Search Results (37,101)

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations. Full article

This study explores the interrelationship between green energy adoption, economic growth, and innovation in promoting sustainable development within OECD countries. Using a random forest regression model, the research analyzes secondary data from 2013 to 2022 to identify the most significant contributors to sustainable development. The random forest model was selected for its ability to handle non-linear relationships and feature importance ranking, providing a comprehensive understanding of the variables’ impacts. The analysis reveals that green energy adoption has the strongest influence on the human development index (HDI), with an importance score of 0.43, followed by gross domestic product (GDP) and the global innovation index (GII). These findings underscore the pivotal role of green energy adoption, amplified by economic growth and technological innovation, in advancing sustainable development. While the study focuses on OECD countries, the insights offer valuable implications for global sustainability initiatives. The evidence supports the argument that prioritizing green energy, supported by economic and innovative drivers, is crucial for achieving broader sustainable development goals. This research provides a methodological contribution by demonstrating the effectiveness of machine learning models in analyzing complex sustainability data and offers empirical evidence that informs policy and future research in a broader context. Full article

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers. Full article

This paper experimented with a methodology of machine learning modelling using virtual samples generated by fast CFD (Computational Fluid Dynamics) simulations in order to predict the greenhouse natural ventilation. However, the output natural ventilation rates using fast two-dimensional (2D) CFD models are not always consistent with the three-dimensional (3D) one for all the scenarios. The first contribution of this paper is a proposed comparative modelling methodology between two-dimensional and three-dimensional CFD studies, regarding its validity, especially when buildings are in rows. The results show that the error of the ventilation rate prediction could exceed 50%, if 2D models are not properly used. Subsequently, in those scenarios where the 2D and the 3D models had equal accuracy, nearly one thousand samples were generated using fast 2D CFD simulations to train a natural ventilation rate regression tree model. This model is efficient to deal with the combined effect of wind pressure and thermal gradients under various vent configurations, with only four necessary inputs. In addition, by analyzing the wind speed distribution contour of the outdoor wind field around the greenhouse rows, the optimal wind speed-measuring locations were determined to eliminate interference for predicting the natural ventilation rate. Full article

Objective: This study assesses the association of metabolic drugs (specifically hypoglycemic and hypolipemic agents) with cardiovascular diseases (CVD) among the Lebanese population and patients’ subgroups. Methods: A nationwide cross-sectional retrospective study was carried out in Lebanon. The survey collected information on sociodemographic characteristics, lifestyles, comorbidities, and medication use. Logistic regression models were employed to analyze the data and determine associations between CVD and metabolic drugs. Stratification analyses were performed based on diabetes and dyslipidemia status. Results: The study found significant associations with CVD among the 2048 participants. Higher scores on the Lebanese Mediterranean Diet Score (LMDS; ORa = 1.06), hypertension (ORa = 1.71), diabetes (ORa = 1.75), dyslipidemia (ORa = 1.89), family history of CVD (ORa = 1.58), and smoking (previous: ORa = 1.63, current: ORa = 2.15) were linked to increased CVD odds. Higher income (intermediate: ORa = 0.64, high: ORa = 0.40) was inversely related to it. A subsequent model that included hypoglycemic and lipid-lowering medications yielded similar results. However, neither hypoglycemic nor lipid-lowering medications demonstrated a significant association with CVD risk. A third regression model was conducted by taking the classes of drugs as an independent variable. Also, the result revealed that all the classes of medication were not associated with the risk of CVD. Stratification by diabetes revealed LMDS and hypertension as risk factors in both groups. Among non-diabetic participants, dyslipidemia (ORa = 2.40), current smoking (ORa = 2.28), and higher income (intermediate: ORa = 0.57, high: ORa = 0.62) were linked to CVD. Among people with diabetes, a family history of CVD (ORa = 2.69) increased the CVD odds, while being an employer (ORa = 0.49) lowered it. Stratification by dyslipidemia showed consistent risk factors, and higher LMDS (ORa = 1.07), diabetes (ORa = 2.14), hypertension (ORa = 1.79), and previous smoking (ORa = 1.95) were linked to CVD without dyslipidemia. Being a female (ORa = 0.52) and having a lower income (ORa = 0.40) were associated with lower CVD odds in those with dyslipidemia. Subgroup analyses showed that medications were not significantly associated with CVD odds among patients with diabetes or hyperlipidemia. Conclusions: This study’s findings highlight the importance of addressing modifiable risk factors and socioeconomic factors to reduce the burden of CVD. Targeted interventions and longitudinal research are necessary to optimize preventive strategies and improve the management of CVD in individuals using hypoglycemic and hypolipemic agents in low- and medium-income countries. Full article

