Search Results (14,619)

The residential building sector is a significant source of global energy consumption and carbon emissions, especially in rapidly changing rural areas. In China, the shift from vernacular courtyard dwellings to modern rural housing has altered the relationship among architectural form, thermal comfort (TC), and energy use. Vernacular dwellings in northern China employ passive strategies, such as courtyard-centred layouts, high thermal-mass envelopes, and natural ventilation, to achieve summer comfort with minimal energy input. In contrast, modern dwellings (brick–concrete) depend more on mechanical cooling and consume more electricity. This study investigates how dwelling type, spatial configuration, building materials, courtyard configuration, thermal comfort, and housing satisfaction interact to shape residential environmental adaptability in rural Handan, Hebei Province. A questionnaire survey of 383 households was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). To supplement perceptual data, summer electricity consumption was monitored in 20 typical dwellings from June to August 2025, and on-site measurements of air temperature, relative humidity, and courtyard air velocity were conducted in six representative cases. The results indicate that dwelling type significantly affects spatial configuration and courtyard form, while spatial configuration and courtyard characteristics together influence material performance. Thermal comfort is identified as a key mediating variable with a strong direct impact on housing satisfaction. Field measurements confirm that vernacular dwellings have lower summer electricity consumption, more stable thermal conditions, improved humidity regulation, and higher courtyard air velocity, indicating superior passive cooling potential. These findings provide empirical evidence that incorporating vernacular passive design principles into contemporary rural housing can improve thermal comfort and reduce energy dependence, thereby supporting climate-responsive, low-carbon rural revitalization strategies. Full article

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts. Full article

The deployment of large language models (LLMs) in commercial environments depends critically on the availability of robust digital infrastructure, scalable computing resources, and mature cloud architectures. This study examines how macro-level digital infrastructure, in particular cloud computing adoption, conditions the ability of the European retail sector to deploy and benefit from large language models (LLMs). Using a country-year panel of EU member states from 2017 to 2023, we estimate fixed-effects regressions to quantify the association between enterprise cloud use and retail trade volume growth, and implement an event-study design to explore dynamic responses around changes in cloud uptake. The results show that increases in cloud adoption are significantly associated with higher retail trade growth added and productivity, with especially strong effects in emerging Eastern European markets. We identify a digital threshold of around 20% of enterprises using cloud services, above which the marginal impact on retail performance becomes notably larger. These findings highlight cloud infrastructure as a key enabling condition for LLM-enabled retail applications and inform EU digital and industrial policy targeting regional digital disparities. Full article

Image deraining is a crucial preprocessing task for enhancing the robustness of high-level vision systems under adverse weather conditions. However, most of the existing methods are limited to a single RGB color space, and it is difficult to effectively separate high-frequency rain streaks from low-frequency backgrounds, resulting in color distortion and detail loss in the restored image. Therefore, a rain removal network that combines dual-color space and frequency domain priors is proposed. Specifically, the devised network employs a dual-branch Transformer architecture to extract color and structural features from the RGB and YCbCr color spaces, respectively. Meanwhile, a Hybrid Attention Feedforward Block (HAFB) is constructed. HAFB achieves feature enhancement and regional focus through a progressive perception selection mechanism and a multi-scale feature extraction architecture, thereby effectively separating rain streaks from the background. Furthermore, a Wavelet-Gated Cross-Attention module is designed, including a Wavelet-Enhanced Attention Block (WEAB) and a Dual Cross-Attention module (DCA). This design enhances the complementary fusion of structural information and color features through frequency-domain guidance and bidirectional semantic interaction. Finally, experimental results on multiple datasets (i.e., Rain100L, Rain100H, Rain800, Rain12, and SPA-Data) demonstrate that the proposed method outperforms other approaches. Full article

