Search Results (31)

Coupled high temperature and dynamic loading often leads to the complicated degradation of performance in industrial kilns, enclosures, or other concrete structures, which constitutes a serious hazard to the safety of concrete structure. To bridge this research gap, this study investigates not only the mechanical response but also the damage mechanisms of normal concrete (NC), basalt fiber-reinforced concrete (BFRC), and steel fiber-reinforced concrete (SFRC) under the coupled effects of high temperature and dynamic loading. Test specimens were conditioned for ambient conditions, 200 °C, 400 °C, and 600 °C, and underwent quasi-static and dynamic splitting tensile tests using the Split Hopkinson Pressure Bar (SHPB) with strain rates varying between 24 and 91 s⁻¹. Significantly, the high-temperature-induced degradation of all types of concrete is remarkably suppressed by fibers, especially steel fibers. The best thermal degradability resistance was displayed by the SFRC with the highest remaining residual dynamic strength, peak strain, and energy dissipation, especially in the most severe (600 °C, 0.15 MPa) circumstances among these three types of materials. All materials revealed a clear strain rate strengthening effect. An empirical model, integrating the coupling effect of strain rate, temperature, and fiber type in DIF, was also developed, yielding better prediction capability than those already available. This reveals that the comprehensive performance of SFRC can meet structure requests, so it is suitable for applications involving steel fiber in environments characterized by high temperature and high strain rates. Full article

Urban green spaces, as vital land use components, play a crucial role in promoting public mental health and well-being. This study investigates the differential restorative benefits and stress relief pathways in urban green spaces for populations with varying stress thresholds. This study employed a controlled experiment (pre-test–free activity–post-test) with 120 park users, integrating subjective scales (DASS-21, SRRS, etc.). We innovatively stratified participants by stress threshold to analyze recovery mechanisms. Key findings reveal: (1) Park visits were associated with significant restorative benefits across all stress groups (p < 0.05), yet the recovery patterns and potential pathways appear to be stress-threshold-dependent. (2) Our findings suggest distinct patterns: low-stress individuals benefit via cognitive-behavioral routes (environmental awareness, dynamic activities), while medium-high stress groups rely more on physiological regulation (environmental enclosure, static relaxation). (3) Crucially, these mechanisms suggest stratified landscape design strategies: multi-sensory interactive spaces for low-stress, static rest areas for medium-stress, and low-interference, high-enclosure meditative environments for high-stress individuals. However, given the single-group pre-post design, observed benefits should be interpreted as associations and plausible pathways rather than definitive causal effects. By introducing stress threshold stratification into restorative landscape research, this study provides actionable, evidence-based guidelines for optimizing urban green space planning and design. It offers a crucial scientific foundation for creating healthier, more inclusive, and sustainable urban environments that effectively address diverse mental health needs and contribute to public health promotion through sustainable land use practices. Full article

To meet the increasing demands for structural lightweighting in electric vehicles (EVs), carbon fiber reinforced plastic (CFRP) has been gradually introduced to reduce weight and enhance passenger safety in automotive engineering. The battery-pack enclosure is a key structural component for EVs, as it significantly influences the driving distance, safety, and road handling of EVs. This study presents a top-down design approach and topology optimization for a lightweight CFRP battery pack enclosure reinforced with cross-shaped stiffeners. The main objective is to develop an efficient composite enclosure that meets performance targets while accommodating the demands of cost-effective mass production. The composite battery pack enclosure was fabricated using the compression molding process. Topology optimization was carried out in the preliminary design stage on the structural shape and geometric parameters following a top-down design approach. Experimental tests recorded maximum deformations of 0.56 mm and 10.33 mm under in-plane and lateral loads, respectively. The final prototype product achieved a total mass of 4.78 kg with a rapid curing cycle of 10–15 min. In conclusion, a lightweight composite battery-pack enclosure with cross-shaped stiffeners was successfully manufactured, integrating a top-down design approach with topology optimization. This study demonstrates an effective design approach to achieving an optimal balance of lightweight, cost-effectiveness, and production efficiency for EV battery-pack enclosures. Full article

