Search Results (200)

Teleoperation emerged as a promising fallback for situations beyond the capabilities of automated vehicles. Nevertheless, teleoperation still faces challenges, such as reduced situational awareness. Since situational awareness is primarily built through the remote operator’s visual perception, the graphical user interface (GUI) design is critical. In addition to video feed, supplemental informational elements are crucial—not only for the predominantly studied remote driving, but also for emerging desk-based remote assistance concepts. This work develops a GUI for different teleoperation concepts by identifying key informational elements during the teleoperation process through expert interviews (N = 9). Following this, a static and dynamic GUI prototype was developed and evaluated in a click dummy study (N = 36). Thereby, the dynamic GUI adapts the number of displayed elements according to the teleoperation phase. Results show that both GUIs achieve good system usability scale (SUS) ratings, with the dynamic GUI significantly outperforming the static version in both usability and task completion time. However, the results might be attributable to a learning effect due to the lack of randomization. The user experience questionnaire (UEQ) score shows potential for improvement. To enhance the user experience, the GUI should be evaluated in a follow-up study that includes interaction with a real vehicle. Full article

Millimeter-wave frequencies are crucial for meeting the high-capacity, low-latency demands of 5G communication systems, thereby driving the need for compact, high-gain antenna arrays capable of efficient beamforming. This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 × 6 linear series-fed microstrip patch antenna array for 5G millimeter-wave communication operating at 28 GHz. The proposed antenna is fabricated on a low-loss Rogers RO3003 substrate and incorporates an integrated symmetric two-way microstrip power divider to ensure balanced feeding and phase uniformity across elements. The antenna achieves a simulated peak gain of 11.5 dBi and a broad simulated impedance bandwidth of 30.21%, with measured results confirming strong impedance matching and a return loss better than −20 dB. The far-field radiation patterns demonstrate a narrow, highly directive beam in the E-plane, and the H-plane results reveal beam tilting behavior, validating the antenna’s capability for passive beam steering through feedline geometry and element spacing (~0.5λ). Surface current distribution analysis confirms uniform excitation and efficient radiation, further validating the design’s stability. The fabricated prototype shows excellent agreement with the simulation, with minor discrepancies attributed to fabrication tolerances. These results establish the proposed antenna as a promising candidate for applications requiring compact, high-gain, and beam-steerable solutions, such as 5G mm-wave wireless communication systems, point-to-point wireless backhaul, and automotive radar sensing. Full article

Biofloc technology (BFT), traditionally centered on feed supplementation and water purification in aquaculture, harbors untapped multifunctional potential as a sustainable resource management platform. This review systematically explores beyond conventional applications. BFT leverages microbial consortia to drive resource recovery, yielding bioactive compounds with antibacterial/antioxidant properties, microbial proteins for efficient feed production, and algae biomass for nutrient recycling and bioenergy. In environmental remediation, its porous microbial aggregates remove microplastics and heavy metals through integrated physical, chemical, and biological mechanisms, addressing critical aquatic pollution challenges. Agri-aquatic integration systems create symbiotic loops where nutrient-rich aquaculture effluents fertilize plant cultures, while plants act as natural filters to stabilize water quality, reducing freshwater dependence and enhancing resource efficiency. Emerging applications, including pigment extraction for ornamental fish and the anaerobic fermentation of biofloc waste into organic amendments, further demonstrate its alignment with circular economy principles. While technical advancements highlight its capacity to balance productivity and ecological stewardship, challenges in large-scale optimization, long-term system stability, and economic viability necessitate interdisciplinary research. By shifting focus to its underexplored functionalities, this review positions BFT as a transformative technology capable of addressing interconnected global challenges in food security, pollution mitigation, and sustainable resource use, offering a scalable framework for the future of aquaculture and beyond. Full article

