Search Results (2,114)

The rapid growth of live e-commerce, a sector valued at over USD 100 billion worldwide, demonstrates its transformative impact on the retail industry, especially in markets like China, where platforms such as Taobao Live and TikTok Shop have markedly altered consumer interaction. This transition is further expedited by Netflix-like entertainment marketing methods, which have demonstrated the capacity to enhance consumer retention by as much as 40%. As organizations adjust to this evolving landscape, it is essential to optimize supply chain strategies to align with these dynamic, consumer-centric environments. This paper examines the complexity of decision-making in live e-commerce supply chains, specifically regarding Netflix-inspired marketing strategies. The primary aim of this study is to design a game-theoretic framework that examines the interactions between producers and online celebrity retailers (OCRs) across different power dynamics. As live commerce integrates digital retail with immersive experiences, businesses must optimize pricing, quality, and marketing strategies in real-time. We present engagement-driven marketing as a strategic variable and incorporate consumer regret and switching costs into the demand function. To illustrate practical trade-offs in strategy, we incorporate a multi-criteria decision-making (MCDM) layer with AHP-TOPSIS, assessing profit, consumer surplus, engagement score, and channel efficiency. The experiment results indicate that Netflix-style marketing markedly increases demand and profit in retailer-led frameworks, whereas centralized tactics enhance overall channel performance. TOPSIS analysis prioritizes high-effort, high-engagement methods, whereas the Stackelberg experiment underscores the influence of power dynamics on profit distribution. This study presents an innovative integrative decision-making methodology for enhancing live-streaming commerce tactics in data-driven and consumer-focused markets. Full article

In multi-user wireless communication scenarios, signal degradation caused by channel fading and co-channel interference restricts system capacity, while traditional enhancement schemes face challenges of high coordination complexity and hardware integration. This paper proposes an electromagnetic focusing method using a single-layer transmissive passive metasurface. A high-efficiency metasurface array is fabricated based on PCB technology, which utilizes subwavelength units for wide-range phase modulation to construct a multi-user energy convergence model in the WiFi band. By optimizing phase gradients through the geometric phase principle, the metasurface achieves collaborative wavefront manipulation for multiple target regions with high transmission efficiency, reducing system complexity compared to traditional multi-layer structures. Measurements in a microwave anechoic chamber and tests in an office environment demonstrate that the metasurface can simultaneously create signal enhancement zones for multiple users, featuring stable focusing capability and environmental adaptability. This lightweight design facilitates deployment in dense networks, providing an effective solution for signal optimization in indoor distributed systems and IoT communications. Full article

The urgent need to mitigate rising global CO₂ emissions demands the development of efficient carbon capture technologies. This study addresses the persistent challenge of sintering-induced performance degradation in CaO-based sorbents during high-temperature CO₂ capture. A novel solvent/nonsolvent synthetic strategy to fabricate CaO/CaAl-layered double oxide (LDO) composites was developed, where CaAl-LDO serves as a nanostructural stabilizer. The CaAl-LDO precursor enables atomic-level dispersion of components, which upon calcination forms a Ca₁₂Al₁₄O₃₃ “rigid scaffold” that spatially confines CaO nanoparticles and effectively mitigates sintering. Thermogravimetric analysis results demonstrate exceptional cyclic stability; the composite achieves an initial CO₂ uptake of 14.5 mmol/g (81.5% of theoretical capacity) and retains 87% of its capacity after 30 cycles. This performance significantly outperforms pure CaO and CaO/MgAl-LDO composites. Physicochemical characterization confirms that structural confinement preserves mesoporous channels, ensuring efficient CO₂ diffusion. This work establishes a scalable, instrumentally simple route to high-performance sorbents, offering an efficient solution for carbon capture in energy-intensive industries such as power generation and steel manufacturing. Full article

Soft manipulators have garnered significant research attention in recent years due to their flexibility and adaptability. However, the inherent flexibility of these manipulators imposes limitations on their load-bearing capacity and stability. To address this, this study compares various variable stiffness technologies and proposes a novel design concept: leveraging the phase-change characteristics of low-melting-point alloys (LMPAs) with distinct melting points to fulfill the variable stiffness requirements of soft manipulators. The pneumatic structure of the manipulator is fabricated via 3D-printed molds and silicone casting. The manipulator integrates a pneumatic working chamber, variable stiffness chambers, heating devices, sensors, and a central channel, achieving multi-stage variable stiffness through controlled heating of the LMPAs. A steady-state temperature field distribution model is established based on the integral form of Fourier’s law, complemented by finite element analysis (FEA). Subsequently, the operational temperatures at which the variable stiffness mechanism activates, and the bending performance are experimentally validated. Finally, stiffness characterization and kinematic performance experiments are conducted to evaluate the manipulator’s variable stiffness capabilities and flexibility. This design enables the manipulator to switch among low, medium, and high stiffness levels, balancing flexibility and stability, and provides a new paradigm for the design of soft manipulators. Full article

Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

Effective flood management in agricultural fields has become increasingly important due to the rising frequency and intensity of rainfall events driven by climate change. This study investigates the applicability of urban flood analysis models—SWMM (1D) and K-Flood (2D)—to irrigated agricultural fields with artificial drainage systems. A case study was conducted in a rural area near the Sindae drainage station in Cheongju, South Korea, using rainfall data from an extreme weather event in 2017. The models simulated inland flooding and were validated against flood trace maps provided by the Ministry of the Interior and Safety (MOIS). Receiver Operating Characteristic (ROC) analysis showed a true positive rate of 0.565, a false positive rate of 0.21, and an overall accuracy of 0.731, indicating reasonable agreement with observed inundation. Scenario analyses were also conducted to assess the effectiveness of three improvement strategies: reducing the Manning coefficient, increasing pump station capacity, and widening drainage channels. Among them, increasing pump capacity most effectively reduced flood volume, while channel widening had the greatest impact on reducing flood extent. These findings demonstrate the potential of urban flood models for application in agricultural contexts and support data-driven planning for rural flood mitigation. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

The cement sheath is critical for ensuring the long-term safety and operational efficiency of oil and gas wells. However, complex geological conditions and operational stresses during production can induce cement sheath deterioration and cracking, leading to reduced zonal isolation, diminished hydrocarbon recovery, and elevated operational expenditures. This study investigates the development of a novel microbial self-healing well cement slurry system, employing fly ash as microbial carriers and sustained-release microcapsules encapsulating calcium sources and nutrients. Systematic evaluations were conducted, encompassing microbial viability, cement slurry rheology, fluid loss control, anti-channeling capability, and the mechanical strength, permeability, and microstructural characteristics of set cement stones. Results demonstrated that fly ash outperformed blast furnace slag and nano-silica as a carrier, exhibiting superior microbial loading capacity and viability. Optimal performance was observed with additions of 3% microorganisms and 3% microcapsules to the cement slurry. Microscopic analysis further revealed effective calcium carbonate precipitation within and around micro-pores, indicating a self-healing mechanism. These findings highlight the significant potential of the proposed system to enhance cement sheath integrity through localized self-healing, offering valuable insights for the development of advanced, durable well-cementing materials tailored for challenging downhole environments. Full article

This study examines the contributions of renewable energy transition (RET) and environmental innovation (EI) to environmental performance in G7 countries from 2003 to 2021, with a focus on the transmission channels of green finance and environmental governance. Using the Augmented Mean Group (AMG) estimator and confirming robustness through the Dynamic Common Correlated Effects Mean Group (DCCE-MG) method, the study explores both direct and indirect effects of RET and EI on two key environmental indicators: the Environmental Performance Index and the Load Capacity Factor. The results reveal that both RET and EI have a significant impact on environmental performance. Moreover, green finance and environmental governance serve as crucial channels through which RET and EI exert their influence. These findings underscore the importance of developing effective financial instruments and robust regulatory frameworks to translate energy and innovation policies into tangible environmental benefits. By highlighting the interplay between technological advancement, financial capacity, and institutional quality, this study provides novel insights into the environmental policy landscape of advanced economies and offers guidance for designing integrated strategies to achieve long-term sustainability goals. Full article

Cold-formed steel (CFS) channel beams are increasingly used as primary structural elements in modern construction due to their lightweight and high-strength characteristics. To accommodate building services, these members often feature perforations—typically circular and unstiffened—produced by punching. Recent studies indicate that adding edge stiffeners, particularly around circular web openings, can improve flexural strength. Extending this idea, attention has shifted to hexagonal web perforations; however, limited research exists on the bending performance of hexagonal cold-formed steel channel beams (HCFSBs). This study presents a detailed nonlinear finite element (FE) analysis to evaluate and compare the flexural behaviour of HCFSBs with unstiffened (HUH) and edge-stiffened (HEH) hexagonal openings. The FE models were validated against experimental results and expanded to include a comprehensive parametric study with 810 simulations. Results show that HEH beams achieve, on average, a 10% increase in moment capacity compared to HUH beams. However, when evaluated using current Direct Strength Method (DSM) provisions, moment capacities were underestimated by up to 47%, particularly in cases governed by lateral–torsional or distortional buckling. A reliability analysis confirmed that the proposed design equations yield accurate and dependable strength predictions. Full article

