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Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal quantification of biological processes. While multiple methods and tools exist to correct images post-acquisition, performing drift correction of large 3D videos using open-source solutions remains challenging and time-consuming. Here we present a new tool developed for ImageJ/Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using 2D cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg performs better than other state-of-the-art open-source drift correction tools and significantly outperforms them in speed (5x to 60x). We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis. Fast4DReg is available onGitHub.

The impact of ultraviolet (UV) radiation on public health is severe and can cause sunburn, skin aging and cancer, immunosuppression, and eye damage. Minimization of exposure to solar UV is required in order to reduce the risks of these illnesses to the public. Greater public awareness and a prediction of the ultraviolet index (UVI) is considered an essential task for the minimization of solar UV exposures. This research has designed artificial intelligence based deep learning models to predict multistep UVI index. It has developed a convolutional neural network integrated with long short-term memory network (CLSTM) as the main model to forecast UVI for Brisbane with latitude − 27.47 and longitude 153.02, the capital city of Queensland, Australia. Solar zenith angle (SZA) data were used together with UVI as inputs in the CLSTM for 10-min, 30-min and 60-min UVI prediction. The CLSTM model was benchmarked against long short-term memory network (LSTM), convolutional neural network (CNN), Deep Neural Network (DNN), multilayer perceptron (MLP), extreme learning machine (ELM), random forest regression (RFR), Extreme Gradient Boosting (XGB), and Pro6UV Deterministic models. The experimental results showed that the CLSTM model outperformed these models with RMSE = 0.3817, MAE = 0.1887, RRMSE = 8.0086%, MAPE = 4.6172% and APB = 3.9586 for 10-min prediction. In addition to that, these metrics for 30-min and 60-min prediction were RMSE = 0.4866/0.5146, MAE = 0.2763/0.3038, RRMSE = 10.4860%/11.5840%, MAPE = 8.1037%/9.6558% and APB = 5.9546/6.8386, respectively. Thus, the CLSTM model can yield improved UVI prediction for both the public and the government agencies.

Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, the prediction of students’ final course grades with continuous assessment marks is a useful toolkit for degree program educators. Predictive models are practical tools used to evaluate the effectiveness of teaching as well as assessing the students’ progression and implementing interventions for the best learning outcomes. This study develops a novel multivariate adaptive regression spline (MARS) model to predict the weighted score (Formula presented.) (i.e., the course grade). To construct the proposed MARS model, Introductory Engineering Mathematics performance data over five years from the University of Southern Queensland, Australia, were used to design predictive models using input predictors of online quizzes, written assignments, and examination scores. About 60% of randomised predictor grade data were applied to train the model (with 25% of the training set used for validation) and 40% to test the model. Based on the cross-correlation of inputs vs. the (Formula presented.), 12 distinct combinations with single (i.e., M1–M5) and multiple (M6–M12) features were created to assess the influence of each on the (Formula presented.) with results bench-marked via a decision tree regression (DTR), kernel ridge regression (KRR), and a k-nearest neighbour (KNN) model. The influence of each predictor on (Formula presented.) clearly showed that online quizzes provide the least contribution. However, the MARS model improved dramatically by including written assignments and examination scores. The research demonstrates the merits of the proposed MARS model in uncovering relationships among continuous learning variables, which also provides a distinct advantage to educators in developing early intervention and moderating their teaching by predicting the performance of students ahead of final outcome for a course. The findings and future application have significant practical implications in teaching and learning interventions or planning aimed to improve graduate outcomes in undergraduate engineering program cohorts.

