We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating ...scientific data while retaining a high level of accuracy for diverse metrics across a large range of input variables. We demonstrate a successful application of the transfer learning concept: we train the network to simulate showers for electrons from a reduced range of primary energies, we then train further for a five times larger range (the model could not train for the larger range directly). The same concept is extended to generate showers for other particles depositing most of their energies in electromagnetic interactions (photons and neutral pions). In addition, the generation of charged pion showers is also explored, a more accurate effort would require additional data from other detectors not included in the scope of the current work. Our further contribution is a demonstration of using GAN-generated data for a practical application. We train a third-party network using GAN-generated data and prove that the response is similar to a network trained with data from the Monte Carlo simulation. The showers generated by GAN present accuracy within
10
%
of Monte Carlo for a diverse range of physics features, with three orders of magnitude speedup. The speedup for both the training and inference can be further enhanced by distributed training.
Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a ...significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins. In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model. Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98. Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.
Many industries use various dyes to beautify their products and discharge the waste into the water without proper treatment. Such wastewater is not only dangerous for aquatic life but it is also ...toxic to human life and can cause numerous problems, such as skin diseases, and some dyes are carcinogenic or even mutagenic as well. Rhodamine-B (RhB) is one of those synthetic organic dyes which is widely used in textile, paper making, leather manufacturing, stained glass work, cosmetics, and many other industries owing to its high tinting strength, high stability, and bright colour. Therefore, it is essential to either remove or reduce its concentration before releasing it into aquatic streams, as well as to minimize or control the cause of several diseases. Several physical and chemical methods have been used for the removal of different dyes from wastewater; nevertheless, adsorption is one of the best techniques used for the removal of dyes due to its high efficiency and low cost. In this regard, we used Chamaecyparis lawsoniana (C. lawsoniana) fruit as a bio-adsorbent for the removal of RhB from an aqueous solution. An 85.42% dye adsorption was achieved at optimized conditions (pH 2, 40 ppm initial dye concentration, 105 min, and 50 mg adsorbent). Adsorption occurs by pseudo-second-order kinetics, according to kinetic studies. Several samples from various sources, including tap water, distilled water, river water, and filtered river water, were tested for RhB removal, and the study revealed good results even in river water. Thus, C. lawsoniana fruit can be used for its real-world application.
Oral mesenchymal stem cell populations in humans have been discovered in close vicinity to oral mucosal tissues and both primary (deciduous) and secondary (permanent) teeth. All these different kinds ...of stem cells have the ability to divide and replenish themselves, however they vary in their gene expression profiles and their capacity to give rise to distinct cell lineages. They all have multipotentiality i.e. chondrogenic, osteogenic, adipogenic, and neurogenic. Due to their relative accessibility, these cell types may form a source of stem cells with substantial potential for application in tissue regeneration. In this review, discoveries outlining stem cell potential are discussed on various aspects as, are their various applications in orthodontics i.e. orthodontic tooth movement, fixing external root resorption, correcting craniofacial anomalies, accelerating craniofacial distraction osteogenesis, recreating the TMJ, and ensuring a stable maxillary expansion.
This This paper analyzes the effect of ICT infrastructure availability on FDI inflow in D8countries (Bangladesh, Indonesia, Iran, Egypt, Nigeria, Malaysia, Pakistan and Turkey). Panel data for the ...period 1997-2018 has been used and the analysis has been done using the fix effect model suggested by Hausman specification test. The result shows positive and significant effect between ICT infrastructure and FDI inflows, along with other controlling variables like market size, trade openness, in case of macroeconomic variable that is exchange rate it has negative but significant effect on FDI inflows.
Abstract
The preventive effect of quercetin on arsenic stimulated reproductive ailments in male Sprague Dawely (SD) rats was investigated. Twenty rats were divided into four groups. The first group ...served as a control and was provided tap water. The second group of rats was treated with sodium arsenite at the dose of 50 ppm in drinking water. The third group served as a positive control and received an oral dose of quercetin (50 mg/kg). In the fourth group, quercetin (50 mg/kg) was co-administered orally with arsenic (50 ppm in drinking water). All the treatments were carried out for 49 days. Arsenic treatment resulted in adverse morphological and histopathological changes in testis of rats including reduced epithelial height and tubular diameter, and increased luminal diameter. In contrast, these adverse effects of arsenic were eliminated by co-administration of quercetin. Additionally arsenic treatment significantly increased testicular thiobarbituric acid reactive substance (TBARS) levels while catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), and glutathione reductase (GSR) activities, and plasma and intra-testicular testosterone concentrations, were decreased significantly. Lipid peroxidation (LPO) was significantly suppressed and depleted antioxidant defense mechanism was restored by the quercetin co-treatment. Also quercetin treatment resulted in a marked increase in plasma and testicular testosterone concentrations. On the basis of these findings, it was concluded that quercetin may be used as a potential therapeutic drug against arsenic induced reproductive toxicity.
Generative Adversarial Networks (GANs) have gained notoriety by generating highly realistic images. The present work explores GAN for simulating High Energy Physics detectors, interpreting detector ...output as three-dimensional images. The demands and requirements of a scientific simulation are quite stringent, as compared to the domain of visual images. Image characteristics such as pixel intensity and sparsity, for example, have very different distributions. Moreover, detector simulation requires conditioning on physics inputs, and domain knowledge becomes essential. We, therefore, adjust the pre-processing and incorporate physics-based constraints in the loss function. We also introduce a multi-step training process based on transfer learning by breaking up the task complexity. Validation of the results primarily consists of a detailed comparison to full Monte Carlo in terms of several physics quantities where a high level of agreement is found (ranging from a few percent up to 10% across a large particle energy range). In addition, we assess the performance by physics unrelated metrics, thereby proving further the variability and pertinence through diverse standpoints. We have demonstrated that an image generation technique from vision can successfully simulate highly complex physics processes while achieving a speedup of more than three orders of magnitude in comparison to the standard Monte Carlo.
The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use ...in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.
Over the last two decades, Pakistan’s energy demand has grown exponentially with very diminutive measures taken by the government to fulfill the needs. The large power plant projects are cumbersome, ...take years to be completed and require plenty of time to get fully operational. The idea of distributed generation works well in this case. Renewable energy comes well into play when we talk about distributed generation but the dependability of renewable energy resources on back-up such as batteries makes them unappealing. The objective of this paper is to practically implement a backup for the renewable energy resources using a mechanical storage such as CAES (Compressed Air Energy System). The proposed model is a composite technology, which comprises of EES (Electrical Energy Storage) and electrical power supply system. Solar energy driven compressor is used to compress the air in a storage tank, which is used on demand to drive the generator coupled air turbine. The fact that the developed system is solar powered, no other fuel is used with air and it uses mechanical storage instead of conventional storage like batteries, which makes the developed prototype system efficient, economical and durable as compared to the existing CAES. This paper focuses on the thermodynamic investigation, design and finally implementing a prototype CAES for a small load as an un-interrupted power supply system.
Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid. However, system inputs like renewable energy generation process, ...conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort. Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics. The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs. The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS). Extensive simulations are conducted to validate the employed algorithm. Simulation results illustrate that the proposed algorithm performs real-time energy optimization and reduces the time average energy cost of 20.15% while meeting the user's energy and comfort requirement.
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