Search Results (25,257)

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies for which there are few effective biomarkers for diagnosis, prognosis, and treatment monitoring. Given the paucity of data in the literature, this study aimed to evaluate the biomarker potential of selected miRNAs (miR-222-3p, miR-3154, miR-3945, miR-4534, and miR-4742) and their protein targets in the context of PDAC. Methods: The expression levels of miRNA candidates were quantified by real-time quantitative PCR in lymphocyte samples from 46 PDAC patients and 50 healthy controls. In silico analyses were performed to identify potential target genes and proteins. ELISA was used to measure protein expression in both groups. Statistical analyses included ROC curve analysis, linear regression, and correlation analyses. In addition, correlations between miRNA/protein expression and clinicopathologic characteristics, including survival, were investigated. Results: miR-222-3p and miR-3154 were significantly downregulated in PDAC patients compared to controls (p < 0.001). Among the dual miRNA combinations, miR-222-3p and miR-4534 showed the highest discriminatory power (AUC = 0.629, p = 0.022). The miR-222-3p expression was significantly increased in patients with a history of alcohol consumption (p = 0.02). Significant correlations were observed between miR-3154 expression and T-stage (p = 0.01) and between perineural invasion and miR-222-3p levels (p = 0.02). Survival analysis showed that high miR-3945 expression was significantly associated with shorter overall survival (p = 0.001). Elevated levels of ESR1, HCFC1, and EPC1 were significantly associated with lymphatic invasion (p < 0.05), while high KCNA1 expression correlated with shorter survival (p = 0.006), indicating its potential as a negative prognostic biomarker. Linear regression analysis revealed a significant positive correlation between miR-3945 and KCNA1 expression (β = 0.259, p = 0.038), indicating a possible regulatory interaction. A borderline correlation was also found between miR-4742 and EPC1 expression (p = 0.055). Conclusions: This study identifies several miRNAs and associated proteins with diagnostic and prognostic significance in PDAC. The results emphasize the clinical relevance of integrating multi-layered analyses of miRNA–protein interactions. The observed associations highlight the role of these molecular markers in tumor progression and patient survival and offer promising opportunities for future research and clinical application in precision oncology. Full article

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies. Full article

Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts. Full article

Vibration monitoring plays a crucial role in assessing the condition and operational safety of electric drive systems. In many industrial applications, scalar control is widely used due to its simplicity and reliability, yet its influence on vibration transmission within interconnected machines remains insufficiently explored. This study addresses the problem of understanding how mechanical vibrations are transmitted between a scalar-controlled induction motor coupled with an AC generator. A comparative experimental investigation was conducted using two different configurations of drive units, incorporating either an induction or a synchronous generator. Vibrations were measured at various operating speeds and analysed using different sensor types to ensure repeatability and reliability of the results. The findings have revealed distinct patterns of vibration transmission between the motor and generator, highlighting the importance of drive system configuration and measurement methodology. A novel approach to data presentation is proposed by normalising vibration levels between machines, offering a clearer interpretation of vibration amplification or damping effects. The results contribute to the development of diagnostic techniques and the optimisation of scalar-controlled drive designs. Full article

Climate change has prompted the adoption of sustainable measures to reduce greenhouse gas (GHG) emissions, particularly in urban transportation. The integration of renewable energy sources, such as solar energy, offers a promising strategy to enhance sustainability in urban transit systems. This study assessed solar irradiation along the tram route in Cuenca—an Andean city characterized by distinctive topographic and climatic conditions—with the aim of evaluating the technical feasibility of integrating solar energy into the tram infrastructure. A descriptive, applicative, and longitudinal approach was adopted. Solar irradiation was monitored using a system composed of a fixed station and a mobile station, the latter installed on a tram vehicle. Readings carried out over fourteen months facilitated the analysis of seasonal and spatial variability of the available solar resource. The fixed station recorded average irradiation values ranging from 3.80 to 4.61 kWh/m²·day, while the mobile station reported values between 2.60 and 3.41 kWh/m²·day, revealing losses due to urban shading, with reductions ranging from 14.7% to 18.8% compared to fixed-site values. It was estimated that a fixed photovoltaic system of up to 1.068 MWp could be installed at the tram maintenance depot using 580 Wp panels, with the capacity to supply approximately 81% of the annual electricity demand of the tram system. Complementary solar installations at tram stops, stations, and other related infrastructure are also proposed. The results demonstrate the technical feasibility of integrating solar energy—through fixed and mobile systems—into the tram infrastructure of Cuenca. This approach provides a scalable model for energy planning in urban transport systems in Andean contexts or other regions with similar characteristics. Full article

