Science and Technology: Developments and Applications Vol. 5

The Large Fan-In and high-performance gates are essential to make portable electronic devices. The efficient realization of high Fan-in XOR gate defines the performance of digital circuits like adders, magnitude comparators etc. In this paper, an efficient realization of three input two-level XOR(Exclusive-OR) is presented. The design of low power and high-speed proposed XOR gate involves the combination of pass and transmission gates. The main objective to achieve this is based on the selection of input signals to propagate and maintain the good logic swing. The exclusive OR gate is a fundamental building primitive for adders which are mostly used in almost all the arithmetic circuits. Two methods were used to design the proposed XOR, one (i.e. Pass_gate) is purely based on pass transistors with 8 MOSFET and the second method (Modified_Pass_gate) uses transmission gates with 12 transistors. The Modified_Pass_gate offers 86.14% and 6.66% power dissipation reduction compared to static and Pass_gate XOR respectively and 77.18% and 50.94% less propagation delay compared to static and Pass_gate XOR respectively, at the supply voltage of 0.7v with input signal frequency of 3GHz. The simulation is performed based on a 32 nm technology node (PTM-models) using the Hspice Synopsis simulation tool. From the simulation, it is evident that a definite advantage is in favor of the proposed XOR gate designs. Especially, for the combination, at 0.7v and at 3GHz, the average power

A selected group of zwitterionic buffers has been proposed for physiological use. The authors have undertaken the determination of pH values for one buffer solution of TES without NaCl and nine buffer solutions with NaCl yielding an ionic strength I = 0.16 mol \(\cdot\) kg^-1 similar to that of blood. The TES was obtained from Research Organics in Cleveland, OH. The experimental method for further crystallization and the assay have been previously reported in detail. These buffer solutions have been evaluated in the temperature range of \(5^{\circ}\)C to \(55^{\circ}\)C using an extended version of the Debye-Hückel equation. The pH values are reported using 1) the Debye-Hückel extension of the Bates-Guggenheim convention in the temperature range \(5^{\circ}\)C to \(55^{\circ}\)C and 2) with and without liquid junction correction at \(25^{\circ}\)C and \(37^{\circ}\)C . These TES buffer solutions are recommended as secondary standard references for pH measurements in the range of pH 7.2 to 7.5 for physiological applications with an ionic strength of I = 0.16 mol \(\cdot\) kg^-1 .

Brain cancer, characterized by the uncontrolled growth of abnormal cells in the brain, is a severe neurological disorder that can be either primary or metastatic. Early detection and accurate classification of brain tumors are crucial for effective management and improved patient outcomes. Brain tumors are classified based on various factors such as their nature, cell origin, grade, and progression stage. Traditional methods of detection, segmentation, and classification are time-consuming, require extensive expertise, and are prone to errors. Artificial Intelligence (AI), including its subtypes Machine Learning (ML) and Deep Learning (DL), holds promise for improving accuracy and expediting detection. AI-based technologies can be categorized into binary classification (e.g., determining whether a tumor is malignant or benign) and multimodal classification (e.g., categorizing tumors into various types). Most AI applications in brain tumor classification focus on radiological images, particularly Magnetic Resonance Imaging (MRI).

AI-based technologies must achieve high accuracy to be effectively integrated into real-life clinical practice. This chapter summarizes the current advances in AI techniques for brain tumor classification, highlighting their potential and ongoing challenges.

Artificial Intelligence has seamlessly changed our daily practices through automated techniques, robotics support, advanced Machine Learning algorithms, Expert systems and many more supporting tools. The influence and augmentation of technology have streaked out manual processing scope and opportunities. The influence and practice of AI in various domains Like Education, medicine, transport, and industry is quite apparent and bothering as well. Experts are now unfolding and exploring the “man vs machine“ aspect and are trying to sightsee the other side of this emergence. This manuscript is also working in the same aspect and particularly concentrating on areas where AI is a challenge to sustainability. Through the study, authors are trying to create thoughtfulness in various sustainability aspects by identifying the sectors where AI adoption is leaving harmful footprints for the environment and other aspects of humanity.

The thermal environment is an important factor that affects human performance. Research has shown that temperature can have a significant impact on muscular strength and endurance. Understanding the impact of temperature on muscular strength and endurance has important implications for optimizing performance in various settings, including athletic, military, and occupational environments. The goal of this study was to perform an empirical inquiry into the effects of various temperature conditions on muscle strength and endurance. The study assessed the handgrip strength and sit-up endurance of physically active people in a controlled laboratory setting at four distinct temperatures (10°C, 20°C, 30°C, and 40°C). The findings showed that severe temperatures (40°C) had a negative influence on muscle strength and endurance, whereas intermediate temperatures (20°C and 30°C) had a favorable effect. These findings have significance for the design of places requiring peak physical performance, such as athletic facilities, military operations, and workplace safety. Future research could investigate other physical performance factors, assess individual differences in response to thermal conditions, analyse non-linear effects of temperature on performance, and evaluate the impact of thermal environment on physical performance in real-world settings.

