Search Results (12,603)

Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge since no single approach performs best under all conditions. To address this issue, this paper proposes an AI-driven framework that evaluates scenario-specific functional requirements—such as coverage, connectivity, and network lifetime—to identify the optimal node scheduling algorithm from a pool that includes Hidden Markov Models (HMMs), BAT, Bird Flocking, Self-Organizing Maps (SOFMs), and Long Short-Term Memory (LSTM) networks. The framework was evaluated using a neural network trained on simulated data and tested across five real-world scenarios: healthcare monitoring, military operations, industrial IoT, forest fire detection, and disaster recovery. The results clearly demonstrate the effectiveness of the proposed framework in identifying the most suitable algorithm for each scenario. Notably, the LSTM algorithm frequently achieved near-optimal performance, excelling in critical objectives such as network lifetime, connectivity, and coverage. The framework also revealed the complementary strengths of other algorithms—HMM proved superior for maintaining connectivity, while Bird Flocking excelled in extending network lifetime. Consequently, this work validates that a scenario-aware selection strategy is essential for maximizing WSN dependability, as it leverages the unique advantages of diverse algorithms. Full article

Acute lymphoblastic leukemia (ALL) remains the most frequent pediatric malignancy, accounting for approximately 34% of all pediatric cancers, with remarkable improvements in survival (approximately 85%) due to advances in chemotherapy, radiotherapy, and supportive care. However, as survival rates have increased, new challenges have emerged—particularly the growing prevalence of obesity and metabolic syndrome among survivors. This review compiles evidence from the past decade on the relationship between leukemia treatment, obesity, and metabolic risk. The findings indicate that cranial radiotherapy, corticosteroid use, and younger age at diagnosis are key risk factors for excessive weight gain and long-term metabolic disturbances. Genetic factors such as FTO, MC4R, and LEPR polymorphisms may further influence susceptibility to obesity. Nutritional analyses highlight poor diet quality, insufficient micronutrient intake, and high-fat, energy-dense dietary patterns in survivors. Beyond endocrine dysfunction, obesity and metabolic syndrome are associated with elevated cardiovascular morbidity and reduced quality of life. Personalized medicine approaches—integrating genomics, metabolomics, and lifestyle data—hold promise for targeted prevention and intervention strategies. Early detection, continuous metabolic monitoring, and health education remain essential components in the long-term management of childhood leukemia survivors. In this review, we analyzed the dietary patterns of children and long-term leukemia survivors explaining why higher rates of obesity and comorbidities appear during or after treatments, and discussed interventions to prevent these conditions. Full article

Periodontitis (PD) is a multifactorial, progressive inflammatory disease affecting the teeth-supporting tissues, characterized by an imbalance of the oral microbiota and the presence of bacterial biofilms leading to host response. Nowadays, reliable biochemical markers for early and objective diagnosis, and for predicting disease progression, are still lacking. Our previous proteomic investigations revealed the significant overexpression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in periodontal pocket tissue, gingival crevicular fluid (GCF), and tooth-surface-collected material (TSCM) from PD patients in comparison to periodontally healthy controls, proposing it as a possible biomarker of PD. This study aimed to evaluate the expression of GAPDH in saliva, a more accessible, non-invasive, and clinically relevant oral sample. The whole saliva was analyzed by a preliminary mass spectrometry-based proteomic approach, identifying significantly increased levels of GAPDH also in salivary samples from periodontal-affected subjects. These data were further validated by enzyme-linked-immunosorbent assay (ELISA). Additionally, protein–protein interaction networks were generated through the Human Protein Atlas database, using different datasets (OpenCell, IntAct, and BioGRID). Bioinformatic analysis provided noteworthy GAPDH-associated networks potentially relevant to periodontal pathology. The scientific significance of this study lies in the detection of salivary GAPDH as a novel strategy to advance periodontal clinical diagnostics from the perspective of a non-invasive screening test. In correlation with other protein markers, salivary GAPDH could constitute a promising set of distinctive and predictive targets to enhance early diagnosis of PD, disease monitoring, and treatment planning in periodontology. Full article

