Search Results (8,555)

Rechargeable zinc-based batteries (ZBBs) have attracted considerable attention for use in large-scale energy storage systems due to their inherent high safety, low cost, and environmental friendliness. However, the practical applicability of ZBBs is limited by challenges related to the anode—such as uncontrollable zinc dendritic growth, the hydrogen evolution reaction (HER), and corrosion—which lead to significant polarization, capacity degradation, and unsatisfactory Coulombic efficiency of the ZBBs. Polyacrylamide (PAM)-based hydrogels have emerged as promising electrolyte materials to address these challenges due to their superior mechanical properties, flexibility, high ionic conductivity, and structural designability. Considering the rapid increase in research attention regarding this topic, we comprehensively summarize recent progress in PAM-based hydrogels as electrolytes for ZBBs in this study. First, we discuss the key challenges associated with Zn anodes in ZBBs, together with corresponding optimization strategies. Next, we detail the fundamental structure, properties, and synthesis of PAM-based hydrogels. Then, the relationships among synthetic methods, nano/microstructures, and electrochemical properties are systematically reviewed and discussed. Finally, prospects for the rational design and application of PAM-based hydrogels in ZBBs are summarized. Full article

With the rapid advancement of wearable technologies, high-performance flexible sensors have garnered significant research interest. This study presents a PAM-5 hydrogel characterized by exceptional tensile strain (425%), superior compressive modulus (325 kPa), and notable ionic conductivity (1.1 S/m), serving as a robust mechanical framework and electrical foundation for developing advanced sensors. The PNP-5 integrated hydrogel sensor fabricated from this material demonstrates an extensive sensing range (2–53 kPa), remarkable sensitivity, and rapid response time (~321 ms), with its outstanding performance attributed to the synergistic structural design. Furthermore, the sensor exhibits excellent durability, maintaining consistent voltage output (~6.5 mV) across 1000 compression cycles, confirming its long-term operational stability. Through real-time monitoring of physiological signals and biomechanical movements including finger bending, respiration, and grasping, combined with spatial pressure mapping experiments using a 5 × 5 array touchpad, the device’s potential applications in wearable sensing platforms and human–machine interface systems are effectively demonstrated. This self-powered hydrogel sensor not only advances the performance metrics of flexible electronic devices but also establishes a solid experimental basis for future development of intelligent materials in health monitoring and interactive technologies. Full article

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

Small and medium-sized enterprises (SMEs) are particularly vulnerable when crisis support systems are delayed, unreliable, or difficult to access. This study examines how South African SMEs maintained strategic continuity during COVID-19 by developing the concept of relational substitution, defined as a process in which owner-managers compensate for weak formal support by internalizing continuity work within the employment relationship. The study is based on a secondary qualitative analysis of 16 semi-structured interviews with SME owners in the Western Cape, South Africa, complemented by a targeted evidence review to inform boundary-condition assessment. The findings show that owner-managers assembled relational continuity bundles that combined labor flexibility, retention intent, transparent communication, and visible well-being support. Owners presented these bundles as efforts to sustain cooperation, trust, and operational functioning when enacted through fairness logics such as voice, transparency, equal sacrifice, and relational care. The study contributes to SME resilience and management and organization studies by distinguishing relational substitution from sustainable human resource management, organizational justice, relational leadership, and institutional fragility, and by specifying the firm-level and institutional conditions under which this mechanism is more likely to support strategic continuity. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

Among a broad range of promising applications, the use of cellular solids as lightweight structural components is an important field of research that requires reliable predictions of their stiffness and strength. Predictive and general models should not depend on extensive parameter-fitting experiments and should not rely on computationally intensive numerical calculations for each new set of geometric parameters and loading conditions. An overview of models for 2D, 2.5D, and three-dimensional structures will be presented. Most 2D and 2.5D models neglect out-of-plane behaviour and the face sheets used in sandwich panels. 3D studies, mainly by finite element models (FEMs), are often limited to a narrow set of geometries and simple loading conditions. Elastic anisotropy is well covered, but calculating yield surfaces remains a challenge. Simplified models based on structural mechanics are rare and often limited in scope. They offer a flexible, computationally efficient approach for simulating truss-based materials. For more advanced designs, parameter-based FEMs must be developed for any loading condition to facilitate the generalised incorporation of 3D cellular solids in mechanical design. Artificial intelligence and machine learning are promising approaches for making optimal use of experimental and FEM results across multidimensional parameter spaces. Full article

Gold nanoparticles (AuNPs) have emerged as versatile antiviral nanoplatforms because their size, morphology, plasmonic properties, and surface chemistry can be precisely engineered. In this review, we summarize the core design principles of antiviral AuNPs from a structure–function–mechanism perspective. We first outline representative synthetic and interface-programming routes for AuNP preparation, including citrate reduction, Brust–Schiffrin synthesis, seed-mediated growth, green synthesis, direct thiol-conjugation, and mixed-ligand shell strategies, emphasizing how these approaches define particle size, morphology, surface accessibility, interfacial composition, and downstream biofunctionalization potential. We then discuss major surface engineering strategies, including polyethylene glycol, nucleic acids, antibodies and nanobodies, peptides, glycans, antiviral drugs, and biomimetic coatings, with particular attention to how ligand density, orientation, flexibility, and interfacial stability determine biological performance. Next, we examine how functionalized AuNPs inhibit different stages of the viral life cycle, including viral attachment and entry, intracellular replication, assembly and egress, photothermal inactivation, and immune modulation or vaccine delivery. Finally, we highlight current challenges, including incomplete structure–activity relationships, dynamic nano–bio interactions under physiological conditions, limited standardization across studies, and translational barriers related to safety, reproducibility, and scale-up. This review provides a conceptual framework for the rational development of next-generation AuNP-based antiviral nanotherapeutics. Full article

