Journal of Scientific Research and Technology

Investigation Into Impact Of Nano Silica On Concrete’s Compressive Strength

2025-05-03T06:20:34+00:00

Use of nanotechnology in concrete has brought fresh perspective to ongoing endeavours to enhance its characteristics. The four main ingredients of concrete are water, aggregate, sand, and cement. Among most ubiquitous building materials, concrete is indispensable. As their very tiny particle size, nanomaterials have the probability to alter microstructure of concrete assets. Adding 230 nm nano silica to concrete for increasing its compressive strength is the focus of this research. U se of 0.35%, 0.65%, and 1% nano silica in place of cement was subject of an experimental enquiry. Its accompanying experiments demonstrate that concrete's early-age compressive strength and total compressive strength are both significantly increased. A higher proportion of nano silica resulted in a stronger material.

Brachyspira-Induced Gut Microbiome Disruption in Livestock: Mechanisms, Consequences, and Interventions

2025-05-05T07:04:16+00:00

Brachyspira species, particularly B. hyodysenteriae and B. pilosicoli, are major enteric pathogens in livestock, contributing to gut microbiome disruption and significant economic losses. This review synthesizes current knowledge on the mechanisms through which Brachyspira species alter gut microbial communities, impair mucosal integrity, and modulate host immune responses. Infection is consistently associated with reduced microbial diversity, an increase in opportunistic taxa, compromised nutrient absorption, and skewed immune signaling—factors that collectively exacerbate clinical outcomes and prolong recovery. The economic burden is compounded by decreased growth rates, poor feed conversion, and production losses, especially in swine and poultry. While dietary and probiotic strategies show promise in restoring microbial balance, standardization of diagnostic and intervention protocols remains limited. This paper highlights the need for longitudinal studies with quantitative metrics to better elucidate the link between microbiome dysbiosis, host physiology, and productivity. Advancements in sequencing, immunoprofiling, and microbial therapeutics offer opportunities to mitigate disease impact and improve livestock health and resilience.

The Role of Artificial Intelligence in Strategic Business Decision Making

2025-05-06T18:13:50+00:00

Artificial Intelligence (AI) is transforming investment decision-making in financial institutions by leveraging machine
learning, natural language processing, and predictive analytics. These technologies enable rapid processing of vast
datasets, accurate market forecasting, and tailored investment strategies, leading to enhanced returns and operational
efficiencies. AI-driven tools analyze market data, sentiment, and alternative sources to uncover insights, optimize
portfolios, and improve risk management, offering a competitive edge in volatile markets. However, challenges such as
algorithmic biases, which can perpetuate unfair outcomes, cybersecurity vulnerabilities that threaten sensitive data,
and regulatory complexities due to opaque AI models require robust oversight. This paper explores AI’s opportunities,
including cost reduction and personalized services, alongside risks like over-reliance on automation and ethical
concerns. Through case studies on hedge funds, robo-advisors, and high-frequency trading, we assess AI’s impact,
emphasizing the need for human-AI collaboration to ensure ethical decision-making. We also examine future trends,
such as sustainable investing and quantum computing, which promise to further reshape finance. Data analysis
highlights AI’s potential and pitfalls, providing recommendations for responsible adoption, including transparency,
staff training, and regulatory engagement, to balance innovation with accountability.

The Role Of Blockchain And Smart Contracts In Business Law

2025-05-06T19:43:41+00:00

Blockchain technology and smart contracts are transforming the legal landscape of global business transactions by enhancing security, transparency, and efficiency. These decentralized technologies eliminate intermediaries, reducing transactional costs while increasing trust through immutable records. However, their legal implications remain complex, as traditional contract law struggles to adapt to self-executing agreements that lack centralized enforcement mechanisms.
The rise of decentralized finance (DeFi) further complicates the future of business law, as it challenges conventional regulatory models by removing centralized authorities from financial transactions. The legal treatment of decentralized autonomous organizations (DAOs), tokenized assets, and smart contract- based lending platforms remain ambiguous, raising concerns about fraud, compliance, and enforcement. As blockchain adoption grows, legislative reforms will be necessary to establish legal certainty while fostering technological advancements.
The integration of artificial intelligence and blockchain in legal services may also redefine contract formation, enforcement, and dispute resolution, shaping the next era of business law. A harmonized global legal approach will be essential to mitigate risks while leveraging blockchain’s potential to create a more efficient and trustworthy commercial ecosystem.

