Flows with Slip of Oldroyd-B Fluids over a Moving Plate

A general investigation has been made and analytic solutions are provided corresponding to the flows of an Oldroyd-B fluid, under the consideration of slip condition at the boundary. The fluid motion is generated by the flat plate which has a translational motion in its plane with a time-dependent velocity. The adequate integral transform approach is employed to find analytic solutions for the velocity field. Solutions for the flows corresponding to Maxwell fluid, second-grade fluid, and Newtonian fluid are also determined in both cases, namely, flows with slip on the boundary and flows with no slip on the boundary, respectively. Some of our results were compared with other results from the literature. The effects of several emerging dimensionless and pertinent parameters on the fluid velocity have been studied theoretically as well as graphically in the paper

Factors Influencing the Usage of Edtech Platforms in Bangladesh

With the introduction of new and improved technologies, the conception and rapid growth of educational technology or Edtech has quickly gained success all over the world. Bangladesh is no different to this trend, as a very lucrative Edtech sector has emerged within the last few years. This recent influx in Edtech platform expansion and usage raises the need for further investigation as to what factors influence the usage of the Edtech platforms in Bangladesh. To address this research gap, this study was conducted among 222 people who were aware of Edtech platforms and their functionality in Bangladesh. The hypothesis testing showed that there was a relationship between Edtech technology and improvement of cloud computing in Bangladesh. Factor analysis was done to identify the most prominent factors, namely ‘User Convenience’, ‘Quality of Learning’, ‘Instructing Capability’, and ‘User Capability’. The quantitative analysis results mostly coincided with the qualitative data derived from literature review as well as the expert interviews. Future researches should be carried out to more geographical locations such as rural areas with a wider range of demographic variable like income level and family size to better understand the Edtech usage factors in Bangladesh. Keywords: Blended Learning, Digital Platforms, Educational Accessibility, Learning Management Systems (LMS), Virtual Learning Environments (VLE) DOI: 10.7176/EJBM/15-18-01 Publication date: November 30th 202

Transition in Tuber Quality Attributes of Potato (Solanum tuberosum L.) Under Different Packaging Systems During Storage

The suitability of different packaging materials i.e. jute, nylon, polypropylene, cotton, low density polyethylene, medium density polyethylene, and high density polyethylene were studied for tubers of the premium potato (Solanum tuberosum L.) variety “Lady Rosetta”. After harvest, potato tubers were washed, sorted, graded, cured, and subsequently stored in different packaging materials at ambient temperature (25±2 °C). Changes in quality attributes of potato tubers under different packaging materials were studied on the basis of their physico-chemical and functional parameters. Overall results revealed that packaging materials had a significant (p ≤ 0.05) effect on many important quality attributes. Generally, weight loss, glucose and glycoalkaloid amounts, and polyphenol oxidase and peroxidase activities increased, while ascorbic acid contents decreased with increasing storage time. Total phenolic contents and radical scavenging activity showed a nearly parabolic trend during the storage period. Amongst the different packaging materials employed, potatoes stored in polypropylene and low-density polyethylene presented the best overall retention of vital quality attributes during 63 days storage. However, the higher tensile strength of polypropylene packaging made it a more durable and thus more effective material for prolonged potato tuber storage, a characteristic having clear advantages when used in typical marketing supply chains

Phenyl N-cyclo­hexyl­carbamate

In the title compound, C13H17NO2, the dihedral angle between the benzene ring and the basal plane of the cyclo­hexyl ring is 49.55 (8)°. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds, forming chains propagating in [010]

Metabolic dysregulation in early onset psychiatric disorder before and after exposure to antipsychotic drugs