Accurate and rapid detection of Volvariella volvacea (straw mushroom) fruiting bodies at harvestable maturity is a critical prerequisite for automated industrial cultivation. However, existing detection methods often yield high false-negative and false-positive rates when processing a small-scale, densely distributed, and heavily occluded targets against complex straw substrate backgrounds. Furthermore, these methods frequently struggle to balance the competing requirements of architectural efficiency (such as parameter volume and computational complexity) and real-time performance for edge computing. To address these challenges, this study proposes a YOLO11n-CPDM, a lightweight detection model based on an improved YOLO11n architecture. The model incorporates synergistic optimizations across feature extraction, fusion, and reconstruction. First, a Dual Coordinate Attention Feature Extraction mechanism is integrated into the C3k2 bottleneck blocks of the backbone network. This enhances target perception in complex, occluded environments by concurrently modeling global context and local salient features. Second, within the neck network, the standard attention module is replaced with the PnPNystraAttention module, coupled with the DySample dynamic upsampling operator. This modification strengthens contextual relationships among multi-scale features and improves spatial consistency during reconstruction while preserving linear computational complexity. Finally, the detection head is optimized using MBConv blocks based on an inverted residual structure to minimize parameter volume. Experimental results on a custom V. volvacea dataset demonstrate that the proposed YOLO11n-CPDM model achieves significant performance gains, with Precision (P), Recall (R), and Mean Average Precision (mAP50) reaching 86.8%, 87.5%, and 88.4%, respectively. These figures represent improvements of 2.7, 3.0, and 3.2 percentage points over the baseline YOLO11n model. Additionally, the model size is reduced to 4.8 MB (a 12.7% decrease), while achieving inference speeds of 42.7 FPS on Jetson AGX Orin and 21.2 FPS on Jetson Nano, outperforming the baseline model on both embedded platforms. Consequently, the proposed model effectively enhances detection performance in complex environments while maintaining excellent lightweight characteristics and deployment flexibility, providing a solid technical foundation for intelligent perception and automated harvesting of V. volvacea. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

The escalating security risks associated with unauthorized unmanned aerial vehicles (UAVs) in advancing smart cities necessitate the development of robust active countermeasures. This work presents a novel approach centered on a UAV-borne flexible net system and provides a rigorous investigation into its complex nonlinear dynamics. This study establishes a lumped-mass, semi-spring–damper dynamic model of the flexible capture net, characterizing its key dynamic properties, including deployment performance, aerodynamic attitude, and the high-impact phenomena of collision and entanglement with the target UAV. To verify the reliability of the proposed method, numerical simulations are combined with field tests for systematic validation. Comparative analysis reveals excellent quantitative agreement, with over 80% conformity in the net’s spatial configuration between simulated and experimental results. This paper illuminates the fundamental principles governing energy dissipation and transient tension dynamics pre- and post-capture. This study provides preliminary evidence for the feasibility of the proposed method and identifies key directions for future investigation. The findings offer guidance for the design and optimization of future systems intended to neutralize low, slow, and small (LSS) aerial threats. Full article

Multimodal recommender systems are challenged by heterogeneous modality noise and coarse-grained feature fusion. Specifically, existing frequency-domain methods typically apply symmetric filtering across modalities, ignoring their distinct spectral characteristics. Consequently, symmetric filtering cannot simultaneously satisfy the denoising requirements of visual features and the semantic preservation requirements of textual features, leading to suboptimal multimodal representations. Meanwhile, current fusion strategies mainly operate at the instance level with static modality weights, lacking flexibility to dynamically adjust feature channels for user-specific collaborative contexts. To address these issues, this paper proposes MFA-GCN, a multimodal recommendation framework that combines asymmetric spectral filtering, multiview graph enhancement, and behavior-guided channel attention. For visual modalities, a multiscale frequency-domain module integrating 1D convolution and self-attention is adopted to suppress high-frequency disturbances while preserving informative structures. For textual modalities, a lightweight complex-domain scaling strategy is introduced to adjust spectral energy while maintaining semantic consistency. In addition, auxiliary user–user and item–item graphs are constructed to supplement sparse user–item interactions and provide richer collaborative signals. A behavior-guided channel attention mechanism is further used to dynamically refine multimodal representations. Experiments on three public Amazon datasets demonstrate that MFA-GCN consistently outperforms several representative baselines. Full article

