| 1 |
Author(s):
Emiliano C. De Catalina.
Country:
Philippines
Research Area:
Electrical Engineering
Page No:
1-11 |
Electrical Method in Automated On-Off and Speed-Selection Switching of 220V AC Electric Fan
Abstract
This paper is concerned with the 220VAC electric fan. Most electric fans sold in the market today are still manually operated, except for a few automated ones. Mostly, the ON-OFF and SPEED-SELECTION switching is still manually done by the user. The purpose of this paper is to present an innovation on the said manually-operated ON-OFF and SPEED-SELECTION switching of the common household 220VAC electric fan. Here, the switching is to be automated by means of ambient temperature; but no microcontroller, with its program, no internet would be used. Generally, this innovation is an automated ON-OFF and SPEED-SELECTION switching using electrical method. As temperature rises, the electric fan itself automatically switches ON and selects to the higher SPEED. As temperature lowers, the electric fan also automatically selects to the lower SPEED, corresponding to a pre-set temperature, and, switches OFF as temperature lowers even more. This study utilizes the applied technological research design. It applies scientific knowledge to solve a problem or to develop a new method. The findings of this study show that the innovation is feasible, technically doable, and implementable. In conclusion, the innovation is manufacturable, integrable, and adoptable in the current designs of the 220VAC electric fans commonly sold in the market today.
| 2 |
Author(s):
Harshdeep Mishra, Gagan Sharma, Nidhi Sharma, Ashutosh Tripathi.
Country:
India
Research Area:
Computer Science
Page No:
12-23 |
AI-Based Predictive Waste Reduction System in Supermarkets: A Dashboard-Driven Approach
Abstract
Food waste represents one of the significant global sustainability challenges, with supermarkets being substantial contributors due to inadequacies in demand forecasting and inventory management. This paper presents an AI-Based predictive waste reduction system designed for supermarket environments. The system integrates machine learning-driven risk scoring, real-time inventory monitoring, and an interactive analytics dashboard to identify high-risk products and deliver actionable recommendations such as dynamic discounting, stock redistribution, and donation management. Built on a modular React.js/TypeScript architecture with Tailwind CSS styling, the platform offers multi-store support, configurable alert thresholds, and a responsive user interface. Empirical testing demonstrates the system’s capacity to reduce food waste by up to 47% and generate daily savings of approximately $2,847/store through proactive interventions. The paper details the system architecture, feature design, database schema, and evaluation results. It also looks ahead to real world use, highlighting how the system could relate to IoT sensor and integrated with cloud-based machine learning to make it practical, scalable and ready for use.
| 3 |
Author(s):
Sagar Pathak, Bidhya Shrestha.
Country:
United States
Research Area:
Computer Science
Page No:
24-39 |
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment
Abstract
This research presents the implementation of a Deep Q-Learning Network (DQN) for a self-driving car on a 2-dimensional (2D) track, aiming to enhance the performance of the DQN network. It covers the development of custom driving environment with pygame on the track around the University of Memphis map and design and implementation of the DQN model. The algorithm utilizes data from 7 sensors collected by sensors installed in the car, based on the distance between the car and the track. These sensors are positioned in front of the vehicle, spaced 20 degrees apart, enabling them to sense a wide area ahead. We successfully implemented DQN and also modified the DQN with priority-based action selection mechanism and referred to it as modified DQN. The model is trained on 1000 episodes and the average reward received by agent is found to be around 40, which is around 60% higher than the original DQN and around 50% higher than the vanilla neural network.
| 4 |
Author(s):
K. D. Jagtap, B. D. Karande, S. V. Badgire.
Country:
India
Research Area:
Mathematics
Page No:
40-57 |
On Existence and Locally Attractivity Results for Fractional Order Nonlinear Random Integral Equation
Abstract
In this paper, we investigate the existence and qualitative behaviour of solutions for a class of nonlinear random integral equation of fractional order in R_+=[0,∞). The analysis is carried out within the framework of Banach algebra, employing a hybrid fixed-point theorem as the principal tool. The problem is considered under the assumptions of Lipschitz continuity and Caratheodory conditions, which ensures the measurability and continuity properties required for the existence of random solutions. In addition to proving the existence of such solutions, we establish their local attractivity, thereby demonstrating the stability of the system in a probabilistic sense. Also we have proved existence of extremal solutions. The theoretical results presented in this work contribute to the growing field of fractional calculus and stochastic analysis by providing rigorous framework for studying fractional random integral equations. To illustrate the applicability of the main results, we provide a concrete example that verifies the theoretical findings and highlights the practical relevant of the proposed approach.
| 5 |
Author(s):
Kufre A. Mkpedem, Mary O. Durojaye.
