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Current paper reports the findings of a content analysis of Open Educational Resources. Twenty three studies were collected for the analysis. The studies were collected from different journals depending on open access policy. Every study was categorised by author, year of publication, title of the study, author characteristics and analysis. The analysis refers to the information about Open Educational Resources variables. Therefore the investigators categorize the research papers based on OER. An analysis of research papers was formed in conclusion and finalizing the common dimensions of content analysis of OER studies.

An ancient B.T.M. with P.C.M. was controlled through the issues of high inflexibility of phase change material, leakage problems and very low conductivity in thermal energy. This research paper reports a facile batter thermal management and creativity along with induced non-rigid phase change material composites. This battery model can be determined by the flexible phase change material composites along with an intervention due to the recovery in shape and non-rigidity of flexible phase change components. This assemble was modelled to be efficient and compact without any requirement for grease. A constant state reveals various stages of phase change material which has various properties in thermal efficiency. A unified state was linked with the recovery shape of flexible phase change components, which can cause a low resistance in FCPCM and battery. Battery thermal management demonstrates the perfect process of thermal control power. If the battery was discharged from 90 to 10% of charge, then the temperature of flexible phase change components depends upon battery thermal management. It was 44.5 °C during the 3.5 °C rate, which was 29.8 °C lower than no phase change material. It also reveals low-temperature oscillation inside the long-time process and range of heat of recovered phase change material. The performance of battery thermal management and its flexibility will give perceptions of passive battery thermal management systems.

The recent trends in material science are focused toward composite materials. It has been used in a lot of real-life applications. In composite material, recently researchers concentrated on the na...

An ancient BTM with PCM was controlled through the issues of high inflexibility of phase change material, leakage problems and very low conductivity in thermal energy. This research paper reports a facile batter thermal management and creativity along with induced non-rigid phase change material composites. This battery model can be determined by the flexible phase change material composites along with an intervention due to the recovery in shape and non-rigidity of flexible phase change components. This assemble was modeled to be efficient and compact without any requirement for grease. A constant state reveals various stages of phase change material which has various properties in thermal efficiency. A unified state was linked with the recovery shape of flexible phase change components which can cause a low resistance in FCPCM and battery. Battery thermal management demonstrates the perfect process of thermal control power. If the battery was discharged from 90 to 10% of charge, then the temperature of flexible phase change components depends upon battery thermal management. It was 44.5°C during the 3.5°C rate which was 29.8°C lower than no phase change material. It also reveals low-temperature oscillation inside the long-time process and range of heat of recovered phase change material. The performance of battery thermal management and its flexibility will give perceptions of passive battery thermal management systems.

A Meta analytical review investigated the study habits and academic achievement of school and college students. In this study the sample consists of twenty seven reviews based on study habits and academic achievement/ performance. The sample of the review based on inclusion criteria such as study habits and academic achievement. The review was collected by following details such as title of the study, Name of the author, Sample of the study, correlation Value and findings of the study. The Meta analysis is on the relationship between study habits and academic achievement. The main aim of the study is to find the fixed effects, random effects model values, Heterogeneity and forest plots. A Meta analysis of research paper was formed the conclusion.

Chemical precipitated and followed by calcinations is a versatile process for synthesising undoped ZnO and Mn (5, 10 and 15 wt%) doped ZnO nanoparticles at pH-10 and calcinations temperature of 450...

Magnetic Resonance Imaging (MRI) is an emerging research area, employed extensively in radiology for the diagnosis of various neurological diseases. Here, as the classification of MRI gains significant importance for the tumor diagnosis, various techniques are developed in the literature for the automatic classification of MRI images. In this paper, a clustering scheme, namely gaussian hybrid fuzzy clustering (GHFC) is developed by hybridizing the fuzzy c-means (FCM) clustering and sparse FCM along with the Gaussian function for the segmentation purpose. After segmenting the image, the suitable features are extracted from the image and given to the Exponential cuckoo based Radial Basis Neural Network (Exponential cuckoo based RBNN) classifier. The features serve as the training information for the Exponential cuckoo based RBNN classifier, and it finally detects the training class. Simulation of the proposed work is done using BRATS and SIMBRATS databases, and the results are compared with several state-of-art techniques. Simulation results depict that the proposed GHFC along with the RBNN classifier achieved improved accuracy and mean squared error results with the values of 0.8952, and 0.0074, respectively, for the BRATS dataset and 0.8719 and 0.0036, for the SIMBRATS dataset.

The objective of this work is to investigate the effect of graphite addition to Al/SiC composites manufactured by stir casting process. The reinforcement content of graphite was varied from 2.5 wt....

