Search Results (1,182)

Background: CKD remains substantially underdiagnosed and inadequately monitored. Objectives: To evaluate whether a laboratory-integrated CDSS enables opportunistic CKD detection and longitudinal surveillance in Primary Care via automated KDIGO stratification and progression detection. Methods: We implemented a CDSS embedded within the laboratory information system at Hospital Universitari i Politècnic La Fe, a tertiary university hospital in València, Spain. A one-time baseline profile screened patients with diabetes, hypertension, or age 60–80 years without documented advanced CKD, while a follow-up profile targeted patients with advanced CKD or those previously assessed by baseline screening. Both profiles applied KDIGO risk stratification and automated detection of renal and albuminuric progression based on predefined criteria, classifying each patient into one of three action categories: no CKD or low-risk requiring no action; CKD requiring Primary Care monitoring; or CKD meeting criteria for automated nephrology referral. Missed opportunities for albuminuria monitoring and economic impact of the intervention were evaluated. Results: During a 4-month period, the CDSS processed 10,733 requests, of which 10,414 were evaluable. No further action was required in 8661 patients (83.2%), monitoring was recommended in 1591 (15.3%), and nephrology referral was triggered in 162 (1.6%). Among 82 patients with albuminuric progression, 39% had not undergone ACR testing for more than two years despite ongoing Primary Care contact. Following implementation, ACR testing increased by 15.0% compared with the previous year, resulting in an additional analytical cost of EUR 1550.54. Conclusions: The CDSS enabled systematic CKD detection and surveillance in Primary Care, identifying silent renal and albuminuric progression and supporting nephrology referral at minimal cost. Full article

Counting bacterial colonies by hand is one of the most common tasks in microbiology, but it is slow and often yields different results depending on who does the counting. This paper presents an automated system for detecting and classifying bacterial colonies from Petri plate images. We tested four models: Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and a hybrid CNN+Vision Transformer (CNN+ViT). The system uses OpenCV for image preprocessing and extracts features such as area, perimeter, and circularity. Colonies are then classified by size and health condition. Confusion matrix results show that SVM and ANN achieved the highest overall accuracy (95.6%), while CNN+ViT eliminated false positives entirely. A Streamlit-based web interface allows real-time colony analysis directly in laboratory environments. Full article

This paper presents the design, implementation, and experimental validation of an educational robotic assembly mini-line based on the WLKATA Mirobot platform. The system was developed as a laboratory-scale demonstrator for centralized coordination of multiple robotic stations and for demonstrating selected industrial automation concepts, with potential for future extension toward Industrial Internet of Things (IIoT) applications. The main objective is to develop and functionally validate a centralized seven-station robotic assembly system capable of simulating selected manufacturing and assembly processes under controlled laboratory conditions. The practical implementation includes the mechanical design and layout of the assembly mini-line, robotic programming, and the development of communication and control mechanisms. A centralized supervisory control architecture based on an Arduino Mega 2560 and RS485 master–slave communication was implemented to coordinate seven WLKATA robotic stations and manage the production sequence. Experimental evaluation included repeated individual workstation tests and 30 complete production cycles. The results demonstrate the functional feasibility of centralized coordination and successful execution of the defined assembly sequence under the evaluated laboratory conditions. This paper evaluates the boundaries of deterministic behaviour in low-cost multi-robot networks utilizing a master–slave physical layer under dense operational sequencing. The identified limitations and requirements for future work include automated data logging, quantitative communication-performance monitoring, and long-term system testing. Full article

Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels. Full article

Early identification of individuals with abnormal glucose metabolism is essential for timely intervention and prevention of diabetes-related complications. Routine laboratory testing generates large amounts of clinical data that may support automated glycemic classification through machine learning approaches. This study aimed to develop and evaluate a machine learning framework for the classification of HbA1c-defined glycemic status using routinely available clinical laboratory features. A retrospective dataset of 1434 individuals with available glycemic measurements was analyzed. Participants were categorized into HbA1c-defined normoglycemic, prediabetic-range, or diabetic-range groups. Three concurrent classification tasks were examined: HbA1c-defined dysglycemia classification, diabetic-range HbA1c classification, and multiclass HbA1c-defined glycemic-status classification. Demographic, biochemical, and hematological variables were used as predictors. Data preprocessing included missing-value handling, feature filtering, and outlier treatment. Several supervised learning algorithms were evaluated, including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Multinomial Logistic Regression. Model performance was assessed using train–test validation and cross-validation with accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. For dysglycemia, Gradient Boosting achieved the highest AUC (0.848), while Random Forest achieved the highest accuracy (0.801) and sensitivity (0.908). For diabetic-range HbA1c, Random Forest achieved the highest AUC (0.864), whereas SVM achieved the highest accuracy (0.794). In multiclass classification, Random Forest achieved the highest accuracy (0.610), while Gradient Boosting achieved the highest macro-AUC (0.796) and macro-F1 score (0.603). Pairwise comparisons showed no statistically significant superiority of any classifier after Holm correction. Clinical-baseline and ablation analyses demonstrated that fasting glucose accounted for a substantial proportion of discrimination, with only modest incremental value from additional laboratory variables. These findings support cautious interpretation of routine laboratory-based classification models pending further validation and clinical-utility assessment. Full article

Background: Medication non-adherence is a major driver of poor therapeutic outcomes, and computer vision methods that observe facial movements offer a non-contact route to verifying oral medication intake. Objective: To map and synthesize the existing literature on computer vision techniques applicable to the monitoring of medication intake, focusing on face tracking methods, oral movement detection and deglutition recognition, and to assess their potential in supporting automatic medication adherence verification systems. Eligibility criteria: Peer-reviewed articles, conference papers, patents, theses and preprints published from 2016 onward, written in English or Portuguese, applying facial landmark tracking or face analysis to ingestion-related movements (mouth opening, hand-to-mouth motion, pill placement, mastication or deglutition); studies confined to object/pill detection without facial analysis, or to general food intake without transferability to medication, were excluded. Sources of evidence: A systematic screening of 362 initial records was conducted across six main electronic databases and repositories: Google Scholar, PubMed, ScienceDirect, arXiv, IEEE Xplore, and Espacenet. Charting methods: Data were charted with a standardized, pilot-tested extraction form capturing bibliographic attributes, dataset type, experimental environment, face tracking approach, tools/models, and target movements; extraction was performed by a single reviewer. Following the screening process, a final selection of 34 relevant studies was included for detailed analysis and mapping. Results: Among the 34 included studies, 14 employ facial landmarks, 11 utilize temporal deep learning models, 6 apply facial action models and 3 rely on hybrid multimodal approaches that combine video analysis, object detection and temporal modeling. Tasks such as detecting mouth opening or tracking pill-to-mouth movement show promising results, while accurately detecting deglutition remains a technical challenge due to high sensitivity and individual variability. Limitations: The majority of the literature relies on private or institutional datasets (31 studies) and operates in controlled laboratory environments (22 studies); only 2 studies evaluated their methods via independent external datasets, which limits the generalization of current solutions to real-world telemonitoring scenarios. Conclusions: The literature indicates the existence of solid technical foundations for developing automated medication intake verification systems. To advance the field toward practical deployment, future research must address the need for more diverse datasets, real-world validation and more robust, adaptable modeling frameworks. Full article

The accurate characterization of human immunodeficiency virus (HIV) genetic diversity and drug resistance is critical for effective surveillance and treatment, yet current sequencing technologies face limitations in sensitivity and scalability for community-level implementation. We present G-HIV, an integrated platform combining long-read sequencing (G-seq500) with an automated bioinformatics pipeline. G-HIV processes raw FastQ data to generate automated reports on point mutations, drug resistance predictions, viral quasispecies diversity, and haplotype networks via a two-step analytical approach. Applied to 44 HIV-1 plasma samples (42 used in the final comparison after excluding 2 samples with low-quality Sanger chromatograms), G-HIV detected 3–48 candidate minority variants per sample that were not observed by Sanger sequencing, identifying drug-resistant quasispecies in two samples with undetectable Sanger signals, and revealed mixed infection cases (e.g., inter-subtype CRF07_BC/CRF08_BC) through phylogenetic analysis. G-HIV addresses an integration of long-read sequencing with a fully automated, one-stop bioinformatics pipeline designed for frontline laboratories without specialized bioinformatics expertise—providing a scalable solution for community-based resistance surveillance and personalized therapy optimization in resource-limited settings. This research addresses an integrated long-read sequencing and automated bioinformatics platform for rapid and precise HIV-1 surveillance. G-HIV surpasses conventional approaches like Sanger sequencing in resolution, efficiency, and accessibility for community-level surveillance. By integrating long-read sequencing, streamlining workflows and eliminating the need for specialized bioinformatics expertise, G-HIV is positioned to become a new solution, providing more effective one-stop services for HIV-1 prevention and control. Full article

