and mechanical behavior of the materials involved and their interaction. A 1D thermal model for multilayered combustion chambers of hybrid rocket engines and solid rocket motors is developed, taking into consideration the thermal behavior of charring ATPSs during phase change and the capability of implementing an ablation process. A stress model is also implemented to assess the structural integrity of the combustion chamber that undergoes pressure and thermal loads. A numerical finite-difference model is used to implement analytical models and simulate the behavior of the materials. Bibliographic data and finite element analysis tools are used to evaluate and verify the models developed. Lastly, six different materials are used as a case study, and a parametric optimization is applied to obtain the minimum-mass designs using the materials selected. Full article

Palatability is a critical determinant of pet food performance, directly influencing voluntary intake, nutrient utilization, and therapeutic efficacy. In this systematic review, we examine peer-reviewed research publications, patent filings, and commercial product data pertaining to palatant technologies in dry and wet pet food from 2014 to 2024. Major palatant classes—including fats, proteins, yeast extracts, and novel plant-derived or insect-based hydrolysates—are evaluated for their physicochemical properties, flavor-release mechanisms, and stability during processing. We analyze formulation techniques such as microencapsulation, Maillard-reaction enhancement, and multilayer coating systems, focusing on their impact on aromatic compound retention and palatability consistency. Patent landscape assessment identifies over 15 key innovations in delivery systems, life-stage-specific palatant modulation, and dual-phase release architectures. Dual-phase release architectures are defined as systems that deliver active compounds in two sequential phases, such as immediate and sustained release. Sensory evaluation methodologies—ranging from multivariate preference mapping to descriptive analysis—are critically appraised to correlate human-panel metrics with canine and feline feeding behavior. We also discuss strategic integration of palatants at different processing stages (pre-conditioning, extrusion, and post-extrusion) and the challenges of balancing taste masking with nutritional requirements, particularly in formulations containing alternative proteins for sustainability. Despite rapid market growth in functional palatant-infused products, peer-reviewed literature remains relatively limited, suggesting opportunities for further research on species-specific flavor drivers, synbiotic flavor–nutrient interactions, and novel delivery platforms. This comprehensive overview of palatant science, patent innovations, and market evolution provides evidence-based guidance for researchers, formulators, and veterinarians seeking to optimize organoleptic properties and consumer acceptance of next-generation pet foods. Full article

The thermal insulation performance of liquid hydrogen (LH₂) storage tanks is critical for long-distance transportation. The active cooled shield (ACS) technologies, such as the liquid nitrogen cooled shield (LNCS) and the vapor hydrogen cooled shield (VHVCS) are important thermal insulation methods. Many researchers installed the VHVCS inside the multilayer insulation (MLI) and obtained the optimal position. However, the MLI layer is often thinner than the vacuum interlayer between the inner and outer tanks, and there is a large vacuum interlayer between the outermost side of MLI and the inner wall of the outer tank. It is unknown whether the insulation performance can be improved if we install ACS in the mentioned vacuum interlayer and separate a portion of the MLI to be installed on the outer surface of ACS. In this configuration, the number of inner MLI (IMLI) layers and the ACS position are interdependent, a coupling that has not been thoroughly investigated. Therefore, thermodynamic models for MLI, MLI-LNCS, and MLI-VHVCS schemes were developed based on the Layer-by-Layer method. By applying Robin boundary conditions, the temperature distribution and heat leakage of the MLI scheme were predicted. Considering the coupled effects of IMLI layer count and ACS position, a co-optimization strategy was adopted, based on an alternating iterative search algorithm. The results indicate that for the MLI-LNCS scheme, the optimal number of IMLI layers and LNCS position are 36 layers and 49%, respectively. For the MLI-VHVCS scheme, the optimal values are 21 layers and 39%, respectively. Compared to conventional MLI, the MLI-LNCS scheme achieves an 88.09% reduction in heat leakage. However, this improvement involves increased system complexity and higher operational costs from LN₂ circulation. In contrast, the MLI-VHVCS scheme achieves a 62.74% reduction in heat leakage, demonstrating that using sensible heat from cryogenic vapor can significantly improve the thermal insulation performance of LH₂ storage tanks. The work of this paper provides a reference for the design and optimization of the insulation scheme of LH₂ storage tanks. Full article