Source water reservoirs in the lower reaches of the Yangtze River are increasingly threatened by algal contamination, driven by fluctuations in upstream water quality. To ensure stable reservoir operation and protect downstream drinking water sources, physical enclosures are widely used. However, most algal pollution in reservoirs consists of microalgae (diameters < 100 μm), and conventional algae barriers are effective primarily against visible algal blooms but perform poorly against microscopic algal clusters. To address this limitation, we developed a composite microfiltration physical enclosure system by integrating a microfiltration membrane, supported by a mechanical layer, onto physical enclosures. The algal removal performance of this system was evaluated from lab-scale tests to field-scale applications. Results demonstrated that the composite membrane exhibited excellent interception efficiency against algal aggregates, with algae density in the filtered water reduced by over 80%. The composite enclosure effectively filters multiple algae species, significantly reducing the risk of algae entering downstream water treatment plants, thereby alleviating the burden of traditional processes and reducing operating costs. Full article

Integrating bioluminescent organisms as passive lighting sources in the built environment is currently a hot topic. However, there are several limitations facing the implementation and up-scaling of these naturally bioluminescent organisms in the built environment on architectural and urban scales, such as the scale, sensitivity, enclosure, and difficulty of maintenance. Moreover, there are complex technicalities and operational aspects of conventional bioreactors that host these bioluminescent agents, especially in terms of managing their recharge and effluent, not to mention their high maintenance cost. The current work offers a sustainable, stand-alone, bioluminescent urban screen system employing Aliivibrio fischeri CECT 524 bioink on 3D-printed customized scaffolds as bioreceptive panel design based on a field-diffusion pattern to host the bioluminescent bacterial bioink. The field-diffusion pattern was employed thanks to its proven efficiency in entrapment of the various microbial cultures. Three different growth media were tested for culturing Aliivibrio fischeri CECT 524, including Luria Bertani Broth (LB), the Tryptone Soy Broth (TSB), and the standard Marine Broth (MB). The results revealed that the Marine Broth (MB) media achieved the highest bioluminescent intensity and duration. The maximum light emission typically in range of ~490 nm of blue–green light captured by a conventional reflex camera (human eye vision) was observed for 10 consecutive days in complete darkness after 3–10 s, at a room temperature of 25 °C. This was visible mainly at the thin curvilinear peaks of the 3D-printed field pattern. P1 achieved the highest performance in terms of visible blue–green light, and a duration of 10 days of active bioluminescence was achieved without the need for refilling, thanks to the high number of peaks and narrow wells at <0.5 cm of its field-diffusion pattern. This study proves the efficiency of this biomimetic pattern in terms of the bioreceptivity of the bioluminescent bacterial bioink. Furthermore, the proposed 3D-printed urban screens proved their economic sustainability in terms of affordability and their minimized production processes, in addition to their easy maintenance and recharge. These results qualify these 3D-printed bioluminescent urban screens for easy and decentralized adoption and application on an architectural and urban scale. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Explosion-proof safety evaluation is critical for coal mine equipment operating in hazardous environments. Traditional methods rely on physical explosion testing, which is time-consuming, costly, and impractical for large-scale or complex systems. We propose a real-time virtual measurement method based on an improved combined surrogate model to address these limitations. A digital twin framework is constructed by integrating internal explosion transmission data with physical models of gas deflagration and enclosure impact mechanics. A transient multi-physical reduced-order model is developed using Latin hypercube sampling and machine learning. The core prediction model, RBF-GMSE, combines a radial basis function surrogate model and a generalized mean square error model through adaptive weighting. This model is trained on dimension-reduced finite element data and used to predict explosion-induced stress, strain, and displacement in real time. A virtual measurement system is implemented using this framework, enabling accurate, dynamic safety evaluation of explosion-proof equipment. Validation against simulation data shows a maximum prediction error below 1.89% and an average correlation coefficient of 0.9779, confirming the model’s high accuracy and robustness. This approach offers an intelligent solution for efficient and precise acquisition of explosion-proof safety characteristics in coal mine equipment. Full article