The growing demand for engineers with practical competencies in industrial automation requires educational approaches that effectively integrate theoretical knowledge with hands-on experience. This study proposes and evaluates a hybrid virtual–physical learning framework for programmable logic controller (PLC) education by combining Factory I/O simulation with deployment on a physical PLC laboratory system. The proposed framework was evaluated using a quasi-experimental pre-test/post-test design with control and experimental groups involving undergraduate engineering students assigned to control and experimental groups. Educational effectiveness was assessed using five complementary performance indicators: knowledge gain, programming errors, task completion time, deployment success rate, and student confidence. The results indicated that students using the proposed framework achieved higher knowledge gains, committed fewer programming errors, completed programming tasks more efficiently, and reported greater confidence than students following a conventional laboratory approach. The findings indicate that integrating virtual industrial simulation with physical PLC implementation may support competency development and students’ preparation for real industrial automation tasks. The proposed framework provides a practical methodology for competency-based PLC education and may contribute to the modernization of engineering curricula by strengthening the connection between virtual simulation and real-world industrial practice. Full article

Quality control in green coffee bean production is critical for food safety and economic sustainability. A persistent gap in existing automated inspection systems is their inability to capture both sides of each bean, which leads to systematic under-detection of surface defects. This work presents three contributions: (i) a novel mechatronic apparatus that mechanically guarantees dual-sided imaging of every bean, (ii) a public 12-class dataset of green coffee bean defects, and (iii) an embedded, real-time inspection pipeline validated on low-cost hardware. The apparatus sequentially presents each bean, from a standard 350 g sample, to two 16-megapixel cameras under controlled LED illumination. A dataset of 9600 images spanning 12 classes (11 defects and 1 normal) was generated from expert-classified samples and enriched through data augmentation. Four convolutional neural network (CNN) architectures, VGG-16, VGG-19, ResNet-50 and YOLOv8, were trained and benchmarked using precision, recall, F1-score and mean average precision. YOLOv8 achieved the best overall performance, with a precision of 97.4%, a recall of 99.6%, an F1-score of 0.930 and a mean average precision of 96.5%, outperforming VGG-16 (accuracy 86.07%), VGG-19 (accuracy 67.03%) and ResNet-50 (accuracy 87.76%). Dual-sided acquisition raised mean per-class detection accuracy from 0.727 to 0.908, a relative gain of 25.7% over an equivalent single-sided configuration. Deployed in real-time “track” mode on a Raspberry Pi 4, the system simultaneously classifies defects and counts beans by category, processing a 350 g sample in approximately 38 min. Combining mechanical innovation with lightweight deep learning enables practical, scalable, and cost-effective quality control for laboratories specialized in coffee analysis. Full article

Residual creases generated during the leather spreading process exhibit highly variable morphologies and irregular feature distributions, causing significant challenges for feature extraction and leading to low localization accuracy. To address these issues, this paper proposes the Dynamic, Cross-Calibrated, and Edge-Enhanced YOLOv8n-Pose (DCE-YOLOv8n-Pose) algorithm. Instead of providing regional approximations, this framework outputs precise spatial coordinates for robotic grasping by integrating three synergistic components in a progressive network flow. First, dynamic snake convolution adaptively perceives the continuous geometric features of elongated creases; subsequently, an efficient multi-scale attention mechanism provides cross-dimensional weight calibration to suppress highly homochromatic background interference and correct spatial misalignments; finally, an edge-enhanced content-aware reassembly of features module preserves high-frequency gradients and prevents feature fracturing during multi-scale fusion. For comprehensive evaluation, a dataset comprising 700 original laboratory images was constructed. To prevent data leakage, the dataset was partitioned into training and validation sets based on individual leather specimens, ensuring that images of the same leather piece do not appear in both sets. Additionally, an independent test set of 500 images collected from an actual processing plant was designed for industrial validation. Experimental results indicate that, at an Intersection over Union (IoU) threshold of 0.5, the DCE-YOLOv8n-Pose model achieves a bounding box mean average precision (mAP@0.5) of 91.8% and a keypoint mAP@0.5 of 85.1%, with a keypoint precision of 87.9%. The computational load is maintained at 9.2 GFLOPs, alongside an inference speed of 114.3 FPS. Furthermore, consistent convergence across four independent training runs substantiates the model’s reliability in reducing missed detection rates and localization deviations. In conclusion, the proposed algorithm demonstrates practical applicability for the visual guidance of automated leather spreading equipment by balancing detection precision and inference speed, thereby offering an effective coordinate reference for subsequent robotic stretching operations. Full article

