Search Results (254)

Accurate estimation of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing the reliability and efficiency of energy storage systems. This study explores custom deep learning models to predict RUL using a dataset from the Hawaii Natural Energy Institute (HNEI). Three approaches are investigated: an Encoder-only Transformer model, its enhancement with SimSiam transfer learning, and a CNN–Encoder hybrid model. These models leverage advanced mechanisms such as multi-head attention, robust feedforward networks, and self-supervised learning to capture complex degradation patterns in the data. Rigorous preprocessing and optimisation ensure optimal performance, reducing key metrics such as mean squared error (MSE) and mean absolute error (MAE). Experimental results demonstrated that Transformer–CNN with Noise Augmentation outperforms other methods, highlighting its potential for battery health monitoring and predictive maintenance. Full article

Thrust vectoring technology significantly improves the manoeuvrability and environmental adaptability of unmanned aerial vehicles by dynamically regulating the direction and magnitude of thrust. In this paper, the principles and applications of mechanical thrust vectoring technology, fluidic thrust vectoring technology and the distributed electric propulsion system are systematically reviewed. It is shown that the mechanical vector nozzle can achieve high-precision control but has structural burdens, the fluidic thrust vectoring technology improves the response speed through the design of no moving parts but is accompanied by the loss of thrust, and the distributed electric propulsion system improves the hovering efficiency compared with the traditional helicopter. Addressing multi-physics coupling and non-linear control challenges in unmanned aerial vehicles, this paper elucidates the disturbance compensation advantages of self-disturbance rejection control technology and the optimal path generation capabilities of an enhanced path planning algorithm. These two approaches offer complementary technical benefits: the former ensures stable flight attitude, while the latter optimises flight trajectory efficiency. Through case studies such as the Skate demonstrator, the practical value of these technologies in enhancing UAV manoeuvrability and adaptability is further demonstrated. However, thermal management in extreme environments, energy efficiency and lack of standards are still bottlenecks in engineering. In the future, breakthroughs in high-temperature-resistant materials and intelligent control architectures are needed to promote the development of UAVs towards ultra-autonomous operation. This paper provides a systematic reference for the theory and application of thrust vectoring technology. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

Impurities in polypropylene random copolymer (PPR) raw materials can seriously affect the performance of the final product, and efficient and accurate impurity detection is crucial to ensure high production quality. In order to solve the problems of high small-target miss rates, weak anti-interference ability, and difficulty in balancing accuracy and speed in existing detection methods used in complex industrial scenarios, this paper proposes an enhanced machine vision detection algorithm based on YOLOv11. Firstly, the FasterLDConv module dynamically adjusts the position of sampling points through linear deformable convolution (LDConv), which improves the feature extraction ability of small-scale targets on complex backgrounds while maintaining lightweight features. The IR-EMA attention mechanism is a novel approach that combines an efficient reverse residual architecture with multi-scale attention. This combination enables the model to jointly capture feature channel dependencies and spatial relationships, thereby enhancing its sensitivity to weak impurity features. Again, a DC-DyHead deformable dynamic detection head is constructed, and deformable convolutions are embedded into the spatial perceptual attention of DyHead to enhance its feature modelling ability for anomalies and occluded impurities. We introduce an enhanced InnerMPDIoU loss function to optimise the bounding box regression strategy. This new method addresses issues related to traditional CIoU losses, including excessive penalties imposed on small targets and a lack of sufficient gradient guidance in situations where there is almost no overlap. The results indicate that the average precision (mAP@0.5) of the improved algorithm on the self-made PPR impurity dataset reached 88.6%, which is 2.3% higher than that of the original YOLOv11n, while precision (P) and recall (R) increased by 2.4% and 2.8%, respectively. This study provides a reliable technical solution for the quality inspection of PPR raw materials and serves as a reference for algorithm optimisation in the field of industrial small-target detection. Full article

