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

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

Data Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.This research received no external funding

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks

Data Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications.This research received no external funding