In this work, we present the design and evaluation of the X2000, a new development kit created to simplify and accelerate research for advanced driver-assistance systems (ADAS). The X2000 is a complete ADAS development kit for the Ford Mach-E. It includes a forward-facing vehicle-mounted camera, vehicle-mounted AI computer, controller area network flexible data-rate (CAN-FD) and 12 V power connections, and a CAN-FD interface to the vehicle’s forward radar. Central to the kit is a novel ADAS software architecture designed for readability and extensibility. Included in the design are software modules for the following: (1) camera and radar interfacing; (2) image processing; (3) AI model inference; (4) data logging; (5) steering and velocity planning; (6) low-level vehicle controls for steering, acceleration, and braking; (7) lane centering visualization to the car’s 17-inch touchscreen. To build on a proven system, the X2000 integrates the AI model, planner, low-level controls, and radar interfacing software from Openpilot. We build on the excellent work of the Openpilot team while creating a highly simplified system. Openpilot features 17 software processes and 77 inter-process messages, while the X2000 uses 6 processes and 7 inter-process messages. Full article

The socio-ecological transition requires not only technological innovation but also new ways of recognizing the social, environmental, and territorial value generated by collective action. Many of these positive externalities remain invisible in conventional assessment frameworks, limiting the legitimacy, financing, and scaling of local sustainability initiatives. This article presents a strategic framework designed to identify and structure positive externalities in collective self-consumption and other transformative projects. The method combines four components: (i) normative identification through the Sustainable Development Goals; (ii) balanced multi-stakeholder participation to surface diverse perspectives; (iii) perceptive mapping using an adapted Kano model; and (iv) strategic articulation. The framework was applied in two contrasting contexts: an energy community centered on shared renewable production, and a women’s empowerment program focused on capability-building and social innovation. These applications do not aim at empirical replication or the validation of results, but at examining how the framework supports collective recognition and strategic structuring in different organizational settings. Across these distinct settings, it led to the formulation of coherent and actionable strategic roadmaps, illustrating how positive externalities can inform governance choices, strengthen institutional legitimacy, and support long-term project consolidation. These results suggest that collective recognition enables externalities to structure strategic action beyond their original sector, demonstrating the potential transferability of the approach. Developed within a research program supported by the French Agency for Ecological Transition (ADEME) and the national urban-transition initiative (PUCA), the framework provides a practical decision architecture for structuring shared value within coordinated strategies. Full article

Two-dimensional (2D) materials offer exceptional electrical, optical, and mechanical properties but face challenges in terms of scalability, stability, and integration. Hybridizing 2D materials with polymers provides an effective route to overcome these limitations by enabling tunable interfaces, mechanical compliance, chemical functionality, and three-dimensional device processability. This review summarizes the fundamental structural configurations of 2D–polymer hybrids, including embedded composites, stacked heterostructures, covalently functionalized interfaces, polymer-encapsulated layers, and fiber–network architecture, and describes how their interfacial interactions dictate charge transport, environmental robustness, and mechanical behavior. We also highlight major fabrication strategies, such as solution dispersion, in situ polymerization, and vapor-phase deposition. Finally, we discuss emerging applications in sensors, optoelectronics, neuromorphic systems, and energy devices, demonstrating how synergistic coupling between 2D materials and functional polymers enables enhanced sensitivity, programmable electronic states, broadband photodetection, and improved electrochemical performance. These insights provide design guidelines for future multifunctional and scalable 2D–polymer hybrid platforms. Full article

The ligand-driven self-assembly of metal clusters offers a powerful strategy for constructing discrete molecular architectures with tunable magnetic and structural properties. By judiciously selecting appropriate multidentate ligands, researchers can direct the formation of polynuclear metal assemblies with diverse nuclearities, geometries, and topologies. Coordination-driven processes commonly stabilize such assemblies where multidentate ligands operate as templates and linkers. These will also determine how the metal centers are arranged in space and how they connect to each other. These clusters can take on shapes that range from basic bridging dimers to more complicated icosahedral and cubane-type motifs. They often have excellent symmetry and strong frameworks. Magnetically, these clusters are a great place to study exchange interactions, spin frustration, and the behavior of single-molecule magnets (SMMs). The magnetic characteristics depend on things like the type of metal ions, the bridging ligands, the overall shape, and the local coordination environment. Interestingly, a large number of ligand-assembled clusters exhibit high spin ground states and slow magnetization relaxation, which makes them attractive options for quantum information storage and molecular spintronic devices. This review connects coordination chemistry, supramolecular design, and molecular magnetism of pyridine–amine–carboxylate frameworks, offering insights into fundamental magnetic phenomena and guiding the development of next-generation functional materials. Continued exploration of ligand frameworks and metal combinations holds the potential to yield novel clusters with enhanced or unprecedented magnetic characteristics. Full article

