Search Results (121)

Insect bites are a common cause of skin irritation and can contribute to disease transmission through vector-borne pathogens. Early identification of the likely biting organism can assist preliminary guidance (e.g., monitoring for warning signs, considering exposure history) and may reduce complications through timely follow-up. This paper studies a compact attention-guided learning framework for multiclass insect-bite image classification under strict storage constraints. A teacher network (BiteAI-T) based on MobileNetV3-Small is trained with spatial attention pooling to emphasize lesion-relevant regions while maintaining an efficient backbone. A lightweight depthwise-separable student (BiteAI-S) is trained using multi-level knowledge distillation that combines softened-logit matching with intermediate supervision through attention-map alignment and pooled-feature matching. Model storage is further reduced through weight-only quantization-aware training using an LSQ-inspired learnable scaling factor; BatchNorm running statistics are frozen during quantization fine-tuning to improve stability. Experiments on an eight-class dataset (ants, bed bugs, chiggers, fleas, mosquitos, no bites, spiders, ticks) show that BiteAI-T reaches 93.75% test accuracy. For deployment, we export (i) a TorchScript Lite teacher artifact (BiteAI-T_Lite, 2.35 MB) and (ii) a weight-only int8 student artifact (BiteAI-S_int8, 0.992 MB). Comparative results are also reported for an SVD-compressed + fine-tuned FP16 variant (92.66% test accuracy, 2.84 MB), illustrating accuracy–size trade-offs across compression strategies. Full article

To address the challenges associated with inter-module energy interaction and mode adjustment at load ports in distributed energy systems in the context of the energy transition, this paper proposes and designs a multi-port wireless energy interaction system based on LC series resonance and multi-coil magnetic coupling. The system aims to facilitate flexible energy interaction among power sources, energy storage units, and loads, as well as multi-modal port regulation. The system employs a multi-coil coupled full-bridge topology combined with a phase-shift control strategy to achieve energy exchange and power regulation among multiple ports. To meet the power demands of different ports, a port state control method incorporating a mode preset mechanism is proposed, enabling the intermediate port to switch seamlessly among input (source), output (load), and active relay modes. This paper analyzes the operating modes of a single port and establishes the dynamic mathematical model of the overall three-coil system as well as the small-signal model of the port output. Furthermore, it investigates the energy interaction mechanism to derive the operating characteristics and conditions under different modes, and elucidates the energy relay mechanism with zero active power consumption. A three-port hardware experimental platform was constructed based on a dsPIC33 controller. Experimental results indicate that: (1) the prototype achieved a maximum transmission power of 100 W; (2) the peak system efficiency reached $83.1\%$ under different load conditions; and (3) during mode switching, the system response time was less than 200 ms with no significant overshoot. The study demonstrates that the proposed topology and control strategy effectively realized dynamic energy interaction and seamless mode switching among multiple ports, providing a theoretical basis and engineering reference for multi-port energy interaction and wireless power transfer networks. Full article

Recent laboratory experiments in an intermediate-scale Hele-Shaw cell, designed to represent a coarse sand aquifer, demonstrate that mixing-induced calcite precipitation leads to the formation of a self-organized, heterogeneous porous medium. This morphology, characterized by elongated carbonate structures and internal preferential flow channels, induces strong anomalous transport features, including early solute arrival, distinct double-peak breakthrough curves, and pronounced tailing. In this article, we investigate the link between this precipitation-induced heterogeneity and solute transport by implementing varying permeability scenarios, derived from experimental image analysis, into a transport model. Our analysis reveals that while a standard dual-permeability approach, which simply delineates the total precipitated area, captures the flow diversion responsible for the emergence of the double peak, it fails to reproduce the transition between peaks and the late-time tailing. To address this, we introduce a novel triple-permeability model that incorporates internal preferential flow channels within the high-precipitation zones. By resolving the internal structure of these zones, the triple-permeability model accurately captures the dual-peak transition and tailing behavior. These findings provide critical insights for applications such as geological carbon sequestration and enhanced oil recovery. Although determining exact internal structures in field settings is challenging, our results demonstrate that effective transport models must account for the internal heterogeneity of high-precipitation zones, rather than treating them as uniform barriers, to accurately predict the channeling effects that govern injectivity and long-term storage security. Full article

