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

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

IF 2.829

IF 6.019

IF 2.829

IF 0.822

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IF 0.340

IF 0.822

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IF 3.83

IF 10.556

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IF 0.975

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IF 0.975

IF 0.860

IF 2.834

IF 1.836

IF 0.480

Science Direct

Science Direct

IF 0.664

IF 5.353

Science Direct

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.

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.

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.

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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Dr. Md. Abdullah Al Mashud is a distinguished academic, researcher, and scientific leader currently serving as a Senior Visiting Research Scientist at the Center for Nanotechnology, Coppin State University, USA. A native of Bangladesh, he has built a multidisciplinary research career integrating computational science, nanotechnology, biomedical engineering, and molecular simulation. He completed his B.Sc. (Honors) degree in 2003 and M.Sc. in Electrical and Electronic Engineering (EEE) in 2005. In 2021, he earned his Ph.D. in Medical Physics from the Faculty of Engineering and Technology at Islamic University, Bangladesh. Dr. Mashud began his academic career in 2008 as a Lecturer in the Department of Medical Physics and Biomedical Engineering at Gono Bishwabidyalay, Bangladesh. In 2010, he joined the Department of Electrical and Electronic Engineering at Islamic University, Bangladesh, where he was promoted to Assistant Professor, Associate Professor, and Professor in 2012, 2020, and 2023, respectively. Currently he is a full-time Professor of EEE at Islamic University, Bangladesh. His research interests encompass computer-aided drug design, bioinformatics, computational materials science, medicinal chemistry, molecular docking, molecular dynamics simulation, and computational modeling of nanocrystals and quantum dots. Under his leadership, the Biophysics and Biomedicine Research Lab for Drug Design and Nano Material Science was established with more than sixteen advanced simulation platforms supporting interdisciplinary scientific research at Islamic University, Bangladeh. Beyond academia, Dr. Mashud is actively involved in social and scientific development initiatives. He is the Founder and Chairman of Greenford Nano and Cancer Foundation (www.greenford.org.bd) and Greenford Samaj Unnayan Sangstha (www.sus.greenford.org.bd) in Bangladesh. These organizations dedicated to research advancement on nanotechnology, education, healthcare awareness (especially, Cancer) and community development.

Nano Synthesis, Computer Aided Drug Design, Bioinformatics, Computational Material Science, Molecular Dynamic Simulation, Solar Cell, Nano Technology

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

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

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.

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.

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.

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.

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.

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: 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.

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.

In recent years, significant progress has been achieved in the field of perovskite solar cells (PSCs), particularly those comprised of organic–inorganic lead halides, resulting in a remarkable record efficiency of 25.20%. However, the persistent issue of lead toxicity poses a considerable barrier to their widespread commercial adoption. To address this challenge, this study focuses on the optimization of lead-free germanium-based halide PSCs using SCAPS-1D simulation software. In our investigation, CsGeI₃ serves as the absorber layer, TiO₂ functions as the electron transport layer (ETL), Cu₂O is utilized as the hole transport layer (HTL), and various metals are employed as the back metal contact (BMC). The optimization of the BMC leads to the establishment of the FTO/TiO₂/CsGeI₃/Cu₂O/Ni structure. Subsequent optimization steps include fine-tuning the thickness of the absorber, ETL, and HTL layers, as well as optimizing acceptor doping and defect density in the absorber and HTL layers. Sequentially, donor doping and defect density in the ETL are investigated. Interfacial defect densities, along with the impact of temperature, series resistance, and shunt resistance on the photovoltaic performance, are also considered. Upon completion of these optimization procedures, the final device exhibits notable performance characteristics, including an open-circuit voltage (V_OC) of 1.165 V, fill factor (FF) of 88.52%, short-circuit current density (J_SC) of 24.40 mA/cm², and a power conversion efficiency (PCE) of 25.16%. Furthermore, the optimized structure without Cu₂O HTL demonstrates impressive performance, yielding a PCE of 25.15%. The outcomes presented in this study hold promise for the development of advanced lead-free PSCs, paving the way for high conversion efficiencies in future solar energy technologies.

