Relationship between profitability and financial factors of hospitals after a period of austerity and health care reforms: evidence from Greece.

PURPOSE: The reduction of government expenditure in the healthcare system, the difficulty of finding new sources of funding and the reduction in disposable income per capita are the most important problems of the healthcare system in Greece over the last decade. Therefore, studying the profitability of health structures is a crucial factor in making decisions about their solvency and corporate sustainability. The aim of this study is to investigate the effect of economic liquidity, debt and business size on profitability for the Greek general hospitals (GHs) during the period 2016-2018. DESIGN/METHODOLOGY/APPROACH: Financial statements (balance sheets and income statements) of 84 general hospitals (GHs), 52 public and 32 private, over a three-year period (2016-2018), were analyzed. Spearman's Rs correlation was carried out on two samples. FINDINGS: The results revealed that there is a positive relationship between the investigated determinants (liquidity, size) and profitability for both public and private GHs. It was also shown that debt has a negative effect on profitability only for private GHs. PRACTICAL IMPLICATIONS: Increasing the turnover of private hospitals through interventions such as expanding private health insurance and adopting modern financial management techniques in public hospitals would have a positive effect both on profitability and the efficient use of limited resources. ORIGINALITY/VALUE: These results, in conjunction with the findings of the low profitability of private hospitals and the excess liquidity of public hospitals, can shape the appropriate framework to guide hospital administrators and government policymakers.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

Introduction: To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Methods: Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. Results: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. Discussion: These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Effects of residual feed intake on the economic traits of fast-growing meat ducks.

Feed efficiency (FE) is a crucial economic indicator of meat duck production. The objective of this study was to assess the impact of residual feed intake (RFI), defined as the difference between the actual and expected feed intake based on animal's production and maintenance requirements, on the growth performance (GP), slaughter and internal organ characteristics of fast-growing meat ducks. In total, 1,300 healthy 14-day-old male fast-growing meat ducks were housed in individual cages until slaughter at the age of 35 d. The characteristics of the carcass and internal organs of 30 ducks with the highest RFI (HRFI) and the lowest RFI (LRFI) were respectively determined. RFI, the feed conversion ratio (FCR), and average day feed intake (ADFI) were significantly lower in the LRFI group than the HRFI group (P < 0.001), while there were no significant differences in marketing BW or BW gain (BWG) (P > 0.05). The thigh muscle and lean meat yields were higher, and the abdominal fat content was lower (P < 0.001) in the LRFI group, while there were no significant differences in other carcass traits between the groups (P > 0.05). The liver and gizzard yields were significantly higher (P < 0.001) in the LRFI group, while there were no significant differences (P > 0.05) in intestinal length between the groups. RFI was highly positively correlate with FCR and ADFI (P < 0.01), but negatively correlated the yields of thigh muscle, lean meat, liver, and gizzard, and positively correlated with abdominal fat content. These results indicate that selection for low RFI could improve the FE of fast-growing meat ducks without affecting the marketing BW and BWG, while increasing yields of thigh muscle and lean meat and reducing abdominal fat content. These findings offer useful insights into the biological processes that influence FE of fast-growing meat ducks.

Nutritional Quality Assessment of Miscellaneous Cassava Tubers Using Principal Component Analysis and Cluster Analysis.

Cassava is a staple crop in developing countries because its starchy roots provide essential dietary carbohydrates. The aim of this research was to conduct a comprehensive inquiry and scientific evaluation of the nutritional value of cassava tubers. Eight nutritional characteristics were examined in native and imported cassava variants: starch, reduced sugar, anthocyanins, protein, dietary fiber, quinic acid, vitamin C, and dry matter content. Principal component analysis (PCA) was conducted to minimize the dimensionality of the nutritional markers. A scientific assessment technique was developed to calculate a composite score for the various cassava samples. Analysis of the data revealed noticeable variance among the samples' nutritional indicators, suggesting varying degrees of association. Starch had a substantial positive link with lower sugar, protein, and dry matter content (p < 0.01). Anthocyanins and quinic acid interacted favorably (p < 0.05), and a positive link between protein and dry matter content was observed (p < 0.05); however, protein and dietary fiber interacted negatively (p < 0.05). The contribution rate of the top three PCA factors was over 76%, demonstrating that these factors incorporated the primary information acquired from the eight original nutritional indices, while maintaining excellent representativeness and impartiality. The experimental results showed a preliminary nutritional grade for 22 cassava tuber samples. The top five types were Guangxi Muci, Gui Cassava 4, Glutinous Rice Cassava, Huifeng 60, and Dongguan Hongwei. In the cluster analysis, the levels of similarity between the data showed that the 22 types of cassava tubers could be grouped into five categories, each with their own set of nutrients. This study promotes the directed breeding of cassava species and offers a theoretical foundation for creating and using various cassava varieties. Furthermore, this work lays the groundwork for a systematic and dependable technique for the quality assessment, comprehensive evaluation, and reasonable classification of cassava species and similar crops.

