Search Results (399)

A parallel efficient global optimization (EGO) algorithm with a pseudo expected improvement (PEI) multi-point sampling criterion, proposed in recent years, is developed to adapt the capabilities of modern parallel computing power. However, a comprehensive and clear discussion on the impact of different point-filling strategies on the optimization performance of the parallel EGO algorithm is still lacking, limiting its theoretical reference for practical applications and technological advancements. To address this gap, this study comprehensively investigates the optimization performance of the parallel EGO algorithm based on the PEI multi-point sampling criterion by analyzing the impact of different point-filling strategies under kriging surrogate models of varying fidelity. Therefore, nine benchmark test functions with different optimization problem characteristics were selected as optimization test objects, and the results were systematically analyzed from the perspectives of convergence performance, optimization efficiency, and algorithmic diversity. The analysis results indicate that the higher-fidelity kriging surrogate model enhances the stability of the parallel EGO algorithm in terms of convergence performance, optimization efficiency, and algorithmic diversity. Full article

The objective of this study was to perform a preliminary in vitro characterization of Lactiplantibacillus plantarum BG112, assessing its safety and technological features for potential application as a culture starter for an industrial fermented dry meat product. In vitro assays assessed its viability, probiotic properties, and safety for use in food formulations. The strain was characterized through morphological and biochemical tests, carbohydrate fermentation profiling, and various in vitro assays based on FAO/WHO criteria for probiotic selection. These included proteolytic activity, auto-aggregation capacity, tolerance to simulated gastric juice and bile salts, antimicrobial activity, and resistance to sodium chloride, nitrite, and low pH. Safety evaluations were also performed by testing antibiotic susceptibility, hemolytic activity, and DNAse production. The results showed that L. plantarum BG112 exhibited strong tolerance to adverse environmental conditions typically found during sausage fermentation and ripening, along with significant inhibitory activity against pathogenic bacteria, such as Escherichia coli O157:H7, Salmonella Typhimurium, and Staphylococcus aureus. The strain also demonstrated no hemolytic or DNAse activity and presented a favorable antibiotic sensitivity profile, meeting key safety requirements for probiotic use. Further studies using meat matrices and in vivo models are needed to validate these findings. This study contributes to the early-stage selection of safe and technologically suitable strains for use in fermented meat products. These findings support the potential application of L. plantarum BG112 as a safe and effective starter culture in the development of high-value, premium fermented meat products, aligned with current consumer demand for health-enhancing and natural foods. Full article

Passengers’ trust in Shared Autonomous Vehicles (SAVs) can be affected by different factors, such as their attitudes toward new technologies and perceptions of the vehicles’ reputation. While the existing literature has begun to explore these issues, there is limited research investigating how industrial design in SAVs can enhance passengers’ trust levels. To address this gap, this study responds to the central question: How can passengers’ trust in the vehicle itself and in fellow passengers be enhanced through design intervention? This question conceptualises trust in the vehicle and trust in strangers as an integrated trust issue within the SAV context. To fill this gap, this study adopts a project-grounded methodology. The design work is guided by five trust principles: anthropomorphic design, a defensible space, system transparency, personalisation features, and a restorative environment. Drawing on insights from an auto-ethnography of current ride-sharing services, these principles are further explored and applied to identify design opportunities for both the physical and digital elements of SAVs. The final conceptual SAV design demonstrates how different design elements can be orchestrated to engender user trust. The outcome contributes to ongoing design practices and helps researchers and designers better understand trust design for SAVs. Full article

