Search Results (506)

The COVID-19 pandemic has had profound impact, necessitating a deeper understanding of factors influencing virus transmission. The negative impacts have weakened the economy and changed billions of lives around the world. COVID-19 is a new virus, and a lot of studies have tried to investigate its effect on, for example, the economy or environment. This research reveals new approaches to recognizing and stopping the spread of this virus with its connection to weather conditions and relevant parameters. By analyzing how temperature and humidity affect COVID-19 spread, alongside evaluating the effectiveness of initial public policies, this study addresses the critical gap in research by investigating the interplay between climate conditions and government regulations during the early stages of the pandemic in South Korea. This dual approach provides a comprehensive framework for understanding how environmental and policy factors jointly influence pandemic dynamics, offering valuable lessons for future global health crises. Although it focuses only on the first phase of South Korea COVID-19 regulations, outcomes show that these regulations were notably effective against the COVID-19 pandemic. The outcomes prove that higher temperature and higher relative humidity lead to lower transmission. Hence, based on the results during winter, the number of infections would be expected to speed up again. Full article

Maize is a staple crop important for food security that millions globally depend upon as an energy source, primarily due to its high starch and fat content. For growth and disease resistance, maize production requires a balanced intake of essential nutrients, including nitrogen (N), phosphorus (P) and potassium (K). This study investigated the relationship between soil nutrient levels and maize disease occurrences in the Mopani District Municipality, Limpopo Province, South Africa. Soil and maize leaves were collected using a systematic sampling approach. Grids of 10 × 10 m were created, covering a maize field. Forty soil samples were collected a day before the planting date and sent to the laboratory for analysis of N, P and K. During the tasseling stage of the maize plant, 40 maize leaf samples were collected and sent to the laboratory for disease identification. Maize leaves were classified as healthy, southern corn leaf blight (Bipolaris maydis), northern corn leaf blight (Exserohilum turcicum), maize streak disease (Maize streak virus), nitrogen-deficient or phosphorus-deficient. Generalized Linear Models (GLMs) with a corrected Akaike Information Criterion (AICc) showed a significant relationship between low soil nutrient levels of N, P and K and maize disease occurrence (p < 0.0001). The interaction of the N*P*K model had the lowest AIC value (AICc = 28.53), indicating the necessity of considering synergistic effects in maize disease management. All the model performances had a delta AICc = 0. These findings highlight the significance of comprehensive soil management strategies in enhancing the disease resistance, well-being and yields of maize crops. Full article

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts. Full article

Background: Out-of-hospital cardiac arrest (OHCA) is associated with high mortality and cerebral disability in survivors. Current models of risk prediction and survival are mainly based on resuscitation duration. We examined the prognostic value of circulating biomarkers in predicting mortality and severe cerebral disability for OHCA survivors, alongside traditional clinical risk indicators. Methods: Biomarkers including BNP, troponin I, and galectin-3 were measured at hospital admission in resuscitated OHCA patients. Prognostic significance for mortality and cerebral disability involving circulating biomarkers, resuscitation duration, demographics, and laboratory and clinical characteristics was examined via univariate and multivariate Cox proportional hazards regression models. The incremental prognostic value of the index covariates was examined through model diagnostics, focusing on the Akaike information criterion (AIC) and Harrell’s concordance statistic (c-statistic). Results: In a combinatorial analysis of 144 OHCA survivors (median follow-up 5.7 years (IQR 2.9–6.6)), BNP, galectin-3, arterial pH, and resuscitation time were significant predictors of all-cause death and severe cerebral disability, whereas troponin I levels were not. Multivariate regression, adjusting for BNP, arterial pH, and resuscitation time, identified galectin-3 as an independent predictor of long-term mortality. Multiple linear regression models also confirmed galectin-3 as the strongest predictor of cerebral disability. The incorporation of galectin-3 into models for predicting mortality and cerebral disability enhanced fit and discrimination, demonstrating the incremental value of galectin-3 beyond traditional risk predictors. Conclusions: Galectin-3 is a significant, independent long-term risk predictor of cerebral disability and mortality in OHCA survivors. Incorporating galectin-3 into current risk stratification models may enhance early prognostication and guide targeted clinical interventions. Full article

