Tribological Characterisation of Graphene Oxide as Lubricant Additive on Hypereutectic Al-25Si/Steel Tribopair

The performance of a lubricant greatly depends on the additives it involves. However, recently used additives produce severe pollution when they are burned and exhausted. Therefore, it is necessary to develop a new generation of green additives. Graphene oxide (GO) is considered to be environmentally friendly. The scope of this study is to explore the fundamental tribological behavior of graphene, the first existing 2D material, and evaluate its performance as a lubricant additive. The friction and wear behavior of 0.5 wt% concentrations of GO particles in ethanol and SAE20W50 engine oil on a hypereutectic Al-25Si alloy disc against steel ball was studied at 5 N load. GO as an additive reduced the wear coefficient by 60–80% with 30 Hz frequency for 120 m sliding distance. The minimum value of the coefficient of friction (0.057) was found with SAE20W50 + 0.5 wt% GO. A possible explanation for these results is that the graphene layers act as a 2D nanomaterial and form a conformal protective film on the sliding contact interfaces and easily shear off due to weak Van der Waal's forces and drastically reduce the wear. Scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and Raman spectroscopy were used for characterization of GO and wear scars

Rethinking policy and institutional imperatives for integrated watershed management: Lessons and experiences from semi-arid India

This study investigates the institutional and policy issues that limit effective participation of resource users in community watershed programs and identifies key lessons for harnessing collective action and its effectiveness in achieving economic and environmental outcomes. It shows that spatial and temporal attributes of watersheds and the associated market failures that accelerate degradation of agricultural and environmental resources require innovative policy and institutional arrangements for coordinating use and management of resources. Under enabling policies, IWM can effectively contribute towards diversification of production into high-value products, reversal of resource degradation, growth in the incomes of the poor and enhance the ability to mitigate the effect of drought. However, the degree of biophysical and social complexity within watershed communities could often undermine incentives for collective action, thwart distribution of benefits against the landless and resource-poor households and even lead to depletion of groundwater resources. Governments and other stakeholders have a unique role to play in kick-starting the process of transformation through strategic natural resource and productivity-enhancing investments that strengthen local capacity for collective action and generate local public goods. Such collective investments could serve as building blocks for private productivity-enhancing and risk-mitigating investments as they boost profitability of productive assets (land and labor) and encourage farmer adoption of beneficial conservation practices. The lessons and experiences also show that integrating interventions along watershed frontiers would require a flexible learning alliance of institutions and cross-disciplinary teams with complementary skills and competencies

Variable indicators for optimum wavelength selection in diffuse reflectance spectroscopy of soils

Diffuse reflectance spectroscopy (DRS) operating in 350–2500 nm wavelength range is fast emerging as a rapid and non-invasive technique for analyzing multiple soil attributes. Because the spectral reflectance values in this range of wavelengths are highly co-linear, it is important to select relevant spectral information from the reflectance spectra to build a robust spectral algorithm. The objective of this study is to examine the utility of different variable indicators such as partial least squares regression (PLSR) coefficients (β), variable influence on projection, squared residual (SqRes), correlation coefficient (r), biweightmidcorrelation (bicor), mutual information based adjacency value (AMI), signal-to-noise ratio (StN), covariance procedures (CovProc) and their combinations in conjunction with an ordered predictor selection (OPS) approach for selecting optimum number of spectral variables (NSV) which could improve DRS model performance. The approach was tested with the PLSR models of pH, organic carbon, extractable iron (Fe), sand and clay contents and geometric mean diameter in Vertisols and Alfisols. The prediction accuracy of best models selected via OPS approach was found to be superior to full-spectrum (NSV = 2048) model for all the soil attributes. The percent decrease in RMSE value was found to be highest for Fe (14%, NSV = 79) in Alfisols followed by pH (9%, NSV = 660) in Vertisols while it varied between 3 and 8% for other soil attributes. Although the results were not conclusive in favor of one specific variable indicator, the CovProc and bicor were found to be more appropriate for accurate and moderate DRS models in this study, respectively. The overall results of this study advocate the use of OPS approach with variable indicators and their combinations as a promising strategy to develop simple and effective DRS models for soils

Acute Appendicitis among Saudi and Non-Saudi Patients: A Cross-Sectional Based Study

