Search Results (10,546)

Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership. Full article

Background/Objectives: Given the narrow therapeutic range of tacrolimus and substantial inter-individual variability in trough levels, both total daily dose and the trough level-to-dose ratio are commonly used to guide dose optimization. In this study, Life-Cycle Pharma tacrolimus was compared with immediate-release tacrolimus in a real-world setting. Methods: This longitudinal observational study included kidney transplant recipients at two Hungarian university clinics. Sixty-three (63) patients completed the study and were included in the statistical analysis. They received either Life-Cycle Pharma-tacrolimus (n = 40) or immediate-release tacrolimus (n = 23) as maintenance therapy in the two study arms, each combined with everolimus or mycophenolic acid and corticosteroids. Patients were enrolled 4–6 weeks after transplantation and prospectively followed for 48 months. Tacrolimus trough level, total daily dose and their ratio were recorded at each of the seven follow-up visits during the 48-month study period. Epidemiological data, patient characteristics, laboratory parameters (including eGFR, de novo donor-specific antibodies, and CMV and BK virus incidence), and acute rejection episodes were monitored. Results: The mean age at enrolment was 53.35 years, and 41 patients (65.08%) were male. A stable therapeutic maintenance trough level was achieved in both study arms. Life-Cycle Pharma tacrolimus required a 30% lower total daily dose than immediate-release tacrolimus to achieve comparable exposure. A gradual decline in eGFR was observed in the immediate-release tacrolimus arm (a mean decrease of 6.06 mL/min/1.73 m² over 4 years) from a baseline level of 58.52 mL/min/1.73 m² (±16.69), whereas GFR increased in the Life-Cycle Pharma tacrolimus arm (a mean increase of 4.76 mL/min/1.73 m² over the same period) from a significantly lower baseline level of 46.55 mL/min/1.73 m² (±17.04). Conclusions: Both formulations provided effective long-term maintenance immunosuppression in kidney transplant recipients and maintained stable trough levels. Life-Cycle Pharma tacrolimus represents a potential option for dose minimization, and it also helped to stabilize renal function despite the worse baseline condition. Full article

Background: Pacemakers enable continuous long-term surveillance of atrial fibrillation detected by implanted devices. Circulating biomarkers reflecting endothelial dysfunction, inflammation, and myocardial stress may help identify patients at risk for atrial fibrillation (AF) progression and higher arrhythmic burden. Methods: This analysis included patients from the prospective ACaSA study (NCT05127720) with a dual chamber pacemaker (Microport^® BOREA DR or TEO DR) and monitored weekly via remote monitoring technology (SMARTVIEW^®). Individuals with permanent AF or single-chamber systems were excluded. Baseline plasma concentrations of angiopoietin-2 (ANGPT2), growth differentiation factor-15 (GDF-15), fibroblast growth factor-23 (FGF-23), bone morphogenetic protein-10 (BMP10), and tumor necrosis factor–related apoptosis-inducing ligand receptor-2 (TRAIL-R2) were quantified using enzyme-linked immunosorbent assays. N-terminal pro-B-type natriuretic peptide (NT-proBNP) was measured using electrochemiluminescence immunoassay. Biomarkers were log₂-transformed, with values below assay detection limits imputed at half the lower limit of detection. Two endpoints were assessed following a 30-day blanking period: (1) progression to persistent AF, defined as ≥7 consecutive days with >99% daily AF burden, analyzed using Cox regression; and (2) AF burden, calculated as total AF time normalized to monitored days and categorized as <25%, 25–75%, or >75%, analyzed using multinomial logistic regression. Multivariable models were adjusted for age, sex, heart failure, diabetes, and prior myocardial infarction; Cox models were limited to age, sex, and heart failure due to fewer events. Results: A total of 223 patients were included (median age 75 years; 37.2% women). During follow-up, 28 patients (13.3%) progressed to persistent AF. Higher baseline ANGPT2 was the strongest predictor of progression (HR per doubling 1.83, 95% CI 1.27–2.66, p = 0.001), followed by GDF-15 (HR 1.52, 95% CI 1.03–2.24, p = 0.036). In the burden analysis, ANGPT2 demonstrated a pronounced graded relationship with arrhythmic load, with markedly increased odds of high (>75%) AF burden (OR 8.31, 95% CI 2.63–26.26, p < 0.001). GDF-15 independently predicted both medium (OR 2.05, p = 0.025) and high burden (OR 2.32, p = 0.037). NT-proBNP displayed a borderline association with high burden (OR 2.02, p = 0.061). No significant associations were observed for FGF-23, BMP10, or TRAIL-R2. Conclusions: In continuously monitored pacemaker patients, ANGPT2 and GDF-15 emerged as key biomarkers associated with AF disease severity. ANGPT2 was strongly linked to both progression to persistent AF and high AF burden, whereas GDF-15 consistently predicted higher AF burden and also contributed to risk of progression. These findings highlight endothelial and inflammatory pathways as potential markers of atrial disease progression. Full article

