Search Results (62,755)

This paper presents the design and fabrication of a triboelectric nanogenerator based on a Quadruple Parallel-cavity Helmholtz Resonator (4C–HR TENG) for the efficient harvesting of noise energy in marine engine room environments. The device utilizes sound waves to drive periodic contact and separation between polytetrafluoroethylene (PTFE) particles in the resonant cavity and the vibrating diaphragm as well as the upper electrode plate, thereby converting sound energy into mechanical energy and finally into electrical energy. The device consists of an acoustic waveguide with a length of 350 mm and both width and height of 60 mm, along with a Helmholtz Resonator with a diameter of 60 mm and a height of 40 mm. Experimental results indicate that under resonance conditions with a sound pressure level of 109.8 dB and a frequency of 110 Hz, the device demonstrates excellent output performance, achieving a peak output voltage of 250 V and a current of 4.85 μA. We analyzed and investigated the influence mechanism of key parameters (filling ratio, sound pressure level, the height between the electrode plates, and particle size) on the output performance. Through COMSOL Multiphysics simulation analysis, the sound pressure enhancement effect and the characteristic of concentrated diaphragm center displacement at the first-order resonance frequency were revealed, verifying the advantage of the four-cavity structure in terms of energy distribution uniformity. In practical applications, the minimum responsive sound pressure level corresponding to the operating frequency range of the 4C–HR TENG was determined. The output power reaches a maximum of 0.27 mW at a load resistance of 50 MΩ. At a sound pressure level of 115.1 dB, the device can charge a 1 μF capacitor to 4.73 V in just 32 s and simultaneously illuminate 180 LEDs in real-time, demonstrating its potential for environmental noise energy harvesting and micro-energy supply applications. This study provides new insights and experimental evidence for the efficient recovery of noise energy. Full article

Hydrothermal carbonization (HTC) represents a promising thermochemical method for converting wet biomass under moderate aqueous conditions into carbon-rich materials, characterized by specific attributes. Notwithstanding the increasing interest surrounding HTC, the current literature remains fragmented regarding the precise mechanisms by which process parameters influence hydrochar formation, its properties, and sustainable utilization. Consequently, the primary objective of this review is to systematically elucidate the fundamental mechanisms that govern HTC, to identify key parameters impacting hydrochar yield and quality, and to assess the sustainability and prospective contributions of HTC within the context of circular economy principles. This paper elaborates on the reaction pathways of hydrolysis, dehydration, decarboxylation, and aromatization that dictate the structural alterations and carbon densification of hydrochars. It emphasizes the roles of temperature, residence time, solid/liquid ratio, catalysts, and feedstock composition in jointly determining hydrochar yield, elemental composition, aromaticity, porosity, and energy density. Additionally, recent advancements, including microwave-assisted HTC, catalytic modifications, and post-activation techniques, are reviewed to enhance hydrochar functionality for applications in energy, adsorption, catalysis, and soil enhancement. Challenges remain regarding the scale-up of the process, reactor design, standardization of hydrochar properties, and the sustainable management or valorization of process water. This review integrates mechanistic insights with recent technological progress to position HTC as a versatile and sustainable method for producing high-value hydrochars, thereby underscoring its potential role in future biorefineries and circular economy initiatives. Full article

The increasing size of recent deep neural networks, particularly when applied to learning across multiple domains, limits their deployment in resource-constrained environments. To address this issue, this study proposes a lightweight neural architecture with a parallel structure of convolutional layers to enable efficient and scalable multi-domain learning. The proposed network includes an individual feature extractor for domain-specific features and a common feature extractor for the shared features. This design minimizes redundancy and significantly reduces the number of parameters while preserving classification performance. To evaluate the proposed method, experiments were conducted using four image classification datasets: MNIST, FMNIST, CIFAR10, and SVHN. These experiments focused on classification settings where each image contained a single dominant object without relying on large pretrained models. The proposed model achieved high accuracy while significantly reducing the number of parameters. It required only 3.9 M parameters for learning across the four datasets, compared to 33.6 M for VGG16. The model achieved an accuracy of 98.87% on MNIST and 85.83% on SVHN, outperforming other lightweight models, including MobileNet v2 and EfficientNet v2b0, and was comparable to ResNet50. These findings indicate that the proposed architecture has the potential to support multi-domain learning while minimizing model complexity. This approach may be beneficial for applications in resource-constrained environments. Full article