Green Synthesis of Metal Nanoparticles is becoming a more common method for producing nanoparticles with a diameter of 1–100 nm that may be employed in a variety of medical applications. The antibacterial efficacy of silver nanoparticles (AgNPs) derived from Cinnamomum tamala (Tejpata) leaf extract against antibiotic-resistant Pseudomonas aeruginosa is investigated in this study. Green AgNP synthesis is safe, cost-effective, and ecologically friendly. The biosynthesized AgNPs were studied using UV-Visible spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Dynamic Light Scattering (DLS), X-ray Diffraction (XRD), and Transmission Electron Microscopy (TEM). The AgNPs were virtually spherical, with an average size of 25–30 nm, according to TEM observations. Biochemical and molecular identification were used to isolate multidrug-resistant P. aeruginosa from the hospital's drainage water. The antibacterial potential of AgNPs against P. aeruginosa is determined using the agar diffusion method. Silver nanoparticles produced from Cinnamomum tamala (Tejpata) leaf extract were shown to be effective in inhibiting four strains of P. aeruginosa. According to the agar disc diffusion method, AgNPs had the largest inhibition zone of 17.67 ± 0.577 mm, while aqueous extract had 5.67 ± 0.5777 mm, indicating that AgNPs had antibacterial activity. This study on AgNPs might assist with managing multidrug resistant pathogenic bacteria and be a possible source of medicinal application due to its potential antibacterial effect.

The increasing incidence of Monkeypox virus (Mpox) and Marburg virus (MARV) infections worldwide presents a significant challenge to global health, as limited treatment options are currently available. This study investigates the potential of several O-rhamnosides and Kaempferol-O-rhamnosides as Mpox and MARV inhibitors using molecular modeling methods, including ADMET, molecular docking, and molecular dynamics/MD simulation. The effectiveness of these compounds against the viruses was assessed using the Prediction of Activity Spectra for Substances (PASS) prediction. The study’s primary focus is molecular docking prediction, which demonstrated that ligands (L07, L08, and L09) bind to Mpox (PDB ID: 4QWO) and MARV (PDB ID: 4OR8) with binding affinities ranging from -8.00 kcal/mol to -9.5 kcal/mol. HOMO-LUMO based quantum calculations were employed to determine the HOMO-LUMO gap of frontier molecular orbitals (FMOs) and to estimate chemical potential, electronegativity, hardness, and softness. Drug similarity and ADMET prediction assessments of pharmacokinetic properties revealed that the compounds were likely non-carcinogenic, non-hepatotoxic, and rapidly soluble. Molecular dynamic (MD) modeling was used to identify the most favorable docked complexes involving bioactive chemicals. MD simulations indicate that varying types of kaempferol-O-rhamnoside are necessary for successful docking validation and maintaining the stability of the docked complex. These findings could facilitate the discovery of novel therapeutic agents for treating illnesses caused by the Mpox and MARV viruses.

Background: Head and neck cancer (HNC) is on the rise worldwide, endangering lives and straining healthcare systems in both developing and developed nations. Despite the availability of a number of therapy options, the success rate for treating and controlling head and neck cancer remains dismal. To combat the aggressiveness and drug resistance of Epstein-Barr virus (EBV)-positive Head-Neck cancer cells, this study looks into the potential of Euphorbia tirucalli (pencil cactus) leaf extract.

Objectives: The goal of this study is to identify prospective therapeutic candidates from the extract of Euphorbia tirucalli (pencil cactus) leaves, which have the ability to inhibit Epstein-Barr virus (EBV)-positive Head- Neck cancer cells.

Materials and Methods: The thirteen most important chemical components found in Euphorbia tirucalli (pencil cactus) leaves were analyzed by means of molecular modeling techniques such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), Quantum Mechanics (QM) calculation, molecular docking, and molecular dynamics (MD) simulations. Using the Prediction of Activity Spectra for Substances (PASS) model, we assess the potency of these compounds. Important molecular properties such as chemical potential, electronegativity, hardness, and softness can be determined with the use of quantum chemical calculations employing HOMO-LUMO analysis. These drugs' safety and toxicological characteristics are better understood to assessments of their pharmacokinetics and ADMET. Finally, molecular dynamics simulations are employed to verify binding interactions and assess the stability of docked complexes.

