Search Results (7,875)

Based on the self-determination theory and social presence theory, this study examined how high-tech and high-touch orientations in logistics service quality (LSQ) influence consumer satisfaction in omni-channel retailing. LSQ was modelled as two second-order constructs: high-tech orientation (timeliness, physical facilities, and ease of return) and high-touch orientation (employees’ knowledge, flexibility, and responsiveness to delivery discrepancies). Survey data from 455 consumers were analyzed using structural equation modelling. Both orientations significantly improved satisfaction, with high-tech orientation showing a slightly stronger effect, reflecting the digital literacy of the predominantly young sample. The findings extended self-determination theory and social presence theory by offering a dual-orientation perspective and practical guidance for balancing high-tech and high-touch in omni-channel logistics service design. Full article

The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs). Full article

Background: Cardiac-respiratory dual gating in SPECT (DG-SPECT) is an emergent technique for alleviating motion blurring artifacts in myocardial perfusion imaging (MPI) due to both cardiac and respiratory motions. Moreover, the attenuation artifact may arise from the spatial mismatch between the sequential SPECT and CT attenuation scans due to the dual gating of SPECT data and non-gating CT images. Objectives: This study adapts a four-dimensional (4D) cardiac SPECT reconstruction with post-reconstruction respiratory motion correction (4D-RMC) for dual-gated SPECT. In theory, a respiratory motion-matched attenuation correction (MAC) method is expected to yield more accurate reconstruction results than the conventional motion-averaged attenuation correction (AAC) method. However, its potential benefit is not clear in the presence of practical imaging artifacts in DG-SPECT. In this study, we aim to quantitatively investigate these two attenuation methods for SPECT MPI: 4D-RMC (MAC) and 4D-RMC (AAC). Methods: DG-SPECT imaging (eight cardiac gates and eight respiratory gates) of the NCAT phantom was simulated using SIMIND Monte Carlo simulation, with a lesion (20% reduction in uptake) introduced at four different locations of the left ventricular wall: anterior, lateral, septal, and inferior. For each respiratory gate, a joint cardiac motion-compensated 4D reconstruction was used. Then, the respiratory motion was estimated for post-reconstruction respiratory motion-compensated smoothing for all respiratory gates. The attenuation map averaged over eight respiratory gates was used for each respiratory gate in 4D-RMC (AAC) and the matched attenuation map was used for each respiratory gate in 4D-RMC (MAC). The relative root mean squared error (RMSE), structural similarity index measurement (SSIM), and a Channelized Hotelling Observer (CHO) study were employed to quantitatively evaluate different reconstruction and attenuation correction strategies. Results: Our results show that the 4D-RMC (MAC) method improves the average relative RMSE by as high as 5.42% and the average SSIM value by as high as 1.28% compared to the 4D-RMC (AAC) method. Compared to traditional 4D reconstruction without RMC (“4D (MAC)”), these metrics were improved by as high as 11.23% and 27.96%, respectively. The 4D-RMC methods outperformed 4D (without RMC) on the CHO study with the largest improvement for the anterior lesion. However, the image intensity profiles, the CHO assessment, and reconstruction images are very similar between 4D-RMC (MAC) and 4D-RMC (AAC). Conclusions: Our results indicate that the improvement of 4D-RMC (MAC) over 4D-RMC (AAC) is marginal in terms of lesion detectability and visual quality, which may be attributed to the simple NCAT phantom simulation, but otherwise suggest that AAC may be sufficient for clinical use. However, further evaluation of the MAC technique using more physiologically realistic digital phantoms that incorporate diverse patient anatomies and irregular respiratory motion is warranted to determine its potential clinical advantages for specific patient populations undergoing dual-gated SPECT myocardial perfusion imaging. Full article

The complex structures and diverse object categories in indoor environments pose significant challenges for point cloud semantic segmentation. To address the insufficient capability of extracting local features in complex scenes, this paper proposes a point cloud segmentation network based on neighborhood feature enhancement termed PKA-Net. First, to obtain richer and more discriminative feature representations, we design a local feature encoding module that extracts geometric features, color information, and spatial information from local regions of the point cloud for joint feature encoding. Furthermore, we enhance the hierarchical feature extraction by integrating Kolmogorov–Arnold Networks (KAN) to form the SAPK module, improving the network’s ability to fit complex geometric structures. A residual structure is also adopted to optimize feature propagation and alleviate the problem of gradient vanishing. Finally, we propose the dual attention mechanism C-MSCA, which dynamically selects and strengthens key features through the synergistic action of channel and spatial attention, enhancing the network’s perception of local details and global structure. To evaluate the performance of the proposed PKA-Net, extensive experiments were conducted on the S3DIS dataset. Experimental results demonstrate that PKA-Net improves OA by 2.1%, mAcc by 2.9%, and mIoU by 4% compared to the baseline model. It outperforms other mainstream models, delivering enhanced overall segmentation performance. Full article