Hydrogen, as an alternative energy carrier, presents significant prospects for the transition to more environmentally friendly energy solutions. However, its efficient and safe storage remains a challenge, as materials with high adsorbent capacity and long-term storage capability are required. This study focuses on the synthesis and characterization of a composite material comprising carbon fiber and manganese dioxide (MnO₂/CFs), for the purpose of hydrogen storage. Carbon fiber was chosen as the basis for the composition of the composite material due to its large active surface area and its excellent mechanical, thermal, and electrochemical properties. The deposition of MnO₂ on the surface of carbon fibers took place through two different synthetic pathways: electrochemical deposition and chemical synthesis under different conditions. The electrochemical method enabled the production of a greater amount of oxide with optimized structural and chemical properties, whereas the chemical method was simpler but required more time to achieve comparable or lower-capacity performance. Elemental analysis of the electrochemically produced composites showcased an average of 40.5 ± 0.05 wt% Mn presence, which is an indicator of the quantity of MnO₂ on the surface responsible for hydrogen storage, while the chemically produced composites showcased an average of 7.6 ± 0.05 wt% Mn presence. Manganese oxide’s high specific capacity and reversible redox reaction participation make it suitable for hydrogen storage applications. The obtained results of the hydrogenated samples through physicochemical characterization indicated the formation of the MnOOH intermediate. Regarding these findings it may be remarked that carbon fiber/MnO₂ composites are promising candidates for hydrogen storage technologies. Finally, the fabricated carbon fiber/MnO₂ composites were applied successfully as working electrodes for analysis of the [Fe(CN)₆]^3−/4− redox system in aqueous KCl solutions. Full article

In this work, a 3D microfluidic paper-based analytical device (3D-µPAD) was developed for the smartphone-based colorimetric determination of phosphate in environmental samples. The assay relied on the formation of a blue-colored product (molybdenum blue) in the detection area of the 3D-µPAD upon reduction of the heteropolyacid H₃PMo₁₂O₄₀ formed in the presence of phosphate. A number of experimental parameters were optimized, including geometric aspects of 3D-µPADs, digitization and image processing conditions, the amount of chemicals deposited in specific areas of the 3D-µPAD, and the reaction time. In addition, the stability of the device was evaluated at three different storage temperatures. Under optimal conditions, the working range was found to be from 4 to 25 mg P/L (12–77 mg PO₄⁻³/L). The limits of detection (LOD) and quantification (LOQ) were 0.015 mg P/L and 0.05 mg P/L, respectively. The repeatability and intermediate precision of a 5 mg P/L standard were 4.8% and 7.1%, respectively. The proposed colorimetric assay has been successfully applied to phosphorous determination in various waters, soils, and sediments, obtaining recoveries in the range of 94 to 107%. The ready-to-use 3D-µPAD showed a greener profile than the standard method for phosphate determination, being affordable, easy-to-use, and suitable for citizen science applications. Full article

Spray freeze-drying (SFD) is a novel technique for formulating dry powders, particularly for pulmonary drug delivery via dry powder inhalers (DPIs). Despite their low density and excellent aerodynamic properties, such powders are affected by high cohesiveness due to their surface properties. Sugars such as mannitol (MAN), trehalose, raffinose, and sucrose are commonly used in SFD. MAN is widely employed due to its high MAN—ice eutectic temperature—at which MAN and water (ice) form a stable eutectic mixture—and its crystallinity. However, crystallinity can impact the microparticles’ (MPs) cohesiveness, since MAN exhibits distinct polymorphs (α, β, δ) with peculiar properties. This study provides valuable insights for the development of DPI formulations by ensuring precise control over MAN polymorphism, ultimately enhancing formulation stability and performance. We introduced, for the first time, an intermediate freezing (IF) step within the SFD process to modulate MAN polymorphism, demonstrating its synergy with optimised storage temperature conditions. Furthermore, polyvinylpyrrolidone, 2-hydroxypropyl beta cyclodextrin, dextran, and polysorbate 80 were employed as polymorphism-controlling agents for MAN, contributing to the development of stable formulations with reduced particle cohesion and improved storage stability at room temperature. For the first time, this study shows that MAN polymorphism in SFD can be controlled to drive dry powder inhaler performance. Full article