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed. Full article

The Harrist Church in Ivory Coast, West Africa, emerged from the ministry of Liberian William Wadé Harris who baptized between 10,000 and 200,000 people during his eighteen-month evangelistic tour, 1913–1915. This story is full of linguistic complexities and anomalies. Harris himself spoke only English and his own local Liberian Glebo language. He was therefore compelled to work through expatriate English-speaking merchants, knowledgeable of and conversant in local languages, as interpreters and translators in addressing the twelve ethnic groups who heard and accepted his message. Harris encouraged new converts to compose hymns in their own indigenous languages by transforming musical genres embedded in their local musical traditions. Additionally fascinating is that during this early colonial period, the twelve ethnic groups impacted by Harris’s ministry lived in almost total isolation from each other and developed their own hymn traditions for thirty-five years (1914–1949), unaware of the existence of churches and worship patterns in neighboring ethnic districts. Only in 1949 did they suddenly become acquainted with the broader, multi-musical, multilingual reality of the Harrist movement. Since then, individual musicians and choirs from local congregations have gradually begun to sing a few of each other’s songs, though the challenge of becoming a truly multicultural, multiethnic church remains a work in progress. Documentation of these developments include written colonial and early Protestant and Catholic missionary sources and a large number of eye-witness interviews. Primary research methods employed here come from four intersecting disciplines and theoretical frameworks: orality studies, with particular focus on oral sources in constructing historical narrative; religious phenomenology; mission history; and ethnodoxological research. Full article

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility. Full article

Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R² = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity. Full article

Many expectant mothers use CBD to alleviate symptoms like nausea, insomnia, anxiety, and pain, despite limited research on its long-term effects. However, CBD passes through the placenta, affecting fetal development and impacting offspring behavior. We investigated how prenatal CBD exposure affects the insular cortex (IC), a brain region involved in emotional processing and linked to psychiatric disorders. The IC is divided into two territories: the anterior IC (aIC), processing socioemotional signals, and the posterior IC (pIC), specializing in interoception and pain perception. Pyramidal neurons in the aIC and pIC exhibit sex-specific electrophysiological properties, including variations in excitability and the excitatory/inhibitory balance. We investigated IC’s cellular properties and synaptic strength in the offspring of both sexes from mice exposed to low-dose CBD during gestation (E5–E18; 3 mg/kg, s.c.). Prenatal CBD exposure induced sex-specific and territory-specific changes in the active and passive membrane properties, as well as intrinsic excitability and the excitatory/inhibitory balance, in the IC of adult offspring. The data indicate that in utero CBD exposure disrupts IC neuronal development, leading to a loss of functional distinction between IC territories. These findings may have significant implications for understanding the effects of CBD on emotional behaviors in offspring. Full article

The composition of high rockfill dam materials is complex, and the mechanical parameters are uncertain and correlated in unknown ways due to the influences of the environment and construction, leading to complex deformation mechanisms in the dam–foundation system. Statistical characteristics of material parameters are the basis for deformation and stress analysis of high core rockfill dams, and using an inaccurate distribution model may result in erroneous analysis results. Furthermore, empirically evaluated distribution types of parameters are susceptible to the influence of small sample sizes, which are common in the statistics of geotechnical engineering. Therefore, proposing a multi-dimensional joint distribution model for parameters based on small-sample data is of great importance. This study determined the interval estimation values of Duncan–Chang E-B model parameters—such as the mean value and coefficient of variation for the core wall, rockfill, and overburden materials—using parameter statistical analysis, bootstrap sampling methods, and Akaike information criterion (AIC) optimization. Additionally, the marginal distribution types of each parameter were identified. Subsequently, a multi-dimensional joint distribution model for Duncan–Chang model parameters was constructed based on the multi-dimensional nonlinear correlation analysis of parameters and the Copula function theory. The application results for the PB dam demonstrate that joint sampling can effectively reflect the inherent correlation laws of material parameters, and that the results for stress and deformation are reasonable, leading to a sound evaluation of the cracking risk in the core wall of high core rockfill dams. Full article

The purpose of this study is to investigate the role of AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims to investigate the mediating roles of OC and GI, as well as the moderating role of knowledge sharing culture (KNC). This study used quantitative methodology and utilized a survey to collect data from 421 employees in different organizations in Bangladesh. We used the structural equation modeling (SEM) technique to analyze the data. This study finds that AI capability significantly influences OC, GI, and SP. OC and GI work as mediators, and KNC serves as a moderator among the suggested relationships. This study is notable for its novelty in examining multiple unexplored aspects in the current body of research. This research also provides valuable insights for policymakers and practitioners regarding the effective integration of AI to enhance organizational competitiveness. Full article