The early detection of neurological and psychiatric disorders is critical for optimizing patient outcomes and improving the efficacy of healthcare delivery. This study presents a novel multiclass machine learning (ML) framework designed to classify epilepsy, migraine, and schizophrenia simultaneously using electroencephalography (EEG) signals. Unlike conventional approaches that predominantly rely on binary classification (e.g., healthy vs. diseased cohorts), this work addresses a significant gap in the literature by introducing a unified artificial neural network (ANN) architecture capable of discriminating among three distinct neurological and psychiatric conditions. The proposed methodology involves decomposing raw EEG signals into constituent frequency subbands to facilitate robust feature extraction. These discriminative features were subsequently classified using a multilayer ANN, achieving performance metrics of 95% sensitivity, 96% specificity, and a 95% F1-score. To enhance clinical applicability, the model was optimized for potential integration into real-time diagnostic systems, thereby supporting the development of a rapid, reliable, and scalable decision support tool. The results underscore the viability of EEG-based multiclass models as a promising diagnostic aid for neurological and psychiatric disorders. By consolidating the detection of multiple conditions within a single computational framework, this approach offers a scalable and efficient alternative to traditional binary classification paradigms. Full article

Precoding technology is one of the core technologies that significantly impacts the performance of massive Multiple-Input Multiple-Output (MIMO) Integrated Sensing and Communication (ISAC) systems. Traditional precoding methods, due to their inherent limitations, struggle to adapt to complex channel conditions. Although more advanced neural network-based precoding schemes can accommodate complex channel environments, they suffer from high computational complexity. To address these issues, this paper proposes a KAN-and-Attention based ISAC Precoding (KAIP) scheme for massive MIMO ISAC systems. KAIP extracts channel interference features through multi-layer attention mechanisms and leverages the nonlinear fitting capability of the Kolmogorov–Arnold Network (KAN) to generate precoding matrices, significantly enhancing system performance. Simulation results demonstrate that compared with conventional precoding schemes, the proposed KAIP scheme exhibits significant performance enhancements, including a 70% increase in sum rate (SR) and a 96% decrease in computing time (CT) compared with fully connected neural network (FCNN) based precoding, and a 4% improvement in received power (RP) over the precoding based on convolutional neural network (CNN). Full article

Polarization-sensitive photodetection is critical for advanced optical systems, yet achieving simultaneous high-fidelity recognition of the circularly polarized (CP) and linearly polarized (LP) light with compact designs remains challenging. Here, we use COMSOL 5.6 software to demonstrate a silicon metasurface-integrated MCT photodetector that resolves both CP and LP signals through a single ultrathin platform. The device deciphers LP states via four orientation-specific linear gratings for differential detection, while chiral symmetric silicon nanostructures enable direct CP discrimination with an exceptional extinction ratio of 30 dB. The proposed architecture combines two breakthroughs: (1) superior polarization reconstruction capability, achieved via the synergy of grating-induced polarization selectivity and chiral near-field enhancement, and (2) a fabrication-simplified process that eliminates multilayer stacking or complex alignment steps. This work establishes a new paradigm for miniaturized, high-performance polarization optics, with potential applications in polarization imaging, quantum communication, and hyperspectral sensing. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article

As a core component in quantum cryptography, Quantum Random Number Generators (QRNGs) face dual critical challenges: insufficient randomness enhancement and limited compatibility with post-processing algorithms. This study proposes an Adaptive Feedback Compensation Algorithm (AFCA) to address these limitations through dynamic parameter feedback and selective encryption strategies. The AFCA dynamically adjusts nonlinear transformation intensity based on real-time statistical deviations, retaining over 50% of original bits while correcting local imbalances. Experimental results demonstrate significant improvements across QRNG types: the Monobit Test p-value for continuous QRNGs increased from 0.1376 to 0.9743, and the 0/1 distribution deviation in discrete QRNGs decreased from 7.9% to 0.5%. Compared to traditional methods like von Neumann correction, AFCA reduces data discard rates by over 55% without compromising processing efficiency. These advancements provide a robust solution for high-security quantum communication systems requiring multi-layered encryption architectures. Full article

