Advancement Of Science Research Articles

A Comprehensive Review on Analytical Methods of Rifampicin

Rifampicin, a cornerstone in the treatment of tuberculosis and other bacterial infections, necessitates robust analytical methods for its accurate determination in pharmaceutical formulations and biological matrices. This review aims to provide a comprehensive summary of the analytical methods developed and validated for rifampicin quantification, encompassing various chromatographic, spectroscopic, and other analytical techniques reported in the literature. The review begins with an overview of the physicochemical properties, pharmacological significance, and regulatory requirements pertinent to rifampicin analysis. Additionally, the review highlights the importance of method robustness, specificity, sensitivity, and stability-indicating capability in ensuring the quality and safety of rifampicin-containing formulations. Overall, this review serves as a comprehensive reference for researchers, analysts, and regulatory authorities involved in the development, validation, and quality control of analytical methods for rifampicin, facilitating the advancement of pharmaceutical sciences and therapeutic interventions aimed at combating infectious diseases effectively

Moving window sparse partial least squares method and its application in spectral data

With the advancement of data science and technology, the complexity and diversity of data have increased. Challenges arise when dealing with a larger number of variables than the sample size or the presence of multicollinearity due to strong correlations among variables. In this paper, we propose a moving window sparse partial least squares method that combines the sliding interval technique with sparse partial least squares. By utilizing sliding interval partial least squares regression to identify the optimal interval and incorporating sparse partial least squares for variable selection, the proposed method offers innovations compared to traditional partial least squares (PLS) approaches. Monte Carlo simulations demonstrate its performance in variable selection and model prediction. We apply the method to seawater spectral data, predicting the concentration of chemical Oxygen demand. The results show that the method not only selects reasonable spectral wavelength intervals but also enhances predictive performance.

Modernization and Inheritance of Folk Beliefs in the Digital Age: A Case Study in the Southeastern Coastal Areas of China

With the processes of urbanization and population migration in China, local traditional folk beliefs are facing a crisis of inheritance, including the loss of believers and the decline of religious buildings. However, in the southeastern coastal areas of China, with the development of society and the advancement of science and technology, folk beliefs have shown a trend of modernization, gained widespread attention from young people, and shown a good trend of inheritance. This study focuses on the modernization of folk beliefs in the southeastern coastal areas of China, exploring how folk beliefs are adapted to contemporary life and the key role of information technology in the protection of religious buildings. The study found that the modernization of folk beliefs in China’s southeastern coastal areas is mainly reflected in the portrayal of gods in cartoonish images, the popularity of music, and the modernization of communication methods. By analyzing the modernization process of folk beliefs in the southeastern coastal areas of China, this study reveals the adaptation and development of folk beliefs in modern society. Based on the reality of contemporary Chinese society, this study also explores the future modernization trend of folk beliefs and discusses the possibilities and potential risks of the application of digital technology in folk belief inheritance.

Cnn-trans model: A parallel dual-branch network for fundus image classification

The existence of fundus diseases not only endangers people’s vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance..

Elucidating structure-property relationships of guar gum biomolecules: insights from -polynomial and QSPR modeling.

This study investigates the quantitative structure-property relationship (QSPR) modeling of guar gum biomolecules, focusing on their structural parameters. Guar gum, a polysaccharide with diverse industrial applications, exhibits various properties such as viscosity, solubility, and emulsifying ability, which are influenced by its molecular structure. In this research, -polynomial and associated topological indices are employed as structural descriptors to represent the molecular structure of guar gum. The -polynomial and associated topological indices capture important structural features, including size, shape, branching, and connectivity. By correlating these descriptors with experimental data on guar gum properties, predictive models are developed using regression analysis techniques. The analysis revealed a strong correlation between the boiling point and molecular weight and all the considered topological descriptors. The resulting models offer insights into the relationship between guar gum structure and its properties, facilitating the optimization of guar gum production and application in various industries. This study demonstrates the utility of -polynomial and QSPR modeling in elucidating structure-property relationships of complex biomolecules like guar gum, contributing to the advancement of biomaterial science and industrial applications.