Antipsychotic Drugs (APDs) are being widely prescribed to treat various disorders, including schizophrenia and bipolar disorder; however, abnormal glucose metabolism and weight gain have been reported with Atypical Anti-Psychotic drugs (AAPDs) that can lead to insulin-resistance and type 2 diabetes mellitus. The study was designed to assess various biochemical parameters including insulin and blood sugar before and after exposure to APDs in order to exclude the involvement of psychiatric disorders and certain other factors in metabolic dysregulations. Fifty seven APDs-naïve patients with first episode psychosis were divided into six groups who received olanzapine, quetiapine, risperidone, aripiprazole, haloperidol or combination of olanzapine with escitalopram and haloperidol. The serum samples were taken before the intake of the first dose and then on follow-up. Decrease in the level of elevated insulin and glucose was observed post-treatment in some patients, while others were observed whose insulin and glucose levels increased post-treatment, yet some patients did not show any disturbance in the insulin and glucose levels. It is concluded that psychiatric disorders by itself, narcotics, cigarette smoking and use of oral snuff may be also be implicated in metabolic dysregulations. The effects of APDs on insulin and glucose in healthy volunteers might be different than in patients with psychiatric disorders

Melatonin rescues the mice brain against cisplatin-induced neurodegeneration, an insight into antioxidant and anti-inflammatory effects

Herein, we evaluated the neuroprotective effect of melatonin against cisplatin-induced oxidative damage, neuroinflammation, and synaptic dysfunction in mice. Cisplatin was administered at a dose of 2 mg/kg for eleven consecutive days to induce symptoms of cognitive impairment and neurodegeneration, while melatonin was administered at a 20 mg/kg dose for thirty consecutive days. We used various experimental techniques such as western blotting, immunofluorescence analysis, and oxidative stress marker assays to support our notion. Moreover, for cognitive impairment, we conducted behavioral analyses such as Morris Water Maze (MWM) and Y-Maze tests. The results indicated that melatonin attenuated oxidative stress by upregulating the expression of NF-E2-related factor-2 (Nrf2) dependent anti-oxidative protein levels. Similarly, melatonin positively modulated the expression of Sirt1 (a member of the sirtuin family), Phospho-AMPKα (Thr172), peroxisome proliferator-activated receptor (PPARγ), PPAR gamma coactivator 1 alpha (PGC-1α) coupled to downregulation of neuroinflammatory mediators and markers such as nuclear factor kappa-B (NF-κB), tumor necrosis factor-alpha (TNF-α), and interleukin-1 beta (IL-1β). Moreover, melatonin significantly upregulated the expression of synaptic markers such as postsynaptic density protein -95 (PSD-95), synaptosomal-associated protein 23 (SNAP-23), and synaptophysin compared to the cisplatin alone group. Furthermore, the results of behavior tests suggested that melatonin significantly improved the cognitive functions of the cisplatin injected mice

Critical Behavior of La0.8Ca0.2Mn1−xCoxO3 Perovskite (0.1 ≤ x ≤ 0.3)

The critical properties of La0.8Ca0.2Mn1−xCoxO3 (x = 0, 0.1, 0.2 and 0.3) compounds were investigated by analysis of the magnetic measurements in the vicinity of their critical temperature. Arrott plots revealed that the paramagnetic PM-ferromagnetic (FM) phase transition for the sample with x = 0 is a first order transition, while it is a second order transition for all doped compounds. The critical exponents β, γ and δ were evaluated using modified Arrott plots (MAP) and the Kouvel-Fisher method (KF). The reliability of the evaluated critical exponents was confirmed by the Widom scaling relation and the universal scaling hypothesis. The values of the critical exponents for the doped compounds were consistent with the 3D-Heisenberg model for magnetic interactions. For x = 0.1, the estimated critical components are found inconsistent with any known universality class. In addition, the local exponent n was determined from the magnetic entropy change and found to be sensitive to the magnetic field in the entire studied temperature range.This work has been supported by the Tunisian Ministry of Scientific Research and Technology and Institute Neel at Grenobl

Exploration of black boxes of supervised machine learning models: A demonstration on development of predictive heart risk score

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated

Development of nonlaboratory-based risk prediction models for cardiovascular diseases using conventional and machine learning approaches

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities