Search Results (155)

Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs. Full article

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

The displacement of bridge towers is relatively large under strong wind action. Changes in tower displacement can reflect the usage status of the bridge towers. Therefore, it is necessary to conduct performance warning research on tower displacement under strong wind action. In this paper, the triple standard deviation method, multiple linear regression method, and interpolation method are used to preprocess monitoring data with skipped points and missing anomalies. An improved multi-rate data fusion method, validated using simulated datasets, was applied to correct monitoring data at bridge tower tops. The fused data were used to feed predictive models and generate structural performance alerts. Spectral analysis confirmed that the fused displacement measurements achieve high precision by effectively merging the low-frequency GPS signal with the high-frequency accelerometer signal. Structural integrity monitoring of wind-loaded bridge towers used modeling residuals as alert triggers. The efficacy of this proactive monitoring strategy has been quantitatively validated through statistical evaluation of alarm accuracy rates. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

In order to improve energy utilization efficiency and the flexibility of resource transfer in oceanic-island-group microgrids, a water–electricity–hydrogen flexible scheduling strategy based on a multi-rate hydrogen-powered ship is proposed. First, the characteristics of the seawater desalination unit (SDU), proton exchange membrane electrolyzer (PEMEL), and battery system (BS) in consuming surplus renewable energy on resource islands are analyzed. The variable-efficiency operation characteristics of the SDU and PEMEL are established, and the effect of battery life loss is also taken into account. Second, a spatio-temporal model for the multi-rate hydrogen-powered ship is proposed to incorporate speed adjustment into the system optimization framework for flexible resource transfer among islands. Finally, with the goal of minimizing the total cost of the system, a flexible water–electricity–hydrogen hybrid resource transfer model is constructed, and a certain island group in the South China Sea is used as an example for simulation and analysis. The results show that the proposed scheduling strategy can effectively reduce energy loss, promote renewable energy absorption, and improve the flexibility of resource transfer. Full article

To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. Full article

As adaptive multi-rate (AMR) speech applications become increasingly widespread, AMR-based steganography presents growing security risks. Conventional steganalysis methods often assume known embedding rates, limiting their practicality in real-world scenarios where embedding rates are unknown. To overcome this limitation, we introduce a novel framework that integrates a multi-scale transformer architecture with multi-task learning for joint classification and regression. The classification task effectively distinguishes between cover and stego samples, while the regression task enhances feature representation by predicting continuous embedding values, providing deeper insights into embedding behaviors. This joint optimization strategy improves model adaptability to diverse embedding conditions and captures the underlying relationships between discrete embedding classes and their continuous distributions. The experimental results demonstrate that our approach achieves higher accuracy and robustness than existing steganalysis methods across varying embedding rates. Full article

As a crucial component of the Beijing–Hangzhou Grand Canal’s hydraulic engineering, the Jiangnan Canal has historically played a pivotal role in China’s development as a key hydraulic infrastructure. This water conservancy project, connecting northern and southern water systems, not only facilitated regional economic integration but also nurtured unique cultural landscapes along its course. The Jiangnan Canal and its adjacent cities were selected as the study area to systematically investigate 334 tangible cultural heritage (TCH) sites and 420 intangible cultural heritage (ICH) elements. Through integrated Geographical Information System (GIS) spatial analyses—encompassing nearest neighbor index, kernel density estimation, standard deviation ellipse assessment, multi-ring buffer zoning, and Geodetector modeling, the spatiotemporal distribution features of cultural heritage were quantitatively characterized, with a focus on identifying the underlying driving factors shaping its spatial configuration. The analysis yields four main findings: (1) both TCH and ICH exhibit significant spatial clustering patterns across historical periods, with TCH distribution displaying an axis-core structure centered on the canal, whereas ICH evolved from dispersed to clustered configurations. (2) The center of gravity of TCH is primarily around Taihu Lake, while that of ICH is mainly on the south side of Taihu Lake, and the direction of distribution of both is consistent with the direction of the canal. (3) Multi-ring buffer analysis indicates that 77.2% of TCH and 49.8% of ICH clusters are concentrated within 0–10 km of the canal, demonstrating distinct spatial patterns: TCH exhibits a gradual canal-dependent density decrease with distance, whereas ICH reveals multifactorial spatial dynamics. (4) Human activity factors, particularly nighttime light intensity, are identified as predominant drivers of heritage distribution patterns, with natural environmental factors exerting comparatively weaker influence. These findings provide empirical support for developing differentiated conservation strategies for canal-related cultural heritage. The methodology offers replicable frameworks for analyzing heritage corridors in complex historical landscapes, contributing to both applied conservation practices and theoretical advancements in cultural geography. Full article

