December 2024 Volume 10 Issue 4

ENGINEERING & TECHNOLOGY JOURNAL

STUDY OF REDUCTION FORMULAE FOR GENERALIZED ?? ?FUNCTION AND ITS APPLICATIONS IN ARTIFICIAL INTELLIGENCE AND ROBOTICS IN LIFE SCIENCES

Aaqib Hamid Dar ; Bharti Saxena ; Kirti Verma ; Ramakant Bharadwaj

  • Pages: 1-8
    • <p>Reduction formulas are particularly useful when dealing with integrals of powers of basic functions like trigonometric functions logarithmic functions, and exponential functions. For example, if we have an integral involving a power of a trigonometric function, a reduction formula can help express it in terms of integrals involving lower powers, making the computation more manageable. Reduction formulas can handle integrals involving polynomials of any degree. This is significant because direct integration of high-degree polynomials can be complex and time-consuming. By applying reduction formulas, we can express the integral of a higher degree polynomial as a combination of integrals involving lower degree polynomials or simpler functions. Products of Reduction formulas are also applicable to integrals involving products of transcendental functions, such as the product of a trigonometric function and an exponential function. The use of reduction formulas allows breaking down the complex integral into simpler components, making it easier to evaluate. The versatility of reduction formulas extends to a wide range of mathematical expressions, enabling their application to various types of functions and integrals. The ??-function mentioned in our context seems to represent a generic function with parameters, and reduction formulas can be used to obtain specific results by specializing these parameters. Reduction formulas provide a systematic approach to solving higher order integrals. By expressing integrals in terms of lower order integrals, a step-by-step reduction process simplifies the computation. In this research paper we discuss Reduction Formulae for the Generalized ??-function.</p>

STRENGTH AND DURABILITY ANALYZING CONCRETE BUILT FROM RECYCLED STONE AGGREGATE

Abhay Yadav , Satish Parihar

  • Pages: 1-9
    • <p>This paper discusses the suitability of producing concrete with 100 % recycled aggregate to meet durability and strength requirements for different applications. Aggregate strength, gradation, absorption, specific gravity, shape and texture are some of the physical and mechanical characteristics that contribute to the strength and durability of concrete. In general, the quality of recycled aggregate depends on the loading and exposure conditions of the demolished structures. Therefore, the experimental program was focused on the evaluation of physical and mechanical properties of the recycled aggregate over a period of 6 months. In addition, concrete properties produced with fine and coarse recycled aggregate were evaluated. Several concrete mixes were prepared with 100 % recycled aggregates and the results were compared to that of a control mix. SEM was conducted to examine the microstructure of selected mixes. The results showed that concrete with acceptable strength and durability could be produced if high packing density is achieved.</p>

A Structure Planning Approach for Semi-Rigid Base Asphalt Advance Pavement That Relies On Optimization of Elasticity

Yogesh Chauhan , Satish Parihar

  • Pages: 1-11
    • <p>The early damage of the semi-rigid base asphalt pavement is related to the pavement structure modulus&rsquo;s unreasonable matching. In this study, three typical pavement structures were selected to analyze the pavement structures&rsquo; influence on the pavement service life. A three-dimensional finite element pavement structure model was established. The independent variables are subgrade modulus, base course modulus, and subbase modulus. The deflection, the bottom tensile stress, and maximum shear stress were chosen as the evaluation indexes. The effect of the modulus on the mechanical response of the pavement structure was analyzed. The optimal modulus combination of the pavement structure was determined through multi-factor range analysis. The mechanical response and fatigue life before and after the optimization pavement structure were compared. The results showed that the field measured modulus of Structure 1 and 2 was higher than the design modulus. Moreover, while the modulus of base course and subbase course was increased, the deflection gradually reduced. The base course&rsquo;s bottom tensile stress and the subbase were increased, and the maximum shear stress was basically unchanged. After the modulus combination optimized pavement structure, the mechanical response was significantly reduced. The fatigue life based on the deflection and bottom tensile stress, and the laboratory normalized fatigue equation were significantly increased. By the combination of fatigue performance of pavement materials and pavement structure, it was possible to provide an effective optimization method for the design of semi-rigid base asphalt pavement in this research work.</p>

A Review of Detection and Classification of Brain Tumor Disease Using Ensemble Methods

Sumit Yadav, Hari Om Sharan

  • Pages: 1-4
    • <p>This paper examines the developments in ensemble approaches for brain tumour illness detection and classification. In medical imaging, ensemble techniques including bagging, boosting, and stacking have greatly improved diagnostic accuracy and dependability. The benefits and limits of these techniques, as well as their uses in the investigation of brain tumours and potential avenues for future research, are covered in the paper. Any approach's primary objective is to detect and classify brain tumours, either as a primary task or as a health indicator. From conventional techniques to innovative deep architectures, ensemble approaches have currently attained state-of-the-art performance on the majority of machine learning applications. In this chapter, I go over the basic ideas and limitations related to human brain classification that were used by earlier researchers. Using sophisticated ensemble techniques, the latest developments in developing and improving a discriminative model to manage the classification, identification, and brain structural parcellation tasks are examined. Some recent publications that use ensemble approaches to identify brain illnesses are given special attention throughout this study.</p>

AI Applications in Predictive Toxicology in Drug Development

Shweta Dwivedi Saumya Singh, Syed Adnan Afaq Vishal Agarwal

  • Pages: 1-7
    • <p>AI applications in predictive toxicology are revolutionizing the way toxicity is assessed in drug development, reducing both the time and costs associated with traditional methods. By leveraging machine learning algorithms and deep learning models, AI can predict the toxicological profile of new compounds with remarkable accuracy, minimizing the need for extensive animal testing. These systems analyze vast amounts of chemical, biological, and clinical data to detect patterns that indicate potential toxic effects. Predictive toxicology powered by AI not only enhances drug safety but also accelerates the drug development pipeline by identifying high-risk compounds early in the process. This paper explores the advancements in AI-driven toxicology, its applications in drug safety assessment, and prospects for regulatory integration.</p>
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