The process of soil stabilization is a fundamental requirement before road infrastructure development is possible. Different binding materials have been used worldwide as soil stabilizers. In this study, water treatment waste (i.e., alum sludge (AS)) was used as a soil stabilizer. Alum sludge can work not only as a low-cost soil stabilizer but also can solve the problem of waste management at a large scale. Utilization of alum waste can be a sustainable solution and environmentally friendly exercise. Thus, in consideration of the pozzolanic properties of alum, it was applied as a binder, similar to cement or lime, to stabilize the soil with the addition of 2%, 4%, 6%, 8%, and 10% of dry soil by weight. To analyze the resulting improvement in soil strength, the California Bearing Ratio (CBR) test was conducted in addition to three other tests (i.e., particle size analysis, Atterberg’s limits test, and modified proctor test). The soil bearing ratio was significantly improved from 6.53% to 16.86% at the optimum level of an 8% addition of alum sludge. Furthermore, the artificial neural networks (ANNs) technique was applied to study the correlations between the CBR and the physical properties of soil, which showed that, at 8% optimum alum sludge, maximum dry density, optimum moisture content, and plasticity index were also at maximum levels. This study will help in providing an eco-friendly soil stabilization process as well as a waste management solution.

The global increasing food demand can be met by efficient energy utilization in mechanized agricultural productions. In this study, input–output energy flow along with CO2 emissions for different wheat production cases (C-I to C-V) were investigated to identify the one that is most energy-efficient and environment-friendly case. Data and information about input and output sources were collected from farmers through questionnaires and face-to-face interviews. Input and output sources were converted into energy units by energy equivalents while CO2 emissions were calculated by emission equivalents. Data envelopment analysis (DEA) was conducted to compare technical efficiencies of the developed cases for optimization of inputs in inefficient cases. Results revealed that case C-Ⅴ (higher inputs, larger fields, the tendency of higher fertilizer application and tillage operations) has the highest energy inputs and outputs than the rest of the cases. Moreover, it possesses the lowest energy use efficiency and energy productivity. The highest CO2 emissions (1548 kg-CO2/ha) referred to C-Ⅴ while lowest emissions per ton of grain yield were determined in C-Ⅳ (higher electricity water pumping, moderate energy input). The grain yield increases directly with input energy in most of the cases, but it does not guarantee the highest values for energy indices. C-Ⅲ (moderate irrigations, educated farmers, various fertilizer applications) was found as an optimum case because of higher energy indices like energy use efficiency of 4.4 and energy productivity of 153.94 kg/GJ. Optimum input and better management practices may enhance energy proficiency and limit the traditionally uncontrolled CO2 emissions from wheat production. Therefore, the agricultural practices performed in C-Ⅲ are recommended for efficient cultivation of wheat in the studied area.

The ongoing global warming and changing patterns of precipitation have significant implications for crop yields. Process-based models are the most commonly used method to assess the impacts of projected climate changes on crop yields. In this study, the crop-environment resource synthesis (CERES)-Maize 4.6.7 model was used to project the maize crop yield in the Shaanxi Province of China over future periods. In this context, the downscaled ensemble projections of 17 general circulation models (GCMs) under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) were used as input for the calibrated CERES-Maize model. Results showed a negative correlation between temperature and maize yield in the study area. It is expected that each 1.0 °C rise in seasonal temperature will cause up to a 9% decrease in the yield. However, the influence of CO2 fertilization showed a positive response, as witnessed by the increase in the crop yield. With CO2 fertilization, the average increase in the maize crop yield compared to without CO2 fertilization per three decades was 10.5%, 11.6%, TA7.8%, and 6.5% under the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. An elevated CO2 concentration showed a pronounced positive impact on the rain-fed maize yield compared to the irrigated maize yield. The average water use efficiency (WUE) was better at elevated CO2 concentrations and improved by 7–21% relative to the without CO2 fertilization of the WUE. Therefore, future climate changes with elevated CO2 are expected to be favorable for maize yields in the Shaanxi Province of China, and farmers can expect further benefits in the future from growing maize.

