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•Vanadium containing polyoxometalate was supported on polyvinylalcohol (PVA) via sol–gel method in mild temperature.•Catalytic activity tested on ODS of actual gasoline and model ...sulfur compounds.•(N(tBu)4)4HPW10V2O40-PVA nanocomposite was shown be able to scavenge mercaptans (with high yield) in gasoline by H2O2 as an oxidant.•Activity of nanocomposite is much higher than unsupported polyoxometalates.
In this manuscript, phosphotungestovanadate (N(tBu)4) 4HPW10V2O40 (PWV) as a Keggin-type polyoxometalate was synthesized and immobilized on poly vinyl alcohol (PVA) via sol–gel method. The materials characterized by FT-IR, XRD, UV–vis, SEM and 31P NMR spectroscopy. Catalytic activity of synthesized nanocomposite was tested on oxidative desulphurization of actual gasoline and results are compared with that of model sulphur compounds. This Keggin-type supported catalyst was shown to be able to have oxidative desulphurization of model sulphur compounds and actual gasoline with high yield. The addition of acetic acid enhanced the conversion. The advantages of this method lie in its mild condition, low cost, large scale, simplicity and environmentally friendly route.
Abstract Ensuring the security of China’s rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that ...integrates multi-source data, including climate, remote sensing, soil properties and agricultural statistics from 2000 to 2017. The research evaluates six artificial intelligence (AI) models including machine learning (ML), deep learning (DL) models and their hybridization to predict rice production across China, particularly focusing on the main rice cultivation areas. These models were random forest (RF), extreme gradient boosting (XGB), conventional neural network (CNN) and long short-term memory (LSTM), and the hybridization of RF with XGB and CNN with LSTM based on eleven combinations (scenarios) of input variables. The main results identify that hybrid models have performed better than single models. As well, the best scenario was recorded in scenarios 8 (soil variables and sown area) and 11 (all variables) based on the RF-XGB by decreasing the root mean square error (RMSE) by 38% and 31% respectively. Further, in both scenarios, RF-XGB generated a high correlation coefficient (R 2 ) of 0.97 in comparison with other developed models. Moreover, the soil properties contribute as the predominant factors influencing rice production, exerting an 87% and 53% impact in east and southeast China, respectively. Additionally, it observes a yearly increase of 0.16 °C and 0.19 °C in maximum and minimum temperatures (T max and T min ), coupled with a 20 mm/year decrease in precipitation decline a 2.23% reduction in rice production as average during the study period in southeast China region. This research provides valuable insights into the dynamic interplay of environmental factors affecting China’s rice production, informing strategic measures to enhance food security in the face of evolving climatic conditions.
Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for ...drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980-2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.
The coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for ...water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradient boosting (XGB) and random forest-extreme gradient boosting (XGB-RF) were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest and lowest temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout based on four scenarios (input combinations). The main findings were; the highest CU value was 86.7% in the square system of 2520 sprinkler under 200 kPa, 0.5 m height and 0.855 m3/h (Nozzle 2.5 mm). Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge and 1 m height. For applied machine learning, the highest values of R2 were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R2 were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the sprinkler height had the lowest impact on modeling of the water distribution uniformity.
Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four ...machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield.
Triple-negative breast cancer (TNBC) subtype is among the most aggressive cancers with the worst prognosis and least therapeutic targetability while being more likely to spread and recur. Cancer ...transformations profoundly alter cellular metabolism by increasing glucose consumption via glycolysis to support tumorigenesis. Here we confirm that relative to ER-positive cells (MCF7), TNBC cells (MBA-MD-231) rely more on glycolysis thus providing a rationale to target these cells with glycolytic inhibitors. Indeed, iodoacetate (IA), an effective GAPDH inhibitor, caused about 70% drop in MDA-MB-231 cell viability at 20 μM while 40 μM IA was needed to decrease MCF7 cell viability only by 30% within 4 hours of treatment. However, the triple negative cells showed strong ability to recover after 24 h whereas MCF7 cells were completely eliminated at concentrations <10 μM. To understand the mechanism of MDA-MB-231 cell survival, we studied metabolic modulations associated with acute and extended treatment with IA. The resilient TNBC cell population showed a significantly greater count of cells with active mitochondria, lower apoptotic markers, normal cell cycle regulations, moderately lowered ROS, but increased mRNA levels of p27 and PARP1; all compatible with enhanced cell survival. Our results highlight an interplay between PARP and mitochondrial oxidative phosphorylation in TNBC that comes into play in response to glycolytic disruption. In the light of these findings, we suggest that combined treatment with PARP and mitochondrial inhibitors may provide novel therapeutic strategy against TNBC.
The aim of this paper is to investigate whether characteristics of the first lactation (FL) curve of Iranian Holstein cows are associated with survival. Cows with least 10 test-days of milk ...production in their FL were used. The persistency of lactation (PL) and survival were estimated using a random regression model by restricted maximum likelihood with the ECHIDDNA software. We also used the Wood model to parameterize each individual lactation curve and then analyzed various curve characteristics using an animal model. The predicted breeding value (EBV) of the characteristics of the lactation curve of the cows from day 40 to 305 was predicted. The EBV of the production range (PR) and the slope of line in increasing phase (m40,Peak) of production curve of sires with higher survival EBV were lower than other sires (P < 0.05). The estimates of PL were independent of survival estimate. Therefore, the PR from 40th day after calving can be considered as a definition of PL because the lower the PR, the flatter is the milk production curve. Genetic evaluation of young bulls for survival needs the data of death or culling of their daughters. Therefore, the bulls can genetically be evaluated for survival according to the PL and m40,Peak of FL information of their daughters.
Abstract
The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO
2
) by plant photosynthesis, which currently accounts for about ...one-quarter of anthropogenic CO
2
emissions per year. This study aimed to detect the variability of carbon productivity using the standardized evapotranspiration deficit index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34%, 30.5%, and 29.6% of cropland area respectively, and 25%, 31.5%, and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.
Typically, the underrepresentation of female engineers in education, employment, and leadership is a worldwide social issue. The present study investigates the critical employment challenges and ...barriers for female engineers in Yemeni’s unstable, conservative, and poor society. The quantitative methodology was based on two constructed questionnaires targeting female engineering graduates from 2012 to 2021 at Taiz University and executive HR managers. The results indicated that the ongoing civil war, employability attributes, personal attitudes, low wages, the conservative society, and marriage–family beliefs are the most apparent barriers to female engineers’ employment in Yemen. Nearly 40% of female engineers are frustrated with not having a job, almost the same percentage stopped seeking a job, and about two-thirds did not register with government employment offices. This study prompts engineering colleges to frequently update their programs to cope with rapid developments and to include employability courses in their curricula. Furthermore, this study advises female engineers to consult experts before enrolling in engineering programs and to practice training and employability skills immediately after graduation. We want to encourage such social studies in Yemen that are concerned with female issues to underpin their participation in society and to make use of unutilized resources.
Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural ...systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.
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