Search Results (17,195)

Atmospheric sulfur (S) deposition, a key component of acid deposition, poses risks to ecosystems, human health, and sustainable development. In China, decades of coal-dominated energy use caused severe S pollution, but recent emission-control policies and energy restructuring have sought to reverse this trend. However, the effectiveness and regional differences in these measures remain insufficiently quantified. Here, we combined continuous observations from 43 monitoring sites (2013–2023), satellite-derived SO₂ vertical column density, and multi-source environmental datasets to construct a high-resolution record of wet S deposition. A random forest model, validated with R² = 0.52 and RMSE = 1.2 kg ha⁻¹ yr⁻¹, was used to estimate fluxes and spatial patterns, while ridge regression and SHAP analysis quantified the relative contributions of emissions, precipitation, and socioeconomic factors. This framework allows us to assess both the environmental and health-related sustainability implications of sulfur deposition. Results show a nationwide decline of more than 50% in wet S deposition during 2013–2023, with two-thirds of sites and 95% of grids showing significant decreases. Historical hotspots such as the North China Plain and Sichuan Basin improved markedly, while some southern provinces (e.g., Guizhou, Hunan, Jiangxi) still exhibited high deposition (>20 kg ha⁻¹ yr⁻¹). Over 90% of the reduction was attributable to emission declines, confirming the dominant effect of sustained policy-driven measures. This study extends sulfur deposition records to 2023, demonstrates the value of integrating ground monitoring with remote sensing and machine learning, and provides robust evidence that China’s emission reduction policies have delivered significant environmental and sustainability benefits. The findings offer insights for region-specific governance and for developing countries balancing economic growth with ecological protection. Full article

This study presents a comprehensive assessment of lighting conditions in the Intensive Care Units (ICUs) of two major hospitals in the Democratic Republic of Congo (DRC): Hospital du Cinquantenaire in Kinshasa and Jason Sendwe Hospital in Lubumbashi. A mixed-methods approach was employed, integrating continuous illuminance monitoring with structured staff surveys to evaluate visual comfort in accordance with the EN 12464-1 standard for indoor workplaces. Objective measurements revealed that more than 52.2% of the evaluated ICU workspaces failed to meet the recommended minimum illuminance level of 300 lux. Subjective responses from healthcare professionals indicated that poor lighting significantly reduced job satisfaction by 40%, lowered self-rated task performance by 30%, decreased visual comfort scores from 4.1 to 2.6 (on a 1–5 scale), and increased the prevalence of well-being symptoms (eye fatigue, headaches) by 25–35%. Frequent complaints included eye strain, glare, and discomfort with posture, with these issues often exacerbated during the rainy season due to reduced natural daylight. The study highlights critical deficiencies in current lighting infrastructure and emphasizes the need for urgent improvements in clinical environments. Moreover, inconsistent energy supply to these healthcare settings also impacts the assurance of visual comfort. To address these shortcomings, the study recommends transitioning to energy-efficient LED lighting, enhancing access to natural light, incorporating circadian rhythm-based lighting systems, enabling individual lighting control at workstations, and ensuring a consistent power supply via the integration of solar inverters to the grid supply. These interventions are essential not only for improving healthcare staff performance and safety but also for supporting better patient outcomes. The findings offer actionable insights for hospital administrators and policymakers in the DRC and similar low-resource settings seeking to enhance environmental quality in critical care facilities. Full article

Liquid biopsies promise major advantages for cancer screening and diagnosis. By detecting biomarkers in peripheral blood samples, liquid biopsies reduce the need for invasive techniques and provide important genetic information integral to the emerging molecular classification of cancers. Unfortunately, the concentrations of most biomarkers, particularly circulating tumour nucleic acids, are vanishingly small—beyond the sensitivity and specificity of most assays. Clustered Regularly Interspaced Short Palindromic Repeats diagnostics (herein labelled ‘CRISPR-Dx’) use gene editing tools to detect, rather than modify, nucleic acids with extremely high specificity. These tools are commonly combined with isothermal nucleic acid amplification to also achieve sensitivities comparable to high-performance laboratory-based techniques, such as digital PCR. CRISPR assays, however, are inherently well suited to adaptation for point-of-care (POC) use, and unlike antigen-based POC assays, are significantly easier and faster to develop. In this review, we summarise current CRISPR-Dx platforms and their analytical potential for cancer biomarker discovery, with an emphasis on enhancing early diagnosis, disease monitoring, point-of-care testing, and supporting cancer therapy. Full article

Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R² increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

Air quality is crucial for public health and the environment, which makes it important to both monitor and forecast the level of pollution. Polluted air, containing harmful substances such as particulate matter, nitrogen oxides, or ozone, can lead to serious respiratory and circulatory diseases, especially in people at risk. Air quality forecasting allows for early warning of smog episodes and taking actions to reduce pollutant emissions. In this article, we review air pollutant concentration forecasting methods, analyzing both classical statistical approaches and modern techniques based on artificial intelligence, including deep models, neural networks, and machine learning, as well as advanced sensing technologies. This work aims to present the current state of research and identify the most promising directions of development in air quality modeling, which can contribute to more effective health and environmental protection. According to the reviewed literature, deep learning–based models, particularly hybrid and attention-driven architectures, emerge as the most promising approaches, while persistent challenges such as data quality, interpretability, and integration of heterogeneous sensing systems define the open issues for future research. Full article

Desertification threatens the sustainability of dryland ecosystems, yet many existing monitoring frameworks rely on static maps, coarse spatial resolution, or lack temporal forecasting capacity. To address these limitations, this study introduces PyGEE-ST-MEDALUS, a novel spatiotemporal framework combining the full MEDALUS desertification model with deep learning (CNN, LSTM, DeepMLP) and machine learning (RF, XGBoost, SVM) techniques on the Google Earth Engine (GEE) platform. Applied across Tebessa Province, Algeria (2001–2028), the framework integrates MODIS and Sentinel-1/-2 data to compute four core indices—climatic, soil, vegetation, and land management quality—and create the Desertification Sensitivity Index (DSI). Unlike prior studies that focus on static or spatial-only MEDALUS implementations, PyGEE-ST-MEDALUS introduces scalable, time-series forecasting, yielding superior predictive performance (R² ≈ 0.96; RMSE < 0.03). Over 71% of the region was classified as having high to very high sensitivity, driven by declining vegetation and thermal stress. Comparative analysis confirms that this study advances the state-of-the-art by integrating interpretable AI, near-real-time satellite analytics, and full MEDALUS indicators into one cloud-based pipeline. These contributions make PyGEE-ST-MEDALUS a transferable, efficient decision-support tool for identifying degradation hotspots, supporting early warning systems, and enabling evidence-based land management in dryland regions. Full article

Various types of interfering signals are an integral part of ECGs recorded using wearable electronics, specifically during field monitoring, outside the controlled environment of a medical doctor’s office, or laboratory. The frequency spectrum of several types of interfering signals overlaps significantly with the ECG signal, making effective filtration impossible without losing clinically relevant information. In this article, we proceed from the practical assumption that it is unnecessary to analyze the entire ECG recording in real long-term recordings. Conversely, in the preprocessing phase, it is necessary to detect unreadable segments of the ECG signal. This paper proposes a novel method for automatically detecting unreadable segments distorted by superimposed interference in ECG recordings. The method is based on a convolutional neural network (CNN) and is comparable in quality to annotation performed by a medical expert, but incomparably faster. In a series of controlled experiments, the ECG signal was recorded during physical activities of varying intensities, and individual segments of the recordings were manually annotated based on visual assessment by a medical expert, i.e., divided into four different classes based on the intensity of distortion to the useful ECG signal. A deep convolutional model was designed and evaluated, exhibiting a 87.62% accuracy score and the same F1-score in automatic recognition of segments distorted by superimposed interference. Furthermore, the model exhibits an accuracy and F1-score of 98.70% in correctly identifying segments with visually detectable and non-detectable heart rate. The proposed interference detection procedure appears to be sufficiently effective despite its simplicity. It facilitates subsequent automatic analysis of undisturbed ECG waveform segments, which is crucial in ECG monitoring using wearable electronics. Full article