Myosin-X (MYO10), a molecular motor localizing to filopodia, is thought to transport various cargo to filopodia tips, modulating filopodia function. However, only a few MYO10 cargoes have been described. Here, using GFP-Trap and BioID approaches combined with mass spectrometry, we identified lamellipodin (RAPH1) as a novel MYO10 cargo. We report that the FERM domain of MYO10 is required for RAPH1 localization and accumulation at filopodia tips. Previous studies have mapped the RAPH1 interaction domain for adhesome components to its talin-binding and Ras-association domains. Surprisingly, we find that the RAPH1 MYO10-binding site is not within these domains. Instead, it comprises a conserved helix located just after the RAPH1 pleckstrin homology domain with previously unknown functions. Functionally, RAPH1 supports MYO10 filopodia formation and stability but is not required to activate integrins at filopodia tips. Taken together, our data indicate a feed-forward mechanism whereby MYO10 filopodia are positively regulated by MYO10-mediated transport of RAPH1 to the filopodium tip.

Background: To evaluate the difference in the level of TSH in diabetic and non diabetic patients at the time of the diagnosis of hypothyroidism. Methods: 100 diagnosed cases of hypothyroidism, 50 with diabetes and 50 without diabetes were studied. The level of TSH at the time of diagnosis and other information were obtained from the medical records. Results: The mean TSH in patients with the history of diabetes at the diagnosis of hypothyroidism was 19.9616±26.990 and in those without the history of the diabetes was 10.4797±6.503 (p value 0.018). The females with diabetes had higher level of TSH level at the time of diagnosis of hypothyroidism than females without diabetes (p value 0.045). There was no statistically significant difference in the level of TSH in males with and without diabetes at the time of diagnosis of hypothyroidism. Conclusion: Patients with diabetes mellitus had higher level of TSH at the time of diagnosis of hypothyroidism in comparison to those without diabetes. Early identification of the raised TSH levels in diabetic patients and timely intervention will help to reduce the chances of adverse cardiovascular outcomes and diabetic kidney disease in this group of patients.

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-minute) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a Total Sky Imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m-2 s-1, mean absolute error MAE = 150.24 μ mol of photons m-2 s-1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate & McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.

To support alternative forms of energy resources, the prediction of global incident solar radiation (Irad) is critical to establish the efficacy of solar energy resources as a free and clean energy, and to identify and screen solar powered sites. Solar radiation data for construction of energy feasibility studies are not available in many locations due to the absence of meteorological stations, especially in remote or regional sites. To surmount the challenge in solar energy site identification, the universally gridded data integrated into predictive models used to generate reliable Irad forecasts can be considered as a viable medium for future energy utilization. The objective of this paper is to review, develop and evaluate a suite of machine learning (ML) models based on the artificial neural network (ANN) versus several other kinds of data-driven models such as support vector regression (SVR), Gaussian process machine learning (GPML) and genetic programming (GP) models for the prediction of daily Irad generated through the European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis fields. The performance of the ML models are benchmarked against several statistical tools: auto regressive moving integrated average (ARIMA), Temperature Model (TM), Time series and Fourier Series (TSFS) models. To train these models, 87 different predictor variables from the ERA-Interim reanalysis dataset (01-January-1979 to 31-December-2015) were extracted for 5 solar-rich metropolitan sites (i.e., Brisbane, Gold Coast, Sunshine Coast, Ipswich and Toowoomba, Australia) targeted against surface level Irad available from the measured Scientific Information for Land Owners dataset. For daily forecast models, a total of the 20 most important predictors related to the Irad dataset were screened with nearest component analysis: “fsrnca” feature selection, and partitioned into training (70%), validation (15%) and testing (15%) sets for model design. To benchmark the ANN, TSFS and TM models were developed with Fourier series and regression analysis, respectively and the statistical performance was benchmarked with root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), Mean Bias error (MBE), Legates and McCabe Index (E1), and relative MAE, RMSE and diagnostic plots. The performance of ANN was significantly better than the other models (SVR, GPML, GP, TM), resulting in lower RMSE (1.715–2.27 MJm−2/day relative to 2.14–5.90 MJm−2/day), relative RMSE (9.07–12.47 vs 10.98–29.15), relative RMAE (7.97–11.74 vs 9.27–33.96) and larger WI, ENS and E1 (0.938–0.967 vs. 0.462–0.955, 0.935–0.872 vs. 0.355–0.915, 0.672–0.783 vs. 0.252–0.740). Additionally, models assessed with predictors grouped into El Nino, La Nina and the positive, negative and neutral periods of Indian Ocean Dipole, affirmed the merits of ANN model (RRMSE ≤ 11%). Seasonal analysis showed that ANN was an elite tool over SVR, GPML and GP for Irad prediction. The study concludes that an ANN approach integrated with ECMWF fields, incorporating physical interactions of Irad with atmospheric data, is an efficacious alternative to forecast solar energy and assist with energy modelling for solar-rich sites that have diverse climatic conditions to further support clean energy utilization.