Results: The molecular docking analysis identifies ligands (01), (02), and (10) as strong competitors, with strong binding affinity for the Epstein-Barr virus (EBV)-positive Head-Neck cancer cell line. Not only do the ligands (01), (02), and (10) match the criteria for a potential new inhibitor of head-neck cancer, but they also outperform the present FDA-approved treatment.

Conclusion: Taraxerol, euphol, and ephorginol, three phytochemicals isolated from the leaves of the Euphorbia tirucalli (pencil cactus), have been identified as effective anti-cancer agents with the potential to serve as a foundation for novel head-neck cancer therapies, particularly those targeting the Epstein-Barr virus (EBV)-overexpressing subtype of this disease. An effective, individualized treatment plan for head-neck cancer is a long way off, but this study is a major step forward that could change the lives of patients and reduce the global burden of this disease.

Breast cancer, being among the most frequent and fatal cancers in women, is an enormous issue globally. The critical requirement for novel treatment methods is underscored by its high mortality rate and relentless advancement. Even though breast cancer is one of the world’s most common causes of death, the therapeutic avenue is still limited. The aim of this work is to investigate the potential inhibitory effects of specific compounds present in leaf extract from Mangifera indica on the growth of drug-resistant breast cancer protease PDB ID 3w32. The chemical compounds present in Mangifera indica leaves were used to analyze using molecular modeling techniques, such as molecular docking, molecular dynamics (MD) simulations, quantum mechanics (QM) calculations, and the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) method, in order to examine three key chemical constituents: quercetin (08), catechin (09), and elagic acid (10). The ligands undergo extensive testing to figure out how effective they are against the 3w32-overexpressing breast cancer protein. Quantum calculations retaining HOMO-LUMO analysis might identify important characteristics of molecules, such as chemical potential, electronegativity, hardness, softness, and orbital energy gaps. According to the molecular docking inquiry, ligands 08, 09, and 10 are strong candidates with strong binding affinity for the breast cancer protein that overexpresses 3w32. The protein binding site stability of the chosen natural ligands was verified by MD simulation. These three ligands not only surpass the efficacy of the FDA-approved treatment, but also fulfill the requirements for a possible new inhibitor of breast cancer.

With regard to current environmental concerns, the development of lead-free substitutes, such as CsSnCl₃ perovskites, has become increasingly important, as lead-based perovskite solar cells (PSCs) are hampered in their advancement by stability and toxicity. Using SCAPS 1d simulation software, the lead-free absorber material CsSnCl₃, which has a bandgap of 1.51 eV, was investigated numerically in this work. The FTO/ETL/CsSnCl₃/C₆TBTAPH₂/Au structure is examined and improved to achieve the highest efficiency achievable, using MZO, PC₆₁BM, CdS, and ZnSe as ETL to create four distinct structures. The absorber’s layer thickness, acceptor, and defect density have been carefully optimized. The influence of additional factors, such as the thickness of the HTL and ETL, the donor and defect density of the ETL and acceptor and defect density of HTL is also seen. Following the final optimization, the FTO/MZO/CsSnCl₃/C₆TBTAPH₂/Au structure showed a PCE of 32.11%, 1.36 V for V_OC, 25.87 mA/cm² for J_SC, and 90.59% for FF. The above discoveries have the potential to significantly advance lead-free, ecologically sustainable double perovskite solar cells (PSCs), making them more effective as well as creating the path for their eventual mainstream use.

Background

As the HIV-based complication is still going on with the high infection and mortality rate, it requires a novel strategy to combat this infection due to the unavailability of proper therapeutic. Therefore, we utilize integrated immuno and bioinformatics approaches in this study to design a peptide vaccine against HIV infection by targeting its complete genome.

Methods

The complete genome sequence was analyzed, and the potential B and T cells were predicted. Among the predicted epitopes, the promising ones were selected and further used with the adjuvant and linker to formulate a vaccine candidate. The vaccine was modeled, and its activity and stability towards the TLRs were analyzed via docking and dynamics (500ns). The vaccine-generated immune activity and expression via in-silico cloning were also evaluated.

