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Exploring the role of plant associating bacteria as bioinoculants and their beneficial effects in phytostimulation: A review
No full text567-575With an increase in global demand for food without unwanted environmental issues stresses a need for sustainable agriculture. Up till now, conventional agricultural methods focused on obtaining great crop yields from the use of chemical fertilizers but overlooked the hazardous concerns that are leading to soil depletion. These chemical fertilizers adversely affect soil structure, decrease fertility, damage soil flora, and lead to soil erosion. In this scenario, understanding the natural mechanisms of plant-microbe interactions in the rhizospheric environment can potentially lead a way towards eco-friendly agriculture, as the plant associating bacteria prompting phytostimulation can be the key players in unlocking sustainable alternative for conventional fertilizers. Plant growth-promoting bacteria (PGPB) are a distinct class of soil microorganisms that promote plant growth and yields by enhancing nutrient delivery and shielding the plants against diseases. Nitrogen-fixing bacteria such as Rhizobium and Azotobacter, for instance, fix atmospheric nitrogen into a usable form for plants, which minimizes synthetic fertilizers' requirement. Some other PGPB genera such as Pseudomonas and Bacillus induce root and shoot elongation by synthesizing phytohormones. These bacteria also provide protection to plants by synthesizing antimicrobial substances and increasing the competitive nature of the rhizosphere. Bacteria like Azospirillum, Enterobacter, and Flavobacterium also stimulate plant growth by producing phytohormones under specific environmental conditions. Utilization of PGPB as bio-stimulants in agriculture is a promising method for sustainable agriculture, minimizing dependence on chemical fertilizers and maintaining soil health. This approach would play an important role in sustaining a balanced ecosystem along with increasing agricultural productivity
Evaluating antimicrobial potential of ozonated water in preliminary trials
No full text443-448In response to the growing demand for sustainable disinfection solutions, ozonated water emerges as a promising antimicrobial agent. This study evaluates the efficacy of ozonated water against microbial strains such as Staphylococcus aureus, Pseudomonas aeruginosa and Candida albicans under standardized laboratory conditions. Sterile distilled water was ozonated using a portable ozone generator of 13 W for 30 min. Ozone concentration was confirmed using test strips. Nutrient broth and peptone water were used for bacterial and fungal growth, respectively. The study had three groups: negative control (uninoculated media), positive control (media inoculated with microbes) and test group (ozonated media inoculated with microbes). Microbial growth was assessed by solution turbidity after 24 h for bacterial species and 48 h for fungi. Additionally, the bactericidal and fungicidal activities were determined by transferring 10 L of the test group sample into fresh nutrient broth and peptone water, followed by incubation. The results confirmed antimicrobial activity of ozonated water. Also, the bactericidal and fungicidal activity of ozonated group confirmed by the clear broth in the test group, like the negative control, suggests its applications in various domains including healthcare. Further research is necessary to evaluate its compatibility with various materials for optimal applications
Acorus calamus L. and Parthenium hysterophorus L. plant extracts potential as wood preservative against Gloeophyllum striatum decay fungus
No full text413-424This study aimed to assess the antifungal resistance of Acorus calamus and Parthenium hysterophorus plant extracts. Wood samples were treated with plant extracts at different concentrations: 0.25, 0.50, 1.00, 1.50, and 2%. For a period of 12 weeks, treated wood samples (Pinus roxburghii were tested for resistance to the brown rot decay fungus Gloeophyllum striatum. Wood samples treated with petroleum ether extract of A. calamus at 2% concentration showed the lowest mass losses (10.75%), and wood samples treated with the methanolic extract of P. hysterophorus showed the lowest mass losses (13.61%) at 2% concentration. Plant extracts of A. calamus and P. hysterophorus showed the highest antifungal activity and percentage fungus growth inhibition at 2.0% concentration. Maximum colonization was noticed for untreated wood samples, and the lowest was noticed at 2.0%. Antifungal properties of A. calamus and P. hysterophorus extracts were confirmed by a decay index test. After the decay test, chemical properties of wood samples were evaluated to confirm the efficiency of plant extracts, and it was observed that minimum losses of soluble extractives, lignin, and holocellulose of treated wood samples occurred at 2.0% concentration of extract. As per the findings of the present investigation, selected botanicals can be used for wood preservation and reducing the mass losses
CMOS Mutator Circuits based on VD-DIBA for Realization of Meminductor/Memcapacitor and its Application