This work presents a systematic methodology for the preliminary design and optimization of electromechanical actuators, aimed at minimizing overall mass and rotational inertia while satisfying torque and speed requirements. The proposed approach integrates dimensionless scaling relationships, derived and corrected from catalog data, with a genetic algorithm that performs multi-parameter optimization across different actuator architectures. The algorithm enables the exploration of non-linear and multi-modal design spaces, allowing the identification of balanced solutions between mechanical efficiency and dynamic performance, employing custom functions for individual generation, constraint handling, and compatibility verification to ensure feasible and consistent architecture designs throughout the optimization process. A case study on the steering system of an aircraft nose landing gear illustrates the method’s ability to define optimal design parameters in real mechanical systems. Linear and non-linear dynamic analyses confirmed the compliance of the optimized design with control and stability requirements. The study demonstrates how the developed custom constrained genetic optimization approach can effectively support the early design phase, reducing the computational effort required in further stages and improving the overall consistency of electromechanical actuator development. Full article

Micro-foundational HRM has advanced our understanding of how employees perceive and respond to HR practices, yet explanations of how HR systems can generate and sustain coordinated action in day-to-day work remain underspecified. This article presents a theory-building integrative review that specifies a constrained, generative mechanism grounded in observable interaction episodes. We propose a functional architecture that assigns constructs to distinct explanatory roles: enabling states (Role A), interaction episodes as the behavioral engine (Role B), and emergent coordination products (Role C). Psychological safety is positioned as an enabling condition that shifts the likelihood and quality of enactment, whereas collective leadership behavior (CLB) is defined as response-inclusive influence episodes (an influence attempt plus an observable response such as uptake, contestation, neglect, or sanction). We formalize a recursive safety–CLB cycle in which response patterns update subsequent safety and influence dispersion over time, which can yield divergent coordination trajectories even when HR conditions are broadly similar. The framework generates discriminant predictions about response profiles, dispersion versus centralization of influence, and temporal signatures, and it clarifies minimal design requirements for testing recursion with episode-level and intensive longitudinal evidence. We discuss implications for micro-foundational HRM, measurement alignment, and testable design-relevant implications for HR system design as an interaction-relevant cue environment. Full article

This paper presents an all-digital fractional-N phase-locked loop (ADPLL) operating in the 2.86–3.2 GHz range, optimized for IoT and high-frequency RF transceiver applications demanding stringent phase noise performance, fast settling time, and high integration capability. The key innovation lies in the introduction of a bandpass delta-sigma time-to-digital converter (BPDSTDC) that achieves high-resolution phase detection, an extended detection range of ±2$\pi$, and superior noise-shaping characteristics, completely eliminating the complex calibration procedures typically required in conventional TDC designs. The proposed architecture synergistically combines the BPDSTDC with digital down-conversion blocks to extract phase error at baseband, a divider chain integrated with phase interpolators achieving 1/4 fractional resolution to suppress in-band quantization noise, and a wide-bandwidth digital loop filter (>1 MHz) ensuring fast dynamic response and robust stability. The bandpass delta-sigma modulator is implemented with compact resonator structures and a flash quantizer, achieving an optimal balance among resolution, power consumption, and silicon area. The incorporation of highly linear phase interpolators extends fractional frequency synthesis capability without requiring complex digital-to-time converters (DTCs), significantly reducing design complexity and calibration overhead. Fabricated in a 180-nm CMOS technology, the proposed chip demonstrates robust measured performance. The band-pass delta-sigma TDC achieves a low integrated rms timing noise of 183 fs within a 1-MHz bandwidth. Leveraging this low TDC noise, the complete ADPLL exhibits a measured in-band phase noise of −120 dBc/Hz at a 1-MHz offset for a 3.2-GHz output frequency while operating with a loop bandwidth exceeding 1 MHz. This corresponds to a normalized phase noise of −216 dBc/Hz. The system operates from a 1.8-V supply and consumes 10 mW, achieving competitive performance compared with prior noise-shaping TDC-based all-digital PLLs. Full article