Country:
United Kingdom
Research Area:
Mathematics
Page No:
58-71 |
Polynomial Approximation Analysis of Transient Mixed Convection Flow in A Vertical Micro-Annulus with Viscous Dissipation
Abstract
This paper presents an analytical assessment of transient mixed convection flow and heat transfer in a vertical micro-annulus, taking into account internal heat generation, viscous dissipation, and temperature-dependent viscosity. The Boussinesq approximation is used to develop the governing momentum and energy equations, which are then translated into dimensionless form. A polynomial approximation method is used to generate closed-form solutions, which are then evaluated via symbolic computation. The effect of critical dimensionless factors, such as the Reynolds number, Peclet number, Eckert number, and heat generation coefficients, on fluid velocity and temperature distributions is investigated. The findings show that internal heat generation greatly increases fluid temperature while decreasing flow velocity. Increasing Eckert and Peclet numbers reduces temperature profiles, while increasing Reynolds numbers decreases fluid velocity. The research also reveals a linked interaction between heat and flow fields, which is controlled by viscosity variations and energy dissipation effects. The findings have important implications for thermal management and flow control in micro-scale systems including microchannels, heat exchangers, and cooling devices, where accurate prediction of linked heat and fluid flow is critical.
| 6 |
Author(s):
Shatakshi Singh, Satwick Pandey, Smriti Jaiswal.
Country:
India
Research Area:
Computer Science
Page No:
72-84 |
AI-Enabled Smart Energy Optimization System for Consumption Forecasting and Cost Reduction
Abstract
Managing household electricity consumption in India is challenging due to complex slab-based tariff structures and limited consumer awareness of appliance-level energy usage. This paper presents a Next Generation Smart Energy Optimizer, an AI-driven web-based system designed to predict electricity consumption and optimize household energy costs. The proposed system integrates machine learning, a slab-aware billing engine, and large language model (LLM)-based recommendations to provide comprehensive decision support. A Random Forest regression model, trained on real-world household data, predicts appliance-wise monthly energy consumption with high accuracy. The billing engine simulates real Distribution Company (DISCOM) tariff structures, generating detailed cost breakdowns including energy charges, fixed charges, and applicable duties. Additionally, an LLM-based advisory module produces personalized and context-aware recommendations to help users reduce consumption and expenses. The system is implemented using a Python–Flask backend, PostgreSQL database, and a browser-based frontend, ensuring scalability and accessibility. Experimental evaluation shows strong performance, achieving a mean absolute percentage error (MAPE) of 6.8% compared to actual electricity bills. The proposed framework bridges the gap between consumption prediction and actionable insights, offering a practical solution for intelligent household energy management.
| 7 |
Author(s):
Maribel M. Estioco, Wences Love Ylanan, Jezreel Roy Mamhot, Jeza Senining, Erica Pearl Faith Sarueda.
Country:
Philippines
Research Area:
Information Technology
Page No:
85-98 |
Development and Performance Evaluation of a Web-Based Facial Recognition Attendance System for Higher Education Institutions
Abstract
The web-based facial recognition attendance system, designed and evaluated in this project, provided an efficient, accurate, and secure means of taking attendance at J.H. Cerilles State College. Face-to-face identification can eliminate errors in manual and pen-and-paper recording, such as late manual logs and logbook signing for absent students. This system utilizes the 4D development model: Discover, Design, Develop, and Deploy for automating this traditional process. The web system is built with HTML, CSS, JavaScript, PHP, MySQL, and face-api.js for browser-based real-time face detection and recognition. Security for user login, face enrollment, automatic attendance recording, role-based management, and record export are among the main features of this system. In terms of system evaluation, the tests undertaken included alpha, beta, load, stress, compatibility, usability, and functionality testing. Analysis of these tests showed high user acceptance. The total weighted mean across these tests was 4.73, corresponding to Strongly Agree. The system demonstrated consistent response time and maintained accurate recognition performance with 30 users concurrently operating. Stress testing successfully identified 249/265 users, achieving 93.96% recognition accuracy during peak usage. Compatibility testing revealed that the system functions correctly in current web browsers like Google Chrome and Brave and includes proper privacy measures. The result shows that the system reduced the workload on admin staff, reduced attendance errors, and increased the transparency of the attendance system. We conclude that the web-based facial recognition attendance system is suitable and flexible to apply in a higher education system.