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.

An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms

With the rapid advancement of science and technology, chemical and physical research has entered an era of complexity and high dimensionality, where traditional research paradigms struggle to optimize vast chemical parameter searches. The Machine Chemist platform was developed in this context, leveraging big data and intelligent models to automate chemical synthesis, characterization, and testing processes. This study aims to predict the y1, y2 and y3 properties and classes of 2,580 molecules based on their physicochemical properties and improve model accuracy through data analysis and modeling. Data preprocessing involved removing missing values, duplicates, and outliers using the quartile method, resulting in an analyzable dataset. A scatter plot of y2 and id suggested a univariate polynomial function relationship, leading to the construction of a univariate nonlinear regression model. The model achieved a high prediction accuracy, with a low root mean square error and a high coefficient of determination. Additionally, the relationship between class and y1~y3 , x1~x100 indicators was examined, revealing mostly nonlinear relationships. A random forest model was established to classify 2,580 molecules into 1 to 4 classes based on the properties of 200,000 chemical molecules. The model's performance was evaluated using decision trees, confusion matrices, precision, and recall metrics. Finally, the SHAP method assessed the impact of feature indicators on classification outcomes, contributing to the development of a reliable molecular category prediction model.

Approaching scalable quantum memory with integrated atomic devices

Quantum memory, which maps photonic quantum information into a stationary medium and retrieves it at a chosen time, plays a vital role in the advancement of quantum information science. In particular, the scalability of a quantum memory is a central challenge for quantum network that can be overcome by using integrated devices. Quantum memory with an integrated device is highly appealing since it not only expands the number of memories to increase data rates, but also offers seamless compatibility with other on-chip devices and existing fiber network, enabling scalable and convenient applications. Over the past few decades, substantial efforts have been dedicated to achieving integrated quantum memory using rare earth ions doped solid-state materials, color centers, and atomic gases. These physical platforms are the primary candidates for such devices, where remarkable advantages have been demonstrated in achieving high-performance integrated quantum memory, paving the way for efficiently establishing robust and scalable quantum network with integrated quantum devices. In this paper, we aim to provide a comprehensive review of integrated quantum memory, encompassing its background and significance, advancement with bulky memory system, fabrication of integrated device, and its memory function considering various performance metrics. Additionally, we will address the challenges associated with integrated quantum memory and explore its potential applications. By analyzing the current state of the field, this review will make a valuable contribution by offering illustrative examples and providing helpful guidance for future achievements in practical integrated quantum memory.

Toward a science-based classification of processed foods to support meaningful research and effective health policies.

Processed foods have been part of the American diet for decades, with key roles in providing a safe, available, affordable, and nutritious food supply. The USDA Food Guides beginning in 1916 and the US Dietary Guidelines for Americans (DGA) since 1980 have included various types of commonly consumed processed foods (e.g., heated, fermented, dried) as part of their recommendations. However, there are multiple classification systems based on "level" of food processing, and additional evidence is needed to establish the specific properties of foods classified as "highly" or "ultra"-processed (HPF/UPFs). Importantly, many foods are captured under HPF/UPF definitions, ranging from ready-to-eat fortified whole grain breakfast cereals to sugar-sweetened beverages and baked goods. The consequences of implementing dietary guidance to limit all intake of foods currently classified as HPF/UPF may require additional scrutiny to evaluate the impact on consumers' ability to meet daily nutrient recommendations and to access affordable food, and ultimately, on health outcomes. Based on a meeting held by the Institute for the Advancement of Food and Nutrition Sciences in May 2023, this paper provides perspectives on the broad array of foods classified as HPF/UPFs based on processing and formulation, including contributions to nutrient intake and dietary patterns, food acceptability, and cost. Characteristics of foods classified as UPF/HPFs are considered, including the roles and safety approval of food additives and the effect of food processing on the food matrix. Finally, this paper identifies information gaps and research needs to better understand how the processing of food affects nutrition and health outcomes.