With the widespread use of high-density polyethylene (HDPE) pipes in various industrial and municipal applications, ensuring the structural integrity of their joints is crucial. This paper presents a novel defect detection method based on a microwave resonant probe, designed to perform efficient and non-destructive evaluation of butt fusion joints in HDPE pipes. The experimental setup integrates a microwave antenna and resonant cavity to record real-time variations in resonance frequency and S₂₁ magnitude while scanning the pipe surface. This method effectively detects common defects, including cracks, holes, and inclusions, within the butt fusion joints. The results show that the microwave resonant probe exhibits high sensitivity in detecting HDPE pipe defects. It can identify different sizes of cracks and holes, and can distinguish between talc powder and sand particles. This technique offers a promising solution for pipeline health monitoring, particularly for evaluating the quality of welded joints in non-metallic materials. Full article

Medicinal plants remain central to traditional healthcare, yet their increasing integration into modern pharmacology necessitates robust toxicological evaluation. Origanum majorana L. (sweet marjoram), widely used in culinary and folk medicine, contains diverse secondary metabolites with both therapeutic and potential genotoxic activities. Despite its popularity, systematic in vivo and in vitro safety assessments remain limited. This study aimed to comprehensively evaluate the acute oral toxicity, cytotoxicity, and genotoxicity of O. majorana methanolic extract, providing baseline toxicological data to support its safe traditional use and potential pharmaceutical applications. The methanol extract of O. majorana leaves was tested in NIH-3T3 fibroblasts for cytotoxicity and genotoxicity. In vivo acute oral toxicity was assessed in rats according to OECD Guideline 420, with animals monitored over 14 days for clinical signs, hematological and biochemical alterations, and histopathological changes. The extract preserved fibroblast viability above 90% across all tested concentrations (10–200 µg/mL), indicating absence of cytotoxicity. However, comet and micronucleus assays revealed dose-dependent DNA damage, suggesting genotoxic potential at higher exposures. In vivo, no mortality or overt systemic toxicity was observed at doses up to 2000 mg/kg. Hematological analyses showed immunomodulatory shifts (increased neutrophils and monocytes, reduced eosinophils), while biochemical profiles indicated hepatoprotective and cardioprotective effects, with reduced ALT, AST, and LDH levels. Histopathological evaluation revealed only mild, focal changes consistent with adaptive rather than irreversible responses. O. majorana extract demonstrates a favorable acute safety profile with preserved hepatic and renal function, hematological modulation, and absence of in vitro cytotoxicity. Nevertheless, dose-dependent genotoxicity warrants caution for concentrated formulations. According to GHS classification, the extract aligns with Category 5 (acute oral toxicity, lowest hazard) and Category 2 (germ cell mutagenicity). These findings underscore the importance of dose management and further long-term genotoxicity studies before translational applications in nutraceutical or biomedical fields. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. 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

Background/Objectives: The execution of clinical trials is increasingly constrained by operational complexity, regulatory requirements, and variability in site performance. These challenges have direct implications for the reliability of trial outcomes. However, standardized methods to evaluate site-level performance remain underdeveloped. This study introduces the Clinical Trial Site Performance Measure (CT-SPM), a novel framework designed to systematically capture site-level operational quality and to provide a scalable short form for routine monitoring. Methods: We conducted a multicenter study across six Italian academic hospitals (January–June 2025). Candidate performance indicators were identified through a systematic review and expert consultation, followed by validation and reduction using advanced statistical approaches, including factor modeling, ROC curve analysis, and nonparametric scaling methods. The CT-SPM was assessed for structural validity, discriminative capacity, and feasibility for use in real-world settings. Results: From 126 potential indicators, 18 were retained and organized into four domains: Participant Retention and Consent, Data Completeness and Timeliness, Adverse Event Reporting, and Protocol Compliance. A bifactor model revealed two higher-order dimensions (participant-facing and data-facing performance), highlighting the multidimensional nature of site operations. A short form comprising four items demonstrated good scalability and sufficient accuracy to identify underperforming sites. Conclusions: The CT-SPM represents an innovative, evidence-based instrument for monitoring trial execution at the site level. By linking methodological rigor with real-world applicability, it offers a practical solution for benchmarking, resource allocation, and regulatory compliance. This approach contributes to advancing clinical research by providing a standardized, data-driven method to evaluate and improve performance across networks. Full article

