Search Results (917)

Background: Cluster sets (CSs) maintain velocity and power in compound movements by employing similar propulsion strategies or maintaining impulse through different mechanisms. This study aimed to explore the effect of four CS conditions on back squat (BS) propulsion and provide models for estimating changes in propulsion based on repetition and set number. Methods: Twenty male participants (age = 28.3 ± 3.1 years, stature = 1.74 ± 8.21 m, body mass = 84.80 ± 7.80 kg, BS 1RM = 140.90 ± 24.20 kg) completed four data collection sessions. Each session consisted of three sets of five repetitions at 80% 1RM BS with three minutes of unloaded inter-set rest, using varying intra-set rest intervals. Experimental conditions included 0 s (TRAD), 10 s (CS10), 20 s (CS20), and 30 s (CS30) inter-repetition rest, randomly assigned to sessions in a counterbalanced order. Ground reaction force data were collected on dual force platforms sampling at 1000 Hz, from which net propulsive impulse (J_PROP), mean force (MF), and propulsion time (t_PROP) were calculated. Conditions and sets were analysed using a 4 × 3 (CONDITION*SET) repeated-measures ANOVA to assess differences between conditions and sets, and linear mixed models (LMMs) were used to provide regression equations for each dependent variable in each condition. Results: The ANOVA revealed no significant interactions for any dependent variable. No main effects of CONDITION or SET were observed for J_PROP. The main effects of CONDITION showed that MF was significantly lower in TRAD than CS20 (g = 0.757) and CS30 (g = 0.749). t_PROP was significantly higher in TRAD than CS20 (g = 0.437) and CS30 (g = 0.569). The main effects of SET showed that MF was significantly lower in S2 (g = 0.691) and S3 (g = 1.087) compared to S1. t_PROP was significantly higher in S2 (g = 0.866) and S3 (g = 1.179) compared to S1. LMMs for CS20 and CS30 revealed no significant effect (p > 0.05) between repetition or set number and dependent variables. Conclusions: The results suggest that CS20 and CS30 maintain J_PROP by limiting MF and t_PROP attenuation. This is less rest than that suggested by the previous literature, which may influence programming decisions during strength and power mesocycles to maximise training time and training density. LMMs provide accurate estimates of BS propulsive force attenuation when separating repetitions by up to 30 s, which may help practitioners optimise training load for long-term adaptations. Full article

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. Full article

Background and objectives: Antibiotic resistance in Cutibacterium acnes is undermining topical macrolides and clindamycin, prompting renewed interest in zinc oxide nanoparticles (ZnO-NPs) as non-antibiotic alternatives. We aimed to (i) determine the antimicrobial and anti-inflammatory performance of topical ZnO-NP formulations across in vitro, animal and early human models; (ii) identify physicochemical parameters that modulate potency and tolerance; and (iii) delineate translational gaps and priority design elements for randomised trials. Methods: We systematically searched PubMed, Scopus and Web of Science until 1 June 2025 for in vitro, animal and human studies that evaluated ≤100 nm ZnO-NPs applied topically to C. acnes cultures, extracting data on bacterial load, lesion counts, biophysical skin parameters and acute toxicity. Eight eligible investigations (five in vitro, two animal, one exploratory human) analysed particles 20–50 nm in diameter carrying mildly anionic zeta potentials. Results: Hyaluronic acid-coated ZnO-NPs achieved a sixteen-fold higher selective kill ratio over Staphylococcus epidermidis at 32 µg mL¹, while centrifugally spun polyvinyl alcohol dressings reduced C. acnes burden by 3.1 log₁₀ on porcine skin within 24 h, and plant-derived nanogels generated inhibition zones that were 11% wider than benzoyl-peroxide’s 5%. In human subjects, twice-daily 0.5% hyaluronic–ZnO nanogel cut inflammatory-lesion counts by 58% at week four and lowered transepidermal water loss without erythema. Preclinical safety was reassuring, zero mortality among animals at 100 µg mL¹ and no irritation among patients, although high-dose sunscreen-grade ZnO (20 nm) delayed rat wound closure by 38%, highlighting dose-dependent differences. Conclusions: Collectively, the evidence indicates that nanoscale reformulation markedly augments zinc’s antibacterial and anti-inflammatory performance while maintaining favourable acute tolerance, supporting progression to rigorously designed, adequately powered randomised trials that will benchmark ZnO-NPs against benzoyl peroxide and retinoids, optimise dosing for efficacy versus phototoxicity, and establish long-term dermatological safety. Full article

