Search Results (15,316)

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom. Full article

Soft robotics has emerged as a promising approach for safe human–machine interaction, adaptive manipulation, and bioinspired motion, yet its progress relies on accurate material characterization and structural analysis of actuators. This study presents the mechanical behavior and stress analysis of a stereolithography-printed pneumatic actuator with antagonistic architecture, fabricated using Elastic 50A resin V2. Uniaxial tensile tests were performed according to ASTM D412 to derive material parameters, which were fitted to hyperelastic constitutive models. The Yeoh model was identified as the most accurate and implemented in finite element simulations to predict actuator deformation under multiple pressurization modes. Results revealed critical stress zones and established operational pressure limits of 110–130 kPa, beyond which the material approaches its tensile strength. Experimental testing with a controlled pneumatic system validated the numerical predictions, confirming both bending and multidirectional actuation as well as structural failure thresholds. The integration of material characterization, numerical modeling, and experimental validation provides a robust workflow for the design of SLA-fabricated antagonistic actuators. These findings highlight the advantages of combining digital fabrication with antagonistic actuation and material modeling to expand the understanding of soft robots’ behavior. Full article

Wirelessly charging unmanned aerial vehicles (WCUAVs) can complete charging tasks without human intervention and may help us efficiently collect various types of geographically dispersed data in unmanned data collection systems (UDCSs). However, the limited number of wireless charging stations and longer wireless charging times also pose challenges to minimizing the Age of Information (AoI). Here, we provide a heuristic method to minimize AoI for WCUAVs. Firstly, the problem of minimizing AoI is modeled as a trajectory optimization problem with nonlinear constraints involving n sensor nodes, a data center, and a limited number of wireless charging stations. Secondly, to solve this NP-hard problem, an improved artificial plant community (APC) approach is proposed, including a single-WCUAV architecture and a multi-WCUAV architecture. Thirdly, a benchmark test set is designed, and benchmark experiments are conducted. When the number of WCUAVs increased from 1 to 2, the total flight distance increased by 12.011% and the average AoI decreased by 45.674%. When the number of WCUAVs increased from 1 to 10, the total flight distance increased by 87.667% and the average AoI decreased by 78.641%. The experimental results show that the proposed APC algorithm can effectively solve AoI minimization challenges of WCUAVs and is superior to other baseline algorithms with a maximum improvement of 9.791% in average AoI. Due to its simple calculation and efficient solution, it is promising to deploy the APC algorithm on the edge computing platform of WCUAVs. Full article

Platelet-rich plasma (PRP) is an autologous blood-derived concentrate increasingly utilized in regenerative medicine for its ability to accelerate healing and tissue repair. PRP is broadly classified by leukocyte content, fibrin architecture, and platelet concentration, with classification systems developed to standardize characterization. Preparation methods, including single- or double-spin centrifugation and buffy coat techniques, influence the final composition of PRP, determining the relative proportions of platelets, leukocytes, plasma proteins, and extracellular vesicles. These components act synergistically, with platelets releasing growth factors (e.g., VEGF, PDGF, TGF-β) that stimulate angiogenesis and matrix synthesis, leukocytes providing immunomodulation, plasma proteins facilitating scaffolding, and exosomes regulating intercellular signaling. Mechanistically, PRP enhances tissue repair through four key pathways: platelet adhesion molecules promote hemostasis and cell recruitment; immunomodulation reduces pro-inflammatory cytokines and favors M2 macrophage polarization; angiogenesis supports vascular remodeling and nutrient delivery; and serotonin-mediated pathways contribute to analgesia. These processes establish a regenerative microenvironment that supports both structural repair and functional recovery. Clinically, PRP has been applied across multiple specialties. In orthopedics, it promotes tendon, cartilage, and bone healing in conditions such as tendinopathy and osteoarthritis. In dermatology, PRP enhances skin rejuvenation, scar remodeling, and hair restoration. Gynecology has adopted PRP for ovarian rejuvenation, endometrial repair, and vulvovaginal atrophy. In dentistry and oral surgery, PRP accelerates wound closure and osseointegration, while chronic wound care benefits from its angiogenic and anti-inflammatory effects. PRP has also favored gingival recession coverage, regeneration of intrabony periodontal defects, and sinus grafting. Although preparation heterogeneity remains a challenge, PRP offers a versatile, biologically active therapy with expanding clinical utility. Full article

