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This study aims to measure the relationship between technological progress, renewable energy, and green economic growth (GEG). This study uses a data envelopment analysis (DEA) estimation method to evaluate the association between government expenditure on research and development (R&D), renewable energy deployment, and GEG in the Economic Community of West African States (ECOWAS) between 1990 and 2018. The estimates revealed an inconsistent GEG indicator in the analysis, suggesting the lesser impact disposition of public policy. In addition, the energy efficiency ratio of ECOWAS subregion is under 0.50, implying energy poverty in the sub-region. Many people do not have sufficient energy to heat and cool their homes to enough temperature and meet their basic needs and energy security concerns. This research discovered that a percentage growth increase in renewable energy deployment results in a 3.2% increase in growth in sustainable performance. Alongside an essential effect of one percentage point growth in R&D expenditure boosts economic system sustainable performance to 4.4% combined with a supported effect of one percent. This research reveals that the ECOWAS government expenditure on human resources and R&D of sustainable energy resources would result in a low carbon growth via an advanced technological production process; nevertheless, the impacts are varied in the various countries in ECOWAS. (c) 2022 Elsevier Ltd. All rights reserved.

The study analyzes the relationship between external debt and economic growth in the South Asian region. The panel ordinary least square (OLS), fixed effect, Quantile regression, and robust output regression were used to analyze the World Bank data from 2000 to 2018. South Asian countries, Afghanistan, Bangladesh, Bhutan, India, Pakistan, Sri Lanka, Maldives, and Nepal, were included in the assessment. The analysis exhibited that external debt has a negative impact, and on the other hand, external debt stock has a positive impact on economic growth. The robust regression analysis substantiated the findings and yielded total external debt and external debt service impact of 39% and 31%, respectively. The study also showed that gross capital formation and trade openness have a positive effect on economic growth. Moreover, compared to domestic debt, Threshold analysis reveals that the external debt becomes a drag on growth and instigates a more substantial adverse effect on growth (due to the rising indebtedness of a country). Thus, the study serves as a base for policymakers and government officials to upsurge economic growth while reducing the foreign debt of the economy. Even though the presence of high borrowing costs, better institutional quality can help alleviate the adverse impact of external borrowing on growth.

Global warming and pollution resulted from the increased power demand are some of the present era challenges associated with power generation through fossil fuels. To provide a solution, renewable energy systems are gaining much interest to capture energy through different energy harvesting devices. Among the energy harvesting devices, the thermoelectric generators (TEGs) are the ones with the ability to directly convert thermal energy into electrical energy without any environmental effects. However, their conventional fabrication methods are costly and possess environmental effects. This research reports an organic TEG fabricated through a cost-efficient and environmentally friendly process known as the direct ink write technique. The TEGs are printed onto a glass substrate, the most common insulator used in houses, and building windows with two different lengths. The active materials used are poly (3,4-ethylene dioxythiophene) polystyrene sulfonate (PEDOT: PSS) ink which is an organic semiconducting material and silver ink. The maximum power outputs of 5.17 ± 0.5 nW and 4.08 ± 0.5 nW are recorded for TEGs with different characteristic lengths of 30 and 40 mm at a temperature gradient of 120 °C. Furthermore, the performance of TEG is also characterized for solar thermal energy harvesting. The results obtained are reasonably linear, stable, and comparable to previously reported.

The use of surface electromyogram (sEMG) has grown in the field of gait analysis, prostheses and exoskeleton. Surface electromyogram can directly reflect the human intention for locomotion modes and can be used as a source of control for lower limb prosthesis. The variations factors such as non-Gaussian nature of sEMG signal and mobility of amputees have been observed to degrade the activity recognition performance. This study investigates the properties of the sEMG signal with the purpose of determining the discriminant features to classify the feature space into various activities especially in the context of amputees. To address the variations in activity recognition performance, this study proposed the magnitude of bispectrum as a novel feature extraction method that is invariant to the variations factors and an unsupervised feature reduction method was used to extract the discriminant features. Furthermore, sEMG signals from eleven wearable sensors located on the lower limb muscles were recorded from six subjects including four able-bodies, one unilateral transtibial, and one unilateral transfemoral amputee during walking and ramp activities. Distinct muscles were selected using the L1-norm method. Effective classifier namely support vector machine and linear discriminant analysis were used to classify the multi-class sEMG signal patterns. The experimental results consistently showed an average accuracy of 99.7%. Further evaluation on three different types of prostheses revealed that the proposed method is more robust compared to the existing methods. The promising results of this study can be applied potentially in the control of lower limb wearable devices such as prostheses/exoskeletons.