Oligodendrocyte precursor cells (OPCs) are a distinct and dynamic glial population that retain proliferative and migratory capacities throughout life. While traditionally recognized for differentiating into oligodendrocytes (OLs) and generating myelin to support rapid nerve conduction, OPCs are now increasingly appreciated for their diverse and non-canonical roles in the central nervous system (CNS), including direct interactions with neurons. A notable feature of OPCs is their expression of diverse ion channels that orchestrate essential cellular functions, including proliferation, migration, and differentiation. Given their widespread distribution across the CNS, OPCs are increasingly recognized as active contributors to the development and progression of various neurological disorders. This review aims to present a detailed summary of the physiological and pathological functions of ion channels in OPCs, emphasizing their contribution to CNS dysfunction. We further highlight recent advances suggesting that ion channels in OPCs may serve as promising therapeutic targets across a broad range of disorders, including, but not limited to, multiple sclerosis (MS), spinal cord injury, amyotrophic lateral sclerosis (ALS), psychiatric disorders, Alzheimer’s disease (AD), and neuropathic pain (NP). Finally, we discuss emerging therapeutic strategies targeting OPC ion channel function, offering insights into potential future directions in the treatment of CNS diseases. Full article

Sudden Unexpected Death in Epilepsy (SUDEP) is a leading cause of mortality among individuals with epilepsy, particularly those with drug-resistant forms. This review explores the complex multisystem mechanisms underpinning SUDEP, integrating recent findings on brain, cardiac, and pulmonary dysfunctions. Background/Objectives: The main objective of this review is to elucidate how seizures disrupt critical physiological systems, especially the brainstem, heart, and lungs, contributing to SUDEP, with emphasis on respiratory control failure and autonomic instability. Methods: The literature from experimental models, clinical observations, neuroimaging studies, and genetic analyses was systematically examined. Results: SUDEP is frequently preceded by generalized tonic–clonic seizures, which trigger central and obstructive apnea, hypoventilation, and cardiac arrhythmias. Brainstem dysfunction, particularly in areas such as the pre-Bötzinger complex and nucleus tractus solitarius, plays a central role. Genetic mutations affecting ion channels (e.g., SCN1A, KCNQ1) and neurotransmitter imbalances (notably serotonin and GABA) exacerbate autonomic dysregulation. Risk is compounded by a prone sleeping position, reduced arousal capacity, and impaired ventilatory responses. Conclusions: SUDEP arises from a cascade of interrelated failures in respiratory and cardiac regulation initiated by seizure activity. The recognition of modifiable risk factors, implementation of monitoring technologies, and targeted therapies such as serotonergic agents may reduce mortality. Multidisciplinary approaches integrating neurology, cardiology, and respiratory medicine are essential for effective prevention strategies. Full article

The swift advancement of fifth-generation (5G) wireless technology brings forth a range of enhancements to address the increasing demand for data, the proliferation of smart devices, and the growth of the Internet of Things (IoT). This highly interconnected communication environment necessitates using multiple-input multiple-output (MIMO) systems to achieve adequate channel capacity. In this article, a 2-port MIMO system using two flipped parallel 1 × 2 arrays and a 2-port MIMO system using two opposite 1 × 4 arrays designed and fabricated antennas for 5G wireless communication in the sub-6 GHz band, are presented, overcoming the limitations of previous designs in gain, radiation efficiency and MIMO performance. The designed and fabricated single-element antenna features a circular microstrip patch design based on ROGER 5880 (RT5880) substrate, which has a thickness of 1.57 mm, a permittivity of 2.2, and a tangential loss of 0.0009. The 2-port MIMO of two 1 × 2 arrays and the 2-port MIMO of two 1 × 4 arrays have overall dimensions of 132 × 66 × 1.57 mm³ and 140 × 132 × 1.57 mm³, respectively. The MIMO of two 1 × 2 arrays and MIMO of two 1 × 4 arrays encompass maximum gains of 8.3 dBi and 10.9 dBi, respectively, with maximum radiation efficiency reaching 95% and 97.46%. High MIMO performance outcomes are observed for both the MIMO of two 1 × 2 arrays and the MIMO of two 1 × 4 arrays, with the channel capacity loss (CCL) ˂ 0.4 bit/s/Hz and ˂0.3 bit/s/Hz, respectively, an envelope correlation coefficient (ECC) ˂ 0.006 and ˂0.003, respectively, directivity gain (DG) about 10 dB, and a total active reflection coefficient (TARC) under −10 dB, ensuring impedance matching and effective mutual coupling among neighboring parameters, which confirms their effectiveness for 5G applications. The three fabricated antennas were experimentally tested and implemented using the MIMO Application Framework version 19.5 for 5G systems, demonstrating operational effectiveness in 5G applications. Full article