Internet of Things (IoT) applications are rapidly adopting low-power wide-area network (LPWAN) technology due to its ability to provide broad coverage for a range of battery-powered devices. LoRaWAN has become the most widely used LPWAN solution due to its physical layer (PHY) design and regulatory advantages. Because LoRaWAN has a broad communication range, the coverage of the gateways might overlap. In LoRa technology, packets can be received concurrently by multiple gateways. Subsequently, the network server selects the packet with the highest receiver strength signal indicator (RSSI). However, this method can lead to the exhaustion of channel availability on the gateways. The optimisation of configuration parameters to reduce collisions and enhance network throughput in multi-gateway LoRaWAN remains an unresolved challenge. This paper introduces a novel low-complexity model for ZBMG-LoRa, mitigates the collisions using channel utilisiation, and categorises nodes into distinct groups based on their respective gateways. This categorisation allows for the implementation of optimal settings for each node’s subzone, thereby facilitating effective communication and addressing the identified issue. By deriving key performance metrics (e.g., network throughput, energy efficiency, and probability of effective delivery) from configuration parameters and network size, communication reliability is maintained. Optimal configurations for transmission power and spreading factor are derived by our method for all nodes in LoRaWAN networks with multiple gateways. In comparison to adaptive data rate (ADR) and other related state-of-the-art algorithms, the findings demonstrate that the novel approach achieves higher packet delivery ratio and better energy efficiency. Full article

In this study, the effects of plasma assistance on the electron beam physical vapour deposition (EB-PVD) process were investigated using an industrial coater (Smart Coater ALD Vacuum Technologies GmbH) equipped with a dual hollow cathode system. This configuration enabled the generation of a plasma environment during the deposition of the ceramic top coat onto a metallic substrate. The objective was to assess how plasma assistance influences the microstructure and thickness distribution of 7% wt. yttria-stabilised zirconia (YSZ) thermal barrier coatings (TBCs). Coatings were deposited with and without plasma assistance to enable a direct comparison. The thickness uniformity and columnar morphology of the 7YSZ top coats were evaluated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The mechanical properties of the deposited coatings were verified by the scratch test method. The results demonstrate that, in the presence of plasma, columnar grains become more uniformly spaced and exhibit sharper, well-defined boundaries even at reduced substrate temperatures. XRD analysis confirmed that plasma-assisted EB-PVD processes allow for maintaining the desired tetragonal phase of YSZ without inducing secondary phases or unwanted texture changes. These findings indicate that plasma-assisted EB-PVD can achieve desirable coating characteristics (uniform thickness and optimised columnar structure) more efficiently, offering potential advantages for high-temperature applications in aerospace and power-generation industries. Continued development of the EB-PVD process with the assistance of plasma generation could further improve deposition rates and TBC performance, underscoring the promising future of HC-assisted EB-PVD technology. Full article

Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two approaches are explored: a hybrid model combining Stochastic Differential Equations (SDEs) with Neural Networks (NNs) and Deep Learning models, in particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and the Combination of Convolutional Neural Networks (CNNs) and LSTM. Notably, while SDE-NN models are well suited for predictions in cases where data patterns are chaotic and lack consistent trends, incorporating stochastic processes increases the complexity of learning within SDE models. Moreover, it is worth mentioning that while SDE-NNs cannot be classified as purely “white box” models, they are also not entirely “black box” like traditional Neural Networks. Instead, they occupy a middle ground, offering improved interpretability over pure NNs while still leveraging the power of Deep Learning. This balance is precious in fields such as wind power prediction, where accuracy and understanding of the underlying physical processes are essential. The evaluation of the results demonstrates the effectiveness of the SDE-NNs compared to traditional Deep Learning models for wind power prediction. The SDE-NNs achieve slightly better accuracy than other Deep Learning models, highlighting their potential as a powerful alternative. Full article

The combined influence of specimen size and strain rate on the mechanical behaviour of small-diameter bamboo culms remains insufficiently characterised. This study investigates the longitudinal compressive strength of Chimonobambusa utilis through axial compression tests on specimens measuring 15 × 15 × 5 mm, 18 × 18 × 6 mm, and 21 × 21 × 7 mm under strain rates of 10⁻⁴, 10⁻³, and 10⁻² s⁻¹. Coupling experimental data with theoretical analysis, this study develops a size–strain rate interaction model to quantitatively assess the effects of specimen size and strain rate on the compressive strength of small-diameter bamboo. Increasing specimen size reduced strength and shifted failure modes from shear to buckling and splitting. At a strain rate of 10⁻⁴ s⁻¹, strength decreased from 73.35 MPa for the 15 × 15 × 5 mm specimens to 62.84 MPa for the 21 × 21 × 7 mm specimens. Conversely, increasing the strain rate from 10⁻⁴ s⁻¹ to 10⁻² s⁻¹ for the 15 × 15 × 5 mm specimens increased strength from 73.35 MPa to 80.27 MPa, indicating suppressed crack propagation. The Type II Weibull model exhibited higher predictive accuracy and parameter stability than the Type I variant. Coupling the Type II Weibull function with a power-law strain rate term and an interaction exponent developed a predictive equation, achieving relative errors below 5%. The findings demonstrate that specimen size predominantly governs strength, whereas strain rate exerts a secondary but enhancing influence. The proposed coupling model enables reliable axial load prediction for small-diameter bamboo culms, supporting material selection and dimensional optimisation in structural applications. Full article

Glacial-lake outburst floods (GLOFs) threaten more than three million residents of south-east Kazakhstan, yet quantitative data on lake growth and storage are scarce. We inventoried 154 lakes on the northern flank of the Ile-Alatau and selected four moraine-dammed basins with the greatest historical flood activity for detailed study. Annual lake outlines (2016–2023) were extracted from 3 m PlanetScope imagery with a Normalised Difference Water Index workflow, while late-ablation echo-sounder surveys (2023–2024) yielded sub-metre bathymetric grids. A regionally calibrated area–volume power law translated each shoreline to water storage, and field volumes served as an independent accuracy check. The lakes display divergent trajectories. Rapid thermokarst development led to a 37% increase in the surface area of Lake 13bis, expanding from 0.039 km² to 0.054 km² over a 5-year period. In contrast, engineering-induced drawdown resulted in a 44% reduction in the area of Lake 6, from 0.019 km² to 0.011 km². Lakes 5 and 2, which are supplied by actively retreating glaciers, exhibited surface area increases of 4.8% and 15%, expanding from 0.077 km² to 0.088 km² and from 0.061 km² to 0.070 km², respectively. The empirical model reproduces field volumes to within ±25% for four lakes, confirming its utility for rapid hazard screening, but overestimates storage in low-relief basins and underestimates artificially drained lakes. This is the first study in Ile-Alatau to fuse daily 3 m multispectral imagery with ground-truth bathymetry, delivering an 8-year, volume-resolved record of lake evolution. The results identify Lake 5 and Lake 2 as priority targets for early-warning systems and demonstrate that sustained intervention can effectively suppress GLOF risk. Incorporating these storage trajectories into regional disaster plans will sharpen evacuation mapping, optimise resource allocation, and inform transboundary water-hazard policy under accelerating climate change. Full article