Background/Objectives: Adequate nutrition in older adults is essential to maintaining health, functionality, and quality of life, particularly in long-term care hospitals (HACLEs). Previous studies suggest that dissatisfaction with hospital food is linked to longer stays, more complications, and negative perceptions of care. Given these concerns, this study aimed to assess patients’ experiences with hospital food over a 15-year period in a HACLE in Spain, identify key influencing factors, and validate an updated PREM (Patient Reported Experience Measure) tool for food services. Methods: A retrospective, observational, repeated cross-sectional study was conducted using annual PREM surveys administered between 2011 and 2025 to patients on oral diets. Psychometric validation of the updated 8-item version (2024) was conducted. Results: Out of 1618 surveys, 1540 were included in the final analysis. The updated PREM showed strong internal consistency (α = 0.85, ω = 0.87), a two-factor structure (food quality and service conditions), and adequate model fit. Perceptions worsened after a catering company change in 2022 but improved following the implementation of new food distribution carts in 2025. The PREM total score showed a strong positive association with the global satisfaction item, providing supportive evidence based on a closely related anchor measure (Spearman’s rho = 0.80, 95% CI 0.77–0.82; p < 0.001). Scores differed significantly by diet type: patients receiving a pureed diet reported the highest average satisfaction score, followed by those on a soft diet and a regular diet. The group on a soft diet excluding foods that pose a choking hazard had the lowest mean score. Conclusions: The validated PREM scale is a reliable tool to monitor patient experience with hospital food. It enables early detection of quality issues and supports targeted improvements. Routine use in long-term care settings may foster personalized, patient-centered nutrition strategies and enhance care quality. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

Quantifying the effects of land-use changes on soil fertility is essential for agricultural planning, yet long-term analyses combining field and remote sensing data remain scarce in Hungary. This systematic review followed PRISMA 2020 guidelines to assess arable land fertility trends between 2000 and 2020. A comprehensive search of WoS, Scopus, and Google Scholar identified 202 records, with 106 studies meeting inclusion criteria. Eligibility required empirical soil data collected from Hungarian arable lands. Among these, 17% reported declines in SOC, 13% indicated nutrient depletion, 36% observed stable or lost fertility, and 34% documented improvements. Regarding monitoring methods, 41% relied solely on field sampling, 44% applied GIS or spatial analyses, and 15% incorporated remote sensing indices such as NDVI. Evidence revealed spatial–temporal heterogeneity: fertility declines occurred in intensively cultivated regions, while western Transdanubia showed stability. Trends were linked to land-use intensification and intermittent reductions in agricultural area. Integration of remote sensing indices, such as NDVI, with field observations enhanced detection of spatial and temporal patterns. These findings underscore the need for harmonised monitoring frameworks, precision agriculture tools, and predictive modelling to support sustainable soil management. Identifying fertility-decline zones informs policy aligned with the EU Soil Strategy 2030 and supports Hungary’s agricultural resilience. Full article

Background/Objectives: Digital health interventions are increasingly used to support person-centered care (PCC) in chronic disease management, yet it remains unclear which PCC components are most consistently enabled by digital tools and how these relate to outcomes. This study synthesized evidence on digitally supported PCC for adults with chronic conditions, examining how interventions operationalize PCC and which clinical, patient-reported, and implementation outcomes are reported. Methods: A structured literature synthesis was conducted according to PRISMA guidelines across a heterogeneous evidence base, including randomized and pragmatic trials, observational studies, qualitative studies, and systematic reviews. The review protocol was pre-registered in the Open Science Framework (OSF) Registries. Results: Across 16 included studies, digital solutions most consistently supported PCC through enhanced situational awareness via self-monitoring, strengthened partnership through two-way communication and coaching, and reinforced shared documentation through co-created health plans. Benefits were reported most consistently for process and experience outcomes, such as perceived access to support, engagement, and empowerment. Evidence for sustained long-term clinical improvements, such as glycemic control, was mixed and frequently limited by short follow-up periods and variation in intervention integration. Conclusions: Digitalization can strengthen PCC when embedded within relational care models and organizational workflows that translate patient-generated data into meaningful action. Future work should utilize clearer PCC operationalization, longer follow-up, and routine reporting of equity outcomes, alongside targeted training for healthcare professionals delivering PCC in digital encounters. Full article

