The current study purposes to assess the impact of institutional pressures (coercive, mimetic, and normative) on environmental performance of the firm along with the mediation of implementation of ...environmental management accounting and moderation of environmental proactivity between them. Quantitative data are collected through structured questionnaire from 500 middle‐level and top‐level managers and owners of manufacturing firms of Pakistan. Data are analyzed through SPSS and AMOS in which structural equation modeling in performed to assess hypotheses. Findings show that coercive, mimetic, and normative pressures are significant derivers of environmental performance. It is further found that coercive, mimetic, and normative pressures significantly enhance the implementation of environmental management accounting, which in turn enhances the environmental performance of the firm. Furthermore, environmental proactivity is found to be a significant moderator between mimetic pressure and environmental performance while no moderation by environmental proactivity is confirmed in relationships of coercive pressure and normative pressure with environmental performance. The current findings are anticipated to assist practitioners in strategy development and execution to improve environmental performance of the firm and policymaking to control environmental impacts of businesses and improve environmental conditions in the country. The current study is the first one to empirically examine the moderating role of environmental proactivity and mediating role of execution of environmental management accounting between institutional pressures and environmental performance in context of Pakistan; so, it will open new areas of discussion and analysis for researchers in the domain of institutional theory.
Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human ...efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.
•Very optimistic results for the automated discrimination of schizophrenia using state-of-the-art 3D deep learning architecture.•For the classification, we have used 3D convolutional neural networks ...architectures.•We achieve very high diagnostic accuracy with an area under the curve of 0.9982 and accuracy of 98.09% (p < 0.001).•With this accuracy this research may be translated into an excellent tool to assist clinicians.•3D ICA based functional connectivity networks were used as the input features of the classifier.
This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 patients with schizophrenia and 72 healthy controls) was obtained from a publicly available dataset using a three-dimensional convolution neural network 3D-CNN based deep learning classification framework and ICA based features.
We achieved 98.09 ± 1.01% ten-fold cross-validated classification accuracy with a p-value < 0.001 and an area under the curve (AUC) of 0.9982 ± 0.015. In addition, differences in functional connectivity between the two groups were statistically analyzed across multiple resting-state networks. The disconnection between the visual and frontal network was prominent in patients, while they showed higher connectivity between the default mode network and other task-positive/ cerebellar networks. These ICA functional network maps served as highly discriminative three-dimensional imaging features for the discrimination of schizophrenia in this study.
Due to the very high AUC, this research with more validation on the cross diagnosis and publicly available dataset, may be translated in future as an adjunct tool to assist clinicians in the initial screening of schizophrenia.
Selenium nanoparticles (SeNPs) have advantages over other nanomaterials because of the promising role of selenium in the stabilization of the immune system and activation of the defense response. The ...use of SeNPs and their supplements not only have pharmacological significance but also boost and prepare the body's immune system to fight the pathogens. This review summarizes the recent progress in the biogenesis of plant-based SeNPs by using various plant species and the role of secondary metabolites on their biocompatible functioning. Phyto-synthesis of SeNPs results in the synthesis of nanomaterials of various, size, shape and biochemical nature and has advantages over other routine physical and chemical methods because of their biocompatibility, eco-friendly nature and in vivo actions. Unfortunately, the plant-based SeNPs failed to attain considerable attention in the pharmaceutical industry. However, a few studies were performed to explore the therapeutic potential of the SeNPs against various cancer cells, microbial pathogens, viral infections, hepatoprotective actions, diabetic management, and antioxidant approaches. Further, some of the selenium-based drug delivery systems are developed by engineering the SeNPs with the functional ligands to deliver drugs to the targeted sites. This review also provides up-to-date information on the mechanistic actions that the SeNPs adopt to achieve their designated tasks as it may help to develop precision medicine with customized treatment and healthcare for the ailing population.
