The therapeutic aspects of the endocannabinoid system (ECS) for cancer and their development: from nature to laboratory

The endocannabinoid system (ECS) is a group of neuromodulatory lipids and their receptors, which are widely distributed in mammalian tissues. ECS regulates various cardiovascular, nervous, and immune system functions inside cells. In recent years, there has been a growing body of evidence for the use of synthetic and natural cannabinoids as potential anticancer agents. For instance, the CB1 and CB2 receptors are assumed to play an important role inside the endocannabinoid system. These receptors are abundantly expressed in the brain and fatty tissue of the human body. Despite recent developments in molecular biology, there is still a lack of knowledge about the distribution of CB1 and CB2 receptors in the human kidney and their role in kidney cancer. To address this gap, we explore and demonstrate the role of the endocannabinoid system in renal cell carcinoma (RCC). In this brief overview, we elucidate the therapeutic aspects of the endocannabinoid system for various cancers and explain how this system can be used for treating kidney cancer. Overall, this review provides new insights into cannabinoids' mechanisms of action in both in vivo and in vitro models, and focuses on recent discoveries in the field

Performance Evaluation of RAKE Receiver for UWB Systems using Measured Channels in Industrial Environments

The industrial environments are an important scenario for ultra wideband (UWB) communication systems. However, due to large number of metallic scatterers in the surroundings, the multipath offered by UWB channels is dense with significant energy. In this paper, the performance of RAKE receivers operating in a non line-of-sight (NLOS) scenario in these environments is evaluated. The channels used for the evaluation are measured in a medium-sized industrial environment. In addition, a standard IEEE 802.15.4a channel model is used for comparison with the results of the measured data. The performance of partial RAKE (PRake) and selective RAKE (SRake) is evaluated in terms of uncoded bit-error-rate (BER) using different number of fingers. The performance of maximal ratio combining (MRC) and equal gain combining (EGC) is compared for the RAKE receiver assuming perfect knowledge of the channel state. Finally, based on the simulation results, conclusions are drawn considering the performance and complexity issues for system design in these environments

Autism Spectrum Disorder Detection Based on Wavelet Transform of BOLD fMRI Signals using Pre-trained Convolution Neural Network

Autism spectrum disorder (ASD) is a mental disorder and the main problem in ASD treatment has no definite cure, and one possible option is to control its symptoms. Conventional ASD assessment using questionnaires may not be accurate and required evaluation of trained experts. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported for ASD detection. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. This study aims to present several convolutional neural networks as tools for ASD detection based on temporal dynamic features classification and improve the ASD prediction results. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as GoogLenet, DenseNet201, ResNet18, and ResNet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbours (KNN). The best prediction results are 85.9% accuracy achieved by extracted the features from DenseNet201 network and classified these features by KNN classifier. Comparison with previous studies, has indicated the good  potential of the proposed model for diagnosis of  ASD cases. From another perspective, the presented method can be applied for analysis of rs-fMRI data on other type of brain disorders

LATEST TOURNAISIAN (EARLY CARBONIFEROUS) CONODONTS FROM THE TABAI LIMESTONE, TIRAH, NORTHWESTERN PAKISTAN

A new stratigraphic unit, the Tabai Limestone of the poorly known Tirah area of northwest Pakistan, is one of several Early Carboniferous carbonate units distributed along the North Gondwana margin, some connected with transgressive interludes. The Tabai Limestone has produced latest Tournaisian (Early Carboniferous) conodonts indicative of the middle of the anchoralis-latus Zone

Detecting Zero-day Polymorphic Worms with Jaccard Similarity Algorithm

Zero-day polymorphic worms pose a serious threat to the security of Mobile systems and Internet infrastructure. In many cases, it is difficult to detect worm attacks at an early stage. There is typically little or no time to develop a well-constructed solution during such a worm outbreak. This is because the worms act only to spread from node to node and they bring security concerns to everyone using Internet via any static or mobile node. No system is safe from an aggressive worm crisis. However, many of the characteristics of a worm can be used to defeat it, including its predictable behavior and shared signatures. In this paper, we propose an efficient signature generation method based on string similarity algorithms to generate signatures for Zero-day polymorphic worms. Then, these signatures are practically applied to an Intrusion Detection System (IDS) to prevent the network from such attacks. The experimental results show the efficiency of the proposed approach compared to other existing mechanisms

Statistical Analysis and Comparison of Optical Classification of Atmospheric Aerosol Lidar Data

In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models (GLM) and regression tree techniques are used to further analyze the performance of the LIDAR parameter-based aerosol classification methods. The goal of GLM and regression tree analyses is to compare and contrast distinct classification data schemes, and compare the results with the measured aerosol reflection data in the atmosphere. The detailed statistical comparisons and analyses shows that the optical methods adopted in this study for classification and prediction of various harmful aerosol types such as soot, carbon monoxide (CO), sulfates (SOx), and nitrates (NOx) are efficient under appropriate functional distributions. The article offers a method for natural ordering of the aerosol types

Composite Multi-Criteria Decision Analysis for Optimization of Hybrid Renewable Energy Systems for Geopolitical Zones in Nigeria

