Search Results (15,913)

The increasing demand for fresh fruits and vegetables has been accompanied by a rise in foodborne illness outbreaks linked to fresh produce. Traditional antimicrobial washing treatments, such as chlorine and peroxyacetic acid, have limitations in efficacy and pose environmental and worker health concerns. This study evaluated the effectiveness of organic acids (citric, malic, and lactic acid) and power ultrasound, individually and in combination, for the reduction in Salmonella enterica and Listeria monocytogenes on four fresh produce types: romaine lettuce, cucumber, tomato, and strawberry. Produce samples were inoculated with bacterial cocktails at 8–9 log CFU/unit and treated with organic acids at 2 or 5% for 2 or 5 min, with or without power ultrasound (40 kHz). Results showed that pathogen reductions varied based on the produce matrix with smoother surfaces such as tomato, exhibiting greater reductions than rougher surfaces (e.g., romaine lettuce and strawberry). Lactic and malic acids were the most effective treatments, with 5% lactic acid achieving a reduction of >5 log CFU/unit for S. enterica and 4.53 ± 0.71 log CFU/unit for L. monocytogenes on tomatoes. The combination of organic acids and power ultrasound demonstrated synergistic effects, further enhancing pathogen reduction by <1.87 log CFU/unit. For example, S. enterica on cucumbers was reduced by an additional 1.87 log CFU/unit when treated with 2% malic acid and power ultrasound for 2 min compared to malic acid alone. Similarly, L. monocytogenes on strawberries was further reduced by 1.84 log CFU/unit when treated with 5% malic acid and power ultrasound for 2 min. These findings suggest that organic acids, particularly malic and lactic acids, combined with power ultrasound, may serve as an effective hurdle technology for enhancing the microbial safety of fresh produce. Future research can include validating these treatments in an industrial processing environment. Full article

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. Full article

In this article, thermal investigation methods for electrically insulating and thermally conductive substrate materials will be presented. The investigations were performed in their real-world application environment, i.e., in the form of IGBT (insulated gate bipolar transistor) module substrate plates. First, the overall thermal resistance and thermal structure function of the system in a multivariable parameter space were revealed using CFD (computational fluid dynamics) simulations. Afterwards, thermal transient testing was performed on real samples, with the help of which the thermal resistance values of the modules were determined using the thermal dual interface test method. The presented tests are not intended to determine material parameters, but to rank different substrate materials based on their thermal performance. Full article

High demand for sustainable solutions in the construction industry determines the significant relevance of developing new eco-friendly composites with a reduced carbon impact on the environment. The main aim of this study is to investigate the possibility and efficiency of using technical sulfur (TS) as a modifying additive for geopolymer composites and to select the optimal content of polypropylene fiber (PF). To assess the potential of TS, experimental samples of geopolymer solutions based on metakaolin and fly ash were prepared. The TS content varied from 0% to 9% by weight of binder in 3% increments. In the first stage, the density, compressive and flexural strength, capillary water absorption and microstructure of hardened geopolymer composites were tested. The TS additive in an amount of 3% was the most effective and provided an increase in compressive strength by 12.6%, flexural strength by 12.8% and a decrease in capillary water absorption by 18.2%. At the second stage, the optimal PF content was selected, which was 0.75%. The maximum increases in strength properties were recorded for the composition with 3% TS and 0.75% PF: 8% for compression and 32.6% for bending. Capillary water absorption decreased by 12.9%. The geopolymer composition developed in this work, modified with TP and PF, has sufficient mechanical and physical properties and can be considered for further study in order to determine its competitiveness with cement composites in real construction practice. Full article

Addressing the challenges of bulky, low-efficiency sound-insulation materials at low frequencies, this work proposes an acoustic metamaterial based on curve fractal channels. Each unit cell comprises a concentric circular-ring channel recursively iterated: as the fractal order increases, the channel path length grows exponentially, enabling outstanding sound-insulation performance within a deep-subwavelength thickness. Finite-element and transfer-matrix analyses show that increasing the fractal order from one to three raises the number of bandgaps from three to five and expands total stop-band coverage from 17% to over 40% within a deep-subwavelength thickness. Four-microphone impedance-tube measurements on the third-order sample validate a peak transmission loss of 75 dB at 495 Hz, in excellent agreement with simulations. Compared to conventional zigzag and Hilbert-maze designs, this curve fractal architecture delivers enhanced low-frequency broadband insulation, structural lightweighting, and ease of fabrication, making it a promising solution for noise control in machine rooms, ducting systems, and traffic environments. The method proposed in this paper can be applied to noise reduction of transmission parts for ceramic automation production. Full article

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Titanium alloys are frequently subjected to surface treatments to enhance their biocompatibility and corrosion resistance in biological environments. Plasma electrolytic oxidation (PEO) is an environmentally friendly electrochemical technique capable of forming oxide layers characterized by high corrosion resistance, biocompatibility, and strong adhesion to the substrate. In this study, the PEO process was performed using a low-melting-point ternary eutectic electrolyte composed of Ca(NO₃)₂–NaNO₃–KNO₃ (41–17–42 wt.%) with the addition of ammonium dihydrogen phosphate (ADP). The use of this electrolyte system enables a reduction in the operating temperature from 280 to 160 °C. The effects of applied voltage from 200 to 400V, current frequency from 50 to 1000 Hz, and ADP concentrations of 0.1, 0.5, 1, 2, and 5 wt.% on the growth of titanium oxide composite coatings on a Ti-6Al-4V substrate were investigated. The incorporation of Ca and P was confirmed by phase and chemical composition analysis, while scanning electron microscopy (SEM) revealed a porous surface morphology typical of PEO coatings. Corrosion resistance in Hank’s solution, evaluated via Tafel plot fitting of potentiodynamic polarization curves, demonstrated a substantial improvement in electrochemical performance of the PEO-treated samples. The corrosion current decreased from 552 to 219 nA/cm², and the corrosion potential shifted from −102 to 793 mV vs. the Reference Hydrogen Electrode (RHE) compared to the uncoated alloy. These findings indicate optimal PEO processing parameters for producing composite oxide coatings on Ti-6Al-4V alloy surfaces with enhanced corrosion resistance and potential bioactivity, which are attributed to the incorporation of Ca and P into the coating structure. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article

