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Applications of neuroscience in education practices: A research review in cognitive neuroscience
The human brain is the most complex and mysterious organ in the body responsible for learning. Applications of neuroscience and genetics need to be comprehended to modulate teaching and learning practices in education. Considering the scope for application of advanced sciences in education practices, this book chapter simplifies and reviews ten critical research findings relevant for students and teachers for classroom applications and for modulating learning patterns for different age groups. The concept is also relevant for parents and the academic fraternity at large. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Applying Artificial Bee Colony Algorithm to Improve UWSNs Communication
The research in this study aims at implementing the ABC algorithm to enhance the communication within UWSNs. The ABC algorithm, motivated by the CPG approach being analogous to that of honey bees searching for food, specifies optimal values for critical parameters of the network such as energy consumption, reliability in data transfer, and scalability. From the analyses conducted in this exposition, it is apparent that the envisaged methodology outperforms other conventional routing parlances in the following ways: minimal energy usage, high data delivery ratios, low packet drops, and longest network lifetime. Therefore, from the above results it can be concluded that, the said ABC algorithm is helping in achieving a better result in terms of improved underwater communication as well as in mitigating with the difficulties of UWSNs. 2024 IEEE. -
Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis
More than fifty percent of all liver cognate deaths are caused by alcoholic liver disease (ALD). Excessive drinking over the time leads to alcohol-related steatohepatitis and fatty liver, this in turn can lead to alcoholic liver fibrosis (ALF) and in due course alcohol-related liver cirrhosis (ALC). Detecting ALD at an early stage will reduce the treatment cost to the patient and reduce mortality. In this research, a two-step model is developed for predicting the liver cirrhosis using different ensemble classifiers. Among 41 features recorded during data collection, only 15 features arefound to be effective determinants of the class variable. The proposed stacked ensemble technique for ALD prediction is compared with other ensemble models such as random forest, AdaBoost, and bagging. Through experimentation, it is observed that the proposed model with XGBoost and decision tree as base models and logistic regression as Meta model exhibits prediction accuracy of 93.86%. The prediction accuracy of theproposed stacked ensemble technique is 0.2% better in prediction accuracy and 0.3% reduced error rate in comparison with random forest classifier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Applying talent acquisition to the test: Assessing productivity in facilities organization /
Pramana Research Journal, Vol.9, Issue 2, pp.197-207, ISSN No: 2249-2976. -
Appraisal of prolyl 4-hydroxylase alpha subunit gene polymorphisms in Spondyloepimetaphyseal dysplasia of Handigodu type (SEMDHG)
Background: The Handigodu variant of Spondyloepimetaphyseal Dysplasia (SEMDHG) is a severe, progressive osteoarthritic disorder characterized by chronic pain and joint degeneration. Clinically, the disorder presents in three distinct phenotypic forms, each exhibiting varying degrees of stature reduction and disease severity. Urine analysis of affected individuals reveals an elevated peptide-bound proline to 4-hydroxyproline ratio relative to controls, suggesting disruptions in collagen metabolism. Given the critical role of prolyl 4-hydroxylase enzymes in stabilizing collagen structure, this study undertook a comprehensive sequence analysis of all three isoforms of prolyl 4-hydroxylase in both affected and unaffected individuals to elucidate potential molecular underpinnings of the disorder. Method: The entire exonic regions and 2000 base pairs upstream of the translation start sites of the P4HA1, P4HA2, and P4HA3 genes were sequenced in a cohort of 300 individuals, comprising 166 affected and 134 unaffected individuals. Results: Sequence analysis of the ? (I), ? (II), and ? (III) subunit genes identified three novel SNPs and a 39-bp deletion variant, in addition to ten previously reported SNPs catalogued in dbSNP. The SNP rs28384495 in P4HA1, the 39-bp deletion variant, and a novel mutation (SNP3) in P4HA3 exhibited significantly different allele frequencies between patients and controls. Genotype association analysis revealed that SNPs in P4HA1 and P4HA3 were associated with Type 2 and Type 3 HD under various genetic models. Notably, all Type 2 HD patients were heterozygous for the 39-bp deletion, whereas all Type 3 HD patients were homozygous for the variant. Haplotype analysis corroborated the findings of the genotype association analysis. Conclusion: This study is the first to account an association between the P4H gene and disease. Further research is needed to evaluate the functional implications of the identified mutations. 2024 -