Results

A total of 6 B cells, 7 CTL, and 6 HTL were identified as an immunodominant epitope and used for vaccine formulation. These epitopes were fused together via linkers, and their efficiency and constancy were enhanced with Adjuvant, PADRE epitope, and His-tag. Further, the formulated vaccine shows high population coverage and stable features based on the 2D and 3D assessments. The docking investigation demonstrated the strong activity of the vaccine towards the TLR2 and TLR3, having binding affinity − 10.8 kcal/mol-1 and − 15.8 kcal/mol-1, and also disclosed remarkable constancy based on the 500ns simulation period. The vaccine-assisted immune simulation and expression level in the vector revealed a robust immune response towards the host based on the vaccination and a significant expression level.

Conclusions

Based on the integrated approach and validation steps, the overall finding suggests that the formulated vaccine may have strongly immunodominant properties and could combat the infection.

Plant-mediated biosynthesis of nanoparticles has gained significant attention in Biomedical fields. This study focused on the isolation of multidrug-resistant diarrheagenic fecal pathogens and their control by green-synthesized silver nanoparticles. Furthermore, we assessed the antioxidant and cytotoxic properties of Piper chaba leaf-synthesized nanoparticles. We isolated and identified multiple antibiotic-resistant fecal pathogens, including Enterococcus gallinarum, Klebsiella pneumoniae, and Escherichia coli, through biochemical assays and 16S rRNA gene sequencing. Then the synthesis of silver nanoparticles was visually confirmed by a color change in the sample solutions and further confirmed through UV–Vis spectroscopy, FTIR, TEM, DLS, and XRD analyses. UV–Vis spectroscopy revealed a surface plasmon resonance (SPR) peak at 440 nm. Meanwhile, FTIR analysis identified functional groups such as haloalkanes, alkenes, and alcohols contributing to the synthesis and stabilization of the nanoparticles. DLS showed the nanoparticles were 56.66 nm in size with monodispersity, and XRD confirmed the presence of face-centered cubic crystals with an average size of 16.06 nm. Furthermore, TEM images showed spherical-shaped nanoparticles at various resolutions. The DPPH (2, 2-diphenyl-1-picrylhydrazyl) assay indicated antioxidant activity, with a maximum radical scavenging activity (% RSA) of 69.28 %. In addition, the brine shrimp lethality assay (BSLA) demonstrated low cytotoxicity with an LC₅₀ value of 912.01 μg/mL. The nanoparticles effectively inhibited these multidrug-resistant pathogens at varying doses, with minimum inhibitory concentrations of 4 μg/mL against Klebsiella pneumoniae and Escherichia coli, and 16 μg/mL against Enterococcus gallinarum. These findings suggest that green-synthesized silver nanoparticles could be a promising therapeutic approach for controlling antibiotic-resistant fecal pathogens.

Perovskite solar cells (PSCs) are an intriguing field of study due to their unique characteristics. This study aims to utilize the SCAPS-1D simulator to study the behaviour of absorbers in perovskite photocells that incorporate silver cadmium fluoride (AgCdF₃). In this study, AgCdF₃-based absorber material combined with 4 distinct ETLs namely PC₆₀BM, SnS₂, ZnSe and CdZnS and C₆TBTAPH₂ HTL in the structural proposed configuration of FTO/ETL/AgCdF₃/C₆TBTAPH₂/Au. The materials have been chosen based on the proper band alignments. The influence of thickness, acceptor density, defect density for both HTL and absorber layer in addition to the donor density, defect density, and thickness of the ETLs are observed carefully. Several parameters including the thickness of absorber and ETL and acceptor density of absorber layer is optimized to find best performances. After all the optimization, the FTO/PC₆₀BM/AgCdF₃/C₆TBTAPH₂/Au structure has provided the highest power conversion efficiency (PCE) of 31.32 %, along with 0.86 V of open circuit voltage (V_OC), 41.90 mA/cm² of short circuit current (J_SC) and 86.65 % of fill factor (FF). This simulation research allows scientists to develop cost-effective and efficient PSCs, advancing solar technology.