Evaluation of water resources security in Anhui Province based on GA-BP model.

Water resources security is an important cornerstone of regional sustainable development, but the current evaluation system of water resources security is not scientific, and the measurement of safety level has not been optimized by combining algorithms. In this paper, indicators are selected according to the actual situation in Anhui Province. Firstly, correlation analysis (CA) and principal component analysis (PCA) are used to reduce the dimensionality of indicators, and then, the scientific evaluation is carried out based on genetic algorithm optimized back propagation neural network (GA-BP). This paper improves the generalization ability of the evaluation model and overcomes the shortcomings of the traditional model, which is slow in convergence and easy to fall into local optimality. The results showed that the water resources security level showed an obvious improvement trend from 2006 to 2020 and stabilized at a relatively safe level from 2014 to 2020. The subsystem of water resources environmental security is the least secure, followed by the subsystem of social and economic security, and the security of water resources regulation and response is basically stable at a relatively safe level. The conclusion of this study can provide decision-making basis for the relevant research of government, society, and scientific community.

Assessment of the salt tolerance of diverse bread wheat (Triticum aestivum L.) genotypes during the early growth stage under hydroponic culture conditions.

Objectives: Soil salinity affects the growth of crop plants, leading to reduced productivity, and is a major challenge for wheat production worldwide. Various adaptations and mitigation approaches in combination with tolerant wheat genotypes can be useful for the sustainability of crop production in saline environments. However, the development of salt-tolerant wheat genotypes is one of the best and most efficient solutions for obtaining desirable yields. Considering these issues, an investigation was carried out under hydroponic nutrient culture conditions to assess the genetic variability and selection of salt-tolerant wheat genotypes by categorizing inequitable morphophysiological and genetic variability as well as multivariate analysis. Methods: To meet the objectives of this study, 100 wheat genotypes were tested hydroponically in 0 (control) and 15 dS m-1 salt solutions. Conclusion: For all the wheat genotypes grown under saline conditions, the shoot length (SL), root length (RL), shoot fresh weight (SFW), root fresh weight (RFW), total fresh weight (TFW), shoot dry weight (SDW), root dry weight (RDW), and total dry weight (TDW) decreased significantly. Furthermore, significant variation was observed among the genotypes in terms of their characteristics only under saline conditions. In the case of genetic diversity analysis, a high genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), genetic advance in the percentage of the mean (GAM) and high heritability (h2b) were recorded for all tested wheat genotypes based on the SDW, RDW and TDW. Correlation analysis for both genotypic and phenotypic relationships revealed strong positive correlations for TDW, SDW, TFW and SFW. Principal component analysis (PCA) revealed that TDW, TFW, SDW, and SFW were the most discriminative variables for the wheat genotypes, which was confirmed by discriminant function analysis (DFA). PCA-biplot analysis also revealed significant positive correlations between SDW and SFW and between TDW and TFW. Hierarchical cluster analysis was performed for ten clusters based on the relative performance of the genotypes, where the genotypes were characterized into salt-tolerant, medium-salt-tolerant, medium-salt-susceptible and salt-susceptible groups. Among the genotypes, G11, G25 and G29 under cluster VII were categorized as salt tolerant based on their outstanding performance in terms of characteristics only under saline conditions. D2 analysis proved that the wheat genotypes of this cluster were highly divergent from the other cluster genotypes; as a result, these genotypes might be utilized as parents in the development of salt-tolerant wheat genotypes. The current study concluded that SDW and TDW could be employed as criteria for selecting and defining salt-tolerant genotypes during the early growth stage of wheat.

[Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021].

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

Ammonia distribution and ecological risk assessment in nine fresh lakes in China.

