Search Results (1,931)

Soil organic carbon (SOC) and total nitrogen (TN) are fundamental indicators of soil fertility and long-term agricultural sustainability. However, intensive cultivation, residue removal, and imbalanced fertilization have resulted in substantial declines in SOC and TN across many agroecosystems, particularly in Northeast China. This study investigated SOC and TN dynamics within the 0–35 cm profile of four representative soils in Northeast China under a continuous maize cropping system. Five treatments were assessed: conventional tillage (CT), deep tillage (DT), deep tillage with straw (SDT), deep tillage with organic fertilizer (MDT), and deep tillage combined with straw and organic fertilizer (SMDT). Compared with DT, organic amendment treatments increased SOC and TN contents in the 0–20 cm layer by 9.41–57.57% and 5.29–60.76%, respectively. The SMDT treatment achieved the highest SOC and TN stocks (65.03 Mg ha⁻¹ and 7.91 Mg ha⁻¹) and enhanced nutrient accumulation in the 20–35 cm layer. In the subsoil, the ratio of soil C and N (C/N) under SMDT increased by 3.11%, 11.08%, 2.10%, and −7.01% across the four soils, indicating improved C–N balance and reduced nutrient stratification. SOC and TN stocks were linearly correlated with cumulative C input, confirming that organic amendments were among the main drivers of C and N sequestration. Mantel and path analyses further revealed that clay content and mean annual precipitation enhanced SOC and TN storage by improving soil structure and C–N balance through increased C input and reduced bulk density. Overall, deep tillage combined with amendments strengthened C–N coupling, improved soil fertility, and provided a mechanistic basis for reconstructing fertile tillage layers and sustaining productivity in Northeast China. Full article

Feeding unmanned aerial vehicles (UAVs) in aquaculture face critical challenges due to time-varying mass, strong coupling, and environmental disturbances, which hinder the effectiveness of conventional control strategies. This paper proposes a robust dual-loop model predictive control (MPC) framework optimized by an adaptive niche radius genetic algorithm (ANRGA). The outer loop employs MPC for position regulation using virtual acceleration inputs, while the inner loop applies MPC for attitude stabilization with dynamic inertia adaptation. To overcome the limitations of manual weight tuning, ANRGA adaptively optimizes the weighting factors, preventing premature convergence and improving global search capability. System stability is theoretically ensured through Lyapunov analysis and the small-gain theorem, even under variable-mass dynamics. MATLAB simulations under representative trajectories—including spiral, figure-eight, and feeding cruise paths—demonstrate that the proposed ANRGA-MPC-MPC achieves position errors below 0.5 m, enhances response speed by approximately 58% compared with conventional MPC, and outperforms benchmark controllers in terms of accuracy, robustness, and convergence. These results confirm the feasibility of the proposed method for precise and energy-efficient UAV feeding operations, providing a promising control strategy for intelligent aquaculture applications. Full article

Modelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for representing utility networks; however, most existing implementations rely on relational databases, where connectivity must be reconstructed through joins rather than represented as explicit relationships. This creates challenges when managing densely connected network structures. This study introduces the UNADE–Labelled Property Graph (UNADE-LPG) model, a graph-based representation that maps the classes, relationships, and constraints defined in the UNADE Unified Modelling Language (UML) schema into nodes, edges, and properties. A conversion pipeline is developed to generate UNADE-LPG instances directly from CityGML UNADE datasets encoded in GML, enabling the population of graph databases while maintaining semantic alignment with the original schema. The approach is demonstrated through two case studies: a schematic network and a real-world water system from Frankston, Melbourne. Validation procedures, covering structural checks, topological continuity, classification behaviour, and descriptive graph statistics, confirm that the resulting graph preserves the semantic structure of the UNADE schema and accurately represents the physical connectivity of the network. An analytical path-finding query is also implemented to illustrate how the UNADE-LPG structure supports practical network-analysis tasks, such as identifying connected pipeline sequences. Overall, the findings show that the UNADE-LPG model provides a clear, standards-aligned, and operationally practical foundation for representing utility networks within graph environments, supporting future integration into digital-twin and network-analytics applications. Full article

Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive lifelong planning A* algorithm (WA-LPA*) is proposed for energy-aware path planning in dynamic wind environments. WA-LPA* constructs a composite heuristic function incorporating wind-field alignment factors and integrates a hierarchical height-aware optimization strategy. Meanwhile, an adaptive replanning mechanism is designed based on the change characteristics of the wind field. Simulation experiments conducted across representative scenarios demonstrate that, compared to conventional algorithms that neglect wind-field effects, WA-LPA* achieves energy efficiency improvements of 5.9–29.4%. Full article

Ancient walled cities represent key material evidence for early state formation and human–environment interaction on the northeastern Tibetan Plateau. However, traditional field surveys are often constrained by the vastness and complexity of the plateau environment. This study proposes an improved deep learning framework, AC-YOLOv11, to achieve automated detection of ancient city remains in the Qinghai Lake Basin using 0.8 m GF-2 satellite imagery. By integrating a dual-path attention residual network (AC-SENet) with multi-scale feature fusion, the model enhances sensitivity to faint geomorphic and structural features under conditions of erosion, vegetation cover, and modern disturbance. Training on the newly constructed Qinghai Lake Ancient City Dataset (QHACD) yielded a mean average precision (mAP@0.5) of 82.3% and F1-score of 94.2%. Model application across 7000 km² identified 309 potential sites, of which 74 were verified as highly probable ancient cities, and field investigations confirmed 3 new sites with typical rammed-earth characteristics. Spatial analysis combining digital elevation models and hydrological data shows that 75.7% of all ancient cities are located within 10 km of major rivers or the lake shoreline, primarily between 3500 and 4000 m a.s.l. These results reveal a clear coupling between settlement distribution and environmental constraints in the high-altitude arid zone. The AC-YOLOv11 model demonstrates strong potential for large-scale archaeological prospection and offers a methodological reference for automated heritage mapping on the Qinghai–Tibet Plateau. Full article

The surface defect detection of printed circuit boards (PCBs) plays a crucial role in the field of industrial manufacturing. However, the existing PCB defect detection methods have great challenges in detecting the accuracy of tiny defects under the complex background due to its compact layout. To address this problem, we propose a novel YOLO-AMBA-EASPP-BiFPN (YOLO-AEB) network based on the YOLOv10 framework that achieves high precision and real-time detection of tiny defects through multi-level architecture optimization. In the backbone network, an adaptive multi-branch attention mechanism (AMBA) is first proposed, which employs an adaptive reweighting algorithm (ARA) to dynamically optimize fusion weights within the multi-branch attention mechanism (MBA), thereby optimizing the ability to represent tiny defects under complex background noise. Then, an efficient atrous spatial pyramid pooling (EASPP) is constructed, which fuses AMBA and atrous spatial pyramid pooling-fast (ASPF). This integration effectively mitigates feature degradation while preserving expansive receptive fields, and the extraction of defect detail features is strengthened. In the neck network, the bidirectional feature pyramid network (BiFPN) is used to replace the conventional path aggregation network (PAN), and the bidirectional cross-scale feature fusion mechanism is used to improve the transfer ability of shallow detail features to deep networks. Comprehensive experimental evaluations demonstrate that our proposed network achieves state-of-the-art performance, whose F₁ score can reach 95.7% and mean average precision (mAP) can reach 97%, representing respective improvements of 7.1% and 5.8% over the baseline YOLOv10 model. Feature visualization analysis further verifies the effectiveness and feasibility of YOLO-AEB. Full article

Head and neck squamous cell carcinoma (HNSCC) is the most common and aggressive histologic subtype of head and neck cancer (HNC), difficult to treat effectively. Here, we discuss several studies on human and mouse HNSCC cell lines arising from the mucosal epithelium of various anatomical sites, as well as recent studies using murine models, focused on targeting key checkpoints in the glutamine (Gln) metabolism pathway, either alone or in synergy with other signaling pathways, as a potential therapeutic strategy for HNSCC. Emerging evidence demonstrates a complex interplay between Gln metabolism and pathways mediating altered cellular mechanisms, including ferroptosis, immune system evasion, mitochondrial energy production, and oncogenic transcriptional control. This review examines currently available gene expression databases and protein expression analyses of Gln metabolism-related components in tissue samples from HNSCC patients. From a translational perspective, the co-administration of pharmaceutical agents and biologic products targeting distinct molecular pathways, integrated with radiotherapy (RT) or chemotherapy (CT), may produce superior anti-HNSCC efficacy, thereby improving clinical outcomes and extending patient survival. Multimodal strategies represent a key direction in precision oncology, enabling personalized therapeutic interventions to suppress metastatic dissemination and disease progression more effectively. Therefore, an integrated therapeutic approach represents a promising path to defeat HNSCC. Full article

