Journal of Scientific & Industrial Research (JSIR)

Formulation of Corchorus olitorius Leaves and Jowar-based Ready-to-Eat Snack

2024-12-20T17:18:51+0530

India has a great heritage of traditional snack foods. Murukku is a traditional snack of Telangana state, India, which has a distinct culture and food habits. A ready-to-eat, nutritious snack was developed by blending sorghum flour, Corchorus olitorius leaves, and other ingredients in optimal proportions. The developed product's sensory evaluation, nutrient, and phytonutrient composition were analysed. This nutritional powerhouse and sensory delight snack was crafted with a perfect blend of taste and texture. The unique combination of ingredients, carefully balanced flavours, and textures makes it a treat for the senses. Based on the sensory data, 10% Corchorus olitorius leaves incorporated murukku (JMCO- Jowar murukku with 10% incorporation of Corchorus olitorius) was selected for further study and compared with the control (JMC). The addition of Corchorus olitorius in murukku significantly (p < 0.01) enhanced the ash content (4.26%), crude protein level (16.87%), and crude fibre (3.84%), while reducing the carbohydrate content (45.56%) and energy content (480.6 kcal/100g). Adding leaf powder significantly
(p < 0.01) enhanced the vitamin and mineral content of murukku, providing a wealth of nutritional benefits. Total phenol, flavonoid, tannin, and antioxidant activity of murukku (JMCO) were 122.14 mg GAE/100g, 60.3 mg RE/100g, 20.97 mg TAE/100g, and 5.57% respectively. Corchorus olitorius leaves and jowar-based ready-to-eat murukku have good sensory, nutritional, and phytonutrient properties.

Antimicrobial Activities of Lipoxazolidinone A Purified from Lactobacillus apis YMP3 against Various Infant Diarrheal Pathogens and its Cytotoxicity against Normal Mammalian Cells

2025-04-01T11:57:34+0530

Infant diarrheal infections continue as one of the critical challenges of global health issues, demanding the development of safe therapeutic solutions; hence, this study was performed. This study explores the antimicrobial potential of lipoxazolidinone A against various infant diarrheal infectious pathogens and its cytotoxicity against mammalian cells. As reported earlier in our studies, this compound was produced and purified from a probiotic Lactobacillus apis YMP3. The antimicrobial study was performed in this study against a panel of 12 clinical bacterial pathogens causing infant diarrheal infections, collected from the Thanjavur Government Medical College, Thanjavur, Tamil Nadu, India. The purified lipoxazolidinone A showed appreciable antimicrobial activities against seven pathogens viz., Vibrio cholerae, Proteus mirabilis, Salmonella typhi, Vibrio vulnificus, Bacillus cereus, Escherichia coli, and Salmonella paratyphi, at the minimum inhibitory concentration of 100, 125, 150, 150, 150, 175, and 200 µg/ml, respectively, and the remaining five pathogens, viz., Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella oxytoca, Shigella dysenteriae, and Shigella boydii, revealed partial growth inhibition of 73, 56, 37, 27 and 21% at the highest tested concentration (200 µg/ml), respectively. The cytotoxicity studies of lipoxazolidinone A using two normal mammalian cell line cultures, viz., primary mouse embryonic fibroblast cells (NIH/3T3) and Madin-Darby Canine kidney cells (MDCK), evidenced negligible toxicity till 400 µg/ml. These findings reveal that lipoxazolidinone A is a promising antimicrobial candidate, proving its appreciable inhibitions against pathogenic bacteria and nontoxicity towards mammalian cells. The mammalian cell-safe antimicrobial activities of lipoxazolidinone A suggest futuristic research for the potential drug development in treating many infant gastrointestinal infections.

Isolation of Multi Stress Tolerant Yeast for Ethanol Production at Elevated Temperature