This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems. Full article

To address the challenge of physically realizing fractional-order electrical networks, this study proposes an implementation method for a mechatronic inerter–spring–damper (ISD) suspension based on a fractional-order biquadratic transfer function. Building upon a previously established model of a mechatronic ISD suspension, the influence of parameter perturbations on the suspension’s dynamic performance characteristics was systematically investigated. Positive real synthesis was employed to determine the optimal five-element passive network structure for the fractional-order biquadratic electrical network. Subsequently, the Oustaloup filter approximation algorithm was utilized to realize the integer-order equivalents of the fractional-order electrical components, and the approximation effectiveness was analyzed through frequency-domain and time-domain simulations. Bench testing validated the effectiveness of the proposed method: under random road excitation at 20 m/s, the root mean square (RMS) values of the vehicle body acceleration, suspension working space, and dynamic tire load were reduced by 7.86%, 17.45%, and 2.26%, respectively, in comparison with those of the traditional passive suspension. This research provides both theoretical foundations and practical engineering solutions for implementing fractional-order transfer functions in vehicle suspensions, establishing a novel technical pathway for comprehensively enhancing suspension performance. Full article

This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this information into a single map. To achieve this, a web-based platform was developed to allow the simultaneous, real-time visualization of all robots in operation. However, the main challenge of this research lies in the heterogeneity of the fleet, which comprises robots of different makes and models from various manufacturers, each using distinct data formats. The proposed approach addresses this by facilitating fleet monitoring and management, ensuring a greater efficiency and coordination in the robot movement. The results demonstrate that the platform improves the traceability and operational supervision, promoting the optimized management of mobile robots. It is concluded that the proposed solution contributes to industrial automation by providing an intuitive and centralized interface, enabling future expansions for new functionalities and the integration with other emerging technologies. The proposed system demonstrated efficiency in updating and supervising operations, with an average latency of 120 ms for task status updates and an interface refresh rate of less than 1 s, enabling near real-time supervision and facilitating operational decision-making. Full article

This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices—the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel’s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination ($R^2$) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing. Full article

Hyperuricemia arises from urate overproduction and/or underexcretion. Probiotics offer the potential for alleviating hyperuricemia by degrading urate precursors. This study characterized Enterococcus faecium YC07 isolated from the traditional Chinese fermented food Jiangshui, which demonstrated efficient biodegradation of nucleosides (urate precursors), converting 2.0 g/L to nucleobases within 48 h. Whole genome sequencing revealed a 2.53 Mb draft genome (59 contigs, 38.21% GC content) containing 2387 protein-coding genes. Genomic and phenotypic analysis confirmed its probiotic potential, including high tolerance of simulated gastric fluid (98.89% survival) and intestinal fluid (44.51% survival), and strong adhesion capacity (24.16% auto-aggregation, 35.48% hydrophobicity), pathogen inhibition, and antioxidant activity. The identified antibiotic resistance genes and virulence factors were assessed alongside acute oral toxicology, cytotoxicity, antibiotics susceptibility, hemolysis, and enzymatic activity assays, confirming safety. Furthermore, successful pilot-scale fermentation in a 100 L fermenter demonstrated industrial feasibility. These findings established E. faecium YC07 as a safe and effective probiotic candidate for functional foods targeting hyperuricemia management. Full article

Listeriosis is a disease associated with the consumption of food contaminated with Listeria monocytogenes. Probiotic lactic acid bacteria (LAB) or their postbiotics have been of interest for their anti-listerial effect. This study focused on isolating LAB from artisanal cheeses and characterizing their potential as probiotics. Twelve LAB isolates exhibiting typical LAB traits were evaluated for their ability to survive in simulated gastric juice, hydrolyze bile salts, auto-aggregate, hydrophobicity, and antagonistic activity against L. monocytogenes. The four most promising LAB strains demonstrated anti-listerial probiotic potential and were identified as Latilactobacillus (Lat.) curvatus SC076 and Lactiplantibacillus (Lact.) paraplantarum SC291, SC093, and SC425. The antimicrobial activity of these strains was mainly attributed to bacteriocin-like substances and organic acids. While three Lact. paraplantarum strains were resistant to ampicillin, Lat. curvatus was sensitive to all tested antibiotics. All selected strains exhibited no hemolytic, gelatinase, and lecithinase activity. Exposure to LAB supernatants resulted in a significant reduction in the adhesion and intracellular count of L. monocytogenes in Caco-2 cells, with Lat. curvatus SC076 showing the most significant effect. Based on its probiotic characteristics, Lat. curvatus SC076 is a promising candidate for functional foods, pending further in vivo studies to assess its potential in the food industry. Full article

