Journal of Scientific & Industrial Research (JSIR)

Classifying and Standardizing Parameters of Voice towards Categorizing Individual Variations for Personalised Biomedicine

2023-10-12T14:43:00+0530

Most of the eminent scholars of Unani medicine like Hippocrates, Galen and Avicenna have mentioned about four temperaments on the basis of four types of Humours. They believed that every person has certain characteristic features morphologically, physiologically and psychologically, which are distinct from one another on the basis of dominant humour. The normal proportion of these humours with each other is responsible for health while their imbalance causes disease and it has diagnostic and treatment values. Voice is one of the physiological functions and has been extensively documented in the literature of traditional medicine. Therefore, an adequate dataset of actual voice samples was taken from the random population and it was hypothesized that categorization of people can be done on the bases of their voice characteristics. There is a strong co-relation between voice characteristics and temperament of different individuals, research on voice characteristics was taken to interpret the observations in terms of four temperaments which was not done earlier. The study was carried out on healthy volunteers of the age group between 25–35 years (both the sexes). Zoom recorder was used to record the voice samples and then with the help of PRAAT software a voice report was obtained (shows readings of various voice parameters). Among various parameters in the voice report only eight were chosen according to the need of the study. Then statistical analysis was done to find the differences of individuals on the basis of their voice characteristics. Data suggests that not all voice parameters are equally efficient for categorization; however, rate of speech, pulse, and voice breaks can be used to divide people into four broad categories. The clinical advantage of categorizing voice is discussed with an aim to develop it as a tool for personalized medicine.

Designing and Spectral Investigations of S-benzyl β-(N-3-(2-nitrophenyl)allylidene)dithiocarbazate as a potential 2019-nCoV main Protease Inhibitor

2024-12-18T16:36:21+0530

The current work reports NS Schiff base S-benzyl β-(N-3-(2-nitrophenyl)allylidene)dithiocarbazate (HL) formulated as C₁₇N₃O₂S₂H₁₅. The synthesized Schiff base ligand (HL) was investigated by ¹HNMR, FT-IR, mass, Raman and UV-Vis analysis. The binding energies (−∆G) of HL for DNA, proteins (3C5W) & main protease (7BRO) were −19.6648, −20.5016 & −4.8 kj/mol., docking study showed significant therapeutic proficiency of (HL) to be used as 2019-nCoV main protease inhibitor. Anti-bacterial activity of (HL) has been checked against two multi-resistant bacterial strains E. coli (Gram −ve) and S. aureus (Gram +ve) showing notable bacteriostatic activity. The thermal stability of (HL) was studied by heating the (HL) at controlled rate of 15ºC per minute under N₂ atmosphere, up to the temperature range of 500°C. To support experimental findings theoretical investigation of (HL) was performed with the DFT-RB3LYP calculation method using basis set 6−311++G (D,P) for geometry optimization found −1536.98 a.u minimum energy and dipole moment 5.8005 Debye. FT-IR, Raman, and UV-Vis were perlustrated by both experimental and theoretical techniques. VEDA−4 software package was involved to scrutinize the percentage contribution of different segments from (HL) in FT-IR spectra obtained by Gaussian09 software. Comprehensive observations identified 111 fundamental vibrational modes, along with the potential energy distribution percentage (PED%) for each vibration and from dihedral angle calculations, indicating that HL possesses a non-linear geometry. This research explores novel bioactive alkyl/aryl NS chelating metal complexes as single crystals to enhance nano-bio interactions and expand electronic applications.

Innovative Pathways in Leather Industry: A Comprehensive Review of Sustainable Technologies and Strategies

2024-06-24T16:49:49+0530

The leather industry has undergone substantial transformations due to technological innovations, fostering more sustainable and efficient production methods. Several studies have been reported on innovative technologies and strategies employed in this industry so far. Despite the available literature in the field, a thorough analysis that summarizes and evaluates the recent advances in those innovations is still in its infancy. This review aims to provide an extensive overview of the latest developments in innovative technologies utilized in the leather industry. The findings from the literature indicate that technologies such as nanotechnology, artificial intelligence, augmented reality, additive manufacturing, and total quality management are at the forefront of this evolution. These state-of-the-art technologies have significantly transformed traditional production methods, addressing pressing environmental and sustainability challenges. Overall, these innovations have enabled manufacturers to enhance their business performance while addressing ecological and sustainability concerns at the same time.

