Search Results (96)

Technological innovation in commercial Remotely Piloted Aircraft Systems (RPASs) is advancing rapidly. However, their operational efficiency remains limited by the need for on-site skilled human operators. Semi-autonomous drone-in-a-box (DIAB) systems are emerging as a practical solution, enabling automated, repeatable missions for applications such as construction site monitoring, security, and critical infrastructure inspection. Beyond industry, these systems hold significant promise for scientific research, particularly in long-term environmental monitoring where cost, accessibility, and safety are critical factors. In this technology demonstration, we detail the system implementation, discuss flight-planning challenges, and assess the overall feasibility of deploying a DJI Dock 2 DIAB system for remote monitoring of the Miles Ridge landslide in the Yukon Territory, Canada. The system was installed approximately 2.5 km from the landslide and operated remotely from across the country in Montreal, QC, about 4000 km away. A total of five datasets were acquired from July to September 2025, enabling three-dimensional reconstruction of the landslide surface from each acquisition. A comparison of extracted cross-sections demonstrated high reproducibility and accurate co-registration across acquisitions. This study highlights the potential of DIAB systems to support reliable, low-maintenance monitoring of remote landslides. Full article

Microplastics are increasingly recognized as contaminants of emerging concern in wastewater systems, where treatment plants act both as sinks and as point sources. However, Central Asian wastewater infrastructures are under-represented in the literature, and global syntheses are hindered by strong methodological heterogeneity (sampling regimes, size cut-offs, QA/QC). This PRISMA-guided critical review compiles and harmonizes data from 63 WWTP studies worldwide (402 matrix-stage observations), including the few available case studies from Kazakhstan and neighboring countries, to benchmark Central Asian plants against a global envelope and identify methodological and infrastructure gaps. Globally, influent concentrations cluster around a median ≈65 particles/L, while final/tertiary effluents show a median ≈2.2 particles/L. Median removal efficiency is 85.5% for secondary and 95.0% for tertiary/advanced trains, with ≈10³–10⁵ particles/kg DW typically retained in sludge. Across influent, effluent and sludge, fibers and fragments of PE, PP and PET dominate polymer morphology patterns, with similar PET/PE/PP signatures also reported in downstream river water. Central Asian influents fall within global interquartile ranges, but secondary-only facilities tend to yield effluents in the upper half of the global distribution. Overall, the review provides a first integrated, methodologically explicit assessment of WWTP microplastics in Central Asia and underscores the need for protocol harmonization, longitudinal monitoring, and targeted upgrades of polishing steps and sludge management in arid hydrosystems. Full article

Conservation programmes for two local dairy cattle breeds—Latvian Brown old type (BV) and Latvian Blue (LZ)—commenced in 2004. The aim of this study was to evaluate genetic diversity in the BV and LZ local cattle populations using SNP data. This study was based on genotype data from 96 BV and 75 LZ cows and 20 BV and 18 LZ bulls. The SNPs were determined using the GGP 100K bovine SNP BeadChip. Quality control (QC) and genotype data analysis were performed using PLINK v1.9. The observed heterozygosity was moderate, at around 0.4, for both breeds. Inbreeding coefficients were estimated based on homozygosity runs (F_ROH) to compare recent and ancient inbreeding in the BV and LZ populations. Therefore, the ROH segments were divided into segments with the four classes (1–4 Mb, 4–8 Mb, 8–16 Mb, and above 16 Mb). Shorter ROH regions (ROH < 4 Mb) predominated in the genome. ROH regions with lengths above 16 Mb covers 4–6% of the genome in BV and 11% in LZ population. The average inbreeding coefficient for approximately three generations (F_ROH>16) was 2.30% and 4.87% for BV and LZ cows (p < 0.05), respectively, and 2.59% and 3.85% for BV and LZ bulls, respectively. This study demonstrates that inbreeding has increased from generation to generation (F_ROH>16 is higher compared with F_ROH<16) in both populations. The level of current inbreeding in LZ is higher compared with that in the BV breed. The overall level of inbreeding in the BV and LZ populations is low, but there is a high level of inbreeding among a few animals. The impact of inbreeding on cow productivity has been observed in the LZ and BV cow populations. As a result, breeding organisations need to monitor and control the level of inbreeding and prevent the loss of genetic diversity in these animal populations. Breeders should minimize mating among close relatives; introduce genetically unrelated animals, use pedigree, and genomic information in controlling rates of inbreeding. Full article

