Search Results (5,032)

Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This work introduces an automated pipeline for generating large-scale IoT network datasets by bringing together the Contiki-NG firmware, parameterized topology generation, and Slurm-based orchestration of Cooja simulations. The system supports a variety of network structures, scalable node counts, randomized battery allocations, and routing protocols to reproduce diverse failure modes. As a case study, we conduct over 10,000 Cooja simulations with 15–75 battery-powered motes arranged in sparse grid topologies and operating the RPL routing protocol, consuming 1300 CPU-hours in total. The simulations capture realistic failure modes, including unjoined nodes despite physical connectivity and cascading disconnects caused by battery depletion. The resulting graph-structured datasets are used for two prediction tasks: (1) estimating the last successful message delivery time for each node and (2) predicting network-wide spatial coverage. Graph neural network models trained on these datasets outperform baseline regression models and topology-aware heuristics while evaluating substantially faster than full simulations. The proposed framework provides a reproducible foundation for data-driven analysis of energy-limited IoT networks. Full article

Polyvinyl chloride (PVC) is a widely used polymer in composite systems due to its versatility and processability, with growing use in advanced engineering applications. This study presents the formulation, processing optimisation, and detailed characterisation of a hot-dip PVC-based plastisol composite coating developed for electrical insulation in electric vehicle (EV) battery systems. A series of plastisol formulations with varying filler contents were prepared and applied via dip-coating at withdrawal speeds of 5, 10, and 15 mm s⁻¹. The 5 mm s⁻¹ withdrawal speed resulted in the most uniform coatings with thicknesses of 890–2100 µm. Mechanical testing showed that lower filler content significantly improved performance: Group 1 (lowest filler) exhibited the highest tensile strength (11.9 N mm⁻²), elongation at break (465%), tear strength (92 N mm⁻¹), and abrasion resistance. SEM and EDX analyses confirmed more homogeneous filler dispersion in Group 1, while FTIR spectra indicated stronger polymer–plasticiser interactions. Contact-angle measurements showed an increase of ${38}^{\circ }$ in low-filler samples, indicating enhanced surface hydrophobicity. Furthermore, Group 1 coatings demonstrated superior dielectric strength (22.1 kV mm⁻¹) and excellent corrosion resistance, maintaining integrity for over 2000 h in salt-spray testing. These findings highlight the importance of filler optimisation in balancing mechanical, electrical, and environmental performance. The proposed PVC-based composite coating offers a durable, cost-effective solution for next-generation EV battery insulation systems and has potential applicability in other high-performance engineering applications. Full article

Pragmatics concerns how people use language and other expressive means, such as nonverbal and paralinguistic cues, to convey intended meaning in the context. Difficulties in pragmatics are common across distinct clinical conditions, motivating validated assessments such as the Assessment Battery for Communication (ABaCo); whether Large Language Models (LLMs) can serve as reliable coders remains uncertain. In this exploratory study, we used Generative Pre-trained Transformer (GPT)-4o as a rater on 2025 item × dimension units drawn from the responses given by 10 healthy older adults (M = 69.8) to selected ABaCo items. Expert human coders served as the reference standard to compare GPT-4o scores. Agreement metrics included exact agreement, Cohen’s κ, and a discrepancy audit by pragmatic act. Agreement was 89.1% with κ = 0.491. Errors were non-random across acts (χ²(12) = 69.4, p < 0.001). After Benjamini–Hochberg False Discovery Rate correction across 26 cells, only two categories remained significant: false positives concentrated in Command and false negatives in Deceit. Missing prosodic and gestural cues likely exacerbate command-specific failures. In conclusion, in text-only settings, GPT-4o can serve as a supervised second coder for healthy-aging assessments of pragmatic competence, under human oversight. Safe clinical deployment requires population-specific validation and multimodal inputs that recover nonverbal cues. Full article