The Bekasi River Basin is highly vulnerable to severe and recurrent flooding, as evidenced by significant infrastructure and environmental damage during major events. This study investigates the catastrophic floods of 2016, 2020, 2022, and 2025 by implementing the Rainfall-Runoff-Inundation (RRI) model to simulate key hydrological processes. After validation using historical water level data, the model performed effectively, achieving the highest coefficient of determination (R² = 0.75) and lowest root mean square error (RMSE = 0.66) at Cileungsi Station. In contrast, the lowest R² = 0.02, and the highest RMSE = 3.74 at Pondok Gede Permai (PGP) Station. The results reveal a concerning trend of worsening 5-year flood events, with the 2025 flood reaching a peak inundation depth exceeding 3 m and affecting an area of 2.97 km², caused by a rainfall threshold of more than 180 mm/day. Furthermore, the model shows a rapid hydrological response, with a time lag of approximately 7 h or less between peak rainfall and flood onset across three monitoring stations. Analysis indicates these severe floods were primarily triggered by heavy rainfall combined with significant land cover changes. The findings provide valuable insights for flood prediction and mitigation strategies in this vulnerable region. Full article

Gully erosion is a major driver of land degradation globally, particularly in semi-arid regions where it is fundamentally controlled by rainfall and runoff dynamics. Understanding how rainfall translates into runoff in gullied landscapes is crucial for predicting erosion processes and modelling runoff to inform land management strategies. In this study, rainfall-runoff analysis was conducted using high-resolution rainfall and runoff data from intensely monitored alluvial gullies in the semi-arid regions of northern Australia. Runoff responses were strongly seasonal, with flashy but low-volume flows during the early wet season (October–November) and prolonged, high-discharge events during peak rainfall months (December–March). Antecedent soil moisture had a limited influence on runoff generation, likely due to rapid wetting–drying cycles and shallow infiltration depths. Notably, rainfall-runoff behavior diverged with catchment-to-gully area ratio (A_ca): linear runoff to rainfall responses were observed where gullies were eroded to the catchment limit (A_ca ≈ 1) whereas high-A_ca systems (A_ca > 5) exhibited threshold, stepwise behavior with upslope contributions activating at ~26 mm event rainfall. Field infiltration tests showed upslope catchment infiltration capacity was ~70% higher than on gully floors (~36 vs. 21 mm h⁻¹). This indicates greater near-surface storage and delayed upslope runoff, consistent with an activation threshold for upslope contributions. Mean rainfall–runoff ratios were higher in low-A_ca gullies (≈0.52–0.68) than in high-A_ca systems (≈0.40–0.46). These findings have implications for rainfall-runoff modelling, process-based understanding of gully erosion and gully management in semi-arid environments. Full article

As the environmental burden of traditional plastics continues to grow, bioplastics (BPs) have emerged as a promising alternative due to their renewable origins and potential for biodegradability. However, the most popular anaerobic systems (ASs)—anaerobic digestion (AD), acidogenic fermentation (AF), and enzyme hydrolysis (EH)—for BPs degradation still face many challenges, e.g., low degradation efficiency, process instability, etc. As a sustainable clean energy technology, bioelectrochemical systems (BESs) have demonstrated strong potential in the treatment of complex organic waste when integrated with ASs. Nevertheless, research on the synergistic degradation of BPs using BES-ASs remains relatively limited. This review systematically summarizes commonly used anaerobic degradation methods for BPs, along with their advantages and limitations, and highlights the BES-AS as an innovative strategy to enhance BPs degradation efficiency. BESs can accelerate the decomposition of complex polymer structures through the activity of electroactive microorganisms, while also offering benefits such as energy recovery and real-time process monitoring. When coupled with anaerobic digestion, the BES-AS demonstrates significant synergistic effects, improving degradation efficiency and promoting the production of high-value-added products such as volatile fatty acids (VFAs) and biogas, thereby showing great application potential. This review outlines current research progress, identifies key knowledge gaps in mechanism elucidation, system design, source recovery, etc., and proposes future research directions. These include system optimization, microbial community engineering, development of advanced electrode materials, and omics-based mechanistic studies. Advancing multidisciplinary integration is expected to accelerate the practical application of BES-ASs in BP waste management and contribute to achieving the goals of sustainability, efficiency, and circular utilization. Full article