Breast cancer survivorship is a recognized risk factor for sexual dysfunction, with various clinical, sociocultural, and psychological factors potentially interacting differently across populations. This study compared sexual dysfunction, anxiety, and depression between females with breast cancer and those without, aiming to identify associated factors. A total of 362 females participated, including 227 with sexual dysfunction and 135 controls. Among them, 195 are breast cancer survivors, while 167 have no personal history of cancer. Key variables were analyzed using Student’s t-test for quantitative data and Fisher’s exact test for categorical data, while logistic regression models were used to assess the association between sexual dysfunction and various factors. Multivariate analysis revealed that, in sexually active females, breast cancer survivorship increased the odds of sexual dysfunction 2.7-fold (95% CI: 1.17–6.49; p = 0.020). Anxiety was significantly associated with sexual dysfunction, regardless of cancer status (AdOR 6.00; 95% CI: 2.50–14.43; p < 0.001). The interaction between cancer survival and anxiety further increased the odds of sexual dysfunction by more than 11-fold (AdOR 11.55; 95% CI: 3.81–35.04; p < 0.001). Additionally, obesity was found to be a protective factor among cancer survivors (AdOR 0.149; 95% CI: 0.027–0.819; p = 0.029). In conclusion, breast cancer has a significant impact on sexual function, with psychological factors like anxiety playing a crucial role. Addressing these issues requires a holistic, patient-centered approach that considers the complex interplay of physical, emotional, and sociocultural factors. Full article

The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and reducing waste. However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based intrusion detection system (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS—Host IDS) or, in general, within the traffic of a network (NIDS—Network IDS). The proposed work performs a comparative analysis and an ablative study among recent machine learning-based NIDSs to develop a benchmark of the different proposed strategies. The proposed work compares both shallow learning algorithms, such as decision trees, random forests, Naïve Bayes, logistic regression, XGBoost, and support vector machines, and deep learning algorithms, such as DNNs, CNNs, and LSTM, whose approach is relatively new in the literature. Also, the ensembles are tested. The algorithms are evaluated on the KDD-99, NSL-KDD, UNSW-NB15, IoT-23, and UNB-CIC IoT 2023 datasets. The results show that the NIDS tools based on deep learning approaches achieve better performance in detecting network anomalies than shallow learning approaches, and ensembles outperform all the other models. Full article

Background: Chronic kidney disease patients undergoing hemodialysis (HD) are at a high risk of developing sarcopenia. This study aimed to validate the performance of ultrasound (US) measurements of the vastus lateralis (VL) for estimating muscle mass and diagnosing sarcopenia in CKD patients with HD. Methods: Forty-six patients were enrolled in this study. Muscle thickness (MT) and echo intensity (EI) of VL, physical performance, and biochemical markers were collected to establish a linear regression model for predicting appendicular skeletal muscle mass (ASM), using dual-energy X-ray absorptiometry (DXA) as the reference standard. The model’s performance was validated, and its diagnostic accuracy for sarcopenia was also evaluated. Results: An ASM prediction model was derived: −20.17 + 1.90 × MT_VL (cm) + 1.58 × male + 0.16 × Height (cm) + 0.09 × Weight (kg) + 0.05 × Age (year), with a standard estimated error of 1.44 kg and adjusted R-squared of 0.84. The model exhibited high correlation and an acceptable limit of agreement, compared to DXA measurement. EI displayed a negative correlation with ASM and MT. Conclusions: The ASM adjusted with BMI demonstrated superior performance in diagnosing sarcopenia compared to the ASM adjusted with height. Ultrasound provides a cost-effective bedside tool for evaluating muscle conditions in HD patients. Full article