To address the urgent need for real-time and high-precision torque perception in robotic manipulators operating in complex environments, this study focuses on the structural optimization design of joint torque sensors. By proposing a novel hourglass-hole spoke-type elastic body structure, a systematic parametric optimization study was conducted with the objectives of improving material utilization and output sensitivity. To enhance optimization efficiency, single-factor experiments and explanatory notes on parameter selection ranges were incorporated to identify factors significantly influencing the target response and to determine their appropriate experimental ranges. Building upon this, the Box–Behnken experimental design method was employed, combined with response surface methodology, to perform multi-objective optimization on the key dimensions of the elastic body. Experimental results demonstrate that the optimized sensor structure achieved a 13.1% improvement in material utilization and an 11.9% increase in sensitivity. The baseline sensitivity of the final sensor reached 0.558 mV/N·m, representing a 19.2% enhancement compared to the optimized dumbbell-hole structure, while material utilization was also improved by 3.1%. This study proposes a novel high-sensitivity hourglass-hole spoke-type elastic body configuration and establishes an efficient response surface optimization framework applicable to the structural design of joint torque sensors fabricated from linear elastic materials, offering new insights for the design and optimization of high-sensitivity torque sensors. Full article

Droplet impact on porous mesh surfaces is a common phenomenon in fields such as thermal management systems, biomedical manufacturing, and precision agriculture. As a substrate with microstructures, the mesh surface allows liquid penetration upon droplet impact. The resulting loss of liquid mass significantly alters the impact dynamics of the residual droplet on the surface. This study experimentally compares the behavior of water droplets impacting superhydrophobic mesh surfaces with different pore sizes against that on smooth surfaces. It focuses on analyzing how liquid penetration affects parameters such as spreading time (t_s), maximum spreading factor (β_max), contact time (t_c), and droplet height (h). The results show that the substantial liquid loss induced by large-pore meshes directly leads to a marked decrease in spreading time and maximum spreading factor. Furthermore, the “pancake bouncing” phenomenon observed on the superhydrophobic mesh surfaces significantly shortens the contact time, providing a new perspective for minimizing the contact duration between droplets and solid surfaces. By establishing the correlation between pore size and droplet impact behavior, this study provides key structural design guidelines for applications such as advanced printing systems and efficient pesticide spraying, thereby achieving the goal of proactively regulating liquid dynamics through surface microstructure. Full article

In light of the evident water scarcity and the challenges posed by climate change, this study aimed to evaluate the physiological, phenological, and productive responses of the potato crop (var. Superchola) under water deficit conditions, with the goal of optimizing water use in Tungurahua Province, Ecuador. Crop tolerance to water stress was assessed using drainage lysimeters under a completely randomized block design with three treatments and three replications: 100% ETo, 75% ETo, and 50% ETo. Soil and climatic parameters were characterized, and the crop coefficient (Kc) was calculated and adjusted for each phenological stage. The results showed that, although the full irrigation treatment (100% ETo) yielded the highest production, the application of a moderate water deficit (75% ETo) achieved a 16.2% water saving without significantly affecting crop yield or development. The maximum Kc value recorded was 1.22 during the maximum crop development stage. Full article