Silicon carbide (SiC), a wide-bandgap semiconductor, is renowned for its exceptional performance in power electronics and extreme-temperature environments. However, precision low-loss laser slicing of SiC is impeded by energy divergence and crack delamination induced by refractive-index-mismatch interfacial aberrations. This study presents an integrated laser slicing system based on a liquid crystal on silicon spatial light modulator (LCOS-SLM) to address aberration-induced focal elongation and energy inhomogeneity. Through dynamic modulation of the laser wavefront via an inverse ray-tracing algorithm, the system corrects spherical aberrations from refractive index mismatch, thus achieving precise energy concentration at wanted depths. A laser power attenuation model based on interface reflection and the Lambert–Beer law is established to calculate the required laser power at varying processing depths. Experimental results demonstrate that aberration correction reduces focal depth to approximately one-third (from 45 μm to 15 μm) and enhances energy concentration, eliminating multi-layer damage and increasing crack propagation length. Post-correction critical power measurements across depths are consistent with model predictions, with maximum error decreasing from >50% to 8.4%. Verification on a 6-inch N-type SiC ingot shows 90 μm damage thickness, confirming system feasibility for SiC laser slicing. The integrated aberration-correction approach provides a novel solution for high-precision SiC substrate processing. Full article

Multi-layer multi-pass welding plays an important role in manufacturing industries such as nuclear power plants, pressure vessel manufacturing, and ship building. However, distortion or welding defects are still challenges; therefore, welding monitoring and quality control are essential tasks for the dynamic adjustment of execution during welding. The aim was to propose a machine vision system for monitoring and surface quality evaluation during multi-pass welding using a line scanner and infrared camera sensors. The cross-section modelling based on the line scanner data enabled the measurement of distortion and dynamic control of the welding plan. Lack of fusion, porosity, and burn-through defects were intentionally generated by controlling welding parameters to construct a defect inspection dataset. To reduce the influence of material surface colour, the proposed normal map approach combined with a deep learning approach was applied for inspecting the surface defects on each layer, achieving a mean average precision of 0.88. In addition to monitoring the temperature of the weld pool, a burn-through defect detection algorithm was introduced to track welding status. The whole system was integrated into a graphical user interface to visualize the welding progress. This work provides a solid foundation for monitoring and potential for the further development of the automatic adaptive welding system in multi-layer multi-pass welding. Full article

This article closely examines the Georgian folk tale Master and Pupil, focusing on the intermedial transformation of its sequential narrative structure as an instance of oral storytelling. The tale is analyzed within the broader discourses of performativity, voice, and narrative subjectivity through the lenses of performance theory, media formalism, and the Aarne–Thompson–Uther (ATU) classification system (Type 325). The study reveals a transition in the tale from silence to vocal authority; here, voice functions not only as a means of communication but also as a vehicle for resistance, transformation, and the negotiation of identity. Master and Pupil emerges, beyond a magical apprenticeship narrative, as a multilayered performance of disembodiment and symbolic transmission through an intermedial perspective; in this context, musicality and vocality operate as liminal forces. The pupil’s acquisition of voice signifies both a narrative rupture and a restructuring of hierarchical relations. Furthermore, the article situates the tale within the broader matrix of the Georgian oral storytelling tradition, demonstrating how recurring motifs surrounding the transformation of voice reflect culturally embedded patterns of media convergence and embodied knowledge. By foregrounding the tale’s intermedial dynamics, this study reframes folk tales as a fluid site of aesthetic, cultural, and epistemic negotiations. Full article