Search Results (5,252)

The present work explores the effects of dental splints from a mechanical standpoint, aiming to provide a practical tool for the surgical decision-making process regarding splint cross-section dimensions. Our investigation centers on the anatomical structure of a pentamorphic dental arch encompassing central and lateral incisors and one canine on each side. Using parametric in silico models built up by means of an ad-hoc procedure, geometry, material properties, and boundary conditions are defined on a parametric anatomical model that can be tailored using RX-derived geometrical information. Two general cases have been considered, one with the splint and the other splintless, and a sensitivity analysis has been performed by varying the splint section height and thickness. The results show the diminishing mobility at the apex and basis of the diseased incisors, demonstrating the effectiveness of the periodontal treatment. Moreover, the stress due to physiological loads moves away from diseased teeth toward the healthy ones due to the splint effects, focusing on the splint–glue–canine contact zone and highlighting the crucial role played by the canine in fixing the entire dental structure. To establish a preliminary mechanical assessment of the dental structure’s safety and to confine its actual value within a mechanically reasonable range, a synthetic “traffic-light” indicator of stress-based failure risk is proposed. It is felt that the tool proposed in this study can help surgeons assess the pre-operative patient-specific mechanical effects of the splint treatment, driving the design and choice of periodontal splints. By linking splint geometry to mechanical safety via a stress-based indicator, the method supports the optimized design and selection of splints, improving treatment reliability while preserving comfort and clinical effectiveness. Full article

As a prevalent integrated structure-insulation system, sandwich insulation wall panels have emerged as a critical structural configuration for zero- and nearly zero-energy green buildings, owing to their high construction efficiency and superior thermal insulation performance which directly aligns with the core goals of sustainability and sustainable energy utilization in the built environment. However, connectors penetrate the insulation layer and form systematic thermal bridges, which cause substantial heat loss and become a key bottleneck limiting further improvement in the overall thermal performance of wall systems. This study established three-dimensional numerical models of sandwich insulation wall panels with four typical connectors (fiber-reinforced polymers (FRPs), clamp-type stainless steel, plate-type stainless steel, and truss-type stainless steel) using Ansys Fluent 2021R1. The model reliability was verified by calibrated hot-box experiments, with relative errors between simulation and experimental results ranging from 2.1% to 16.1%. Systematic numerical simulations were then performed to investigate the effects of connector type, insulation material, climate zone, inner–outer temperature difference, connector quantity, and wall dimensions on the thermal bridge effect. The results indicated that FRP connectors caused the minimal heat flux increment (only 0.27%), followed by clamp-type stainless steel connectors (9.59%), while plate-type and truss-type stainless steel connectors led to significant increments (27.17% and 27.62%, respectively). The lower the heat transfer coefficient (K-value) of the wall was, the more prominent the connector-induced thermal bridge effect was. Within the typical temperature difference range, the heat flux increment of each connector remained stable, and polyurethane (PU) insulation exhibited a more significant inhibitory effect on thermal bridges than extruded polystyrene (XPS) under the same K-value. Linear fitting formulas for the relationship between wall K-value/temperature difference and the heat flux correction coefficient were derived, with high goodness-of-fit. The maximum impact of connectors on wall thermal performance did not exceed 30%. This study provides theoretical support and design references for the selection of connectors, material optimization, and thermal performance calculation of sandwich insulation wall panels, contributing to the promotion of energy-saving building envelope technologies. Full article

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

Non-invasive electrocardiogram (ECG) and respiratory signals are easy to acquire via low-cost sensors, making them promising alternatives for sleep staging. However, existing methods using these signals often yield insufficient accuracy. To address this challenge, we incrementally optimized the sleep staging model by designing a structured experimental workflow: we first preprocessed respiratory and ECG signals, then extracted fused features using an enhanced feature selection technique, which not only reduces redundant features, but also significantly improves the class discriminability of features. The resulting fused features serve as a reliable feature subset for the classifier. In the meantime, we proposed a hybrid optimization algorithm that integrates the snake optimization algorithm (SO) and ant colony optimization algorithm (ACO) for automated hyperparameter optimization of support vector machines (SVMs). Experiments were conducted using two PSG-derived public datasets, the Sleep Heart Health Study (SHHS) and MIT-BIH Polysomnography Database (MIT-BPD), to evaluate the classification performance of multimodal features compared with single-modal features. Results demonstrate that the bimodal staging using SHHS multimodal signals significantly outperformed single-modal ECG-based methods, and the overall accuracy of the SHHS dataset was improved by 12%. The SVM model optimized using the hybrid Snake–ACO algorithm achieved an average accuracy of 89.6% for wake versus sleep classification on the SHHS dataset, representing a 5.1% improvement over traditional grid search methods. Under the subject-independent partitioning experiment, the wake versus sleep classification task maintained good stability with only a 1.8% reduction in accuracy. This study provides novel insights for non-invasive sleep monitoring and clinical decision support. Full article