This study investigated the ability of an electronic nose system (E-nose) to detect early signs of fungal contamination in the red plum variety ‘Black Splendor’. We focused on identifying changes in volatile organic compounds (VOCs) that occur with decay. For this purpose, we compared two groups of plums: a control group (healthy plums) and a group inoculated with Monilinia laxa. VOCs from both groups were analyzed and quantified using gas chromatography/mass spectrometry (GC/MS). In parallel, E-nose signals were recorded at two key moments of fungal development: an early and an intermediate phase. The results revealed a strong correlation between E-nose signals and the aromatic profile characteristic of fungal contamination in plums. Linear discriminant analysis (LDA) models, developed from the E-nose data, achieved 100% differentiation between healthy and infected samples. Furthermore, these models discriminated with 100% accuracy between healthy plums and those with incipient contamination. These findings demonstrate that E-nose technology serves as a reliable, non-destructive approach for real-time assessment of plum quality throughout storage. Full article

This paper presents an optimal-design methodology for small-scale Integrated Energy Systems (IESs) that couple electricity and heat in distributed networks. A hybrid reliability assessment integrates probabilistic state enumeration with scenario-based simulation. Mathematically, the design is cast as a stochastic, reliability-driven ranking: time-sequential Monte Carlo (MC) produces estimators of Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Self-Sufficiency Rate (SSR), which are normalized and combined into a Composite Reliability Index (CRI) that orders candidate siting/sizing options. The case study is the D-campus microgrid with Photovoltaic (PV), Combined Heat and Power (CHP), Fuel Cell (FC), Battery Energy Storage Systems (BESSs), and Heat Energy Storage Systems (HESSs; also termed TESs), across multiple siting and sizing scenarios. Results show consistent reductions in LOLP and EENS and increases in SSR as distributed energy resource capacity increases and resources are placed near critical nodes, with the strongest gains observed in the best-performing configurations. The CRI also reveals trade-offs across intermediate scenarios. The operational concept of the campus Energy Management System (EMS), including full operating modes and scheduling logic, is developed to maintain a design focus on reliability-driven decision making. Probability-based formulations, reliability metrics, and the sequential MC setup underpin the proposed ranking framework. The proposed method supports Distributed Energy Resource (DER) sizing and siting decisions for reliable, autonomy-oriented IESs. Full article

This study evaluates the application of an electronic nose (E-nose) system as a non-destructive tool for the early detection of Monilinia laxa infection in yellow nectarines (Prunus persica var. nectarine, cv. “Kinolea”) through the analysis of volatile organic compounds (VOCs). Two experimental groups were established: a control group of healthy fruit and a treatment group inoculated with the pathogen. The VOCs emitted by both groups were identified and quantified using gas chromatography-mass spectrometry (GC-MS). Simultaneously, the responses of the E-nose were recorded at three critical stages of fungal development: early, intermediate, and advanced. The electronic nose used consists of a set of 11 commercial metal oxide semiconductor (MOX) sensors. The signals from these sensors showed a strong correlation with the VOC profiles associated with fungal deterioration. Linear discriminant analysis (LDA) models based on E-nose data successfully distinguished between healthy and infected samples with 97% accuracy. Furthermore, the system accurately classified samples into three stages of contamination—control, early infection, and advanced infection—with 96% classification accuracy. These findings demonstrate that E-nose technology is an effective, rapid, and non-invasive method for the real-time monitoring of post-harvest fungal contamination in nectarines, offering significant potential for improving quality control during storage and distribution. Full article