Generative artificial intelligence (GenAI) holds significant potential to enhance university students’ learning. However, over-reliance on it to complete academic tasks poses a risk to academic achievement by potentially encouraging cognitive outsourcing. Despite this growing concern and an expanding body of research on GenAI usage, the mechanisms through which GenAI dependency and perceived teacher caring affect their academic achievement and self-efficacy remain underexplored. Based on the theory of media system dependence, this study explores the mechanisms through which university students’ dependency on GenAI affects their academic outcomes, focusing on the mediating role of self-efficacy and moderating role of perceived teacher caring. A survey was conducted with 418 university students from Chinese public universities who had used GenAI for an extended period. The results revealed that GenAI dependency positively predicts false self-efficacy and negatively predicts academic achievement, exhibiting a significant Dunning–Kruger effect. Perceived teacher caring moderates the relationship between GenAI dependency and self-efficacy. High perceived teacher caring mitigates the Dunning–Kruger effect but has a weak moderating effect on academic achievement. These findings enhance the explanatory power of the media system dependency theory in educational contexts and reveal the pathways through which GenAI dependency and teacher caring affect learning processes and outcomes. This study expands the theoretical implications of teacher caring in the digital age and provides empirical evidence to aid higher education administrators in optimising AI governance and teachers in improving instructional interventions. Full article

Spiking neural networks (SNNs) have emerged as a promising model for energy-efficient, event-driven processing of asynchronous event streams from Dynamic Vision Sensors (DVSs), a class of neuromorphic image sensors with microsecond-level latency and high dynamic range. Nevertheless, challenges persist in optimising training and effectively handling spatio-temporal complexity, which limits their potential for real-time applications on embedded sensing systems such as object tracking and recognition. Targeting this neuromorphic sensing pipeline, this paper proposes the Dynamic Tracking with Event Attention Spiking Network (DTEASN), a novel framework designed to address these challenges by employing a pure SNN architecture, bypassing conventional convolutional neural network (CNN) operations, and reducing GPU resource dependency, while tailoring the processing to DVS signal characteristics (asynchrony, sparsity, and polarity). The model incorporates two innovative, self-developed components: an event-driven multi-scale attention mechanism and a spatio-temporal event convolver, both of which significantly enhance spatio-temporal feature extraction from raw DVS events. An Event-Weighted Spiking Loss (EW-SLoss) is introduced to optimise the learning process by prioritising informative events and improving robustness to sensor noise. Additionally, a lightweight event tracking mechanism and a custom synaptic connection rule are proposed to further improve model efficiency for low-power, edge deployment. The efficacy of DTEASN is demonstrated through empirical results on event-based (DVS) object recognition and tracking benchmarks, where it outperforms conventional methods in accuracy, latency, event throughput (events/s) and spike rate (spikes/s), memory footprint, spike-efficiency (energy proxy), and overall computational efficiency under typical DVS settings. By virtue of its event-aligned, sparse computation, the framework is amenable to highly parallel neuromorphic hardware, supporting on- or near-sensor inference for embedded applications. Full article

A mycelium is a network of hyphae that possesses the ability to self-assemble and grow into various shapes, acting as a natural binder that minimises the need for intensive chemical and energy processes, making it an alternative capable of forming structures that may eventually outperform traditional fibres such as animal leather and polyester. In this work, two mycelium mats were created, and their thickness, water absorption, coverage, and tear strength for the sewing process were determined. Fibre mats were grown in vitro or on a jute substrate. The mats were treated with salt, tannin or citric acid solutions, then air- or oven-dried. In general, the treatment that least modified the colour and appearance of the mycelium mats was citric acid, and when dried by airflow, the thickness averaged 1.4 mm. The highest tear strengths were 10.55 N/mm and 12.7 N/mm for the mycelium mats treated with citric acid without and with jute, respectively. A high percentage of water absorption was observed, reaching 267% (mycelium mats treated with tannins and dried at 65 °C) and 28% (mycelium mats treated with citric acid and air-dried). In general, all mycelium mats can be sewn, except for those treated with citric acid, which have a viscous texture and require slow sewing to prevent the mycelium from breaking. The Trametes fungus can be utilised in the production of mycelial materials, allowing for the optimisation of growth conditions to obtain mycelial mats that meet the requirements for use as an environmentally friendly alternative in the textile and related industries. Full article