Background and objectives: Numerous studies and meta-analyses have established the efficacy of telemonitoring for blood pressure and other components of metabolic syndrome in improving disease management. Nevertheless, the adoption of telemonitoring technologies is often hindered by personal, technological, and systemic barriers. In Slovakia, where patient–physician contact rates are high, there is limited research on patients’ perspectives regarding telemedicine adoption for cardiovascular risk management. The objective of this study was to examine patients’ perspectives on and perceived obstacles to the use of telemonitoring for arterial hypertension and dyslipidemia in Slovakia. Methods: This cross-sectional, questionnaire-based survey targeted a cohort of 18,053 patients. The survey instrument was designed to gather data on several key areas: patient demographic characteristics, blood pressure measurement habits, the utilization of smart technologies, perceived benefits and barriers to telemonitoring, and patients’ knowledge of their lipid profiles and cardiovascular risk factors. Statistical analysis included chi-square tests, ANOVA, and effect size calculations with 95% confidence intervals (CI). Results: A total of 1787 patient responses (9.9%) were collected. Among the respondents, 67.4% (n = 1204) had arterial hypertension, while 7.9% (n = 95) were on non-pharmacological therapy. Only 21.2% (n = 255) of hypertensive patients measured their blood pressure daily, with a significantly higher proportion of men than women (28.6% vs. 12.7%, p = 0.011, Cohen’s d = 0.42). The most frequent users of blood pressure monitoring were in the 31–45 age group (p = 0.001, η² = 0.08). A total of 19.4% (n = 347) of respondents used wearable devices, and 6.3% (n = 113) used blood pressure monitors connected to an application. Smart technology use was significantly more common in the 31–45 age group (p = 0.01, Cramer’s V = 0.15). Moderate interest in telemedicine was expressed by 69.8% (n = 1247) of respondents, though only 27.4% (n = 490) showed strong interest. The majority of patients (73.8%, n = 1319) did not know their LDL-C levels, and 45.7% (n = 817) of those who did had elevated levels. Conclusions: The findings suggest that while interest in telemedicine methods for the management of arterial hypertension and dyslipidemia exists among Slovak patients, it is more moderate than initially assumed. Importantly, expressed willingness to participate in a study should not be directly equated with readiness to adopt new technologies in daily practice. Successful integration of telemonitoring into the Slovak healthcare system will therefore require not only patient engagement but also active support from healthcare providers to overcome practical and motivational barriers. These findings highlight the need for targeted implementation strategies that address the specific barriers identified in the Central and Eastern European healthcare context. Full article

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. In recent years, mortality rates have declined due to antismoking policies, earlier detection, and the advent of targeted therapies and immunotherapy, particularly for non-small cell lung cancer (NSCLC), which accounts for 85% of all cases. With improved survival, however, LC patients are increasingly exposed to competing causes of mortality, among which cardiovascular disease (CVD) is highly prevalent, affecting 30–50% of patients and contributing to nearly 30% of deaths. This burden reflects both shared risk factors and the cardiotoxic potential of radiotherapy, chemotherapy, and immunotherapy. Beyond acute adverse cardiovascular events during treatment, real-world data indicate that immune checkpoint inhibitors (ICIs) may also exert chronic cardiovascular effects, significantly accelerating the atherosclerotic process in multimorbid patients. These findings underscore the importance of accurate baseline assessment and aggressive management of cardiovascular risk factors in LC patients—particularly in the adjuvant and neoadjuvant settings, where longer survival is anticipated. Moreover, long-term monitoring should be implemented through a tailored, multiparametric strategy that integrates novel biomarkers and advanced artificial intelligence–assisted imaging techniques. Achieving this ambitious goal requires the close collaboration of a multidisciplinary team, with cardiologists playing a pivotal role. This review will address the complexity of LC patients, focusing on the interplay of cardio-immuno-metabolic factors, summarizing the cardiovascular impact of immunotherapy across metastatic, locally advanced, and perioperative settings, and outlining practical strategies for the management of these vulnerable patients. Full article