Enhancing freeze-thaw tolerance in baker's yeast: strategies and perspectives.

Frozen dough technology is important in modern bakery operations, facilitating the transportation of dough at low temperatures to downstream sales points. However, the freeze-thaw process imposes significant stress on baker's yeast, resulting in diminished viability and fermentation capacity. Understanding the mechanisms underlying freeze-thaw stress is essential for mitigating its adverse effects on yeast performance. This review delves into the intricate mechanisms underlying freeze-thaw stress, focusing specifically on Saccharomyces cerevisiae, the primary yeast used in baking, and presents a wide range of biotechnological approaches to enhance freeze-thaw resistance in S. cerevisiae. Strategies include manipulating intracellular metabolites, altering membrane composition, managing antioxidant defenses, mediating aquaporin expression, and employing adaptive evolutionary and breeding techniques. Addressing challenges and strategies associated with freeze-thaw stress, this review provides valuable insights for future research endeavors, aiming to enhance the freeze-thaw tolerance of baker's yeast and contribute to the advancement of bakery science.

Development of the Venture Scientist Mindset among Japanese Basic Researchers

Japanese basic researchers, known for their dedication to the advancement of science without any expectation of economic benefit, are conventionally regarded as virtuous professionals. However, current social demand requires researchers to adopt a venture mindset, implement their research outcomes for societal benefit, and contribute to society through business. In this paper, I highlight the importance of overcoming the "valley of death" between society and researchers to create useful intersections between science and business, aimed at application of research outcomes to the society and encouraging a lifestyle and challenges as venture scientists who can contribute to the generation of new industries.

Komunikasi Dakwah Dalam Konversi Nilai Pendidikan Agama Islam Guna Pembentukan Karakter Sosial Bermasyarakat

During the era of globalisation, we will be presented with the disharmony of the crisis of human values with the tension of Islamic norms along with the advancement of science and technology (IPTEK). Therefore, the correlation between aspects of religion / da'wah, social communication, and science needs to be integrated in the delivery of Islamic religious education and social character building. The main focus is how da'wah and Islamic religious education can build social character in accordance with Islamic values in the midst of rapid social change. The study presented in this research uses a literature review to collect, evaluate and synthesise information from various sources, thus building a comprehensive understanding of the topic discussed. This research found that da'wah and Islamic religious education are very important in building social character in accordance with Islamic values. The concept of effective da'wah communication can help in transforming the values of Islamic religious education into relevant da'wah applications in the modern era. Islamic religious education aims to improve the quality of human resources and create a decent situation for life and the environment. Da'wah and Islamic religious education have an important role in building social character in accordance with Islamic values. In the modern era, da'wah must be able to adapt to the development of science and technology and globalisation to remain relevant and effective. This research emphasises the importance of integration between da'wah and communication to achieve sustainable da'wah goals.

Dakwah Berbasis Pemberdayaan Ekonomi Pada Program Misykat DT Peduli Di Kelurahan Rahma Kota Lubuklinggau

During the era of globalisation, we will be presented with the disharmony of the crisis of human values with the tension of Islamic norms along with the advancement of science and technology (IPTEK). Therefore, the correlation between aspects of religion / da'wah, social communication, and science needs to be integrated in the delivery of Islamic religious education and social character building. The main focus is how da'wah and Islamic religious education can build social character in accordance with Islamic values in the midst of rapid social change. The study presented in this research uses a literature review to collect, evaluate and synthesise information from various sources, thus building a comprehensive understanding of the topic discussed. This research found that da'wah and Islamic religious education are very important in building social character in accordance with Islamic values. The concept of effective da'wah communication can help in transforming the values of Islamic religious education into relevant da'wah applications in the modern era. Islamic religious education aims to improve the quality of human resources and create a decent situation for life and the environment. Da'wah and Islamic religious education have an important role in building social character in accordance with Islamic values. In the modern era, da'wah must be able to adapt to the development of science and technology and globalisation to remain relevant and effective. This research emphasises the importance of integration between da'wah and communication to achieve sustainable da'wah goals.