Online customer reviews contain rich signals about product value but are difficult to convert into strategy-ready evidence. This study proposes an end-to-end framework that maps review text to the Value Proposition Canvas (VPC) and quantifies alignment between user needs and product performance. Using customer reviews for three Samsung Galaxy Watch generations, an LLM extracts six dimensions (Customer Jobs, Pains, Gains, Feature Gaps, Emotions, Usage Context). Extracted phrases are embedded with a transformer model, clustered via K-means with data-driven k selection, and labeled by an LLM to form an interpretable taxonomy. Subsequently, the analysis derives frequency profiles, a gap density indicator, a context–gap matrix, and a composite Product–Market Fit (PMF) score that balances gain rate, gap rate, and coverage with sensitivity analysis to alternative weights. The findings show predominantly positive affect, with unmet needs concentrated in battery endurance and interaction stability. Productivity- and interaction-centric jobs attain the highest PMF score, while several monitoring-centric jobs are comparatively weaker. Significant cross-generation differences in job composition indicate evolving usage priorities across successive releases. The framework provides a scalable, reproducible path from unstructured VOC to decision support, enabling data-driven prioritization for product and UX management while advancing theory-grounded analysis of customer value. Full article

The accelerating global demand for cobalt, driven primarily by lithium-ion batteries, has intensified the search for alternative sources of supply. Mine tailings represent a promising secondary resource, particularly in regions with extensive mining histories such as Chile. This study evaluates cobalt leaching from copper tailings using a deep eutectic solvent (DES), choline chloride–citric acid (ChCl–CA), with controlled addition of hydrogen peroxide. The tailings were subjected to pretreatments (froth flotation, chlorination, and thermal roasting) and then leached with choline chloride–citric acid-based DES or H₂SO₄. Temperature, leaching time, and solid–liquid ratio were evaluated. Results show that roasting significantly enhanced cobalt recovery when followed by citric acid or DES leaching, reaching up to 100% Co recovery. Under optimized conditions, DES-based leaching was effective and selective in a polymetallic matrix and achieved recoveries comparable to or better than acid leaching without generating toxic emissions. Although flotation and chlorination had limited effects on overall recovery, the results demonstrate the viability of integrated and cleaner technologies for valorizing tailings that contain critical metals such as cobalt. Full article

China is developing renewable energy bases (REBs) in the desert and Gobi regions. However, the intermittency of renewable energy and the temporal mismatch between peak renewable generation and peak load demand severely disrupt the power supply reliability of these REBs. Hydrogen storage technology, characterized by high energy density and long-term storage capability, is an effective method for enhancing the power supply reliability. Therefore, this paper proposes a REB planning model in the desert and Gobi regions considering seasonal hydrogen storage introduction as well as electricity-carbon-hydrogen markets trading. Furthermore, a combination scenario generation method considering extreme scenario optimization is proposed. Among which, the extreme scenarios selected through an iterative selection method based on maximizing scenario divergence contain more incremental information, providing data support for the proposed model. Finally, the simulation was conducted in the desert and Gobi regions of Yinchuan, Ningxia Province, China, primarily verifying that (1) the REB incorporating hydrogen storage can fully leverage hydrogen storage to achieve seasonal and long-term electricity transfer and utilization. The project has a payback period of 10 years, with an internal rate of return of 13.30% and a return on investment of 16.34%, thus showing significant development potential. (2) Compared to the typical battery-involved REB, the hydrogen-involved energy storage facility achieved a 59.39% annual profit, a 10.98% internal rate of return, a 14.93% return on investment, and a 1.51% improvement in power supply reliability by sacrificing a 52.49% increase in construction cost. (3) Compared to REB planning based only on typical scenarios, the power supply reliability of REBs based on the proposed combination scenario generation method improved by 8.58%. Full article

The growth of lithium dendrites and the associated “dead lithium” issue significantly impair the performance and cycle life of lithium metal batteries. This study utilizes a phase-field model under constant-current discharge conditions to simulate the dissolution process of lithium dendrites. The results demonstrate that the non-uniform dissolution of lithium dendrites is a primary cause of their stripping and subsequent dead lithium formation. Specifically, a high charging voltage and a high reaction rate constant aggravate dendrite growth and dead lithium accumulation. Although a high discharging voltage accelerates dendrite dissolution, it readily induces stripping at the dendrite roots, generating more dead lithium. In contrast, increasing the temperature, enhancing the interface mobility, adjusting the anisotropy strength to a moderate level, and constructing semi-circular initial nuclei can effectively mitigate dead lithium by promoting a more uniform dissolution process. This research provides a theoretical foundation for optimizing battery operational parameters and electrode designs to improve capacity and safety. Full article