Bare soil expansion in urban forests, driven by persistent high-intensity trampling, degrades both macro-scale natural resources and micro ecological conditions. Targeted interventions are therefore essential. In this study, trampled bare ground in forest parks and artificially cultivated Ophiopogon japonicus were used as experimental models We employed trampled bare ground in forest parks as well as artificially cultivated O. japonicus as experimental models. Five treatments were implemented: enclosure control (CK), ploughing (F), Bacillus thuringiensis NL-11 application (J), biochar addition (C), and co-application of B. thuringiensis NL-11 with biochar (JC). Our results indicate that, compared with CK, biochar treatments reduced soil bulk density by 30%, increased soil porosity by 89%, and improved water-holding capacity. The soil nitrate nitrogen content in the NL-11 treatment was increased by 113.8% compared with CK, while the co-application of NL-11 with biochar exhibited the highest sucrase and urease activities. Notably, the co-application of B. thuringiensis NL-11 with biochar exhibited the most pronounced effects on aboveground biomass, plant height, and root development, followed by the B. thuringiensis NL-11 treatment. Microbial β-diversity shifts under co-application of B. thuringiensis NL-11 with biochar treatment strongly correlated with soil enzyme activation and plant growth enhancement (Mantel test, p < 0.05). Correlation analysis confirmed that exogenous nutrient inputs significantly influenced enzyme activities, thereby promoting plant development. These results highlight the effectiveness of integrating microbial inoculation with biochar to restore trampled urban forest soils. Full article

The storage enclosures are vital for stabilizing the micro-environment within, facilitating preventive conservation efforts, and enabling energy savings by reducing the need for extensive macro-environmental control within the room. However, real-time conformity monitoring of the micro-environment to ensure compliance with preventive conservation specifications poses a practical challenge due to a limitation in implementing physical sensors for each enclosure. This study aims to address this challenge by using an ANN (Artificial Neural Network)-based prediction for temperature and RH (Relative Humidity) changes in response to macro-environmental fluctuations. A numerical model was developed to simulate transient heat and mass transfer between macro- and micro-environments and then employed to determine an acceptable macro-environmental range for sustainable preventive conservation and to generate a dataset to train a sequence-to-sequence ANN model. This model was specially designed for 24 h real-time prediction of heat and mass transfer and to simulate the micro-environmental conditions under varying levels of control accuracy over the macro-environment. The effectiveness of the prediction model was tested through a real trial application in the laboratory, which revealed a robust prediction of micro-environments inside different enclosures under various macro-environmental conditions. This modeling approach offers a promising solution for monitoring the micro-environmental conformity and further implementing the relaxing control strategy in the macro-environment without compromising the integrity of the collections stored inside the enclosures. Full article

Commercial wearable systems for surface electromyography (sEMG) acquisition often trade bandwidth, synchronization, and battery life for miniaturization, and their proprietary designs inhibit reproducibility and cost-effective customization. To address these limitations, we developed MOT, a fully wireless, multichannel platform built from commodity components that can be replicated in academic laboratories. Each sensor node integrates an AD8232 analog front-end configured for 19–690 Hz bandwidth (59 dB mid-band gain) with a 12-bit successive approximation ADC sampling at 1 kS/s. Packets of 120 samples are broadcast via the low-latency ESP-NOW 2.45 GHz protocol to a central hub, which timestamps and streams data to a host PC over USB-UART. Bench tests confirmed the analog response and showed mains interference at least 40 dB below voluntary contraction levels; the cumulative packet loss remained below 0.5% for six simultaneous channels at 100 m line-of-sight, with end-to-end latency under 3 ms. A 180 mAh Li-ion cell was used to power each node for 1.8 h of continuous operation at 100 mA average draw, and the complete sensor, including enclosure, was found to weigh 22 g. MOT reduced a 60 Hz artifact magnitude by up to 22 dB while preserving signal bandwidth. The hardware, therefore, provides a compact and economical solution for biomechanics, rehabilitation, and human–machine interface research that demands mobile, high-fidelity sEMG acquisition. Full article