This paper develops a Sustainable Artificial Intelligence-Driven Wind Power Forecasting System (SAI-WPFS) to enhance the integration of renewable energy while minimizing the environmental footprint of deep learning computations. Although deep learning models such as CNN, LSTM, and GRU have achieved high accuracy in wind power forecasting, existing research rarely considers the computational energy cost and associated carbon emissions, creating a gap between predictive performance and sustainability objectives. Moreover, limited studies have addressed the need for a balanced framework that jointly evaluates forecast precision and eco-efficiency in the context of large-scale renewable deployment. Using real-time data from the Dumat Al-Jandal Wind Farm, Saudi Arabia’s first utility-scale wind project, this study evaluates multiple deep learning architectures, including CNN-LSTM-AM and GRU, under a dual assessment framework combining accuracy metrics (MAE, RMSE, R²) and carbon efficiency indicators (CO₂ emissions per computational hour). Results show that the CNN-LSTM-AM model achieves the highest forecasting accuracy (MAE = 29.37, RMSE = 144.99, R² = 0.74), while the GRU model offers the best trade-off between performance and emissions (320 g CO₂/h). These findings demonstrate the feasibility of integrating sustainable AI into wind energy forecasting, aligning technical innovation with Saudi Vision 2030 goals for zero-carbon cities and carbon-efficient energy systems. Full article

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article

The development of Digital Twin (DT) technology in Battery Management Systems (BMSs) presents a transformative approach for maintenance, monitoring, and predictive diagnostics, especially in the demanding field of space applications. DTs, through their three-layer structure, provide an accurate and dynamic virtual representation of the physical entity, continuously updated via bidirectional data exchange provided by the communication link. Given the promising capabilities of the DT approach in real-time applications, its integration into BMSs is straightforward, as it can enhance monitoring and prediction of nonlinear electrochemical systems, such as space-grade lithium-ion batteries, supporting the mitigation of ageing effects under the unique constraints of the space environment. Despite notable progress in BMS technologies, the choice of estimation techniques consistent with the DT paradigm remains insufficiently defined. This survey examines the state of the art with the aim of bridging the conceptual framework of DTs and existing battery management algorithms, identifying the methodologies most suitable in accordance with DT architectures and principles. The scope of this paper is to provide researchers and engineers with a comprehensive overview of the advancements, key enabling technologies, and implementation strategies for Digital Twins in space BMSs, ultimately contributing to more reliable and efficient space missions. Full article

Cooperative pursuit using multi-UAV systems presents significant challenges in dynamic task allocation, real-time coordination, and trajectory optimization within complex environments. To address these issues, this paper proposes a reinforcement learning-based task planning framework that employs a distributed Actor–Critic architecture enhanced with bidirectional recurrent neural networks (BRNN). The pursuit–evasion scenario is modeled as a multi-agent Markov decision process, enabling each UAV to make informed decisions based on shared observations and coordinated strategies. A multi-stage reward function and a BRNN-driven communication mechanism are introduced to improve inter-agent collaboration and learning stability. Extensive simulations across various deployment scenarios, including 3-vs-1 and 5-vs-2 configurations, demonstrate that the proposed method achieves a success rate of at least 90% and reduces the average capture time by at least 19% compared to rule-based baselines, confirming its superior effectiveness, robustness, and scalability in cooperative pursuit missions. Full article

In the digital era, commodity barcodes serve as a bridge between the physical and digital worlds and are widely used in retail checkout systems. To meet the broader application demands for product identification, this paper proposes a method for locating, semantically segmenting barcodes in complex backgrounds, decoding hidden information, and recovering these barcodes in wide field-of-view images. This method integrates self-attention mechanisms and reparameterization techniques to construct a RepSAU-Net model. Specifically, this paper first introduces a barcode image dataset synthesis strategy adapted for deep learning models, constructing the SBS (Screen Stego Barcodes) dataset, which comprises 2000 wide field-of-view background images (Type A) and 400 information-hidden barcode images (Type B), totaling 30,000 images. Based on this, a network architecture (RepSAU-Net) combining a self-attention mechanism and RepVGG reparameterization technology was designed, with a parameter count of 32.88 M. Experimental results demonstrate that this network performs well in barcode segmentation tasks, achieving an inference speed of 4.88 frames/s, a Mean Intersection over Union (MIoU) of 98.36%, and an Accuracy (Acc) of 94.96%. This research effectively enhances global information capture and feature extraction capabilities without significantly increasing computational load, providing technical support for the application of data-embedded barcodes. Full article