Background/Objectives: Porcine epidemic diarrhea virus (PEDV), particularly the emerging GII genotype, poses a severe threat to the swine industry in affected regions, primarily in Asia. Current vaccines based on classical strains often provide limited cross-protection against these heterogeneous variants, though it should be noted that these vaccines are primarily designed to induce maternal immunity in sows. The objective of this study was to develop a novel inactivated vaccine using an emerging PEDV GIIc variant and evaluate its immunogenicity and cross-protective efficacy against heterologous strains. Methods: A novel PEDV strain, designated PEDV-HeN2024, was isolated from clinical samples and identified through cell culture, immunofluorescence assay (IFA), genetic sequencing, and phylogenetic analysis. An inactivated vaccine was prepared by emulsifying the purified virus with ISA 201 VG adjuvant (1:1, v/v). Immunogenicity was assessed in piglets by measuring virus-neutralizing antibody titers and PEDV-specific IgG levels. Cross-protective efficacy was evaluated through in vitro neutralization assays and in vivo challenge studies with homologous GIIc and heterologous GIIa and GIIb strains. Results: The isolated PEDV-HeN2024 strain demonstrated pathogenicity, causing severe diarrhea and 100% mortality in PEDV-naïve neonatal piglets. Sera from vaccinated animals showed potent cross-neutralizing activity against homologous GIIc, as well as heterologous GIIa and GIIb strains. In challenge studies, vaccinated piglets were significantly protected against clinical disease, showing no diarrhea or viral shedding, and maintained normal intestinal architecture. Conclusions: The inactivated vaccine developed from the emerging PEDV GIIc variant elicits robust humoral immunity and provides cross-protection against prevalent heterologous GII strains. These findings highlight its potential as a promising spectrum vaccine candidate for controlling PEDV outbreaks. This study underscores the importance of using recently circulating strains for vaccine development to overcome the limitations of current vaccines. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

This work proposes new architecture, supported by analytical modelling and computer-aided design (CAD) simulations, for a highly sensitive monolayer graphene-gated AlGaN/GaN HEMT terahertz (THz) detector operating at room temperature (RT). The monolayer graphene gate acts as a surface plasmon absorber for the incident THz radiation. The carrier density perturbation caused by incident THz energy on the monolayer graphene surface is then capacitively coupled to the two-dimensional electron gas (2DEG) channel of the HEMT structure underneath. The channel is partially depleted for increased mobility and nonlinearity with potential asymmetry across the channel for consistent photogeneration. The Drude absorption of THz radiation initiates intraband transitions in monolayer graphene, thereby reducing phonon losses. These reduced phonon losses enable RT THz detection. Based on our simulations, the proposed detector architecture can generate a responsivity of 2.12 × 10⁶ V/W at 1 THz with a broadband bandwidth of 2 THz. Full article

Self-supervised contrastive learning has demonstrated remarkable effectiveness in learning visual representations without labeled data, yet its application to 3D local feature learning from point clouds remains underexplored. Existing methods predominantly focus on complete object shapes, neglecting the critical challenge of recognizing partial observations commonly encountered in real-world 3D perception. We propose a momentum contrastive learning framework specifically designed to learn discriminative local features from randomly sampled point cloud regions. By adapting the MoCo architecture with PointNet++ as the feature backbone, our method treats local parts of point cloud as fundamental contrastive learning units, combined with carefully designed augmentation strategies including random dropout and translation. Experiments on ShapeNet demonstrate that our approach effectively learns transferable local features and the empirical observation that approximately 30% object local part represents a practical threshold for effective learning when simulating real-world occlusion scenarios, and achieves comparable downstream classification accuracy while reducing training time by 16%. Full article