This paper presents the design, implementation, and testing of a traffic monitoring platform based on 5G-connected Ultra-Wideband (UWB) radars deployed on a university campus. The development of both connected radars and an IoT platform is detailed. The connected radars integrate commercial components, including a Raspberry Pi (RPi), a UWB radar, a standard enclosure, and a custom communication board featuring a 5G module. The IoT platform, which receives data from the radars via MQTT, is scalable, easily deployable, and supports radar management, data visualization, and external data access via an API. The solution was deployed and tested on campus, demonstrating real-time operation over a commercial 5G network with an estimated median latency between the radar and server of 75 ms. A preliminary evaluation conducted on a single radar during peak-hour traffic on a double-lane road, representing a challenging scenario, indicated a high detection rate of 94.81%, a low false detection rate of 1.02%, a high classification accuracy of 97.29%, and a high direction accuracy of 99.66%. These results validate the system’s capability to deliver accurate traffic monitoring. Full article

3D-Kagome lattice sandwich panels are mainly composed of upper and lower panels and a series of symmetrically and periodically arranged lattices, known for their excellent high specific stiffness, high specific strength, and energy absorption capacity. The inherent geometrical symmetry of the 3D-Kagome lattice plays a crucial role in achieving superior mechanical stability and load distribution efficiency. This structural symmetry enhances the uniformity of stress distribution, making it highly suitable for automotive vibration suppression, such as battery protection for electric vehicles. In this study, a polyurethane foam-filled, symmetry-enhanced 3D-Kagome sandwich panel is designed following an optimization of the lattice structure. A novel fabrication method combining precision wire-cutting, interlocking core assembly, and in situ foam filling is employed to ensure a high degree of integration and manufacturability of the composite structure. Its mechanical properties and energy absorption characteristics are systematically evaluated through a series of experimental tests, including quasi-static compression, three-point bending, and low-speed impact. The study analyzes the effects of core height on the structural stiffness, strength, and energy absorption capacity under varying loads, elucidating the failure mechanisms inherent to the symmetrical lattice sandwich configurations. The results show that the foam-filled sandwich panels exhibit significant improvements in mechanical performance compared to the unfilled ones. Specifically, the panels with core heights of 15 mm, 20 mm, and 25 mm demonstrate increases in bending stiffness of 47.3%, 53.5%, and 51.3%, respectively, along with corresponding increases in bending strength of 45.5%, 53.1%, and 50.9%. The experimental findings provide a fundamental understanding of foam-filled lattice sandwich structures, offering insights into their structural optimization for lightweight energy-absorbing applications. This study establishes a foundation for the development of advanced crash-resistant materials for automotive, aerospace, and protective engineering applications. This work highlights the structural advantages and crashworthiness potential of foam-filled Kagome sandwich panels, providing a promising foundation for their application in electric vehicle battery enclosures, aerospace impact shields, and advanced protective systems. Full article

This manuscript presents a real-time monitoring system for urban garbage levels using Time-of-Flight (ToF) sensing technology. The experiment employs the VL53L8CX sensor, which accurately measures distances, along with an ESP32-S3 microcontroller that enables IoT connectivity. The ToF-Node IoT system, consisting of the VL53L8CX sensor connected to the ESP32-S3, communicates with an IoT gateway (Raspberry Pi 3) via Wi-Fi, which then connects to an IoT cloud. The ToF-Node communicates with the IoT gateway using Wi-Fi, and after with the IoT cloud, also using Wi-Fi. This setup provides real-time data on waste container capacities, facilitating efficient waste collection management. By integrating sensor data and network communication, the system supports informed decision-making for optimizing collection logistics, contributing to cleaner and more sustainable cities. The ToF-Node was tested in four scenarios, with a PCB measuring 40 × 18 × 4 mm and an enclosure of 65 × 40 × 30 mm. We used an office trash box with a height of 250 mm (25 cm), and the ToF-Node was located on the top. Results demonstrate that the effectiveness of ToF technology in environmental monitoring and the potential of IoT to enhance urban services. For detailed monitoring, additional ToF sensors may be required. Data collected are displayed in the IoT cloud for better monitoring and can be viewed by level and volume. The ToF-Node and the IoT gateway have a combined power consumption of 153.8 mAh Full article