Biomimetic microstructured surfaces offer a promising passive strategy for drag reduction in marine and aerospace applications. This study employs computational fluid dynamics (CFD) simulations to systematically investigate the drag reduction performance and mechanisms of groove-type microstructures, addressing both geometry selection and dimensional optimization. Three representative geometries (V-groove, blade-groove, and arc-groove) were compared under identical flow conditions (inflow velocity 5 m/s, Re = 7.5 × 10⁵) using the shear-stress-transport (SST k-ω) turbulence model, and the third-generation Ω criterion was employed for threshold-independent vortex identification. The results establish a clear performance hierarchy: blade-groove achieves the highest drag reduction rate of 18.2%, followed by the V-groove (16.5%) and arc-groove (14.7%). The analysis reveals that stable near-wall microvortices form dynamic vortex isolation layers that separate the high-speed flow from the groove valleys, with blade grooves generating the strongest and most fully developed vortex structures. A parametric study of blade-groove aspect ratios (h⁺/s⁺ = 0.35–1.0) further demonstrates that maintaining h⁺/s⁺ ≥ 0.75 preserves effective vortex-isolation layers, whereas reducing h⁺/s⁺ below 0.6 causes vortex collapse and performance degradation. These findings establish a comprehensive design framework combining geometry selection (blade-groove > V-groove > arc-groove) with dimensional optimization criteria, providing quantitative guidance for practical biomimetic drag-reducing surfaces. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

With the dual challenges of global energy scarcity and worsening environmental issues, the efficient and selective conversion of CO₂ into CH₄-an environmentally friendly fuel with high energy density—offers considerable application potential. In this study, a 2D-Cu₂O/carbon nitride (2D-Cu₂O/CN) heterojunction catalyst was successfully prepared. Notably, 2D-Cu₂O/CN shows enhanced light absorption capacity, reduced charge-transfer resistance, and efficient separation of photogenerated electron–hole pairs. It exhibits a CH₄ yield of 14.1 μmol·g⁻¹·h⁻¹, 4-fold higher than that of CN. This study provides a feasible approach for the design of high-efficiency photocatalysts for CO₂ reduction to CH₄. Full article

Gas injection is a well-established method for enhancing oil recovery by improving oil mobility, primarily through viscosity reduction. While its application in heavy oil reservoirs is extensively studied, the specific impact of carbon dioxide (CO₂) injection pressure on fluid viscosity reduction and the ultimate recovery factor from light oil reservoirs has not been fully investigated. To address this gap, this experimental study systematically explores the effects of CO₂ injection pressure and reservoir permeability on light oil recovery. This study conducted miscible, near-miscible, and immiscible gas injection experiments on two core samples with distinct permeabilities (13.4 md and 28 md), each saturated with light oil. CO₂ was injected at five different pressures, including conditions ranging from immiscible to initial reservoir pressure. The primary metrics for evaluation were the recovery factor (measured at gas breakthrough, end of injection, and abandonment pressure) and the viscosity reduction of the produced oil. The results conclusively demonstrate that CO₂ injection significantly enhances light oil production. A direct proportional relationship was established between both the injection pressure and the recovery factor and between permeability and overall oil production at the gas breakthrough. However, a key finding was the inverse relationship observed between permeability and viscosity reduction: the lower-permeability sample (13.4 md) consistently exhibited a greater percentage of viscosity reduction across all injection pressures than the higher-permeability sample (28 md). This unexpected trend is aligned with the inverse relationship between the permeability and the recovery factor after the gas breakthrough. This outcome suggests that enhanced CO₂ solubility, driven by higher confinement pressures within the nanopores of the lower-permeability rock, promotes a localized, near-miscible state. This effect was even evident during immiscible injection, where the low-permeability sample showed a noticeable viscosity reduction and superior long-term production. These findings highlight the critical role of pore-scale confinement in governing CO₂ miscibility and its associated viscosity reduction, which should be incorporated into enhanced oil recovery design for unconventional reservoirs. Full article

Background/Objectives: This study explored how attribute framing (lifestyle-focused vs. technology-focused product descriptions) and popularity cueing (presence or absence of a “best-seller” label) influenced purchase likelihood for a fictitious selection of hearing aids (HAs) among Canadian adults aged 40 years and above. The study further aimed to investigate whether the effects observed were unique to HAs or applicable to less-specialized consumer technology contexts. Method: A 2 × 2 × 2 mixed experimental design compared attribute framing and popularity cueing effects across HAs and notebook computers at three technology levels (entry-level, midrange, and premium). Participants (n = 122) provided ratings indicating their purchase likelihood for each product. Results: Attribute framing showed no significant influence on purchase decisions across technology levels. The presence of a popularity cue that the midrange HA was the best-seller negatively affected purchase likelihood for the entry-level HA, with higher purchase likelihood ratings observed when this cue was absent. Participants expressed stronger purchase likelihood for premium HAs compared to premium notebook computers. Notably, these two effects were not statistically significant following correction for multiple comparisons. Conclusions: Popularity cues for HAs may have inadvertent consequences for consumer perceptions of models with differing technology levels. Findings also suggest potentially greater willingness to invest in premium health-related technologies versus familiar consumer technology. Further research involving current HA users or candidates is needed to better understand these findings. Full article