Changing the topography of electrodes by ultrafast laser ablation has shown great potential in enhancing electrochemical performance in lithium-ion batteries. The generation of microstructured channels within the electrodes creates shorter pathways for lithium-ion diffusion and mitigates strain from volume expansion during electrochemical cycling. The topography modification enables faster charging, improved rate capability, and the potential to combine high-power and high-energy properties. In this study, we present a preliminary exploration of this approach for sodium-ion battery technology, focusing on the impact of laser-generated channels on hard carbon electrodes in sodium-metal half-cells. The performance was analyzed by employing different conditions, including different electrolytes, separators, and electrodes with varying compaction degrees. To identify key factors contributing to rate capability improvements, we conducted a comparative analysis of laser-structured and unstructured electrodes using methods including scanning electron microscopy, laser-induced breakdown spectroscopy, and electrochemical cycling. Despite being based on a limited sample size, the data reveal promising trends and serve as a basis for further optimization. Our findings suggest that laser structuring can enhance rate capability, particularly under conditions of limited electrolyte wetting or increased electrode density. This highlights the potential of laser structuring to optimize electrode design for next-generation sodium-ion batteries and other post-lithium technologies. Full article

Voice shopping brings consumers convenience and retailers a new channel to reach buyers, which is an important component of online shopping. However, it has received a tepid response recently. Two issues must first be addressed to promote voice shopping: why consumers have a low acceptance of voice shopping and how to motivate their use. Since technology is intended to serve practical purposes, it is necessary to achieve a fit among voice shopping, online shopping tasks, and consumers. Accordingly, this study elaborates on fit and integrates the task-technology fit (TTF) theory (for objective fit) and the technology acceptance model (TAM, for subjective fit) to build a research model in which task, technology, and individual characteristics affect fit that will stimulate voice shopping intention. Using Structural Equation Modeling to analyze the data collected from 425 consumers who do not accept voice shopping, the results show that both objective fit and subjective fit can determine voice shopping intention; however, some critical task, technology, and individual characteristics cannot affect either aspect of fit, indicating that consumers still do not believe voice is workable in online shopping. These findings make suggestions for a purposeful upgrade of the voice shopping experience, which helps promote voice shopping and ultimately contributes to the prosperity of online shopping. This study also offers insights into what constitutes fit and its roles in the integrated model of the TTF theory and TAM. Full article

Pre-embedded sensors for concrete structure monitoring face bottlenecks in power supply and data transmission. Existing power supply solutions such as photovoltaic systems and batteries suffer from drawbacks including energy randomness and structural damage to concrete caused by their installation methods. Additionally, commercial wireless communication signals exhibit issues like strong attenuation and poor security during propagation. This paper proposes a hybrid electromagnetic field decoupled parallel transmission technology for power and signals. It utilizes the inherent decoupling characteristic of electric and magnetic fields within the near-field range to construct independent power/signal transfer channels, and achieves independent full-duplex transmission of uplink/downlink data via orthogonal coupling plates. Mathematical models for the power and signal channels are established, and finite element simulations are conducted. A parameter design method for the power compensation network and signal filtering circuit is also proposed. An experimental setup is built, with a coupler outer dimension of 200 mm × 200 mm, a coupling distance of 10 mm, and a thickness of 16 mm for both the transmitting and receiving sides. Experimental results show that the system achieves power transmission with a power of 100 W and an efficiency of 82%, while simultaneously realizing full-duplex communication with a bidirectional rate of 9600 bit/s. Moreover, no bit errors occur within 300,000 characters of bidirectional data. Full article

MN arrays are highly beneficial for transdermal drug delivery, primarily due to reduced pain and improved compliance. However, complex processing and restricted design freedom limit traditional fabrication. We utilized stereolithography (SLA) 3D printing as a breakthrough method to achieve the one-step production of MN arrays with customized geometries (structure, size, tip angle), enabling tunable release profiles and eliminating cumbersome manufacturing steps. After fabricating conical and grooved MN arrays and validating them in an ex vivo porcine skin model, we confirmed their functionality. Notably, the grooved design provided excellent penetration, requiring only 2.2 N of force for full insertion (at 18.92° tip angle), and demonstrated an enhanced drug loading capacity of 4.8 μg released over 2 h. These results underscore the significant potential of 3D-printed, channel-structured MN arrays as a new generation of high-performance transdermal delivery devices. Full article