The integration of Artificial Intelligence (AI) systems into critical decision-making processes necessitates robust mechanisms to ensure trustworthiness, ethical compliance, and human oversight. This paper introduces trustSense, a novel assessment framework and tool designed to evaluate the maturity of human oversight practices in AI governance. Building upon principles from trustworthy AI, cybersecurity readiness, and privacy-by-design, trustSense employs a structured questionnaire-based approach to capture an organisation’s oversight capabilities across multiple dimensions. The tool supports diverse user roles and provides tailored feedback to guide risk mitigation strategies. Its calculation module synthesises responses to generate maturity scores, enabling organisations to benchmark their practices and identify improvement pathways. The design and implementation of trustSense are grounded in user-centred methodologies, with defined personas, user flows, and a privacy-preserving architecture. Security considerations and data protection are integrated into all stages of development, ensuring compliance with relevant regulations. Validation results demonstrate the tool’s effectiveness in providing actionable insights for enhancing AI oversight maturity. By combining measurement, guidance, and privacy-aware design, trustSense offers a practical solution for organisations seeking to operationalise trust in AI systems. This work contributes to the discourse on governance of trustworthy AI systems by providing a scalable, transparent, and empirically validated human maturity assessment tool. Full article

Currently, recognition and assessment of human posture have become significant topics of interest, particularly through the use of visual sensor systems. These approaches can effectively address the drawbacks associated with traditional manual assessments, which include fatigue, variations in experience, and inconsistent judgment criteria. However, systems based on visual sensors encounter substantial implementation challenges when a large number of such sensors are used. To address these issues, we propose a human posture recognition and assessment system architecture, which comprises four distinct subsystems. Specifically, these subsystems include a Visual Sensor Subsystem (VSS), a Posture Assessment Subsystem (PAS), a Control-Display Subsystem, and a Storage Management Subsystem. Through the cooperation of subsystems, the architecture has achieved support for parallel data processing. Furthermore, the proposed architecture has been implemented by building an experimental testbed, which effectively verifies the rationality and feasibility of this architecture. In the experiments, the proposed architecture was evaluated by using pull-up and push-up exercises. The results demonstrate that the proposed architecture achieves an overall accuracy exceeding 96%, while exhibiting excellent real-time performance and scalability in different assessment scenarios. Full article

The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring. Full article

The implementation of Euro 7 emission standards demands advanced real-time NO_x monitoring systems for diesel vehicles. Existing unified models inadequately capture phase-dependent emission mechanisms during cold-start, urban, and highway operation. This study develops a novel Mixture of Experts (MoE) architecture with data-driven phase classification based on aftertreatment thermal dynamics. Real-world data from a Euro 6d commercial vehicle (3247 PEMS samples) were classified into three phases, cold (<70 °C coolant temperature), hot low-speed (<90 km/h), and hot high-speed (≥90 km/h), validated through t-SNE analysis (silhouette coefficient = 0.73). The key innovation integrates thermal–kinematic domain knowledge with specialized XGBoost regressors, achieving R² = 0.918 and a 58% RMSE reduction versus unified models (RMSE = 1.825 mg/s). The framework operates within real-time constraints (1.5 ms inference latency), integrating autoencoder-based anomaly detection (95.2% sensitivity) and Model Predictive Control (11–13% NO_x reduction). This represents the first systematic phase-specific NO_x modeling framework with validated Euro 7 OBM compliance capability, providing both methodological advances in expert allocation strategies and practical solutions for next-generation emission control systems. Full article

In today’s rapidly evolving data environment, secure and efficient storage solutions are fundamental to supporting the robust development of the data economy. Trustworthy data space serves as an innovative technological framework for addressing critical challenges in data circulation. It is specifically designed to facilitate the secure exchange in data elements and overcome trust barriers in cross-organizational data sharing. However, current decentralized storage architectures still have significant implementation gaps. Practical deployment and system integration remain substantial challenges for existing technological solutions. To address these issues, this paper first conducts a systematic analysis of existing trusted data storage methods. On this basis, it proposes a data-secure storage mechanism based on polynomial commitment. This mechanism uses polynomial commitment to implement data storage and verification, thereby ensuring data integrity and consistency. Meanwhile, it integrates homomorphic signature technology to guarantee the authenticity of data sources without disclosing original data. Additionally, a data modification recording function is introduced to ensure the traceability of all operations. Experimental results show that the proposed scheme achieves superior performance in three key aspects: communication overhead, storage efficiency, and data update costs. Full article

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation. Full article