Surendra Shrestha

This work introduces a simulation-driven framework for reliably reproducing experimental trends pertaining to two-dimensional thin films, thus rendering advanced heterostructure research feasible for facilities without sophisticated equipments. Zhang and his colleagues fabricated a molybdenum disulfide (MoS2) device in a ground-signal-ground (G-S-G) configuration and derived its equivalent electrical circuit. This research simulates a MoS2-graphene based coplanar transmission line (CPTL) in a G-S-G configuration using Ansys HFSS. Thereafter an accurate equivalent electrical model circuit was designed and calculated for its hetero-junction device using Advance Design System simulator. For validation the characteristic curves of the simulated and modeled devices are compared with the measurements obtained for the metallic MoS2 proposed by Zhang and his colleagues, which are used in this study for validation purposes rather than as a foundation for the proposed model. The validation results confirms that the proposed simulation method can consistently replicate experimental trends. Furthermore, the effect of the combined properties of MoS2 and graphene on performance is evaluated based on S-parameters, and its high-frequency behavior is assessed through scattering parameter analysis (S11, S21), particularly at 5.9 GHz. Among the four different configurations assessed in this study, the one with the graphene monolayer positioned on the source side of the MoS2 signal line exhibited improved high-frequency performance in terms of I–V and C–V characteristics, as well as a low parasitic effect. Therefore, it can potentially be utilized in future devices and next-generation Wi-Fi spectra.

Offline handwritten signature verification is critical for secure authentication in legal, financial, and administrative domains. Traditional systems struggle with intra-user variability and skilled forgeries, while writer-dependent approaches lack scalability. Unlike digital data validation, verifying physical signatures is a complex task due to variations in writing styles, intra-writer inconsistency, and skilled forgery attempts. This research introduces a writer-independent deep learning approach using a Bidirectional Recurrent Convolutional Siamese Network (BRCSN) to address these challenges. The proposed model combines convolutional neural networks (CNNs) to extract spatial features and bidirectional gated recurrent units (Bi-GRUs) to learn sequential stroke patterns, allowing the system to capture both local and global dependencies from static signature images. A triplet loss function is employed to optimize the network by minimizing the distance between genuine signature pairs and maximizing the distance between forged ones in the embedding space. The system is evaluated on benchmark multi-lingual signature datasets and achieves high classification accuracy, recall, and F1-score, while maintaining low false acceptance and rejection rates. Importantly, the BRCSN model performs consistently across diverse users without requiring writer-specific training, making it well-suited for real-time deployment in legal, financial, and administrative applications. Inference time is kept under 100 milliseconds, supporting practical use cases. By eliminating the need for retraining and leveraging spatial- temporal learning through BRCSN, this research contributes to the advancement of secure and scalable biometric systems capable of robust forgery detection.

We used three spectrum sliced even and odd channels using a pair of 50-GHz Arrayed-waveguide gratings (AWGs) demonstrating the possibility of 25-GHz spaced ultra densed spectrum-sliced wavelength-division[1]multiplexed passive optical network. Those AWGs have an offset of 25 GHz with respect to each other. For the demultiplexing of the channels, we used a pair of 50-GHz AWGs. We intended to use these AWGs as it gives better bit-error rate performances and has two times larger channel bandwidth than conventional 25-GHz AWGs . Moreover, the increased spectral overlap between adjacent channels produces relatively small crosstalk since the channels are incoherent and the irintensity noise is absorbed by the reflective semiconductor amplifiers. The 25-GHz spaced spectrum-sliced spectrum-sliced WDM passive optical network can be realized more easily using 50-GHz AWGs since 50-GHz AWGs are more available in the market than 25- GHz AWGs.

The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users’ power consumption and improved the spectral efficiency.

With the advancement of technology, fake news is more widely exposed to users. Fake news may be found on the Internet, news sources and social media platforms. The spread of the fake news has harmed both individuals and society. The way to observe fake news using the stance detection technique is the focus of this paper. Given a set of news body and headline pairs, stance detection is the task of automatic detection of relationships among pieces of text. Pre-trained GloVe word embedding is used for the word to vector representation as it can capture the inter-word semantic information. The LSTM neural network had been shown efficient in deep learning applications because it can capture sequential information of input data. In this paper, it is found that the LSTM-based encoding decoding model using pre-trained GloVe word embedding achieved 93.69% accuracy on the FNC-1 dataset.

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

Kathmandu Upatyaka Khanepani Limited (KUKL) currently uses 35 surface and 57 groundwater sources to supply water for Nepal's capital, Kathmandu. It is necessary to understand if the Melamchi Water Supply Project (MWSP) can assist lean period water supply by indirectly increasing groundwater storage, through diverting excess water supply to groundwater recharge zones. The current study analyzed long-term groundwater depletion to assess available groundwater storage, followed by assessment of groundwater balance for the Kathmandu Valley. Results show that total groundwater extraction for Kathmandu was 69.44 million cubic meters (MCM) and drawdown of the groundwater surface was 15–20 m since the construction of wells in 1984/85, indicating substantial overexploitation. Results indicate that the ongoing unmet demand of 170 MCM/year can be easily satisfied if groundwater storage is recharged effectively, as underground water storage potential is 246 MCM/year due to a groundwater depletion rate of 2–10 m. From results, it is evident that that the timely implementation of the MWSP can help ease ongoing water stress and aid in reversing the damage caused to groundwater storage. In the long run, MWSP can supply water and recharge groundwater during monsoon periods, thus improving the quality of life and socio-economic status in Kathmandu.

This study aimed to develop magnetic Fe₃O₄/sugarcane bagasse activated carbon composite for the adsorption of arsenic (III) from aqueous solutions. Activated carbon (AC) was prepared from sugarcane bagasse by chemical activation using H₃PO₄ as an activating agent at 400 °C. To enhance adsorption capacity for arsenic, the resultant AC was composited with Fe₃O₄ particles by facile one-pot hydrothermal treatment. This method involves mixing the AC with aqueous solution of iron (II) chloride tetrahydrate, polyvinyl pyrrolidone (PVP), and ethanol. Batch adsorption experiments were conducted for the adsorption of As (III) onto the composite. The effects of pH, adsorbent dosage, and contact time on the arsenic adsorption were studied. The result showed that the composite could remove the arsenic from the water far more effectively than the plain AC. The highest percentage of arsenic removal was found at pH at 8, adsorbent dose of 1.8 g/L, and contact time of 60 min. Langmuir and Freundlich adsorption isotherm was used to analyze the equilibrium experimental data. Langmuir model showed the best fit compared to the Freundlich model with a maximal capacity of 6.69 mg/g. These findings indicated that magnetic Fe₃O₄/sugarcane bagasse AC composite could be potentially applied for adsorptive removal of arsenic (III) from aqueous solutions.