This study investigates the impact of different duct designs on the energy-harvesting performance of oscillating-wing systems in both partially and fully confined environments. Numerical simulations were conducted to examine the effects of straight, convergent–straight, and convergent–divergent duct configurations on the aerodynamic forces and overall energy extraction efficiency. Under partial confinement, the convergent–divergent duct demonstrated a significant improvement of 67.5% in power output over the ductless baseline configuration. This enhancement is attributed to the increased incoming flow velocity and amplified pressure difference around the wing, which improve the effectiveness of energy generation. However, the straight and convergent–straight ducts reduced the harvester’s performance due to the diminished flow velocity within each duct. Under full confinement, all duct configurations substantially enhanced energy-harvesting performance, with the convergent–straight duct providing the highest efficiency gain (84.9%). This improvement is primarily due to the increased velocity and pressure differential across the wing surfaces, which maximise the heaving force and overall energy generation performance. These findings highlight the critical role of duct geometry in optimising energy-harvesting performance, both in partially confined and fully confined flow environments. Full article

This study investigates the subsequent stages of recrystallisation in Interstitial-Free (IF) steel subjected to an unconventional continuous annealing process with a controlled thermal gradient. A cold-rolled steel strip was exposed to varying annealing temperatures along its length, enabling the analysis of microstructural evolution during the course of recrystallisation. The microstructure and stored energy were assessed at various positions along the strip using Electron Backscatter Diffraction (EBSD). The results underscore the significant influence of local misorientation and structural inhomogeneity on orientation selection during recrystallisation. The remaining non-recrystallised volume fraction (NRF) strongly correlates with the average misorientation gradient, obeying a phenomenological power-law correspondence with an exponent of ~3.7. This indicates that the recrystallisation process is highly sensitive to small changes in local orientation gradients. These findings highlight the crucial role of stored energy distribution for texture evolution, particularly during the early stages of recrystallisation in continuous annealing. It is observed that g-fiber grains, in comparison to a-fiber grains, are much more susceptible to grain fragmentation and therefore develop more robust intra-granular misorientation gradients, allowing for successful nucleation events to occur. In the present study, these phenomena are documented in a statistically representative manner. These insights are valuable for optimising thermal processing in interstitial-free (IF) steels. Full article

The subject of this paper is how certain electrode array parameters affect the operating characteristics of electrohydrodynamic (EHD) propulsion systems. The focus is on how changes in the shapes and arrangements of electrodes, such as the diameter of the coronating conductor, effective electrode length and the spacing between electrodes, influence the formation and behaviour of the corona discharge and the resulting ion-induced airflow. A modular experimental setup was created to allow for a systematic study of each parameter in controlled atmospheric conditions using a high-voltage DC power supply. The study includes both the theoretical background and experimental methods, in order to explore the connections between the electric field distribution, ion mobility and propulsion force generation. By measuring the current, voltage and flow velocity, the impacts of design changes on the propulsion behaviour are examined. The findings help to improve the understanding of EHD propulsion mechanics and lay the groundwork for optimising electrode designs in future applications. This research supports the ongoing work to create compact, quiet and efficient propulsion technologies for use in lightweight aerial vehicles, precise fluid control and other engineering areas, where solid-state thrust systems have clear benefits over traditional methods. Full article

