This study investigates the impact of globalization, financial development, and energy utilization on environmental sustainability in the Gulf Cooperation Council (GCC) countries. GCC countries are ...currently experiencing higher demand and utilization of energy resources, high global integration, and improvements in the financial sector that poses serious environmental sustainability challenges. We have employed a relatively comprehensive proxy, i.e., ecological footprint for environmental sustainability and more advanced and robust econometric strategies (second-generation) to examine the impact of globalization, financial development, and energy utilization on environmental sustainability in the GCC countries, which have a significant departure from the extant literature. The results of this study show that globalization, financial development, and energy utilization are significantly deteriorating the environmental quality in the GCC countries. Additionally, in order to account for the national heterogeneity, we have performed country-specific analysis and interestingly, results reveal that globalization, financial development, and energy utilization negatively influence the environmental sustainability in each sample country that is consistent with the findings of overall panel. Furthermore, the findings are robust to various robustness checks that we have performed for checking the reliability of our main findings. This study also offers some useful policy implications to the stakeholder in general and specifically concerning the GCC countries for promoting their environmental sustainability.
•Pretreatments of lignocellulose materials were reviewed for the improvement of AD.•Chemical pretreatments produced various inhibitors which inhibit the AD process.•Combination of physical & ...biological pretreatments can be improved the AD process.
To overcome the energy demand and environmental issues by applying ecofriendly techniques to produce green energy are getting high attentions in these days. Lignocellulosic materials are the most abundant resource of biomass on earth and these materials have become vital for anaerobic digestion. However, the structure of lignocellulosic materials is difficult to degrade during anaerobic digestion. Physical, thermal, chemical and thermochemical techniques have been widely used for the pretreatment of lignocellulosic materials, which might facilitate the degradation of pretreated lignocellulosic materials during anaerobic digestion. Further, in recent years many updated and advanced techniques have also been developed for the pretreatment of lignocellulosic materials. Therefore, this study systematically summaries the novel techniques and chemicals used for physical, thermal, chemical and thermochemical pretreatments of lignocellulosic materials for the enhancement of anaerobic digestion. In addition, the mechanism, applications, advantages and disadvantages and challenges of each type of the pretreatment have also been identified. Finally, this study will provide new ideas for the development of new pretreatment techniques.
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Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine learning methods have surfaced to diagnose ...Alzheimer's disease. Resting-state fMRI is a neuroimaging modality that has been widely utilized to study brain activity related to neurodegenerative diseases. In literature, the previous studies are limited to the binary classification of Alzheimer's disease and Mild Cognitive Impairment. The application of computer-aided diagnosis for the numerous advancing phases of Alzheimer's disease, on the other hand, remains understudied. This research analyzes and presents methods for multi-label classification of six Alzheimer's stages using rs-fMRI and deep learning. The proposed model solves the multi-class classification problem by extracting the brain's functional connectivity networks from rs-fMRI data and employing two deep learning approaches, Stacked Sparse Autoencoder and Brain Connectivity Graph Convolutional Network. The suggested models' results were assessed using the k-fold cross-validation approach, and an average accuracy of 77.13% and 84.03% was reached for multi-label classification using Stacked Sparse Autoencoders and Brain Connectivity Based Convolutional Network, respectively. An analysis of brain regions was also performed by using the network's learned weights, leading to the conclusion that the precentral gyrus, frontal gyrus, lingual gyrus, and supplementary motor area are the significant brain regions of interest.
•The study presents a framework for multi-label classification of six Alzheimer's disease stages using state-of-the-art deep learning methodology by extracting the brain's functional connectivity networks from the rs-fMRI.•The work is exemplary for Alzheimer's disease diagnosis using resting-state fMRI in order to study brain activity related to neurodegeneration.•A novel pipeline is presented to process rs-fMRI data was used to extract functional connectivity maps from a benchmark dataset on Alzheimer's disease.•State-of-the-art two deep learning methods are applied, and the performance of the models is evaluated using cross-validation, where results are better than other methods.•An analysis of brain regions has been performed using the network's learned weights, leading to the identification of the significant brain regions of interest.
