Search Results (17,388)

Abstract: Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized. Full article

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection. Full article

Forecasts about the aging trend of the world population agree on identifying increased life expectancy as a serious risk factor for the financial sustainability of social healthcare systems if not properly supported by innovative care management policies. Such policies should include the integration within traditional healthcare services of assistive technologies as tools for prolonging healthy and independent living at home, but also for introducing innovations in clinical practice such as long-term and remote health monitoring. For their part, solutions for active and assisted living have now reached a high degree of technological maturity, thanks to the considerable amount of research work carried out in recent years to develop highly reliable and energy-efficient wearable sensors capable of enabling the development of systems to monitor activity and physiological parameters over time, and in a minimally invasive manner. This work reviews the role of wearable sensors in the design and development of assisted living solutions, focusing on human activity recognition by joint use of onboard electromyography sensors and inertial measurement units and on the acquisition of parameters related to overall physical and psychological conditions, such as heart activity and skin conductance. Full article

The transition towards more sustainable packaging calls for improving our ability to predict, control, and inhibit microbial growth. Despite the importance of modified atmosphere packaging (MAP) in food preservation, the exact relations between MAP gases (CO₂, O₂, N₂) and microbial behavior are still poorly understood. Addressing this major knowledge gap requires a specific infrastructure to gain precise control over the gas composition during storage time. Thus, this study aimed at developing and validating an innovative gas-washing bottle incubation system (GBIS) with an adapted pH methodology for monitoring microbial growth in liquid media under different well-controlled conditions. Listeria monocytogenes—a psychrotrophic pathogen raising severe safety concerns under refrigerated conditions—was used as a representative microorganism. The results showed that daily gas flushing effectively dominated overnight headspace variations, allowing incubating L. monocytogenes and daily sampling for 13 days under static conditions. Subsequently, storage experiments were performed at 4 °C under stable pH and anaerobic conditions with different CO₂ levels (20–40–60%). Significant growth reduction was observed from 6.0 to 4.8 log CFU/mL as CO₂ increased from 20% (pH = 6.7) to 60% (pH = 6.2) (p ≤ 0.05). Overall, GBIS shows great potential in data collection for predictive modeling and, consecutively, for boosting decision-making in the food packaging sector. Full article

The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy. Lasting neuropsychological effects have also been recorded in individuals following SARS-CoV-2 infection. These include anxiety, depression, and cognitive dysfunction, suggesting a lasting impact on mental health. The neuroinvasive potential of the virus, inflammatory responses, and the role of angiotensin-converting enzyme 2 (ACE2) in neuroinflammation are critical factors in neuropsychiatric COVID-19 manifestations. In addition, the review highlights the importance of monitoring biomarkers to assess Central Nervous System (CNS) involvement. Management strategies for these neuropsychiatric conditions include supportive therapy, antiepileptic drugs, antithrombotic therapy, and psychotropic drugs, emphasizing the need for a multidisciplinary approach. Understanding the long-term neuropsychiatric implications of COVID-19 is essential for developing effective treatment protocols and improving patient outcomes. Full article

Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications. Full article

Background: Mechanical ventilation is a critical but resource-intensive treatment. Automated tools are common in screening diagnostics, whereas real-time, continuous trend analysis in mechanical ventilation remains rare. Current techniques for monitoring lung conditions are often invasive, lack accuracy, and fail to isolate respiratory resistance—making them impractical for continuous monitoring and diagnosis. To address this challenge, we propose an automated, non-invasive condition monitoring method to support pulmonologists. Methods: Our method leverages ventilation waveform time-series data in controlled modes to monitor lung conditions automatically and non-invasively on a breath-by-breath basis while accurately isolating respiratory resistance. Results: Using statistical classification and regression models, the approach achieves 99.1% accuracy for ventilation mode classification, 97.5% accuracy for feature extraction, and 99.0% for predicting mechanical lung parameters. The models are both computationally efficient (720 K predictions per second per core) and lightweight (24.5 MB). Conclusions: By storing breath-by-breath predictions, pulmonologists can access a high-resolution trend of lung conditions, gaining clear insights into sudden changes without speculation and streamlining diagnosis and decision-making. The deployment of this solution could expand domain knowledge, enhance the understanding of patient conditions, and enable real-time dashboards for parallel monitoring, helping to prioritize patients and optimize resource use, which is especially valuable during pandemics. Full article

Moulting is a crucial yet challenging life-history trait to study in seabirds, particularly in the wild. Public aquariums offer valuable opportunities to collect detailed data, which, although not directly transferable to wild populations, provide important insights. At Oceanário de Lisboa, six Fratercula arctica individuals were monitored over seven years to document moulting patterns. The start and end of each moult were consistently recorded around the spring and autumn equinoxes. Pre-alternate moults lasted between 17 and 73 days, while pre-basic moults ranged from 11 to 48 days, with primary moults occurring between the two. This study is the first to document an asynchrony between the primary and the pre-alternate moults in F. arctica, highlighting a previously unreported aspect of the species’ moulting process. This seven-year time series and its findings prompt a call for action for further studies in controlled conditions, to investigate this pattern under different conditions and across puffins’ life stages. Such data could be crucial for developing more effective conservation strategies for this vulnerable species. These findings emphasize the importance of continued monitoring and research on ex situ puffin populations to expand our understanding of their moulting behaviour and its implications for wild populations. Full article

