Browsing by Author "Shahin Uddin, Muhammad"

Now showing 1 - 5 of 5

Fiber design and performance analyses for optical multiplexing: terahertz optical communications
(2024-04-25) Kabir, Md Anowar; Kumar Paul, Bikash; Hossain, Md Selim; Shahin Uddin, Muhammad; Morshed, Monir
Multiplexing is the process of combining multiple signals at a single channel. Orbital Angular Momentum (OAM) is one of the main multiplexing techniques for optical data transmissions. This paper examines and suggests a hollow core with four layers of semilunar air-hole-shaped circular photonic crystal fiber (PCF) capable of transmitting terahertz (THz) OAM information-carrying modes. By using the full vector finite element method (FEM), OAM multiplexing is analyzed for the proposed fiber. All the THz OAM-based factors are analyzed at a frequency band ranging from 400 GHz to 800 GHz. For the first time, some important PCF factors such as effective refractive index difference (ERID), dispersion profile (DP), OAM purity, confinement loss (CL), effective mode area (ERA), and numerical aperture (NA) are quantitatively discussed with applications. The proposed design supports 50 OAM modes with ERID up to 10−3. The PCF has a CL of approximately 10–10 dB cm−1 and the lowest dispersion profile is 0.3581 ps/THz/cm. Furthermore, the OAM purity is around 97%. Nonetheless, the proposed design can be used in THz-OAM transmission and high optical fiber communications.
Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease
(2024-12-19) Hosen, Md. Faruk; Abul Basar, Md.; Shahin Uddin, Muhammad; Yasmin, Mst. Farjana; Morshed, Monir
Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions.
Integrated Bioinformatics and Machine Learning Analysis Uncovers Key Pathways and Therapeutic Targets for Hypertension and Chronic Kidney Disease
(2024-12-20) Wasima, Jeba; Hosen, Md. Faruk; D Cruze, Francis Rudra; Shahin Uddin, Muhammad
Hypertension is a serious cardiovascular disease that substantially raises morbidity and mortality rates worldwide. People who have high blood pressure have been found to have an increased risk of developing chronic kidney disease (CKD) in recent years. The goal of this research is to use modern bioinformatics approaches to find potential treatment candidates and clarify the underlying biological pathways linked to both hypertension and CKD. Sample from individuals with CKD and hypertension were taken from two publicly available microarray datasets, GSE33463 and GSE66494. Consistent differentially expressed genes (DEGs) were found following thorough pre- processing and Python analysis. A Venn diagram was used to show where these DEGs’ regulatory crossings were. The most functionally important genes were then identified via topological analysis after protein-protein interaction (PPI) networks were built. UBC, ARRIB1, FADD and EIF3D have been identified as important hub genes. These concordant DEGs are tightly linked to the Toll-like receptor signaling pathway, which is a crucial mechanism in the control of the immunological response, according to pathway enrichment analysis performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG).In order to better understand gene relationships, future research will examine modular network studies, transcription factor (TF), microRNA (miRNA) network regulation, and gene ontology (GO) analysis. Concordant DEGs have been used to select a number of possible medicinal molecules, providing a promising path forward for therapeutic research.
Investigation of Gas Sensor Based on Differential Optical Absorption Spectroscopy Using Photonic Crystal Fiber
(Alexandria Engineering Journal, Elsevier, 2020-12) Paul, Bikash Kumar; Ahmed, Kawsar; Dhasarathan, Vigneswaran; Al-Zahrani, Fahad Ahmad; Aktar, Mst. Nargis; Shahin Uddin, Muhammad; H.Aly, Arafa
This study presented a novel shape photonic crystal fiber based gas sensor for the first time. Perfectly circular shape air hole are arranged to form core and cladding region. Based on full vector finite element method (FV-FEM) and circular shape perfectly matched layer (PML) the designed sensor has been investigated. The model field, effective mode index, sensitivity, confinement loss, effective mode area, nonlinearity, V parameter of the sensor fiber fundamental mode are investigated through FEM based commercial software package COMSOL Multiphysics. Numerical simulations evidences that the sensor shows the sensitivity responses of 64.69% and confinement loss of 4.38 × 10-06 dB/cm at the transmission wavelength λ = 1.55 µm. Besides, the sensor achieves single mode operation over the whole operating wavelength. Based on these excellent optical behaviors of the designed sensor it can be undoubtedly expect that, this sensor will play an influential role detecting gas molecules as well as PCF based optical sensing areas.
MN1 DN121 Vg19: A Multi-stream Ensemble Model for Detecting External Damage on Tomato Surface
(2025-06-12) Juthy, Most. Jebun Nahar; Tauhid; Hosen, Md. Faruk; Abul Basara, Md.; Aktar, Mst. Nargis; Shahin Uddin, Muhammad
The use of advanced computer vision and powerful deep-learning capabilities is increasingly important in the agricultural industry, particularly for monitoring crops and detecting diseases. However, detecting external damage to fruit crops remains a significant challenge. In our study, we introduced a multi-stream ensemble model called MN1_DN121_Vg19, which is designed to identify external damage on tomato surfaces. We separately used different pre-trained approaches, including InceptionV3, ResNet50, ResNet101, VGG16, VGG19M obileNetV1, and DenseNet121, and we examined how well they performed. After evaluating their performance, We merged the three wellknown pre-trained methods: MobileNetV1, DenseNet121, and VGG19, and extracted the learned features from these models. After that, we fine-tuned some of the top layers to effectively learn the features from our used dataset. The top 10 layers of DenseNet121 and MobileNet, as well as the top 4 layers of VGG19, were fine-tuned to create the merged model. The accuracy of the suggested multi-stream ensemble model was 98.51% using a dataset of 6500 images across 4 classes. When compared to existing pre-trained models, our merged model demonstrated superior performance across all metrics.