With the development of industry and economy, ammonia nitrogen pollutions in surface water are of great concern worldwide. This study investigated the historical contents of total ammonia nitrogen (TAN) and unionized ammonia molecules (NH3) in nine fresh lakes in China during 2014-2022. Three different classification methods (flood season, season, and geographical distribution) were used to analyze the concentration variation of TAN and NH3. The concentration of TAN first decreased and then increased in the flood season, showing a lower concentration in summer and a higher concentration in winter. The variation trend of NH3 was in an opposite way with TAN. Correlation analysis between ammonia and 10 water quality parameters and 4 pollution emission and treatment parameters showed that the correlation coefficient between TAN and total phosphorus (total nitrogen) was 0.44 (0.43), respectively. The correlation coefficients between average annual TAN concentration and total emissions (waste water treatment input) were 0.35 (0.53), respectively. Combined with ecotoxicity data from a series of aquatic species, the ecological risks of TAN and NH3 in lakes were evaluated using hazard quotient and joint probability curve methods. From 2014 to 2022, the probability of 5% species affected in the acute ecological risk of TAN and NH3 is lower than 0.01, but for the chronic ecological risk of TAN and NH3, the probabilities of 5% species affected are 0.003-0.030 and 0.04-0.14, respectively. The chronic ecological risks were higher than the acute ecological risks, and high risks in plateau lakes like Dianchi Lake should be paid more attention to.

[Occurrence Characteristic and Risk Assessment of Microplastics in Sishui River (Xingyang Section)].

Urban rivers are the main receptors and transporters of microplastic pollution. Understanding the occurrence and environmental risk of microplastics in urban rivers can provide theoretical basis for further control of microplastic pollution. The Sishui River, a tributary of the Yellow River, was selected as the research object. A total of nine water samples were collected from sewage outlets of the Sishui River (Xingyang section). The microplastics in the collected samples were characterized by their sizes, shapes, and colors using a microscope. It was found that microplastics were mostly in the form of transparent fibers and fragments in the water body of sewage outlets, of which the size below 500 µm was relatively high. In addition, PET and PE polymers were identified as the main types using a laser infrared imager. The correlation analysis showed that there was a significant correlation between the PET and PE, indicating that they were similar in origin. The results of the environmental risk assessment showed that the type of microplastics was the main factor affecting the assessment results, whereas the risk values of six sewage samples containing PVC were high. However, the value of pollution load index revealed a low risk level of pollutants in the study area.

[Spatio-temporal variations of vegetation cover in semi-arid regions and its response to climate change: A case study of Xilin Gol, Inner Mongolia, China]. / åŠå¹²æ—±åŒºæ¤è¢«è¦†ç›–æ—¶ç©ºå˜åŒ–ç‰¹å¾åŠå ¶å¯¹æ°”å€™å˜åŒ–çš„å“åº”: 以锡林郭勒盟为例.

The quantitative analysis of spatio-temporal variations of vegetation cover and its correlation with climate are of great significance for understanding of ecological environment, ecological civilization construction, and sustainable development in semi-arid areas. We investigated the spatio-temporal variations of normalized difference vegetation index (NDVI) and its response to climate change during 2000-2020 in Xilin Gol, Inner Mongolia, by using trend analysis, regression analysis and partial correlation analysis based on the data of MODIS-NDVI, tempe-rature, precipitation, digital elevation model. The results showed that vegetation cover in Xilin Gol had been increased from 2000 to 2020, which generally included three phases, i.e., stable fluctuation, rapid growth, and steady growth. The mean NDVI showed a zonal increasing distribution from southwest to northeast, and had a strong correlation with elevation and population density in Xilin Gol region. The high values of NDVI were mainly in the east, with a significant increasing trend, and the low values were in the southwest, with a local degradation. The sensitivity of vegetation cover to climate change showed spatial and temporal variations. The spatial variation of vegetation was more sensitive to temperature and the interannual variation was sensitive to annual precipitation. In summary, vegetation cover improved overall in Xilin Gol, but there was degradation in some areas. We should formulate differentiated and precise vegetation restoration and ecological environmental protection policies.

Spatiotemporal evolution characteristics and driving forces of vegetation cover variations in the Chengdu-Chongqing region of China under the background of rapid urbanization.

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China's economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.

The influence of knowledge-intensive service industry agglomeration in the Yangtze River Delta urban agglomeration on regional economy.

