Assam Don Bosco University Journals
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Screening of Amylase- and Cellulase-Producing Endophytic Bacteria from Cissus quadrangularis: A short report
No full textEndophytic bacteria are increasingly recognised as valuable biological resources in biotechnology due to their potential to synthesize a wide range of industrially relevant enzymes ability. This study explores the isolation, from Cissus quadrangularis, a medicinal vine traditionally used to increase healing process of fractured bone. By combining classical microbiological methods– serial dilution, plating, staining, and biochemical assays characterization, and screening of amylase- and cellulase-producing endophytic bacteria with functional enzyme screening, the study highlights the potential of plant-associated bacteria promising natural sources for environmentally friendly and scalable enzyme production. This research indicates that exploring plantassociated bacteria for relevant enzymes can produce valuable functional enzymes, for industrial use
INFLUENCE OF SELF GRAVITY ON THE MOTION OF IONIZING SHOCK FRONT IN A NON-IDEAL GAS
No full textThe influence of self gravity on ionizing shock front motion in a non-ideal gas in presence of a spatially decreasing magnetic field is examined. The medium ahead of the shock is considered to possess a negligible electrical conductivity and it is assumed that the initial density of the gas obeys a power law. The parametric variations of the variables are observed in the area in the wake of the shock front along with the similarity solutions are also derived and the influences of governing parameters are examined systematically
PREPARATION OF AN HERBAL MOSQUITO REPELLANT AND ITS STABILITY STUDY
No full textMosquito-borne diseases are a persistent global health concern, necessitating the development of safe and efficient repellents. This work explores the formulation and efficacy of a mosquito repellent liquid derived from natural ingredients, with a focus on essential oils such as lemongrass oil and okra extract used as a surfactant. Lemongrass oil, known for its high citronellal content, provides a potent repellent effect, while okra extracts containing mucilage acts as a natural surfactant, facilitating the homogeneous mixing of essential oils with water, thereby enhancing the formulation's stability and application of the repellent. The preparation process involved extracting essential oil from lemongrass, through reflux extraction method and obtaining mucilage from okra pods. The lemon grass was collected from the Assam don bosco university campus, tapesia gardens, Sonapur782402, Assam, India. The repellent formulations were then created by emulsifying varying concentrations of lemongrass oil with okra mucilage. The surface active agent present in okra helps in stabilizing the oil/water emulsion. The repellents stability was optimized against the concentration of various components and time. The okra/water extract concentration at 13.3 % v/v was optimized in this present study due to the formulation stability for 20 days. The repellent was found to be stable for 20 days at the LO/water concentration 17 % (v/v), which was also optimized. After 25 days from the day of preparation, the repellent was destabilized due to coagulation. For efficacy testing, proper scientific facility is required concerning the safety aspect. The inclusion of okra mucilage improved the stability of repellent suspension and ease of application
SYMMETRY REALIZATION UNDER (5+4) SCHEME IN THE CONTEXT OF MINIMAL EXTENDED SEESAW MECHANISM
Full text linkThis work includes the Abelian group symmetry realization of two-zero textures of neutrino mass matrices ( ) under Minimal Extended Seesaw (MES) Mechanism. MES is an extension of type-I seesaw mechanism which incorporates an additional scalar singlet field ‘S’ apart from the three right-handed neutrinos. MES mechanism deals with form of Dirac neutrino mass matrix and right-handed Majorana neutrino mass matrix , along with form of which couples the right-handed neutrinos and the singlet scalar ‘S’. In this work, we present the cyclic group symmetry realization of those textures of , and which are viable in realizing the two-zero textures of under MES mechanism. In doing so, the Standard Model is extended to include few scalar doublets to realize the zero textures of , scalar singlet to realize and few scalar singlets to realiz
A study on the Role of Financial and Non-financial incentives on Employee Performance
Full text linkThe purpose of this study is to investigate how employee performance at Global IME Bank Limited in Dharan, Nepal is affected by both monetary and non-monetary incentives. A demographic profile of the 64 participants in the study highlighted their varied age groups, gender representation, employment levels, and years of service. ANOVA was one of the statistical techniques used to assess the data. The results demonstrate that employee happiness and performance are highly influenced by both non-financial (recognition, career progression prospects, work-life balance, and working environment) and financial incentives (compensation, bonuses, profit-sharing, and retirement benefits). With p-values of.000, the models' high F-statistics of 225.42 and 192.56, respectively, show that they are highly predictive for financial and non-financial incentives.To further emphasize the robustness of the results, the study received a high reliability score of 0.96. The study suggests continual evaluation and enhancement of incentive programs to sustain their efficacy, concluding that a smart combination of non-financial and financial incentives is essential for improving employee performance.Keywords: Employee performance, financial incentive, non-financial incentive