Fused deposition modeling (FDM) is one of the most widely adopted additive manufacturing (AM) technologies due to its accessibility and versatility; however, ensuring process reliability and product quality remains a significant challenge. This work introduces a novel methodology to evaluate the fidelity of programmed extruder head trajectories and speeds against those executed during the printing process. The approach integrates infrared thermography and image processing. A type-V ASTM D638-14 polylactic acid (PLA) specimen was fabricated using 16 layers, and its G-code data were systematically compared with kinematic variables extracted from long-wave infrared (LWIR) thermal images. The results demonstrate that the approach enables the detection of deviations in nozzle movement, providing valuable insights into layer deposition accuracy and serving as an early indicator for potential defect formation. This thermal image–based monitoring can serve as a non-invasive tool for in situ quality control (QC) in FDM, supporting process optimization and improved reliability of AM polymer components. These findings contribute to the advancement of smart sensing strategies for integration into industrial additive manufacturing workflows. Full article

Accurate 3D reconstructions of architecture, engineering, and construction AEC structures using UAV photogrammetry are often hindered by occlusions, excessive image overlaps, or insufficient coverage, leading to inefficient flight paths and extended mission durations. This work presents a BIM-aware, autonomous UAV trajectory generation framework wherein a compact, geometrically valid viewpoint network is first derived as a foundation for path planning. The network is optimized via Integer Linear Programming (ILP) to ensure coverage of IFC-modeled components while penalizing poor stereo geometry, GSD, and triangulation uncertainty. The resulting minimal network is then sequenced into a global path using a TSP solver and partitioned into battery-feasible epochs for operation on active construction sites. Evaluated on two synthetic and one real-world case study, the method produces autonomous UAV trajectories that are 31–63% more compact in camera usage, 17–35% shorter in path length, and 28–50% faster in execution time, without compromising coverage or reconstruction quality. The proposed integration of BIM modeling, ILP optimization, TSP sequencing, and endurance-aware partitioning enables the framework for digital-twin updates and QA/QC monitoring, accordingly, offering a unified, geometry-adaptive solution for autonomous UAV inspection and remote sensing. Full article

Traditional shelf-life (SL) determination in bakery products relies primarily on subjective sensory evaluation, limiting both predictive capability and technological transfer. This study aimed to develop an objective, data-driven framework by integrating statistical and Machine Learning (ML) methods to identify and quantify the core determinants of bread SL. Samples were produced under a 2 × 2 × 2 factorial design (Fermentation, Temperature, Packaging), with continuous monitoring of physicochemical and atmospheric parameters. Three-way ANOVA confirmed that Storage x Temperature (η² ÷ 0.41) and Modified Atmosphere Packaging (η² ÷ 0.36) were the dominant factors. The optimal synergy (4 °C + ATM) achieved a 100% Success Rate, extending SL to 54 days vs. 16 days under ambient conditions. For prediction, a Generalized Linear Model (GLM) was developed for binary classification and rigorously validated via 10-fold cross-validation. The GLM achieved an Overall Accuracy of 89% (AUC 92%), uniquely identifying pH and Total Titratable Acidity (TTA) as the most influential predictors. In conclusion, GLM provides a robust tool for objective SL prediction. The integrated ANOVA–GLM framework achieved a 3.3-fold SL extension and 92% predictive accuracy. The findings confirm that preservative effectiveness is not solely due to the process itself, but is mediated by the resulting chemical acidity, offering a scalable framework for Real-Time Quality Control (QC) in the food industry. Full article