Heavy metal ions (HMIs) threaten ecosystems and human health due to their carcinogenicity, bioaccumulativity, and persistence, demanding highly sensitive, low-cost real-time detection. Electrochemical sensing technology has gained significant attention owing to its rapid response, high sensitivity, and low cost. Molybdenum disulfide (MoS₂), with its layered structure, tunable bandgap, and abundant edge active sites, demonstrates significant potential in the electrochemical detection of heavy metals. This review systematically summarizes the crystal structure characteristics of MoS₂, various preparation strategies, and their mechanisms for regulating electrochemical sensing performance. It particularly explores the cooperative effects of MoS₂ composites with other materials, which effectively enhance the sensitivity, selectivity, and detection limits of electrochemical sensors. Although MoS₂-based materials have made significant progress in theoretical and applied research, practical challenges remain, including fabrication process optimization, interference from complex-matrix ions, slow trace-metal enrichment kinetics, and stability issues in flexible devices. Future work should focus on developing efficient, low-cost synthesis methods, enhancing interference resistance through microfluidic and biomimetic recognition technologies, optimizing composite designs, resolving interfacial reaction dynamics via in situ characterization, and establishing structure–property relationship models using machine learning, ultimately promoting practical applications in environmental monitoring, food safety, and biomedical fields. Full article

Leaf nitrogen and chlorophyll, crucial crop status indicators, enable precision fertilization through rapid monitoring. This study investigated greenhouse tomatoes subjected to varying nitrogen concentrations in nutrient solutions. Hyperspectral data from leaves across ten nitrogen levels, different growth stages, and leaf positions were integrated with synchronously measured nitrogen and chlorophyll contents. The analysis systematically revealed differences in these indicators under nitrogen stress at various growth stages and leaf positions. The 12-step “coarse–fine–optimal” feature wavelength selection strategy was proposed to identify sensitive spectral bands. The PLSR model was established with a strong predictive performance. Using the optimal model, indicator values for each pixel were retrieved and visualized via pseudocolor imaging, illustrating the distribution of physiological parameters at different scales and growth stages, and aiding in the interpretation of nitrogen stress responses. This study provides a scientific basis for optimizing nitrogen fertilization strategies, contributing to improved tomato yield and quality, reduced environmental impact, and the sustainable development of facility-based agriculture. Full article

Data is an important resource for gaining knowledge about the behavior and condition monitoring of machines, enabling the estimation of parameters and the prediction of failures. However, in industrial environments, sensor interruptions often create gaps in the time series, which affects the reliability of the data. To overcome this challenge, this paper proposes an imputation strategy based on recurrent neural networks, in particular long short-term memory (LSTM) models, within a multivariate encoder–decoder architecture. This approach utilizes correlations between variables to reconstruct missing values, resulting in more complete and robust datasets. Experimental results with multivariate time series show that the proposed method achieves accurate imputation, with errors as low as RMSE = 2.33 and $R^2$ = 0.90 for some variables. Comparisons with alternative architectures, including GRU and Dense networks, show that LSTM excels in specific cases (e.g., VL3, $R^2$ = 0.45), while the Dense architecture provides more stable performance across most variables. In particular, the Dense model achieved the best overall balance between accuracy and robustness, reaching RMSE = 2.33 and $R^2$ = 0.90 for the best-performing variables, while the LSTM achieved the lowest error values in targeted scenarios, confirming its suitability for capturing complex temporal dependencies. Overall, this study highlights the feasibility of using recurrent neural networks to exploit temporal correlations for reliable data recovery, even under conditions of signal interruption in factory environments. Full article

Alexandrium spp., globally recognized as harmful algal bloom (HAB) species, pose severe threats to marine ecosystems, fisheries, and public health. Based on 469 occurrence records and 24 marine environmental variables, this study employed the Biomod2 ensemble modeling framework to predict the potential distribution of Alexandrium spp. under current and future climate scenarios, and to assess the role of key environmental factors and the spatiotemporal dynamics of habitat centroid shifts. The results revealed that (1) the ensemble model outperformed single models (AUC = 0.998, TSS = 0.977, Kappa = 0.978), providing higher robustness and reliability in prediction; (2) salinity range (bio18, 19.1%) and mean salinity (bio16, 5.8%) were the dominant factors, while minimum temperature (bio23) also showed strong constraints, indicating that salinity determines “whether persistence is possible,” while temperature influences “whether blooms occur”; (3) under present conditions, high-suitability habitats are concentrated in Bohai Bay, the Yangtze River estuary to the Fujian coast, and parts of Guangdong; (4) climate change is predicted to drive a southward shift of suitable habitats, with the most pronounced expansion under the high-emission scenario (RCP8.5), leading to the emergence of new high-risk areas in the South China coast and adjacent South China Sea; (5) centroid analysis further indicated a pronounced southward migration under RCP8.5 by 2100, highlighting a regional reconfiguration of ecological risks. Collectively, salinity and temperature are identified as the core drivers shaping the ecological niche of Alexandrium spp., and future warming is likely to exacerbate HAB risks in southern China. This study delineates key prevention regions and proposes a shift from reactive to proactive management strategies, providing scientific support for HAB monitoring and marine ecological security in China’s coastal waters. Full article