Estimation of Reliability of Zimbardo Time Perspective Inventory using Bayesian Approach for the Promotion of Quality Education and Life Long Learning in Engineering Discipline

2025-05-14T07:41:11+00:00

Commonly reported frequentist measures of reliability like Cronbach’s alpha and McDonald’s omega, along with the less reported Greatest lower bound coefficient suffer from either underestimation or overestimation of reliability coefficients. Moreover, there is inherent uncertainty associated with the obtained estimates. An alternative approach to resolve this issue can be estimation of reliability of psychological instruments using the Bayesian approach, involving the reporting of posterior distribution of the obtained estimate. In this context, the Zimbardo Time Perspective Inventory Short Form by [17], is chosen for estimation of the reliability of its dimensions, partly due to the lack of consensus in reporting of stable estimates of this estimand in multiple contexts. The data was collected from 187 computer science engineering students of Lovely Professional University, Phagwara, Punjab, during their regular classroom session. R Package ‘Bayesrel’ was used to find the Bayesian and Frequentist frameworks-based reliability coefficients of the five dimensions of the scale using RStudio Ver. 2022.12.0+353. Except for the dimensions Present Hedonistic and Future time perspective, the reliability of rest the other three dimensions of time perspective were found to fall short of the acceptable benchmark of 0.6 [31]. The graphical posterior predictive check figures showed good fit between eigen values based posterior unidimensional model and data implied covariance matrices. The psychometric implications of the study in the context of Indian engineering students are discussed.

NLP-Powered IoT Assistant for Multilingual Classrooms: Bridging Communication Gaps in Education

2025-05-14T09:38:52+00:00

Communication challenges in linguistically diverse classrooms can limit students' participation and comprehension. This paper presents a novel educational assistant that combines natural language processing (NLP) and Internet of Things (IoT) technologies to facilitate seamless multilingual interaction. By providing live translation, emotional state recognition, and contextual instructional cues, the system supports both teachers and students in navigating linguistic diversity. Designed in alignment with Universal Design for Learning (UDL) principles, the tool aims to foster inclusive practices by adapting to the dynamic needs of learners. Initial pilot scenarios reveal improvements in engagement and concept clarity. The paper details the assistant’s system design, implementation considerations, and practical benefits for adaptive teaching in multilingual settings. This assistant is designed to operate seamlessly within existing classroom ecosystems, minimizing training burdens and supporting diverse linguistic profiles.

Opencv-Based License Plate: Algorithms And Implementations

2025-05-15T04:06:10+00:00

Fast growth in vehicle populations requires effective methods for automating tasks related to vehicle identification and supervision. This article presents a new approach to automatic detection of cognitive marks from live video flows using the OpenCV Computer Vision library and the Optical Character Optical Character Tesseract. The aim of the proposed system is to increase the accuracy and reliability of recognition marks and at the same time ensure real -time processing requirements. The methodology includes a multi -stage process. Initially, the frames are captured from a live input video and then preliminarily processed using OpenCV techniques, such as changing change, noise reduction and edge detection. Subsequently, extraction in the field of interest (ROI) is carried out to isolation of candidates for license plates within each framework. The extracted ROI is further refined by analysis of contour and geometric properties to improve the accuracy of the license plate detection. After the detection phase, the TESSERACT OCR engine is used to perform characters recognition in detected areas of cognitive marks. The system architecture facilitates smooth integration between OpenCV and Tesseract, allowing effective data exchange and processing. The recognized characters are then validated using techniques after processing to ensure accurate extraction of the cognitive mark numbers. Experimental results on a diverse set of live input scenarios show the effectiveness of the proposed system in accurate detection and recognition of cognitive marks in real time.