High-resolution water quality maps derived from imaging spectroscopy provide valuable insights for environmental monitoring and management, but the processing of all pixels of large datasets is extremely computationally intensive and limits the speed of map production. We demonstrate a superpixel approach to accelerating water quality parameter inversion on such data to considerably reduce time and resource needs. Neighboring pixels were clustered into spectrally similar superpixels, and bio-optical inversions were performed at the superpixel level before a nearest-neighbor interpolation of the results back to pixel resolution. We tested the approach on five example airborne imaging spectroscopy datasets from Hawaiian coastal waters, comparing outputs to pixel-by-pixel inversions for three water quality parameters: suspended particulate matter, chlorophyll-a, and colored dissolved organic matter. We found significant reduction in computational time, ranging from 38 to 2625 times faster processing for superpixel sizes of 50 to 5000 pixels (200 to 20,000 m²). Using 1000 paired output values from each example image, we found minimal reduction in accuracy (as decrease in R² or increase in RMSE) of the model results when the superpixel size was less than 750 2 m × 2 m resolution pixels. Such results mean that this methodology could reduce the time needed to produce regional- or global-scale maps and thereby allow environmental managers and other stakeholders to more rapidly understand and respond to changing water quality conditions. Full article

Bacterial infection of the lower respiratory tract frequently occurs in mechanically ventilated patients and may develop into life-threatening conditions. Yet, existing diagnostic methods have moderate sensitivity and specificity, which results in the overuse of broad-spectrum antibiotics administered prophylactically. This study aims to evaluate the suitability of volatile bacterial metabolites for the breath-based test, which is used for diagnosing Ventilation-Associated Pneumonia (VAP). The in vitro experiments with pathogenic bacteria most prevalent in VAP etiology (i.e., Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were performed to identify bacteria-derived metabolites using a specially designed cultivation system enabling headspace sampling for GC-MS analysis. Thirty-nine compounds were found to be significantly metabolized by tested species and, therefore, selected for monitoring in the exhaled breath of critically ill, mechanically ventilated (MV) patients. The emission of volatiles from medical respiratory devices was investigated to estimate the risk of spoiling breath results with exogenous pollutants. Bacterial metabolites were then evaluated to differentiate VAP patients from non-infected MV controls using Receiver Operating Characteristic (ROC) analysis, with AUC, sensitivity, and specificity calculated. Nine bacterial metabolites that passed verification through a non-parametric ANOVA test for significance and LASSO penalization were identified as key discriminators between VAP and non-VAP patients. The diagnostic model achieved an AUC of 0.893, with sensitivity and specificity values of 87% and 82.4%, respectively, being competitive with traditional methods. Further validation could solidify its clinical utility in critical care settings. Full article

Background and objectives: Nephrocalcinosis (NC) is a common condition characterized by the deposition of calcium salts in the kidneys of very preterm infants due to tubular immaturity, intensive treatment and nutritional supplements. However, optimal vitamin D supplementation remains unclear. In most patients, NC spontaneously resolves within the first year of life, but long-term kidney function data are lacking. The aim was to study nephrocalcinosis in very preterm infants, assess risk factors and evaluate vitamin D’s impact during the first month with a 2-year follow-up. Material and Methods: This was a prospective observational study conducted over a 3-year period in infants with a gestational age of less than 32 weeks. The patients’ data were compared between the NC and control groups based on kidney ultrasound results at discharge. In the first month, the mean vitamin D intake from all sources as well as biochemical markers of calcium metabolism were collected. Patients diagnosed with NC were referred to a pediatric nephrologist after discharge. Results: NC was found in 35% of a cohort of 160 infants, more common in those with a gestational age <28 weeks. Risk factors were associated with higher morbidity and necessary treatment. At 28 days, serum 25-hydroxy vitamin D levels differed between NC and control groups (p < 0.05). The NC group with GA ≥ 28 weeks had higher vitamin D intake (p < 0.05), hypercalciuria and calcium/creatinine ratio (p < 0.01) and lower parathyroid hormone levels (p < 0.05). Follow-up showed resolution in 70% at 12 months and 90% at 24 months. Conclusions: The prevalence of NC in very preterm infants is significant, associated with lower maturity and higher morbidity. Careful vitamin D supplementation and biochemical monitoring of Ca metabolism from the first month of life should support bone health and limit the risk of nephrocalcinosis. Due to the high incidence of NC in very preterm infants, long-term follow-up is essential. Full article