In this paper, a high-voltage chopper and ping-pong auto-zeroing operational amplifier was designed for industrial and automotive applications. Based on chopper stabilization, the proposed circuit introduces a novel chopper switch control signal that varies with the input common-mode voltage. This scheme effectively suppresses the reference offset caused by the chopper switches and prevents transistor breakdown under high-voltage conditions. Additionally, the ping-pong auto-zero structure was optimized by employing a six-channel parallel first-stage amplifier, which further reduced the charge injection and ripple introduced by the chopper switches. The amplifier was implemented using an SMIC (Semiconductor Manufacturing International Corporation) 180 nm 1P5M BCD (Bipolar-CMOS-DMOS) process with a chip area of 4.211 mm². The post-layout simulation results show that, under a 55 V supply, the amplifier achieves an input-referred noise Power Spectral Density (PSD) of 6.2 $\mathrm{nV}/\sqrt{\mathrm{Hz}}$ and an input offset voltage of 32 $\mu \mathrm{V}$, while the output voltage swings from 0.2 V to 53.4 V with a unity gain bandwidth of 3.2 MHz, which meets the requirements for high-voltage, high-resolution signal processing. Full article

Industrial defect detection in edge computing environments faces critical challenges in balancing accuracy, efficiency, and adaptability under data scarcity. To address these limitations, we propose the Hybrid Anomaly Detection System (HyADS), a novel lightweight framework for edge-based industrial defect detection. HyADS integrates three synergistic modules: (1) a feature extractor that integrates Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) to capture robust texture features, (2) a lightweight U-net autoencoder that reconstructs normal patterns while preserving spatial details to highlight small-scale defects, and (3) an adaptive patch matching module inspired by memory bank retrieval principles to accurately localize local outliers. These components are synergistically fused and then fed into a segmentation head that unifies global reconstruction errors and local anomaly maps into pixel-accurate defect masks. Extensive experiments on the MVTec AD, NEU, and Severstal datasets demonstrate state-of-the-art performance. Notably, HyADS achieves state-of-the-art F1 scores (94.1% on MVTec) in anomaly detection and IoU scores (85.5% on NEU/82.8% on Seversta) in segmentation. Designed for edge deployment, this framework achieves real-time inference (40–45 FPS on an RTX 4080 GPU) with minimal computational overheads, providing a practical solution for industrial quality control in resource-constrained environments. Full article

Ensuring efficient long-term quality control of the raw mix remains a priority for the cement industry, supporting initiatives to lower the CO₂ footprint by incorporating significant amounts of alternative fuels and raw materials in clinker production. This study presents an effective method for creating a robust auto-tuner for proportional–integral–differential (PID) controller control of the lime saturation factor (LSF) of the raw mix using artificial neural networks (ANNs). This auto-tuner, combined with a previously studied robust PID controller, forms an integrated system that adapts to process changes and maintains low long-term variance in LSF. The ANN links each of the three PID gains to the process dynamic parameters, with the three ANNs also interconnected. We employed the Levenberg–Marquardt method to optimize the ANNs’ synaptic weights and applied the weight decay method to prevent overfitting. The industrial implementation of our control system, using the auto-tuner for 16,800 h of raw mill operation, shows an average LSF standard deviation of 2.5, with fewer than 10% of the datasets exceeding a standard deviation of 3.5. Considering that the measurement reproducibility is 1.44 and assuming a low mixing ratio of the raw meal in the silo equal to 2, the LSF standard deviation in the kiln feed approaches the analysis reproducibility, indicating that disturbances in the raw meal largely diminish in the kiln feed. In conclusion, integrating traditional, well-established tools like PID controllers with newer advanced techniques, such as ANNs, can yield innovative solutions. Full article