This article presents an overall examination of how small temperature fluctuations affect P-wave velocity (V_p) measurements and their uncertainties in concrete using embedded piezoelectric transducers. This study highlights the fabrication of custom transducers tailored for long-term concrete monitoring. Accurate and reliable estimation of ultrasonic wave velocities is challenging, since they can be impacted by multiple experimental and environmental factors. In this work, a reliable methodology incorporating correction models is introduced for the quantification of uncertainties in ultrasonic absolute and relative velocity measurements. The study identifies significant influence quantities and suggests uncertainty estimation laws, enhancing measurement accuracy. Determining the onset time of the signal is very time-consuming if the onset is picked manually. After testing various methods to pinpoint the onset time, we selected the Akaike Information Criterion (AIC) due to its ability to produce sufficiently reliable results. Then, signal correlation was used to determine the influence of temperature (20 °C to 40 °C) on V_p in different concrete samples. This technique proved effective in evaluating velocity changes, revealing a persistent velocity decrease with temperature increases for various concrete compositions. The study demonstrated the capability of ultrasonic measurements to detect small variations in the state of concrete under the influence of environmental variables like temperature, underlining the importance of incorporating all influencing factors. Full article

Artepillin C, drupanin, and plicatin B are prenylated phenylpropanoids that naturally occur in Brazilian green propolis. In this study, these compounds and eleven of their derivatives were synthesized and evaluated for their in vitro antimicrobial activity against a representative panel of oral bacteria in terms of their minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values. Plicatin B (2) and its hydrogenated derivative 8 (2′,3′,7,8-tetrahydro-plicatin B) were the most active compounds. Plicatin B (2) displayed strong activity against all the bacteria tested, with an MIC of 31.2 μg/mL against Streptococcus mutans, S. sanguinis, and S. mitis. On the other hand, compound 8 displayed strong activity against S. mutans, S. salivarius, S. sobrinus, Lactobacillus paracasei (MIC = 62.5 μg/mL), and S. mitis (MIC = 31.2 μg/mL), as well as moderate activity against Enterococcus faecalis and S. sanguinis (MIC = 125 μg/mL). Compounds 2 and 8 displayed bactericidal effects (MBC: MIC ≤ 4) against all the tested bacteria. In silico studies showed that the complexes formed by compounds 2 and 8 with the S. mitis, S. sanguinis, and S. mutans targets (3LE0, 4N82, and 3AIC, respectively) had energy score values similar to those of the native S. mitis, S. sanguinis, and S. mutans ligands due to the formation of strong hydrogen bonds. Moreover, all the estimated physicochemical parameters satisfied the drug-likeness criteria without violating the Lipinski, Veber, and Egan rules, so these compounds are not expected to cause problems with oral bioavailability and pharmacokinetics. Compounds 2 and 8 also had suitable ADMET parameters, as the online server pkCSM calculates. These results make compounds 2 and 8 good candidates as antibacterial agents against oral bacteria. Full article

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targeted Ahmedabad city in India and employed the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using an augmented Dickey–Fuller test, and the Akaike information criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and prediction accuracy. The model achieved an RMSE of 1.0265, indicating a high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model’s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model’s applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further. Full article

This study analyzes drought conditions in the Aegean region using monthly precipitation data from nine stations between 1972 and 2020. The Standardized Precipitation Index (SPI) was calculated for 1-, 3-, 6-, 9-, and 12-month periods to evaluate drought conditions at different timescales and station-specific conditions. The results indicate that short-term droughts are more frequent but shorter in duration, while longer periods exhibit fewer but more prolonged droughts. The relative frequency of drought across all periods ranges between 9% and 27%. The İzmir and Denizli stations were highlighted due to their representation of coastal and inner regions, respectively. The findings show that coastal stations, like İzmir, experience more frequent wet years compared to inner stations like Denizli, which have more dry years. Time series linear autoregressive (AR) models, using SPI-12 data, were developed to represent long-term drought trends and forecasts. The best-fitting models were determined using AIC, AICC, FPE, and Var(e) criteria, with AR(2) generally being the most suitable, except for Denizli. This integrated analysis of SPI and AR models provides a robust basis for understanding regional precipitation regimes and predicting future droughts, aiding in the development of effective drought mitigation strategies and water resource management. Future research is anticipated to extend this analysis to encompass all of Turkey and explore various time series models’ applicability. Full article