To address the issue of uneven temperature distribution during the tire vulcanization process based on electromagnetic heating, this study focuses on the hot plate of a tire vulcanizing machine. An octagonal hot plate with dimensions of 1380 mm × 1380 mm × 60 mm was adopted, and temperature sensors were installed to monitor temperature changes in real time. Through electromagnetic simulation, the effects of current intensity, frequency, and coil-to-hot-plate distance on temperature uniformity were studied. The simulation results show that the temperature difference increases with current intensity and current frequency, while the temperature difference decreases with the increase in coil-to-hot-plate distance. To minimize the temperature gradient, the coil layout was structurally optimized based on the geometric features of the hot plate to improve magnetic field distribution. Several coil arrangements were designed and compared, including uniform, dual-ring, multi-ring, and the newly proposed flower-shaped configuration. It shows that the multi-ring circular coil has the best uniformity when heating a circular hot plate, and the flower-shaped coil has best temperature uniformity when heating an octagonal hot plate. Experimental validation using an industrial-scale prototype confirmed that the optimized design reduced temperature variation to within ±2 degrees Celsius. This work contributes a practical and geometrically informed coil design strategy for improving the temperature uniformity and energy efficiency of electromagnetic heating systems in industrial tire vulcanization. Full article

Surface defect detection plays an important role in particleboard quality control. But it still faces challenges in detecting coexisting multi-scale defects and weak texture defects. To address these issues, this study proposed PBD-YOLO (Particleboard Defect-You Only Look Once), a lightweight YOLO-based algorithm with multi-scale feature fusion and weak texture enhancement capabilities. In order to improve the ability of the algorithm to extract weak texture features, the SPDDEConv (Space to Depth and Difference Enhance Convolution) module was introduced in this study, which reduced the loss of information in the down-sampling process through space-to-depth transformation and enhanced the edge information of weak texture defects through difference convolution. This approach improved the mAP (mean average precision) of weakly featured but edge-sensitive defects (such as scratches) by as much as 20.9%. In order to improve the algorithm’s ability to detect multi-scale defects, this study introduced the ShareSepHead (Share Separated Head) and C2f_SAC (C2f module with Switchable Atrous Convolution) modules. ShareSepHead fused feature maps from different scales of the neck network by adding a convolutional layer with shared weights, and the C2f_SAC module adaptively fused multi-rate receptive fields through a switching mechanism. The synergistic effect of ShareSepHead and C2f_SAC improved the detection accuracy of multi-scale defects by 10.6–20.8%. The experimental results demonstrated that PBD-YOLO achieved 85.6% mAP at 50% intersection over union (IoU) and 81.4% recall, surpassing YOLOv10 by 5.5% and 13%, respectively, while reducing parameters by 11.3%. In summary, it could be better to meet the need of accurately detecting surface defects on particleboard. Full article

The global navigation satellite system (GNSS) struggles to deliver the precision and reliability required for positioning, navigation, and timing (PNT) services in environments with severe interference. Fifth-generation (5G) cellular networks, with their low latency, high bandwidth, and large capacity, offer a robust communication infrastructure, enabling 5G base stations (BSs) to extend coverage into regions where traditional GNSSs face significant challenges. However, frequent multi-sensor faults, including missing alarm thresholds, uncontrolled error accumulation, and delayed warnings, hinder the adaptability of navigation systems to the dynamic multi-source information of complex scenarios. This study introduces an advanced, tightly coupled GNSS/5G/IMU integration framework designed for distributed PNT systems, providing all-source fault detection with weighted, robust adaptive filtering. A weighted, robust adaptive filter (MCC-WRAF), grounded in the maximum correntropy criterion, was developed to suppress fault propagation, relax Gaussian noise constraints, and improve the efficiency of observational weight distribution in multi-source fusion scenarios. Moreover, we derived the intrinsic relationships of filtering innovations within wireless measurement models and proposed a time-sequential, observation-driven full-source FDE and sensor recovery validation strategy. This approach employs a sliding window which expands innovation vectors temporally based on source encoding, enabling real-time validation of isolated faulty sensors and adaptive adjustment of observational data in integrated navigation solutions. Additionally, a covariance-optimal, inflation-based integrity protection mechanism was introduced, offering rigorous evaluations of distributed PNT service availability. The experimental validation was carried out in a typical outdoor scenario, and the results highlight the proposed method’s ability to mitigate undetected fault impacts, improve detection sensitivity, and significantly reduce alarm response times across step, ramp, and multi-fault mixed scenarios. Additionally, the dynamic positioning accuracy of the fusion navigation system improved to 0.83 m ($1\sigma$). Compared with standard Kalman filtering (EKF) and advanced multi-rate Kalman filtering (MRAKF), the proposed algorithm achieved 28.3% and 53.1% improvements in its $1\sigma$ error, respectively, significantly enhancing the accuracy and reliability of the multi-source fusion navigation system. Full article

