Search Results (7,627)

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

Ecological security evaluation serves as the cornerstone for ecological management decision-making and spatial optimization. This study focuses on the Dongping Lake Basin. Based on the Pressure–State–Response (PSR) model framework, it integrates ecological risk, ecosystem health, and ecosystem service indicators. Utilizing methods including Local Indicators of Spatial Association (LISA), Transition Matrix, and GeoDetector, it analyzes the spatio-temporal evolution characteristics and driving mechanisms of watershed ecological security from 2000 to 2020. The findings reveal that the Watershed Ecological Security Index (WESI) exhibited a trend of “fluctuating upward followed by periodic decline”. In 2000, the status was “relatively unsafe”. It peaked in 2015 (index 0.332, moderately safe) and experienced a slight decline by 2020. Spatially, a significantly clustered pattern of “higher in the north and lower in the south, higher in the east and lower in the west” was observed. In 2020, “High-High” clusters of ecological security aligned closely with Shandong Province’s ecological conservation red line, concentrating in core protected areas such as the foothills of the Taihang Mountains and Dongping Lake Wetland. Level transitions were characterized by “predominant continuous improvement in low levels alongside localized reverse fluctuations in middle and high levels,” with the “relatively unsafe” and “moderately safe” levels experiencing the largest transfer areas. Geographical detector analysis indicates that the Human Interference Index (HI), Ecosystem Service Value (ESV), and Annual Afforestation Area (AAA) were key drivers of watershed ecological security change, influenced by dynamic interactive effects among multiple factors. This study advances watershed-scale ecological security assessment methodologies. The revealed spatio-temporal patterns and driving mechanisms provide valuable insights for protecting the ecological barrier in the lower Yellow River and informing ecological security strategies within the Dongping Lake Watershed. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

Background: Empowerment during pregnancy is linked to improved maternal and infant health outcomes and greater maternal well-being. Group Antenatal Care (GANC), a participant-centered model of care, promotes empowerment, active engagement, and the deconstruction of hierarchy between participants and care providers. It combines health assessment, interactive learning, and community building. While empowerment is a core concept of GANC, the ways it manifests and the elements that facilitate it remain unclear. Method: We conducted a generic qualitative study across four organizations in Brussels, using multiple data collection methods. This included interviews with 13 participants and 21 observations of GANC sessions, combining both the insider and outsider perspective. An adapted version of the Pregnancy-Related Empowerment Scale (PRES) guided the interviews guide and thematic analysis. Results: We identified seven themes that capture how empowerment occurs in GANC: peer connectedness, provider connectedness, skillful decision-making, responsibility, sense of control, taking action, and gaining voice. Several aspects of GANC contribute to empowerment, particularly the role of facilitators. Conclusions: This study highlights how GANC enhances empowerment during pregnancy through interpersonal, internal, and external processes. Important components within GANC that support this process include the group-based format and the interactive nature of the discussions. The presence of skillful GANC facilitators is an essential prerequisite. In a diverse and often vulnerable context like Brussels, strengthening empowerment through GANC presents challenges but is especially crucial. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

Background/Objectives: Pancreatic cancer (PC) is a diagnosis with a poor prognosis which can be associated with significant distress and may hinder a patient’s ability to understand treatment details. Educating patients based on their learning preferences (LPs) and emotions may allow for personalized, enhanced care. Methods: This prospective project enrolled patients with non-metastatic PC. Phase 1 utilized the Learning Preference Barometer (LPB) and Emotional Journey Barometer (EJB), which are digital instruments co-designed by CANCER101 (C101) and the Health Collaboratory, to assess patient LPs and emotional states. Phase 2 provided information prescriptions aligned with LPs through C101’s Prescription to Learn^® (P2L) platform. Collected data included demographics, treatment, LPs (auditory, kinesthetic, linguistic, visual), patient engagement with P2L, and patient emotional states with qualitative verbal validation. Descriptive variables were used to report outcomes. Results: Primary LPs in the 47 participating patients were as follows: linguistic 45%, visual 34%, auditory 11%, and kinesthetic 9%, with secondary preferences in the majority (53%). Those patients (66%) who accessed P2L had linguistic and visual preferences; the majority accessed 1- 2 resources out of the 25 provided. Resources accessed aligned to 88% of patient LPs. The majority of patients (60%) initiated treatment prior to initial EJB, and 40% were treatment naive. Common baseline emotions were optimistic (47% vs. 36%, respectively), satisfied (11% vs. 25%), acceptance (11% vs. 11%), and overwhelmed (5% vs. 11%). Conclusions: Assessing LPs and emotional state allows for personalized patient education and clinical encounters for PC patients. Future work includes examining the effects of personalized approaches on patient satisfaction, decision-making, health outcomes, and the overall patient–clinician relationship. Full article