2025-01-22T16:16:23+0530

In recent decades, bioethanol and biofuels have emerged as alternatives to fossil fuels globally. Utilizing dairy waste (whey) as substrate is lucrative option for utilizing it and producing ethanol. Therefore, in deliberating the cost-effectiveness of ethanol fermentation there is a requirement of a microorganism strain that can convert substrate (lactose) into ethanol at higher temperatures. The study aims to isolate multi-stress tolerant lactose fermenting yeast strain for economical production of ethanol. Out of 215 samples of lactose fermenting yeast 9 strains were selected for ethanol production. Taxonomical identification and multi-stress tolerance (thermal, sugar, ethanol) of the 9 strains were conducted. After optimizing ethanol production at different temperatures (37℃, 40℃, 42℃, 45℃), significant ethanol production (8% v/v) could be observed at 37℃ in 40 h from 15% w/v lactose (YPL medium) while at 40℃ and 42℃ the productivity of ethanol slightly decreased, i.e., 7.5% v/v after 30 h with 0.5% residual lactose and at 45℃, only 3.5% v/v ethanol has been produced with approximately 7.5% w/v residual lactose. Thus, the isolated Kluyveromyces sp. 6C17 strain showed notable stress tolerance, making it promising for economical bioethanol production using lactose containing substrates at high temperature.

High Gain Microstrip Patch Antenna using Frequency Selective Surface for 5G Energy Harvesting Applications

2025-04-01T10:50:42+0530

This study introduces a novel microwave power transmission method designed to wirelessly power electronic devices, addressing the issues associated with energy storage and wired power sources. The antenna, fabricated using Roger’s RT/5880 substrate measuring 50 × 50 × 1.575 mm, functions within the 3.4 – 3.6 GHz C-band frequency range. The design has been tuned for a broad response and improved axial ratio, resonating throughout a bandwidth of 3.2 – 6.2 GHz, appropriate for WLAN, WiMAX, and 5G applications, with an average gain of 5 dBi. A technique for enhancing the gain of the monopole antenna was employed, utilizing a single-layer 4 × 4 metallic Frequency Selective Surface (FSS) reflector, which produced a band-stop filter response throughout a frequency range of 0.5 – 7 GHz and attained an antenna gain of 10 dB. Additionally, a rectifier circuit was incorporated to enhance power conversion efficiency and output voltage, employing SMD-Schottky diode type HSMS-2850-TR1 components that optimize the design and minimize its size. The rectifier circuit demonstrates an efficiency of 64.5% at an input power of 12 dBm with a 2 kΩ resistive load, producing a maximum voltage of 3.5 V with an input power of 13 dBm and a 10 kΩ load. This design enables the rectenna to operate efficiently in diverse contexts, providing a Power Dynamic Range (PDR) of (−30 to 30) dBm, ensuring a reliable power supply for devices even in low-power conditions. This technique is ideally suited for energizing a range of wireless sensors and other IoT applications.

Assessing Barriers to IoT Implementation in Circular Systems using Spherical Fuzzy AHP Approach

2024-11-13T16:03:01+0530

The implementation of Internet of Things (IoT) technologies is vital for enabling circular systems that enhance resource efficiency and sustainability. However, several barriers hinder the successful implementation of IoT in such frameworks. This study employs the Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) to identify, classify, and prioritize these barriers under uncertainty. Based on a combination of literature review and expert input, the barriers were grouped into four main categories: technical, financial, data and security, and infrastructure-related challenges. The findings reveal that data privacy and security concerns, technological complexity, and high initial costs are the most critical obstacles. Among all, data-related issues emerged as the top concern, reflecting the importance of trust, legal clarity, and cyber security in IoT implementation. Technical and financial barriers also significantly impact implementation, while infrastructure challenges are comparatively less severe. The study provides a structured decision-making framework to guide stakeholders in addressing high-priority barriers, facilitating the integration of IoT into circular economy models.

Addressing Catastrophic Forgetting in Neural Networks: A Review and Comparative Analysis of Algorithms for Crack Detection

2025-06-19T12:51:16+0530

Deep neural networks have demonstrated impressive results on a huge area of interest, but they suffer from catastrophic forgetting – when learning new tasks, they tend to forget what has been previously learned. Catastrophic forgetting in neural networks has garnered significant attention in recent years. The problem is too complex to generate general conclusions independently of the types of analyzed networks and specific issues. This paper addresses catastrophic forgetting in neural networks applied to crack detection. Given the limited research in this area, the existing research gap is highlighted in this study by reviewing recent works on catastrophic forgetting that explore various learning methods to mitigate forgetting. A comprehensive survey of techniques is conducted. Three representative algorithms used in crack detection datasets are evaluated - one from each major approach. Finally, a comparative analysis establishes a foundation for a novel algorithm that integrates the strengths of these different approaches.

