Journal of Scientific & Industrial Research (JSIR)

Geotechnical Design and Rock Support Evaluation of Dimapur–Kohima Railway Tunnel, Nagaland, India

Arun Kumar — 2025-03-11

This study evaluates the rock support requirements for a specific tunnel design, aligning with guidelines from the Indian Railways Schedule of Dimensions for 1676 mm Broad Gauge (BG), Revised (2004), and Addendum and Corrigendum Slip (ACS) No. 26. By examining geological, geotechnical, and geo-engineering parameters along the tunnel alignment—including rock mass characteristics and in situ stress conditions—the study aims to identify optimal design and support systems. Using RocLab software, key rock mass properties like deformation modulus and shear strength were evaluated, while RS2 (Finite Element Method software) assessed primary tunnel support needs. For full-face excavation and tunnel cross sections without invert, the recommended support classes III and IV require bolts of 3–3.5 m in length and 10–15 cm shotcrete. For cross sections with invert, classes IV, V, and VI support systems are advised. The maximum observed deformation for Class VI tunnels ranged from 80–150 mm, with a yielding radius of approximately 8 m, indicating poor rock quality in this section. To mitigate deformation risks, a high-strength support system with yielding capabilities is suggested, along with pipe roofing. However, further evaluation through 3D simulations is recommended to assess the pipe roof’s effects on stress and deformation. Finally, Class VII supports are proposed for their robust design, especially in areas with shallow overburden and low-stress environments, capable of managing substantial loads. This real-time study offers valuable insights for academicians and consultants focused on tunnel support assessment and related geotechnical applications.

Miniaturized Bandpass Filter with Controllable Transmission Zero using Low-Temperature Co-fired Ceramic (LTCC) Technology

Avjit Roy Choudhury — 2025-03-11

The trend toward miniaturization of satellite payloads necessitates the development of increasingly smaller and more efficient receiver and transmitter systems. Consequently, the components that make up these receivers and transmitters must be compact and utilize multi-layer technology to create hybrid architectures. Microstrip-based high-rejection filters necessitate a significant amount of space, which hinders the implementation of radio frequency systems in package (SiPs). The objective of this study is to develop compact filters for satellite receivers. This research focuses on constructing a filter that has improved selectivity to reject unwanted image frequencies while keeping the filter order the same. A third-order grounded combline resonator loaded with a capacitor has been chosen as the fundamental configuration for the proposed bandpass filter design. The paper introduces a new method to improve the selectivity of the combline resonator bandpass filter by incorporating a transmission zero, which is achieved by introducing a U-shaped coupling structure between non-adjacent resonators in a separate layer. The position of the transmission zero can be accurately controlled by adjusting the length of the U-shaped coupling transmission line. An additional coupling pad has been inserted between the resonator and the U-shaped pattern on a separate layer to guarantee the coupling between the resonators and adjust the bandwidth. An innovative method was employed to build and simulate a filter for the Ku-band frequency range of 12.7 GHz to 13.0 GHz. The filter has a 1-dB absolute bandwidth of 310 MHz and achieves a rejection of 26 dB at 11.6 GHz, the image frequency for a standard payload receiver. The filter's dimensions are 6.5 mm × 2.58 mm × 0.89 mm, making it an essential component of the RF SiP.

Improved Machine Learning based Approach for Autotuning PID Controller using Genetic Algorithms and Parallel Processing

Aghil Ahmadi — 2025-03-11

PID controllers are widely applied in approximately 95% of continuous control systems across process industries, making them a cornerstone of control engineering. Despite their widespread use, these controllers are often inadequately tuned. This study proposes an intelligent adaptive Proportional-Integral-Derivative (PID) controller for managing complex, uncertain processes. To enhance the capabilities of traditional PID controllers, an advanced machine learning approach using Deep Neural Networks (DNNs) is implemented. To optimize the configuration of the neural network and reduce computational load, a Genetic Algorithm (GA)-based structural learning technique is used, combined with parallel computing to accelerate training. Simulation results show that the proposed controller achieves an RMSE of 0.70 in the absence of disturbances, outperforming the Standard PID (0.95 RMSE) and Shallow Neural PID (0.74 RMSE). Under a 20 dB SNR disturbance, the proposed approach maintains robust performance with an RMSE of 0.79, compared to the Standard PID (1.10 RMSE) and Shallow Neural PID (0.86 RMSE). These findings highlight the superiority of the proposed innovatively tuned controller over both standard PID controllers and recently introduced intelligent network-based tuners, particularly in the presence of uncertainties.