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

AI-based educational chatbots can expand access to learning, but many remain limited to text-only interfaces and fixed infrastructures, while purely generative responses raise concerns of reliability and consistency. In this context, we present ROboMC, a portable and multimodal system that combines a validated knowledge base with generative responses (OpenAI) and voice–text interaction, designed to enable both text and voice interaction, ensuring reliability and flexibility in diverse educational scenarios. The system, developed in Django, integrates two response pipelines: local search using normalized keywords and fuzzy matching in the LocalQuestion database, and fallback to the generative model GPT-3.5-Turbo (OpenAI, San Francisco, CA, USA) with a prompt adapted exclusively for Romanian and an explicit disclaimer. All interactions are logged in AutomaticQuestion for later analysis, supported by a semantic encoder (SentenceTransformer—paraphrase-multilingual-MiniLM-L12-v2’, Hugging Face Inc., New York, NY, USA) that ensures search tolerance to variations in phrasing. Voice interaction is managed through gTTS (Google LLC, Mountain View, CA, USA) with integrated audio playback, while portability is achieved through deployment on a Raspberry Pi 4B (Raspberry Pi Foundation, Cambridge, UK) with microphone, speaker, and battery power. Voice input is enabled through a cloud-based speech-to-text component (Google Web Speech API accessed via the Python SpeechRecognition library, (Anthony Zhang, open-source project, USA) using the Google Web Speech API (Google LLC, Mountain View, CA, USA; language = “ro-RO”)), allowing users to interact by speaking. Preliminary tests showed average latencies of 120–180 ms for validated responses on laptop and 250–350 ms on Raspberry Pi, respectively, 2.5–3.5 s on laptop and 4–6 s on Raspberry Pi for generative responses, timings considered acceptable for real educational scenarios. A small-scale usability study (N ≈ 35) indicated good acceptability (SUS ~80/100), with participants valuing the balance between validated and generative responses, the voice integration, and the hardware portability. Although system validation was carried out in the eHealth context, its architecture allows extension to any educational field: depending on the content introduced into the validated database, ROboMC can be adapted to medicine, engineering, social sciences, or other disciplines, relying on ChatGPT only when no clear match is found in the local base, making it a scalable and interdisciplinary solution. Full article

This paper proposes a novel AC side series arc fault (SAF) identification method based on Generalized Lissajous Trajectory (GLT) learning for low-voltage residential circuits. The method addresses challenges in detecting SAFs—characterized by high concealment, random occurrence, and limitations in existing protection devices—by leveraging the Hilbert transform to map current signals into 2D Generalized Lissajous Trajectories. These trajectories amplify key SAF features (e.g., zero-break distortion and random pulses). A ResNet50-based image recognition model achieves high-precision fault detection under specific load types, with a validation accuracy of up to 99.91% for linear loads and 98.93% for nonlinear loads. The algorithm operates within 1.6 ms, enabling real-time circuit breaker tripping. The proposed method achieves higher recognition accuracy with lower computational cost compared to other image-based methods. In this paper, an adjustable load signal modeling approach is proposed to visualize the current signal using GLT and complete the lightweight identification based on ResNet network, which provides new ideas and methods for series arc fault detection. Full article

The widespread adoption of electric vehicles (EVs) is contingent on high-power fast-charging infrastructure that can also provide grid stabilization services through bidirectional power flow. While the constituent power stages of such off-board chargers are well-known, a critical research gap exists in their system-level integration, where sub-optimal dynamic interaction between independently controlled stages often leads to DC-link instability and poor transient performance. This paper presents a rigorous, system-level study to address this gap by developing and optimizing a unified control framework for a high-power bidirectional EV fast-charging system. The system integrates a three-phase active front-end rectifier with an LCL filter and a four-phase interleaved bidirectional DC/DC converter. The methodology involves a holistic dynamic modeling of the coupled system, the design of a hierarchical control strategy augmented with a battery current feedforward scheme, and the system-wide optimization of all Proportional–Integral (PI) controller gains using the Artificial Bee Colony (ABC) algorithm. Comprehensive simulation results demonstrate that the proposed optimized control framework achieves a critically damped response, significantly outperforming a conventionally tuned baseline. Specifically, it reduces the DC-link voltage settling time during charging-to-discharging transitions by 74% (from 920 ms to 238 ms) and eliminates voltage undershoot, while maintaining excellent steady-state performance with grid current total harmonic distortion below 1.2%. The study concludes that system-wide metaheuristic optimization, rather than isolated component-level design, is key to unlocking the robust, high-performance operation required for next-generation EV fast-charging infrastructure, providing a validated blueprint for future industrial development. Full article