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning. Full article

In higher education, institutional quality is traditionally assessed through metrics such as academic programs, research output, educational resources, and community services. However, it is important that their activities align with student expectations, particularly in relation to interactive learning environments, learning management system interaction, curricular and co-curricular activities, accessibility, support services and other learning resources that ensure academic success and, jointly, career readiness. The growing popularity of student engagement metrics as one of the key measures to evaluate institutional efficacy is now a feature across higher education. By monitoring student engagement, institutions assess the impact of existing resources and make necessary improvements or interventions to ensure student success. This study presents a comprehensive analysis of student feedback from the StudentSurvey.ie dataset (2016–2022), which consists of approximately 275,000 student responses, focusing on student self-perception of engagement in the learning process. By using classical topic modelling techniques such as Latent Dirichlet Allocation (LDA) and Bi-term Topic Modelling (BTM), along with the advanced transformer-based BERTopic model, we identify key themes in student responses that can impact institutional strength performance metrics. BTM proved more effective than LDA for short text analysis, whereas BERTopic offered greater semantic coherence and uncovered hidden themes using deep learning embeddings. Moreover, a custom Named Entity Recognition (NER) model successfully extracted entities such as university personnel, digital tools, and educational resources, with improved performance as the training data size increased. To enable students to offer actionable feedback, suggesting areas of improvement, an n-gram and bigram network analysis was used to focus on common modifiers such as “more” and “better” and trends across student groups. This study introduces a fully automated, scalable pipeline that integrates topic modelling, NER, and n-gram analysis to interpret student feedback, offering reportable insights and supporting structured enhancements to the student learning experience. Full article

Cave systems are a kind of natural laboratory for interdisciplinary research on karstogenesis in the context of global changes. In this study, we investigate the concentration of ²²²Rn at 65 points in 37 representative caves of Bulgarian karst through continuous monitoring with passive and active detectors with a duration of 1 to 13 years. The concentration changes strongly both in the long term and seasonally, with values from 0.1 to 13 kBq m⁻³. These variations are analyzed from different perspectives (location and morphological features of the cave system, cave climate, ventilation regime, etc.). The seasonal change in the direction and intensity of ventilation is a leading factor determining the gas composition of the cave atmosphere during the year. Parallel measurements of ²²²Rn and CO₂ concentrations in the cave air show that both gases have a similar seasonal fluctuation. Cases of coincidences of an anomalous increase in the concentration of ²²²Rn with manifestations of seismic activity and micro-displacements along tectonic cracks in the caves have also been registered. The dependencies between the ²²²Rn concentration in the caves and in the soil above them are also discussed, as well as the possible connections between global trends in climate change and trends in ²²²Rn emissions. Special attention is paid to the risks of radiation exposure in show caves. A calculation procedure has been developed to achieve the realistic assessment of the effective dose of cave guides. It is based on information about the annual course of the ²²²Rn concentration in the respective cave and the time schedule of the guides’ stay in it. The calculation showed that the effective dose may exceed the permitted limits, and it is thus necessary to control it. Full article

NO₂ is a toxic gas mainly generated by combustion processes, such as vehicle emissions and industrial activities. It is a key contributor to smog, acid rain, ground-level ozone, and particulate matter, all of which pose serious risks to human health and the environment. Conventional resistive gas sensors, typically based on metal oxide semiconductors, detect NO₂ by resistance modulation through surface interactions with the gas. However, they often suffer from low responsiveness and poor selectivity. This study investigates NO₂ detection using nanoporous zinc oxide thin films integrated into a resistor structure and floating-gate field-effect transistor (FGFET). Both Silvaco-Atlas simulations and experimental fabrication were employed to evaluate sensor behavior under NO₂ exposure. The results show that FGFET provides higher sensitivity, faster response times, and improved selectivity compared to resistor-based devices. In particular, FGFET achieves a detection limit as low as 89 ppb, with optimal performance around 400 °C, and maintains stability under varying humidity levels. The enhanced performance arises from quantum well effects at the floating-gate Schottky contact, combined with NO₂ adsorption on the ZnO surface. These interactions extend the depletion region and confine charge carriers, amplifying conductivity modulation in the channel. Overall, the findings demonstrate that FGFET is a promising platform for NO₂ sensors, with strong potential for environmental monitoring and industrial safety applications. Full article