This study develops a transparent MATLAB-based numerical model for simulating lead-free perovskite solar cells (PSCs), providing full equation-level control and reproducible device analysis. Unlike black-box tools such as SCAPS-1D, the framework offers open access to all physical parameters and faster computation. Validation against reported CsGeI₃/TiO₂/Cu₂O/Ni (∼25% PCE) and CsSnCl₃/MZO/C₆TBTAPH₂/Au (∼32% PCE) structures shows <5% deviation from benchmark results, confirming model accuracy. By combining SnF₂ passivation, MoO_x dipole contact, and a multi-layer anti-reflection coating, the optimised Pb-free design (Model C) achieves ∼35% efficiency—a 12.5% gain in PCE—with 3% higher Voc and 2% higher fill factor when compared to previous Sn-based PSC models. For Pb-free PSCs, this is the first MATLAB-based open-access modelling framework that combines optical and interfacial engineering, providing researchers and students with a scalable, instructive, and repeatable platform to investigate next-generation photovoltaic design.

Introduction: Nanoparticles obtained through green synthesis play remarkable roles in biomedical applications. Urinary tract infections (UTIs) are a nightmare for the mass population, especially for women, and quinolone-resistant UTI bacteria worsen the situation. Our current investigation aimed to control quinolone-resistant pathogenic UTI bacteria with green-synthesized silver nanoparticles (AgNPs).

Methods: Visual observation of color change, UV-Vis spectroscopic analysis, FTIR (Fourier Transform Infrared Spectroscopy), DLS (Dynamic Light Scattering), XRD (X-ray Diffraction), and TEM (Transmission Electron Microscopy) techniques were used to effectively characterize the biosynthesized AgNPs. Klebsiella variicola, Pseudomonas sp., and Staphylococcus epidermidis bacteria were isolated and identified using biochemical and molecular identification techniques from urine samples of hospitalized patients with UTI. These bacteria showed quinolone resistance to up to fourth-generation antibiotics.

Results and Discussion: The results elucidated the synthesis of spherical-shaped nanosilvers coated with Punica granatum polyphenols. These biosynthesized AgNPs showed moderate polydispersity and narrow distribution. The antibacterial efficiency of the AgNPs was determined against isolated bacterial strains. Klebsiella variicola and Staphylococcus epidermidis exhibited the highest sensitivity to the nanoparticles. Nanoparticles at a concentration of 128 μg/ml inhibited bacterial growth to a great extent and gave a maximum inhibition zone of 14.67 ± 0.577 mm in diameter for both bacterial strains. In addition, toxicity analysis of synthesized nanoparticles via brine shrimp lethality assay (BSLA) showed a very low cytotoxicity level (2398.83 μg/ml), depicting safety for human use.

Conclusion: We can conclude that Punica granatum leaf-synthesized AgNPs could possess significant biomedical applications as potential antibacterial agents due to their bactericidal activity and low cytotoxicity.

Background: This study explores the eco-friendly synthesis of silver nanoparticles (AgNPs) using Mangifera indica leaf extract as a diminishing and settling agent. These AgNPs are notable for their antimicrobial activity.

Objective: This study's objective is to produce silver nanoparticles using mango leaf concentrate and assess the antimicrobial efficacy of biosynthesized silver nanoparticles against antibioticresistant bacteria.

Method: Comprehensive analysis techniques, including UV-visible spectroscopy, FTIR, DLS, XRD, and TEM, were employed to characterize the properties of the biosynthesized AgNPs. DPPH and BSLA assays were done to measure the antioxidant and cytotoxicity levels. The Kirby Bauer Disc diffusion method was incorporated to analyze the bacterial MIC and zone of inhibition.

Result: AgNPs were produced using the green synthesis method. UV results confirmed the presence of SPR at 427nm at 1:70 ratios. TEM results demonstrated that these nanoparticles are spherical. DLS result showed an average particle size of about 41.02nm. FTIR and XRD analysis provide strong evidence of the synthesis of silver nanoparticles with a crystalline structure using mango leaf extract. The silver nanoparticles produced exhibited less cytotoxicity with maximum antioxidant activity at 200μg/mL. AgNPs had strong inhibitory effects on three strains of antibiotic- resistant bacteria: Bacillus cereus, Staphylococcus aureus, and Escherichia coli. Notably, the disc diffusion method revealed that the AgNPs generated the maximum and minimum inhibitory zone of Bacillus cereus at 16 and 8mm at 64 and 8 μg/mL, respectively.

