Assessment of the Nexus between Groundwater Extraction and Greenhouse Gas Emissions Employing Aquifer Modelling

AbstractOne of the main sources of Greenhouse Gas Emissions (GHG) is electricity consumption which is getting used for different purposes.Water pumping, especially, pumping from deep groundwater resources consumes a lot of energy. In arid and semi-arid areas, in which groundwater is the only source of water, water pumping is done for different purposes such as agricultural, industrial and urban uses. Kerman plain is one of these arid and semi-arid areas which is located in South East of Iran. Groundwater reliance and aquifer decline are the most prominent challenges that this area is faced with in recent years. This challenges increase the demand for more electricity consumption to pump water from the aquifer so that CO2 emissions will be increased. A large percentage of water extraction from the aquifer is used for agricultural purposes. In this paper, by modelling Kerman plain aquifer with MODFLOW software by using Geographical Information System (GIS) database and also studying height of groundwater table from 1999 to 2012, electricity energy consumption of groundwater extraction for agricultural, industrial and urban water supply is calculated and the CO2 emissions trends resulted from electricity energy consumption is evaluated. Then model results are examined for a business as usual (BAU) scenario of changes in water resources. As a result the amount of CO2 emitted from groundwater abstraction by three mentioned sectors is calculated for specified time horizon. Finally, some suggestions are presented for reducing greenhouse gas emissions for the time horizon

The prevalence of metabolic syndrome in psoriatic arthritis patients, a hospital-based cross-sectional study on Iranian population

Background: Psoriasis is a T-cell mediated chronic inflammatory disorder with multiple skin, nails and joints involvement. The reported prevalence of psoriatic arthritis varies from 5 to 42 cases per 100 psoriasis patients. Insulin resistance is believed to be central to the pathogenesis of metabolic syndrome, a constellation of major risk factors for cardiovascular diseases, including atherogenic dyslipidemia, truncal adiposity, hypertension and hyperglycemia. The association of psoriasis and psoriatic arthritis with metabolic syndrome is increasingly being reported. Although the literature relating psoriatic arthritis to metabolic syndrome is accumulating, there is still a paucity of evidence, especially from Asia. Here, we examined the prevalence of metabolic syndrome and its components in patients with psoriatic arthritis. Methods: The study was performed among outpatients attending the specialty clinic and rheumatology ward of Rasoul-e-Akram general hospital between January 2014 and April 2015. A consecutive sample of 80 patients diagnosed as having psoriatic arthritis was studied. Age, gender, body mass index, blood pressure and waist circumference, and history of smoking of patients were measured and asked at the enrolment visit. Venous samples were taken after 8 h of overnight fasting for the estimation of serum lipid profile, glucose and uric acid levels. Also an ultrasonographic examination was done for detection of non-alcoholic fatty liver disease. Results: 46 patients (57.5) were male and 34 patients (42.5) were female. Mean age of the participants was 43 years (SD: 11.3). The prevalence of abnormal components of metabolic syndrome was 53.8 for BMI, 48.8 for TG level, 50 for HDL, 46.3 for LDL, 45 for Cholesterol, 23.8 for FBS, 46 for waist circumflex in men and 47.7 in women and 42.5 for uric acid. 40 of the patients had abnormal SBP and 41.2 had abnormal DBP. Thirty percent of the participants were current smokers and 43.8 had NAFLD on ultrasonographic examination. Conclusion: 51.3 of patients had metabolic syndrome according to the adult treatment panel III criteria for adult Asian patients. � 2016, Tehran University of Medical Sciences. All rights reserved

An investigation on relationship of chemical indices of kilka (Clupeonella engrauliformis) with weight loss during cold storage at -18C

We studied the relationship between physical and chemical properties of frozen kilka with weight loss for packed and unpacked products during storage at amal 8 ' C. Statistical analysis of the results including variance, Duncan test and ANOVA showed relationships existed between changes in Total Volatile Nitrogen (TVN), Peroxide Value (PV), pH, moisture, organoleptic properties of frozen packed and unpacked kilka with product weight losses during cold storage at -18°C. The statistical treatment of the results showed that weight losses for packed samples in comparison to unpacked one at the level of P<0.0I was significant. The weight losses, changes of TVN, PV, pH and moisture losses for unpacked samples were 1.5, 1.35, and 4.5, 132 and 1.32 times more in comparison to the packed one, respectively. Also, the statistical analysis of the results showed a correlation between weight losses in unpacked samples of frozen kilka and the measured factors. The results of chemical and physical properties measured for packed samples of frozen kilka during cold storage and their statistical analysis showed a significant correlation P<0.01 between weight losses and the changes in TVN from 7 to 28mg/100gr, PV from 2.28 to 6.01meq/kg, pH from 6.08 to 6.37 and 1.72% loss in the moisture of the samples. According to these results and the organoleptic tests, the shelf life for packed and unpacked frozen kilka in cold storage at 48°C, is recommended 60 and 30 days, respectively

Immunomodulatory Effect of Toll-Like Receptor-3 Ligand Poly I:C on Cortical Spreading Depression

The release of inflammatory mediators following cortical spreading depression (CSD) is suggested to play a role in pathophysiology of CSD-related neurological disorders. Toll-like receptors (TLR) are master regulators of innate immune function and involved in the activation of inflammatory responses in the brain. TLR3 agonist poly I:C exerts anti-inflammatory effect and prevents cell injury in the brain. The aim of the present study was to examine the effect of systemic administration of poly I:C on the release of cytokines (TNF-α, IFN-γ, IL-4, TGF-β1, and GM-CSF) in the brain and spleen, splenic lymphocyte proliferation, expression of GAD65, GABAAα, GABAAβ as well as Hsp70, and production of dark neurons after induction of repetitive CSD in juvenile rats. Poly I:C significantly attenuated CSD-induced production of TNF-α and IFN-γ in the brain as well as TNF-α and IL-4 in the spleen. Poly I:C did not affect enhancement of splenic lymphocyte proliferation after CSD. Administration of poly I:C increased expression of GABAAα, GABAAβ as well as Hsp70 and decreased expression of GAD65 in the entorhinal cortex compared to CSD-treated tissues. In addition, poly I:C significantly prevented production of CSD-induced dark neurons. The data indicate neuroprotective and anti-inflammatory effects of TLR3 activation on CSD-induced neuroinflammation. Targeting TLR3 may provide a novel strategy for developing new treatments for CSD-related neurological disorders. © 2014, Springer Science+Business Media New York

Immunomodulatory Effect of Toll-Like Receptor-3 Ligand Poly I:C on Cortical Spreading Depression

The release of inflammatory mediators following cortical spreading depression (CSD) is suggested to play a role in pathophysiology of CSD-related neurological disorders. Toll-like receptors (TLR) are master regulators of innate immune function and involved in the activation of inflammatory responses in the brain. TLR3 agonist poly I:C exerts anti-inflammatory effect and prevents cell injury in the brain. The aim of the present study was to examine the effect of systemic administration of poly I:C on the release of cytokines (TNF-α, IFN-γ, IL-4, TGF-β1, and GM-CSF) in the brain and spleen, splenic lymphocyte proliferation, expression of GAD65, GABAAα, GABAAβ as well as Hsp70, and production of dark neurons after induction of repetitive CSD in juvenile rats. Poly I:C significantly attenuated CSD-induced production of TNF-α and IFN-γ in the brain as well as TNF-α and IL-4 in the spleen. Poly I:C did not affect enhancement of splenic lymphocyte proliferation after CSD. Administration of poly I:C increased expression of GABAAα, GABAAβ as well as Hsp70 and decreased expression of GAD65 in the entorhinal cortex compared to CSD-treated tissues. In addition, poly I:C significantly prevented production of CSD-induced dark neurons. The data indicate neuroprotective and anti-inflammatory effects of TLR3 activation on CSD-induced neuroinflammation. Targeting TLR3 may provide a novel strategy for developing new treatments for CSD-related neurological disorders. © 2014, Springer Science+Business Media New York

Energy conditions in f(R) gravity and Brans-Dicke theories

The equivalence between f(R) gravity and scalar-tensor theories is invoked to study the null, strong, weak and dominant energy conditions in Brans-Dicke theory. We consider the validity of the energy conditions in Brans-Dicke theory by invoking the energy conditions derived from a generic f(R) theory. The parameters involved are shown to be consistent with an accelerated expanding universe.Comment: 9 pages, 1 figure, to appear in IJMP

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Data Availability Statement: The EEG dataset is available online at https://mindbigdata.com/opendb/ (Accessed on 12 February 2020).Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights.This research received no external funding

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying ``hit'' molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug to its protein target. A major drawback of the approaches is that they require exceptional computing capabilities to consider for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently proposed learning paradigm that is able to leverage low-precision binary vector arithmetic to build efficient representations of the data that can be obtained without the need for gradient-based optimization approaches that are required in many conventional machine learning and deep learning approaches. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated for a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encoding algorithms that leverage the extended connectivity fingerprint (ECFP) algorithm. We show that HDC-based inference methods are as much as 90 times more efficient than more complex representative machine learning methods and achieve an acceleration of nearly 9 orders of magnitude as compared to inference with molecular docking. We demonstrate multiple approaches for the encoding of molecular data for HDC and examine their relative performance on a range of challenging molecular property prediction and drug-protein binding classification tasks. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools

Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network

Data Availability Statement: The EEG-ImageNet dataset used in this study is publicly available in this address: https://tinyurl.com/eeg-visual-classification (accessed on 10 October 2022).Copyright © 2022 by the authors. Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.This research received no external funding