Mycotoxins are responsible for a multitude of diseases in both humans and animals, resulting in significant medical and economic burdens worldwide. Conventional detection methods, such as enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), are highly effective, but they are generally confined to laboratory settings. Consequently, there is a growing demand for point-of-care testing (POCT) solutions that are rapid, sensitive, portable, and cost-effective. Lateral flow assays (LFAs) are a pivotal technology in POCT due to their simplicity, rapidity, and ease of use. This review synthesizes data from 78 peer-reviewed studies published between 2015 and 2024, evaluating advances in nanoparticle-based LFAs for detection of singular or multiplex mycotoxin types. Gold nanoparticles (AuNPs) remain the most widely used, due to their favorable optical and surface chemistry; however, significant progress has also been made with silver nanoparticles (AgNPs), magnetic nanoparticles, quantum dots (QDs), nanozymes, and hybrid nanostructures. The integration of multifunctional nanomaterials has enhanced assay sensitivity, specificity, and operational usability, with innovations including smartphone-based readers, signal amplification strategies, and supplementary technologies such as surface-enhanced Raman spectroscopy (SERS). While most singular LFAs achieved moderate sensitivity (0.001–1 ng/mL), only 6% reached ultra-sensitive detection (<0.001 ng/mL), and no significant improvement was evident over time (ρ = −0.162, p = 0.261). In contrast, multiplex assays demonstrated clear performance gains post-2022 (ρ = −0.357, p = 0.0008), largely driven by system-level optimization and advanced nanomaterials. Importantly, the type of sample matrix (e.g., cereals, dairy, feed) did not significantly influence the analytical sensitivity of singular or multiplex lateral LFAs (Kruskal–Wallis p > 0.05), confirming the matrix-independence of these optimized platforms. While analytical challenges remain for complex targets like fumonisins and deoxynivalenol (DON), ongoing innovations in signal amplification, biorecognition chemistry, and assay standardization are driving LFAs toward becoming reliable, ultra-sensitive, and field-deployable platforms for high-throughput mycotoxin screening in global food safety surveillance. Full article

Electrochemical separations use an ionic current to drive the flow of ions across an ion exchange membrane to produce dilute and concentrated streams. The economics of these systems is challenging because passing an ionic current through a dilute solution often requires a small cell gap to lower the ionic resistance and the use of a low current density to minimize the voltage drop across the dilute product stream. Lower salt concentration in the product stream improves the fraction of the salt recovered but increases the electricity cost due to high ohmic losses. The electricity cost is managed by lowering the current density which greatly increases the balance of the plant. The cell configuration demonstrated in this study eliminates the need to pass an ionic current through the diluted product stream. Ionic current passes only through the concentrated product stream, which allows use of high current density and smaller balance of the plant. The cell has three chambers with an anion and cation membrane separating the cathode and anode, respectively, from the concentrated product solution. The device uses zero-gap membrane electrode assemblies to improve the cell voltage and system performance. As ions concentrate in the center compartment, the solution resistance decreases, and the product is recovered with a lower voltage penalty compared to traditional electrodialysis. This lower voltage drop allows for faster feed flow rates and higher current density. Additionally, the larger cell gap for the product provides opportunities for systems with solids suspended in solution. It was found that the ion collection efficiency increased with current due to enhanced convective mass transfer in the feed streams. Full article

Temperature polarization is a critical factor influencing the performance of membrane distillation. The presence of temperature polarization causes the temperature of the fluid near the membrane surface to be different from that in the bulk region, reducing the effective temperature difference across the membrane and thus diminishing the transmembrane mass transfer driving force. This study investigates the monitoring of temperature polarization and its effects on the transmembrane mass transfer performance in a typical air gap membrane distillation system. A set of thermocouples within a feed module were employed to monitor and capture the development of the temperature polarization profile. The test results reveal that temperature polarization reduces the effective temperature difference across the membrane, leading to a certain difference between the theoretical estimation and experimental values of the mass transfer coefficient across the porous membrane. To address this issue, the temperature polarization factor was further analyzed as a metric to quantify the impact of temperature polarization on the transmembrane flux in membrane distillation, with a detailed discussion of its range and implications. Full article

Forward osmosis (FO) is a membrane separation process driven by the osmotic pressure difference between a high-salinity draw solution (DS) and a low-salinity feed solution (FS). This pressure-free dewatering method is highly energy efficient, making it suitable for concentration and resource recovery. However, conventional FO systems using series-connected modules suffer from progressive DS dilution and FS concentration, leading to a reduction in the osmotic driving force and thereby limiting the overall performance. To address this issue, we propose a novel hybrid FO module configuration in which the FS flows in series while the DS is split and distributed in parallel across moules. This configuration was evaluated using an experimentally validated FO module model and RO simulation tools. Under seawater (600 mM NaCl) as DS and brackish water (10 mM NaCl) as FS, a conventional three-stage FO module achieved an enrichment ratio of 2.5 with an energy consumption of 0.151 kWh/m³. In contrast, the proposed draw solution split distribution (DSSD) achieved an enrichment ratio of 12.5 at a reduced energy consumption of 0.137 kWh/m³. In comparison, a reverse osmosis system consuming 0.58 kWh/m³ achieved a similar enrichment ratio of 12.3. These results demonstrate the high energy efficiency and dewatering capacity of the proposed FO configuration, highlighting its potential for industrial applications in food processing, beverage production, pharmaceuticals and agriculture. Full article