Knowledge-intensive service industry has become an important part of social economy, which not only promotes the innovation of modern service industry, but also promotes the upgrading and transformation of manufacturing industry. The agglomeration of knowledge-intensive service industry is not only the inevitable result of economic growth, but also the premise of sustained economic growth. Moreover, China's national economy is transforming from an industrialized economy to a service-oriented and knowledge-based economy, and the importance of knowledge-intensive service industry is increasing day by day. This paper constructs a theoretical framework of the influence of population agglomeration on the economic development of urban agglomerations. The article holds that population agglomeration promotes the economic development of urban agglomerations through the upgrading of industrial structure and the promotion of human capital. In the future, we need to focus on the similarities and differences between population agglomeration and economic development of urban agglomerations in central China, explore the factors of economic development of urban agglomerations, and provide reasonable suggestions for the government to formulate relevant economic policies. This paper, from an innovative research perspective, based on the perspective of population agglomeration, studies the path of its role in regional economic development, which can make up for this defect to some extent. Moreover, the research object has been innovated and the research method has been improved. On the basis of the above, the panel data of the Yangtze River Delta from 2012 to 2021 is selected as the research sample, the location entropy index is taken as the explanatory variable, the regional gross domestic product (GDP) is taken as the explanatory variable, and the rationality of economic theory and the significance of econometric test are considered. Finally, the data are analyzed and tested by regression. The empirical analysis of the Yangtze River Delta urban agglomeration proves the importance of knowledge-intensive service industry agglomeration to regional economic development. Finally, based on the contribution of knowledge-intensive service industry agglomeration to regional economy, the development of knowledge-intensive service industry and the promotion of industrial agglomeration are studied.

Multi-model exploration of groundwater quality and potential health risk assessment in Jajpur district, Eastern India.

The presence of fluoride and nitrate is a serious groundwater quality issue in India impacting human health. In the present study, 14 different hydrochemical parameters for 76 groundwater samples collected from the Jajpur district of Odisha, India, were evaluated. Entropy-weighted water quality index (EWQI), fixed-weight groundwater quality index (GWQI), principal component analysis (PCA), and rotated factor loading-based water quality index (PCWQI) were employed to assess groundwater quality. About 65.79 ± 4.68%, 33.55 ± 3.95%, and 0.66 ± 0.76% of the samples were rated as "excellent," "good," or "medium" quality, respectively, across the four different water quality indices, with a nominal rating discrepancy of 13.15%. Though 86% of samples consistently received excellent or good ratings across all WQI frameworks, concentrations of F- and NO3- in 36.8% and 11.84% of the samples exceeded the WHO permissible limit. In health risk assessment, about 38.15% of samples surpassed the F- hazard quotient (HQ > 1) posing non-carcinogenic health risks for children. The non-carcinogenic health risks due to NO3- were evident in 55.26% and 11.84% of samples for children and adults, respectively. The higher concentration of NO3- in some of the water samples, together with its positive correlation with HCO3-, may worsen groundwater pollution. The moderate correlation between Ca2+ and HCO3- (r = 0.410) and the insignificant correlation between Mg2+ and HCO3- (r = 0.234) suggests calcite dissolution is far more common than dolomite.

Optimizing spatial patterns of ecosystem services in the Chang-Ji-Tu region (China) through Bayesian Belief Network and multi-scenario land use simulation.

To foster an ecological civilization and ensure sustainable development of population resources, a critical focus of China's land spatial planning initiatives is the complex interplay among the prudent utilization of regional natural resources, stable socio-economic growth, and ecological environment preservation and governance. This interplay is vital for improving the population's quality of life, enhancing national resilience, and fostering the development of an ecological civilization. Within this context, this research focuses on the Chang-Ji-Tu region, employing a hybrid InVEST-Bayesian Belief Network-PLUS model. This approach facilitates a comprehensive assessment of habitat quality (HQ), carbon sequestration (CS), soil conservation (SC), crop production (CP), and total ecosystem services (TES) spanning 2005 to 2020, to optimize spatial structures based on these assessments. The findings indicate significant insights: (1) temporally, both HQ and CS exhibit an initial ascent followed by a subsequent decline, while SC demonstrates a rise, subsequent decrease, and then a gradual increase. CP shows a consistent increase, and TES initially decreases before experiencing a rise. Spatially, regions exhibiting high CP are primarily located in the northwest, in contrast to the southeast, demonstrating lower values. Conversely, HQ, CS, and SC exhibit lower values in the northwest and higher values in the southeast. TES exhibits considerable variability in the northwest and a more equilibrated distribution in the southeast. (2) A positive correlation is observed between land use/cover changes (LUCC) and HQ, CS, and SC, while a negative correlation is noted with CP and TES. (3) In exploring potential scenarios for economic development (ED), natural development (ND), and ecological protection (EP) in 2035, our study categorizes TES into distinct zones: ecological protection prohibited zones, ecological buffer prohibited zones, construction and development suitable zones, and restricted zones for arable land protection. This study, grounded in the current ecosystem status, thoroughly analyzes spatial structural optimization, offering crucial insights for future land spatial planning and ecological restoration.