Weeds are one of the most important biological constraint to cotton production, and resulting in a yield losses of up to 90%. The evolution of hundreds of resistant weed species, the lack of new herbicide chemistries, and the increase in weed management costs are all making weed management more arduous for the growers. Hence, a field experiment was conducted to investigate the efficacy of allelopathic crop water extracts (ACWEs) alone and in combination with one third rate of S-metolachlor (717 g a.i. ha−1) for effective weed management in cotton. The treatments investigated were; weedy check, sorghum + brassica water extract (WE) at 1.5 L ha−1, sorghum + sunflower WE at 1.5 L ha−1, sorghum + brassica + sunflower WE at 1.5 L ha−1, sorghum + brassica WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i.ha−1, sorghum + sunflower WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i.ha−1, sorghum + brassica WE + sunflower WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i.ha−1, and S-metolachlor at recommended rate of 2.15 kg a.i. ha−1. Results revealed that pre-emergent application of sorghum + brassica water extract (WE) at 1.5 L ha−1 was the best treatment in terms of effective dry biomass reduction (40%) of Trianthema portulacastrum and Cyperus rotundus, and increase in seed cotton yield (12%). The second best treatment was sorghum + sunflower WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i. ha−1 with yield increase of 11% over the weedy control. In comparison, S-metolachlor at recommended rate 2.15 kg a.i. ha−1 recorded only 4% decrease in weeds dry biomass reduction and 8% increase in seed cotton yield. Hence, it can be predicted that binary combination of sorghum and brassica WE at 1.5 L ha−1 or binary combination of sorghum + sunflower WE at 1.5 L ha−1 with one third dose of S-metolachlor (717 g a.i. ha−1) can be used for effective weed management and increase in seed cotton yield. Furthermore, adoption of this technique will also reduce the herbicide application, which is not only beneficial for the ecosystem but, also minimize the evolution of herbicide-resistant weed species. Keywords: Allelopathy, Chemical weed control, Herbicide, Weeds, Biomass, Seed cotton yield, Environment

Energy indices provide a clear summary to measure the energy performance of the country which is a prerequisite for policy making. Increasing rate of greenhouse gases (GHGs) emissions are causing dangerous climate changes which seem irreversible. Therefore, it is vital to rank countries with the highest GHGs and carbon emissions in order to develop low carbon economies. This requires a comprehensive statistical analysis. In this study, we develop an aggregated composite index (ACI) of energy security and environmental sustainability for each of the world’s highest GHGs and CO2 emitting countries. Our index is based on a comprehensive set of indicators including carbon emission and energy metrics. Composite indicator has been used to combine all the indicators in a holistic way. Higher values show a better efficiency and vice versa. The analysis reveals that there are considerable differences in performances of top and low ranked countries, Canada and Brazil respectively. We ranked the countries based on their efficiency score from top-down. Analysis also provides a roadmap and guidelines for the future policymakers.

Energy security assessment provides a benchmark for policy analysis and identifies the challenges for ensuring energy supplies as well. This paper develops a composite index for assessing the oil supply risk of South Asian countries. The index is based on a comprehensive set of indicators including the ratio of imported oil over GDP, geopolitical risk, market liquidity, GDP per capita, ratio of oil imports over consumption, diversification, oil price volatility, US$ volatility and transportation risk. Results reveal that India is the least oil vulnerable country while Afghanistan and Bangladesh are the most oil vulnerable countries. India's leading score reflects a higher potential to change the oil suppliers while Afghanistan, Bangladesh, Bhutan, and Nepal have the least score confirming them as the highest supply risk associated countries. Policies such as adopting renewable energy technologies, generating nuclear power, diversifying export sources and cutting down oil subsidies can help reduce the impact of oil supply risk.