Appraisal of the potential of endophytic bacterium Bacillus amyloliquefaciens from Alternanthera philoxeroides: A triple approach to heavy metal bioremediation, diesel biodegradation, and biosurfactant production
Endophytic microbes have been associated with many positive traits due to their endurance mechanisms. The current study was designed at exploring the potential of the endophytic bacterium Bacillus amyloliquefaciens MEBAphL4 isolated from Alternanthera philoxeroides for biosurfactant production and bioremediation efficiency. This endophyte, isolated from the polluted Madiwala lake in Bangalore, displayed elevated resistance to Cr and Pb till 2000 mg/L. The metal removal efficiency was found to be higher for Cr (25.7 %) at pH 6 and for Pb (92.3 %) at pH 9. Further, the present study also describes biosurfactant production with good emulsification ability (E24-52 %) and stability over a range of pH (8?12), temperature (2040C) and salinity (515 %). Biosurfactant production was enhanced 1.18-fold using the Response Surface Methodology approach and characterised by Fourier Transformation Infra-red Spectroscopy and Ultra-Performance Liquid Chromatography- Mass Spectrometry showing the presence of lipopeptides, fengycin, iturin and surfactin of molecular weights 1463.65, 1043.44 and 1012.56 Da respectively. The potential application of the biosurfactant in degrading various hydrocarbons was evaluated, demonstrating its effectiveness in bioremediation of oil-contaminated sites. Specifically, diesel biodegradation was measured at 56.460.95 %. These findings underscore the potential of B. amyloliquefaciens in environmental applications such as heavy metal biosorption and the bioremediation of contaminated sites, particularly those affected by oil spills and correlates to UN SDG6 of clean water and sanitation. 2024 Elsevier Ltd -
Approach for Collision Minimization and Enhancement of Power Allocation in WSNs
Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption. 2021 Debabrata Singh et al. -
Approach for Preprocessing in offline Optical Character Recognition (OCR)
offline optical character recognition (offline OCR) is one of the important applications of pattern recognition. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step be-comes essential for any image identification task. Lots of research has been performed in numerous jobs towards this preprocessing in the literature. Here, an attempt has been made to summarize different procedures and aspects of preprocessing adopted in implementing these preprocessing techniques. This is done in the hope that this may help the research community towards the gaining of knowledge of different preprocessing techniques used in offline OCR. offline OCR has several applications, such as old manuscript digitization, signature authentication, bank cheque automatic clearance and postal letter sorting, etc. Finally, an overall summary in a concise way has been presented based on different preprocessing techniques used in offline OCR. 2022 IEEE. -
Approach Towards Web for Exploring the Suitable Job for Individuals
In light of future work challenges, true human resource management (HRM) must be rebuilt. This involves over time human resource development; it must also contain the concept of sustainability to move from consuming to generating human resources. The labor market is constantly changing, with nontraditional jobs becoming increasingly important, especially in light of the current COVID-19 legislation. A useful teaching strategy in a variety of academic fields, including career development, is experiential learning. Important elements for establishing experiential learning programs at the institutional level are also covered by researchers. Our framework may assist businesses in identifying the type of experiential learning that best fits their objectives and setting for professional training. It can also help ensure that the training is successfully designed and delivered. 2024 IEEE. -
Approaches on redesigning entrepreneurship education
All over the world there is an emergence of a self-reliant life. This instilled a spark in entrepreneurship, especially during the wake of a pandemic world. The paradigm shift from dependency to self-reliance demands a set of skills and techniques as prerequisites to thrive in this competitive world. This chapter introduces a couple of innovative pedagogy strategies that can be inculcated in educational institutions, which will give rise to efficient entrepreneurs who can face adversaries and make an efficient contribution to society. The chapter aims to integrate realistic learning activities for fostering capability development in entrepreneurship education. Capability enhancement in entrepreneurship education includes activities that improve the knowledge, skills, and talents of potential entrepreneurs. The chapter aims to develop a model that further illustrates how the educational entrepreneurial experience could be explored. 2023, IGI Global. -
Approaches Towards A Recommendation Engine for Life Insurance Products