DNA-binding studies of a variety of rhenium(I) tricarbonyl complexes are known. Primarily the rhenium complexes bind to DNA through intercalation or minor groove or both. We synthesized a novel organometallic salt of the type, [Re(µ-H)Re]+[Re(µ-OR)3Re]-. The UV-vis and emission spectroscopic titrations and viscosity studies indicate that the salt binds to DNA through partial intercalation. A variety of mono-, di-, and trinuclear rhenium(I) carbonyl complexes are known to exhibit cytotoxicity against numerous cancer cell lines. Examples of the cytotoxicity of tetranuclear rhenium (I) tricarbonyl complexes are rare. We have found that the tetranuclear, novel organometallic salt is highly potent on numerous cancer cells. The IC50 values (concentrations required to induce 50% cell deaths) are 0.220 µM on BxPC-3 pancreatic cancer cells, 0.298 µM on estrogen receptor positive MCF-7 breast cancer cells, 0.948 µM on triple negative MDA-MB-231 breast cancer cells, and 0.300 µM on U-937 lymphoma cells.

Designing predictive models of global solar radiation can be an effective renewable energy feasibility studies approach to resolve future problems associated with the supply, reliability and dynamical stability of consumable energy demands generated by solar-powered electrical plants. In this paper we design and present a new approach to predict the monthly mean daily solar radiation (GSR) by constructing an extreme learning machine (ELM) model integrated with the Moderate Resolution Imaging Spectroradiometer (MODIS)-based satellite and the European Center for Medium Range Weather Forecasting (ECMWF) Reanalysis data for solar rich cities: Brisbane and Townsville, Australia. A self-adaptive differential evolutionary ELM (i.e., SaDE-ELM) is proposed, utilizing a swarm-based ant colony optimization (ACO) feature selection to select the most important predictors for GSR, and the SaDE-ELM is then benchmarked with nine different data-driven models: a basic ELM, genetic programming (GP), online sequential ELM with fixed (OS-ELM) and varying (OSVARY-ELM) input sizes, and hybridized model including the particle swarm optimized-artificial neural network model (PSO-ANN), genetic algorithm optimized ANN (GA-ANN), PSO-support vector machine model (PSO-SVR), genetic algorithm optimized-SVR model (GA-SVR) and the SVR model optimized with grid search (GS-SVR). A comprehensive evaluation of the SaDE-ELM model is performed, considering key statistical metrics and diagnostic plots of measured and forecasted GSR. The results demonstrate excellent forecasting capability of the SaDE-ELM model in respect to the nine benchmark models. SaDE-ELM outperformed all comparative models for both tested study sites with a relative mean absolute and a root mean square error (RRMSE) of 2.6% and 2.3% (for Brisbane) and 0.8% and 0.7% (for Townsville), respectively. Majority of the forecasted errors are recorded in the lowest magnitude frequency band, to demonstrate the preciseness of the SaDE-ELM model. When tested for daily solar radiation forecasting using the ECMWF Reanalysis data for Brisbane study site, the performance resulted in an RRMSE ≈ 10.5%. Alternative models evaluated with the input data classified into El Nino, La Nina and the positive and negative phases of the Indian Ocean Dipole moment (considering the impacts of synoptic-scale climate phenomenon), confirms the superiority of the SaDE-ELM model (with RRMSE ≤ 13%). A seasonal analysis of all developed models depicts SaDE-ELM as the preferred tool over the basic ELM and the hybridized version of ANN, SVR and GP model. In accordance with the results obtained through MODIS satellite and ECMWF Reanalysis data products, this study ascertains that the proposed SaDE-ELM model applied with ACO feature selection, integrated with satellite-derived data is adoptable as a qualified tool for monthly and daily GSR predictions and long-term solar energy feasibility study especially in data sparse and regional sites where a satellite footprint can be identified.