This paper presents the design, modeling and experimental validation of an on-grid photovoltaic system with self-consumption, sized for the sustainable supply of a water pumping station. The system, composed of 68 photovoltaic panels, uses an architecture based on a Boost DC-DC converter controlled by the Perturb and Observe algorithm, raising the operating voltage to a high-voltage DC bus to maximize the conversion efficiency. The study integrates dynamic performance analysis through simulations in the Simulink environment, testing the stability of the DC bus under sudden irradiance shocks, with rigorous experimental validation based on field production data. The simulation results, which indicate a peak DC power of approximately 34 kW, are confirmed by real monitoring data that records a maximum of 35 kW, the error being justified by the high efficiency of the panels and system losses. Long-term validation, carried out over three years of operation (2023–2025), demonstrates the reliability of the technical solution, with the system generating a total of 124.68 MWh. The analysis of energy flows highlights a degree of self-consumption of 60.08%, while the absence of chemical storage is compensated for by injecting the surplus of 49.78 MWh into the national grid, which is used as an energy buffer. The paper demonstrates that using the grid to balance night-time or meteorological deficits, in combination with a stabilized DC bus, represents an optimal technical-economic solution for critical pumping infrastructures, eliminating the maintenance costs of the accumulators and ensuring continuous operation. Full article

Microbiological analysis is crucial for identifying species, assessing infections, and diagnosing infectious diseases, thereby supporting both research studies and medical diagnosis. In response to these needs, accurate and efficient identification of bacterial colonies is essential. Conventionally, this process is performed through manual counting and visual inspection of colonies on agar plates. However, this approach is prone to several limitations arising from human error and external factors such as lighting conditions, surface reflections, and image resolution. To overcome these limitations, an automated bacterial colony counting and classification system was developed by integrating a custom-designed imaging device with advanced deep learning models. The imaging device incorporates controlled illumination, matte-coated surfaces, and a high-resolution camera to minimize reflections and external noise, thereby ensuring consistent and reliable image acquisition. Image-processing algorithms implemented in MATLAB were employed to detect bacterial colonies, remove background artifacts, and generate cropped colony images for subsequent classification. A dataset comprising nine bacterial species was compiled and systematically evaluated using five deep learning architectures: ResNet-18, ResNet-50, Inception V3, GoogLeNet, and the state-of-the-art EfficientNet-B0. Experimental results demonstrated high colony-counting accuracy, with a mean accuracy of 90.79% ± 5.25% compared to manual counting. The coefficient of determination (R² = 0.9083) indicated a strong correlation between automated and manual counting results. For colony classification, EfficientNet-B0 achieved the best performance, with an accuracy of 99.78% and a macro-F1 score of 0.99, demonstrating strong capability in distinguishing morphologically distinct colonies such as Serratia marcescens. Compared with previous studies, this research provides a time-efficient and scalable solution that balances high accuracy with computational efficiency. Overall, the findings highlight the potential of combining optimized imaging systems with modern lightweight deep learning models to advance microbiological diagnostics and improve routine laboratory workflows. Full article

Carbon fiber reinforced polymers (CFRPs) are widely used in aerospace and wind power applications, but the complex failure mechanisms of their connection structures pose challenges for connection design. This study aims to investigate the influence of bonding interface inclination angle and connection method on the failure mechanisms of CFRP joints under bending loads. The study investigated two design parameters: the joint geometry of the bonding interface (single-slope, transition-slope, and single-step) and the connection methods (bonding, bolting, and hybrid bonding–bolting). Finite element simulations analyzed the mechanical performance and failure modes under different design parameters. Bending tests validated the mechanical properties of the joint interface, validating the effectiveness of the numerical simulation. The study found that under bonded connections, the bending load increased with the slope of the connection interface, with improvements of 21.87% and 39.75%, respectively. The main reason is stress concentration caused by sharp geometric discontinuities. The hybrid connection had the highest peak load, with improvements of 38.38% and 43.91% compared to the other connection methods. Hybrid bonding–bolting connections further optimized structural performance and damage tolerance. This study reveals the damage mechanisms of the bonding interface and provides a reliable prediction method for aerospace and wind turbine blade applications. Full article