| 8 |
Author(s):
A. Kavya, Dr. D. Jayachitra.
Country:
India
Research Area:
Computer Science
Page No:
99-111 |
An Integration of Cryptography and Coding Theory
Abstract
The usage of the internet is increasing, so the message transmission must be protected. The real message is called plaintext. When a message is sent, it is encrypted into ciphertext, and it is assumed that it will reach the receiver without any errors. But in real situations, errors can occur at any time. The RSA algorithm is applied for both encryption and decryption to ensure message security. After transmission error detection and correction are performed using coding techniques, if the error is a single bit, it is corrected using the Hamming code, and otherwise, the BCH code is used. This paper combines cryptography and coding theory to improve security and reliability in the secure communication system.
| 9 |
Author(s):
Dattatray Gadkari.
Country:
India
Research Area:
Materials Science
Page No:
112-140 |
Diffusion controlled entirely detached growth of in₀.₅Ga₀.₅Sb by VDS process: Role of interface stability, thermodiffusion, and quasi-microgravity condition
Abstract
This study reports, for the first time, the diffusion-controlled growth of In₀.₅Ga₀.₅Sb crystals under entirely detached conditions using the Vertical Directional Solidification (VDS) process in a vacuum-sealed quartz ampoule. The detached configuration eliminates crystal–wall contact, suppresses buoyancy-driven convection, and establishes a quasi-microgravity environment governed by diffusion and thermodiffusion. Crystal growth was achieved under controlled conditions of gap width (70–250μm), axial temperature gradient (10–32 °C·cm⁻¹), and translation rate (≤ 3 mm·h⁻¹), ensuring a stable and planar crystal–melt interface. Mass transport is governed by coupled Fickian diffusion and Soret-driven thermodiffusion, leading to systematic solute redistribution along the growth axis. A polarity transition from n-type to p-type conduction occurs near the equiatomic composition (~50% GaSb), confirming thermodiffusion-controlled segregation. Key transport parameters, including boundary layer thickness (δ ≈ 0.05–0.3 cm), diffusion time (~10² s), and a low Peclet number (Pe ≪ 1), confirm diffusion-dominated growth with negligible convection. The constitutional supercooling criterion is satisfied under detached conditions, ensuring interface stability and defect-free solidification. Structural, electrical, and optical characterizations confirm high crystalline quality, reduced dislocation density, and excellent compositional uniformity. A dimensionless diffusion framework is introduced to quantify the transition from gravity-influenced to diffusion-dominated regimes. The results demonstrate that the VDS process enables quasi-equilibrium and quasi-microgravity solidification and provides a viable terrestrial alternative to alike microgravity-based crystal growth of high-quality In₀.₅Ga₀.₅Sb.
| 10 |
Author(s):
S. Dhanushya, Dr. D. Jayachitra.
Country:
India
Research Area:
Computer Science
Page No:
141-149 |
A Survey on Artificial Intelligence Applications in Image Processing Across Diverse Fields
Abstract
Artificial Intelligence (AI) has emerged as a powerful tool in image processing by enabling automated analysis, feature extraction and intelligent decision-making from visual data. With advancements in computational power and data availability, AI-based image processing techniques are increasingly applied across multiple domains such as healthcare, agriculture, astronomy, finance, security and industrial automation. This paper presents a comprehensive survey of recent research works that integrate AI methods with image processing to address domain-specific challenges and improve system performance. Key techniques including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Support Vector Machines (SVM), Optical Character Recognition (OCR) and hybrid models are systematically reviewed and compared. Their applications in tasks such as medical image segmentation, weed detection, satellite image enhancement, astronomical image preprocessing and document automation are discussed. The survey highlights significant improvements in accuracy, efficiency and automation achieved through AI-driven approaches when compared to traditional image processing techniques. In addition, major challenges such as limited annotated datasets, high computational requirements, generalization issues and ethical concerns related to privacy are identified. Finally, the paper outlines future research directions including explainable AI, federated learning and lightweight models for real-time and edge-based deployment. This study aims to serve as a useful reference for researchers and practitioners seeking to apply AI techniques in image processing across diverse real-world applications.
| 11 |
Author(s):
Anjali Priya, Sushil Kumar, Rishi Anand Arya.