Background/Objectives: Late diagnosis hampers effective treatment of non-small cell lung cancer (NSCLC). This study evaluated whether circulating microRNAs (miRs), miR-155 and miR-3196, measured in liquid biopsy peripheral blood mononuclear cells (PBMCs), can serve as potential non-invasive biomarkers for NSCLC diagnosis, patient stratification, therapy monitoring, and prognosis. Methods: RNA was isolated from PBMCs of 136 NSCLC patients and 64 healthy donors. RT–qPCR quantified miR expression in PBMCs after predefined QC filtering: miR-155-3p (NSCLC n = 63; controls n = 28), miR-3196 (NSCLC n = 55; controls n = 28), and miR-155-5p (NSCLC n = 23; controls n = 12). Diagnostic performance was assessed using receiver operating characteristic (ROC) analyses, reporting area under the curve (AUC), and threshold-dependent sensitivity/specificity. Survival was analyzed with Kaplan–Meier/Cox methods. Associations with clinicopathological variables (stage, metastasis, smoking, EGFR, and KRAS status), treatment response (chemotherapy, immunotherapy, TKIs), and survival outcomes were examined. Results: miR-155-3p was upregulated in NSCLC, whereas miR-3196 was downregulated relative to controls; AUCs were 0.881 and 0.784, respectively. At high-sensitivity operating points, specificity was lower (≈29–30%), consistent with PBMC miRs reflecting both immune activation and tumor burden. In adenocarcinoma, miR-155-3p was associated with advanced stage, metastatic disease and smoking history. miR-3196 aligned with features of metastatic progression. During systemic therapy (chemotherapy, immunotherapy, TKIs), circulating levels of both miRs tended to normalize. Notably, normalization of miR-155-3p levels was associated with improved overall survival, supporting its prognostic value and utility for treatment monitoring. Conclusions: Circulating miR-155-3p and miR-3196 in PBMCs are promising screening/monitoring non-invasive candidates rather than stand-alone NSCLC diagnostics at current thresholds. Combining these miRs with additional biomarkers and/or clinical covariates and tuning decision thresholds may enhance specificity for diagnostic use. While preliminary, these findings warrant validation in large, prospective studies with standardized protocols to enable clinical implementation. Full article

This study evaluates AM dimensional performance using multivariate quality control methods. Three-dimensionally printed products include multivariate correlated quality characteristics (QCs) that should be evaluated together. Furthermore, the same 3D-printed product can be produced by various additive manufacturing techniques, necessitating a comparative analysis to figure out which process provides superior quality. This study evaluates three AM processes—electron beam melting (EBM), fused deposition Modeling (FDM), and stereolithography (SLA)—to assess their performance in multivariate quality control. The research methodology focuses on monitoring, evaluating, and comparing these three AM processes. A standardized benchmark specimen is designed and fabricated using each AM process. Seven critical dimensional QCs were identified, and their specification limits were established based on ISO standards. Data collection was conducted using a high-precision measurement technique. This study used an improved Multivariate Exponentially Weighted Moving Average (MEWMA) control chart for process monitoring to detect deviations. The subsequent process evaluation used Multivariate Process Capability Indices (MPCIs) to assess conformance to specification limits. Then, a sensitivity study was conducted to assess the variability within each AM process. The findings identify the QC that contributes most to variation in each AM process and show clear differences in dimensional performance among EBM, SLA, and FDM, supporting process selection for precision applications. Full article

The Black Sea is a semi-enclosed basin subject to intense anthropogenic pressures and transboundary pollution, making reliable and comparable monitoring data essential for large-scale environmental assessments. However, national practices differ considerably, hindering data integration and coordinated reporting under international frameworks. This study, conducted within the Horizon 2020 project “Advancing Black Sea Research and Innovation to Co-develop Blue Growth within Resilient Ecosystems” (BRIDGE-BS), evaluated pollutant surveillance methodologies with a focus on heavy metals and priority organic contaminants (polycyclic aromatic hydrocarbons, polychlorinated biphenyls, organochlorine pesticides). Standard Operating Procedures (SOPs) were collected from institutions across Black Sea countries and systematically compared for water, sediment, and biota matrices. The analysis revealed shared reliance on internationally recognized techniques but also heterogeneity in sediment fraction selection, digestion and extraction conditions, instrumental approaches, and quality assurance/quality control (QA/QC) documentation. To complement this assessment, an intercalibration (IC) exercise was organized through the QUASIMEME proficiency testing scheme, accompanied by a follow-up structured questionnaire sent to participant institutions. While individual results remain confidential, collective feedback highlighted common challenges in calibration, blank correction, certified reference materials (CRMs) availability, digestion variability, instrument maintenance, and the reporting of uncertainty and detection limits. Together, these findings confirm that harmonization in the Black Sea requires not only improved comparability of laboratory methods but also the future alignment of assessment methodologies, including indicators and thresholds, to support coherent, basin-wide environmental evaluations under regional conventions and EU directives. Full article

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging—Sensors and Predictive QC; Supply Chain—Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005–2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost–benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