In this study, the impact of the electric motor size and the hybridization ratio of a Fuel Cell Electric Bus on its vehicle performance (i.e., gradeability and acceleration) and fuel consumption was investigated using the ADVISOR software. The investigation first involved a parametric analysis with different electric motor and fuel cell sizes for the dynamic performance metrics, specifically the 0–60 km/h vehicle acceleration and the maximum gradeability (%) at a constant speed of 20 km/h. The results revealed that the acceleration is most sensitive to fuel cell power. Regarding gradeability, a more complex relationship was observed: when the electric motor power was below 215 kW, gradeability remained consistently low regardless of the fuel cell size. However, for motors exceeding 215 kW, fuel cell power then became a significant influencing factor on the vehicle’s climbing capability. Subsequently, the analysis focused on the effect of the hybridization ratio, which represents the power balance between the fuel cell and the energy storage system, varied between 0 and 0.8. Results showed that increasing the hybridization ratio decreases gradeability and acceleration performance and increases total energy consumption. This trade-off is quantitatively illustrated by the results over the Central Business District (CBD) driving cycle. For instance, the pure battery-electric configuration (a hybridization ratio of 0), featuring a 296 kW battery system, recorded a gradeability of 12.4% and an acceleration time of 16.3 s, while consuming 28,916 kJ. At an intermediate hybridization ratio of 0.4 (composed of a 118.4 kW fuel cell and a 177.6 kW battery), performance remained high with a gradeability of 12.2% and an acceleration of 17.3 s, but the energy consumption increased to 43,128 kJ. Finally, in the fuel-cell-dominant configuration with a hybridization ratio of approximately 0.8 (a 236.8 kW fuel cell and a 59.2 kW battery), gradeability dropped to 8.4%, acceleration time deteriorated to 38.9 s, and total energy consumption increased further to 52,678 kJ over the CBD driving cycle. Full article

Background/Objectives: Periradicular disease is largely microbial in origin. Even gutta-percha (GP) cones manufactured under aseptic conditions can acquire contaminants during handling or storage, undermining otherwise adequate canal preparation. To assess residual antimicrobial activity on GP cones after brief exposure to five endodontic disinfectants: sodium hypochlorite (NaOCl) 1%, 2.5%, 5.25%; chlorhexidine (CHX) 2%; and glutaraldehyde 2% against Enterococcus faecalis and Candida albicans. Methods: Standardized GP cones were dipped for 5–120 s, blotted on neutralizing gauze, and placed on agar inoculated with either organism. Using an agar diffusion approach, inhibition-zone diameters were recorded at 0, 24, and 48 h. Data were summarized using descriptive statistics (means, standard deviations, and 95% confidence intervals) for each disinfectant–dip-time combination. Results: By 24 h, inhibition zones were observed for most disinfectants; for C. albicans, glutaraldehyde 2% showed no measurable effect. At later time points, performance depended on both disinfectant and contact time. For E. faecalis, NaOCl 2.5% and 5.25% yielded the largest zones at 48 h (20–21 mm at 120 s), whereas NaOCl 1% was smaller (10 mm) and glutaraldehyde 2% modest (9 mm). For C. albicans, NaOCl 2.5% and CHX 2% were most effective at 48 h (17–19 mm at 120 s); NaOCl 5.25% was intermediate, NaOCl 1% weak, and glutaraldehyde 2% showed no measurable antifungal effect. Longer immersions (≥45 s) consistently increased inhibition zone diameters. Conclusions: Residual antimicrobial activity on GP cones depends on both the agent and the immersion time. For E. faecalis, higher concentration NaOCl produced the largest zones at short contact time, whereas for C. albicans, CHX 2% and NaOCl 2.5% provided the most reliable carryover. Selecting an appropriate concentration and allowing sufficient dip time may reduce reinfection risk at obturation. Full article

