Material science,Nanotechnology, Non-linear optics, Optical Fiber Communication, Electromagnetic waves, Electronics

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Md. Nozibul Haque currently working as Professor in the department of Electrical and Electronic Engineering, Islamic University, Kushtia-7003, Bangladesh.

• Geoelectrical Resistivity • GIS • Satellite Remote Sensing • Hydrogeology • Climate.

https://www.researchgate.net/profile/Md_Haque133

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volume 4, P:701

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Applied Geophysics, Environmental Science, Ground Water Modelling and Wireless Mobile Communication

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Mohammad Ruhul Amin Bhuiyan was born on 10th December 1974 in Bangladesh. He received the B.Sc. (Hons) and M.Sc. degree in Applied Physics and Electronic Engineering from Rajshahi University, Bangladesh in 1994 and 1995. The Ph.D. degree (worked in Experimental Physics Division, Bangladesh Atomic Energy Center) from Islamic University, Kushtia, Bangladesh in 2008. He is currently a Professor in the Department of Electrical and Electronic Engineering, Islamic University, Bangladesh. The post-doctoral research has been carried out in Electrical and Electronics Engineering at Cankiri Karatekin University and Manisa Celal Bayar University, Turkey. His current interest is development and characterization of nanostructure thin-film solar cell and thermoelectric materials. His work has produced two books and nearly 45 peer-reviewed scientific international and national research articles.

Renewable Energy, Thin Films Solar Cell, Nanostructure TE Materials

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https://scholar.google.com/citations?hl=en&user=4ua-ZDMAAAAJ

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IF 2.834

IF 0.860

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IF 3.83

IF 0.975

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IF 10.556

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IF 6.019

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A seasoned professor having 25 years experience of teaching and research in undergraduate and postgraduate levels in public university. Having wide experience of working at different levels of the university. Keynote speaker. Quality Assurance Expert, External examiner for PhD thesis for home and abroad. Reviewer for Elsevier journals. TPC for many IEEE sponsored international conferences. Editorial board member for national and international journals.

Wireless networking, Wireless communication. Image processing

https://www.researchgate.net/profile/Md_Ali15

https://scholar.google.com/citations?user=DWSxbuQAAAAJ&hl=en

Nanomaterials/nanocomposite (Organic/Inorganic) synthesis/processing via electrochemical and Sol Gel technique and their characterization/metrology for optoelectronic applications. Thin and thick film deposition via Physical Vapor Deposition (PVD), Pu

https://www.researchgate.net/profile/M_Alam10

https://scholar.google.com/citations?hl=en&user=H3lfBVkAAAAJ

M. Jalal Uddin received his Ph.D. degree in Electronics and Convergence Engineering from KwangWoon University, Seoul, Republic of Korea in 2019 and Master of Science (MS) degree in Nanomolecular Science from Jacobs University Bremen, Germany in 2013. Currently, he is researching as a Post-Doctoral Fellow in the Bio-IT Convergence Lab, KwangWoon University, Seoul, Republic of Korea. His research interests include Smart-biochips, Biosenors, and Wearable Devices for Point-of-Care (PoC) applications. His has nearly 25 peer-reviewed scopus-indexed research articles and four journal covers.

Research are includes Smart-biochips, Biosenors, and Wearable Devices for Point-of-Care (PoC) applications.

https://www.researchgate.net/profile/Jalal-Uddin-6

https://scholar.google.com/citations?hl=en&user=QknzDFQAAAAJ

I am Professor Humayun Kabir completed my PhD in Electronic Engineering from Kwangwoon University, Republic of Korea. Now serving in one of the prominent Public University in Bangladesh. I love teaching and do research on topics related to Electrical and Electronic Engineering

System Modeling; Artificial Intelligence/Machine learning; Context-Aware System ; Internet of Things(IoT); Wireless Communication; Embedded System

https://www.researchgate.net/profile/M_Humayun_Kabir2

https://scholar.google.com/citations?hl=en&view_op=list_works&gmla=AJsN-F5qnQz38q3YUqiBL2nVNpvRbjUbj-dpHnV9zs2bDzZfhRdwP5iYisKo9gU0Kp7W9R1VqOH4MckitNsffdDDegOdFPJ6KE6GYaTzTcHymXQ8BywU8rg&user=uPGKXvoAAAAJ