The General Mathematics Course ‘Mathematics and Life Sciences’ for Non-science and Engineering Students

In modern society, alongside the rapid advancement of science and technology, the role and importance of mathematics are increasingly being emphasized. Such advancements, including data analysis and artificial intelligence development, rely fundamentally on mathematical thinking and theory, which have become essential elements across all academic disciplines. Against this backdrop, the role of university general mathematics education has become undeniably important. It is necessary not only for students in science and engineering, but also for those in the humanities and social sciences, to have access to balanced mathematical education. To this end, universities must focus on developing a variety of general mathematics courses. This paper presents a detailed case study of the design and operation of a basic general mathematics course titled “Mathematics and Life Sciences,” offered to students in the humanities and social sciences at Y University in Seoul. The course is structured to enable students to learn essential basic mathematics theories that are crucial across various disciplines and to apply them to phenomena in life sciences. In doing so, students will gain a better understanding of certain interdisciplinary connections as well as enhance their thinking and problem-solving skills. Additionally, this paper analyzes the educational effects and positive feedback from students obtained through a decade of continuous improvement in the content and operation of this course. Based on this analysis, this paper proposes future directions for development and research tasks in the educational field. This research is expected to contribute to the exploration of the direction in which university general mathematics education should head.

재판업무 지원 AI 도입에 관한 헌법적 고찰

In the contemporary intelligence information society, artificial intelligence (AI) is being utilized in various sectors due to the advancement of science and technology. Amidst this trend, the legal technology (LegalTech) industry, which integrates technology with legal advisory and litigation services, has been experiencing a leap in growth. Furthermore, the application of artificial intelligence in the judicial duties of judges is garnering significant interest and expectation for speeding up legal proceedings and enhancing the efficiency of judicial administration. Artificial intelligence used in the judiciary can be broadly classified into two types. First, there is AI that entirely replaces human judgment, exemplified by the initiative in Estonia in 2019 to implement AI for conducting small claims trials. Second, there is AI designed to assist human judges, such as algorithms that help draft verdicts based on similar case precedents or provide appropriate sentencing recommendations for defendants in criminal trials. In this regard, some state courts in the United States have incorporated the COMPAS algorithm into sentencing procedures within criminal justice processes. This surge in AI development seems to have an international impact on judicial procedures, influencing the judicial policies of the courts in South Korea significantly. Specifically, National Court Administration has announced plans to alleviate the workload of judges and address the issue of trial delays through the introduction of what is referred to as the ‘Next-Generation Electronic Litigation System.’ However, while AI technology may assist in accelerating trial processes, there are considerable concerns regarding the protection of litigants' right to a fair trial and democratic legitimacy. Above all, the potential for data and algorithm errors in judicial-use AI could compromise the fairness of trials or lead to miscalculation due to biased legal decisions. This study examines the constitutional issues concerning the advent of AI judges in the era of artificial intelligence are explored, particularly the infringement of the litigants' right to a fair trial, the problems of restoring the original state following miscalculation, and the responsibilities of judges. Furthermore, the study examines legislative trends abroad concerning the use of AI in judicial tasks, and seeks legislative improvement strategies for the principles and premises that must be adhered to by AI utilized in the judiciary during the era of artificial intelligence.