Recommender engines are powerful tools in today's world to overcome the problem of over choice. As the world is moving towards information overload, the life insurance industry is no more immune than any other domain. Three broad categories of life insurance plans are namely - Endowment, Term and ULIP. This paper discusses a variety of ML models that aim to classify the right fit product category for a new customer (extendable to existing customers) on a real-time life insurance company dataset. The dataset used for the modelling were of 2 kinds. The first kind contained features of customer demographics - age, location, education and occupation. The second dataset included these customer demographics as well as the bureau information of the respective customers which included multiple features describing their credit history. By the means of clustering, collaborative filtering approaches were tried on. Also, the problem was tackled using predictive modelling techniques such as Random Forest, Decision Trees and XGBoost. 2021 IEEE. -
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Approximate Binary Stacking Counters for Error Tolerant Computing Multipliers
To increase the power and efficiency of VLSI circuits, a new, creative multiplying methodology is required. Multiplication is a crucial arithmetic operation for many of these applications. As a result, the newly proposed error-tolerant computing multiplier is a crucial component in the design of approximate multipliers that are both power and gate efficient. We have created approximative multipliers for several operand lengths using this suggested method and a 45-nm library. Depending on their probability, the approximation for the accumulation of changing partial products varies. In compared to approximate multipliers that were previously given, the proposed circuit produces better results. When column-wise generate elements are added to the modified partial product matrix using an OR gate, the output is usually accurate. The amount of energy used, and its silicon area have been considerably reduced in the suggested multiplier when compared to traditional multipliers by 41.92% and 18.47%, respectively. One of the platforms that these suggested multipliers are suitable for is the image processing application. 2024 IEEE. -
Aquila Optimizer Based Optimal Allocation of Soft Open Points for Multi-Objective Operation in Electric Vehicles Integrated Active Distribution Networks
The appropriate position and sizing of soft open points (SOPs) for reducing the detrimental impact of electric vehicle (EV) load penetration and renewable energy (RE) variation on active distribution networks (ADNs) are provided in this study. Soft open points (SOPs) have been used to create a multi-objective framework that considers loss minimization and voltage profile enhancement. The non-linear multi-variable complicated SOP allocation problem is solved for the first time using a modern meta-heuristic Aquila optimizer (AO). The modified IEEE 33-bus benchmark and IEEE 69-bus ADNs are used in the simulations. Before SOPs, the average real power loss in IEEE 33-bus AND was 370.329 kW, but after SOPs, it was reduced to 259.356 kW (i.e., 29.96 percent reduction). Similarly, effective SOPs integration in the IEEE 69-bus resulted in a loss reduction of 81.07 percent. AO's computational efficiency is also compared to that of multiobjective particle swarm optimization (MOPSO), particle swarm optimization (PSO), and cuckoo search algorithm (CSA). The AO has produced better results in terms of lower losses, improved voltage profile despite variations in EV load penetration, and RE and load volatility in ADNs, according to the results 2022. International Journal of Intelligent Engineering and Systems.All Rights Reserved -
AR and Online Purchase Intention Towards Eye Glasses
Augmented reality (AR) can be a potent tool for Indian online eyewear marketers by bridging the gap between online and offline purchasing experiences and meeting the needs of social validation and sensory engagement, which are preferences of Indian consumers. The present research explores how augmented reality (AR) technology affects Indian consumers' intentions to buy glasses online. A combination of descriptive and exploratory research design was used on the sample size of 236 consumers. Data was analyzed using frequency table and Structured Equation modelling (SEM) to identify the relationship amongst the variables. The findings indicate that accessibility to product information, telepresence, and perceived ease of use are important variables impacting purchase intention. AR can bridge the gap between online and offline experiences, meet consumer preferences, and create trust and confidence. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. 2024 IEEE. -
Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification
Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset. 2022 Elsevier Ltd -
Arabica Coffee Bean Grading into Specialty and Commodity Type Based on Quality Using Visual Inspection