An IoT-Based Edge Computing Lossless Compression Approach for Enhancing Energy Efficiency in Networks

2024-06-05T12:20:36+0530

As the Internet of Things (IoT) maintains to increase the inexperienced control of the huge amounts of records generated becomes increasingly more crucial. One of the major issues is big energy intake associated with transmitting records throughout networks. Addressing this issue is vital for the sustainability and feasibility of IoT infrastructures, mainly in packages stressful actual-time records processing and assessment. This paper targets to introduce a completely unique, energy-green technique for IoT compression that minimizes strength intake at some stage in records transmission. By leveraging edge computing, that seeks the machine data closer to its supply, thereby decreasing transmission distances and related electricity costs. A three-layered framework is introduced to achieve lossless compression by capturing network packets of different data sizes. The framework comprises IoT layer, Edge layer and Cloud layer. The framework is carried out at the brink of the community, enhancing statistics, decreasing power consumption, and ensuring security from cyber threats. The results are evaluated using metrics affecting data compression such as Root Mean Squared Error (RMSE) and Peak Signal to Noise Ratio (PSNR). The experimental results show that the proposed compression approach achieves the lowest power consumption rate as 80%, 85%, 90% and 88% in case of image, sensor, financial and textual data types respectively. Furthermore, the proposed framework achieves the highest PSNR value (92.14) and the lowest RMSE value (0.6653) thereby validating the performance of the given IoT-based framework. It shows that the proposed approach is better than existing compression techniques and recent review studies.

Feature Selection for Biomedical Data Classification: Statistical vs. Swarm Intelligence Methods

2024-12-12T12:43:31+0530

Applying machine learning methods to large datasets with numerous features presents challenges in terms of training time and model complexity. Feature selection is crucial for reducing data dimensions, improving classification accuracy, and optimizing model interpretability. This study aims to enhance the classification of integrated biomedical data to identify thrombophilia diagnosis. The dataset consists of 71 features from 35 women (22 healthy, 13 with thrombophilia), and three classification algorithms (K Nearest Neighbors, Random Forest, Support Vector Machine) are used to evaluate model performance. Identifying key features related to thrombophilia diagnosisis performed using both filter methods and wrapper methods based on swarm intelligence algorithms. Those methods are analyzed and compared as potential approaches for the feature selection process. The wrapper method outperformed the filter methods for clinical and biological data, achieving a classification accuracy of 0.97 compared to 0.91, while selecting only 4 key features compared to 10. For demographic data, both methods produced the same classification accuracy (0.83), but the wrapper method reduced the number of features. These findings demonstrate that wrapper methods based on swarm intelligence algorithms improve model performance and facilitate more efficient data management, which holds significant practical applications for thrombophilia diagnostics. Additionally, the study highlights the advantage of applying the Bat Algorithm in the feature selection process for thrombophilia prediction, contributing to both the novelty and utility of the approach.

A Single–Degree–of–Freedom Solution Procedure to Determine Dynamic Characteristics of Air–Bearing

2025-02-20T14:31:24+0530

An externally–pressurized journal air–bearing for a heavy, rigid, and balanced rotor is analyzed. The dynamic characteristics of air–bearing are determined during the investigation, at various angular velocities of the journal and angular frequencies of journal vibration. The dynamic characteristics of the air–bearing are found by a numerical simulation procedure. The journal air–bearing system is modeled to have a single–degree–of–freedom. The journal follows a predefined harmonic motion during the simulation. This motion is caused by self–exciting forces resulting from flow dynamics within a real air–bearing. Pressure distribution in the air–bearing is computed by solving the two–dimensional Reynolds equation. Alternating–direction finite–difference method is implemented using MATLAB to find the numerical solutions for pressure. The dynamic load–carrying capacity is calculated via the numerical integration of pressure distribution. The dynamic characteristics of air–bearing are calculated using the time series of displacements, velocities of the geometric center of the journal, and air–bearing forces. The stiffness coefficients and damping coefficients of air–bearing, as well as the eccentricity ratio and attitude angle of the journal, are compared with the findings in the literature. The average percentage differences in the results are attributed to the minor differences in the mathematical models of air–bearing used in this research and the literature. The dynamic stability of the rotor air–bearing system against self–excited vibration can be examined using the dynamic characteristics of the air–bearing.