This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary-type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model’s fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the Grouping Genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)-ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO-(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promising results; thus improving those attained by the 7 alternative models (i.e., hybrid CRO-(ELM)-MARS, CRO-(ELM)-MLR and CRO-(ELM)-SVR and the GGA equivalent models). The study ascertains that the CRO-based hybrid system where a large pool of predictor data are carefully screened through a wrapper-based modelling system and the ELM model is applied as a objective estimation tool can be adopted as a qualified stratagem in solar radiation estimation problems. The proposed hybrid CRO-(ELM)-ELM system exhibits clear advantages over the alternative machine learning approaches tested and also over the other machine learning algorithms used without the feature selection tool; thus advocating its scientific utility in renewable energy applications.

Cisplatin and other metal-based drugs often display side effects and tumor resistance after prolonged use. Because rhenium-based anticancer complexes are often less toxic, a novel series of organorhenium complexes were synthesized of the types: XRe(CO)(3)Z (X = α-diimines and Z = p-toluenesulfonate, 1-naphthalenesulfonate, 2-naphthalenesulfonate, picolinate, nicotinate, aspirinate, naproxenate, flufenamate, ibuprofenate, mefenamate, tolfenamate, N-acetyl-tryptophanate), and their biological properties were examined. Specifically, in hormone-dependent MCF-7 and hormone-independent triple-negative MDA-MB-231 breast cancer cells, the p-toluenesulfonato, 1-naphthalenesulfonato, 2-naphthalenesulfonato, picolinato, nicotinato, acetylsalicylato, flufenamato, ibuprofenato, mefenamato, and N-acetyl-tryptophanato complexes were found to be far more potent than conventional drug cisplatin. DNA-binding studies were performed in each case via UV–Vis titrations, cyclic voltammetry, gel electrophoresis, and viscosity, which suggest DNA partial intercalation interaction, and the structure–activity relationship studies suggest that the anticancer activities increase with the increasing lipophilicities of the compounds, roughly consistent with their DNA-binding activities.

Community forest management helps in mitigating deforestation and forest degradation by addressing the negative aspects of rural livelihoods such as poverty and social exclusion. It is important in regulating global climate by encouraging sequestration of carbon in shoots, roots and soils. We studied the status of community forest management, forest resource harvest and carbon stocks in two community forests of the mid hill region of central and western Nepal. The study was based on primary and secondary data collected through carbon stock measurement from field visits and allometric equations, household surveys, focus group discussions, key informant interviews, and review of past studies. Socioeconomic variables such as gender, age group, livestock and landholding status were related to resource utilization, conservation, and management of community forest. Forest resources such as timber, firewood, fodder and leaf litter were harvested in sustainable ways. People were involved in forest thinning, co-management meetings, guarding and planting trees for forest conservation and management. Density and carbon stock of trees increased gradually in comparison to a previous study. We recommend further research on other community forests for more accurate and better results.