This study aimed at proposing a predictive model for real-time monitoring of epidemic dynamics at the municipal scale in Lombardy region, in northern Italy, leveraging Emergency Medical Services (EMS) dispatch data and Geographic Information Systems (GIS) methodologies. Unlike traditional epidemiological models that rely on official diagnoses and offer limited spatial granularity, our approach uses EMS call data (rapidly collected, geo-referenced, and unbiased by institutional delays) as an early proxy for outbreak detection. The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. Developed in collaboration with the Lombardy EMS agency (AREU), the system is designed for operational applicability, emphasizing simplicity, speed, and interpretability. Despite the complexity of the phenomenon and the use of a five-class output, the model shows promising predictive capacity, particularly for identifying outbreak-free areas. Performance is affected by changing epidemic dynamics, such as those induced by widespread vaccination, yet remains informative for early warning. The framework supports health decision-makers with timely, localized insights, offering a scalable tool for epidemic preparedness and response. Full article

Background/Objective: Executive functions (EFs) are essential in the daily management of arthritis, as they influence treatment adherence, decision-making, and the ability to cope with disease-related challenges. The objective of this study was to compare EFs alongside functional status and quality of life in patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA) and examine their associations with disease activity and clinical variables. Methods: In this cross-sectional study, 140 patients (70 RA, 70 PsA) were assessed using the Stroop-TBAG, Wisconsin Card Sorting Test (WCST), and Adult Executive Functioning Inventory (ADEXI). Functional status and quality of life were measured with the Health Assessment Questionnaire (HAQ) and WHOQOL-BREF, respectively. Correlations with disease activity (DAS28-CRP), age, and disease duration were examined. Results: RA patients had significantly higher disease activity and longer disease duration. They showed poorer performance on the Stroop Test (color–word time: 61.6 ± 14.8 vs. 52.4 ± 10.9 s, p < 0.001; errors: 3.2 ± 2.1 vs. 2.1 ± 1.5, p = 0.001), more WCST perseverative errors (p = 0.002), and higher ADEXI inhibition scores (13.9 ± 2.5 vs. 12.9 ± 3.0, p = 0.013). DAS28-CRP was correlated with EF impairments, disability, and poorer quality of life in RA (p < 0.05). In PsA, EFs remained relatively stable, although higher disease activity was associated with worse HAQ scores (p = 0.001). Treatment type was not linked to EF, but patients on combination therapy reported lower physical (p = 0.009) and psychological (p = 0.014) quality of life, along with higher HAQ scores (p = 0.016). Conclusions: This study revealed that patients with RA exhibit more pronounced executive dysfunction, along with lower ADL skills and quality of life compared to those with PsA. These findings highlight the need for multidimensional assessment strategies in inflammatory arthritis, especially in RA, where cognitive and functional outcomes are closely tied to clinical burden. Full article

This study assessed whether COVID-19 pandemic experiences were associated with excessive alcohol use during the first year of the pandemic in Iowa, a heavy-drinking midwestern US state. We analyzed survey data from 4047 adult residents of Iowa collected in August 2020, focusing on three pandemic-related stressors (e.g., emotional reactions to the pandemic; disruption of daily activities; and financial hardship) and salient social support. Using multiple logistic regression, we tested correlates of increased drinking, heavy drinking, and binge drinking, controlling for demographic characteristics and health status. We found that nearly half (47.6%) of respondents did not change their drinking compared to before the pandemic; however, 12.4% of respondents reported increasing their drinking and 5.3% reported decreasing their drinking. Emotional reactions to the pandemic and disruption of daily activities were associated with higher odds of increased drinking, and rurality was associated with lower odds of increased drinking. No pandemic-related stressor was associated with heavy or binge drinking, but social support was associated with lower odds of binge drinking. Thus, we concluded that some pandemic-related stressors may explain increased drinking but not heavy or binge drinking. Understanding the nuances of alcohol use can inform preventive interventions, policy decisions, and preparations for future catastrophic events. Full article