Design and Analysis of Industrial Material Handling Systems using FEA and Dynamic Simulation Techniques

2025-04-01T10:28:27+0530

This study focuses on the design, simulation, and experimental validation of advanced material handling systems, specifically a vibratory bowl feeder and a paddle mixer, aimed at enhancing automation efficiency in modern industrial environments. The scope encompasses improving part orientation and mixing reliability in sectors such as automotive, pharmaceutical, and food processing industries. A vibratory bowl feeder was custom-designed for nuts and bolts, addressing common challenges such as inconsistent feed rates, jamming, and adaptability. The methodology involved 3D CAD modeling in SolidWorks, finite element analysis (FEA) for structural integrity verification, and dynamic simulation using Algoryx Momentum to predict system behavior under varied operating conditions. A spring-mass model was developed to compute natural frequencies and vibration characteristics. Simulation results were validated through experimentation across a frequency range of 47–79.75 Hz, measuring feed rate and part delivery time. Key findings indicate that the vibratory feeder achieved up to 200 parts per minute and over 95% orientation accuracy. FEA confirmed structural safety with stresses below 312 MPa and a verified natural frequency of 78.4 Hz. Simulation outcomes closely matched experimental results in the 50–60 Hz range but deviated at lower frequencies, highlighting real-world inefficiencies not captured in the model. The study concludes that integrating simulation with physical validation ensures robust design, reduced development costs, and enhanced system efficiency. Future work includes incorporating AI-based control and smart sensors to improve adaptability, accuracy, and energy efficiency. This work establishes a strong foundation for the development of intelligent, high-performance material handling systems.

Design and Analysis of a Solar-Powered Vapour Absorption Refrigeration System using E20 Software

2025-04-01T12:19:48+0530

The utilization of solar energy as future energy source is drawing attention of industrialist and researchers worldwide. The Concentrating Solar Power (CSP) and Solar Photovoltaic Power (SPP) is second largest installed renewable energy source. Solar energy finds potential application in refrigeration system, electricity generation, desalinate water, heat generation etc. The solar powered refrigeration system is more sustainable and environmentally friendly compared to conventional refrigeration systems. In order to explore the applications of solar powered refrigeration system, this study presents the quantitative simulation and performance maximization of a 2 TR solar-powered Vapour Absorption Refrigeration System (VARS) using E20 software motivated by its relevance for medium-scale applications such as commercial cooling and food processing. The integration of solar energy into VARS is an established field of research, with significant contributions. While fundamental principles of solar-driven ammonia-water VARS have been well documented, the application of E20 software would enhance system efficiency through simulation-based optimization. Despite E20 being a commercial tool frequently used in HVAC applications, this study applies it to a specific use case of VARS optimization, offering an improvement in methodology rather than an entirely new concept. This work presents the refining of existing designs using accessible simulation technology for performance optimization. Further, the aim is to bridge the gap between academic studies and practical implementations by demonstrating the feasibility of E20 in VAR system simulation. The performance of the system evaluated yielding a COP of 0.514, which aligns with previously established efficiency ranges.

Assessing Gender Equity in Capacity Building Schemes in Indian Research

2025-02-10T17:09:04+0530

The present study analyzed the gender dimension in capacity building schemes of Government of India, using a case study for Junior Research Fellowship Scheme of Council of Scientific and Industrial Research (CSIR-JRF) which is India’s top fellowship scheme for capacity building at early career development stage in science. The analysis was done for the scholars who qualified and joined the scheme during the decade 2010–2019. The results indicate that the number of female scholars qualified and enrolled (availed fellowship) under this scheme during the decade is overall considerably lower than male. Female scholars’ inclination was more towards life sciences, whereas male counterparts were more inclined towards physical and chemical sciences. The Gender Parity Index (GPI) varies among different states of India, with maximum numbers of qualified scholars (both male and female) were from West Bengal though male pass percentage dominated the qualifying exam, whereas Kerala showed the highest GPI of 3.02 during the last 10 years with more number of enrolled female scholars. However, few states and union territories showed least GPI due to zero enrollment for female scholars despite qualifying exam. This study provides a new dimension for future policy studies based on the knowledge/evidence of gender parity in capacity building schemes.