Agricultural inputs have crucial role to deliver increased productivity and sustained agricultural growth in an economy. However, adequate empirical research is not available related to effects of agricultural inputs use on agricultural productivity in Nepalese context. Thus, empirical study was conducted so as to reveal the effects of agricultural inputs use in agricultural production sector. This study utilized time series data of 30 years (1990 to 2019) retrieved from various secondary data sources of agricultural input supply in Nepal. Then, data obtained were processed and analyzed in two ways: First, Compound Annual Growth Rate (CAGR) of different agricultural inputs were estimated using Log-Lin regression model. Second, the parameters estimates of Cobb-Douglas regression analysis was obtained taking Agricultural GDP as the dependent variable and independent variables viz. cultivated area, irrigated area, population engaged in agriculture, fertilizer consumption, pesticides application, improved seeds used and government expenditure. The results depicted that growth rate of various agricultural inputs use had raised significantly and was in increasing trend during the study period. Furthermore, cultivated area, irrigated area and pesticides application were found statistically significant and had significant impact on growth of agricultural GDP (Gross Domestic Product).

The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model.

This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Network for pattern recognition with the Long Short-Term Memory Network for half-hourly global solar radiation (GSR) forecasting. The Convolution network is applied to robustly extract data input features from predictive variables (i.e., statistically significant antecedent inputs) while Long Short-Term Memory absorbs them for prediction. Half-hourly GSR for Alice Springs (Australia: 01 January 2006 to 31 August 2018) are extracted with stationarity checks applied via unit-root and mutual information test to capture antecedent GSR values required to forecast future GSR. The proposed hybrid model is benchmarked with standalone models as well as other Deep Learning, Single Hidden Layer and Tree based models. The results show that the benchmarked models are not able to generate satisfactory GSR predictions and the proposed hybrid model outperforms all other counterparts. The hybrid model registers superior results with over 70% of predictive errors lying below ±10 Wm−2 and outperforms the benchmark model for 1-Day half-hourly GSR prediction with low Relative Root Mean Square Error (≈1.515%), Mean Absolute Percentage Error (≈4.672%) and Absolute Percentage Bias (≈1.233%). This study ascertains that a proposed hybrid model based on a convolution network framework can accurately predict GSR and enable energy availability to be regularly monitored over multi-step horizons when coupled with a low latency Long Short-Term Memory network. Furthermore, it also concludes that the proposed model can have practical implications in forecasting GSR, capitalizing its versatility as a stratagem in monitoring solar powered systems by integrating freely available solar radiation into a real power grid system.

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmott’s (WI), Legates & McCabe’s ( E 1 ) and Nash–Sutcliffe coefficients ( E N S ) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent.

Heavy metal pollution of water is a significant environmental and public health concern. Current biological strategies for heavy metal removal from water are performed using microbial biopolymers, including polysaccharides, that are already fully formed. This creates limitations in adapting polysaccharides to increase binding affinity for specific metals. We propose that altering the specificity of polysaccharide-producing enzymes could be beneficial to improving metal capture by modified polysaccharides. We assess binding of Cu2+ and Pb2+ metal cations to Neisseria meningitidis-type polysaccharides. All concentrations of metal cations tested were able to completely bind to colominic acid. This polymer is equivalent to the capsular polysaccharide of N. meningitidis serogroup B comprised of a homopolymer of negatively charged sialic acid. There was slightly less binding observed with N. meningitidis serogroup W, which contains repeating units of the neutral sugar galactose and sialic acid. Our work represents the first assessment of the metal-binding properties of these capsular polysaccharides. Future work will seek to optimize metal-binding with Neisseria meningitidis serogroup W polysaccharide.

This paper points out that to understand development processes, examining aid impacts at local level is as important as studying the impacts of development at the macro level. Studying development at local levels entails understanding the different societal processes triggered off by aid including the undesired and unplanned consequences. DOI: 10.3126/opsa.v11i0.3038 Occasional Papers in Sociology and Anthropology Vol.11 2009 221-243

In spite of the prevailing gender norms, which favour men, some legislation passed by the Nepali government acknowledges the role of Nepali women as irrigators. Policies which increase and strengthen women’s participation in the irrigation have been formulated. This article explores how these policies have been implemented and suggests measures which can be taken to further bolster women’s participation in this sector. Water Nepal Vol.11(2) 2004 pp.45-61