This study evaluates glass and carbon fibre-reinforced concrete in terms of performance, durability, environmental impact, and a novel enzymatic self-healing method. An experimental program was conducted on seven concrete mixes, including a plain control and mixes with varying dosages of glass and carbon fibres. Glass and carbon fibres were incorporated at identical dosages of 0.12%, 0.22%, and 0.43% fibre volume fraction ($V_f)$ to enable direct comparison of their performance. The experimental investigation involved a comprehensive characterization of the concrete mixes. Fresh properties were evaluated via slump tests, while hardened properties were determined through compressive and split tensile strength testing. Durability was subsequently assessed by measuring the rate of water absorption, bulk density, and moisture content. Following this material characterization, a cradle-to-gate Life Cycle Assessment (LCA) was conducted to quantify the embodied carbon and energy. Finally, an evaluation of a novel Carbonic Anhydrase (CA)-based self-healing treatment on pre-cracked, optimised fibre-reinforced specimens was conducted. The findings highlight key performance trade-offs associated with fibre reinforcement. Although both fibre types reduced compressive strength, they markedly improved split tensile strength for glass fibres by up to 70% and carbon fibres by up to 35%. Durability responses diverged: glass fibres increased water absorption, while carbon fibres reduced water absorption at low doses, indicating reduced permeability. LCA showed a significant rise in environmental impact, particularly for carbon fibres, which increased embodied energy by up to 141%. The CA enzymatic solution enhanced crack closure in fibre-reinforced specimens, achieving up to 30% healing in carbon fibre composites. These findings suggest that fibre-reinforced enzymatic self-healing concrete offers potential for targeted high-durability applications but requires careful life-cycle optimisation. Full article

In recent years, fractional-order controllers have garnered increasing attention due to their enhanced flexibility and superior dynamic performance in control system design. Among them, the fractional-order Proportional–Integral–Derivative (FOPID) controller offers two additional tunable parameters, $\lambda$ and $\mu$, expanding the design space and allowing for finer performance tuning. However, the increased parameter dimensionality poses significant challenges for optimisation. To address this, the present study investigates the application of FOPID controllers to a two-wheeled self-balancing robot (TWSBR), with controller parameters optimised using intelligent algorithms. A novel Multi-Strategy Improved Beluga Whale Optimization (MSBWO) algorithm is proposed, integrating chaotic mapping, elite pooling, adaptive Lévy flight, and a golden sine search mechanism to enhance global convergence and local search capability. Comparative experiments are conducted using several widely known algorithms to evaluate performance. Results demonstrate that the FOPID controller optimised via the proposed MSBWO algorithm significantly outperforms both traditional PID and FOPID controllers tuned by other optimisation strategies, achieving faster convergence, reduced overshoot, and improved stability. Full article

To address the limitations of traditional models in capturing complex features for concrete strength prediction, this study proposes a hybrid deep learning approach that integrates multiple attention mechanisms with gated recurrent units (GRU). The methodology employs a multi-scale validation framework, conducting three-dimensional validation across three datasets: the Kaggle standard dataset, the lightweight foam concrete dataset, and the self-compacting concrete dataset. Six attention mechanisms (SE attention, dot-product attention, self-attention, etc.) are comprehensively compared to optimise the GRU network structure. A Newton–Raphson-based optimiser (NRBO) enables hyperparameter adaptive tuning. Experimental results show significant improvements over the baseline GRU model: mean R² increased by 6.99%, while RMSE and MAE decreased by 38.5% and 37.5%, respectively. SHAP interpretability analysis confirms that attention mechanisms effectively capture key parameters like SP and VMA in the self-compacting concrete dataset. Based on the findings, this study recommends using self-attention for datasets smaller than 200 samples and selecting the higher-accuracy model between self-attention and stacked attention mechanisms for larger datasets. Full article

Background: Osteoarthritis (OA) of the knee is a highly prevalent and heterogeneous condition. Identifying distinct clinical phenotypes within end-stage knee OA populations may inform tailored preoperative management strategies for individuals awaiting total knee replacement (TKR) surgery. Methods: This cross-sectional study employed exploratory factor analysis to identify clinical presentation patterns among patients with knee OA awaiting TKR in South Africa, using modifiable variables including demographic data, physical examination findings, patient-reported outcomes, and functional measures. Results: Three distinct clinical phenotypes emerged: (1) gait and weight—characterised by poor gait mechanics, obesity, and low self-efficacy; (2) central pain—encompassing central sensitisation, depression, and reduced functional performance; and (3) functional factors—reflecting muscular weakness and functional limitations. Conclusions: This study highlights the heterogeneity in clinical presentations among patients with end-stage knee OA awaiting TKR in South Africa. The identified phenotypes suggest a need for tailored, multidisciplinary preoperative interventions incorporating weight management, pain management, psychological support, targeted exercise programs, and behavioural change strategies to optimise post-surgical outcomes and enhance overall care. Full article