Primary school students, as the main users of classrooms, are directly affected by the lighting environment, which not only affects their visual comfort but also their cognitive performance. This study investigated the effects of different correlated colour temperature (CCT) levels in classroom lighting on the cognitive performance of primary school students based on a multidimensional evaluation combining physiological signals (EEG and EDA) and subjective assessment. In this study, 53 subjects aged 10–13 years old from a primary school in Anshan City were used in a controlled experiment under five CCT conditions (3000 K, 4000 K, 5000 K, 6000 K, and 7000 K) at a constant illumination level of 500 lx. EEG and skin conductance (SC) signals were collected and subjective perceptions of visual comfort and fatigue were assessed while cognitive tasks were carried out. The results showed that students performed best cognitively at a colour temperature of 4000 K, with the lowest EEG absolute power (AP) (p < 0.01) and highest comfort (p < 0.05). Females were more sensitive to colour temperature changes and showed better cognitive performance in cooler colour temperature conditions, while male students performed better in warmer light conditions (p < 0.01). The above findings suggest that optimising the CCT of classroom lighting enhances students’ cognitive functioning and comfort, providing empirical support for lighting design guidelines in educational environments. Full article

Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded DC microgrids (MGs). We propose a hierarchical distributed control framework integrating ANN-based controllers and adaptive event-triggered mechanisms to dynamically regulate power flow and minimise communication. This system utilises a hierarchical coordinated control method (HCCM) with primary virtual resistance droop control integrated with state-of-charge (SoC) management and secondary control for voltage regulation and proportional current distribution through optimised communication networks. The integration of artificial neural network (ANN)-based controllers alongside traditional PI control leads to an improvement in system responsiveness. The control approach dynamically adjusts the trigger parameters to minimise communication overhead with tight voltage regulation. An extensive simulation using MATLAB/Simulink shows how the system can effectively manage variability in renewable energy sources and maintain stable voltage profiles with precise power distribution and minimal bus voltage fluctuations. Simulations confirm enhanced voltage regulation (±0.5% deviation), proportional current sharing (98% accuracy), and 60% communication reduction under load transients (outcomes). Full article

The emergence of the Internet of Things (IoT) has brought about a significant technological shift, coupled with the rise of intelligent computing. IoT integrates various digital and analogue devices with the Internet, enabling advanced communication between devices and humans.The pervasive adoption of IoT has transformed urban infrastructures into interconnected smart cities. Here, we propose a framework that mathematically models and automates power consumption management for IoT devices in smart city environments ranging from residential buildings to healthcare settings. The proposed framework utilises set theoretic association-rule mining and combines unsupervised preprocessing with frequent-item set mining and iterative numerical optimisation to reduce non-critical energy consumption. Readings are first converted into binary transaction matrices; then a modified Apriori algorithm is applied to extract high-confidence usage patterns and association rules. Dimensionality reduction techniques compress these transaction profiles, while the Gauss–Seidel method computes control set points that balance energy efficiency. The resulting rule set is deployed through a web portal that provides real-time device status, remote actuation, and automated billing. These associative rules generate predictive control functions, optimise the response of the framework, and prepare the framework for future events. A web portal is introduced that enables remote control of IoT devices and facilitates power usage monitoring, as well as automated billing. Full article

Laser-Directed Energy Deposition (L-DED) is an additive manufacturing technique used for producing and repairing components, mainly for coating applications, depositing metal matrix composites such as cemented carbides, composed of hard metal carbides and a metallic binder. In this sense, this study evaluated the preparation of a ready-to-press WC-25Co powder as a reliable feedstock for L-DED process. This powder required pre-heat treatment studies to prevent fragmentation during powder feeding, due to the absence of metallurgical bonding between WC and Co particles. In the current study, the Taguchi methodology was used, varying laser power, powder feed rate, and scanning speed to reach an optimised deposition window. The best bead morphology resulted from 2400 W laser power, 11 mm/s scanning speed, and 9 g/min feed rate. Moreover, defects such as porosity and cracking were mitigated by applying a remelting strategy of 2400 W and 9 mm/s. Therefore, a perfect deposition is obtained using the optimised processing parameters. Microstructural analysis of the optimised deposited line revealed a fine structure, comprising columnar and equiaxed dendrites of complex carbides. The average hardness of the deposited WC-25Co powder on a AISI 1045 steel was 854 ± 37 HV0.2. These results demonstrate the potential of L-DED for processing high-performance cemented carbide coatings. Full article