Aquaculture plays a crucial role in meeting the needs of a growing human population and achieving the sustainable development goals outlined in Agenda 2030. However, it is essential that this sector grows sustainably. In this study, we hypothesized that environmental sustainability decreases as the trophic level of farmed species increases and that it is higher in integrated systems compared to monocultures. To test these hypotheses, we conducted a comparative analysis of the environmental sustainability indicators of some aquaculture systems, including the farming of primary producers, filter feeders, and low-trophic phagotrophs. We compiled secondary data on eighteen environmental sustainability indicators from seven aquaculture systems. Five are monocultures, including the farming of macroalgae (Hypnea pseudomusciformis), oysters (Crassostrea gazar) in a tropical environment, oysters in a subtropical environment, as well as tambatinga (hybrid Colossoma macropomum × Piaractus brachypomus) and tambaqui (Colossoma macropomum). Additionally, two are integrated systems: tambaqui raised in hapa nets (small cage-like enclosures) within Amazon river prawn (Macrobrachium amazonicum) ponds, and tambaqui and prawns cohabitating freely in the same ponds. A benchmark tool was utilized to establish reference values for comparing indicators between the systems, and a method was developed to create environmental sustainability indices that integrate all indicators. Environmental sustainability tends to decrease as trophic levels rise, supporting the initial hypothesis. However, the data revealed that Integrated Multi-Trophic Aquaculture (IMTA) systems ultimately have lower environmental sustainability than monocultures, which was contrary to our expectations. Algae and oyster farming were found to be more environmentally sustainable than low-trophic fish farming systems. Among these, the integrated systems did not demonstrate significantly greater sustainability than the monocultures, as initially anticipated. To gain a comprehensive understanding of sustainability, further research on the social and economic sustainability of these systems is necessary. Full article

Nanofluids have the proven capacity to significantly improve the thermal efficiency of a heat exchanging system due to the presence of conductive nanoparticles. The aim of this study is to simulate the forced convection on a non-Newtonian hybrid with a nanofluid (Al₂O₃-TiO₂-H₂O) in a hexagonal enclosure by the Galerkin finite element method (GFEM). The physical model is a hexagonal enclosure in two dimensions, containing a heated cylinder embedded at the center. The bottom, middle left, and right walls of the enclosure are all considered cold ($T_c$), while the top wall is considered to be moving, and the remaining middle, upper left, and right walls have the adiabatic condition. The Prandtl number ($Pr$ = $6.2$), Reynolds number ($Re$ = 50, 100, 300 and 500), power law index (n = $0.6$, $0.8$, $1.0$, $1.2$ and $1.4$), volume fractions of nanoparticles ($\varphi$ = $0.00$, $0.01$, $0.02$, $0.03$ and $0.04$), and Hartmann numbers ($Ha$ = 0, 10, 20 and 30) are considered in the model. The findings are explained in terms of sensitivity tests and statistical analysis for various $Re$ numbers, n, and $Ha$ numbers employing streamlines, isotherms, velocity profiles, and average Nusselt numbers. It is observed that the inclusion of $\varphi$ improves the convective heat transfer at the surging values of $Re$. However, if the augmenting heat transfer requires any control mechanism, integrating a non-zero $Ha$ number is found to stabilize the system for the purpose of thermal efficacy. Full article