No full text371-381In this research, meminductor and memcapacitor mutators have been introduced by utilizing voltage differencing
differential input buffered amplifier in conjunction with a memristor and one capacitor. Notably, the memristor integrated
into the proposed design is composed solely of transistors and a capacitor. It is asserted that CMOS-based memristors offer
substantial advantages over their counterparts constructed from active blocks, as commonly found in existing literature,
particularly in terms of integration, compatibility, power efficiency, reliability, and cost-effectiveness. The memcapacitor
mutator can be easily derived from the meminductor mutator and vice versa by swapping the positions of the capacitor and
memristor. Simulation of the proposed designs is carried out with the help of LTSPICE tool with TSMC 180nm CMOS
technology parameters. The results obtained demonstrate that these designs exhibit commendable performance
characteristics across a wide spectrum of frequencies, and notably, they successfully withstand scrutiny under the nonvolatility
test. Additionally, adaptive learning circuit is designed using the proposed mutator to corroborate the effectiveness
of the design
Machine Learning-based Predictive Models for Early Diagnosis of Liver Disease
No full text575-583Liver disease is a major global health issue, contributing to nearly 2 million deaths annually. Early detection is crucial, yet
traditional diagnostic methods are invasive and costly. This study proposes a machine learning-based framework for liver disease
diagnosis using 30,690 patient records, incorporating demographic details, liver enzyme levels, and bilirubin measurements.
The methodology includes data preprocessing, feature selection, and model evaluation across 13 machine learning algorithms. Key
predictive features—Total Bilirubin, Direct Bilirubin, SGPT, SGOT, and Alkaline Phosphatase— were identified using
Chi-squared test, ANOVA F-value, Mutual Information, and Random Forest Importance. Among the models, Decision
Tree, Bagging Classifier, and XGBoost demonstrated superior performance, achieving over 99% accuracy. The Decision
Tree model exhibited the highest computational efficiency (0.0009 seconds prediction time), making it ideal for real-time
clinical applications. The study underscores the potential of machine learning in non-invasive, scalable, and accurate
liver disease diagnostics. Future work includes extending the model for personalized medicine and advanced liver
disease subtypes
Machine Learning-based Predictive Models for Early Diagnosis of Liver Disease
No full text575-583Liver disease is a major global health issue, contributing to nearly 2 million deaths annually. Early detection is crucial, yet
traditional diagnostic methods are invasive and costly. This study proposes a machine learning-based framework for liver disease
diagnosis using 30,690 patient records, incorporating demographic details, liver enzyme levels, and bilirubin measurements.
The methodology includes data preprocessing, feature selection, and model evaluation across 13 machine learning algorithms. Key
predictive features—Total Bilirubin, Direct Bilirubin, SGPT, SGOT, and Alkaline Phosphatase— were identified using
Chi-squared test, ANOVA F-value, Mutual Information, and Random Forest Importance. Among the models, Decision
Tree, Bagging Classifier, and XGBoost demonstrated superior performance, achieving over 99% accuracy. The Decision
Tree model exhibited the highest computational efficiency (0.0009 seconds prediction time), making it ideal for real-time
clinical applications. The study underscores the potential of machine learning in non-invasive, scalable, and accurate
liver disease diagnostics. Future work includes extending the model for personalized medicine and advanced liver
disease subtypes
Adaptive Hierarchical Clustering and Batch-free Top-K Sequential Pattern Mining for Data Streams
No full text531-543The Sequential Pattern Mining (SPM) is a challenging task in data streams due to huge memory and computational costs
to meet accuracy in mined results. Sequential patterns mined from target stream in traditional batch-based processing results
in pattern loss when the batches are processed independently, where the pattern frequency is determined local to the batch.
However, if a pattern is frequent in the stream and its items appear in various batches, then this pattern never becomes
frequent and hence requires pruning. To address this issue, the sequences are clustered by similarity using Adaptive
Hierarchical Clustering (AHC) and Batch-Free Top-K Sequential Pattern Mining (BFTKSPM) algorithms proposed to mine
approximate sequential patterns over data streams. The BFTKSPM algorithm targets data stream in a continuous and
batch-free manner. The top-k sequential patterns are extracted from data streams and are maintained in an inverted
tree structure. The experimental results of the proposed algorithm are carried out on benchmark datasets for data streams and
it outperforms the existing batch-based methods in terms of execution time, memory, precision, recall, and F1-score