Effect of a New Attachment in Rapier Loom on Properties of Textiles

2025-03-19T15:18:16+0530

Design and development of an innovative automated device focus on eliminating the auxiliary selvedge in shuttleless rapier looms. This auxiliary selvedge continuously contributes to increased material waste, additional labor and time for removal. So, the proposed device integrates cutting-edge automation technologies with existing loom mechanisms to address these challenges. The auxiliary selvedge is by grasping the protruding weft ends after each pick insertion in the weaving process, with consequent cutting and suctioning the projecting ends and so the device presents a sustainable solution for the textile industry by optimizing material utilization and by reducing the production costs. The scaling down in material remnants was quantified, and the quality of fabric is analyzed by testing the yarn density, areal density, tensile strength, tearing strength, bursting strength, air permeability, dimensional stability and crimp percentage for the fabric produced before and after installation of the attachment. A new attachment to the loom significantly reduces yarn wastage and cost, achieving a 62.5% reduction in both, without altering the loom's constructional parameters in the process of weaving and the produced fabric quality. The statistical significance of the fabric properties produced with and without the attachment was evaluated using a t-test. No significant differences were found in yarn density, areal density, tearing strength, and dimensional stability of the fabrics produced with and without the attachment. However, there were slight but statistically significant differences observed in crimp percentage, tensile strength, bursting strength, and air permeability of the fabrics produced with and without the attachment.

Enhancing Environmental Sound Classification with EcoOptiNet: A CNN-based Approach for Low-Frequency Signals

2025-07-02T11:26:07+0530

Time–frequency analysis is widely used to extract meaningful features from raw signals, but it is particularly challenging for low-frequency, non-stationary audio data. Deep neural networks offer strong feature-learning capabilities, yet their effectiveness depends on optimizers that can manage temporal variability and guide models toward stable convergence.
This research introduces EcoOptiNet, an improved optimization algorithm designed for low-frequency time-series audio signals. EcoOptiNet incorporates bias correction, an adaptive learning rate, and a learning warm-up phase (ζ) to prevent abrupt weight updates during early training. In contrast, a cubed-gradient update strategy enhances learning for
non-stationary signals. Audio signal data is transformed into Mel-spectrograms and delta features, capturing both spectral and temporal characteristics, and a Convolutional Neural Network (CNN) architecture is employed for classification.
The algorithm is evaluated on two benchmark environmental sound datasets using standard splits: ESC-50 with 5-fold
cross-validation and UrbanSound8K with 10-fold cross-validation. Experimental results indicate that EcoOptiNet achieves an average accuracy of 98.83% on ESC-50 and 91% on UrbanSound8K, outperforming commonly used optimizers such as Adam, RMSprop, and SGD, while maintaining low variance across folds. These findings demonstrate that EcoOptiNet provides an efficient and robust approach for optimizing deep neural networks on low-frequency, real-world audio signals. The study highlights the algorithm's ability to reliably extract discriminatory features from challenging datasets, offering a practical solution for environmental sound recognition applications where non-stationarity and low-frequency components can hinder traditional training approaches.

Reimagining Sustainability: Examining Consumer Awareness and Behavior towards Purchasing Remanufactured Products

2025-05-13T13:43:05+0530

Remanufacturing and utilizing remanufactured products are key indicators of a country's sustainability efforts due to their significant potential to reduce carbon emissions. The Kingdom of Saudi Arabia has prioritized this area as part of its Vision 2030 goals. While studies have examined consumer behavior toward remanufactured products in countries such as China and India, limited research has explored awareness and behavior in emerging markets pursuing strategic sustainability visions. This study investigates consumer awareness and purchasing behavior toward remanufactured products in Saudi Arabia, an emerging market committed to sustainability under Vision 2030. It introduces a new construct, consumer-suggested facilitators, capturing strategies proposed by consumers to enhance acceptance of remanufactured products—extending prior research in this field. A structured survey with a comprehensive questionnaire was conducted to assess factors influencing awareness, trust, and behavior toward remanufactured products. Findings show a significant correlation between consumer awareness and trust (r = 0.335, p < 0.001) and between perceived cost-benefits and knowledge of product channels (r = 0.465, p < 0.001). The results highlight the need for strategic marketing initiatives, educational campaigns, and supportive policies to accelerate remanufactured product adoption. Insights from this research help optimize supply chain strategies and consumer engagement approaches in the remanufacturing industry.