To elaborate on the effects of hydraulic projects and physicochemical factors on the spatiotemporal distribution of phytoplankton communities, we monitored the phytoplankton communities and related water parameters in the Ganjiang River’s main channel over a five-year period. The survey revealed 65 species across six phyla, with Chlorophyta, Cyanophyta and Bacillariophyta as the most diverse groups. Phytoplankton abundance and biomass exhibited significant seasonal variations (p < 0.001), peaking in summer and autumn and reaching their lowest values in winter and spring. Spatially, phytoplankton abundance and biomass were not significantly different (p > 0.05), the abundance and biomass of Cyanophyta were higher in the two reservoir areas compared to the upstream sampling points. This suggests that the hydraulic projects altered the river’s flow and velocity, which led to a succession in phytoplankton community composition. Correlation analysis showed a strong positive association between the abundance and biomass of both Cyanophyta and Chlorophyta and water temperature (p < 0.001), but showed a significant negative relationship with nitrogen (p < 0.05). In contrast, Bacillariophyta abundance and biomass were positively and significantly correlated with ammonium nitrogen (p < 0.05). Redundancy analysis confirmed that water temperature and nitrogen are the primary environmental variables influencing the phytoplankton community’s succession. The direct alteration of river hydrodynamic characteristics by hydraulic projects, coupled with the reservoir-induced water stratification and its influence on vertical water temperature distribution, ultimately results in the profound reshaping of the phytoplankton community structure through coupled effects with nitrogen cycling. The findings from this study can scientifically inform the ecological scheduling, water quality management and water supply security of the Ganjiang River basin’s cascade reservoirs. Full article

Machine learning-based digital soil mapping often struggles with spatial heterogeneity and long-range dependencies. To address these limitations, this study proposes Multi-Attention Convolutional Neural Networks (MACNN). This deep learning algorithm integrates multiple attention mechanisms to improve mapping accuracy. First, environmental covariates are determined from the soil-landscape model. These are then fed as structured input to the Convolutional Neural Network. Next, by incorporating Transformer self-attention and multi-head attention mechanisms, this study effectively models the long-range dependencies between soil types and features. Concurrently, the Convolutional Block Attention Module (CBAM) is introduced. CBAM features both channel and spatial dual attention, enabling adaptive weighting of crucial feature channels and spatial locations. This significantly enhances the algorithm’s sensitivity to discriminative information. To validate its effectiveness, the proposed MACNN algorithm was used for soil type mapping in Heilongjiang Province. Compared to Random Forest, Decision Tree, and One-Dimensional Convolutional Neural Network algorithms, MACNN demonstrated superior classification performance. It achieved an overall classification accuracy of 81.27%. An ablation study was conducted to investigate the importance of individual modules within the proposed algorithm. The findings indicate that progressively integrating Transformer and CBAM modules into the 1D-CNN baseline significantly enhances algorithm performance through synergistic gains. Therefore, this integrated algorithm offers a feasible solution to improve digital soil mapping accuracy, providing significant reference value for future research and applications. Full article

Differing from traditional isobaric combustion, a pulse detonation-based ramjet (PD-Ramjet) was proposed in this study to enhance the efficiency of traditional ramjets. By using a two-dimensional numerical simulation method, the filling process and detonation initiation process of the hydrogen/air stoichiometric mixture in channels equipped with typical flameholders were studied under the inflow condition of a ramjet combustor, and the influences of the typical flameholders on the filling process and detonation initiation process were analyzed. Single cavity, sudden expansion cavity, central cavity, and V-shaped groove were chosen as typical ramjet flameholders. The simulation and analysis results indicated that the flameholders would affect the filling effect, and the blocking ratio had a great influence on the filling process. The hydrogen volume discharged from the outlet of the channel and the time for mixed gas to reach the outlet were related to the blocking ratio and the cavity aft wall inclination angle. The detonation initiation process revealed that the flameholders promoted the generation of detonation waves. Contrastingly, the detonation wave could not be initiated in the channel without flameholders despite the better filling effect. Moreover, different flameholders would change the position of high-pressure point formation and the time for generating the stable detonation wave. On the whole, the sudden expansion cavity had a lower blockage ratio and also gave consideration to the filling effect and detonation initiation characteristic, making it the most suitable flameholder structure for PD-Ramjet in this study. Full article