The transition to a carbon-neutral society represents one of the most pressing global challenges of the 21st century, requiring a radical transformation in how energy is produced, consumed, and managed. Digitalization has emerged as a pivotal driver in energy transition, offering innovative Pathways to enhance energy efficiency and penetrate renewable energy that should lead to reduced GHG emissions. The aim of the research was to develop a model for the evaluation of digitalization impact on GHG emissions where energy serves as a mediating factor. The data of 27 European Union Member States was employed for the investigation covering the period 2014–2023. Principal Component Analysis was utilized to calculate the composite indicators of digitalization and energy. A comprehensive and systematic analysis of the complex interactions of digitalization, energy and GHG emissions was performed using a path analysis. The findings emphasized the critical role of the rebound effect of digitalization as the advantages associated with energy efficiency and the integration of renewable energy, facilitated by digitalization, are overweighted by increased energy consumption. The research ultimately contributes to a deeper understanding of how digitalization can be measured, guided, and optimized to support sustainable energy and mitigation of climate change. Full article

To address the issue of sound absorption valleys in open-cell aluminum foam and enhance mid-to-high frequency (800–6300 Hz) performance, we developed a novel pore-penetrating 316L stainless steel fiber–aluminum foam (PPFCAF) composite using an infiltration method. The formation mechanism of the pore-penetrating fibers, the resultant pore-structure, and the accompanying sound absorption properties were investigated systematically. The PPFCAF was fabricated using 316L stainless steel fiber–NaCl composites created by an evaporation crystallization process, which ensured the full embedding of fibers within the pore-forming agent, resulting in a three-dimensional fiber-pore interpenetrating network after infiltration and desalination. Experimental results demonstrate that the PPFCAF with a porosity of 82.8% and a main pore size of 0.5 mm achieves a sound absorption valley value of 0.861. An average sound absorption coefficient is 0.880 in the target frequency range, representing significant improvements of 9.8% and 9.9%, respectively, higher than that of the conventional infiltration aluminum foam (CIAF). Acoustic impedance reveal that the incorporated fibers improve the impedance matching between the composite material and air, thereby reducing sound reflection. Finite element simulations further elucidate the underlying mechanisms: the pore-penetrating fibers influence the paths followed by air particles and the internal surface area, thereby increasing the interaction between sound waves and the solid framework. A reduction in the main pore size intensifies the interaction between sound waves and pore walls, resulting in a lower overall reflection coefficient and a decreased reflected sound pressure amplitude (0.502 Pa). In terms of energy dissipation, the combined effects of the fibers and refinement increase the specific surface area, thereby strengthening viscous effects (instantaneous sound velocity up to 46.1 m/s) and thermal effects (temperature field increases to 0.735 K). This synergy leads to a notable rise in the total plane wave power dissipation density, reaching 0.0609 W/m³. Our work provides an effective strategy for designing high-performance composite metal foams for noise control applications. Full article

Accumulation-type water vapor transport (hereafter referred to as AT-WVT) and permeation-type water vapor transport (hereafter referred to as PT-WVT) represent two fundamental modes of water vapor diffusion in asphalt mixtures, exerting distinct impacts on asphalt pavement durability. In this study, the diffusion characteristics of AT-WVT and PT-WVT within three core components of asphalt pavement systems—pure asphalt binder, aggregate matrix, and asphalt mixture void structures—were investigated. The corresponding diffusion coefficients for these three materials were determined through a synergistic approach combining laboratory experiments and theoretical modeling. Three typical asphalt materials (50# asphalt, 70# asphalt, SBS-modified asphalt) and two commonly used aggregates (limestone, diabase) were used. The results show that, for all three materials, the water vapor diffusion coefficient for the AT-WVT mechanism is relatively low, whereas the coefficient for the PT-WVT mechanism is approximately four orders of magnitude greater. The tortuosity factor of moisture diffusion paths in asphalt mixtures is substantially elevated during AT-WVT (tortuosity factor > 2000), as water vapor encounters frequent obstacles caused by the complex microstructural architecture (e.g., asphalt–aggregate interfaces and closed pores). In contrast, PT-WVT exhibits a much lower tortuosity factor (12–18), enabling rapid and direct migration through interconnected channels, such as capillary voids and microcracks. Due to its higher transport efficiency, PT-WVT poses a more critical threat to pavement durability by facilitating rapid moisture intrusion and subsequent damage (e.g., stripping, fatigue cracking). This study elucidates the mechanistic differences between AT-WVT and PT-WVT in asphalt binder, aggregate matrix, and asphalt mixtures, providing a foundation for optimizing asphalt mixture design to enhance long-term durability and performance under hygrothermal loading conditions. Full article