Background: Low-dose antibiotic contamination in animal feed is a persistent global food safety challenge. Transient early-life exposure to low-dose penicillin (LDP) is known to induce metabolic syndrome (MetS) in adult mice, but the underlying mechanisms are unclear. Introduction: This study investigated the role of gut microbiota (GM) and intestinal immunity in mediating the long-term metabolic effects of early-life LDP exposure. Methods: Mice were exposed to LDP transiently during early life. GM composition was analyzed. Intestinal IgA responses were quantified. Bacterial encroachment, systemic and adipose tissue inflammation, and diet-induced MetS were assessed. Germ-free (GF) mice received GM transplants from LDP-exposed or control mice to test causality and persistence. Results: Early-life LDP exposure significantly disrupted GM composition, particularly in the ileum, in 30-day-old mice. These GM alterations caused persistent suppression of intestinal IgA responses, evidenced by reduced IgA-producing cells and sIgA levels. This suppression was constrained to early-life exposure: transferring LDP-modified GM to GF mice produced only a transient reduction in fecal sIgA. The LDP-induced sIgA reduction decreased IgA binding of bacteria, leading to increased bacterial encroachment and systemic and adipose tissue inflammation. These pathological changes exacerbated diet-induced MetS. Discussion: Our findings demonstrate that early-life LDP exposure induces persistent intestinal IgA deficiency through lasting GM alterations initiated in early development. This deficiency drives bacterial encroachment, inflammation, and ultimately exacerbates MetS. Conclusions: The exacerbation of diet-induced metabolic syndrome by early-life LDP exposure occurs through an intestinal sIgA-dependent pathway triggered by persistent GM disruption. This highlights a critical mechanism linking early-life antibiotic exposure, gut immune dysfunction, and long-term metabolic health, with significant implications for food safety. Full article

With the rising global demand for aquatic products, aquaculture has become a cornerstone of food security and sustainability. This review comprehensively analyzes the application of deep learning in sustainable aquaculture, covering key areas such as fish detection and counting, growth prediction and health monitoring, intelligent feeding systems, water quality forecasting, and behavioral and stress analysis. The study discusses the suitability of deep learning architectures, including CNNs, RNNs, GANs, Transformers, and MobileNet, under complex aquatic environments characterized by poor image quality and severe occlusion. It highlights ongoing challenges related to data scarcity, real-time performance, model generalization, and cross-domain adaptability. Looking forward, the paper outlines future research directions including multimodal data fusion, edge computing, lightweight model design, synthetic data generation, and digital twin-based virtual farming platforms. Deep learning is poised to drive aquaculture toward greater intelligence, efficiency, and sustainability. Full article

The dynamic error is the dominant factor affecting multi-axis CNC machining accuracy. Predicting and compensating for dynamic errors is vital in high-speed machining. This paper proposes a novel prediction-model-based approach to predict and compensate for the ball screw feed system’s dynamic error. Based on the lumped and distributed mass methods, this method constructs a parameterized dynamic model relying on the moving component’s position for electromechanical coupling modeling. Using Latin Hypercube Sampling and numerical simulation, a sample set containing the input and output of one control cycle is obtained, which is used to train a Cascade-Forward Neural Network to predict dynamic errors. Finally, a feedforward compensation strategy based on the prediction model is proposed to improve tracking performance. The proposed method is applied to a ball screw feed system. Tracking error simulations and experiments are conducted and compared with the transfer function feedforward compensation. Typical trajectories are designed to validate the effectiveness of the electromechanical coupling model, the dynamic error prediction model, and the feedforward compensation strategy. The results show that the prediction model exhibits a maximum prediction deviation of 1.8% for the maximum tracking error and 13% for the average tracking error. The proposed compensation method with friction compensation achieves a maximum reduction rate of 76.7% for the maximum tracking error and 63.7% for the average tracking error. Full article