This study presents a hybrid approach integrating metaheuristic optimization and machine learning methods to quickly and reliably estimate the optimum design parameters of dome-shaped axially symmetric cylindrical reinforced concrete (RC) walls. A comprehensive dataset was created using the Jaya algorithm to minimize total material cost for hinged and fixed support conditions. For each optimized design case, total wall height (H), dome height (Hd), dome thickness (hd), and fluid unit weight (γ) were considered as input parameters; optimum wall thickness (hw) and total cost were determined as output parameters. Using the obtained dataset, a total of thirteen different regression-based machine learning algorithms, including linear regression-based models, tree-based ensemble methods, and neural network models, were trained and tested. Hyperparameter adjustments for all models were performed using the Optuna framework, and model performances were evaluated using a ten-fold cross-validation method and holdout dataset results. The results showed that machine learning models can learn the optimum design space obtained from metaheuristic optimization outputs with high accuracy. In optimum wall thickness estimation, Gradient Boosting-based models provided the highest accuracy under both hinged and fixed support conditions. In total cost estimation, the Gradient Boosting model stood out under hinged support conditions, while the XGBoost model yielded the most successful results for fixed support conditions. The findings clearly show that no single machine learning model exhibits the best performance for all output parameters and support conditions. The proposed approach offers significantly higher computational efficiency compared to traditional iterative optimization processes and allows for rapid estimation of optimum design parameters without the need for any iterations. In this respect, this study provides an effective decision support tool that can be used especially in the preliminary design phases and contributes to sustainable, cost-effective reinforced concrete structure design. Full article

To address the technical bottlenecks commonly encountered with real-time quantitative PCR instruments, such as insufficient ramp rates and uneven chamber temperature distribution, this study proposes an innovative design scheme for a temperature control system that incorporates key hot air circulation and temperature field regulation technologies. By combining the PCR instruments’ working principles and structural characteristics, the failure mechanisms associated with the temperature control system are systematically analyzed, and a reliability-oriented thermodynamic analysis model is constructed to clarify the functional positioning of core components and to systematically test the airflow uniformity, temperature dynamics, and nucleic acid amplification efficiency. An integrated fixture for airflow rectifier and cruciform frames is designed, which enables precise quantitative characterization of the system temperature uniformity, ramp rates, and amplification efficiency on a multi-condition comparison platform. Through modeling analysis combined with experimental validation, the thermal performance differences among various heating chamber structures are compared, leading to a multidimensional optimization of the temperature control system. The test results demonstrate outstanding core performance metrics for the optimized system: the up ramp reaches 7.5 ± 0.1 °C/s, the down ramp reaches 13.5 ± 0.1 °C/s, and the steady-state temperature deviation is only ±0.1 °C. The total duration for 35 PCR cycles is recorded at 16.3 ± 0.6 min, with a nucleic acid amplification efficiency of 98.9 ± 0.2%. The core performance metrics comprehensively surpass those of mainstream global counterparts. The developed temperature control system is well-suited for practical applications such as rapid detection, providing critical technological support for the iterative upgrade of nucleic acid amplification techniques while laying a solid foundation for the engineering development of high-performance PCR instruments. Full article

This study investigates the two-dimensional (2D) steady-state frictional contact behavior of functionally graded piezoelectric material (FGPM) coatings under a high-speed rigid cylindrical punch. An electromechanical coupled contact model considering inertial effects is established, while a layered model is employed to simulate arbitrarily varying material parameters. Based on piezoelectric elasticity theory, the steady-state governing equations for the coupled system are derived. By utilizing the transfer matrix method and the Fourier integral transform, the boundary value problem is converted into a system of coupled Cauchy singular integral equations of the first and second kinds in the frequency domain. These equations are solved semi-analytically, using the least squares method combined with an iterative algorithm. Taking a power-law gradient distribution as a case study, the effects of the gradient index, relative sliding speed, and friction coefficient on the contact pressure, in-plane stress, and electric displacement are systematically analyzed. Furthermore, the contact responses of FGPM coatings with power-law, exponential, and sinusoidal gradient profiles are compared. The findings provide a theoretical foundation for the optimal design of FGPM coatings and for enhancing their operational reliability under high-speed service conditions. Full article

Accurate analysis of commutation phenomena in permanent magnet DC (PMDC) motors requires simultaneous consideration of electromagnetic field distribution and armature circuit dynamics. Classical circuit-based models are unable to properly capture transient effects occurring in short-circuited coils during commutation, while purely field-based models neglect the influence of the supply circuit. In this paper, a coupled field–circuit model of a PMDC motor with an innovative magnetic circuit based on rectangular NdFeB permanent magnets is presented. The model combines a two-dimensional finite element electromagnetic analysis with a segmented armature circuit and dynamic commutator switching, allowing the electromotive force to be computed individually for each coil based on the actual magnetic field distribution. The novelty of the proposed approach lies in the integration of a non-standard rectangular permanent magnet topology with a coil-resolved field–circuit commutation model, validated on a physical motor prototype. Simulation results are compared with experimental measurements obtained from a laboratory prototype at rotational speeds of 850 and 1000 r/min. The predicted electromagnetic torque shows good agreement with measurements, with deviations below 5%, while the armature current is estimated with an error of up to approximately 20%, primarily due to model simplifications. The developed model provides direct access to transient commutation waveforms and constitutes a practical tool for the analysis and design optimization of PMDC motors operating under dynamic conditions, particularly in cost-sensitive and reliability-oriented applications. Full article