Energy harvesting from human-body-induced motion is mostly challenging due to the low-frequency, high-amplitude nature of the motion, which makes the use of conventional cantilevered spring-mass oscillators unrealizable. Frequency up-conversion by mechanical impact is an effective way to overcome the challenge. However, direct impact on the transducer element (especially, piezoelectric) increases the risk of damaging it and raises questions on the reliability of the energy harvester. In order to overcome this shortcoming, we proposed a transverse mechanical impact driven frequency up-converted hybrid energy harvester for human-limb motion. It utilizes the integration of both piezoelectric and electromagnetic transducers in a given size that allows more energy to be harvested from a single mechanical motion, which, in turn, further improves the power density. While excited by human-limb motion, a freely-movable non-magnetic sphere exerts transverse impact by periodically sliding over a seismic mass attached to a double-clamped piezoelectric bimorph beam. This allows the beam to vibrate at its resonant frequency and generates power by means of the piezoelectric effect. A magnet attached to the beam also takes part in generating power by inducing voltage in a coil adjacent to it. A mathematical model has been developed and experimentally corroborated. At a periodic limb-motion of 5.2 Hz, maximum 93 µW and 61 µW average powers (overall 8 µW·cm⁻³ average power density) were generated by the piezoelectric and the electromagnetic transducers, respectively. Moreover, the prototype successfully demonstrated the application of low-power electronics via suitable AC-DC converters.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things, represented as any items from our daily life that are enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems has been the main concern for the research community. In this paper, different security threats of Zigbee networks in the IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office/corporate environment. Our proposed SIF can predict and protect against various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting against several potential security attacks efficiently, enabling a more secure way of intelligent monitoring in the IoT platform.

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

Recognition of human activity is a key element for building intelligent and pervasive environments. Inhabitants interact with several objects and devices while performing any activity. Interactive objects and devices convey information that can be essential factors for activity recognition. Using embedded sensors with devices or objects, it is possible to get object-use sequencing data. This approach does not create discomfort to the user than wearable sensors and has no impact or issue in terms of user privacy than image sensors. In this paper, we propose a linear model for activity recognition based on the state-space method. The activities and sensor data are considered as states and inputs respectively for linear modeling. The relationship between the states and inputs are defined by a coefficient matrix. This model is flexible in terms of control because all the elements are represented by matrix elements. Three real datasets are used to compare the recognition accuracy of the proposed method to those of other well-known activity recognition model to validate the proposed model. The results indicate that the proposed model achieves a significantly better recognition performance than other models.

Context reasoning is an important issue for a context-aware system. Generally, context reasoning is adopted to deduce new context based on the available contexts. The rule-based reasoning is one of the most well-known methods for context reasoning. However, it is difficult for the rule-based algorithm to reason personalized context, because it requires a large number of rules to apply the user's preferences. To address this weakness, in this paper we suggest the Profile-Applied Reasoning Engine (PARE). PARE is an enhanced rule-based reasoning method which uses profiles while reasoning contexts. By using profiles, PARE can become aware of the context that is preferred by a specific individual. To validate the effectiveness of the proposed reasoning engine, we compared the reasoning result of PARE with traditional rule-based reasoning in smart home domain. PARE shows better outcome for reasoning the personalized contexts than the traditional rule-based reasoning. In addition, by using profiles, a significant number of rules have been omitted and consequently the running time is also decreased. Moreover, PARE occupies less memory space which is restricted with number of variables of a rule. Therefore, PARE optimizes both runtime and memory space, which is valuable when making embedded context-aware system.

A smart space is embedded with several components such as sensors, actuators, and computing devices that enable the sensing and control of the environment, and the inhabitants interact with the devices in the smart space whenever they need to. To model a smart space, a dynamic relationship needs to be established among the elements of the space whereby the interactions with devices are considered a dynamic-process state. In this paper, a linear model of a smart space is presented using a state equation, where the two coefficient matrices and need to be defined to model the smart space, and the coefficient matrix is used to determine the states of the devices; similarly, the situation of the smart space is determined using coefficient . An algorithm is presented to make a linear model from the logical functions that are used to describe the system. This model is flexible in terms of the control of the smart-space environment because the environmental factors are represented by a matrix element. This linear smart-space model is helpful for the control of a context-aware system, and we use an example to illustrate the effectiveness of the proposed model.