The objective of the study is to improve the Gross Domestic Product (GDP) error term modeling, with the Combine White Noise (CWN) Model in Australia. The estimation of the Combine White Noise model passes the stability condition, stationary, serial correlation, and the CWN model estimation yields the best results with minimum information criteria and higher log-likelihood values than EGARCH  and VAR models. The determinant of the residual of the covariance matrix value indicates that CWN is efficient while the determinant of the residual of the covariance matrix value indicates that VAR is not efficient. CWN has the least forecast errors which are indications of the best results when compared with the EGARCH and VAR models dynamic evaluation forecast errors. The minimum forecast error values indicate forecast accuracy. The total results testified that CWN is the right model. To model conditional heteroscedasticity data with leverage effect in Australia and other nations powerfully, CWN is acclaimed. The contribution of this study to the scientific community is that the CWN gives suitable results that improve the weaknesses of the existing models. The CWN forecast output is more reasonable for effective policy making. Implementation of this CWN will boost the economy of the society.

The structural and spectroscopic characterization of the synthesized aromatic organic compound 4-methyl-N-(4-methylphenyl) benzene sulfonamide (abbreviated as 4M4MPBS) was carried out using experimental XRD, FTIR, and FTRAMAN techniques. To support the analytical results, theoretical calculations were performed on 4M4MPBS using the DFT method associated with B3LYP functional with a 6-31G (d,p) basis set. Furthermore, the reactivity of the title compound was studied by the investigation of molecular electrostatic potential (MEP) computed and discussed.

The rapid development of artificial intelligence (AI) has brought disruptive changes across many industries, with large language models (LLMs), Retrieval-Augmented Generation (RAG) systems, and convolutional neural networks (CNNs) featured prominently in this massive transformation. The advent of Artificial Intelligence (AI) in recent years has transformed the technology landscape like never before. More implementations of AI-powered applications have led to advanced and sophisticated supporting technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and Convolutional Neural Networks (CNNs). Application systems using these advanced technologies are transforming businesses across industries. LLM enhances natural language understanding and also aids in generation, facilitating communications closely resembling human interactions. RAG systems integrate retrieval mechanisms with generative capabilities, improving the relevance and accuracy of generated content by leveraging external knowledge bases. Meanwhile, CNNs continue to excel in image processing tasks, driving advancements in computer vision applications. The collective synergies between these technologies help businesses in improving efficiency, user experience, and decision-making.

The study illustrates implementations while revealing the challenges and opportunities presented by these technologies. The research on these technologies underscores the necessity for ongoing research and adaptation in leveraging these technologies to maximize their potential in real-world applications.

The study concluded that the technologies of LLMs, RAG, and CNNs have a far-reaching and disruptive effect on application systems, impacting many industries. LLMs are reshaping the landscape of natural language processing, allowing for more nuanced systems that comprehend, produce, and communicate human language.

This study aims to develop independent teaching materials in the form of electronic modules (e-modules) that use the Team Assisted Individualization (TAI) cooperative model and include HOTS questions on fluid dynamics material. The method used in this study is research and development with the ADDIE (Analyze, Design, Development, Implementation, and Evaluation) development stages. The TAI cooperative model has five stages of learning, namely teams, placement tests, team study, factual tests, and lastly team score and team recognition. The developed e-module also included questions that test students' higher order thinking skills. The validation test of media, material, and learning experts received an average score of 82.81%, while the field trial on physics teachers received an average score of 84.73% and on students 91.01%. These scores show that the developed e-module is suitable for use as independent teaching materials for students. In addition, the calculation of the n-gain test is 0.48 and categorized as moderate according to the gain value interpretation table. This value shows that e-modules that present learning materials with videos, virtual laboratories, worksheets, and HOTS practice questions can improve students' high-level thinking skills.

The objective of this study is to simulate urban growth on the surface and underground, on a municipal scale, using a Geostatistical spatial modeling technique, called Kriging. Ordinary kriging was used as a method of geostatistical interpolation of urban growth, taking into account normal growth trends. Geostatistics is a branch of Statistics that emerged in the 1960s in France with applications to the study of the behavior of spatial phenomena, initially used for prospecting in geology and mining. Later it began to have applications in the environment and social sciences. The research work presented here aims to apply Geostatistics techniques to the dynamics of urban growth in the municipality of Oeiras, in Portugal. The results of the used method allowed us to understand the trends of urban growth on the surface and underground, as well as the advantages and usefulness of the Geostatistical technique applied to urban planning.