Hybrid maize farmers have to face diverse kinds of climate, biological, price and financial risks. Farmers' risk perceptions and risk attitudes are essential elements influencing farm operations and management decisions. However, this important issue has been overlooked in the contemporary studies and therefore there is a dearth of literature on this important issue. The present research is therefore, an attempt to fill this gap. This study aims to quantify hybrid maize farmers' perceptions of disastrous risks, their attitudes towards risk and to explore the impacts of various farm and farm household factors on farmers' risk attitudes and risk perceptions. The present study is conducted in four hybrid maize growing districts of Punjab Province, Pakistan, using cross-sectional data of 400 hybrid maize farmers. Risk matrix and equally likely certainty equivalent (ELCE) method are used to rank farmers' perceptions of four catastrophic risk sources including climate, biological, price and financial risks and to investigate farmers' risk aversion attitudes, respectively. Furthermore, probit regression is used to analyze the determinants affecting farmers' risk attitudes and risk perceptions. The results of the study showed that majority of farmers are risk averse in nature and perceive price, biological and climate to be potential sources of risks to their farm enterprise. In addition, analysis divulges that distance from farm to main market, off-farm income, location dummies for Sahiwal and Okara, age, maize farming experience, access to extension agent, significantly (either negatively or positively) influence farmers' risk attitudes and risk perceptions. The study delivers valuable insights for farmers, agricultural insurance sector, extension services researchers and agricultural policy makers about the local understanding of risks to hybrid maize crop in developing countries, like Pakistan, and have implications for research on farmers' adaptation to exposed risks.

Feature selection is an important preprocessing step in high-dimensional regression and classification problems because it helps to avoid the effect of noise, redundant, and irrelevant features on model performance. A variety of methods for feature selection have been proposed in the literature. However, small perturbations in the training data may produce highly different feature subsets; this is known as instability. Evaluating the stability of feature selection approaches has grown in importance and popularity in recent years. This paper introduces a novel stability estimator for measuring the internal and external stability of features subsets chosen using various methods in random subsampling experiments. The proposed estimator evaluates the similarity of features within selected subset as well as measuring the variation with respect to the number of selected features between selected subsets in different subsampling experiments. Furthermore, the asymptotic normality of the proposed stability estimator for large number of subsamples is also established. Experiments are carried out on both simulated and real-world datasets; where results demonstrate the usefulness of the proposed stability estimator.

Plant defence responses can be triggered by the application of elicitors for example chitosan (β-1,4-linked glucosamine; CHT). It is well-known that CHT induces rapid, local production of reactive oxygen species (ROS) and nitric oxide (NO) resulting in fast stomatal closure. Systemic defence responses are based primarily on phytohormones such as ethylene (ET) and salicylic acid (SA), moreover on the expression of hormone-mediated defence genes and proteins. At the same time, these responses can be dependent also on external factors, such as light but its role was less-investigated. Based on our result in intact tomato plants (Solanum lycopersicum L.), CHT treatment not only induced significant ET emission and stomatal closure locally but also promoted significant production of superoxide which was also detectable in the distal, systemic leaves. However, these changes in ET and superoxide accumulation were detected only in wild type (WT) plants kept in light and were inhibited under darkness as well as in ET receptor Never ripe (Nr) mutants suggesting pivotal importance of ET and light in inducing resistance both locally and systemically upon CHT. Interestingly, CHT-induced NO production was mostly independent of ET or light. At the same time, expression of Pathogenesis-related 3 (PR3) was increased locally in both genotypes in the light and in WT leaves under darkness. This was also observed in distal leaves of WT plants. The CHT-induced endoplasmic reticulum (ER) stress, as well as unfolded protein response (UPR) were examined for the first time, via analysis of the lumenal binding protein (BiP). Whereas local expression of BiP was not dependent on the availability of light or ET, systemically it was mediated by ET.