Rechargeable sodium-ion batteries (SIBs) have attracted considerable attention owing to the natural abundance and accessibility of sodium. Maricite NaMnPO₄, a phosphate-based cathode material with high theoretical capacity, suffers from blocked sodium-ion diffusion channels. In this study, atomistic simulations using pair potentials and density functional theory (DFT) are employed to investigate intrinsic defect mechanisms, sodium-ion migration pathways, and the role of dopant incorporation at Na, Mn, and P sites in generating Na vacancies and interstitials. Among the intrinsic defects, the Na–Mn anti-site cluster emerges as the most favorable, exhibiting a very low formation energy of 0.12 eV, while the Na Frenkel pair (1.93 eV) is the next most stable defect, indicating that sodium diffusion is primarily facilitated by vacancy formation. Nevertheless, sodium-ion mobility in NaMnPO₄ remains limited, as reflected by the relatively high migration activation energy of 1.28 eV. Among the isovalent substitutions, K is predicted to be the most favorable dopant at the Na site, whereas Ca and Cu are the most favorable at the Mn site. Thallium is identified as a promising dopant at the Mn site for generating Na vacancies that facilitate Na-ion migration, while Ge substitution at the P site is predicted to enhance the sodium content in the material. Full article

Electric vehicles (EVs) are experiencing rapid global adoption driven by environmental concerns and fuel security. This article presents a new dual-source converter based on a hybrid modular multilevel configuration (DCHMMC) designed for optimal cell utilisation in EV battery systems. Contrary to conventional converters that can either charge or discharge the cells using a single source, thereby leaving several cells/modules (Ms) idle during each time step, the proposed converter enables the integration of two sources that can utilise the cells simultaneously. This dual source feature minimises idle cells/Ms, enhances energy efficiency, and supports flexible bidirectional power flow. The proposed converter operates in three distinct modes. The first involves dual-source charging for fast charging and improved vehicle availability. The second involves one source charging while the other discharges for dynamic operation. Finally, the last involves dual-source discharging for maximum power delivery and support vehicle-to-grid (V2G) operation. The simulation results demonstrated smooth multilevel sinusoidal output voltages (Vout_a and Vout_b), each with a peak of 350 V, generated simultaneously using 132 cells (six cells per M, 22 Ms). The total harmonic distortion (THD) values for Vout_a and Vout_b were 0.42% and 2.25%, respectively, confirming the high-quality performance. Furthermore, only 0–36 cells and 0–6 Ms were idle during operation, showing improved cell utilisation. Full article

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and application by enabling tailored control over design, pore geometry, materials, and integration. This perspective work examines the opportunities and challenges associated with utilizing additive manufacturing as an enabling battery manufacturing technology. Recent advances in the additive fabrication of electrodes, electrolytes, separators, and integrated devices are examined, exhibiting the potential to acheive electrochemical performance, design adaptability, and sustainability. At the same time, key challenges—including materials formulation, reproducibility, economic feasibility, and regulatory uncertainty—are discussed as limiting factors that must be addressed for achieving the expected results. Rather than being viewed as a replacement for conventional gigafactory-scale production, additive manufacturing is positioned as a complementary fabrication technique that can deliver value in niche, distributed, and application-specific contexts. This work concludes by outlining research and policy priorities that could accelerate the maturation of 3D-printed batteries, stressing the importance of hybrid manufacturing, multifunctional printable materials, circular economy integration, and carefully phased timelines for deployment. Moreover, by enabling customized form factors, improved device–user interfaces, and seamless integration into smart, automated environments, additive manufacturing has the potential to significantly enhance user experience across emerging battery applications. In this context, this perspective provides a grounded assessment of how additive fabrication methods may contribute to next-generation battery technologies that not only improve electrochemical performance but also enhance user interaction, reliability, and seamless integration within automated and control-driven systems. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article