In the electronics sector, artificial intelligence (AI) has grown into a disruptive force that is changing how humans engage with technology and creating new opportunities. AI is expanding the capabilities of electronic devices, granting them higher intelligence, increased intuitiveness, and the ability to comprehend and react to human behavior. The purpose of this approach is to highlight the knowledge structure in artificial intelligence application in agriculture and its challenges within the European Union. A bibliometric analysis was conducted, distinguishing the following items as the main research themes: agriculture 4.0; advanced monitoring and controlling strategies in intelligent agriculture; the automation of agriculture by including practices such as cloud computing, Internet of Things (IoT), big data, blockchain, robotics and AI, information security; new skills, and responsible leadership. The regression analysis revealed that the employers’ assumption of responsibility, by ensuring opportunities for training and development of digital skills, determines the growth of added value (0.013) and its rate (0.0003). Enhancing labor productivity depends on Internet access for the integration of technologies based on artificial intelligence (1.343). An increasing employment rate of low-skilled people affects agricultural production (0.0127). The contributions of this two-dimensional approach consist in supporting the integration of digital technology in agriculture as a condition for achieving the goals of sustainable development. Full article

This study considers seismoacoustic wave propagation through the land–sea interface, i.e., in the presence of a coastal wedge, taking into account the real bottom bathymetry. It is of interest in the problems of coastal monitoring and environmental studies. An effective numerical model based on the finite element method is proposed and implemented. An approximate analytical solution in the fluid and an asymptotic analytical solution for the surface seismic wave on the shore are considered to validate the numerical model. It is shown that in field experiment conditions the hydroacoustic signal generated by an underwater source with a power of ~200 W is transformed into a seismic wave on the shore with an amplitude of units of nanometers at distances of several kilometers, which can be measured by a sensitive sensor. An extensive series of numerical simulations with different model parameters was performed, which allowed us to evaluate the most appropriate propagation medium parameters to match the observed and calculated data. Full article

There is currently no established information for assessing the general welfare conditions and behavior of dairy cows housed in compost-bedded pack barns (CBPs) that allow access to pasture. Therefore, the objective of this study was to evaluate and classify the welfare and behavior of dairy cows in three different housing conditions within CBPs in southern Brazil. During both the cold and hot seasons, nine farms were divided into three groups: CONV (conventional, large, full-time barns), ADAP (conventionally adapted, full-time barns), and PART (part-time barns). The European Welfare Quality^® (WQ^®) protocol takes into account the characteristics of the animals, animal housing, and farm management to set an overall score to assess animal welfare, which is why WQ^® was used in this study. Daytime behavior was monitored over a period of four consecutive hours on two days. The 29 WQ^® measures were grouped into 11 criteria, then into four principles, and finally into the general welfare category. The experimental design employed was a randomized block design in a 2 × 3 factorial scheme (two climatic seasons and three groups), with the means of the measures, principles, and criteria for each group, season, and interaction (group × season) compared using the Tukey test. The diurnal behavior of the cows was described by the average absolute frequency of each observed behavioral measure. There were no differences among the groups in any of the measures assessed by the WQ^® protocol. However, there was a significant increase in both the incidence of diarrhea and the duration of lying down during the cold season. Only the principle of appropriate behavior varied among the groups, with the PART group demonstrating superior scores. Regardless of the season, the welfare of dairy cows maintained in CBPs was classified as “improved”. No abnormalities in behavior were observed among cows housed in the different groups or seasons. Cows in the PART group laid down less frequently during the hot season. Overall, the CBP system provided favorable welfare and behavioral conditions for cows in Brazil, and access to grazing further enhanced the welfare of animals housed in the PART group. Full article

Surfactin, a potent biosurfactant produced by Bacillus subtilis, is synthesized using a non-ribosomal peptide synthetase (NRPS) encoded by the srfAA-AD operon. Despite its association with quorum sensing via the ComX pheromone, the dynamic behavior and in vivo quantification of the NRPS complex remain underexplored. This study established an in vivo quantification system using fluorescence labeling to monitor the availability of surfactin-forming NRPS subunits (SrfAA, SrfAB, SrfAC, and SrfAD) during bioprocesses. Four Bacillus subtilis sensor strains were constructed by fusing these subunits with the megfp gene, resulting in strains BMV25, BMV26, BMV27, and BMV28. These strains displayed growth and surfactin productivity similar to those of the parental strain, BMV9. Fluorescence signals indicated varying NRPS availability, with BMV27 showing the highest and BMV25 showing the lowest relative fluorescence units (RFUs). RFUs were converted to the relative number of NRPS molecules using open-source FPCountR package. During bioprocesses, NRPS availability peaked at the end of the exponential growth phase and declined in the stationary phase, suggesting reduced NRPS productivity under nutrient-limited conditions and potential post-translational regulation. This study provides a quantitative framework for monitoring NRPS dynamics in vivo, offering insights into optimizing surfactin production. The established sensor strains and quantification system enable the real-time monitoring of NRPS availability, aiding bioprocess optimization for industrial applications of surfactin and potentially other non-ribosomal peptides. Full article