Fumonisin B1 (FB1) is the most harmful mycotoxin which prevails in several crops and affects the growth and yield as well. Hence, keeping the alarming consequences of FB1 under consideration, there is still a need to seek other more reliable approaches and scientific knowledge for FB1-induced cell death and a comprehensive understanding of the mechanisms of plant defence strategies. FB1-induced disturbance in sphingolipid metabolism initiates programmed cell death (PCD) through various modes such as the elevated generation of reactive oxygen species, lipid peroxidation, cytochrome c release from the mitochondria, and activation of specific proteases and nucleases causing DNA fragmentation. There is a close interaction between sphingolipids and defence phytohormones in response to FB1 exposure regulating PCD and defence. In this review, the model plant Arabidopsis and various crops have been presented with different levels of susceptibility and resistivity exposed to various concentration of FB1. In addition to this, regulation of PCD and defence mechanisms have been also demonstrated at the physiological, biochemical and molecular levels to help the understanding of the role and function of FB1-inducible molecules and genes and their expressions in plants against pathogen attacks which could provide molecular and biochemical markers for the detection of toxin exposure.

Hundreds of image encryption schemes based on the conventional 2D image space exist in the literature. Very few were built using the 3D image space. In this study, for better security, we present a new RGB (color image) encryption scheme based on the Dynamic 3D scrambled image (D3DSI), 5D multi-wing-hyperchaotic-system and DNA computing. After providing a color image as input, its three components are reshaped in a 1D array. These three components are concatenated to form a single 1D array. A 3D scrambled image with arbitrary dimensions is created. Pixels from the 1D array are shifted randomly to the different cells of the 3D scrambled image. Afterwards, for the realization of the diffusion effects, an XOR operation is performed between the key image and the 3D scrambled image. Next, the pixels data and the DNA key image are DNA-encoded. The pixels data in the form of DNA strands is once again scrambled using the same 3D scrambled image. Once again, the pixels data in DNA strands is XORed with the DNA-encoded key image. Lastly these DNA strands are transformed back to the decimal form. Out of this decimal form, three constituent color components, i.e., red, green and blue are separated and merged together to form the color cipher image. To obtain plaintext sensitivity, an image characteristic has been used to temper the one initial value of the chaotic map being used. The simulation results and the security analysis both portray better security, defiance to the varied threats and a real world applicability of the proposed image cipher.

Solar thermal system has been envisioned for utility scale applications. However, on the low end the domestic applications can be made realizable through low end automation. Performance of typical solar reflector can be significantly enhanced by using a solar tracking system. An efficient & low energy consuming system can result in improved energy harvesting. In the current study, an experimental analysis of automated domestic scale heliostat field using rack and pinion-based mechanism is performed. The mechanism is controlled using relay module with an Arduino UNO R3-SMD. Experiments are performed in actual working conditions of Taxila, Pakistan. The heliostat field is made to reflect the solar rays to a cavity type central receiver (CTCR) having stainless steel tubing. Experimentations were performed under three different scenarios-tracking using one dimensional (1D) with fixed position heliostats, tracking using two dimensional (2D) with 5 variable positioning heliostats, and tracking using 2D with 8 variable positioning heliostats. The performances of each setup is analyzed parameters such as heating fluid, temperature difference (ΔT), absorbed heat (Qabs), and overall efficiency (ƞ). Performance enhancement was observed using 2D tracking with 8 variable positioning heliostats where ΔT was 64.26 °C, Qabs was 489.89 W, with maximum ƞ was 52.8%. In 2D with 5 variable positioning heliostats and 1D with fixed heliostats, the values obtained were 50.3 °C, 385.5 W, 51.83% and 6.523 °C, 50 W, 45.86% respectively.