The research investigates the mediating influence of Green Human Resource Management (GHRM) practices—including environmentally focused recruitment, training, and performance management—on the relationship between green leadership and employees’ green advocacy within Saudi Arabia’s hospitality and tourism industry. Data were gathered through a structured questionnaire administered to supervisors and managers working in five-star hotels, producing 544 valid responses for analysis. The conceptual framework was examined using Structural Equation Modeling (SEM) with the WarpPLS 0.7 software to rigorously assess both the measurement validity and structural associations. The analysis demonstrated that green leadership exerts a positive and significant impact on GHRM practices as well as on employees’ willingness to advocate for environmentally responsible behaviors. Furthermore, GHRM initiatives were found to strengthen employees’ pro-environmental engagement and serve as a partial mediating channel between green leadership and green advocacy. Overall, the findings highlight that institutionalizing sustainable HRM approaches is a fundamental route through which leadership enhances environmental accountability and promotes the long-term economic viability of the hospitality sector. The study concludes by underscoring its theoretical and managerial implications, emphasizing how leadership-guided sustainability actions can foster an organizational culture grounded in ecological responsibility. Full article

Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain. Full article

Welding is a critical joining process in modern manufacturing, with defects contributing to 50–80% of structural failures. Traditional inspection methods are often inefficient, subjective, and inconsistent. To address challenges in weld defect detection—including scale variation, morphological complexity, low contrast, and sample imbalance—this paper proposes ADFE-DET, an adaptive dynamic feature enhancement algorithm. The approach introduces three core innovations: the Dynamic Selection Cross-stage Cascade Feature Block (DSCFBlock) captures fine texture features via edge-preserving dynamic selection attention; the Adaptive Hierarchical Spatial Feature Pyramid Network (AHSFPN) achieves adaptive multi-scale feature integration through directional channel attention and hierarchical fusion; and the Multi-Directional Differential Lightweight Head (MDDLH) enables precise defect localization via multi-directional differential convolution while maintaining a lightweight architecture. Experiments on three public datasets (Weld-DET, NEU-DET, PKU-Market-PCB) show that ADFE-DET improves mAP50 by 2.16%, 2.73%, and 1.81%, respectively, over baseline YOLOv11n, while reducing parameters by 34.1%, computational complexity by 4.6%, and achieving 105 FPS inference speed. The results demonstrate that ADFE-DET provides an effective and practical solution for intelligent industrial weld quality inspection. Full article

Karst collapse columns (KCCs) represent key concealed hazard-inducing factors that threaten the safety of coal mines in North China. To clarify their primary controlling geological factors and evolutionary processes, this study focuses on KCCs in the Hancheng Mining Area, situated on the southeastern margin of the Ordos Basin, China. A comprehensive methodological approach—integrating field surveys, petrographic and mineralogical identification, geochemical analysis, and structural interpretation—was employed to investigate the dominant factors controlling KCC development and their evolutionary mechanisms. The results indicate the following: (1) Thick-bedded dolomites of the 5th Member of the Majiagou Formation (Middle Ordovician Series) serve as the material foundation for karstification. These dolomites were deposited in an oxidized shallow-water tidal flat setting, which endowed them with favorable lithological properties for subsequent dissolution. (2) NE-SW trending erosional grooves within the paleogeomorphology of the Ordovician top surface functioned as preferential flow paths for karst water, channeling fluid movement and intensifying localized dissolution. (3) Multi-phase tectonic activities, particularly extensional deformation during the Himalayan orogeny, created the necessary stress conditions to trigger cave collapse. (4) KCCs undergo a multi-stage formation and evolution process: Starting with the Majiagou Formation’s 5th Member dolomites as the primary lithology, initial modification occurred via Caledonian weathering–crust karstification. Subsequently, compressional tectonism during the Yanshanian orogeny generated void spaces that facilitated deep-seated dissolution. Rapid uplift in the Paleogene exacerbated vertical dissolution, leading to extensive cavity development, which ultimately collapsed under the extensional tectonic regime of the Neogene. This study provides theoretical support for predicting and mitigating sudden water inrushes caused by KCCs in the Hancheng Mining Area. Furthermore, it offers novel insights and a scientific basis for advancing understanding of the developmental mechanisms of North China-type KCCs. Full article