This paper proposes a transmission-path selection algorithm with joint computation and communication resource allocation for sixth-generation (6G) mobile edge computing (MEC) networks enhanced by helper-assisted device-to-device (D2D) communication and reconfigurable intelligent surfaces (RIS). The novelties of this work lie in the joint design of three key components: a helper-assisted D2D uplink scheme, a packet-partitioning cooperative MEC offloading mechanism, and RIS-assisted downlink transmission and deployment design. These components collectively enable diverse transmission paths under strict latency constraints, helping mitigate overload and reduce delay. To demonstrate its performance advantages, the proposed algorithm is compared with a baseline algorithm without helper-assisted D2D or RIS support, under two representative scheduling policies—modified maximum rate and modified proportional fair. Simulation results in single-base station (BS) and dual-BS environments show that the proposed algorithm consistently achieves a higher effective packet-delivery success percentage, defined as the fraction of packets whose total delay (uplink, MEC computation, and downlink) satisfies service-specific latency thresholds, and a lower average total delay, defined as the mean total delay of all successfully delivered packets, regardless of whether individual delays exceed their thresholds. Both metrics are evaluated separately for ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications services. These results indicate that the proposed algorithm provides solid performance and robustness in supporting diverse 6G services under stringent latency requirements across different scheduling policies and deployment scenarios. Full article

Assessing the performance of the smart-grid system (SGS) under uncertainty is essential for ensuring a reliable energy supply from the perspective of the grid operator. In this study, a multistate smart-grid network (MSGN) is developed to evaluate the delivery capability of the SGS. An MSGN consists of multiple interconnected facilities, where nodes represent energy sources or converters and arcs denote feeders. The output of each facility in the MSGN is modeled as multistate, as maintenance activities and partial failures can result in multiple possible output levels. During power delivery, transmission losses may arise due to heat dissipation and feeder aging, potentially resulting in insufficient power supply at the demand side. From a smart-grid management perspective, delivery reliability, defined as the probability that the MSGN can successfully deliver sufficient power from energy sources to the destination under transmission loss, is adopted as a performance index for evaluating SGS capability. To compute delivery reliability, a minimal-path-based algorithm is developed. A practical SGS is presented to demonstrate the applicability of the proposed model and to provide managerial insights into smart-grid performance and operational decision-making. Full article

This work represents the natural continuation of the development of the cognitive architecture developed and named Sophimatics, organically integrating the spatio-temporal processing mechanisms of the Super Time Cognitive Neural Network (STCNN) with the advanced principles of Sophimatics. Sophimatics’ goal is as challenging as it is fraught with obstacles, but its ultimate aim is to achieve a more humanized post-generative artificial intelligence, capable of understanding and analyzing context and evaluating the user’s purpose and intent, viewing time not only as a chronological sequence but also as an experiential continuum. The path to achieving this extremely ambitious goal has been made possible thanks to some previous work in which the philosophical thinking of interest in AI was first inherited as the inspiration for the aforementioned capabilities of the Sophimatic framework, then the issue of mapping concepts and philosophical thinking in Sophimatics’ AI infrastructure was addressed, and finally a cognitive-inspired network such as STCNN was created. This work, on the other hand, addresses the challenge of how to endow the infrastructure with both chronological and experiential time and its powerful implications, such as the innate ability to resolve paradoxes, which generative AI does not have among its prerogatives precisely because of structural limitations. To reach these results, the model operates in the two-dimensional complex time domain ℂ², extending cognitive processing capabilities through the implementation of dual temporal operators that simultaneously manage the real temporal dimension, where past, present, and future are managed and the imaginary one, that considers memory, creativity, and imagination. The resulting architecture demonstrates superior capabilities in resolving informational paradoxes and integrating apparently contradictory cognitive states, maintaining computational coherence through adaptive Sophimatic mechanisms. In conclusion, this work introduces Phase 4 of the Sophimatic framework, enabling management of two-dimensional time within a novel cognitively inspired neural architecture grounded in philosophical concepts. It connects with existing research on temporal cognition, hybrid symbolic–connectionist models, and ethical AI. The methodology translates philosophical insights into formal computational systems, culminating in a mathematical formalization that supports two-dimensional temporal reasoning and paradox resolution. Experimental results demonstrate efficiency, predictive accuracy, and computational feasibility, highlighting potential real-world applications, future research directions, and present limitations. Full article