This paper reveals profound obstacles to sustainable supply chain integration in Industry X.0 in the United Arab Emirates (UAE) by utilizing a hybrid Fuzzy Delphi-TOPSIS approach and enriching the viewpoints of 102 experts in oil/gas (45%), logistics (30%), government (15%), and academia (10%). The top obstacles are a lack of favorable leadership (Fuzzy Delphi Threshold (FDT), FDT = 0.82) and insufficiency of sustainability professionals (FDT = 0.82), with strategy prioritization training (Rank 1, Closeness Coefficient Index (cci) cci = 0.1255) and employee engagement (Rank 2, cci = 0.1499) being among the most important solutions as opposed to technological solutions. Most importantly, AI-related technologies had a low ranking of seventh place because of their lack of implementation, which proves that human capital enhancement is always prioritized before technological adaptation. The oil/gas industry values AI with respect to regulatory compliance commitments to emissions monitoring, whereas SMEs accentuate the problem of training because of the limited resources available to them, which also indicates the societal relevance of the concept of AI to social entrepreneurship and the blockchain-based transparency and access to green technologies. This study contributes (1) a decision-oriented framework bridging the traditional 2050 vision of the UAE and the realities it faces day to day, (2) empirical insights into the need for cultural principals within governance so as to prevent the so-called paperwork syndrome, and (3) a theoretical advancement that sees AI as an enhancer of human-centric methodologies. The conclusions provide policymakers with knowledge of the importance of the ability to contextualize investments in organizational culture prior to technology implementation in order to provide effective sustainability transitions. Full article

This study quantified mercury (Hg) levels in the body hair of domestic and wild animals in four Brazilian states, Paraná, Mato Grosso do Sul, Goiás, and Minas Gerais, by analyzing 169 samples from sows, piglets, free-range pigs, and wild animals. The highest mean Hg concentration (274.93 ± 48.14 µg/kg) was found in wild animals in the Pantanal (MSSilvestre, Mato Grosso do Sul), followed by Minas Gerais (245.09 ± 40.27 µg/kg) and Paraná (193.0 ± 42.45 µg/kg). Levels at the GO, MGM, MSLiv, and PRV sites were significantly lower (p ≤ 0.05), according to the Scott–Knott test. Statistical analysis using ANOVA indicated significant variation in Hg levels between locations (F = 2.36; p ≤ 0.05), with homogeneity of variance (Levene’s test, p = 0.1772). Animals raised in confinement had lower levels than wild animals, which, due to extensive movement and contact with diverse environments, exhibited greater bioaccumulation. Lactating sows showed greater sensitivity than piglets, demonstrating an effect of animal category on metal absorption. The main sources of mercury are anthropogenic activities, such as mining and industrial processes, responsible for the environmental release of the metal. Although the detected levels do not pose an immediate risk to animal health or meat quality, they highlight the need for continuous monitoring, given mercury’s ability to bioaccumulate and affect ecosystems and food security. This work contributes to the understanding of environmental exposure to mercury in Brazil, reinforcing the urgency of effective mitigation strategies to preserve biodiversity and public health. Full article

This study assessed the concentrations and spatial patterns of heavy metals in fine particulate matter with aerodynamic diameter below 2.5 $\mu$m and coarse particulate matter with aerodynamic diameter below 10 $\mu$m in Santo Domingo, Dominican Republic, during 2022. Thirty 24 h samples were collected using portable low-volume samplers across representative urban environments. Elemental concentrations of arsenic, cadmium, chromium, copper, iron, manganese, nickel, lead, vanadium, and zinc were quantified by energy-dispersive X-ray fluorescence. To address data below detection limits, regression on order statistics was applied. Copper and zinc exhibited the highest mean concentrations, pointing to strong anthropogenic inputs, while vanadium and iron showed pronounced spatial variability. Principal component analysis identified traffic and industrial activities as dominant sources. These findings provide baseline evidence for heavy metal pollution in Caribbean urban air and emphasize the need for continuous monitoring and effective regulatory strategies to mitigate potential health risks. Full article