Silver doped cerium oxide nanocomposites were prepared at nitrogen atmosphere using polyindole as reducing agent (Polyindole/Ag-CeO2). The samples were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction study (XRD), scanning electron microscopy coupled with energy dispersive X-ray diffraction (SEM-EDAX), transmission electron microscopy (TEM). The FTIR study revealed the formation of polyindole/Ag-CeO2 nanocomposites. The XRD analysis confirmed the amorphous nature of nanocomposites. The porous structure of the polyindole and spherical shape of the Ag and CeO2 present in the nanocomposites were confirmed by SEM and TEM images. The prepared nanocomposites showed moderate antibacterial and antifungal activities.

A computational analysis has been performed to study the effect of magnetic parameter on magneto-natural convection and entropy generation inside a wavy cabinet filled by Al2O3-H2O nanosuspension. The vertical boundaries of the cavity are designed as wavy and kept at cooled temperature (Tc), while the horizontal borders are modeled as flat and supposed to be thermally insulated except the centrally heated portion on the bottom wall. The higher constant temperature (Th) of the isothermal line source can supply thermal energy in uniform manner. The transformed dimensionless governing equations were solved using finite volume technique along with power-law scheme. The system was optimized for significant variety of controlling parameters namely, number of undulations (N = 0 – 3), volume fraction of nanosuspension ( ϕ = 0.0 – 0.04), magnetic parameter (Ha = 0 and 50), magnetic tilted angle ( ζ = 0 ◦ – 90 ◦ ) and dimensionless heat difference ( Ω = 0.001 – 0.1). The numerical results are analyzed for streamlines, isotherms, entropy isolines, overall Nusselt number and average entropy production strength. From this study, it is noticeable that growth of undulation number causes the reduction of convective heat transfer rate as well as average entropy production intensity. Further, the findings elucidate that the average entropy production intensity and overall heat transfer rate are decreased upon growing magnetic parameter.

This study focused catalytic transesterification process for the production of biodiesel from restaurant based waste cooking oil. Fifteen pre-designed experiments are used to explore the significance of three reaction parameters with three different levels such as methanol to oil ratio, catalyst concentration and agitation speed as well as their combined effect on biodiesel production. Box-Behnken experimental design based on Response Surface methodology (RSM) is utilized for optimizing the process parameters A quadratic model was developed to estimate biodiesel yield, and the R2 value was determined to be 0.99, indicating that the model is accurate. The result showed optimized process parameters for maximum biodiesel yield as methanol:oil molar ratio of 12.97:1, NaOH concentration of 0.1834 wt% and at the agitation speed of 1088 rpm. The obtained results reveal that the yield of 98.45% and the experimental yield of 97.80% respectively which shows the deviation of 0.7%.

The evolution of computing is increasing in a vast manner that will integrate many physical objects and the internet to generate a new interconnection, such as the Internet of Things (IoT). It is estimated that the number of devices that will be interconnected to the internet will be more than trillions until 2025. Due to the lack of interoperability when these devices are interconnected in a vast heterogeneous network, it is tough to define and apply security mechanisms. The IoT networks have been exposed to many vulnerable attacks that disturb the network. Therefore, designing an intrusion detection system that provides additional security tools specific to IoT is needed to apply security mechanisms to detect the attacks in the network. In this paper, we propose a novel hybrid GA-CMIM machine learning algorithm that improves the efficiency in detecting the botnet intrusions with the set of optimal features that are selected from the dataset using a feature selection method.

The role of piperine, a water insoluble alkaloid compound on the nucleation, crystallinity, size, shape and dispersion of hydroxyapatite (HAp) nanoparticles was investigated. Two different concentrations of piperine were used as an organic modifier. FTIR spectra support the transformation of functional groups. The XRD pattern reveals the crystalline nature of the materials. The TEM images disclose the mechanism of nucleation, formation, shape, size and morphology of HAp nanorods. The addition of the piperine during the synthesis altered the morphology of HAp nanorods. Piperine, as water insoluble alkaloid could affect the morphology of the HAp nanoparticles with agglomeration. These types of well defined and shaped HAp nanorods can be used as a delivery service for drug release and in the field of biomaterial research.

The present study investigates the in vitro cytotoxic efficacy of polymer encapsulated bare and Mn-doped ZnO nanoparticles against human liver carcinoma Huh7 cell lines. PEG capped bare and Mn (2.0...

The objective of this endeavor is to investigate the influence of graphite particles addition to the Al/SiC composites manufactured by stir casting method. Al/SiC/Gr hybrid metal matrix composites are preferred replacement for single reinforced composites by virtue of strengthened mechanical and tribological properties. A layered SiC/Gr composite material is required in gas turbine combustor can. Al/SiC/Gr composites are required in bearings, and pistons due to selflubrication, low wear rate and less friction. The inclusion of both strong reinforcement like SiC and smooth reinforcement like graphite enhances hardness and wear resistance of aluminium composites. The reinforcement content of graphite was varied from 2.5 to 7.5 wt % in a step of 2.5 wt. The SiC was fixed at 5 wt. % for all the three specimens, and Al6061 was used as matrix base material. The microstructure and EDAX analyses were required for three samples of hybrid metal matrix composites. Vickers hardness and porosity decreases for Al/SiC/Gr hybrid composites when percentage of graphite content increases. A review of surface modification of Al/SiC/Gr was done to find the different materials used for coating hybrid composites.