Expanding potential of coffee consumers to seek out the freshest and best flavors is a cause for the rise of specialty coffee inthe market. Specialty coffee is grown and harvestedmaintaining an emphasis on quality and clarity of flavor, whereas commodity coffee is harvested for caffeine content. Within those inclusive categories, arabica and robusta are the two types of main branches of coffee that weencounter in the coffee market. Specialty coffees differ significantly from conventional coffees in that they are cultivated at higher altitudes, can be traced, and are professionally processed after being harvested. The quality is constantly examined and understood at every stage, from growth to brewing. Green arabica quality is assessed by counting the defective beans present in the sample. These defects can be primary (Category I) or secondary (Category II). If the primary defects are null and less than five secondary defects, coffee is said to be a specialty.Prior research has been done on classifying the coffee species and differentiating good beans from bad beans. Our research involves the combination of machine learning like K-NN and deep learning convolutional neural networks for classifying specialty coffee from commodity type using computer vision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Due to advancements in infrastructural modulations, architectural design is one of the most peculiar and tedious processes. As the technology evolves to the next phase, using some latest techniques like generative adversarial networks, creating a hybrid architectural design from old and new models is possible with maximum accuracy. Training the model with appropriate samples makes it evident that the designing phase will be simple for even a layman by including proper parameters such as material description, structural engineering, etc. This research paper suggests a hybrid model for an architectural design using generative adversarial networks. For example, merging Romes architectural style with Italys will accurately and precisely recover the pixel-level structure of 3D forms without needing a 2D viewpoint or 3D annotations from a real 2D-generated image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Architecture of FTO/n-CdS/p-SnSe1-xOx/Au Heterojunction Thin Film Diodes by Thermal Evaporation
In this report, FTO/n-CdS/p-SnSe1-xOx/Au heterojunction diodes were fabricated using a homemade precursor followed by dry milling with a facile thermal evaporation method under oxygen atmosphere (10? 2mbar) for the first time. The chemical purity (45.35:45.07:9.58 at.%) and microstructure of the deposited films and device were characterized by energy dispersive x-ray analysis, x-ray photoelectron spectroscopy, and scanning electron microscopy. The crystallographic parameters, a = 11.512 b = 4.163and c = 4.452 with orthorhombic crystal structure and monophase nature were analyzed by powder x-ray diffraction. Raman spectroscopy revealed the vibrational modes, and UVVis-NIR spectroscopy was used to study the direct nature of optical absorption with a band gap of 1.14eV. The currentvoltage (I-V) characteristics of the semiconductor diode were measured in room temperature (25C) and revealed rectifying properties and the cut-off voltage for the device, 0.57V. The obtained results highlight that the use of a p-SnSe1-xOx (SSO) layer as an interface between n-CdS/Au diodes exhibits excellent rectifying behavior and enhanced diode performance. Therefore, the p-SSO layer is a suitable material for heterojuction diodes and optoelectronic switches. Graphical Abstract: [Figure not available: see fulltext.]. 2022, The Minerals, Metals & Materials Society. -
Architecture of monophase InSe thin film structures for solar cell applications
Control of microstructural evolution during the crystallization of InSe thin films is an inevitable strategy to mold their fundamental properties and potential for the fabrication of solar cells. Impact of annealing as well as substrate temperature on the crystallization progress and physical characteristics of thermally evaporated InSe was examined systematically, which eventually dictates the overall performance of resulting device. Structural and compositional characterizations have been thoroughly investigated by X-ray diffraction and energy dispersive X-ray analyses. InSe films form hexagonal structure with a preferred orientation of crystallites along the (004) direction upon crystallization. The layer of InSe is formed by two concomitant processes, deposition and recrystallization. Application of heat treatment resulted in topographical modification, which was probed by an atomic force microscope. Surface roughness was enhanced due to the influence of temperature and thereby the growth of grains. Investigations of electrical and optical properties, thus provided ample evidence for the use of crystallized monophase InSe as an absorber layer in photovoltaic conversion devices. Carrier concentration and mobility of charge carriers estimated from the Hall measurements were found to be 19.43020cm?3 and 2.01cm2V?1s?1 respectively. Moreover, this research work explores power conversion efficiency of p-InSe/n-CdS heterojunction solar cells. 2017 Elsevier B.V.