Efficient abrasive blasting requires precise control of abrasive mass flow, which is governed by both the metering system design and the flow properties of the abrasive material. This study investigates the influence of particle size distribution on the flow behaviour of brown fused alumina during discharge through a commercially available Thomson TV II metering valve. Four abrasive fractions (F220, F80, F46 and F24) were characterized in terms of particle size distribution, bulk density, moisture content, angle of internal friction, and flow function. The discharge behaviour was experimentally evaluated using a custom-built test stand for four valve opening positions. The results showed that the smallest valve opening caused unstable flow conditions, arching, and complete flow blockage for the coarsest fraction, whereas stable and repeatable discharge was achieved for various valve openings. Although the finest fraction exhibited the highest flowability according to the flow function, it did not achieve the highest mass flow rate, indicating that flowability alone was insufficient to explain the observed discharge performance. Instead, the F80 fraction provided the highest discharge performance under all stable operating conditions. These findings indicate that laboratory flowability indices alone cannot reliably predict abrasive feeding performance and should be evaluated together with bulk density and particle size distribution. The results provide practical guidelines for optimizing abrasive metering systems and contribute to improved process stability, abrasive utilization, and operational efficiency in automated abrasive blasting applications. Full article

Existing industrial buildings represent a critical but under-addressed field for operational carbon emission reduction, as retrofit decisions are constrained by production continuity, limited investment capacity, and heterogeneous technical options. This study developed a decision support framework integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate and prioritize operational phase decarbonization retrofit schemes for existing industrial buildings. The framework was applied to the Thyssenkrupp S Plant in eastern China, where ten candidate schemes were identified through an energy audit, on-site investigation, and expert consultation. The results show that heating, ventilation, and air conditioning (HVAC) operational control and temperature set-point optimization ranked first, followed by lighting operational management and automatic control. These management-based measures offer strong near-term applicability because of their low investment, short payback periods, limited implementation disturbance, and immediate emission reduction benefits. Their sustained effectiveness, however, requires standardized procedures, staff education, energy monitoring, and appropriate automation. Rooftop photovoltaics provide the largest annual carbon reduction but have a lower short-term priority because of their high upfront investment. Expert-consistency testing and sensitivity analyses, including criterion weight perturbation, preference scenarios, and Monte Carlo simulation, support the robustness of the leading ranking pattern. The findings support staged retrofit planning that prioritizes durable management measures in the short term, equipment-level efficiency improvements in the medium term, and renewable energy deployment in the long term. Full article

Instrument control remains a practical bottleneck in laboratory automation and self-driving laboratories. Although large language models (LLMs) have shown strong potential in document understanding, code generation, and scientific workflow automation, most existing systems assume that ready-to-use instrument interfaces are already available. However, converting heterogeneous device manuals and communication protocols into tested, reusable software drivers therefore still requires substantial manual effort and iterative hardware-level debugging. In this work, we present ChemAutoAgent, a multi-agent system that converts text-based instrument manuals into tested and reusable laboratory instrument drivers through a staged, evidence-traceable pipeline. We evaluate ChemAutoAgent tested and reusable drivers on three representative instruments—a magnetic stirrer, a peristaltic pump, and a Raman spectrometer—spanning three communication protocols: ASCII/NAMUR, MODBUS RTU, and a custom binary-frame protocol. Following iterative testing and repair, all evaluated driver functions passed the predefined tests for connection establishment, parameter configuration, command execution, and data acquisition. The evaluated drivers required between one and four repair cycles, with autonomous operation ratios ranging from 73% to 81%. A cross-device invocation experiment further demonstrates the feasibility of reusing published drivers in a multi-device workflow. Full article