Objective: We conduct this study to discuss the differences between Saudi and non- Saudi patients with acute appendicitis.Background: Acute appendicitis is one of emergency surgeries in developing and developed countries. Its symptoms are vomiting, lower abdominal pain and decreased appetite. Appendicitis needs urgent surgical prouder to avoid its perforation and associated complications which may lead to death. Method: We conduct cross-sectional based study in one of khamis Mushayt, Saudi Araba. 136 patients diagnosed with acute appendicitis were included and their medical records were reviewed after getting their informed consent.Results: We included 136 patients, 90 were non-Saudi and 46 were Saudi. There were no statistically differences in their diagnosis but the distribution of the diagnosis was different.Conclusion: Acute appendicitis was more prevalent among non-Saudi patients, the diagnosis between both was with no significant differences. Keywords: acute appendicitis, Saudi, non-Saudi, diagnosis, cross-sectional, Saudi Arabia and khamis Mushayt

Dependency measures for assessing the covariation of spectrally active and inactive soil properties in diffuse reflectance spectroscopy

Diffuse reflectance spectroscopy (DRS) is a rapid and noninvasive assessment technique for several spectrally active soil properties (chromophores) such as sand, clay, organic C, and Fe contents. The approach is also used for estimating many spectrally inactive constituents (non-chromophores) based on the assumption of covariation between non-chromophores and chromophores. The linkage between covariation and the ability of DRS to estimate a non-chromophore has not been reported in the literature. In this study, we evaluated the covariation assumption using three dependency measures (Pearson correlation coefficient, r; biweight midcorrelation, bicor; and mutual information based adjacency, AMI), five chromophores (organic C, Fe, clay, and sand contents, and geometric mean diameter), and seven non-chromophores (pH, electrical conductivity, P, K, B, Zn, and Al contents) measured in 247 Alfisol and 249 Vertisol samples. An average dependency index (ADI) was developed for each of the three measures (ADIr, ADIbicor, and ADIAMI). The first derivative of the reflectance in conjunction with partial least squares regression was used for data modeling. Model accuracy was evaluated using residual prediction deviation (RPD). The relationships between RPD values of non-chromophores and the ADI values were examined for different chromophore groups (physical, chemical, and combined). The performance of ADIAMI was found to be superior to ADIr and ADIbicor. The ADIAMI computed using chemical chromophores gave strong linear relationships (R2 = 0.93) between ADIAMI and the RPD of chemical non-chromophores, suggesting that the AMI may be used as a robust dependency measure to assess the covariation of non-chromophores with chromophores in DRS

Mental Models of Soil Management for Food Security in Peri-Urban India

Agricultural development during the Green Revolution brought India food sovereignty but food insecurity persists. Increased crop production was promoted without considering the more holistic impact on food security. Scientists, extension agents, and farmers have different perspectives on how soil health relates to food security. Understanding stakeholders’ perspectives is essential to improving extension communication and mitigating consequences. This study uses qualitative interviews to construct mental models of soil health for food security. The study site is a peri-urban watershed, which is currently participating in the Integrated Farmer Participatory Watershed Management Model (IFPWM). Our study details and defines stakeholders’ mental models of soil health, soil nutrient management, soil sodicity, and food security. A triad belief held by farmers shows the strongly perceived causal relationship between soil health, plant health, and human health. Healthy soil produces healthy food and humans that eat such food will be healthy. Scientists only perceive one condition to achieving food security in the community—food quantity. However, all other stakeholders perceived another risk to food security—food quality. Eating poor quality food is perceived as linked to human health problems in the community. This research suggests the importance of including a fifth dimension of food security, cultural acceptability, within agricultural technology development and dissemination

Bioeconomic modeling of farm household decisions for ex-ante impact assessment of integrated watershed development programs in semi-arid India

The increasing population and urbanization have serious implications for sustainable development in less-favoured areas of developing countries. In an attempt to sustain the long-term productivity of natural resources and to meet the food and non-food demands of growing population in the semi-arid tropics, the Indian government invests and promotes integrated watershed development programs. A comprehensive tool to assess the impacts of watershed development programs on both social well-being and sustainability of natural resource is currently lacking. In this study, we develop a watershed level bioeconomic model to assess the ex-ante impacts of key technological and policy interventions on the socioeconomic well-being of rural households and the natural resource base. These interventions are simulated using data from a watershed community in the semi-arid tropics of India. The model captures the interaction between economic decisions and biophysical processes and using a constrained optimization of household decision model. The interventions assessed are productivity-enhancing technologies of dryland crops and increased in irrigable area through water conservation technologies. The results show that productivity-enhancing technologies of dryland crops increase household incomes and also provided incentives for conserving soil moisture and fertility. The increase in irrigable area enables cultivation of high-value crops which increase the household income but also lead to an increase in soil erosion and nutrient mining. The results clearly indicate the necessity for prioritizing and sequencing technologies based on potential effects and trade-offs on household income and conservation of natural resources