Conclusion: This research indicates that silver nanoparticles synthesized from mango leaves may serve as effective antimicrobial agents, especially against antibiotic-resistant pathogenic bacteria.

3α-Hydroxysteroid dehydrogenase type 3 (3α-HSD3) is a key steroid-metabolizing enzyme involved in the regulation of intratumoral androgen and estrogen levels, predominantly in estrogen receptor-positive (ER⁺) MCF-7 breast cancer cells. Its dysregulation influences proliferation, survival, and resistance to hormone-based therapy. The main goal of this study is to identify potential natural inhibitors of 3α-HSD3 from Terminalia arjuna phytoconstituents to modulate MCF-7 breast cancer cell proliferation. A panel of nine phytochemicals were evaluated through molecular docking, ADMET profiling, HOMO-LUMO energy gap analysis, and prediction of activity spectra for substances (PASS) to assess their drug-likeness and preliminary activity. Subsequently, molecular dynamics (MD) simulations were performed on top candidates to explore the structural stability and interaction dynamics of protein-ligand complexes. The selected compounds exhibited favorable docking scores against 3α-HSD3, with Luteolin(CMP1), Leucocyanidin(CMP2), Gallic Acid(CMP3), and Ellagic Acid(CPM4) forming stable hydrogen bonds with key active site residues. This study showed that the top four compounds may serve as potential 3α-HSD3 inhibitors of breast cancer, warranting further in vivo validation.

Fungal infections are an expanding global health burden, particularly in immunocompromised patients, and clinical management is increasingly limited by antifungal resistance and the narrow repertoire of current drug classes. To explore antifungal-relevant chemotypes from an underinvestigated botanical source, Xantolis assamica leaves were profiled via gas chromatography–mass spectrometry (GC–MS), and the annotated metabolites were prioritized in silico against Aspergillus niger β-1,4-endoglucanase (AnCel5A; PDB ID: 5I77), an underexplored enzyme implicated in fungal cellulose utilization. GC–MS revealed a chemically diverse metabolite set comprising fatty acids and their esters, terpenoids, phenolics, alkaloids, providing a rational basis for structure-based screening. Molecular docking identified CID 20845580 and CID 3083930 as the highest-ranked candidates (−9.9 and −8.9 kcal/mol), which were subsequently evaluated via ADMET profiling to assess developability; CID 20845580 satisfied the Lipinski criteria, whereas CID 3083930 showed a single lipophilicity-associated deviation that highlights a clear optimization target. Both candidates were then examined via 100 ns MD simulation and post-simulation MM-GBSA analysis alongside a reference compound. The CID 3083930 complex exhibited greater dynamic stability and an apo-like flexibility profile, whereas CID 20845580 showed higher structural deviation and residue-level fluctuation. Consistent with these findings, post-simulation MM-GBSA analysis indicated that CID 3083930 retained a more stable energetic profile over time, whereas CID 20845580 showed stronger initial energetic preference but reduced favorability during simulation. Overall, CID 20845580 was prioritized for docking-derived affinity, whereas CID 3083930, identified as onocerin, was prioritized for post-simulation stability, supporting their prioritization for lead optimization and experimental validation.

Application No.202231049880 A; The Patent Office Journal No. 36/2022(Part 1), Dated 09/09/2022; pp-56802

A state-of-the-art-technology was used to design the unbalanced three phase motor controller. The fluctuation of line voltage has been questioned in recent times due to the use of heavy powered machine with three phases. This paper is focused on deriving a control scheme to drive a three phase motor that could be used in industry or households. The very simple circuitry has been employed in this design. To do this, extensive MATLAB analysis and PSpice software was conducted in order to optimize the control system and finally the results are practically verified.

A PC based data acquisition was designed and developed using GM detector and a programmable microcontroller 16F876 of PIC family. This unit was calibrated with the standard Canberra Counter. It gave prompt response directly and has low power consumption. It was tested for gamma ray measurement to continuously monitor a radiological resulting from the natural and man-made sources that threat site security and human health. The system was simple to use, required no additional hardware and allows the selection of data. The collected data were easily being exported to a PC via parallel port. A" C" language program was developed to control the function of the entire system, using PCWH compiler.

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