Drought stress is reducing the production of crops globally. This research was designed to evaluate the role of titanium dioxide (TiO2 NPs) nanoparticles and calcium phosphate on wheat facing drought ...stress. TiO2 NPs were prepared by green synthesis and their characterization (by UV–visible spectroscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX)) was also done. The results showed that TiO2 NPs worked efficiently and improved plant growth under drought. However, the best results were obtained from combined applications of 40 ppm TiO2 NPs and 40 ppm calcium phosphate on plants. They increased root length (33%), shoot length (53%), fresh weight (48%), and dry weight (44%) of wheat as compared to control. The physiological parameters including chlorophyll content, relative water content, membrane stability index, and osmolyte content (proline and sugar) were also improved. The increase in superoxide dismutase, peroxidase and, catalase activity by the combined application of TiO2 NPs and calcium phosphate was 83% and 78%, 74% and 52%, 81%, and 67% in Pakistan-13 and Zincol-16 respectively, as compared to untreated drought exposed plants. They also enhanced the nutrients uptake (including potassium, phosphorus, and nitrogen) that ultimately improved plant biomass. They also maintained the level of growth hormones in plants. These hormones regulate cellular processes and are responsible for germination, development, and plant reaction in drought stress. The increase in the yield was also significant, hence it is recommended that the 40 ppm concentration of TiO2 NPs along with calcium phosphate improves the productivity of wheat under drought stress.
•Drought stress affects growth, physio-biochemical attributes, activity of enzymatic antioxidants and productivity of wheat plants.•Application of Titanium dioxide nanoparticles (TiO2 NPs) mitigated the negative effects of drought stress.•Calcium supplementation improved the growth and development of wheat plantsunder drought stress.•Combined application of TiO2 NPs and calcium improved all parameters under drought stress.
This paper applies modified analytical methods to the fractional-order analysis of one and two-dimensional nonlinear systems of coupled Burgers and Hirota–Satsuma KdV equations. The Atangana–Baleanu ...fractional derivative operator and the Elzaki transform will be used to solve the proposed problems. The results of utilizing the proposed techniques are compared to the exact solution. The technique’s convergence is successfully presented and mathematically proven. To demonstrate the efficacy of the suggested techniques, we compared actual and analytic solutions using figures, which are in strong agreement with one another. Furthermore, the solutions achieved by applying the current techniques at different fractional orders are compared to the integer order. The proposed methods are appealing, simple, and accurate, indicating that they are appropriate for solving partial differential equations or systems of partial differential equations.
Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential ...to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer’s disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South ...Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R
2
value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.
Drought is one of the deadly natural disasters that leave tearstained faces and broken dreams in its wake. Lifecycle as we know it comes to a halt during a dry season in a region. The purpose of this ...study was to observe the temporal and spatial variation of droughts in the rain-fed area of Potohar plateau (22,254 km
2
), Punjab, Pakistan, from 2000 to 2015, through remotely sensed satellite data, available at the database of Google Earth Engine. Potohar consists of four major districts of the country; Chakwal, Attock, Rawalpindi, and Jhelum. From 2000 to 2015, indices calculated were: standard precipitation index (SPI), standard precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), precipitation condition index (PCI), soil moisture condition index (SMCI), and temperature condition index (TCI). In this study, SPI and SPEI pointed out meteorological droughts in 2000, 2001, 2002, 2004, 2009, 2010, and 2012, which were taken as base years for drought in the study. The study concluded that the main factor involved in the drought severity is not one, but rather a combined accumulation of temperature, precipitation, and soil moisture. Soil moisture and precipitation affect the vegetation in the area more so than the temperature of the land surface. Soil moisture was heavily influenced by the amount of precipitation. The land surface temperature was seasonal dependent. The surface temperature was warmest in Chakwal and Attock, while Rawalpindi had the coldest land surface temperature. Soil moisture increased with precipitation. Soil moisture was high in Rawalpindi and Attock during drought years.
Plant extract-based green synthesis of nanoparticles is an emerging class of nanotechnology that has revolutionized the entire field of biological sciences. Green synthesized nanoparticles are used ...as super-growth promoters and antifungal agents. In this study, selenium nanoparticles (SeNPs) were synthesized using Melia azedarach leaves extract as the main reducing and stabilizing agent and characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray (EDX), and fourier transform infrared spectrometer (FTIR). The green synthesized SeNPs were exogenously applied on Mangifera indica infected with mango malformation disease. The SeNPs at a concentration of 30 μg/mL were found to be the best concentration which enhanced the physiological (chlorophyll and membrane stability index), and biochemical (proline and soluble sugar) parameters. The antioxidant defense system was also explored, and it was reported that green synthesized SeNPs significantly reduced the biotic stress by enhancing enzymatic and non-enzymatic activities. In vitro antifungal activity of SeNPs reported that 300 μg/mL concentration inhibited the Fusarium mangiferae the most. This study is considered the first biocompatible approach to evaluate the potential of green synthesized SeNPs to improve the health of mango malformation-infected plants and effective management strategy to inhibit the growth of F. mangifera.
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