Determination of Effective Index of Refraction of Structured Materials (Photonic Crystals)

Photonic crystals have been widely studied by researchers due to their ability to control light propagation in all spatial directions. For example, 2D photonic crystals can be made of dielectric rods arranged in a square lattice. When light propagates through 2D photonic crystals, it experiences multiple scatterings by the crystal centers. The superposition of all scattered waves forms the transmitted field largely dependent upon the effective index of refraction of the structured material. We propose a method to determine the effective index of refraction through such materials with the Finite-Difference Time-Domain (FDTD) model. We set up the interface of two media, such as air and photonic crystal, modeled the refraction of the EM waves at the interface, collected the data from FDTD and analyzed the data in MATLAB using FFT2. The proposed method promises to be a simple tool for determining the effective index of refraction of structured materials

Quantum-Edge Cloud Computing: A Future Paradigm for IoT Applications

The Internet of Things (IoT) is expanding rapidly, which has created a need for sophisticated computational frameworks that can handle the data and security requirements inherent in modern IoT applications. However, traditional cloud computing frameworks have struggled with latency, scalability, and security vulnerabilities. Quantum-Edge Cloud Computing (QECC) is a new paradigm that effectively addresses these challenges by combining the computational power of quantum computing, the low-latency benefits of edge computing, and the scalable resources of cloud computing. This study has been conducted based on a published literature review, performance improvements, and metrics data from Bangladesh on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. We have discussed the integration of quantum cryptography to enhance data integrity, the role of edge computing in reducing response times, and how cloud computing's resource abundance can support large IoT networks. We examine case studies, such as the use of quantum sensors in self-driving vehicles, to illustrate the real-world impact of QECC. Furthermore, the paper identifies future research directions, including developing quantum-resistant encryption and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC promises to overcome the existing limitations of IoT frameworks and set a new standard for the future of IoT applications

Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms

There are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings such as anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness by using several MATLAB functions namely, spectral descriptors, periodicity, and harmonicity. To accomplish the work, we analyze the CREMA-D (Crowd-sourced Emotional Multimodal Actors Data) & TESS (Toronto Emotional Speech Set) datasets of human speech. The audio file contains data that have various characteristics (e.g., noisy, speedy, slow) thereby the efficiency of the ML (Machine Learning) models increases significantly. The EMD (Empirical Mode Decomposition) is utilized for the process of signal decomposition. Then, the features are extracted through the use of several techniques such as the MFCC, GTCC, spectral centroid, roll-off point, entropy, spread, flux, harmonic ratio, energy, skewness, flatness, and audio delta. The data is trained using some renowned ML models namely, Support Vector Machine, Neural Network, Ensemble, and KNN. The algorithms show an accuracy of 67.7%, 63.3%, 61.6%, and 59.0% respectively for the test data and 77.7%, 76.1%, 99.1%, and 61.2% for the training data. We have conducted experiments using Matlab and the result shows that our model is very prominent and flexible than existing similar works.Comment: Journal Pre-print (15 Pages, 9 Figures, 3 Tables

P-gram: positional N-gram for the clustering of machine-generated messages

An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types. In this paper, we propose a new machine language processing (MLP) approach, called P-gram, specifically designed for identifying keywords in, and subsequently clustering, machine-generated messages. First, we introduce a novel concept and technique, positional n-gram, for message keywords extraction. By associating the position as meta-data with each n-gram, we can more accurately discern which n-grams are keywords of a message and which n-grams are parts of the payload information. Then, the positional keywords are used as features to cluster the messages, and an entropy-based positional weighting method is devised to measure the importance or weight of the positional keywords to each message. Finally, a general centroid clustering method, K-Medoids, is used to leverage the importance of the keywords and cluster messages into groups reflecting their types. We evaluate our method on a range of machine-generated (text and binary) messages from the real-world systems and show that our method achieves higher accuracy than the current state-of-the-art tools

Knowledge, attitudes, and fear of COVID-19 during the Rapid Rise Period in Bangladesh

The study aims to determine the level of Knowledge, Attitude, and Practice (KAP) related to COVID-19 preventive health habits and perception of fear towards COVID-19 in subjects living in Bangladesh. Design: Prospective, cross-sectional survey of (n = 2157) male and female subjects, 13–88 years of age, living in Bangladesh. Methods: Ethical approval and trial registration were obtained before the commencement of the study. Subjects who volunteered to participate and signed the informed consent were enrolled in the study and completed the structured questionnaire on KAP and Fear of COVID-19 scale (FCV-19S). Results: Twenty-eight percent (28.69%) of subjects reported one or more COVID-19 symptoms, and 21.4% of subjects reported one or more co-morbidities. Knowledge scores were slightly higher in males (8.75± 1.58) than females (8.66± 1.70). Knowledge was significantly correlated with age (p < .005), an education level (p < .001), attitude (p < .001), and urban location (p < .001). Knowledge scores showed an inverse correlation with fear scores (p < .001). Eighty-three percent (83.7%) of subjects with COVID-19 symptoms reported wearing a mask in public, and 75.4% of subjects reported staying away from crowded places. Subjects with one or more symptoms reported higher fear compared to subjects without (18.73± 4.6; 18.45± 5.1). Conclusion: Bangladeshis reported a high prevalence of self-isolation, positive preventive health behaviors related to COVID-19, and moderate to high fear levels. Higher knowledge and Practice were found in males, higher education levels, older age, and urban location. Fear of COVID-19 was more prevalent in female and elderly subjects. A positive attitude was reported for the majority of subjects, reflecting the belief that COVID-19 was controllable and containable