We introduce CAESAR, a new framework for scientific data reduction that stands for Conditional AutoEncoder with Super-resolution for Augmented Reduction. The baseline model, CAESAR-V, is built on a standard variational autoencoder with scale hyperpriors and super-resolution modules to achieve high compression. It encodes data into a latent space and uses learned priors for compact, information-rich representations. The enhanced version, CAESAR-D, begins by compressing keyframes using an autoencoder and extends the architecture by incorporating conditional diffusion to interpolate the latent spaces of missing frames between keyframes. This enables high-fidelity reconstruction of intermediate data without requiring their explicit storage. By distinguishing CAESAR-V (variational) from CAESAR-D (diffusion-enhanced), we offer a modular family of solutions that balance compression efficiency, reconstruction accuracy, and computational cost for scientific data workflows. Additionally, we develop a GPU-accelerated postprocessing module which enforces error bounds on the reconstructed data, achieving real-time compression while maintaining rigorous accuracy guarantees. Experimental results across multiple scientific datasets demonstrate that our framework achieves up to 10× higher compression ratios compared to rule-based compressors such as SZ3. This work provides a scalable, domain-adaptive solution for efficient storage and transmission of large-scale scientific simulation data. Full article

In remote alpaca breeding regions, access to advanced sperm analysis laboratories is limited. This study validates a practical cytometric method for evaluating sperm viability and acrosomal integrity in epididymal alpaca sperm using early fluorochrome staining, formaldehyde fixation, and intermediate storage. Thirty-two testes were transported at 5 °C, and spermatozoa were collected from the cauda epididymis. After morphometric screening, 26 samples were included. Aliquots were stained with Zombie Green (viability) and FITC–PSA (acrosomal integrity), at time zero. Each aliquot was divided for cytometric analysis at T0 (immediately), T24 (24 h after formaldehyde fixation) and T1w (1 week post-fixation). Fixed samples showed higher viability and acrosomal integrity values (T24: 70.75%, 97.24%; T1w: 71.80%, 97.21%) than T0 (67.63%, 95.89%). This may reflect fluorescence alterations associated with fixation. Strong correlations and Bland–Altman analysis confirmed consistency across time points. This method enables accurate sperm quality evaluation up to one week after collection, offering a useful tool for reproductive monitoring in field conditions without immediate analysis. Further research on ejaculated semen and field protocols is recommended. Full article

A green scheduling problem is proposed in this work, where both constraints on intermediate storage capacity and job transportation requirements are simultaneously considered. An improved discrete pathfinder algorithm (IDPFA) with multi-neighborhood local search is proposed to minimize the maximum completion time and total energy consumption. The algorithm addresses the green flow shop scheduling problem with limited buffers and automated guided vehicle (GFSSP_LBAGV). Firstly, based on the machine speed constraints, the transportation time for moving jobs by the automated guided vehicle (AGV) is incorporated to establish a mathematical model. Secondly, the core idea of the pathfinder algorithm (PFA) is applied to the evolutionary process of the discrete PFA, where three different crossover operations are used to replace the exploration process of the pathfinder, the influence of the pathfinder on the followers, and the mutual learning among the followers. Then, a multi-neighborhood local search is employed to conduct a detailed exploration of high-quality solution spaces. Finally, extensive standard test sets are used to verify the effectiveness of the proposed IDPFA in solving GFSSP_LBAGV. Full article

Warehouses are vital in linking production to consumption, often using a forward–reserve layout to balance picking efficiency and bulk storage. However, replenishing the forward area from reserve storage is prone to delays and congestion, especially during high-demand periods. This study investigates the strategic use of buffer areas—intermediate zones between forward and reserve locations—to enhance flexibility and reduce bottlenecks. Although buffer zones are common in practice, they often lack a structured decision-making framework. We address this gap by developing an optimization model that integrates demand forecasts to guide daily replenishment decisions. To handle the computational complexity arising from large state and action spaces, we implement an approximate dynamic programming (ADP) approach using certainty-equivalent control within a rolling-horizon framework. A real-world case study from an automotive spare parts warehouse demonstrates the model’s effectiveness. Results show that strategically integrating buffer zones with an ADP model significantly improves replenishment timing, reduces direct picking by up to 90%, minimizes congestion, and enhances overall flow of intra-warehouse inventory management. Full article