Piwi interacting RNA (piRNA) is a recognized group of small non-coding RNA molecules. The piRNA molecules are associated with multiple tumors type diagnosis and drug development. It is also linked to regulate gene expression, suppressing transposon and maintains genome integrity. Due to a vital role of piRNAs in biology, the identification of piRNAs and their function has become an important area of research in computational biology. This paper proposes a robust two-layer predictor called “piRNA (2L)-PseKNC” to improve prediction of piRNAs and their function. The proposed predictor employing hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, unsupervised principal component analysis (PCA) algorithm for discriminant feature selection and deep neural network (DNN) as a classifier. The proposed predictor was designed based on two layers approach. The first layer predicts either the encoded sequence belongs to piRNA or non-piRNA sequence and the second layer predict the selected piRNA sequence is functional piRNA or non-functional piRNA sequence. The overall accuracies of the proposed model using 5-fold cross validation test were 94.73% at first layer and 85.21% at second layer, surpassing the existing predictors with accuracies improvement 7.59% and 2.81% at the first layer and at the second layer respectively.

The analysis of omics data together with knowledge-based interpretation can help obtaining important information regarding different biological processes and to reflect the current physiological status of tissue and cells. The main challenge, however, is to analyze high-dimensional gene expression data consisting of a massive amount of redundant genes in extracting disease-related information. To address this problem, gene selection, that eliminates redundant and irrelevant genes, has been a key step. In current article, a feature selection technique is proposed that exploit correlation based overlapping analysis of expression data across classes. The proposed correlation based overlapping score (COS) technique is compared with state-of-the-art gene selection approaches using real-world benchmark microarray datasets. In an experimental evaluation, the COS algorithm outperforms the other methods with minimum misclassification errors obtained via boosting, random forest and k-nearest neighbour (kNN) classifiers. Moreover, the proposed technique is more stable than the other techniques in gene selection.

Graphene has become a worldwide admired material among researchers and scientists equally due to its unique richness in mechanical strength, electrical conductivity, optical and thermal properties. Researchers have explored that the composite materials based on graphene and metal/metal oxide nanostructures possess excellent potential for energy storage technologies. In particular, supercapacitors based on such composite materials have engrossed the extreme interest of researchers for its rapid charging/discharging time, safe operation and longer cyclic constancy. Till now, several fabrication techniques for composite materials and their energy storage applications have been explored. Here, specially, we have concentrated on the hottest research progress for the fabrication of graphene oxide and metal/metal oxide nanocomposites. We also emphasized on the characteristics and properties of supercapacitors fabricated using these composite materials. Moreover, our study is focused on the specific capacitance and cyclic stability of various composites to haul out the most efficient material for supercapacitor applications.

Elastomeric ablative composites for ultrahigh temperature applications were processed and characterized to elucidate the potential of short carbon fibers (SCF) to tailor the thermo-mechanical and ablation characteristics of acrylonitrile butadiene rubber (NBR) composites. SCF was dispersed within NBR using dispersion kneader and two roller mixing mill. Ablation and thermal properties versus back-face temperature elevation during oxy-acetylene flame test, linear/radial ablation rates, percent char yields, insulation index, and thermal conductivity of the fabricated ablatives were measured. Experimental results revealed that the thermo-mechanical and ablation characteristics were significantly improved with increasing SCF concentration in the presence of coupling agent. Improvement in tensile strength, hardness and reduction in elongation at break were obtained with increasing SCF to matrix ratio. The microscopic analysis of the tensile fracture and ablation specimen showed the porosity generation during ablation and uniform dispersion of the impregnated SCF in NBR.