•Greater Cd, Cr, Ni, Mn, Pb recorded in wastewater irrigated soils and food crops.•Cd in soil; Pb, Cd, Cr in irrigated water and food crops surpassed permissible limit.•Wastewater irrigation for food ...crops is a potential threat to human health.•Health risk index >1 for Pb, Cd and Mn in food crops cause potential health risk.
The current study was designed to investigate the potential human health risks associated with consumption of food crops contaminated with toxic heavy metals. Cadmium (Cd) concentration in surface soils; Cd, lead (Pb) and chromium (Cr) in the irrigation water and food crops were above permissible limits. The accumulation factor (AF) was >1 for manganese (Mn) and Pb in different food crops. The Health Risk Index (HRI) was >1 for Pb in all food crops irrigated with wastewater and tube well water. HRI >1 was also recorded for Cd in all selected vegetables; and for Mn in Spinacia oleracea irrigated with wastewater. All wastewater irrigated samples (soil and food crops) exhibited high relative contamination level as compared to samples irrigated with tube well water. Our results emphasized the need for pretreatment of wastewater and routine monitoring in order to avoid contamination of food crops from the wastewater irrigation system.
The optoelectrical and magnetic characteristics of naturally existing iron-based nanostructures, especially hematite and magnetite nanoparticles (H-NPs and M-NPs), gained significant research ...interest in various applications, recently. The main purpose of this Review is to provide an overview of the utilization of H-NPs and M-NPs in various environmental remediation. Iron-based NPs are extensively explored to generate green energy from environmental friendly processes such as water splitting and CO
conversion to hydrogen and low molecular weight hydrocarbons, respectively. The latter part of the Review provided a critical overview to use H-NPs and M-NPs for the detection and decontamination of inorganic and organic contaminants to counter the environmental pollution and toxicity challenge, which could ensure environmental sustainability and hygiene. Some of the future perspectives are comprehensively presented in the final portion of the script, optimiztically, and it is supported by some relevant literature surveys to predict the possible routes of H-NPs and M-NPs modifications that could enable researchers to use these NPs in more advanced environmental applications. The literature collection and discussion on the critical assessment of reserving the environmental sustainability challenges provided in this Review will be useful not only for experienced researchers but also for novices in the field.
Star-shaped three-dimensional (3D) twisted configured acceptors are a type of nonfullerene acceptors (NFAs) which are getting considerable attention of chemists and physicists on account of their ...promising photovoltaic properties and manifestly promoted the rapid progress of organic solar cells (OSCs). This report describes the peripheral substitution of the recently reported highly efficient 3D star-shaped acceptor compound, STIC, containing a 2-(3-oxo-2,3-dihydroinden-1-ylidene)malononitrile (IC) end-capped group and a subphthalocyanine (SubPc) core unit. The 3D star-shaped SubPc-based NFA compound STIC is peripherally substituted with well-known end-capped groups, and six new molecules (S1–S6) are quantum chemically designed and explored using density functional theory (DFT) and time-dependent DFT (TDDFT). Density of states (DOS) analysis, frontier molecular orbital (FMO) analysis, reorganization energies of electrons and holes, open-circuit voltage, transition density matrix (TDM) surface, photophysical characteristics, and charge-transfer analysis of selected molecules (S1–S6) are evaluated with the synthesized reference STIC. The designed molecules are found in the ambience of 2.52–2.27 eV with a reduction in energy gap of up to 0.19 eV compared to R values. The designed molecules S3–S6 showed a red shift in the absorption spectrum in the visible region and broader shift in the range of 605.21–669.38 nm (gas) and 624.34–698.77 (chloroform) than the R phase values of 596.73 nm (gas) and 616.92 nm (chloroform). The open-circuit voltages are found with the values larger than R values in S3–S6 (1.71–1.90 V) and comparable to R in the S1 and S2 molecules. Among all investigated molecules, S5 due to the combination of extended conjugation and electron-withdrawing capability of end-capped acceptor moiety A5 is proven as the best candidate owing to promising photovoltaic properties including the lowest band gap (2.27 eV), smallest λe = 0.00232 eV and λh = 0.00483 eV, highest λmax values of 669.38 nm (in gas) and 698.77 nm (in chloroform), and highest V oc = 1.90 V with respect to HOMOPTB7‑Th–LUMOacceptor. Our results suggest that the selected molecules are fine acceptor materials and can be used as electron and/or hole transport materials with excellent photovoltaic properties for OSCs.