The construction sector is experiencing increasing demand for deep underground structures in urban environments, where excavations frequently intersect fractured aquifers. Such conditions pose significant risks to structural stability and long-term durability due to groundwater inflow and elevated hydrostatic pressures. This study investigates the influence of deep underground construction on fractured aquifer systems using the Abu Dhabi Plaza development in Kazakhstan as a case study. An integrated methodological approach combining hydrogeological monitoring, hydrochemical analysis, and engineering–geological testing was applied. Groundwater levels were monitored using observation wells, while triaxial and uniaxial compression tests were conducted to evaluate the mechanical properties of rock and soil materials. Hydraulic gradients, flow velocities, and hydrostatic pressures were estimated using Darcy’s law and the Boussinesq equation, supported by GIS-based spatial analysis. Groundwater mineralisation is consistently represented in this study by total dissolved solids (TDS), expressed in g/L. The results indicate that groundwater in the Quaternary aquifer is fresh to slightly mineralised, with TDS ranging from 0.47 to 1.50 g/L, whereas groundwater in the fractured Ordovician aquifer exhibits a more stable hydrochemical regime with TDS values of 0.72–0.73 g/L. Statistical analysis identifies two primary controls on groundwater chemistry: (i) natural geochemical processes associated with water–rock interaction and (ii) technogenic influences related to urban activities. Hydrodynamic calculations indicate a hydraulic gradient of approximately 0.136, a filtration velocity of about 0.35 m/day, well discharge reaching 0.11 L/s, and hydrostatic pressure ranging from 1.45 to 2.81 atm. Groundwater drawdown caused by excavation dewatering reached 29–30 m. The findings demonstrate that groundwater inflow is primarily controlled by fracture-controlled permeability and structural heterogeneity within the aquifer system. These results highlight the importance of integrated hydrogeological and hydrochemical assessment, in which TDS serves as the principal quantitative indicator of groundwater mineralisation, for the effective management of groundwater-related risks during deep underground construction. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

Nucleoside analogs (NAs) play a central role in cancer therapy, either through direct cytotoxicity or epigenome reprogramming. They are clinically effective but have shortcomings in their long-term effectiveness because of variable patient responses and the emergence of resistance. There is growing evidence that DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs (ncRNAs) are key factors that determine sensitivity and resistance to NAs. This review summarizes existing evidence on the epigenetic control of cytotoxic and epigenetic nucleoside analogs, discusses predictive biomarkers of human Equilibrative Nucleoside Transporter 1 (hENT1) and deoxycytidine kinase (dCK) promoter methylation, histone modifications, and ncRNA signatures, and assesses the emerging strategies of multi-omic integration. Improvements in detection methods, such as high-resolution sequencing, single-cell profiling, and liquid biopsy, are addressed, along with the issues of reproducibility, tumor heterogeneity, and clinical translation. Epigenetic biomarkers are promising for patient stratification in clinical trials, although a lack of uniformity in technical and methodological approaches currently constrains their full potential. The future focus will be on standardized panels of biomarkers, real-time monitoring, rational combination strategies, and biomarker-directed clinical trial designs. Overall, epigenetic biomarkers are capable of changing nucleoside analog therapy into a more precise, durable, and personalized treatment approach. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