Background/Objectives: Modern automated hematology analyzers combine quantitative cell counting with proprietary algorithms for automated morphological interpretation. While analytical validation traditionally focuses on measurement agreement, less attention has been paid to the evaluation of algorithm-assisted morphological interpretation. This study compared the analytical performance and automated morphological flagging of the Sysmex XN-9100 and Mindray BC-7800/BC-7900 platforms under routine laboratory conditions. Methods: A total of 183 peripheral blood samples collected between October 2025 and February 2026 were analyzed in parallel using both platforms. The study population included healthy individuals and patients with hematological and non-hematological disorders. Analytical agreement for complete blood count (CBC) and leukocyte differential parameters was assessed using Passing–Bablok regression and Bland–Altman analyses. Automated morphological flagging was evaluated in 157 samples using expert optical microscopy as the reference standard. McNemar’s test, Cohen’s kappa coefficient, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated. Results: Excellent agreement was observed for the principal CBC parameters, including white blood cell count, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, and platelet count (correlation coefficients: 0.981–1.000), with minimal proportional bias. Greater variability was found for monocytes, eosinophils, and basophils, particularly at low cell concentrations. Although analytical performance was highly comparable, the two platforms showed different morphological flagging profiles. Both analyzers achieved high negative predictive values (>90%), while differences in sensitivity and specificity across individual flag categories reflected distinct algorithm-assisted classification strategies. Agreement with expert microscopy ranged from fair to moderate for most pathological flags. Conclusions: The Mindray BC-7800/BC-7900 and Sysmex XN-9100 demonstrated excellent analytical agreement for routine CBC testing. However, comparable analytical performance did not necessarily correspond to identical automated morphological interpretation. These results indicate that evaluation of modern hematology analyzers should include both analytical performance and agreement between automated flags and expert microscopy. Full article

Real-time monitoring of radioactive liquid effluents from nuclear power plants (NPPs) is essential for environmental safety assurance, yet existing liquid scintillation counting (LSC) instruments are bulky (>200 kg), laboratory-bound, and incapable of autonomous online deployment. This paper presents the design and implementation of a fully automated online LSC monitoring process integrating seawater sampling, distillation pre-treatment, liquid scintillator mixing, dual photomultiplier tube (PMT) coincidence detection, field-programmable gate array (FPGA)-based digital signal processing, and 4G remote data transmission in a single portable unit weighing 21.59 kg. The automated process executes a complete sample-to-result cycle in approximately 45 min without human intervention. The signal processing chain comprises a dual-PMT coincidence system, a custom two-stage pre-amplifier, a 14-bit 40 MSPS analogue-to-digital converter (ADC; AD9245, Analog Devices, Norwood, MA, USA), and a five-stage FPGA pipeline implementing anti-coincidence rejection, pulse amplitude discrimination, charge comparison method (CCM) waveform discrimination, and convolutional neural network (CNN)-based alpha/beta classification achieving 97.4% accuracy on a Geant4-simulated test set. System performance was validated against a PerkinElmer 1220 QUANTULUS reference spectrometer across a five-point calibration range (0–400 Bq/L; R² = 0.9987, recovery 99.4–101.6%), confirmed via third-party environmental testing (−10 °C to +50 °C, GB/T 2423.1-2008), and verified in field measurements at Tianwan Nuclear Power Plant. The experimentally determined system background is (1.83 ± 0.12) cpm; the calculated minimum detectable activity (MDA) for tritium is 0.073 Bq/mL at 30 min counting time (η = 3.4%, V = 10 mL, T_s = 1800 s per the Currie formulation), satisfying the GB 14587 (the Chinese national standard: Limits of Radioactivity for Liquid Effluents from Nuclear Power Plant) regulatory reference limit of 0.5 Bq/mL with a 7× safety margin. The proposed system is, to the authors’ knowledge, the first reported instrument combining full process automation (including distillation pre-treatment), single-person portability, and real-time 4G remote data transmission for continuous NPP liquid effluent surveillance in high-salinity seawater environments. Full article