Inflammatory Bowel Diseases (IBDs) are complex, multifactorial disorders with no known cure, necessitating lifelong care and often leading to surgical interventions. This ongoing healthcare requirement, coupled with the increased use of biological drugs and rising disease prevalence, significantly increases the financial burden on the healthcare systems. Thus, a number of novel technological approaches have emerged in order to face some of the pivotal questions still associated with IBD. In navigating the intricate landscape of IBD, biosensors act as indispensable allies, bridging the gap between traditional diagnostic methods and the evolving demands of precision medicine. Continuous progress in biosensor technology holds the key to transformative breakthroughs in IBD management, offering more effective and patient-centric healthcare solutions considering the One Health Approach. Here, we will delve into the landscape of biomarkers utilized in the diagnosis, monitoring, and management of IBD. From well-established serological and fecal markers to emerging genetic and epigenetic markers, we will explore the role of these biomarkers in aiding clinical decision-making and predicting treatment response. Additionally, we will discuss the potential of novel biomarkers currently under investigation to further refine disease stratification and personalized therapeutic approaches in IBD. By elucidating the utility of biosensors across the spectrum of IBD care, we aim to highlight their importance as valuable tools in optimizing patient outcomes and reducing healthcare costs. Full article

Introduction: A validated clinical decision tool predictive of favorable functional outcomes following endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) remains elusive. We performed a retrospective case series of patients at our regional Comprehensive Stroke Center, over a four-year period, who have undergone EVT to elucidate patient characteristics and factors associated with a favorable functional outcome after EVT. Methods: We reviewed all cases of EVT at our institution between February 2018 and February 2022 in the extended time window from 6–24 h. Demographic, clinical, imaging, and procedure co-variates were included. A favorable clinical outcome was defined as a modified Rankin scale of 0–2. We included patients with M1 or internal carotid artery occlusion treated with EVT within 6–24 h after symptom onset. We used a univariate and multivariate logistic regression analysis to identify patient factors associated with a favorable clinical outcome at 90 days. Results: Our study included evaluation of 121 patients who underwent EVT at our comprehensive stroke center. Our analysis demonstrates that a higher recanalization score based on the modified Thrombolysis In Cerebral Infarction (mTICI) scale (2B-3) was a strong indicator of a favorable outcome (OR 7.33; CI 2.06–26.07; p = 0.0021). Our data also showed that a higher baseline National Institutes of Health Stroke Scale (NIHSS) score (p = 0.0095) and the presence of pre-existing hypertension (p = 0.0035) may also be predictors of an unfavorable outcome (mRS > 2) per our multivariate analysis. Conclusion: Patients without pre-existing hypertension had more favorable outcomes following EVT in the expanded time window. This is consistent with other multicenter data in the expanded time window that demonstrates greater odds of a poor outcome with elevated pre-, peri-, and post-endovascular-treatment blood pressure. Our data also demonstrate that the mTICI score is a strong predictor of favorable outcome, even after controlling for other variables. A lower baseline NIHSS at the time of thrombectomy may also indicate a favorable outcome. Furthermore, the presence of clinical or radiographic mismatch based on the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) and NIHSS per DAWN and DEFUSE-3 criteria did not emerge as a predictor of favorable outcome, which is congruent with recent randomized controlled trials and meta-analyses. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints. Full article

The accurate and effective prediction of PM2.5 concentrations is crucial for mitigating air pollution, improving environmental quality, and safeguarding public health. To address the challenge of strong temporal correlations in PM2.5 concentration forecasting, this paper proposes a novel hybrid model that integrates the Particle Optimization Algorithm (POA) and Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) network. First, POA is employed to optimize VMD by adaptively determining the optimal parameter combination [k, α], enabling the decomposition of the original PM2.5 time series into subcomponents while reducing data noise. Subsequently, an LSTM model is constructed to predict each subcomponent individually, and the predictions are aggregated to derive hourly PM2.5 concentration forecasts. Empirical analysis using datasets from Beijing, Tianjin, and Tangshan demonstrates the following key findings: (1) LSTM outperforms traditional machine learning models in time series forecasting. (2) The proposed model exhibits superior effectiveness and robustness, achieving optimal performance metrics (e.g., MAE: 0.7183, RMSE: 0.8807, MAPE: 4.01%, R²: 99.78%) in comparative experiments, as exemplified by the Beijing dataset. (3) The integration of POA with serial decomposition techniques effectively handles highly volatile and nonlinear data. This model provides a novel and reliable tool for PM2.5 concentration prediction, offering significant benefits for governmental decision-making and public awareness. Full article