Prioritizing Barriers to Circular Economy Implementation in Toy Industry using Analytical Hierarchy Process

Mangesh Joshi — 2025-03-11

The transition towards a circular economy has gained considerable attention as a promising strategy to mitigate environmental degradation and resource depletion. This study aims to prioritize barriers to circular economy implementation within the toy industry using the Analytical Hierarchy Process (AHP) methodology, driven by the need to identify and address obstacles hindering circular practices in a sector notable for its environmental impact and waste production. The methodology included a comprehensive literature review to identify relevant barriers, expert consultation, and AHP analysis to assign weights and ranks to each factor. The study identified and ranked factors influencing circular economy implementation based on their relative importance. Key findings highlight Lack of Collaboration (weight 0.351), Limited Market Demand (weight 0.109), and Economic Viability (weight 0.092) as primary barriers, with Lack of Infrastructure and Supply Chain Complexity also emerging as critical challenges. These findings emphasize the need for collaborative efforts, innovative solutions, and robust regulatory frameworks to overcome these barriers and promote sustainable practices in the industry. This study provides valuable insights for policymakers, industry stakeholders, and researchers to develop targeted strategies and initiatives for fostering circular economy principles in the toy industry. The novelty of this research lies in its application of the AHP methodology to systematically assess and rank barriers to circular economy implementation in a specific sector, offering a replicable framework for similar analyses in other industries.

An Efficient Early-Onset Plant Disease Prediction using Improved Heuristic-aided Adaptive Ensemble Network with Leaf Image-based Phenotype Data

Thiraviam K — 2025-03-11

Plant-related diseases pose a pressing threat to the agricultural industry, which is already strained to meet growing food demands. Farmers, whose primary income relies on agricultural production, often confront significant challenges as these diseases can severely disrupt crop growth and quality. Without early prediction, such diseases can greatly reduce crop productivity. Hence, to overcome this threat at an early stage, this research concentrates on developing an effective Adaptive Ensemble Network with a novel loss and activation function model for early-onset plant disease prediction using plant leaf imagery. In this method, the vital features are extracted from the Residual Network (ResNet152), Visual Geometry Group (VGG19), and DenseNet161. After attaining the features, the final feature set is obtained by the averaging-based computation. Then, the resultant features are given to the Deep Temporal Convolution Network (DTCN) for prediction of early-onset plant disease, in which the loss and activation functions are newly derived. Furthermore, parameter tuning uses the Modified Update in the Coati Optimization Algorithm (MUCOA). Finally, the validation outcomes of the designed approach are validated against conventional frameworks. The suggested framework performs robustly, achieving maximum accuracy, sensitivity, and specificity values exceeding 93% across both datasets. As a result, the proposed AEN model can be an insightful, farmer-friendly aid in identifying and predicting the early beginnings of plant leaf diseases.

ISAVM: Improved Smart Avian Monitoring using FLANN-based Audio Activity Detection & Speech Enhancement

Jagannath Dayal Pradhan — 2025-03-11

The Avian monitoring system is one of the challenging tasks that helps identify the environmental changes in the forest as well as the overall counts of specific species. Out of several methods available for avian monitoring, audio-based avian monitoring is one of the most efficient and cost-effective tools. By studying bird sounds, a smart society can be built for an enhanced avian surveillance system through the use of speech recognition algorithms. Conventionally, speech characteristics are employed for these tasks, which may not be appropriate given that these acoustic noises deviate from human speech and deteriorate the identification systems’ performance. An application-specific audio activity identification technique is needed since the features of human voice and bird sound differ. As of now, few works have been reported mainly for the bird sound analysis with audio activity detection and speech enhancement schemes. This work has considered and implemented this problem in three steps. In the first stage, an improved voice activity algorithm is designed using a Functional Link Artificial Neural Network model. In the second stage, an effective AdaBoost classifier is used for training and testing. Finally, the developed model, ISAVM: Improved Smart Avian Monitoring System has been checked for improved performance in two standard bird datasets. The evaluation has been done for different preprocessing options with and without audio activity detection and speech enhancement schemes. It has been observed that the proposed model is performing consistently with more than 93% of classification accuracy which is better than the standard avian monitoring models.

DUALBIGRU-UCSA: Deep Learning based Music Emotion Recognition Model

Chung Man Szeto — 2025-03-11

Music Emotion Recognition (MER) is a process to classify emotions perceived in a given piece of music with computational models. There are several problems regarding existing models, due to subjective perception of emotions and individual differences and culture diversity. To overcome these challenges, we developed a Dual Bidirectional Gated Recurrent Unit with Unified Contextual Shuffle Attention Fusion (DualBiGRU-UCSA) model. Here, the primary contribution lies in the practical implementation of bidirectional and gated recurrent units along with developed attention mechanisms to address the requirements for understanding and perceiving complex musical features. Using Bidirectional GRUs, the model taps the information of past and future contexts of music sequences in addition to refining the features of temporal dynamics and feelings. The final model’s performance enhancements involve the integration of bidirectional GRU outputs to the UCSA module through paying much attention and shifting through the feature representations, the module consisting of Shuffle Attention and Multi-Head Location-Aware Attention performs by reducing the unimportant feature representations while enhancing the important patterns and contextual cues. The proposed model performs better in terms of high accuracy, f1-score, negative predictive values, positive predictive values and recall of 96.28%, 96.32%, 96.26%, 96.60% and 96.27% respectively as compared to recent State-of-the-Art techniques.