Multimodal neuroimaging correlates of physical-cognitive covariation in Chilean adolescents. The Cogni-Action Project.

Health-related behaviours have been related to brain structural features. In developing settings, such as Latin America, high social inequality has been inversely associated with several health-related behaviours affecting brain development. Understanding the relationship between health behaviours and brain structure in such settings is particularly important during adolescence when critical habits are acquired and ingrained. In this cross-sectional study, we carry out a multimodal analysis identifying a brain region associated with health-related behaviours (i.e., adiposity, fitness, sleep problems and others) and cognitive/academic performance, independent of socioeconomic status in a large sample of Chilean adolescents. Our findings suggest that the relationship between health behaviours and cognitive/academic performance involves a particular brain phenotype that could play a mediator role. These findings fill a significant gap in the literature, which has largely focused on developed countries and raise the possibility of promoting healthy behaviours in adolescence as a means to influence brain structure and thereby cognitive/academic achievement, independently of socioeconomic factors. By highlighting the potential impact on brain structure and cognitive/academic achievement, policymakers could design interventions that are more effective in reducing health disparities in developing countries.

Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP).

Nagaon is one of the highly flood-prone districts of Assam, India that recurrently experiences devastating floods resulting in the loss of lives and property and wreaking havoc on the district's socioeconomic infrastructure. Identification and mapping of spatial patterns of flood hazards, flood vulnerability, and flood risk zones (FRZs) of the district are, therefore, crucial for flood management and mitigation. The present study, therefore, attempts to delineate the FRZs of more than 930 villages in the Nagaon district by integrating the flood hazard and vulnerability layers in the geospatial environment using the multi-criteria decision analysis and analytical hierarchy process techniques. Here, seven flood hazard and vulnerability indicators are considered to derive each layer separately. The results indicate that about 15.14% of the district's total villages are in the very high FRZ, 27.93% in the high, 46.62% in the moderate, and 10.3% in the low FRZ. Further, bivariate correlation analysis is used to evaluate the results with the percentages of the population, cropland, and animals affected by floods at different temporal scales in order to ensure that the revenue circles with a higher percentage of area under high and very high FRZs genuinely have higher percentages of flood-affected cropland, people, and livestock. The significance of this research is evident in its pragmatic findings that could aid the stakeholders in managing and reducing flood risk at micro-spatial scales.

Effects of 3-month dapagliflozin on left atrial function in treatment-naïve patients with type 2 diabetes mellitus: Assessment using 4-dimensional echocardiography.

BACKGROUND: The sodium-glucose transporter-2 (SGLT-2) inhibitor dapagliflozin can improve left ventricular (LV) performance in patients with type 2 diabetes mellitus (T2DM). However, the effects on left atrial (LA) function in treatment-naïve T2DM patients remain unclear. The aim of our study was 1) to investigate the effects of 3-month treatment with dapagliflozin on LA function in treatment-naïve patients with T2DM using 4-dimensional automated LA quantification (4D Auto LAQ) and 2) to explore linked covariation patterns of changes in clinical and LA echocardiographic variables. METHODS: 4D Auto LAQ was used to evaluate LA volumes, longitudinal and circumferential strains in treatment-naïve T2DM patients at baseline, at follow-up, and in healthy control (HC). Sparse canonical correlation analysis (sCCA) was performed to capture the linked covariation patterns between changes in clinical and LA echocardiographic variables within the treatment-naïve T2DM patient group. RESULTS: This study finally included 61 treatment-naïve patients with T2DM without cardiovascular disease and 39 healthy controls (HC). Treatment-naïve T2DM patients showed reduced LA reservoir and conduit function at baseline compared to HC, independent of age, sex, BMI, and blood pressure (LASr: 21.11 ± 5.39 vs. 27.08 ± 5.31 %, padjusted = 0.017; LAScd: -11.51 ± 4.48 vs. -16.74 ± 4.51 %, padjusted = 0.013). After 3-month treatment with dapagliflozin, T2DM patients had significant improvements in LA reservoir and conduit function independent of BMI and blood pressure changes (LASr: 21.11 ± 5.39 vs. 23.84 ± 5.74 %, padjusted < 0.001; LAScd: -11.51 ± 4.48 vs. -12.75 ± 4.70 %, padjusted < 0.001). The clinical and LA echocardiographic parameters showed significant covariation (r = 0.562, p = 0.039). In the clinical dataset, changes in heart rate, insulin, and BMI were most associated with the LA echocardiographic variate. In the LA echocardiographic dataset, changes in LAScd, LASr, and LASr_c were most associated with the clinical variate. CONCLUSION: Compared with HC, treatment-naïve patients with T2DM had lower LA function, and these patients benefited from dapagliflozin administration, particularly in LA function.