Environmental Management Practices in Oil Refineries: A Case Study of Numaligarh Refinery Limited (NRL), India
Full text linkEnvironmental management in the oil refining sector is crucial due to the industry's significant environmental footprint. This research paper provides an in-depth analysis of the environmental management practices at Numaligarh Refinery Limited (NRL), a prominent refinery in Assam, India. The study evaluates NRL's strategies in waste management, effluent treatment, air quality control, energy efficiency, carbon management, and the adoption of innovative technologies. The paper also assesses the refinery's adherence to environmental regulations and its contributions to sustainable development. The findings highlight NRL's proactive approach to environmental stewardship, which serves as a model for the industry
Spectral Analysis of Musical and Random Noise Signals with Audio Classification: A Comparative Study
Full text linkThis study presents a comparative analysis of two types of audio signals: random noise and music audio. The random noise was recorded in a crowded public bus on an urban road, while the music audio was captured using the same device in a controlled environment, free from external disturbances. Statistical and spectral analyses, including histogram analysis, power spectrum, Fast Fourier Transform (FFT), and Auto-Correlation Function (ACF), were employed to evaluate the signals. The noise signal showed a bell-shaped histogram with samples concentrated around the mean, an ACF that decreased with lag, and the FFT spectrum, which shows that the energy is distributed uniformly across all frequencies. In contrast, the music signal displayed a sharp histogram, distinct voice and music components, rhythmic repetitions, and specific frequencies in its ACF and FFT spectra. Additionally, Support Vector Machine (SVM) classification was performed on both signals. The noise signal was accurately categorized into three noise levels with an accuracy of approximately 82%, while the music audio was categorized into voice, music, and noise components with 90% accuracy. The superior classification performance for music is attributed to the clear separation between voice and music components. This study demonstrates the potential of SVM in audio signal classification, providing a robust approach for distinguishing between noise and music audio
FINDING CONSTRAINED SEQUENTIAL PATTERNS FROM MEDICAL DATASETS
No full textThe problem of mining sequential patterns from supermarket data is an interesting data mining problem and has received a lot of attention to researchers. Mining sequential patterns from supermarket data is actually discovering causal relationship between different itemsets or symptoms that are presents on the patients. Supermarket data contain the records about the information of the itemsets or symptoms of patients besides the patient’s personal information and is ordered in accordance with the time / date of consultation of the patients in the hospital. Such data may offer us the precious information related to the cause and effect one itemset on another on the human body. Although, it gives us the ordering of the occurrence itemsets in the human body, it does not provide us the information about the time intervals within which the successive itemsets may occur in a human body. That is, we don’t know after how much time the next itemset may occur in human body. In this paper, we try address the issue in detail and propose a method of extracting such sequential patterns from such medical data which occurs within a user-specified time intervals. The efficacy of our method is established by experiment conducted with a dataset collected from a private hospital of Saudi Arabi
MACHINE LEARNING FUTURE DRIVE TOWARDS ENHANCED LEARNING- A REVIEW
No full textReal-world applications are strongly associated with data, their storage, their consistency etc. that require Machine Learning (ML) Techniques. While this data is maintained, it may sometimes lead to poor quality of data, insufficient information which has an adverse effect on the applications of Machine Learning techniques. Various data storage techniques may encounter issues in accessing which is adversely correlated to the data privacy, its security and the regulations that encompasses it. Considering these challenges, our study discusses extensive approach to review the existing research that discusses the various modeling techniques of machine learning that can enhance the data regulations. Our study involves various dynamics of data revolutions, Human Computer Integration, Natural Language processing, medical observations and various corporate aspects. We have also discussed the critical analysis of the data privacy integrated with machine learning models and their simulation with neural network and AI based generative models. The short-comings and opportunities that prevail in this field and their potentialities for the future developments are also discussed