Efficient land water management practice and cropping system for increasing water and crop productivity in semi‐arid tropics

In Indian semi-arid tropics (SATs), low water and crop productivity in Vertisols and associated soils are mainly due to poor land management and erratic and low rainfall occurrence. This study was conducted from 2014 to 2016 at the ICRISAT in India to test the effect of broad bed furrows (BBF) as land water management against conventional flatbed planting for improving soil water content (SWC) and water and crop productivity of three cropping systems: sorghum [Sorghum bicolor (L.) Moench]–chickpea (Cicer arientinum L.) and maize (Zea mays)–groundnut (Arachis hypogaea L.) as sequential and pearl millet [Pennisetum glaucum (L.)] + pigeonpea [Cajanus cajan (L.) Millsp.] as intercropping, grown under different nutrients management involving macronutrients (N, P, and K) only and combined application of macro- and micronutrients. The results stated that the SWC in BBF was higher over flatbed by 9.35–10.44% in 0- to 0.3-m, 4.56–9.30% in 0.3- to 0.6-m and 3.85–5.26% in 0.6- to 1.05-m soil depths during the cropping season. Moreover, depletion of the soil water through plant uptake was higher in BBF than in flatbed. Among the cropping systems, sorghum–chickpea was the best in bringing highest system equivalent yield and water productivity with the combined application of macro- and micronutrients. The BBF minimized water stress at critical crop growth stages leading to increase crop yield and water productivity in SATs. Thus, BBF along with the application of macro- and micronutrients could be an adaptation strategy to mitigate erratic rainfall due to climate change in SATs

Economic impact of improved pearl millet production technology in resource-poor rainfed areas of Kurnool District of Andhra Pradesh

Five on-farm trials in Karivemula and Devanakonda watersheds of Kurnool district, Andhra Pradesh, India, demonstrated the economic viability of improved production technologies for pearl millet. The package included improved cultivar (ICTP 8203), seed rate of 4.0 kg/ha, seed treatment with thiram (3 g per kg of seed), and fertilizer dose of 60 kg N per ha and 30 kg P2O5 per ha

Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy

Diffuse reflectance spectroscopy (DRS) operating in wavelength range of 350–2500 nm is emerging as a rapid and non-invasive approach for estimating soil nutrient content. The success of the DRS approach relies on the ability of the data mining algorithms to extract appropriate spectral features while accounting for non-linearity and complexity of the reflectance spectra. There is no comparative assessment of spectral algorithms for estimating nutrient content of Indian soils. We compare the performance of partialleast-squares regression (PLSR), support vector regression (SVR), discrete wavelet transformation (DWT) and their combinations (DWT–PLSR and DWT–SVR) to estimate soil nutrient content. The DRS models were generated for extractable phosphorus (P), potassium (K), sulphur (S), boron (B), zinc (Zn), iron (Fe) and aluminium (Al) content in Vertisols and Alfisols and were compared using residual prediction deviation (RPD) of validation dataset. The best DRS models yielded accurate predictions for P (RPD = 2.27), Fe (RPD = 2.91) in Vertisols and Fe (RPD = 2.43) in Alfisols, while B (RPD = 1.63), Zn (RPD = 1.49) in Vertisols and K (RPD = 1.89), Zn (RPD = 1.41) in Alfisols were predicted with moderate accuracy. The DWT–SVR outperformed all other approaches in case of P, K and Fe in Vertisols and P, K and Zn in Alfisols; whereas the PLSR approach was better for B, Zn and Al in Vertisols and B, Fe and Al in Alfisols. The DWT–SVR approach yielded parsimonious DRS models with similar or better prediction accuracy than PLSR approach. Hence, the DWT–SVR may be considered as a suitable data mining approach for estimating soil nutrients in Alfisols and Vertisols of India