Optical Wireless Communication (OWC) technologies are emerging as promising complements to radio-frequency systems, offering high bandwidth, spatial confinement, and license-free operation. Within this domain, Visible Light Communication (VLC) and Optical Camera Communication (OCC) represent two distinct paradigms with divergent performance and security profiles. While VLC leverages LED-photodiode links for high-speed data transfer, OCC exploits ubiquitous image sensors to decode modulated light patterns, enabling flexible but lower-rate communication. Despite their potential, both remain vulnerable to various attacks, including eavesdropping, jamming, spoofing, and privacy breaches. This work applies—and extends—the Risk Matrix (RM) methodology to systematically evaluate the security of VLC and OCC across reconnaissance, denial, and exploitation phases. Unlike prior literature, which treats VLC and OCC separately and under incompatible threat definitions, we introduce a unified, domain-specific risk framework that maps empirical channel behavior and attack feasibility into a common set of impact and likelihood indices. A normalized risk rank (NRR) is proposed to enable a direct, quantitative comparison of heterogeneous attacks and technologies under a shared reference scale. By quantifying risks for representative threats—including war driving, Denial of Service (DoS) attacks, preshared key cracking, and Evil Twin attacks—our analysis shows that neither VLC nor OCC is intrinsically more secure; rather, their vulnerabilities are context-dependent, shaped by physical constraints, receiver architectures, and deployment environments. VLC tends to concentrate confidentiality-driven exposure due to optical leakage paths, whereas OCC is more sensitive to availability-related degradation under adversarial load. Overall, the main contribution of this work is the first unified, standards-aligned, and empirically grounded risk-assessment framework capable of comparing VLC and OCC on a common security scale. The findings highlight the need for technology-aware security strategies in future OWC deployments and demonstrate how an adapted RM methodology can identify priority areas for mitigation, design, and resource allocation. Full article

To address the challenge of collaborative strategy optimization caused by non-independent and identically distributed data in cross-institutional scenarios, a federated quantitative learning framework integrating Path Attention Aggregation Module (PAAM), Gradient Clipping and Compression (GCC), and a Heterogeneity-Aware Adaptive Optimizer (HAAO) is proposed to achieve efficient return optimization and robust risk control. The framework is validated across multi-market and multi-institutional environments, with experiments covering three key dimensions: return performance, risk management, and communication efficiency. The results demonstrate that the proposed model achieves an annualized return (AR) of 16.57%, representing an approximate 19.7% improvement over the traditional FedAvg model; the Sharpe ratio (SR) increases to 1.25, while the maximum drawdown (MDD) decreases to 15.92% and volatility remains controlled at 8.83%, indicating superior balance between return and risk. In the communication efficiency evaluation, when the number of communication rounds is reduced to 50 and 25, the model maintains accuracy at 94.2% and 92.8%, recall at 93.5% and 91.7%, and precision at 94.8% and 92.3%, respectively. Overall, the proposed framework achieves a dynamic balance between global convergence and risk constraints through path weighting, gradient sparsification, and frequency-domain learning rate adjustment. This research provides a novel and scalable paradigm for distributed financial prediction that ensures both privacy preservation and communication efficiency, demonstrating substantial engineering feasibility and practical applicability in intelligent financial modeling. Full article