Assessment of farmers' knowledge and perceptions of coffee yield reduction due to weeds and their management in Ethiopia.

The home of Coffea arabica is in Ethiopia, where it has high genetic diversity and suitable growing conditions; unfortunately, the national average yields of coffee remain low due to no technical advancements and diverse, complex biotic and abiotic constraints. Hence, this study was conducted in eight major coffee-growing zones of Ethiopia to assess farmers' knowledge and perceptions of coffee yield reduction due to weeds and the farmers' weed control practices. A purposive and random sampling technique was used to generate primary data from coffee growers (N = 320) using a semi-structured questionnaire. Quantitative data were analyzed using a three-stage nested design, and the dependent and independent variables data were subjected to canonical correlation analysis. This study revealed variation in coffee yield (t ha-1) among the assessed areas based on farmers' knowledge of estimating coffee yield. The average yield level ha-1 was very low (0.37 t ha-1) and different among the surveyed areas. The average coffee yield gap as compared to the current national level (0.64 t ha-1) was observed to be 42%, and this low yield was highly correlated with weed infestation (r = 0.879) and type of weeds r = -0.528). This investigation indicated a single factor or association of different factors contributing to the low yield level of coffee in the study areas. Thus, it is concluded that predictor variables accounting for the low yield levels need to be considered when planning future strategies to attain the yield potential of C. arabica in Ethiopia.

Assessment of suitable cultivation region for Pepino (Solanum muricatum) under different climatic conditions using the MaxEnt model and adaptability in the Qinghai-Tibet plateau.

Pepino (Solanum muricatum), a member of the Solanaceae family originating from South America, is cultivated globally. However, the cultivation range and suitable habitat of Pepino have not been extensively studied, which hampers the further development of its cultivation industry. Therefore, we aimed at enrich and expand the planting scope of Pepino. Currently, the main cultivation areas of Pepino in China are the Yunnan-Guizhou Plateau and the Loess Plateau, where the altitude is above 1000 m. In this study, ArcGIS combined with the MaxEnt model was used for prediction, whose area under curve value was 0.949. The main climatic factors affecting the distribution of Pepino are temperature seasonality, annual means temperature, mean temperature of the coldest quarter, elevation, isothermality, and the climate factors, and their cumulative contribution rate of 87.6%. Pepino's main potential distribution areas are located in Yunnan-Guizhou Plateau, Yunnan Province, Hexi Corridor of Loess Plateau, and low altitude areas of Qinghai-Tibet Plateau. The main distribution ranges from 1000 to 2000 m above sea level, and the total suitable area accounts for 20.09% of China's total land area. The prediction results reveal an expanded potential area for Pepino, with no significant migration in the central region of the main potential distribution area by 2050 and 2070. No studies have been conducted on the open-area cultivation of Pepino in northern China. Our findings revealed that the yield and quality in the four experimental sites and final actual cultivation conditions were consistent with the predicted results of MaxEnt. The yiel d per plant in Xunhua and Minhe was significantly different from that in Xining, which was low, and that in Minhe was the highest. Overall, the fruit quality in the Xining region was the lowest among the three regions, which was related to the climatic differences in each region. These results align with the predicted outcomes, indicating that Xining is the least suitable area. Further, these data verify the accuracy of the prediction results. The climate data of the four regions were analyzed simultaneously to elucidate the influence of different climate conditions on the growth of Pepino. Our findings are of considerable significance for introducing characteristic horticultural crops in the Qinghai-Tibet Plateau and using the MaxEnt model to predict the cultivation range of crops.

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods.

This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.