Antecedents of Facebook Updates, and the Role of Personality of Facebook Users in Sri Lanka

This study is to prove the impact of different motives in updating Facebook status and role of personality in Facebook updates. Therefore, the study is focus to identify the impact of motive namely validation, communication and self-expression on Facebook status updates while it is examining the moderation effect of individual personality on original relationship. As study is based on the Facebook users, population of the study consist of all Facebook users and based on the convenience sampling method researchers have selected 252 Facebook users in Sri Lanka as the sample of the study. Data were collected from the sample using a researcher developed questionnaire. In order to prove hypotheses and make inferences, regression analysis was employed in the study. Moreover, regression analysis proved that there is an impact from validation, communication and self-expression on Facebook status updates. Further, the relationship between validation, communication and self-expression with Facebook status update is moderated by the extraversion and openness to experience. Based on the inferences of the study, it can be concluded that validation, communication and self-expression act as motives to update Facebook status and the intensity of that motives depend on the personality of an individua

Synthesis and properties of graphene and graphene/carbon nanotube-reinforced soft magnetic FeCo alloy composites by spark plasma sintering

The effect of the addition of graphene nanoplatelets (GNP) and graphene nanoplatelet/carbon nanotube (GNT) mixtures on the mechanical and magnetic properties of spark plasma sintered soft magnetic FeCo alloys was studied. Three different volume fractions (0.5, 1 and 2 vol%) of GNPs and GNTs were investigated. Ball milling was used to disperse the GNPs in monolithic FeCo powder, while magnetic stirring and ultrasonic agitation were used to prepare hybrid GNT prior to ball milling. The highest saturation induction (B sat) of 2.39 T was observed in the 1 vol% GNP composite. An increase in the volume fraction of the ordered nanocrystalline structure was found to reduce the coercivity (H c) of the composites. The addition of CNTs to the GNP composite prevented grain growth, leading to grain refinement. An 18 % increase in hardness was observed in the 1 vol% GNP composite as compared to the as-received FeCo alloy. A reduction in tensile strength was observed in all of the composite materials, except for the 0.5 vol% GNT composite, for which a value of 643 MPa was observed. Raman spectroscopy indicated a reduction in the defect density of the GNPs after adding CNTs

Energy Balance Assessment in Agricultural Systems; An Approach to Diversification

The energy in agricultural systems is two-fold: transformation and utilization. The assessment and proper use of energy in agricultural systems is important to achieve economic benefits and overall sustainability. Therefore, this study was conducted to evaluate the energy balance of crop and livestock production, net energy ratio (NER), and water use efficiency (WUE) of crops of a selected farm in Sri Lanka using the life cycle assessment (LCA) approach. In order to assess the diversification, 18 crops and 5 livestock types were used. The data were obtained from farm records, personal contacts, and previously published literature. Accordingly, the energy balance in crop production and livestock production was −316.87 GJ ha−1 Year−1 and 758.73 GJ Year−1, respectively. The energy related WUE of crop production was 31.35 MJ m−3. The total energy balance of the farm was 736.2 GJ Year−1. The results show a negative energy balance in crop production indicating an efficient production system, while a comparatively higher energy loss was shown from the livestock sector. The procedure followed in this study can be used to assess the energy balance of diversified agricultural systems, which is important for agricultural sustainability. This can be further developed to assess the carbon footprint in agricultural systems

Wetland water-level prediction in the context of machine-learning techniques:where do we stand?

Abstract Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction

Energy balance assessment in agricultural systems:an approach to diversification

Abstract The energy in agricultural systems is two-fold: transformation and utilization. The assessment and proper use of energy in agricultural systems is important to achieve economic benefits and overall sustainability. Therefore, this study was conducted to evaluate the energy balance of crop and livestock production, net energy ratio (NER), and water use efficiency (WUE) of crops of a selected farm in Sri Lanka using the life cycle assessment (LCA) approach. In order to assess the diversification, 18 crops and 5 livestock types were used. The data were obtained from farm records, personal contacts, and previously published literature. Accordingly, the energy balance in crop production and livestock production was −316.87 GJ ha⁻¹ Year⁻¹ and 758.73 GJ Year⁻¹, respectively. The energy related WUE of crop production was 31.35 MJ m⁻³. The total energy balance of the farm was 736.2 GJ Year⁻¹. The results show a negative energy balance in crop production indicating an efficient production system, while a comparatively higher energy loss was shown from the livestock sector. The procedure followed in this study can be used to assess the energy balance of diversified agricultural systems, which is important for agricultural sustainability. This can be further developed to assess the carbon footprint in agricultural systems

Evaluation of the Impact of Land Use Changes on Soil Erosion in the Tropical Maha Oya River Basin, Sri Lanka

Soil degradation is a serious environmental issue in many regions of the world, and Sri Lanka is not an exception. Maha Oya River Basin (MORB) is one of the major river basins in tropical Sri Lanka, which suffers from regular soil erosion and degradation. The current study was designed to estimate the soil erosion associated with land use changes of the MORB. The Revised Universal Soil Loss Equation (RUSLE) was used in calculating the annual soil erosion rates, while the Geographic Information System (GIS) was used in mapping the spatial variations of the soil erosion hazard over a 30-year period. Thereafter, soil erosion hotspots in the MORB were also identified. The results of this study revealed that the mean average soil loss from the MORB has substantially increased from 2.81 t ha−1 yr−1 in 1989 to 3.21 t ha−1 yr−1 in 2021, which is an increment of about 14.23%. An extremely critical soil erosion-prone locations (average annual soil loss > 60 t ha−1 yr−1) map of the MORB was developed for the year 2021. The severity classes revealed that approximately 4.61% and 6.11% of the study area were in high to extremely high erosion hazard classes in 1989 and 2021, respectively. Based on the results, it was found that the extreme soil erosion occurs when forests and vegetation land are converted into agricultural and bare land/farmland. The spatial analysis further reveals that erosion-prone soil types, steep slope areas, and reduced forest/vegetation cover in hilly mountain areas contributed to the high soil erosion risk (16.56 to 91.01 t ha−1 yr−1) of the MORB. These high soil erosional areas should be prioritized according to the severity classes, and appropriate land use/land cover (LU/LC) management and water conservation practices should be implemented as recommended by this study to restore degraded lands

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p < 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p < 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online