Country:
India
Research Area:
Computer Science
Page No:
150-165 |
MRI-based Detection, Classification and Grade Prediction of Brain Tumor using Transfer Learning and Support Vector Machines
Abstract
Brain Tumors detection, classification and grade prediction according to WHO standards using MRI images remain a highly challenging task. Conventional machine learning and deep learnings models often fails to perform well when the dataset is very small, inaccurate and imbalanced, which is very common in medical imaging. To overcome these limitations, the proposed approach introduces an MRI- based framework for brain tumor detection, classification and grade prediction using transferring learning and support vector machines. MRI is the most widely used imaging modality for brain tumor detection due to its excellent soft-tissue contrast and non-invasive nature. This experiment focuses on the advancement of an intelligent framework for detection, classification and grade prediction of brain tumor using transfer learning techniques. Initially, a basic convolutional-based neural networks is used to detect the MRI images into tumor-positive and tumor-negative groups. Once the presence of tumor is confirmed, a classification technique is applied to find the types of tumors. In the grade prediction stage, machine learning classifiers such as Support Vector Machine are employed to determine tumor aggressiveness. The proposed approach aim is to improve the diagnostic accuracy of 95% or above, support the clinical decision-making and reduce the human intervention. The proposed framework is the combination of detection, classification and grade prediction. Tumor detection using a basic CNNs model, classification using DenseNet121 and tumor grade prediction using ML classifier. The experimental analysis provides the reliability, stable performance, making the system suitable for the real-world application.
| 12 |
Author(s):
Shruti Srivastava, Ayushi Singh, Shimpi Singh.
Country:
India
Research Area:
Artificial Intelligence and Machine Learning Engineering
Page No:
166-176 |
MindGuard AI: A Human-Centred Multimodal Framework for Early Mental Health Risk Assessment
Abstract
Identifying mental health crises at an early stage requires significant effort. An individual can appear healthy despite suffering internally for weeks, and by the time he or she consults a professional, a situation that could easily have been solved using basic help mechanisms transforms into something much more complicated to solve. The following paper introduces MindGuard AI-a multi-channel screening application designed to address that essential phase-the period spanning from initial signs of a psychological problem to the consultation with a specialist. Three distinct signals inform the model simultaneously: a 15-item self-report questionnaire following PHQ-9 and GAD-7 scoring, a BERT powered chat interface capable of assessing emotional tone throughout an informal discussion, and a MobileNetV2 convolutional neural network processing a short video stream captured by a webcam. A weighted fusion function combines all channels' results into one value, and the independent crisis flag continuously monitors for signs of suicide regardless of the resulting score. Our system proved efficient on the set of 240 sessions, showing 91% overall accuracy-an improvement of nearly ten percentage points compared to any single-channel alternative. In addition to describing our algorithm, this paper will also outline its limitations. Matching patterns in the emotional cues does not necessarily imply knowing the patient, and the development of MindGuard AI technology aims at early diagnosis but not as a substitute for the actual physician's judgment.
| 13 |
Author(s):
Dr. Vaibhav Takle.
Country:
India
Research Area:
Physics
Page No:
177-184 |
Hydrothermal Synthesis of SnO₂, TiO₂ Nanomaterials, its Characterization and pH Sensing Application
Abstract
In this research, Hydrothermal method was used to synthesize titanium dioxide (TiO₂) and tin dioxide (SnO₂) as this method is cost effective and have ability to produce highly crystallized material. This research focuses on how synthesis conditions influence structural properties of material and their compatibility for ph sensing application. Characterization technique like XRD, FESEM, UV visible spectroscopy used to determine their crystal structure surface topography, optical properties etc. Crystalline phases of both TiO₂ and SnO₂ were confirmed by XRD analysis. FESEM technique revealed surface morphology and topography as it finds synthesized material are spherical particles, although some degree of accumulation was present. Optical studies analysis done with the help of uv spectroscopy gives us band gap energies of a 3.1 eV for TiO₂ and 3.2 eV for SnO₂ respectively. The range of band gap energies reflects the semiconducting nature of nanomaterials. pH sensing mechanism of synthesized nanomaterials were tested and it is observed that when the pH level changed both TiO₂ and SnO₂ showed changes in their electrical behaviour, so we can say that they are ph sensitive materials. Impedance plot also gives us an idea that these nanomaterials respond well to different pH values. Overall TiO₂ and SnO₂ prepared by hydrothermal method are reliable, stable, low-cost materials for pH sensing
| 14 |
Author(s):