We previously demonstrated that patients with metastatic unresectable stage IIIb–IV melanoma receiving cetirizine (a second-generation H1 antagonist antihistamine) premedication with immunotherapy had better outcomes than those not receiving cetirizine. In this retrospective study, we searched for a gene signature potentially predictive of the response to the addition of cetirizine to checkpoint inhibition (nivolumab or pembrolizumab with or without previous ipilimumab). Transcriptomic analysis showed that inducible T cell costimulator ligand (ICOSLG) expression directly correlated with the disease control rate (DCR) when detected with a loading value > 0.3. A multivariable logistic regression model showed a positive association between the DCR and ICOSLG expression for progression-free survival and overall survival. ICOSLG expression was associated with CD64, a specific marker of M1 macrophages, at baseline in the patient samples who received cetirizine concomitantly with checkpoint inhibitors, but this association was not present in subjects who had not received cetirizine. In conclusion, our results show that the clinical advantage of concomitant treatment with cetirizine during checkpoint inhibition in patients with malignant melanoma is associated with high ICOSLG expression, which could predict the response to immune checkpoint inhibitor blockade. Full article

Aim: Electronic prescribing has allowed for the collection of prescription data in real time in Greece for the first time. Hence, the aim of the current study was to present the characteristics of prescriptions for the Greek population during the period from 2015 to 2021. Methods: This retrospective study was based on data extracted from the nationwide Greek electronic prescription database between January 2015 and December 2021. Descriptive statistics methods were used for the needs of the study. As the basic figures examined depend on the size of the population, in order for the results to be comparable, we estimated the corresponding measures per inhabitant, using population data from the Greek Statistical Authority. Appropriate indicators for the comparison of consumption and expenditure over time were estimated. A study of the trend was also carried out using time series and linear regression models. In order to facilitate the design and implementation of specialized policies, it is useful to identify the drug categories with the highest consumption and expenditure, as well as the geographical areas that present similar characteristics. For the first, ABC analysis was used, which helps to identify the most popular categories of drugs, while for the second, cluster analysis was carried out. Agglomerative clustering was used to divide the regions into similar groups. This hierarchical clustering algorithm classifies the population into several clusters, with areas in the same cluster being more similar, and areas in different clusters being dissimilar. The Ward linkage method with Euclidean distance was used. Results: The analysis of prescription drug consumption and expenditure from 2015 to 2021 revealed significant fluctuations and trends across various drug categories, age groups, and geographical areas. Notably, the quantity of prescriptions increased by 20% since 2015, while expenditure surged by over 30%, with significant spikes following the end of the MoU in 2019 and the onset of the pandemic in 2020. In terms of expenditure, antineoplastic and immunomodulation agents (category L) held the largest share, driven by the introduction of new, costly drugs. The expenditure per inhabitant revealed gender and age disparities, with older populations, particularly women, incurring higher costs. Geographically, drug expenditure, and consumption varied significantly, with distinct regional clusters identified. These clusters, while showing some overlap in consumption and expenditure patterns, also highlighted unique regional characteristics. Conclusions: The insights into prescription drug consumption and expenditure trends offer a valuable basis for developing targeted interventions aimed at optimizing healthcare resource allocation. Moreover, the findings underscore the importance of addressing regional and demographic disparities in pharmaceutical use, thereby contributing to more equitable and cost-effective healthcare strategies. More specifically, the age distribution of prescriptions shows the increase in younger ages, which, as a result, anticipates the overall increase in prescriptions. The knowledge of the most convex categories of medicine, as well as the percentages of the use of generic drugs, shows where interventions should be made, with financial incentives and information through new information channels. The geographic disparities recorded should lead to policies that help the residents of hard-to-reach areas to access prescriptions. In addition, the present study provides a strategic framework for policymakers and healthcare managers to guide future studies and inform decision-making processes. Full article