This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force and torque signals at a 10 kHz sampling rate during the drilling process. These signals are employed for real-time process monitoring, focusing on material change detection and anomaly identification, where anomalies are defined as holes that fail to meet predefined quality criteria. An innovative approach based on unsupervised learning is proposed to enable automatic material change identification, signal segmentation, feature extraction, and hole quality assessment. Specifically, a semi-supervised approach based on a Gaussian Mixture Model (GMM) and 1D Convolutional AutoEncoder (1D-CAE) is employed to detect deviations from normal drilling conditions. The proposed method is benchmarked against state-of-the-art supervised techniques, including logistic regression (LR) and Support Vector Machines (SVMs). Results show that these traditional models struggle with class imbalance, leading to overfitting and limited generalisation, as reflected by the F1 scores of 0.78 and 0.75 for LR and SVM, respectively. In contrast, the proposed semi-supervised approach improves anomaly detection, achieving an F1 score of 0.87 by more effectively identifying poor-quality holes. This study demonstrates the potential of deep learning-based semi-supervised methods for intelligent process monitoring, enabling adaptive control in the drilling process of hybrid stacks and detecting anomalous holes. While the proposed approach effectively handles small and imbalanced datasets, further research into the application of generative AI could enhance performance, aiming for F1 scores above 0.90, thereby supporting adaptation in real industrial environments with high performance. Full article

This study addresses key challenges in auto insurance territory risk analysis by examining the complexities of spatial loss data and the evolving landscape of territorial risks before and during the COVID-19 pandemic. Traditional approaches, such as spatial clustering, are commonly used for territory risk assessment but offer limited predictive capabilities, constraining their effectiveness in forecasting future losses, an essential component of insurance pricing. To overcome this limitation, we propose an advanced predictive modeling framework that integrates spatial loss patterns while accounting for the pandemic’s impact. Our Bayesian-based spatial model captures stochastic spatial autocorrelations among territory rating units and their neighboring regions. This approach enables more robust pattern recognition through predictive modeling. By applying this approach to regulatory auto insurance loss datasets, we analyze industry-level trends in claim frequency, loss severity, loss cost, and insurance loading. The results reveal significant shifts in spatial loss patterns before and during the pandemic, highlighting the dynamic interplay between regional risk factors and external disruptions. These insights provide valuable guidance for insurers and regulators, facilitating more informed decision-making in risk classification, pricing adjustments, and policy interventions in response to evolving spatial and economic conditions. Full article

This paper presents a comparative study of binarization techniques for automated defect detection in dye penetrant testing (DPT) images. We evaluate established methods, including global, adaptive, and histogram-based thresholding, against three novel machine learning-assisted approaches, Soft Binarization (SoBin), Delta Binarization (DeBin), and Convolutional Autoencoder Binarization (AutoBin), using a real-world dataset from an automated DPT system inspecting stainless steel pipes. Performance is assessed with both pixel-level and region-level metrics, with particular emphasis on the influence of defect saturation. Defect saturation is quantified as the mean saturation value of all pixels belonging to a given defect, and defects are grouped into ten categories spanning from low (60–68) to high (132–140) mean saturation. Our results demonstrate that for lower mean defect saturation values, methods such as AutoBin_Triangle, HSV_global_70, and SoBin achieve superior Intersection over Union (IoU) and high true positive rates. In contrast, methods based primarily on global thresholding of the saturation channel tend to perform competitively on images with higher defect saturation levels, reflecting their sensitivity to stronger color signals. Moreover, depending on the method, nearly perfect region-level true positive rates (${\mathrm{TPR}}_{\mathrm{region}}$) or minimal false positive rates (${\mathrm{FPR}}_{\mathrm{region}}$) can be attained, emphasizing the trade-off that different models offer distinct strengths and weaknesses, which necessitates selecting the optimal method based on the specific quality control requirements and risk tolerances of the industrial process. These findings underscore the critical importance of defect saturation as a cue for both human and computer vision systems and provide valuable insights for developing robust automated quality control and predictive quality algorithms. Full article