Activities of Daily Livings (ADLs) refer to the activities that are carried out by an individual for everyday living. Recognition of ADLs is key element for building intelligent and pervasive environments. We propose a two-layer HMM to build a ADLs recognition model that can represent the mapping between low-level sensor data and high-level activity based on the binary sensor data. We used embedded sensor with appliances or object to get object used sequence data as well as object name, type, interaction time, and location. In the first layer, we use location data of object used sensor to predict the activity class and in the second layer object used sequence data to determine the exact activity. We perform comparison with other activity recognition models using three real datasets to validate the proposed model. The results show that the proposed model achieves significantly better recognition performance than other models.

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0https://www.researchgate.net/profile/Md-Abdullah-Al-Mashud

https://scholar.google.com/citations?user=DGSCFT0AAAAJ&hl=en

Structural modifications of ethyl-3-((5-bromopyridin-2-yl)imino)butanoate ester enhances its biological activities. In the present work, the synthesis of a Schiff base by reacting 2-amino-5-bromo pyridine and ethyl acetoacetate was reported. Electronic properties were investigated by the computational method of density functional theory (DFT). The pass prediction score supported anti-viral efficacy which satisfied the Lipiniski rule and overall HOMO-LUMO. The most important part of this investigation was docking study which revealed that the ligands L07 has nearly efficacy to standard drugs and maximum docking score was found against Alpha variant (7EKF) at −6.7 kcal/mol, Beta variant (7ekg) at −6.2 kcal/mol, Gamma variant (7EKC) at −6.5 kcal/mol, Beta variant (7ekg) at −6.2 kcal/mol, SARS-CoV-2 Gamma variant (7EKC) at −6.2 kcal/mol, Delta variant (7V8B) at −6.1 kcal/mol and Omicron variant (7T9J) at −6.3 kcal/mol. Finally, molecular dynamics study was performed to determine the stability and it was reported that the molecules L07 has better stability to be an oral drug against the different variant of SARS-CoV-2 viral pathogen.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fomented a climate of fear worldwide due to its rapidly spreading nature, and high mortality rate. The World Health Organization (WHO) declared it as a global pandemic on 11^th March, 2020. Many endeavors have been made to find appropriate medications to restrain the SARS CoV-2 infection from spreading but there is no specific antiviral therapy to date. However, a computer-aided drug design approach can be an alternative to identify probable drug candidates within a short time. SARS-CoV-2 main protease is a proven drug target, and it plays a pivotal role in viral replication and transcription.

Methods: In this study, we identified a total of 114 essential oil compounds as a feasible anti-SARS-CoV-2 agent from several online reservoirs. These compounds were screened by incorporating ADMET profiling, molecular docking, and 50 ns of molecular dynamics simulation to identify potential drug candidates against the SARS-CoV-2 main protease. The crystallized SARS-CoV-2 main protease structure was collected from the RCSB PDB database (PDB ID 6LU7).

Results: According to the results of the ADMET study, none of the compounds have any side effects that could reduce their druglikeness or pharmacokinetic properties. Out of 114 compounds, we selected bisabololoxide B, eremanthin, and leptospermone as our top drug candidates based on their higher binding affinity scores, and strong interaction with the Cys 145-His 41 catalytic dyad. Finally, the molecular dynamics simulation was implemented to evaluate the structural stability of the ligand-receptor complex. MD simulations disclosed that all the hits showed conformational stability compared to the positive control α-ketoamide.

Conclusions: Our study showed that the top three hits might work as potential anti-SARS-CoV-2 agents, which can pave the way for discovering new drugs, but for experimental validation, they will require more in vivo trials.