The concept of agricultural and environmental sustainability refers to minimizing the degradation of natural resources while increasing crop productions; assessment of inflow and outflow energy resources is helpful in highlighting the resilience of the system and maintaining its productivity. In this regard, the current study evaluated the amount of energy input–output of cotton productions and their environmental interventions. Data are randomly collected from 400 cotton farmers through face-to-face interview. Results suggested that the major energy is consumed by three culprits, i.e., chemical fertilizer, diesel fuel, and irrigation water (11,532.60, 11,121.54, and 4,531.97 MJ ha−1, respectively). Total greenhouse gas (GHG) emission is 1,106.12 kg CO2eq ha−1 with the main share coming from diesel fuel, machinery, and irrigation water. Stimulating data of energies, e.g., energy use efficiency (1.53), specific energy (7.69 MJ kg−1), energy productivity (0.13 kg MJ−1), and net energy gained (16,409.77 MJ ha−1). Further analysis using data envelopment analysis (DEA) showed that low technical efficiency, i.e., 69.02%, is the most probable cause of poor energy use efficiency. The impermanent trend in growth of energy efficiency has been witnessed with plausible potential of energy savings from 4,048.012 to 16,194.77 MJ ha−1 and a reduction of 148.96–595.96 kg CO2eq ha−1 in GHG emission. Cobb–Douglas production function is further applied to discover the associations of energy input to output, which inferred that chemical fertilizer, diesel fuel, machinery, and biocides have significant effect on cotton yield. The marginal physical productivity (MPP) values obliged that the additional use in energy (1 MJ) from fuel (diesel), biocides, and machinery can enhance cotton yield at the rate of 0.35, 1.52, and 0.45 kg ha−1, respectively. Energy saving best links with energy sharing data, i.e., 55.66% (direct), 44.34% (indirect), 21.05% (renewable), and 78.95% (nonrenewable), further unveiled the high usage of nonrenewable energy resources (fossil fuels) that ultimately contributes to high emissions of GHGs. We hope that these findings could help in the management of energy budget that we believe will reduce the high emissions of GHGs.

No abstract

Cancer is the second leading cause of mortality across the globe. Approximately 9.6 million people are estimated to have died due to cancer disease in 2019. Accurate and early prediction of cancer can assist healthcare professionals to devise timely therapeutic innervations to control sufferings and the risk of mortality. Generally, a machine learning (ML) based predictive system in healthcare uses data (genetic profile or clinical parameters) and learning algorithms to predict target values for cancer detection. However, optimization of predictive accuracy is an important endeavor for accurate decision making. Reject Option (RO) classifiers have been used to improve the predictive accuracy of classifiers for cancer like complex problems. In a gene profile all of the features are not important and should be shaved off. ML offers different techniques with their own methodology for feature selection (FS) and the classification results are dependent on the datasets each having its own distribution and features. Therefore, both FS methods and ML algorithms with RO need to be considered for robust classification. The main objective of this study is to optimize three parameters (learning algorithm, FS method and rejection rate) for robust cancer prediction rather than considering two traditional parameters (learning algorithm and rejection rate). The analysis of different FS methods (including t-test, Las Vegas Filter (LVF), Relief, and Information Gain (IG)) and RO classifiers on different rejection thresholds is performed to investigate the robust predictability of cancer. The three cancer datasets (Colon cancer, Leukemia and Breast cancer) were reduced using different FS methods and each of them were used to analyze the predictability of cancer using different RO classifiers. The results reveal that for each dataset predictive accuracies of RO classifiers were different for different FS methods. The findings based on proposed scheme indicate that, the ML algorithms along with their dependence on suitable FS methods need to be taken into consideration for accurate prediction.

The comparison of several associated species and plant genome sequencing efforts has increased in recent years. The inflated level of the genomic variety leads to the discovery that the single reference genomes may not reflect the variability in a species, resulting in the evolution of a pan-genome idea. Pan-genomes exhibit a species' genetic variability and contain mutant genes lacking in some individuals and essential genes present in all individuals. Mutant gene classifications often reveal cross-species parallels, including genes for abiotic and biotic stresses generally concentrated within mutant gene groupings. Here we discuss the history of pan-genomics in plants, investigate the causes of gene variation, deletion, and existence and demonstrate why pan-genomes might assist crop genetics and breeding research.

This study was conducted to evaluate the strength and volume change characteristics of a sedimentary residual soil mixed with bentonite (S1) when treated with three different enzymes. In addition, three reference clays including bentonite, illite, and kaolinite were also treated with enzymes to study the effect on their strength characteristics. Soil samples prepared at the optimum moisture content (OMC) were sealed and cured for four months. After curing, reference clays were tested for unconfined compressive strength (UCS). For swell tests, the S1 soil samples were placed on porous stones, which were immersed in water to allow capillary soaking of the samples. S1 samples were allowed to dry at ambient temperature for shrinkage test until the rate of reduction in volume became negligible. On completion of swell tests, the samples were tested for UCS to determine the decrease in strength due to saturation. No increase in strength and decrease in volume change were observed for any of the enzymes and dosages. Field Emission Scanning Electron Microscope (FESEM) showed some dense packing of particles for treated samples, whereas X-ray diffraction (XRD) did not reveal any change; in fact, the pattern for untreated and treated soil samples were indistinguishable.