Chloride ion penetration is one of the most aggressive threats to reinforced concrete, as it triggers the electrochemical corrosion of steel reinforcement, compromising structural integrity and durability. Chloride ingress occurs through the porous structure of concrete, making permeability control crucial for enhancing structural longevity. Fiber-reinforced concrete (FRC) is widely used to improve durability; however, the effects of different fiber types on chloride resistance remain unclear. This study examines the influence of glass and polypropylene fibers on concrete’s microstructure and chloride penetration resistance. Cylindrical specimens were prepared, including a reference mix without fibers and mixes with 0.25% and 0.50% fiber content by volume. Both fiber types were tested for chloride resistance. The accelerated non-steady-state migration method was employed to determine the resistance coefficients to chloride ion penetration, while X-ray macrofluorescence (MacroXRF) mapped the chlorine infiltration depth in the samples. Compressive strength decreased in all fiber-reinforced samples, with 0.50% glass fiber leading to a 56% reduction in strength. Nevertheless, the XRF results showed that a 0.25% fiber content significantly reduced chloride penetration, with polypropylene fibers outperforming glass fibers. These findings highlight the critical role of fiber type and volume in improving concrete durability, offering insights for designing long-lasting FRC structures in chloride-rich environments. Full article

Volatile organic compounds (VOCs) are highly volatile chemicals in natural and anthropogenic environments, significantly affecting indoor air quality. Major sources of indoor VOCs include emissions from building materials, furnishings, and consumer products. Natural wood products release VOCs, including terpenes and aldehydes, which exert diverse health effects ranging from mild respiratory irritation to severe outcomes, such as formaldehyde-induced carcinogenicity. The temporal dynamics of VOC emissions were investigated, and the toxicological and physiological effects of the VOCs emitted by two types of natural wood, Korean Red Pine (Pinus densiflora) and Japanese Cypress (Chamaecyparis obtusa), were evaluated. Using female C57BL/6 mice as an animal model, the exposure setups included phytoncides, formaldehyde, and intact wood samples over short- and long-term durations. The exposure effects were assessed using oxidative stress markers, antioxidant enzyme activity, hepatic and renal biomarkers, and inflammatory cytokine profiles. Long-term exposure to Korean Red Pine and Japanese Cypress wood VOCs did not induce significant pathological changes. Japanese Cypress exhibited more distinct benefits, including enhanced oxidative stress mitigation, reduced systemic toxicity, and lower pro-inflammatory cytokine levels compared to the negative control group, attributable to its more favorable VOC emission profile. These findings highlight the potential health and environmental benefits of natural wood VOCs and offer valuable insights for optimizing timber use, improving indoor air quality, and informing public health policies. Full article

Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article

Objectives: Burkholderia cepacia complex (Bcc), a Gram-negative organism, is a well-recognized cause of hospital outbreaks, often linked to a contaminated shared source, such as multidose medications. In this study, we report an outbreak of Bcc infections in a tertiary care hospital, associated with the intrinsic contamination of a prepared solution used in interventional radiology (IR) procedures. Additionally, we provide a detailed explanation of the interventions implemented to control and interrupt the outbreak. Methods: Records from the infection control committee from 1 January 2023 to 31 October 2024 were screened to identify cases with Bcc growth in cultured blood, urine, or respiratory samples. Clinical and laboratory data were collected in March 2025. Bacterial identification was performed using conventional methods and MALDI-TOF (Bruker Daltonics, Bremen, Germany). Controls were matched to cases by ward, date of initial growth, and duration of hospitalization. Demographic and clinical data of these patients were systematically collected and analyzed. Microbiological cultures were obtained from environmental objects of concern and certain medications. Results: A total of 82 Burkholderia species were identified. We enrolled 77 cases and 77 matched controls. The source of contamination was identified in ready-to-use intravenous medications (remifentanil and magnesium preparations) in the IR department. These preparations were compounded in advance by the team and were used repeatedly. Although the outbreak originated from contaminated IV medications used in IR, secondary transmission likely affected 28 non-IR patients via fomites, shared environments, and possible lapses in isolation precautions. The mortality rate among the cases was 16.9%. Infection with Bcc was associated with prolonged intensive care unit stays (p = 0.018) and an extended overall hospitalization duration (p < 0.001); however, it was not associated with increased mortality. The enforcement of contact precautions and comprehensive environmental decontamination successfully reduced the incidence of the Bcc outbreak. No pathogens were detected in cultures obtained after the disinfection. Conclusions: The hospital transmission of Bcc is likely driven by cross-contamination, invasive medical procedures, and the administration of contaminated medications. Implementing stringent infection control measures such as staff retraining, updated policies on medication use, enhanced environmental decontamination, and strict adherence to isolation precautions has proven effective in curbing the spread of virulent and transmissible Bcc. Full article