The intensifying global demand for food presents significant challenges for sustainable pig production, particularly in the context of escalating input costs, environmental degradation, and resource scarcity. Life cycle assessment provides a comprehensive framework for quantifying environmental impacts and identifying production hotspots within pig production systems. Feed production and manure management are consistently identified as major contributors, emphasising the need for targeted interventions. Although soybean meal remains a key protein source, its association with deforestation and biodiversity loss is driving an interest in more sustainable alternatives. In temperate climates, faba beans offer a promising, locally sourced option, though their wider adoption is limited by amino acid imbalances and anti-nutritional factors. Grain preservation is another critical consideration, as post-harvest losses and fungal contamination compromise feed quality and animal health. Organic acid preservation has emerged as an energy-efficient, cost-effective alternative to industrial drying, improving storage stability and reducing fossil fuel dependence. Additional nutritional strategies, including dietary crude protein reduction, carbohydrate source modification, feed additive inclusion, and maternal nutritional interventions, can enhance nutrient utilisation, intestinal health, and herd resilience while mitigating environmental impact. This review explores practical feed-based strategies to support sustainable, resilient, and resource-efficient pig production and contribute to global food security. Full article

This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability. Full article

Effective grinding of residual agricultural materials helps to improve yield in the production of chemical compounds through hydrothermal technology. Milling pretreatment has different types of pre-treatment where ball mills, roller mills, and finally, the knife mill stand out. The knife mill being a mill with continuous processing, its multiple benefits and contributions highlight the knife milling process; however, it is a process that is generally carried out with dry biomass that generates extra processing of the biomass before grinding, implying longer times and wear than other equipment. This work presents the design of a knife mill with an adaptation of free convection drying as a joint process of knife milling and drying. The design is based on lignocellulosic biomass, and the knife milling results are presented for two biomasses: peapods and coffee cherries. The knife mill is designed with a motor, a housing with an integrated drive system, followed by a knife system and a feeding system with a housing and finally the free convection drying system achieving particle sizes in these biomasses smaller than 30 mm, depending on the time processed. The data demonstrate the significant impact of particle size on the yields of various platform chemicals obtained from coffee cherry and peapod waste biomass. For coffee cherry biomass, smaller particle sizes, especially 0.5 mm, result in higher total yields compared to larger sizes while for peapod biomass at the smallest particle size of 0.5 mm, the total yield is the highest, at 45.13%, with notable contributions from sugar (15.63%) and formic acid (19.14%). Full article

Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available at the prediction time. Hourly DAEP data from the California Independent System Operator (January 2017 to July 2023) were integrated with exogenous and engineered endogenous features. A custom rolling window cross-validation, with 24 h validation blocks sliding daily across 2372 folds, evaluates an Extreme Gradient Boosting (XGBoost) model’s performance under diverse market conditions, achieving a median mean absolute error of 6.26 USD/MWh and root mean squared error of 8.27 USD/MWh, with variability reflecting market volatility. The feature importance analysis using Shapley additive explanations highlighted the dominance of engineered endogenous features in driving the 24 h lead time forecasts under relatively stable market conditions. Forecasting the DAEP at a runtime of 10 AM on the prior day was used to assess model uncertainty. This involved training random forest, support vector regression, XGBoost, and feed forward neural network models, followed by stacking and voting ensembles. The results indicate the need for ensemble forecasting and evaluation beyond a static train–test split to ensure the practical utility of machine learning for DAEP forecasting across varied market dynamics. Finally, operationalizing the forecast model for bidding decisions by forecasting the DAEP and real-time prices at runtime is presented and discussed. Full article

Bleeding constitutes an essential stage in pre-processing operations for fish products. In China, this process remains entirely manual, characterized by low efficiency, high labor intensity, and operational hazards, creating a pressing demand for versatile automated equipment in the market. To address the mechanization requirements for efficient freshwater fish bloodletting, we developed an automated fish bleeding machine based on gill-based blood extraction principles, incorporating an impact-triggered positional bleeding methodology. The positional triggering mechanism was engineered through kinematic and dynamic analyses of fish sliding trajectories onto the trigger plate, informed by morphological parameters of fish specimens. This design achieved automated positional awareness and bleeding activation. A reciprocating bleeding mechanism was developed by mimicking manual bleeding motions, leveraging the quick-return motion characteristics of an offset crank-slider mechanism. The transmission system combined chain-drive and clutch mechanisms to enable sequential and intermittent power delivery. Experimental validation employed live specimens of snakehead (Channa argus), grass carp (Ctenopharyngodon idellus), and tilapia (Oreochromis mossambicus), with systematic evaluations including sensory assessments, comparative testing, and performance metrics. Results demonstrated a comprehensive sensory evaluation score of 3.8 for bleeding efficacy; significant influence of baffle geometry on performance, identifying V-shaped baffles as optimal; a bleeding success rate averaging 94% with throughput reaching 1417 fish/hour. The integrated workflow—directional feeding, postural constraint, positional triggering, reciprocating bleeding, and automated ejection—established a cyclic mechanized bleeding process with industrial applicability. Full article