Hordeum murinum subsp. leporinum (Link) Arcang. (Syn. Hordeum leporinum) and Hordeum murinum subsp. glaucum (Steud.) Tzvelev. (Syn. Hordeum glaucum) are recognized as problematic weeds across all continents. Both these weedy species have similar morphological features, but they can be distinguished by their ploidy levels, climate adaptability and differences in spikelets and floral structure. These weedy species have now become a serious threat to agricultural productivity and have evolved as invasive weeds due to their genetic diversity and biological, physiological and ecological adaptations. Several biological characteristics like seed dormancy, the enclosed barbed awns, rapid germination and growth rate, and early maturation have made these two weedy species highly invasive and adaptable to Mediterranean type climates. These weedy species pose a serious threat to animal productivity as well. The presence of long awn on the spikelets at the flowering time can cause severe production losses, owing to sheep deaths. These weeds also serve as alternate hosts for insects and pathogens in different crops and pastures. The evolution of herbicide resistance against the main sites of action viz. acetolactate synthase (ALS) inhibitors, acetyl co-enzyme A carboxylase (ACCase) inhibitors, and photosystem I (PS I) inhibitors have made these weedy species more problematic for the farming community. A detailed investigation of the biology, ecology, herbicide resistance mechanisms and management of these weedy species is essential to understand their population dynamics under different management scenarios and develop integrated, multi-tactic strategies to prevent further development and spread of resistance of these weeds.

The present investigation reports the fabrication of thin film composite nanofiltration (TFC-NF) membranes using interfacial polymerization technique for desalination. Ethylene diamine (EDA) and terephthaloyl chloride (TPC) were employed as aqueous and organic phase monomers, respectively to develop polyamide thin layer on the surface of Celgard 2400. The prepared membranes were characterized through Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and atomic force microscopy (AFM). The molecular weight cutoff of TFC-NF membranes was estimated to be below 342 Da using dextrose, sucrose and raffinose solutions. A low cost dead end NF Cell was designed and manufactured domestically to conduct permeation experiments. The effect of different reaction conditions including concentration of monomers, residence time in each monomer and curing temperature on the membrane performance (water flux and MgCl 2 rejection) was also studied. The water flux was augmented linearly at higher applied pressure while MgCl 2 rejection was remained constant. The obtained water flux and MgCl 2 rejection were measured 33 L/m 2 h and 90%, respectively at 7 bar applied pressure. Beside this, membrane stability was analyzed, which confirmed the excellent constancy of ethanol and n -hexane fluxes even after an extended period. Hagen–Poiseuille equation was applied to estimate the TFC-NF membrane pore size ∼0.45 nm.

Proper orientation of plants in the field and management of soil moisture for appropriate utilization of land, water and environmental resources plays a significant role in the optimum development and functioning of vital plant organs. A two factor field experiment was conducted for two consecutive crop growth seasons viz. 2006–07 and 2007–08 at Research and Demonstration Farm, Regional Agricultural Economic Development Centre (RAEDC), Vehari, Pakistan to make a comparison of four different planting geometries viz. planting in 22 cm apart rows under conventional, minimum and zero tillage, respectively and planting in 11 cm apart rows under conventional tillage system. Wheat cultivar, Inqlab-91 was planted late in December. Crop was subjected to five irrigation levels in which irrigation was applied equivalent to 120%, 100%, 80%, 60% or 40% of ETo. Lower soil bulk density and penetration resistances at 10–20 cm soil depth were recorded with conventional tillage with either narrow or wider row spacing as compared to other planting geometries. The maximum values for LAI, LAD, TDM, productive tillers (m−2), 1000-grain weight and grain yield were recorded with planting geometry having 11 cm apart rows under conventional tillage system along with irrigation level of 120% ETo that remained statistically at par with the same planting geometry subjected to the irrigation regime of 100% ETo. This planting geometry also resulted in minimum weed fresh biomass. It is concluded that late planted wheat crop planted in 11 cm wide rows under conventional tillage irrigated @ 100% ETo may serve as an appropriate technology for enhancing the wheat productivity of late sown wheat crop under limited water supplies.

Further structure–activity relationships of a novel series of fungal efflux pump inhibitors with respect to potentiation of the activity of fluconazole against strains of C. albicans and C. glabrata over-expressing ABC-type efflux pumps are systematically explored. Rat protein binding and pharmacokinetics of selected analogues are reported.

Structure-activity relationships of a novel series of fungal efflux pump inhibitors with respect to potentiation of the activity of fluconazole against strains of Candida albicans and Candida glabrata over-expressing ABC-type efflux pumps are systematically explored.