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations. Full article

Monitoring the stability of tailings storage facilities (TSFs) is extremely important due to the catastrophic consequences of instability, which pose a threat to both the environment and human life. For this reason, numerous laboratory and field tests are carried out around dams. An extensive database is collected as part of monitoring and field research. The in-depth analysis of available data can help monitor stability and predict disaster hazards. One of the important factors is displacement, including surface displacements—recorded by benchmarks as well as underground displacements—recorded by inclinometers. In this work, methods were developed to help assess the stability of the TSF in terms of surface and underground displacement based on the simulated data from geodetic benchmarks. The context of spatial correlation was investigated using hot spot analysis, which shows areas of greater risk, indicating the places of correlation of large and small displacements. The analysis of displacements versus time allowed us to indicate the growing exponential trend, thanks to which it is possible to forecast the trend of future displacements, as well as their velocity and acceleration, with the coefficient of determination of the trend matching reaching even 0.97. Additionally, the use of a geographically weighted regression model was proposed to predict the risk of shear relative to surface displacements. Full article

Electrolyte balance is essential for the proper functioning of the body, and imbalances can lead to various health issues, some of which may be life-threatening. Therefore, measuring electrolyte concentrations is becoming increasingly important, particularly for athletes engaged in high-intensity and prolonged physical activity. In this project, we developed a highly sensitive sensing device capable of accurately and rapidly measuring electrolyte concentrations in mixed solutions, providing precise analysis of trace electrolyte levels. The sensor device requires no complex operational procedures and can quickly complete measurements, making it well-suited for point-of-care applications. Integration of regression models further enhances the device’s ability to estimate concentrations in mixed electrolyte solutions. The test results demonstrate that the device can detect subtle concentration variations, with a precision as low as $0.5$ mM. This proposed sensing device offers a cost-effective and efficient solution for real-time monitoring of electrolyte levels in healthcare. Full article

There is an unexplored knowledge gap regarding the relationship between soil quality and mean annual increment (MAI) in forest plantations in Tanzania. Therefore, this study aimed to identify soil quality indicators and their impact on the mean annual increment (MAI) of Pinus patula at Sao Hill (SHFP) and Shume forest plantations (SFP) in Tanzania. The forests were stratified into four site classes based on management records. Tree growth data were collected from 3 quadrat plots at each site, resulting in 12 plots in each plantation, while soil samples were taken from 0 to 40 cm soil depth. Analysis of variance examined the variation in soil quality indicators between site classes at two P. patula plantation sites. Covariance analysis assessed the differences in MAI and stand variables across various site classes, taking into account the differing ages of some stands, with stand age serving as a covariate. Linear regression models explored the relationship between soil quality indicators and MAI, while partial least squares regression predicted MAI using soil quality indicators. The results showed that, at SHFP, sand, organic carbon (OC), cation exchange capacity, calcium (Ca), magnesium (Mg), and available P varied significantly between site classes, while silt, clay, and available P varied significantly at SFP. At SHFP, sand and clay content were positively correlated with MAI, while at SFP, silt content, available P (Avail P), potassium (K), Ca, and Mg showed significant positive correlations. Soil quality indicators, including physical and chemical properties (porosity, clay percentages, sand content, and OC) and only chemical (K, Mg, Avail P, and soil pH) properties were better predictors of the forest mean annual increment at SHFP and SFP, respectively. This study underscores the importance of monitoring the quality of soils in enhancing MAI and developing soil management strategies for long-term sustainability in forests production. Full article

How do transformers model physics? Do transformers model systems with interpretable analytical solutions or do they create an “alien physics” that is difficult for humans to decipher? We have taken a step towards demystifying this larger puzzle by investigating the simple harmonic oscillator (SHO), $\ddot{x}+2\gamma \dot{x}+{\omega }_0^{2}x=0$, one of the most fundamental systems in physics. Our goal was to identify the methods transformers use to model the SHO, and to do so we hypothesized and evaluated possible methods by analyzing the encoding of these methods’ intermediates. We developed four criteria for the use of a method within the simple test bed of linear regression, where our method was $y=wx$ and our intermediate was w: (1) Can the intermediate be predicted from hidden states? (2) Is the intermediate’s encoding quality correlated with the model performance? (3) Can the majority of variance in hidden states be explained by the intermediate? (4) Can we intervene on hidden states to produce predictable outcomes? Armed with these two correlational (1,2), weak causal (3), and strong causal (4) criteria, we determined that transformers use known numerical methods to model the trajectories of the simple harmonic oscillator, specifically, the matrix exponential method. Our analysis framework can conveniently extend to high-dimensional linear systems and nonlinear systems, which we hope will help reveal the “world model” hidden in transformers. Full article