The paper presents a linear optimisation model aimed at improving the design and operational efficiency of home energy systems (HESs). It focuses on integrating photovoltaic (PV) installations, hybrid heating systems, and emerging energy storage systems (ESSs). Driven by the EU climate policy and the evolution of the Polish electricity market, which have caused price volatility, the model examines the economic and technical feasibility of shifting detached and semi-detached houses towards low-emission or zero-emission energy self-sufficiency. The model simultaneously optimises the sizing and hourly operation of electricity and heat storage systems, using real-world data from PV output, electricity and gas consumption, and weather conditions. The key contributions include optimisation based on large data samples, evaluation of the synergy between a hybrid heating system with a gas boiler (GB) and a heat pump (HP), analysis of the impact of demand-side management (DSM), storage capacity decline, and comparison of commercial and emerging storage technologies such as lithium-ion batteries, redox flow batteries, and high-temperature thermal storage (HTS). Analysis of multiple scenarios based on three consecutive heating seasons and projected future conditions demonstrates that integrated PV and storage systems, when properly designed and optimally controlled, significantly lower energy costs for prosumers, enhance energy autonomy, and decrease CO₂ emissions. The results indicate that under current market conditions, Li-ion batteries and HTS provide the most economically viable storage options. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

In this study, an effective diatomaceous earth (Dia)/octadecyltriethoxysilane (OTS)/epoxy resin (EP) with enhanced superhydrophobic and self-cleaning coating was prepared by spraying method, and the effect of OTS modification on the hydrophobicity of Dia materials was investigated through molecular dynamics computational simulation. The results showed that the number of hydrogen bonds and electrostatic interaction energy between diatomite and water molecules were significantly reduced after OTS modification, which significantly enhanced the hydrophobicity of diatomite. The coating exhibits excellent superhydrophobic properties, with a contact angle of up to 152.3°, and has a wide range of applicability, being able to uniformly cover a wide range of substrate surfaces such as glass, wood, and aluminium panels. In addition, it demonstrates excellent self-cleaning capabilities, effectively removing surface contaminants. The mechanical and chemical stability of the coating has also been thoroughly investigated, and it remains superhydrophobic even after abrasion tests and shows excellent stability in acidic or alkaline corrosive environments. Molecular dynamics calculations further elucidated the reason for the change in hydrophobicity of the coatings in acidic and alkaline environments, revealing that the diffusion of water molecules slows down in alkaline environments and solid–liquid interactions are enhanced, resulting in a slight decrease in hydrophobicity. The results of this study not only provide new ideas for the low-cost and environmentally friendly preparation of superhydrophobic materials but also provide a solid theoretical basis and practical guidance for further optimising the material properties. Full article

The recycling of nickel–cadmium batteries poses a significant environmental challenge due to cadmium’s high biotoxicity. This study proposes a green method for recovering cadmium from cadmium oxide (CdO) using carbon (coal) in the presence of a molten binary flux (KCl:NaCl = 0.507:0.493, melting point 667 °C). The flux’s relatively low density and conductivity enable cadmium reduction beneath and through the flux layer. Brown coal (5–25 mm) served as the reductant. The reduction of cadmium from cadmium oxide with carbon (brown coal) took place in the temperature range from 667 °C to 700 °C. To enhance the process, electrovortex flows (EVF) were employed—generated by the interaction between non-uniform AC electric currents and their self-induced magnetic fields resembling conditions in a fluidised bed reactor. The graphite crucible acted as both one of the electrodes, with a graphite rod as the second electrode. As Cd and CdO are denser than both the flux and coal, the reduction proceeded below the flux layer. The flux facilitated CdO transport to the reductant, speeding up the reaction. X-ray diffraction (XRD) and scanning electron microscopy (SEM) confirmed the formation of metallic cadmium beneath and within the flux layer. This method demonstrates the feasibility of flux-assisted cadmium recovery without prior mixing and offers a foundation for further optimisation of sustainable battery recycling. Full article