This research work presents a computational investigation of hafnium(IV) oxide and its crystals doped by Si, Ge and Sn atoms, replacing the oxygen atom in HfO₂. Hafnium(IV) oxide, which has a wide band gap, has been used in power electronics applications as insulator for resistive random access memory (RRAM) in metal–oxide–semiconductor field effect transistors (MOSFETs). However, this gives rise to the serious issue of high resistance in optoelectronic devices. In this work, hafnium(IV) oxide was selected to investigate the change in band gap after large-area-surface doping with Si, Ge, and Sn. The lattice parameters and band gap values were calculated using the Perdew–Burke–Ernzerhof (PBE), revised Perdew–Burke–Ernzerhof (RPBE), Perdew–Wang (PW91), Wu–Cohen (WC), and Perdew–Burke–Ernzerhof for solids (PBE_sol) nonlocal functionals of the generalized gradient approximation (GGA). This first-principles method based on density functional theory (DFT) was employed to investigate the structural geometry, electronic structure, and optical properties using conventional calculations for HfO₂ executed with the computational tools of the CASTEP code from the Materials Studio 8.0 software program. The band gap was recorded at 4.340 eV, 2.033 eV, 1.686 eV, and 3.210 eV for HfO₂, Hf_0.88Si_0.12O₂, Hf_0.88Ge_0.12O₂, and Hf_0.88Sn_0.12O₂ crystals, respectively, through GGA with the PBE functional. Further investigations were conducted for GGA with RPBE, GGA with PW91, GGA with WC, and GGA with PBE_sol. The results revealed band gap values for HfO₂, Hf_0.88Si_0.12O₂, Hf_0.88Ge_0.12O₂, and Hf_0.88Sn_0.12O₂ of 4.427 eV, 2.093 eV, 1.744 eV, and 3.262 eV, respectively, using GGA with WC; 4.333 eV, 2.032 eV, 1.675 eV, and 3.172 eV, respectively, using GGA with PW91; 4.252 eV, 2.002 eV, 1.632 eV, and 3.086 eV, respectively, using GGA with WC; and 4.245 eV, 2.001 eV, 1.629 eV, and 3.076 eV, respectively, using GGA with PBE_sol. However, the PBE functional of GGA showed the best electronic band gap, which was similar to the experimental value of the reference crystal (HfO₂). The density of states (DOS) and partial density of states (PDOS) were also simulated and calculated to evaluate the nature of 6s², 5p⁶, 4f¹⁴, and 5d² orbitals for a Hf atom, 3s² and 2p⁶ orbitals for a Si atom, 4s², 3p⁶, and 3d¹⁰ orbitals for a Ge atom, 4d¹⁰, 5s², and 5p² orbitals for a Sn atom, and 2s and 2p orbitals for an O atom of Hf_0.88Ge_0.12O₂ and Hf_0.88Sn_0.12O₂ crystals. The optical properties (absorption, reflection, refractive index, conductivity, dielectric function, and loss function), electrostatic potential map, and toxicity were calculated for all crystals.

SARS-CoV-2, a new and fast circulating coronavirus strain, infected over 214 countries and territories worldwide and caused global health emergencies. The absence of appropriate medicines and vaccinations has further complicated the condition. SARS-CoV-2 main protease (Mpro) is crucial for its propagation, and it is considered a striking target. This study used several computational approaches to determine the probable antagonist of SARS-CoV-2 Mpro from bioactive phytochemicals of Syzygium aromaticum. A total of 20 compounds were screened through in silico approach. The molecular dynamics simulation studies were then carried out for further insights. We found crategolic acid, oleanolic acid, and kaempferol have considerable binding affinity and important molecular contacts with catalytic pocket residues, His41-Cys145. The pharmacological properties through ADMET analysis also showed that these compounds could be used as safe drug candidates. The molecular dynamics simulation study further confirmed these compound’s stability with Mpro. However, further detailed in-vitro and in-vivo analyses are compulsory to evaluate the real potentiality of identified compounds.

Background: Nanotechnology is an emerging research field on nanoparticles. Nanoparticles are 1-100 nm in one dimension and are important in biomedical science, medical chemistry, atomic physics, and most other known fields. They are used because of their small size, orientation, and physical properties.

Objectives: This study was designed to synthesize silver nanoparticles (AgNPs) using Azadirachta indica (Neem) leaf extract and evaluate their biomedical application.