A Numerical Thermal Analysis of a Battery Pack in an Electric Motorbike Application

Today, electric driven motorbikes (e-motorbikes) are facing multiple safety, functionality and operating challenges, particularly in hot climatic conditions. One of them is the increasing demand for efficient battery cooling to avoid the potential thermal stability concerns due to extreme temperatures and the conventional plastic enclosure of the battery pack. A reliable and efficient thermal design can be formulated by accommodating the battery within an appropriate battery housing supported by a cooling configuration. The proposed design includes a battery pack housing made of high conductive materials, such as copper (Cu) and aluminum (Al), with an adequate liquid cooling system. This study first proposes a potted cooling structure for the e-motorbike battery and numerical studies are carried out for a 72 V, 42 Ah battery pack for different ambient temperatures, casing materials, discharge rates, coolant types, and coolant temperatures. Results reveal that up to 53 °C is achievable with only the Cu battery housing material. Further temperature reduction is possible with the help of a liquid cooling system, and in this case, with the use of coolant temperature of 20◦ C, the battery temperature can be maintained within 28 °C. The analysis also suggests that the proposed cooling system can keep a safe battery temperature up to a 5C rate. The design was also validated for different accelerated driving scenarios. The proposed conceptual design could be exploited in future e-motorbike battery cooling for optimum thermal stability

The transformation of education during the corona pandemic: exploring the perspective of the private university students in Bangladesh

Purpose – In 2020, the education system was preliminary halted by the COVID-19 crisis and went through radical improvisation, and online-based distance learning was the only plausible initiative to continue educational activities ensuring health guidelines properly. However, in reality, such desperate measure in case of a lower-middle-income developing nation lacking proper structural capabilities raised some issues and concerns for both pupils and mentors, and this study aims to explore the practice of online-based distance learning in private universities of Bangladesh and the challenges associated with it. Design/methodology/approach – This exploratory research is qualitative in nature. A total number of 89 undergraduate level university students from different private universities were divided into two main clusters and interviewed in depth. Findings – The findings of this paper revealed that common developing country syndromes like improper technological infrastructure development, limitation to devices or internet accessibility and financial hindrances can disrupt the harmony of the online learning experience. Also, the lack of tech literacy has created a huge tension and psychological inertia among both the teachers and the students. Social implications – The coronavirus pandemic event, with its dreadful influence, is creating immense mental pressures for students to cope well with the online learning system. Comprehending the underlying challenges affiliated with online-based distance learning and enabling faculties or respected personnel with training and development programs to handle impediments better way, this learning initiative can ensure the best outcomes. Originality/value – The significance of this study lies in comprehending the feasibility of online-based education regarding lower-middle-income developing nation context and the realism of such learning process's acceptability considering its actual effectiveness

Modified coptisine derivatives as an inhibitor against pathogenic Rhizomucor miehei, Mycolicibacterium smegmatis (Black Fungus), Monkeypox, and Marburg virus by molecular docking and molecular dynamics simulation-based drug design approach

During the second phase of SARS-CoV-2, an unknown fungal infection, identified as black fungus, was transmitted to numerous people among the hospitalized COVID-19 patients and increased the death rate. The black fungus is associated with the Mycolicibacterium smegmatis, Mucor lusitanicus, and Rhizomucor miehei microorganisms. At the same time, other pathogenic diseases, such as the Monkeypox virus and Marburg virus, impacted global health. Policymakers are concerned about these pathogens due to their severe pathogenic capabilities and rapid spread. However, no standard therapies are available to manage and treat those conditions. Since the coptisine has significant antimicrobial, antiviral, and antifungal properties; therefore, the current investigation has been designed by modifying coptisine to identify an effective drug molecule against Black fungus, Monkeypox, and Marburg virus. After designing the derivatives of coptisine, they have been optimized to get a stable molecular structure. These ligands were then subjected to molecular docking study against two vital proteins obtained from black fungal pathogens: Rhizomucor miehei (PDB ID: 4WTP) and Mycolicibacterium smegmatis (PDB ID 7D6X), and proteins found in Monkeypox virus (PDB ID: 4QWO) and Marburg virus (PDB ID 4OR8). Following molecular docking, other computational investigations, such as ADMET, QSAR, drug-likeness, quantum calculation and molecular dynamics, were also performed to determine their potentiality as antifungal and antiviral inhibitors. The docking score reported that they have strong affinities against Black fungus, Monkeypox virus, and Marburg virus. Then, the molecular dynamic simulation was conducted to determine their stability and durability in the physiological system with water at 100 ns, which documented that the mentioned drugs were stable over the simulated time. Thus, our in silico investigation provides a preliminary report that coptisine derivatives are safe and potentially effective against Black fungus, Monkeypox virus, and Marburg virus. Hence, coptisine derivatives may be a prospective candidate for developing drugs against Black fungus, Monkeypox and Marburg viruses