Background/Objectives: Understanding the diverse neuron types within the hippocampal formation is essential for advancing our knowledge of its fundamental roles in learning and memory. Hippocampome.org serves as a comprehensive, evidence-based knowledge repository that integrates morphological, electrophysiological, and molecular features of neurons across the rodent dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. In addition to these core properties, this open access resource includes detailed information on synaptic connectivity, signal propagation, and plasticity, facilitating sophisticated modeling of hippocampal circuits. A distinguishing feature of Hippocampome.org is its emphasis on quantitative, literature-backed data that can help constrain and validate spiking neural network simulations via an interactive web interface. Methods: To assess and enhance its utility to the neuroscience community, we integrated Google Analytics (GA) into the platform to monitor user behavior, identify high-impact content, and evaluate geographic reach. Results: GA data provided valuable page view metrics, revealing usage trends, frequently accessed neuron properties, and the progressive adoption of new functionalities. Conclusions: These insights directly inform iterative development, particularly in the design of a robust Application Programming Interface (API) to support programmatic access. Ultimately, the integration of GA empowers data-driven optimization of this public resource to better serve the global neuroscience community. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

Given the construction industry’s significant contribution of approximately 39% of global CO₂ emissions, implementing effective carbon reduction strategies is becoming increasingly critical. In this context, Internet of Things (IoT) technologies present promising solutions for monitoring and reducing emissions. However, there is a lack of comprehensive understanding regarding specific IoT applications, implementation barriers, and opportunities for carbon reduction in construction practices. This study investigates the role of IoT in reducing carbon emissions in the construction industry. Following PRISMA guidelines, this study analyzed bibliometric data from Scopus and Web of Science databases using VOSviewer for science mapping visualization. Content analysis was conducted on 17 carefully selected articles to identify key research topics and applications. The analysis identified four mainstream application areas: (1) IoT-based smart monitoring systems for carbon emissions, (2) energy efficiency and management applications, (3) sustainable construction implementation frameworks, and (4) smart cities and other built environment applications. Key findings highlight growing research interest in IoT applications for sustainable construction, with China, the United States, and the United Kingdom leading collaborative efforts. Despite demonstrated carbon reduction potential, significant implementation barriers exist, including technical limitations, organizational resistance, skill gaps, and economic constraints. Key opportunities include Artificial Intelligence (AI) integration, Building information modeling (BIM)-IoT synergies, energy prosumer models, and standardization frameworks. This study provides the first focused review of IoT applications specifically targeting carbon reduction in construction, highlighting a critical technology-practice gap where organizational factors frequently outweigh technological barriers. A proposed socio-technical integration framework in this study bridges technical and organizational elements to overcome adoption barriers. Full article

This paper presents the experimental results of a Machine Learning application for the health monitoring of a conveyor belt. The real-time analysis of the rubber belt condition is a crucial issue in achieving safety and avoiding critical failures and related expenses. The measuring system based on strain gauges was applied to identify the actual state of the belt. Using the Classification Lerner application from MATLAB platform, 22 algorithms were tested, and using the Diagnostic Feature Designer application, the analysis was performed. Three tested ML algorithms were able to classify the states of the conveyor belt with preset damages correctly, exhibiting 100% prediction accuracy. The k-nearest neighbors (KNN) classifiers and neural networks failed to achieve that level of accuracy. Full article

The reliability of health monitoring technologies has become increasingly critical as Ambient Intelligence (AmI) becomes integrated into healthcare. However, a significant gap remains in systematically evaluating reliability, particularly in resource-constrained environments. This study addresses this gap by introducing a comprehensive framework specifically designed to evaluate the reliability of AmI-based health monitoring systems. The proposed framework combines robust simulation-based techniques, including reliability block diagrams (RBDs) and Monte Carlo Markov Chain (MCMC), to evaluate system robustness, data integrity, and adaptability. Validation was performed using real-world continuous glucose monitoring (CGM) and heart rate monitoring (HRM) systems in elderly care. The results demonstrate that the framework successfully identifies critical vulnerabilities, such as rapid initial system degradation and notable connectivity disruptions, and effectively guides targeted interventions that significantly enhance overall system reliability and user trust. The findings contribute actionable insights for practitioners, developers, and policymakers, laying a robust foundation for further advancements in explainable AI, proactive reliability management, and broader applications of AmI technologies in healthcare. Full article