This paper presents eight hybrid renewable energy (RE) systems that are derived from solar, wind and biomass, with energy storage, to meet the energy demands of an average household in the six geopolitical zones of Nigeria. The resource assessments show that the solar insolation, wind speed (at 30 m hub height) and biomass in the country range, respectively, from 4.38–6.00 kWh/m2/day, 3.74 to 11.04 m/s and 5.709–15.80 kg/household/day. The HOMER software was used to obtain optimal configurations of the eight hybrid energy systems along the six geopolitical zones’ RE resources. The eight optimal systems were further subjected to a multi-criteria decision making (MCDM) analysis, which considers technical, economic, environmental and socio-cultural criteria. The TOPSIS-AHP composite procedure was adopted for the MCDM analysis in order to have more realistic criteria weighting factors. In all the eight techno-economic optimal system configurations considered, the biomass generator-solar PV-battery energy system (GPBES) was the best system for all the geopolitical zones. The best system has the potential of capturing carbon from the atmosphere, an attribute that is desirous for climate change mitigation. The cost of energy (COE) was seen to be within the range of 0.151–0.156 US$/kWh, which is competitive with the existing electricity cost from the national grid, average 0.131 US$/kWh. It is shown that the Federal Government of Nigeria favorable energy policy towards the adoption of biomass-to-electricity systems would make the proposed system very affordable to the rural households

The Role of Social Media Platforms in Confronting Intellectual Extremism from Majmaah University Students Perspective

The present study aims to investigate the role of social media platforms in confronting intellectual extremism among Majmaah University students. The researchers conducted the descriptive analytical approach and applied a 20- item questionnaire to a randomly selected sample of (213) students. The results showed that Tik Tok and Snapchat were ranked first among the study sample. Also, there was a moderate negative impact of social media on intellectual security, with a mean of (2.99). This is due to the nature of the content published on networks and its impact on the subscribers. It indicated the importance of social media, more specifically Tik Tok and Snapchat, among the sample of the study. It can be attributed to gender since females are more interested in social networks to publish and share photos. Moreover, the results revealed that social media platforms played a moderate role in resisting intellectual deviation among the participants, with an average mean of (2.96). There were no statistical differences in the impact of social media platforms on young peoples intellectual security and their role in resisting intellectual extremism due to responses to the study questions. The findings of the study call for the necessity of activating the positive role of social media by exploring new approaches to evolve effective alternatives in dealing with social networks by enhancing young peoples media literacy. Contribution/Originality: This study contributed to the existing literature by identifying the role of social media in combating intellectual extremism from the perspective of students at Majmaah University, Saudi Arabia. It also explored the most popular social media platforms among university students and their impact on students intellectual security

Vulnerability to HIV infection among sex worker and non-sex worker female injecting drug users in Dhaka, Bangladesh: evidence from the baseline survey of a cohort study

BACKGROUND: Very little is known about female injecting drug users (IDU) in Bangladesh but anecdotal evidence suggests that they are hidden and very vulnerable to HIV through both their injection sharing and sexual risk behaviors. In order to better understand the risks and vulnerability to HIV of female IDU, a cohort study was initiated through which HIV prevalence and risk behaviors was determined. METHODS: All female IDU (those who had injected in the last six months and were 15 years or older) who could be identified from three cities in the Dhaka region were enrolled at the baseline of a cohort study. The study was designed to determine risk behaviors through interviews using a semi-structured questionnaire and measure prevalence of HIV, hepatitis C and syphilis semiannually. At the baseline of the cohort study 130 female IDU were recruited and female IDU selling sex in the last year (sex workers) versus those not selling sex (non-sex workers) were compared using descriptive statistics and logistic regression. RESULTS: Of the 130 female IDU enrolled 82 were sex workers and 48 were non-sex workers. None had HIV but more sex workers (60%) had lifetime syphilis than non-sex workers (37%). Fewer sex worker than non-sex worker IDU lived with families (54.9% and 81.3% respectively), but more reported lending needles/syringes (29.3% and 14.6% respectively) and sharing other injection paraphernalia (74.4% and 56.3% respectively) in the past six months. Although more sex workers used condoms during last sex than non-sex workers (74.4% and 43.3% respectively), more reported anal sex (15.9% and 2.1% respectively) and serial sex with multiple partners (70.7% and 0% respectively). Lifetime sexual violence and being jailed in the last year was more common in sex workers. CONCLUSION: Female IDU are vulnerable to HIV through their injection and sexual risk behaviors and sex worker IDU appear especially vulnerable. Services such as needle exchange programs should become more comprehensive to address the needs of female IDU

Exergoeconomic and Environmental Modeling of Integrated Polygeneration Power Plant with Biomass-Based Syngas Supplemental Firing

There is a burden of adequate energy supply for meeting demand and reducing emission to avoid the average global temperature of above 2 °C of the pre-industrial era. Therefore, this study presents the exergoeconomic and environmental analysis of a proposed integrated multi-generation plant (IMP), with supplemental biomass-based syngas firing. An in-service gas turbine plant, fired by natural gas, was retrofitted with a gas turbine (GT), steam turbine (ST), organic Rankine cycle (ORC) for cooling and power production, a modified Kalina cycle (KC) for power production and cooling, and a vapour absorption system (VAB) for cooling. The overall network, energy efficiency, and exergy efficiency of the IMP were estimated at 183 MW, 61.50% and 44.22%, respectively. The specific emissions were estimated at 122.2, 0.222, and 3.0 × 10−7 kg/MWh for CO2, NOx, and CO, respectively. Similarly, the harmful fuel emission factor, and newly introduced sustainability indicators—exergo-thermal index (ETI) and exergetic utility exponent (EUE)—were obtained as 0.00067, 0.675, and 0.734, respectively. The LCC of $1.58 million was obtained, with a payback of 4 years, while the unit cost of energy was estimated at 0.0166$/kWh. The exergoeconomic factor and the relative cost difference of the IMP were obtained as 50.37% and 162.38%, respectively. The optimum operating parameters obtained by a genetic algorithm gave the plant’s total cost rate of 125.83 $/hr and exergy efficiency of 39.50%. The proposed system had the potential to drive the current energy transition crisis caused by the COVID-19 pandemic shock in the energy sector