From ancient times, the economic growth of India is mainly based on agricultural output. Agriculture is demographically the broadest economical sector and acts as an important part in the entire socio-economic fabric of India. Nearly half of the population of our country are connected to agriculture as their industry and make a livelihood out of it. At the same time, the crop productivity depends upon based on several climatic and economical factors namely soil type, weather, irrigation, fertilizer, rainfall, and so on. The present advancements in Information Technology for agriculture field become a hot research topic for predicting the crop yield, which can be resolved by the use of data mining techniques. This paper focuses to design a new predictive approach which offers a high yield of paddy crops by the use of data mining models and Hungarian model in Kuruvai season which generally starts from June to July. The data set used in this research for mining process is real data of Kuruvai season are collected from Tamil Nadu Agricultural University (TNAU) AgriTech Portal, Aduthurai, Thanjavur district and a set of three data mining approaches namely Apriori method, Naive Bayes (NB) and J48 classifier are employed to predict the paddy yield. The outcome of various models has been analyzed, and it is shown that the NB model has offered superior outcome over the compared classifier models.

Diabetes is one of the most feared diseases currently faced by humanity. The disease is due to a poor reaction of the body to insulin: it is an important hormone in our body that converts sugar into energy that is necessary for the proper functioning of a normal life. Diabetic disease has serious complications on our body because it increases the risk of developing kidney disease, heart disease, retinal disease, nerve damage, and blood vessels. In this article, we have proposed a decision tree model: SLDPS (Diabetes Prediction System with Supervised Learning). The data set is collected via IoT sensors. The classification accuracy obtained with this model was improved to 94.63% after the rebalancing of the data set and shows a potential relative to other classification models in the literature.

Logistics industry has been the second largest industry to emit the greenhouse gas. Carbon emission can be reduced by analyzing and controlling those influencing factors that affect the implementation of low carbon measures in the logistics industry. In India, Small and Medium level Enterprises (SMEs) account for 60% of all private sector jobs and about 40% of low carbon business. This paper reveals the findings and result of a qualitative statistical study conducted on the SMEs located in the southern parts of India. Based on the existing literature, research hypotheses were formed on the aspects of environmental, economic, and social factors that affect the implementation of low-carbon logistics. To test these hypotheses, a questionnaire was developed and pre-tested by pilot study. Then pre-tested questionnaire was circulated to the various levels of people involved in the logistics activities of SMEs, and responses were obtained. After data collection, a statistical analysis was conducted using SPSS software to prove the research hypotheses.

In the 21st century, people are connecting with technology, and most of the people are spending time in social networks for sharing information. For the most part, spending time on social media is linked with an increased risk of loneliness and depression. Negative effects of social media and social network are affecting the person's mental, emotional, and physical health. The World Health Organization (WHO) reported that 8,000,000 people are dying due to suicide every year, and especially, every 40 seconds teenagers are dying. Generally, most of the teenagers are emotionally imbalanced; most of the suicide is committed on high depression. Depression is the foremost cause of emotional and mental illness. Emotional health is a state of emotional wellbeing, and it's a hidden and invisible factor as well as a factor for good mental health; mental health is a deep-seated problem for most humans. With this background, this chapter discusses the emotional and mental health with respect to technological issues.

Polyindole stabilized silver‒cobalt oxide (Pind/Ag‒Co3O4) nanocomposites were synthesized, characterized and evaluated for their antibacterial and antifungal activities. Polyindole, cobalt oxide and Pind/Ag‒Co3O4 were prepared by chemical oxidative polymerization, co-precipitation and reflux condensation method, respectively. Colloidal nanoparticles of polyindole, cobalt oxide and nanocomposite were characterized by Fourier transform infrared spectrophotometer (FT-IR), X-ray diffraction pattern (XRD), scanning electron microscope with energy dispersive X-ray diffraction study (SEM-EDAX) and high resolution transmission electron microscope with selected area diffraction (HRTEM-SAED). FTIR spectra revealed the functional group’s transformation of cobalt oxide nanoparticles. The crystalline nature of the samples was confirmed by XRD study. The SEM images revealed the various shapes and surface morphology of the nanocomposites. Further, the elemental composition was confirmed by EDAX. The HRTEM images showed shapes such as spherical, cylindrical, rectangular, rods, oval and square. The average particle size of the nanocomposites found to be ∼20 nm which is in good agreement with XRD analysis in terms of the size of the crystalline. The Pind/Ag‒Co3O4 nanocomposites exhibited improved antibacterial and antifungal activities against pathogenic bacteria and fungi. Hence, these nanocomposites could be used as a potential candidate for the biomedical applications.