Over the past decade, educators and administrators in higher education have taken steps toward improving accessibility in teaching and learning. Yet research on supporting students with disabilities in experiential pedagogies, such as community-engaged learning, remains limited, particularly regarding best practices for inclusive instruction. The present study addresses this gap by exploring the perceptions and experiences of students with disabilities in community-engaged learning opportunities, as well as the support mechanisms that may contribute to their meaningful participation in these experiences. Forty-three students with disabilities participated in this qualitative study. Drawing on focus groups, individual interviews, and written responses, the study identifies themes for more inclusive design and delivery, including clearly outlining the physical and digital demands of engagement activities well in advance, designing courses with flexibility in mind, protecting students’ privacy, and including an accessibility statement in the syllabus. While the thematic analysis offers practical recommendations for educators and administrators, aimed at reducing barriers and fostering meaningful participation, the study also advocates for greater theoretical engagement with the personal and relational dimensions of experiential education. Full article

The aim of this article is to identify how research on smart buildings has evolved in the context of the energy transition, with particular emphasis on energy flexibility, grid interaction, market integration, and policy barriers. The study addresses a gap in previous reviews, which have often focused on individual technological domains, building automation, or smart-readiness assessment, while paying less attention to the conditions under which smart buildings become active energy-system resources. A systematic review protocol based on the PRISMA logic was combined with bibliometric mapping and qualitative synthesis. Bibliographic data were retrieved from Scopus on 28 February 2026 and covered 663 English-language journal articles published between 2015 and February 2026. A core set of 63 studies was selected through explicit cluster-based and relevance-based criteria for in-depth qualitative synthesis. The results show a gradual shift from component-level efficiency research towards system-level studies in which smart buildings are analyzed as flexible demand-side assets, distributed energy nodes, and participants in emerging market mechanisms. At the same time, the evidence base remains uneven: many studies rely on simulation or case-specific modeling, while empirical validation, interoperability, occupant behavior, business models, and regulatory implementation remain less mature. The article contributes by distinguishing observed bibliometric patterns from conceptual interpretation and by integrating technological, economic, behavioral, and regulatory evidence into a framework explaining the persistent implementation gap in smart building deployment. Full article

Accurate and reliable localization is a fundamental requirement for autonomous mobile robots operating in structured indoor environments. Adaptive Monte Carlo Localization (AMCL), widely used due to its probabilistic flexibility, suffers from performance degradation in challenging situations such as low-motion, sensor degradation, symmetry ambiguity, and abrupt position changes (kidnapped robot). This study proposes the Confidence-Gated Virtual Scan Motion AMCL (CG-VSM-AMCL) approach, which extends the standard AMCL structure with a selective and confidence-based posterior enhancement mechanism to overcome these limitations. The proposed method integrates beam partitioning, cluster-based dominance analysis, observability-aware gating, and recovery-driven adaptive particle injection components within a holistic architecture. The method was evaluated on a structured department map under seven representative scenarios: cold-start, low-motion, kidnapped robot recovery, odometry bias, scan dropout, world–model mismatch, and symmetry ambiguity. Experimental results demonstrate that the proposed approach systematically reduces localization error, false-lock rate, and convergence time compared to basic AMCL variants, and improves stability under challenging conditions. The significant improvements achieved, particularly in low-motion and symmetry-containing environments, reveal that selectively activated correction strategies can substantially increase localization robustness without altering the fundamental probabilistic structure of AMCL. Full article

Two-dimensional (2D) materials have emerged as a key platform for next-generation electronics due to their atomic thickness and tunable properties. Iron disulfide (FeS₂), known as pyrite, with a bandgap of ~0.95 eV, is suitable for solar energy applications. However, its performance is limited by defects in bulk crystals. Reducing FeS₂ to a single layer eliminates bulk defects and enables strain engineering of the bandgap. In this study, First-principles density functional theory (DFT) calculations are performed using the CASTEP code and the PBEsol functional to examine the structural, electronic, and optical properties of a distorted 1T′-phase FeS₂ monolayer. Full geometry optimization yields lattice parameters a′ = 17.594 Å, b′ = 3.20231 Å, c′ = 5.28091 Å, and Fe–S bond angles of ~75.8° and ~98.2°, confirming symmetry-breaking distortion. The monolayer is dynamically stable, showing no imaginary modes in the phonon dispersion, and remains structurally intact up to 1000 K in molecular dynamics simulations. The unstrained system has an indirect bandgap of 0.70 eV, with the valence band maximum at the Γ point (dominated by S-p states) and conduction band minimum near the X point (Fe-d states). Under mechanical strain (±4%), the bandgap decreases significantly: from 0.70 eV to 0.44 eV under +4% tensile strain along the y-axis, and to 0.53 eV under −4% compressive strain. Biaxial strain causes weaker modulation, reducing the gap to 0.66 eV (+4%) and 0.62 eV (−4%). Optical absorption exceeds 10⁴ cm⁻¹ for photon energies above the bandgap, with tensile strain causing redshifts and compressive strain inducing blueshifts. These findings demonstrate that 2D FeS₂ is mechanically robust, electronically tunable, and optically active, making it a promising candidate material for flexible strain sensors and photovoltaic devices. This work is intended to motivate and inform future synthesis efforts. Full article