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.

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.

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.

Groundwater is the lifeline for the people residing in the mountainous regions of Nepal as they are dependent in this natural resource for drinking and other domestic purposes. The springs and seepages are reported to be drying in many places in Nepalese mountains in the recent time. Such problem is expected to be much more severe in view of climate change condition in the future. In this regard, it is important to establish a methodology to explore groundwater in the mountainous terrain for better planning. In order to delineate groundwater potential zones of rocky aquifers in the mountainous terrain, study has been carried out in Melamchi watershed, lying in the Sindhupalchowk district, Nepal. Various thematic layers have been prepared in GIS and the satellite imageries have been used to extract relevant information. Ranks and weights have been assigned to each thematic layer and its classes, respectively. The rasterized layers have been combined and classifi ed and the resulting map is crossed with the existing springs. It is found that the moderate and high potential classes of the groundwater potential map are represented by the existing springs. This method is expected to be useful at the preliminary watershed level study of groundwater potential in the mountainous terrain of central Nepal.

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.