Search Results (1,098)

Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain. Full article

This study presents a methodology for the development of modern Supervisory Control and Data Acquisition (SCADA) systems aimed at improving the operation and management of concentrated solar power (CSP) plants, leveraging the tools provided by industrial digitization. This approach is exemplified by its application to the CESA-I central tower heliostat field at the Plataforma Solar de Almería (PSA), one of the oldest CSP facilities in the world. The goal was to upgrade the control and monitoring capabilities of the heliostat field by integrating modern technologies such as OPC (Open Platform Communications)) Unified Architecture (UA), a Wi-Fi mesh communication network, and a custom Python-based gateway for interfacing with legacy MODBUS systems. Performance tests demonstrated stable, scalable communication, efficient real-time control, and seamless integration of new developments (smart heliostat) into the existing infrastructure. The SCADA system also introduced a user-friendly Python-based interface developed with PySide6, significantly enhancing operational efficiency and reducing task complexity for system operators. The results show that this low-cost methodology based on open-source software provides a flexible and robust SCADA architecture, suitable for future CSP applications, with potential for further optimization through the incorporation of artificial intelligence (AI) and machine learning. Full article

Developing interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases. This study aimed to create a seizure detection methodology incorporating detailed information from each EEG channel and accounts for frequency band variations linked to the primary brain pathology leading to ICU admission, enhancing our ability to identify epilepsy onset. This study involved 460 video-electroencephalography recordings from 71 patients under monitoring. We applied signal preprocessing and conducted a numerical quantitative analysis in the frequency domain. Various machine learning algorithms were assessed for their efficacy. The k-nearest neighbours (KNN) model was the most effective in our overall sample, achieving an average F1 score of 0.76. For specific subgroups, different models showed superior performance: Decision Tree for ‘Epilepsy’ (average F1 score of 0.80) and ‘Craniencephalic Trauma’ (average F1 score of 0.84), Random Forest for ‘Cardiorespiratory Arrest’ (average F1 score of 0.89) and ‘Brain Haemorrhage’ (average F1 score of 0.84). In the categorisation of seizure types, Linear Discriminant Analysis was most effective for focal seizures (average F1 score of 0.87), KNN for generalised (average F1 score of 0.84) and convulsive seizures (average F1 score of 0.88), and logistic regression for non-convulsive seizures (average F1 score of 0.83). Our study demonstrates the potential of using classifier models based on quantified EEG data for diagnosing seizures in ICU patients. The performance of these models varies significantly depending on the underlying cause of the seizure, highlighting the importance of tailored approaches. The automation of these diagnostic tools could facilitate early seizure detection. Full article

The specific objective of the present study was to develop computational models, by means of which predictions could be performed regarding the quality of the bulk-tank milk in dairy sheep and goat farms. Our hypothesis was that use of specific variables related to the health management applied in the farm can facilitate the development of predictions regarding values related to milk quality, specifically for fat content, protein content, fat and protein content combined, somatic cell counts, and total bacterial counts. Bulk-tank milk from 325 sheep and 119 goat farms was collected and evaluated by established techniques for analysis of fat and protein content, for somatic cell counting, and for total bacterial counting. Subsequently, computational models were constructed for the prediction of five target values: (a) fat content, (b) protein content, (c) fat and protein, (d) somatic cell counts, and (e) total bacterial counts, through the use of 21 independent variables related to factors prevalent in the farm. Five machine learning tools were employed: decision trees (18 different models evaluated), random forests (16 models), XGBoost (240 models), k-nearest neighbours (72 models), and neural networks (576 models) (in total, 9220 evaluations were performed). Tools found with the lowest mean absolute percentage error (MAPE) between the five tools used to test predictions for each target value were selected. In sheep farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 3.95%); for the prediction of fat and protein content combined, neural networks was selected (6.00%); and for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (6.55%); no tool provided useful predictions for fat content and for total bacterial counts. In goat farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 6.17%); for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (4.93% and 5.00%); and for the prediction of total bacterial counts, neural networks was selected (8.33%); no tool provided useful prediction models for fat content and for fat and protein content combined. The results of the study will be of interest to farmers, as well as to professionals; the findings will also be useful to dairy processing factories. That way, it will be possible to obtain a distance-aware, rapid, quantitative estimation of the milk output from sheep and goat farms with sufficient data attributes. It will thus become easier to monitor and improve milk quality at the farm level as part of the dairy production chain. Moreover, the findings can support the setup of relevant and appropriate measures and interventions in dairy sheep and goat farms. Full article