A. Jose Praveen, Sankara Narayanan S T.
Country:
India
Research Area:
Forensic Science
Page No:
185-198 |
Novel Forensic Methodology for Extracting Geolocation Data from Browser Cache Artifacts
Abstract
Modern digital forensics make more use of geolocation evidence, which is helpful to understand where and when users have been active, as well as identify their possible association with a particular place or even an object. Conventional geolocation methods usually consider IP address tracking, GPS coordinates extracted from mobile devices and user-owned media, as well as location services on such devices. However, the current state of web technologies allows one to collect geolocation information not only from IP addresses but also by considering the process of automatic caching online content, including images, maps, and other relevant artifacts generated within the web browser environment. Thus, the purpose of this paper is to discuss a new forensic methodology aimed at collecting geolocation-related information using artifacts stored in the browser cache folder. The main aspects to be considered include cache image recovery, metadata extraction, map tile artifact analysis, as well as finding coordinates based on the information provided by web browser. An experiment was conducted to find out if the suggested approach is appropriate for geolocation investigation.
| 15 |
Author(s):
SENTHIL NATHAN S, MARIMUTHU R.
Country:
India
Research Area:
Computer Science
Page No:
199-208 |
Hybrid Security Framework Integrating Firewall and VPN Technologies
Abstract
The proposed solution introduces SunduVault, which is a cloud-based management framework for managing a WireGuard-based virtual private network with a firewall management panel. End-to-end secure connection to the network is enabled by a PHP/MySQL backend that is responsible for handling user authentication, assigning dynamic IPs, and managing peer onboarding using a REST API. A peer monitoring dashboard along with firewall management capabilities gives live status of connections between clients and the central server, allowing the administrator to apply access control policies without requiring shell-level access. Secure access to the application is ensured by session-based authentication coupled with automatic token refresh, peer metadata storage in MongoDB, and separation of roles among admin and user portals. Experimental results show successful integration of peer onboarding and network segmentation using firewall rules along with real-time peer monitoring dashboard running at under one second refresh intervals.
| 16 |
Author(s):
Sudipto Roy.
Country:
India
Research Area:
Physics
Page No:
209-220 |
A Simple Mathematical Model for Analysing Vote Shares in the Assembly Election in West Bengal in 2026 Following Special Intensive Revision (SIR)
Abstract
This paper presents a simple mathematical model to analyse the impact of the Special Intensive Revision (SIR) of electoral rolls on the vote shares of AITC, BJP & Others in the 2026 West Bengal Assembly Election. Nearly 0.91 crore ineligible voters were deleted from the electoral rolls by the Election Commission of India (ECI). Using the results of 2024 Lok Sabha Election in West Bengal, this model examines how the removal of voters and the anti-incumbency effects may have influenced the results of the 2026 Assembly Election. The model defines key parameters representing the turnout percentage of ineligible voters, the percentage of their votes in favour of AITC (with the remaining in favour of BJP), and the percentage of votes that shifted from AITC and Others to BJP due to anti-incumbency. By solving a system of equations, the study identifies combinations of these parameters that reproduce the observed results of the 2026 West Bengal Assembly Election. The analysis shows that the observed vote shares are consistent with the model under various plausible assumptions regarding voter behaviour. This model suggests that, if SIR had not been conducted, AITC would have received a higher vote share than BJP in 2026 if the anti-incumbency swing was less than about 6.61%. The model further indicates that, for the observed 2026 election results to be reproduced under the present assumptions, the anti-incumbency swing from AITC and Others to BJP must be at least about 6.8%. An analysis involving different combinations of parameter values highlights how electoral roll revisions may meaningfully influence results in close contests. For simplicity, the present study uses only state-wide aggregate data. A limitation is that the constituency-wise information has not been used. The findings demonstrate the usefulness of simple mathematical modelling in understanding the effects of administrative decisions on election outcomes.
| 17 |
Author(s):
Dr. Rupa Pegu.