Global warming and climate change have become one of the most embarrassing and explosive problems/challenges all over the world, especially in third-world countries. It is due to a rapid increase in ...industrialization and urbanization process that has given the boost to the volume of greenhouse gases (GHGs) emissions. In this regard, carbon dioxide (CO
2
) is considered a significant driver of GHGs and is the major contributing factor for global warming. Considering the goal of mitigating environmental pollution, this research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression, and Holt’s methods for forecasting CO
2
emission. It attempts to forecast the CO
2
emission of Bahrain. These methods are evaluated for performance. The neural network model has the root mean square errors (RMSE) of merely 0.206, while the Gaussian Process Regression Rational Quadratic (GPR-RQ) Model has RMSE of 1.0171, and Holt’s method has RMSE of 1.4096. Therefore, it can be concluded that the neural network time series nonlinear autoregressive model has performed better for forecasting the CO
2
emission in the case of Bahrain.
Graphene-based metamaterials are gaining popularity for developing various reconfigurable and electrically tunable optical devices - especially in terahertz (THz) and infrared (IR) bands. Therefore, ...in this paper, we aim to investigate the broadband metamaterial-based absorber that efficiently absorbs the THz radiation ranging from 2.2 to 4.6 THz. The proposed absorber comprises a simple meta-square ring of graphene, which possesses different slots in its structure to induce multiple plasmonic resonances. It is observed that the proposed absorber manifests above 95% absorption for the normally incident THz waves, and it also maintains its absorption value over 80% for different obliquely incident operating conditions. Furthermore, the proposed absorber shows polarization-insensitive features. In addition, the absorption characteristics regulate from 95% to 15% by adjusting the chemical potential of graphene from 1 eV to 0.1 eV. Some of the salient features of the proposed absorber is largest reported bandwidth for single layer absorber with smallest footprint without sacrificing polarization insensitivity or amplitude tunability. From the application point of view, it could provide the pathway for implementing switching, cloaking, smart absorbers, and detection phenomena in the THz range.
There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of ...internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.
Photodegradation of organic pollutants is considered to be the most suitable and cheaper techniques to counter the contamination issues. Metal nanoparticles are considered to be the most effective ...heterogenous photocatalysts for the photodegradation of organic pollutants. Besides, CuO oxide nanoparticles are well-known photocatalysts for photocatalytically degrading organic pollutants. Herein, we reported the synthesis of pure copper oxide nanoparticles (CuO NPs) and nanoclay-supported copper oxide nanoparticles (CuO/NC NPs) by facile chemical reduction technique for swift photodegradation of organic dye. The X-ray diffractogram (XRD) has demonstrated a typical monoclinic phase of CuO NPs. The morphological features via scanning electronic microscopy (FESEM) showed agglomerated morphology of CuO NPs with 372.57 ± 1.76 nm average particle size. The micrographs also revealed the homogenous dispersion of CuO NPs over NC surface in CuO/NC nanocomposite. A polydispersity index (PDI) of 0.39 presented slight variation in the particle size of CuO NPs, which is also supported by the results obtained from atomic force microscopy (AFM), FESEM and transmission electron microscopy (TEM). CuO/NC NPs demonstrated outstanding methyl orange degradation over a very short period of time under simulated light. Using CuO/NC NPs, about 97.18% and 95.96% dye degradations were achieved in merely 4 min, under UV and visible light, respectively. The excellent photodegradation efficacy of CuO/NC NPs can be attributed to the homogenous distribution of CuO NPs, which facilitates the generation of photoexcitons (electrons and holes), enhances charge transfer and minimizes the charge recombination. The NC induced the required photostability by providing sufficient space for NPs distribution.
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