Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.     

Free radical polymerization in multilaminated microreactors: 2D and 3D multiphysics CFD modeling

This paper investigates the modeling of styrene free radical polymerization in two different types of microreactor. A multiphysics model which simultaneously takes into account the hydrodynamics, thermal and mass transfer (convection, diffusion and chemical reaction) is proposed. The set of partial differential equations resulting from the model is solved with the help of the finite elements method either in a 2D or a 3D approach. The different modeled microreactors are on one hand an interdigital multilamination microreactor with a large focusing section, and on the other hand a simple T-junction followed by a straight tube with three different radii. The results are expressed in terms of reactor temperature, polydispersity index, number-average degree of polymerization and monomer conversion for different values of the chemical species diffusion coefficient. It was found that the 2D approach gives the same results as the 3D approach but allows to dramatically reduce the computing time. Despite the heat released by the polymerization reaction, it was found that the thermal transfer in such microfluidic devices is high enough to ensure isothermal conditions. Concerning the polydispersity index, the range of diffusion coefficients over which the polydispersity index can be maintained close to the theoretical value for ideal conditions increases as the tube reactor radius decreases. The interdigital multilamination microreactor was found to act as a tubular reactor of 0.78 mm ID but with a shorter length. This underlines that the use of microfluidic devices can lead to a better control of polymerization reactions.     

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.     

Design of a small single-purpose nuclear desalination plant compatible with the local resources in the middle east

Present plans in the Middle East involve the construction of several nuclear power plants with desalination capabilities to provide power and water to meet the requirements of the anticipated growth in the industrial sector and development of new settlements to improve the socio-economical aspects of population distribution. This effort has been matched by a program initiated by the Development Consultants Association (DCA) to provide the necessary training, manpower and expertise necessary for adequate involvement in siting, design, construction and operation of both nuclear and desalination plants.

In this paper results of the preliminary stages of the design of a 2.6 million gallons per day (1000 cubic meters/day) single-purpose 40 MW thermal MSF-heavy water nuclear plant are reported. Design objectives and philosophy are reviewed. A descriprion is given of the neutronic, thermal, control and mechanical design parameters of the plant. The design is based on utilization of available local material, technology and manpower. The feasibility of production of heavy water in the fertilizer production plants and the extraction of natural uranium from the phospahate mines on the Red Sea coast are considered pointing out further stages of development and future plans.     

Novel and Annotated Long Noncoding RNAs Associated with Ischemia in the Human Heart

Background: Long noncoding RNAs (lncRNAs) have been implicated in the pathogenesis of cardiovascular diseases. We aimed to identify novel lncRNAs associated with the early response to ischemia in the heart. Methods and Results: RNA sequencing data gathered from 81 paired left ventricle samples from patients undergoing cardiopulmonary bypass was collected before and after a period of ischemia. Novel lncRNAs were validated with Oxford Nanopore Technologies long-read sequencing. Gene modules associated with an early ischemic response were identified and the subcellular location of selected lncRNAs was determined with RNAscope. A total of 2446 mRNAs, 270 annotated lncRNAs and one novel lncRNA differed in response to ischemia (adjusted p < 0.001, absolute fold change >1.2). The novel lncRNA belonged to a gene module of highly correlated genes that also included 39 annotated lncRNAs. This module associated with ischemia (Pearson correlation coefficient = −0.69, p = 1 × 10⁻²³) and activation of cell death pathways (p < 6 × 10⁻⁹). A further nine novel cardiac lncRNAs were identified, of which, one overlapped five cis-eQTL eSNPs for the gene RWD Domain-Containing Sumoylation Enhancer (RWDD3) and was itself correlated with RWDD3 expression (Pearson correlation coefficient −0.2, p = 0.002). Conclusion: We have identified 10 novel lncRNAs, one of which was associated with myocardial ischemia and may have potential as a novel therapeutic target or early marker for myocardial dysfunction.     

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers. Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers. At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping. In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase. In this aspect, this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classification (FCMCSO-ASC) technique. The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil. To accomplish this, the FCMCSO-ASC technique incorporates local diagonal extrema pattern (LDEP) as a feature extractor for producing a collection of feature vectors. In addition, the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm. For examining the enhanced soil classification outcomes of the FCMCSO-ASC technique, a series of simulations were carried out on benchmark dataset and the experimental outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.     

Electrospun waterproof breathable membrane with a high level of aerosol filtration

In this study, several nanofibrous polyurethane (PU) webs were electrospun (ES) by changing different effective parameters (e.g., polymeric concentration, voltage, feed flow, etc.). The physical–chemical properties of the webs (i.e., average fiber diameters, thickness, areal density, porosity, contact angle, waterproofness, air permeability (AP), water vapor transmittance, and aerosol filtration) were studied based on the standard test methods. A commercially available waterproof breathable (WPB) fabric was used as a reference for benchmarking. The beads‐free webs with an average fiber diameter as small as 200 nm were achieved from electrospinning of 10 wt % PU in N,N‐dimethylformamide, at feed rate of 0.5 mL/h, applied voltage of 25 kV, and tip‐to‐collector distance of 15 cm. By optimizing the electrospinning parameters, a web with a high level of waterproofness, high AP, and high water vapor transmission rate (WVTR) was obtained. In addition, the selected ES membrane showed very promising aerosol filtration efficiency with complete removal of particles larger than 0.5 µm, and 94% reduction in the concentration of smaller particles. We found a linear empirical equation for the estimation of AP and WVTR based on the average pore size diameter, the membrane thickness, and the porosity with very high regression coefficients (R² > 0.97). © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45660.     

Synthesis of poly(paraphenylene vinylene)—polystyrene‐based rod‐coil block copolymer by atom transfer radical polymerization: Toward a self‐organized lamellar semiconducting material

Atom transfer radical polymerization (ATRP) was used as a versatile route to well‐defined poly(diethylhexyl‐p‐phenylenevinylene‐b‐styrene) (PPV‐b‐PS) semiconducting block copolymers. For this purpose, original conjugated macroinitiators were synthesized from DEH‐PPV and further used for the copolymerization reaction. The microphase‐separated morphologies obtained with the semiconducting PPV‐b‐PS block copolymer fulfill the basic structural requirements required to build efficient organic photovoltaic devices. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Dynamics of Choline-Containing Phospholipids in Traumatic Brain Injury and Associated Comorbidities

The incidences of traumatic brain injuries (TBIs) are increasing globally because of expanding population and increased dependencies on motorized vehicles and machines. This has resulted in increased socio-economic burden on the healthcare system, as TBIs are often associated with mental and physical morbidities with lifelong dependencies, and have severely limited therapeutic options. There is an emerging need to identify the molecular mechanisms orchestrating these injuries to life-long neurodegenerative disease and a therapeutic strategy to counter them. This review highlights the dynamics and role of choline-containing phospholipids during TBIs and how they can be used to evaluate the severity of injuries and later targeted to mitigate neuro-degradation, based on clinical and preclinical studies. Choline-based phospholipids are involved in maintaining the structural integrity of the neuronal/glial cell membranes and are simultaneously the essential component of various biochemical pathways, such as cholinergic neuronal transmission in the brain. Choline or its metabolite levels increase during acute and chronic phases of TBI because of excitotoxicity, ischemia and oxidative stress; this can serve as useful biomarker to predict the severity and prognosis of TBIs. Moreover, the effect of choline-replenishing agents as a post-TBI management strategy has been reviewed in clinical and preclinical studies. Overall, this review determines the theranostic potential of choline phospholipids and provides new insights in the management of TBI.