The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. This study proposes an automatic on-machine measuring apparatus for assessing inner diameter, straightness, and roundness errors. Based on the axial-section measurement principle, an integrated measuring module was designed, including a self-centering mechanism, a diameter measuring sensor, and a positioning reference sensor, all embedded within a control system. On this basis, calculations of the inner diameter, and evaluations of the straightness and roundness errors are presented. Experimental verification is conducted on a blind deep hole with a nominal 100 mm inner diameter and 700 mm depth. Compared with measurements performed on a coordinate measuring machine (CMM), which is limited to a maximum hole depth of 300 mm, the proposed apparatus achieved full-depth on-machine measurements. Meanwhile, the measurement results were consistent with the data obtained by the CMM. The straightness error is considered less than 0.05 mm, and the roundness error is considered less than 0.015 mm. Ultimately, without requiring any additional reference platform, the proposed apparatus shows promise for measuring deep-hole parts on various machine tools, with diameters of no less than 80 mm and theoretically unlimited hole depth. Full article

Introduction: Diabetes mellitus (DM) has a millennia-long history, with early references dating back to ancient Egypt and India. However, it was not until the 20th century that the connection between diabetes and insulin was fully understood. The sequencing of insulin in the 1950s initiated the convergence of biotechnology and diabetes management, leading to the development of recombinant human insulin in 1982. This marked the start of peptide-based therapies in DM. Recombinant peptides for DM treatment: Numerous recombinant peptides have been developed since, starting with modified insulin molecules, with the aim of bettering DM management through fine-tuning the glycemic response to insulin. Peptide-based therapies in DM have expanded substantially beyond insulin to include agonists of Glucagon-like peptide-1 receptor and Glucose-dependent insulinotropic polypeptide receptor, glucagon receptor antagonists, and even peptides exerting multiple receptor agonist effects, for better metabolic control. Insulin pumps, continuous glucose monitoring, and automated insulin delivery systems: The development of modern delivery systems combined with real-time glucose monitoring has significantly advanced diabetes care. Insulin pumps evolved from early large devices to modern sensor-augmented pumps with automated shutoff features and hybrid closed-loop systems, requiring minimal user input. The second-generation systems have demonstrated superior outcomes, proving highly effective in diabetes management. Islet cell transplantation, organoids, and biological pancreas augmentation represent innovative approaches to diabetes management. Islet cell transplantation aims to restore insulin production by transplanting donor beta cells, though challenges persist regarding graft survival and the need for immunosuppression. Organoids are a promising platform for generating insulin-producing cells, although far from clinical use. Biological pancreas augmentation relies on therapies that promote beta-cell (re)generation, reduce stress, and induce immune tolerance. Further biotechnology-driven perspectives in DM will include metabolic control via biotechnology-enabled tools such as custom-designed insulin hybrid molecules, machine-learning algorithms to control peptide release, and engineering cells for optimal peptide production and secretion. Full article

With the continuous development of intelligent manufacturing technology, the application of intelligent feed systems in modern machine tools is becoming increasingly widespread. Digital twin technology achieves the monitoring and optimization of the entire life cycle of a physical system by constructing a virtual image of the system, while neural network controllers, with their powerful nonlinear fitting ability, can accurately capture and simulate the dynamic behavior of complex systems, providing strong support for the optimization control of intelligent feed systems. This article discusses the design and implementation of an intelligent feed system based on digital twins and neural network controllers. Firstly, this article establishes a mathematical model based on the traditional ball screw structure and analyzes the dynamic characteristics and operating mechanism of the system. Subsequently, the mathematical model is fitted using a neural network controller to improve control accuracy and system response speed. The experimental results demonstrate that the neural network controller shows good consistency in fitting traditional mathematical models, not only effectively capturing the nonlinear characteristics of the system but also maintaining stable control performance under complex operating conditions. Full article

This paper explores the integration of decision tree classifiers in the assessment of machining process stability using control charts. The inherent variability in manufacturing processes requires a robust system for the early detection and correction of disturbances, which has traditionally relied on operators’ experience. Using decision trees, this study presents an automated approach to pattern recognition on control charts that outperforms the accuracy of human operators and neural networks. Experimental research conducted on two datasets from surface finishing processes demonstrates that decision trees can achieve perfect classification under optimal parameters. The results suggest that decision trees offer a transparent and effective tool for quality control, capable of reducing human error, improving decision making, and fostering greater confidence among company employees. These results open up new possibilities for the automation and continuous improvement of machining process control. The contribution of this research to Industry 4.0 is to enable the real-time, data-driven monitoring of machining process stability through decision tree-based pattern recognition, which improves predictive maintenance and quality control. It supports the transition to intelligent manufacturing, where process anomalies are detected and resolved dynamically, reducing downtime and increasing productivity. Full article

This study presents a sound-based tool-wear monitoring system designed to overcome the limitations of conventional methods that focus solely on gradual and predictable wear patterns. The proposed system employs low-cost, high-frequency microphones and advanced signal processing—featuring analog/digital filtering, oversampling, signal conditioning, PLL-based synchronization, and feature extraction (ZCR, RMS)—to capture acoustic emissions during machining. Key innovations include optimized microphone placement, a custom PCB, and real-time data transfer via WiFi to MATLAB for analysis. Using the TreeBagger machine-learning algorithm, the system accurately predicts tool wear, detecting both gradual and abrupt wear patterns. Tested on EN 1.4307 (AISI/ASTM 304L) stainless steel, the system demonstrated robust performance in real-time tool-condition assessment. Its scalable and cost-effective design allows for the integration of additional sensors and features, providing a non-invasive and adaptive solution to enhance machining efficiency and reduce operational costs. Full article

The developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process performed in a production line for high-quality bearings. The actuation considered new sensing elements at the machine level and the treatment of the information, fusing the different sources in order to detect quality defects in the grinding process (waviness, burns) and monitoring the state of the tool. At a supervision level, an AI model has been developed for monitoring the complete line and compensating deviations in the dimension of the final assembly. The project also contemplated the hardware architecture for improving the data acquisition and communication among the machines and databases, the data treatment units, and the human interfaces. The resulting system gives feedback to the operator when deviations or potential errors are detected so that the quality issues are recognized and can be amended in advance, thereby reducing the quality cost. Full article

The intelligence of ultra-precision machining processes has become a research focus in the field of precision and ultra-precision manufacturing. Scholars have conducted some fragmented studies on the intelligence of ultra-precision machining processes; however, a systematic review and summary of the intelligent systems and architectures for such processes are still lacking. Therefore, this paper is devoted to reviewing the intelligent systems and architectures for ultra-precision machining processes, focusing on three aspects: machining environment monitoring, cutting process analysis, and intelligent machining system frameworks. The paper first provides an overview of environmental intelligence monitoring from the perspective of the machining environment and then discusses and summarizes monitoring processes, such as tool errors, tool wear, tool setting, and surface measurement, from the perspective of machining process analysis. The intelligent machining system framework is then analyzed and summarized from the perspective of process control. Finally, the paper outlines the overall framework of the intelligent system for ultra-precision machining processes and analyzes its components. This paper provides guidance for the development of intelligent systems in ultra-precision machining processes. Full article

The current cybersecurity ecosystem is proving insufficient in today’s increasingly sophisticated cyber attacks. Malware authors and intruders have pursued innovative avenues to circumvent emulated monitoring systems (EMSs) such as honeypots, virtual machines, sandboxes and debuggers to continue with their malicious activities while remaining inconspicuous. Cybercriminals are improving their ability to detect EMS, by finding indicators of deception (IoDs) to expose their presence and avoid detection. It is proving a challenge for security analysts to deploy and manage EMS to evaluate their deceptive capability. In this paper, we introduce the Hydrakon framework, which is composed of an EMS controller and several Linux and Windows 10 clients. The EMS controller automates the deployment and management of the clients and EMS for the purpose of measuring EMS deceptive capabilities. Experiments were conducted by applying custom detection vectors to client real machines, virtual machines and sandboxes, where various artifacts were extracted and stored as csv files on the EMS controller. The experiment leverages the cosine similarity metric to compare and identify similar artifacts between a real system and a virtual machine or sandbox. Our results show that Hydrakon offers a valid approach to assess the deceptive capabilities of EMS without the need to target specific IoD within the target system, thereby fostering more robust and effective emulated monitoring systems. Full article

Background/Objectives: This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals—cardiological patients, healthy participants, and athletes—based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. Methods: We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups. This research also leveraged explainable artificial intelligence (XAI) techniques to interpret model decisions and investigate feature importance. Results: The highest accuracy, over 89%, was obtained using the dataset that included most important demographic, cardiac, respiratory, and interrelated (causal and information) domain features. The dataset that comprised the most influential features but without demographic data yielded the second best accuracy, equal to 85%. Incorporation of the causal and information domain features significantly improved the classification accuracy. The use of XAI tools further highlighted the importance of these features with respect to each individual group. Conclusions: The integration of ML algorithms with a broad spectrum of cardiorespiratory features provided satisfactory efficiency in classifying pediatric individuals into groups according to their actual health status. This study underscored the potential of ML and XAI in advancing the analysis of cardiorespiratory signals and emphasized the importance of CRC-related features. The established set of features that appeared optimal for the classification of pediatric patients should be further explored for their potential in assessing individual progress through training or rehabilitation. Full article

The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS–SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc² = 0.9885, Rp² = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science. Full article

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of laminated composite structures. Their favorable strength-to-weight ratio renders them ideal for the automotive, marine, and aerospace industries. Among various non-destructive testing (NDT) methods, ultrasonic techniques have emerged as robust tools for detecting and characterizing internal flaws in composites, including delaminations, matrix cracks, and fiber breakages. This review concentrates on recent developments in ultrasonic NDT techniques for the SHM of laminated composite structures, with a special focus on guided wave methods. We delve into the fundamental principles of ultrasonic testing in composites and review cutting-edge techniques such as phased array ultrasonics, laser ultrasonics, and nonlinear ultrasonic methods. The review also discusses emerging trends in data analysis, particularly the integration of machine learning and artificial intelligence for enhanced defect detection and characterization through guided waves. This review outlines the current and anticipated trends in ultrasonic NDT for SHM in composites, aiming to aid researchers and practitioners in developing more effective monitoring strategies for laminated composite structures. Full article