Country:
India
Research Area:
Chemistry
Page No:
221-229 |
Preparation of Organic Ink from Natural Pigments Present in Beta Vulgaris L. (Beetroot) and Curcuma Longa L. (Turmeric) for a Sustainable Environment
Abstract
: Organic inks containing plant based pigments provides sustainable shift on reducing environmental impact and toxicity compared to synthetic inks. Therefore, in this work organic ink formulations from beetroot and turmeric extracts were explored, emphasizing the natural pigment present in it. Four different ink formulations for each extract were prepared by mixing with different proportion of extra virgin olive oil, drumstick resin, and lemon juice, using water or ethanol or both as solvent. Out of all formulations, remarkable result has been achieved using one formulations of each pigment (formulation 3 of beetroot extract which composed of 65% extracted pigment (v/v), 15% ethanol(v/v), 15% water(v/v), 5% lemon juice (v/v), three drops of extra virgin oil, and three drops drumstick resin and formulation 4 of turmeric extract which composed of 55% extracted pigment (v/v), 40% ethanol(v/v), 5% lemon juice (v/v), four drops of extra virgin oil, and four drops drumstick resin). The two best ink formulations retained their vibrant color till 28th day while applied on a paper using ink pen and paintbrush and the ink solutions can be store for 28 days in refrigerator without developing any foul smells.
Keywords- organic ink, natural pigments, light fastness, painting, writing.
| 18 |
Author(s):
Shiv Kumar, Prof. Sunil Kumar Saini, Prof. Sanjiv Kumar Verma.
Country:
India
Research Area:
Medical Science
Page No:
230-241 |
Role of Ki-67, Tumor-Infiltrating Lymphocytes (TILs-CD4 and CD8), and PD-L1 in Predicting pCR After Neoadjuvant Chemotherapy for Breast Cancer
Abstract
Neoadjuvant chemotherapy (NACT) has become a standard treatment approach in breast cancer management, enabling in-vivo assessment of tumour chemosensitivity and facilitating breast-conserving surgery. Pathological complete response (pCR) serves as a validated surrogate endpoint for survival, particularly in aggressive subtypes. This review examines the predictive and prognostic roles of three key biomarkers—Ki-67, tumor-infiltrating lymphocytes (TILs including CD4+ and CD8+ subsets), and PD-L1—in forecasting pCR following NACT. Ki-67, a nuclear proliferation marker, consistently associates with treatment responsiveness, though its predictive value varies by breast cancer subtype and cut-off threshold. TILs, reflecting the immune landscape of the tumour microenvironment, demonstrate subtype-dependent predictive significance, with the strongest correlations observed in triple-negative (TNBC) and HER2-positive cancers. PD-L1, a mediator of immune evasion, emerges as a prognostic marker, particularly in residual disease. Individually and collectively, these biomarkers offer clinically actionable information for treatment stratification, potentially reducing unnecessary chemotherapy exposure and guiding immunotherapy candidacy. Standardised scoring protocols and molecular profiling advances will be essential to embed these biomarkers reliably into routine clinical practice.Keywords: Ki-67; Neoadjuvant chemotherapy; PD-L1; Pathological complete response (pCR); Predictive biomarkers; Tumor-infiltrating lymphocytes (TILs)
| 19 |
Author(s):
Sudhir Kumar Behera, Sujit Kumar Jally.
Country:
India
Research Area:
Environmental Science
Page No:
242-256 |
Trend and Impact Assessment of Floods and Cyclones in Coastal Odisha
Abstract
The state of Odisha situated on the Bay of Bengal periodically experiences loss of life and severe damages from occurrence of various natural hazards such as cyclonic storms, floods, heavy rains, thunderstorms, hail storms, heat wave, droughts etc. The present study aims to find out the degree of impact caused due to the occurrence of flood and cyclone for last five decades along the coastal districts of Odisha. To find out the trend and impact of floods and cyclones on socio-economic variables the UNDPs normalisation method was used to find out the damage index of the taken variables and then the Composite Damage Index (CDI) was obtained by combining all the indices for composite damage assessment of the study area. The district level variation in the impact of flood and cyclone along the Odisha coast shows that the Cuttack district which doesn’t shares any coast line in the Bay of Bengal still ranks one in the Composite Damage Index. Many studies have been done before on the occurrence and impact of natural hazards for the coastal districts of Odisha, but none of the authors have tried to make the comparative spatial-temporal analysis of the damages done by the natural hazards along the coastal Odisha. Therefore, the present paper will surely provide additional information to the policy makers on the changing behavior of the natural hazards over the time and space with regards to its occurrence and impact level.
| 20 |
Author(s):
Bhabataran Bhakat, Dr. Manoranjan Bar.