This study evaluated the impact of biochar on the growth of strawberry plants, combining visual and proximal sensing monitoring. The plants were rooted in soil enriched with biochar, derived from pyrolysis of soft wood at 550 °C and applied in two doses (2 and 15 g/L), and after physical activation with CO₂ at 900 °C; there was also a treatment with no biochar (unaltered). Visual monitoring was based on data logging twice per week of plants’ height and number of flowers and ripe fruits. Proximal sensing monitoring involved a system including a low-cost multispectral camera and a Raspberry Pi 4. The camera acquired nadiral images hourly in three spectral bands (550, 660, and 850 nm), allowing calculation of the normalized difference vegetation index (NDVI). After three months, control plants reached a height of 12.3 ± 0.4 cm, while those treated with biochar and activated biochar grew to 18.03 ± 1.0 cm and 17.93 ± 1.2 cm, respectively. NDVI values were 0.15 ± 0.11 for control plants, increasing to 0.26 ± 0.03 (+78%) with biochar and to 0.28 ± 0.03 (+90%) with activated biochar. In conclusion, biochar application was beneficial for strawberry plants’ growth according to both visual and proximal-sensed measures. Further research is needed to optimize the integration of visual and proximal sensing monitoring, also enhancing the measured parameters. Full article

Virus-associated hepatocellular carcinoma (HCC) remains a major global health burden despite effective antiviral therapies. Chronic infection with hepatitis B (HBV), hepatitis C (HCV), and hepatitis D (HDV) promotes malignant transformation through overlapping pathways of fibrosis, immune dysregulation, and microenvironmental remodeling. YKL-40, a glycoprotein secreted by hepatic stellate cells, hepatocytes under stress, macrophages, and endothelial cells, has emerged as a marker that reflects stromal activation rather than direct hepatocyte injury. Its expression is reinforced by profibrotic and angiogenic circuits, and circulating concentrations correlate with advanced fibrosis, residual risk after viral suppression, and oncologic outcomes. This review synthesizes current evidence on YKL-40 across HBV, HCV, and HDV cohorts, with emphasis on its role in bridging molecular mechanisms to clinical applications. We examine its utility in non-invasive fibrosis assessment, longitudinal monitoring after antiviral therapy, and prognostic modeling in HCC. Particular attention is given to its potential in the liver transplant pathway, where YKL-40 may refine eligibility beyond morphology, inform bridging therapy response, and predict post-transplant recurrence or graft fibrosis. Remaining challenges include its lack of disease specificity, assay variability, and limited multicenter validation. Future integration of YKL-40 into multimarker, algorithm-based frameworks could enable risk-adaptive strategies that align surveillance and transplant decisions with the evolving biology of virus-associated liver disease. Full article

The integration of Internet of Things (IoT) technologies in agriculture has facilitated real-time environmental monitoring, with low-cost IoT-enabled agrometeorological stations emerging as a valuable tool for climate-smart farming. This systematic review examines low-cost IoT-based weather stations by analyzing their hardware and software components and assessing their potential in comparison to conventional weather stations. It emphasizes their contribution to improving climate resilience, facilitating data-driven decision-making, and expanding access to weather data in resource-constrained environments. The analysis revealed widespread adoption of ESP32 microcontrollers, favored for its affordability and modularity, as well as increasing use of communication protocols like LoRa and Wi-Fi due to their balance of range, power efficiency, and scalability. Sensor integration largely focused on core parameters such as air temperature, relative humidity, soil moisture, and rainfall supporting climate-smart irrigation, disease risk modeling, and microclimate management. Studies highlighted the importance of usability and adaptability through modular hardware and open-source platforms. Additionally, scalability was demonstrated through community-level and multi-station deployments. Despite their promise, challenges persist regarding sensor calibration, data interoperability, and long-term field validation. Future research should explore the integration of edge computing, adaptive analytics, and standardization protocols to further enhance the reliability and functionality of IoT-enabled agrometeorological systems. Full article