This article presents a case study from a Social and Health Care School in Denmark, where a rooftop garden was designed to promote student health and support nature-based teaching across subject areas. A novel aspect of the project is the formal integration of the garden into teaching, implying that its long-term impact may extend beyond the students to the end-users they will later encounter in nursing homes and hospitals nationwide. This study applies the Evidence-Based Health Design in Landscape Architecture (EBHDL) process model, encompassing evidence collection, programming, and concept design, with the University of Copenhagen acting in a consultancy role. A co-design process with students and teachers was included as a novel source of case-specific evidence. Methodologically, this is a participatory practice-based case study focusing on the full design and construction processes, combining continuous documentation with reflective analysis of ‘process insights,’ generating lessons learned from the application of the EBHDL process model. This study identifies two categories of lessons learned. First, general insights emerged concerning governance, stakeholder roles, and the critical importance of site selection, procurement, and continuity of design responsibility. Second, specific insights were gained regarding the application of the EBHDL model, including its alignment with Danish and international standardised construction phases. These insights are particularly relevant for project managers in nature-based initiatives. The results also show how the EBHDL model aligns with Danish and international standardised construction phases, offering a bridge between health design methods and established building practice. The case focuses on the EBHDL process rather than verified outcomes and demonstrates how evidence-based and participatory approaches can help structure complex design processes, facilitate stakeholder engagement, and support decision-making in institutional projects. Full article

In recent years, new methods to reuse, repurpose, recycle, and recover decommissioned wind turbine blades (dWTBs) have actively been developed in the wind industry. In this study, the authors address the scientific challenge of repurposing decommissioned wind turbine blades for earthwork applications, particularly as part of retaining structures. A gravity retaining structure made entirely from recycled materials is introduced, consisting of glass fibre-reinforced polymer (GFRP) composite modular units derived from dWTBs. To improve the structure’s sustainability, a mixture of typical sand and lightweight waste materials is considered for filling and backfilling of the GFRP units. In particular, two waste materials are examined—a polymer foil derived from recycled laminated glass and tyre-derived aggregate (TDA) in the form of rubber powder—which are incorporated into the sand matrix in typical dry mass proportions ranging from 2% to 32% and 5% to 20%, respectively, reflecting practical ranges considered in geotechnical backfill applications. The research involved material testing of all recyclates and their mixtures with standard sand, as well as two-dimensional finite-element (2D FE) analysis of a retaining structure using the determined material properties. To facilitate the real-world implementation of this novel technology, a structure was designed to account for ground conditions at a specific site to protect against an existing landslide. In summary, this study presents the concept of a sustainable retaining structure along with results from material tests and an initial design for implementation, supported by FE analysis of overall stability. Full article

With the increasing penetration of renewable energy, power systems are facing greater uncertainty and volatility, which poses significant challenges for Virtual Power Plant scheduling. Existing research mainly focuses on optimizing economic efficiency but often overlooks system reliability and the impact of forecasting deviations on scheduling, leading to suboptimal performance. Thus, this paper presents a reliability-cost bi-objective cooperative optimization model based on a dual-swarm particle swarm algorithm: it introduces positive and negative imbalance price penalty factors to explicitly describe the economic costs of forecast deviations, constructs a reliability evaluation system covering PV, EVs, air-conditioning loads, electrolytic aluminum loads, and energy storage, and solves the multi-objective model via algorithm design of “sub-swarms specializing in single objectives + periodic information exchange”. Simulation results show that the method ensures stable intraday operation of VPPs, achieving 6.8% total cost reduction, 12.5% system reliability improvement, and 14.8% power deviation reduction, verifying its practical value and application prospects. Full article