The detection of ammonia (NH₃) at room temperature is of significant importance for environmental monitoring, industrial safety and early disease diagnosis. In this work, a novel room-temperature ammonia sensor was developed by combining graphene oxide with WO₃ quantum dots. The as-fabricated sensor exhibited excellent comprehensive sensing performance, including high sensitivity, rapid response, outstanding selectivity, and reliable long-term stability. Specifically, when exposed to 10 ppm NH₃, the sensor based on 1.5% GO@WO₃ nanocomposites achieved a frequency shift of 578 Hz, which was 6.4 times that of the pure WO₃ QDs sensor. The theoretical limit of detection (LOD) of the sensor was calculated to be 60 ppb, enabling ppb-level NH₃ detection. In addition, the sensor demonstrated good long-term stability over a two-week period. The enhanced performance of the GO@WO₃ nanocomposite sensor is attributed to the formation of an ohmic contact between GO and WO₃, which eliminates charge transfer barriers, promotes oxygen adsorption, and amplifies the sensing signal. This work provides a simple, efficient, and practical solution for room-temperature NH₃ detection, offering significant advantages over traditional single-component sensors. Full article

Pasture ecosystems in the arid regions of Kazakhstan are highly vulnerable to the combined effects of climatic variability and increasing grazing pressure, while long-term spatial assessments of degradation remain limited. This study develops an integrative remote sensing-based framework for assessing pasture degradation in Atyrau Oblast by combining long-term NDVI time series (2000–2023) with grazing pressure indicators ($K_{sust}$ and LIPS), field observations, and climatic data. The results show that 49.3% of pasturelands are degraded, with statistically significant negative NDVI trends observed across most administrative districts. Areas experiencing pasture overload ($K_{sust}$ > 1.2) spatially coincide with persistent vegetation decline, and significant negative relationships between NDVI and livestock numbers are identified in several districts. The analysis also reveals spatial heterogeneity and lagged responses of vegetation dynamics to grazing pressure under varying climatic conditions. The proposed approach provides a novel integrative framework that links spectral vegetation indicators with climate-adjusted grazing metrics, enabling the identification of degradation hotspots and supporting spatially differentiated pasture management. This framework can be applied in regional land monitoring systems to improve decision-making for sustainable rangeland use under climate change. Full article

Tilia cordata Mill. is a long-lived, ecologically important broadleaved tree species that maintains high genetic diversity despite habitat fragmentation and historical range shifts. In this study, we assessed genetic diversity, clonal structure, and population differentiation in six genetic conservation units (GCUs) in Lithuania using nuclear microsatellite markers. A total of 1109 individuals were successfully genotyped, revealing 979 unique multi-locus genotypes, with 17% of individuals assigned to clonal lineages. Clonal groups were generally small and spatially restricted, indicating localized vegetative regeneration. Genetic diversity was high across all populations, with similar levels of observed and expected heterozygosity, consistent with predominantly outcrossing reproduction. Juvenile cohorts exhibited slightly higher allelic richness and latent genetic potential compared to mature trees, suggesting effective regeneration and maintenance of genetic variation. Genetic differentiation among populations was low but significant (F_ST = 0.013; G″_ST = 0.051), with evidence of clustering corresponding to provenance regions. High gene flow (Nm ≈ 10) likely contributes to weak population structure, although regional differentiation persists. The results demonstrate that Lithuanian T. cordata populations retain a robust genetic framework, combining high within-population diversity with moderate structuring. These findings highlight the importance of conserving multiple GCUs and implementing genetic monitoring to ensure long-term population viability under changing environmental conditions. Full article

Fine-grained energy management in smart-campus buildings requires accurate load forecasting together with reliable and interpretable anomaly diagnosis. This study presents an integrated forecasting–diagnosis framework for building energy systems. Hourly energy demand is modeled using a Transformer-based sequence-to-sequence architecture, in which a domain-aware attention mechanism is introduced to separately represent historical consumption dynamics, environmental influences, and temporal regularities commonly observed in building energy use. Anomaly diagnosis is conducted through a dual-scale strategy that supports both the timely detection of abrupt abnormal events and the identification of gradual performance degradation. Short-term anomalies are detected from forecasting residuals using adaptive thresholds, while long-term anomalies are identified by comparing current residual patterns with same-season historical baselines and validating multi-window trends over a 48 h horizon. The two detection streams are jointly used to distinguish point, pattern, and composite anomalies. To support practical operation and maintenance, SHAP-based explanations are provided to interpret both energy predictions and detected anomalies. Case studies on two educational buildings from the Building Data Genome Project 2 demonstrate that the proposed framework achieves the best overall forecasting performance against both conventional baselines and stronger recent Transformer-based models, with mean absolute percentage errors of approximately 3%. The results indicate that the proposed framework provides a practical solution for data-driven energy monitoring and decision support in smart buildings. Full article