Forests play a critical role in maintaining global ecological balance, regulating climate, and supporting biodiversity. Effective forest management and monitoring relies on the deployment of large-scale wireless sensor networks (WSNs) for real-time data collection, enabling the protection of ecosystems and the early detection of environmental changes. However, such massive deployments pose serious challenges with increasingly scarce radio resources. Sparse code multiple access (SCMA), a non-orthogonal multiple access (NOMA) technique, has been identified as a promising solution for facilitating wireless communications among numerous distributed sensors in large-scale WSNs with improved spectral efficiency. This is essential for application scenarios involving a substantial number of terminal devices, including forest monitoring and management. Codebook design is a critical issue for SCMA systems. It is closely related to the detection performance at the receiver, which in turn has a direct effect on the communication coverage or quality of service (QoS) for the terminal devices. This paper investigates the symbol error rate (SER) performance of SCMA systems over AWGN channels and derives its theoretical upper bound. The optimization objectives for each stage of codebook design are mathematically analyzed for a single resource element (RE), a single device, and multi-device, multi-RE scenarios. On this basis, a lightweight stepwise codebook design scheme is proposed in this paper. Simulation results demonstrate that the proposed codebooks can maintain fairness among devices while guaranteeing detection performance. Full article

This study examines the integration of multi-real-option valuation and security token offering (STO) as an innovative approach to real estate project financing. The case study of Aspen Resort Development serves to illustrate this methodology. The traditional discounted cash flow (DCF) method is frequently ill-suited to the dynamic and uncertain nature of long-term real estate projects, particularly in regard to the ability to adapt to market fluctuations. In order to address these limitations, this study employs a multi-real-option model with a binomial lattice framework, thereby facilitating flexible decision-making in various investment stages. The analysis demonstrates that the STO-based project financing (STO-PF) model offers enhanced financial performance and strategic advantages in comparison to the conventional DCF approach. Furthermore, the STO-PF model has the effect of increasing liquidity, expanding investment accessibility, and improving risk management through the utilization of digital platforms. By quantifying the project’s extended net present value (ENPV), the integration of STOs with real-options models can facilitate optimal investment decisions in the context of a high level of market volatility. Consequently, the STO-PF model is determined to yield a project value (E) of USD 7.34 million and a real-options value (ROV) of USD 3.69 million. This is markedly higher than the net present value (NPV) of USD 3.65 million derived from the traditional project finance (PF) model. Furthermore, the put option for the second investment stage contributes USD 16.45 million to the overall value of the project, thereby demonstrating the flexibility and strategic advantages of the STO framework in comparison to static NPV analysis. The Aspen project serves as a case study, demonstrating the financial viability of phased investments in dynamic market conditions. It contributes to the theoretical understanding of STO-based financing and provides practical insights for developers seeking flexible and innovative financing solutions in the real estate sector. Further research is required to confirm the applicability of STOs in diverse market environments and regulatory contexts. Additionally, in-depth research is necessary to integrate emerging technologies, such as artificial intelligence and machine learning, into multi-real-option-based financial platforms. This integration aims to enhance financial modeling and decision-making processes, as well as to facilitate the integration of digital technologies in this field. Only then can the development and implementation of smart construction development advance. Full article

Citizen participation is key in urban planning, but traditional methods are often limited in terms of accessibility and inclusion. This study investigates how the use of emerging technologies such as Virtual and Augmented Reality (VR/AR), Digital Twin (DT), Building Information Modelling (BIM), Artificial Intelligence (AI), and Geographic Information Systems (GIS) can enhance citizen participation in urban planning. Through the review and analysis of existing literature, combined with the study of cases from cities in Eurasia and North America on the implementation of these technologies in urban and environmental planning, the results indicate that the use of multi-reality technologies facilitates immersive visualization of urban projects, allowing citizens to better understand the implications of proposed changes. Furthermore, the integration of real-time monitoring, such as forest and climate surveillance, improves environmental control. Technologies like AI and GIS also enable greater precision and empowerment in participatory decision-making. Nevertheless, the emergence of these technologies presents a challenge that must be addressed, as it is essential to establish a regulatory framework to ensure their responsible use. In conclusion, these platforms not only increase participation and co-creation but also enable more efficient, sustainable, and inclusive urban planning. Greater adoption of these technologies is suggested to optimize the urban decision-making process. Full article

Hydraulic systems are critical components of mechanical equipment, and effective fault diagnosis is essential for minimizing maintenance costs and enhancing system reliability. In practical applications, data from hydraulic systems are collected with varying sampling frequencies, coupled with complex interdependencies within the data, which poses challenges for existing fault diagnosis algorithms. To solve the above problems, this paper proposes an intelligent fault diagnosis of a hydraulic system based on a multiscale one-dimensional convolution neural network with a multiattention mechanism (MA-MS1DCNN). The proposed method first extracts features from multirate data samples using a parallel 1DCNN with different receptive fields. Next, a Hybrid Attention Module (HAM) is proposed, consisting of two submodules: the Correlation Attention Module (CAM) and the Importance Attention Module (IAM), which aim to meticulously and comprehensively model the complex relationships between channel features. Subsequently, to effectively utilize the feature information of different frequencies, the HAM is integrated into the 1DCNN to form the MA-MS1DCNN. Finally, the proposed method is evaluated and experimentally compared using the UCI hydraulic system dataset. The results demonstrate that, compared to existing methods such as Shapelet, MCIFM, and CNNs, the proposed method shows superior diagnostic performance. Full article