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments. Full article

The flowers of Panax ginseng C. A. Mey. (PG) and Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow (PN) are morphologically indistinguishable after drying, leading to prevalent adulteration that compromises product quality and consumer safety. To address this issue, we developed a rapid, closed-tube molecular authentication method based on high-resolution melting (HRM) analysis. Species-specific primer pairs were designed to target the conserved ITS and rbcL-accD regions, with PNG-2 selected as the optimal candidate owing to its stable genotyping performance and moderate GC content. Our results established GC content, rather than amplicon length, as the primary determinant of the melting temperature (Tm). Notably, the experimentally measured Tm values were consistently 0.7–1.5 °C higher than theoretical predictions, a discrepancy attributable to the stabilizing effect of the saturated fluorescent dye. To ensure maximum diagnostic reliability, the HRM results were cross-validated through a three-tier system comprising ITS2 phylogenetic analysis, agarose gel electrophoresis, and Sanger sequencing. The practical utility and matrix robustness of the assay were further verified using a diversified validation cohort of 30 commercial samples, including 24 floral batches and 6 root-derived products (root slices and ultramicro powders). The HRM profiles demonstrated 100% concordance with DNA barcoding results, effectively identifying mislabeled products across different botanical matrices and processing forms. This methodology, which can be completed within 3 h, provides a significantly more cost-effective and rapid alternative to traditional sequencing-based methods for large-scale market surveillance and industrial quality control. Full article

The growing interest in using edible flowers as functional ingredients has increased the demand for reliable and sustainable strategies to recover and characterize their bioactive compounds. Torch ginger is a tropical species rich in anthocyanins. In this study, an ultrasound-assisted extraction (UAE) method was developed, optimized, and validated for the efficient recovery of anthocyanins from torch ginger flowers, with a clear focus on food-related applications. A Box–Behnken experimental design was applied to evaluate the influence of solvent composition, temperature, solvent-to-sample ratio, and pH on anthocyanin yield, using chromatographic responses. Solvent composition and solvent-to-sample ratio were identified as the most influential parameters, and effective extraction was achieved under mild temperature and pH conditions. The optimized conditions consisted of 84% methanol in water as the extraction solvent, a temperature of 30 °C, a solvent-to-sample ratio of 20:1 (mL g⁻¹), and a pH of 5.6. Kinetic studies revealed that a 5 min extraction time maximized recovery while preventing compound degradation. The method was successfully applied to different torch ginger varieties, revealing a strong correlation between flower color and anthocyanin concentration. This research provides a fast, reliable, and environmentally friendly approach for assessing anthocyanin content in torch ginger flowers. The results support the valorization of this edible flower as a potential source of natural colorants and bioactive ingredients, contributing to ingredient selection, quality control, and the future development of functional foods and clean-label products. Full article

Wave Energy Converter (WEC) buoys operate in aggressive marine environments that impose demanding requirements on structural materials, particularly in terms of moisture resistance, mechanical reliability, and long-term durability. Conventional glass fiber reinforced composites meet these performance requirements but raise sustainability concerns due to their high environmental footprint and limited recyclability. This study addresses this challenge by introducing a systematic, application-driven multi-criteria decision-making (MCDM) framework specifically tailored for material selection in marine renewable energy devices. The novelty of this work lies in the integration of marine durability-dominated criteria weighting with sustainability metrics, moving beyond cost-driven selection approaches commonly reported in the literature. Four cellulosic natural fibers, flax, hemp, kenaf, and sisal, are evaluated as reinforcements for polymer composites intended for point-absorber WEC buoy structures, using conventional E-glass as a baseline reference. Ten performance criteria covering mechanical properties, environmental durability, manufacturing feasibility, and sustainability are defined and objectively weighted using the entropy method to minimize subjective bias. Moisture resistance emerges as the most influential criterion with a weight of 0.142, underscoring its role as a primary degradation mechanism in marine environments, while material cost receives the lowest weight of 0.057, reflecting the prioritization of long-term performance over initial cost. The results identify flax as optimal reinforcement, achieving the highest aggregated score of 4.022 by effectively balancing mechanical performance, resistance to marine exposure, and environmental sustainability. This work introduces a novel decision-support tool for the sustainable design of buoy structures using natural fiber-reinforced composites and establishes a foundation for future optimization of such composites in wave energy applications. Full article