Development of a Mini-tractor Operated Onion De-topper cum Digger

Mude Arjun Naik — 2025-03-11

Harvesting of onion include multiple tasks such as de-topping, digging, soil separation and windrowing. Traditional methods of harvesting are labour demanding, tedious and time-consuming leading to increased labour cost and time. Labour shortages during peak seasons further delay harvesting, which affects product quality and profitability. To address these challenges, a mini-tractor operated onion de-topper cum digger was developed at GBPUA&T, Pantnagar. This intervention combines de-topping, digging, soil separation and windrowing in a single operation suitable for market needs, leading to reduced cost of cultivation and increased profitability among onion growers. The developed machine consists of a de-topping unit that removes onion leaves up to the neck, a digging unit that uproots the de-topped bulbs, a soil separation unit to separate soil adhering to the onions, a power transmission unit to drive de-topping and soil separation unit and a frame for attaching it to the tractor. A Finite Element Analysis (FEA) was also carried out to evaluate structural performance (maximum stress, strain and deflection) acting on the digging blade. Further, the influence of crop and operational parameters (moisture content of onion leaves, cutter bar speed and machine forward speed) on de-topping efficiency was also evaluated. Statistical analysis of the data obtained revealed that de-topping efficiency increases initially with an increase in independent parameters (moisture content of onion leaves, cutter bar speed and machine forward speed) but further increase in independent parameters decreases the de-topping efficiency. The developed machine demonstrated a theoretical field capacity of 0.20 ha/h, an actual field capacity of 0.17 ha/h and a field efficiency of 85%. The developed machine requires 5.88 h/ha costing Rs. 2145 which is 65.6% less than the manual method of harvesting (Rs. 6250/- per ha). The input energy requirement for both manual and machine-assisted (developed machine) were also analyzed, as they are crucial for reducing operational costs and improving the overall sustainability of onion harvesting. The analysis showed that the total input energy needed in manual and with the developed machine were 1381 and 1022 MJ/ha respectively.

Exploring Project Complexity Factors: Case Study of Track-Doubling Line Railway Projects in India

Kavita Bhangale — 2025-03-11

Large projects are crucial as they play an important role, particularly in the economic development of a nation. Globalization and technological advancements enhance the complexity of such projects, which, if not managed properly, lead to poor performance. Although complexity is an important characteristic of large projects, the project-specific complexity factors call for a different approach for each project. Though railway projects are important contributors to economic growth, very few studies focus on the complexity involved in Indian railway projects. Hence, this study fills this knowledge gap by exploring and identifying project complexity factors that can impact project performance in railway projects. The research is based on a qualitative approach. Data were collected through open-ended interviews with project managers involved in the two select cases of brown field doubling line projects, each having unique characteristics. Further, the complexity factors were confirmed by conducting three rounds of Delphi method. The analysis revealed that the project performance is impacted by seven key complexities: organizational, technological, environmental, cultural, infrastructural, communication, and stakeholder management. The study offers findings that are valuable to both future research scholars and practitioners. The identified complexity factors will help project managers plan the projects effectively, leading to smooth execution. Though the findings of this research are based on track-doubling railway projects, they may also apply to other projects of similar characteristics.

Combinatorial Anticancer Effects of the Non-Invasive High Voltage Micro-Second Pulse Electric Field with Low-Dose Curcumin on A549 Cells

Gyanendra Kumar — 2025-03-11

Novel, alternative, and combinatorial approaches to combat cancer with minimal side effects are imperative due to the significant adverse effects associated with conventional therapies. The natural molecule curcumin has been reported to exhibit substantial anticancer activity against cancers, which are a leading cause of mortality worldwide. However, its clinical application is constrained by poor bioavailability. High Voltage Microsecond Pulse Electric Field (HV-µsPEF) therapy has emerged as a promising alternative in cancer treatment and may serve as an effective adjuvant anti-cancer modality. In the present study, the combinatorial effects of HV-µsPEF and low-dose curcumin on the A549 lung cancer cell line were evaluated with the primary objective of minimizing the curcumin dose required for its anticancer efficacy. HV-µsPEF was generated using a previously reported pulse generator and combined with low doses of curcumin to assess their effects on the A549 cell line. Cellular morphology was analyzed through phase-contrast microscopy, while flow cytometry was employed to evaluate the mode of cell death, curcumin uptake, Reactive Oxygen Species (ROS) levels, and Mitochondrial Membrane Potential (MMP) loss. The results demonstrated a highly synergistic induction of cell death in A549 cells, as observed through phase-contrast microscopy and flow-cytometry analyses, attributed to the enhanced uptake of curcumin by cancer cells in the presence of HV-µsPEF. This combinatorial treatment resulted in increased ROS production and significant MMP loss in A549 cancer cells. The findings indicate that combining HV-µsPEF with low doses of curcumin holds promising anticancer potential, effectively reducing the reliance on high doses of curcumin, which are often impractical to achieve in therapeutic applications.