Methods: An aqueous extract of Azadirachta indica was used to synthesize AgNPs. 1 ml of the extract was added drop by drop to 30-60 ml of a 1mM solution of silver nitrate. The AgNPs were characterized by UV-Visible Spectroscopy, Fourier Transform Infrared Spectroscopy, Dynamic Light Scattering Spectroscopy, X-ray Diffraction, Transmission Electron Microscopy, and antibacterial activity studies. The biological synthesis of AgNPs was performed using the aqueous solution of Azadirachta indica leaf extract and AgNO3. A fixed ratio of plant extract to metal ion was used to prepare AgNPs and the formation of the nanoparticles was confirmed through the color change. The nanoparticles were characterized by UV-vis spectrophotometer, FTIR, DLS, XRD and TEM, and were found to range in size from 30-60 nm.

Results: The biosynthesized AgNPs had a bactericidal effect against the antibiotic resistant pathogenic microorganisms Bacillus subtilis, Pseudomonas aeruginosa, and Bacillus cereus.

Conclusion: Silver nanoparticles were synthesized from Azadirachta indica leaf extract and can be used as a therapeutic candidate for biomedical applications.

The aim of this study is to present the results of commissioning of Elekta Synergy linear accelerator (linac). The acceptance test and commissioning were performed for three photon beams energies 4 MV, 6 MV and 15 MV and for the multileaf collimator (MLC). The percent depth doses (PDDs), in-plane and cross-plane beam profiles, head scatter factors (Sc), relative photon output factors (Scp), universal wedge transmission factor and MLC transmission factors were measured. The size of gantry, collimator, and couch isocenter were also measured.

Tailoring optical bandgap of ZnO nanostructured thin films doped with different elements facilitates potential material for photonic applications. Different methods of fabrication process result in different optical and structural properties for the same amount of Mg content. Therefore, details investigation of structural and optical parameters, and their correlation need to be revealed to utilize the fabricated thin films. In this work, Mg-doped ZnO thin film of 200 nm thickness was fabricated by sol-gel spin coating method on a glass substrate for four different Mg content levels. Multiple layers were deposited by a spin coater to increase the film thickness. The prepared thin films were characterized by SEM, XRD, EDS, and UV–Vis spectroscopy. The spectroscopic analysis showed a uniform crystalline nanostructured surface with less structural defects, enhanced transmittance, and higher optical bandgap than that of pure ZnO nanostructured thin film. Change of Mg content from 2% to 8% facilitated tuning of bandgap in the range of 3.30–3.39 eV. Changing trend of structural and optical parameters with Mg content showed non-linear, non-monotonic relation. In-depth analysis of structural and optical properties provides crucial information to form a better view about bandgap dependency on structural parameters.

In this study, we have observed binary mixtures properties for (a) N,N-dimethyl formamide (DMF) + butanol, and (b) DMF + 1-propanol at different temperatures. These are useful solvents because of their various types of applications. Densities, molar volume, excess molar volume, viscosity, and excess viscosity of two binary systems DMF + 1-propanol, and DMF + butanol have been measured at five different temperatures ranging from 303.15 to 323.15 K, respectively. The volumetric and viscometric behaviours of DMF + alcohols are shown as a function of mole fraction of DMF. The molar volume of binary mixtures has been changed according to the chain length of alcohols. For DMF + 1-propanol system, the excess viscosities are found to be negative throughout the entire range of concentration.

This paper describes the performance of one-bit hard combination scheme of cooperative spectrum sensing for different number of cognitive radio users. We evaluate cooperative spectrum sensing by simulating OR rule as a hard combination data fusion rule. Energy detector is used to observe the presence of primary user (PU) signal. It improves the probability of detection by collaborating to detect PUs signal in cognitive radio (CR) system. Simulation result shows that the probability of missed detection is decreasing for both conventional hard combination and 1-bit hard combination scheme with OR rule correspondingly with increasing the probability of false alarm. It is shown that 1-bit scheme has much better performance than the conventional hard combination scheme. This is also shown that the probability of missed detection is decreased even though CR user is increased. Here missed detection probability is decreased when the number of user increases.

I believe that life is wonderful. Learning the way of facing bad times of life makes it more cheerful. I like to go through multidimensional activities with different types of people. I like to enjoy music, play guitar and singing, and playing football, cricket, badminton, and table tennis.

Narrowband Internet of Things (NB-IoT) Channel Estimation, Ad Hoc wireless network, and Electronic System Design.

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Md. Khalid Hossain Jewel

I supervised this work

I supervised this work

I supervised this work