In this paper, we analyse drought variability in Spain (1950–2024) using the Standardised Precipitation–Evapotranspiration Index (SPEI) at 6-, 12-, and 24-month scales. Using 43 long-record meteorological observatories (AEMET), we compute SPEI from quality-controlled (QC), homogenised series, and derive coherent drought regions via clustering and assess trends in the frequency, duration, and intensity of dry episodes (SPEI ≤ −1.5), including seasonality and statistical significance (p < 0.05). Short-term behaviour (SPEI-6) has become more complex in recent decades, with the emergence of a “Catalonia” type and stronger June–October deficits across the northern interior; Mediterranean coasts show smaller or non-significant changes. Long-term behaviour (SPEI-24) is more structural, with increasing persistence and duration over the north-eastern interior and Andalusia–La Mancha, consistent with multi-year drought. Overall, short and long scales converge on rising drought severity and persistence across interior Spain, supporting multi-scale monitoring and region-specific adaptation in agriculture, water resources, and forest management. Key figures are as follows: at 6 months—frequency 0.09/0.08 per decade (Centre–León/Catalonia), duration 0.59/0.50 months per decade, intensity −0.12 to −0.10 SPEI per decade; at 24 months—frequency 0.5 per decade (Cantabrian/NE interior), duration 0.8/0.7/0.4 months per decade (Andalusia–La Mancha/NE interior/Cabo de Gata–Almería), intensity −0.06 SPEI per decade; Mediterranean changes are smaller or non-significant. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Quality assurance programs are critical to ensuring the consistency and reliability of point-of-care surveillance test results. In 2022, we launched Laos’ inaugural quality control (QC) and external quality assessment (EQA) program for national HIV recent infection surveillance. Our study aims to implement the first QC and EQA program for national HIV recent infection surveillance in Laos, utilizing non-infectious dried tube specimens (DTS) for quality control testing. This initiative seeks to monitor and assure the quality of HIV infection surveillance. We employed the Asante HIV-1 Rapid Test for Recent Infection (HIV-1 RTRI) point-of-care kit, using plasma specimens from the Thai Red Cross Society to create dried tube specimens (DTS). The DTS panels, including HIV-1 negative, HIV-1 recent, and HIV-1 long-term samples, met ISO 13528:2022 standards to ensure homogeneity and stability. These panels were transported from the Thai National Institute of Health (Thai NIH) to the Laos National Center for Laboratory and Epidemiology (NCLE) and subsequently shipped to 12 remote laboratories at ambient temperature. The laboratory results were electronically transmitted to Thai NIH 15 days after receiving the panel for performance analysis. The concordance results with the sample types were scored, and laboratories that achieved 100% concordance across all sample panels were considered to have satisfactorily met the established standards. Almost all laboratories demonstrated satisfactory results with 100% concordance across all sample panels during all three rounds of QC: 11 out of 12 (92%) in June, 10 out of 12 (83%) in July, and 11 out of 12 (91%) in August. The two rounds of EQA performed in June and August 2022 were satisfied by 8 out of 11 (72%) and 5 out of 10 (50%) laboratories, respectively. QC and EQA monitoring identified errors such as testing protocol mistakes and insufficient DTS panel dissolution, leading to improvements in HIV recency testing quality. Laboratories that reported errors were corrected and implemented further preventive actions. The QC and EQA program for HIV-1 RTRI identified errors in HIV recent infection testing. Implementing a specialized QC and EQA program for DTS marks a significant advancement in improving the accuracy and consistency of HIV recent infection surveillance. Continuous assessment is vital for addressing recurring issues. Full article

Despite advances in automation, the global manufacturing sector continues to rely heavily on manual package inspection, creating bottlenecks in production and increasing labor demands. Although disruptive technologies such as big data analytics, smart sensors, and machine learning have revolutionized industrial connectivity and strategic decision-making, real-time quality control (QC) on conveyor lines remains predominantly analog. This study proposes an intelligent package integrity monitoring system that integrates waste reduction strategies with both narrow and Generative AI approaches. Narrow AI models were deployed to detect package damage at full line speed, aiming to minimize manual intervention and reduce waste. Using a synthetically generated dataset of 200 paired top-and-side package images, we developed and evaluated 10 distinct detection pipelines combining various algorithms, image enhancements, model architectures, and data processing strategies. Several pipeline variants demonstrated high accuracy, precision, and recall, particularly those utilizing a YOLO v8 segmentation model. Notably, targeted preprocessing increased top-view MobileNetV2 accuracy from chance to 67.5%, advanced feature extractors with full enhancements achieved 77.5%, and a segmentation-based ensemble with feature extraction and binary classification reached 92.5% accuracy. These results underscore the feasibility of deploying AI-driven, real-time QC systems for sustainable and efficient manufacturing operations. Full article