Country:
India
Research Area:
Physics
Page No:
257-269 |
The Structural, Vibrational, Morphological, Optical, and Magnetic Property Studies of Nickel-Doped Manganese Ferrite (Ni₀.₃Mn₀.₇Fe₂O₄) Nanoparticles
Abstract
This study investigated the structural, vibrational, morphological, optical, and magnetic properties of nickel-doped manganese ferrite (Ni₀.₃Mn₀.₇Fe₂O₄) nanoparticles synthesized via co-precipitation route. X-ray diffraction patterns confirmed the formation of a single-phase cubic spinel structure, Fourier Transform Infrared spectroscopy reveals two characteristic absorption bands, corresponding to metal-oxygen stretching vibrations at the tetrahedral and octahedral sites, respectively. Nickel substitution induces a noticeable shift in these bands toward higher wavenumbers, indicating modified vibrational modes. Morphological analysis using Field Emission Scanning Electron Microscopy shows spherical to irregular grain shapes with distinct grain boundaries and increased particle agglomeration at higher doping levels. UV-Visible spectroscopy demonstrates tuning of the optical band gap, with a shift in absorption edge influenced by nickel concentration. Photoluminescence analysis exhibits emission peaks in the visible region, which is attributed to oxygen vacancies and electronic transitions between the octahedral site e_g orbital and the O(2p) level. Finally, Vibrating Sample Magnetometer measurements reveal a systematic variation in saturation magnetization (M_s) and coercivity (H_c). These integrated results highlight the efficacy of nickel doping in engineering multifunctional Mn-ferrite nanoparticles for advanced optoelectronic and magnetic applications.
| 21 |
Author(s):
Rohit Kumar, Sonam Verma.
Country:
India
Research Area:
Materials Science
Page No:
270-282 |
Impact of Geopolitical Trade Shifts on Raw Material Costs in Printing Machines
Abstract
This study investigates the global printing and packaging industry depends mostly on internationally traded raw materials such as paper pulp, inks, BOPP films, adhesives, coatings, and chemicals. Recently, geopolitical trade shifts including wars, sanctions, tariffs, and supply. Chain realignments have significantly affected the availability, pricing, and stability of these materials. The research focuses on how geopolitical disruptions influence raw material costs in the printing industry and analyses their operational and financial consequences. This research shows the impact of conflicts such as the Russia-Ukraine War, the Israel–Hamas conflict, the Gulf War, and the US–China Trade War on paper, petrochemical-based materials, logistics, and energy costs. The research also shows long-term structural changes, including regionalization of supply chains, adoption of recycled materials, and multi-source procurement strategies. The research concludes that geopolitical instability has transformed raw material cost volatility from a temporary challenge into a long-term structural issue for the printing industry.
| 22 |
Author(s):
Sudheer Kumar, Sonam Verma.
Country:
India
Research Area:
Environmental Engineering
Page No:
283-294 |
A Critical Analysis of Environmental Health and Safety Practices in the Modern Printing Industry
Abstract
This research is intended to provide more detail and a more realistic view of what happens at a medium-sized company engaged in the digital printing process in terms of environmental health and safety practices. This research will evaluate different aspects of everyday life for production employees including producing printed materials that are done on a digital printing machine; cutting paper into pieces, laminating items, binding and finishing printed work, storing goods used in production, using cleaning supplies, and shipping goods out. All of these areas of study contain environmental and employee risk that are created by expected fast delivery of goods, being under pressure to keep production machinery operational, and being close to the powered or mechanical devices that run the machines, along with having direct contact with electronic devices and materials used to print, such as toner or ink, and dust from cutting sheets of paper. By identifying many of the circumstances listed above, this research aims to illustrate that a printing company can maintain a strong level of productivity and still require more attention to the safety, health, and environmental issues associated with that level of productivity.