The Significance of Evolutionary Concepts in Biology for the Formation of Evolutionary and Ecological Thinking

This article is devoted to the philosophical analysis of the theoretical and methodological foundations of evolutionary and ecological thinking. The conceptual development of such thinking is a crucial direction in the advancement of modern life sciences. However, within the foundations of evolutionary and ecological interpretation across various biological fields, contradictions between evolutionary and ecological approaches are explicitly evident. This situation arises because the ecological approach views humans as an active factor in the evolution of their environment while simultaneously assuming their natural immutability, i.e., considering them the pinnacle of evolution. The author attempts to identify the principles for selecting evolutionary theories and concepts that underpin the formation of evolutionary and ecological thinking. In this context, the author addresses various branches of evolutionism, such as classical Darwinism, the synthetic theory of evolution, nomogenesis, autogenesis, orthogenesis, catastrophism, neo-Lamarckism, Autopoietic Theory, mutual aid, and symbiosis in evolution. The conclusion is drawn that effective evolutionary and ecological thinking cannot rely solely on the biological characteristics of evolutionism but requires a philosophical understanding of the cultural and historical status of humans in the world.

The Selective SIRT3 Inhibitor 3-TYP Represses Primary Myeloma Growth by Reducing c-Myc Stability.

Multiple myeloma is a hematological cancer that can be treated but remains incurable. With the advancement of science and technology, more drugs have been developed for myeloma chemotherapy that greatly improve the quality of life of patients. However, relapse remains a serious problem puzzling patients and doctors. Thus, developing more highly active and specific inhibitors is urgent for myeloma-targeted therapy. In this study, we identified the SIRT3 inhibitor 3-TYP (3-(1H-1,2,3-triazol-4-yl) pyridine) after screening a histone modification compound library, which showed high cytotoxicity and induced DNA damage in myeloma cells. Furthermore, the inhibitory effect of 3-TYP in our xenograft tumor studies also confirmed that compound 3-TYP could inhibit primary myeloma growth by reducing c-Myc protein stability by decreasing c-Myc Ser62 phosphorylation levels. Taken together, the results of our study identified 3-TYP as a novel c-Myc inhibitor, which could be a potential chemotherapeutic agent to target multiple myeloma.

Horner–Wadsworth–Emmons olefination of proteins and glycoproteins

Chemo-selective modifications of proteins are fundamental to the advancement of biological and pharmaceutical sciences. The search for biocompatible chemical reactions has prompted us to investigate Horner–Wadsworth–Emmons (HWE) olefinations, iconic reactions in organic synthesis that would give rise to new selective protein olefinations. Our choice of HWE olefinations was inspired by the growing number of methods for generating aldehydes as transient reactive groups in proteins and the potential for mild and simple reaction conditions. Here we show that HWE olefination reactions on aldehydes, produced by both chemical and enzymatic methods, are compatible with physiological conditions and highly selective in small and large proteins, including therapeutic antibodies and stable recombinant proteins exemplified by green fluorescent protein. Reaction kinetics can be fine-tuned over orders of magnitude both by judicious use of substituents and pH regulation. The electrophilic nature of the HWE olefination products can be tuned to allow for subsequent nucleophilic additions, including thiol- and phospha-Michael additions. Our results demonstrate that HWE olefination of aldehydes in proteins provides efficient and selective bioconjugation chemistries that are orthogonal to existing methods.

Application of Improved Sparrow Search Algorithm to Path Planning of Mobile Robots.

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning.

Analyzing the strengths and weaknesses of diverse algorithms for solving Multi-Armed Bandit problems using Python

With the rapid advancement of science and technology, the internet has become an integral part of daily life, revolutionizing how people access information and make decisions. In this context, algorithms play a pivotal role in helping individuals make informed choices tailored to their preferences across various domains. Utilizing the MovieLens dataset (https://grouplens.org/datasets/movielens/1m/), which contains a rich compilation of movie ratings and metadata, this study conducts a thorough analysis using Python to assess the performance of four distinct algorithms: Explore-then-Commit (ETC), Upper Confidence Bound (UCB), Thompson Sampling (TS), and Epsilon-Greedy. The comparison reveals that the ETC algorithm excels in applications such as online advertising recommendation and autonomous driving. The UCB algorithm proves more advantageous in financial analysis, where risk management is critical. The TS algorithm is particularly effective in short video recommendation systems, while the Epsilon-Greedy algorithm is well-suited for balancing exploration with reward. Overall, the results indicate that the TS algorithm outperforms the others in general efficacy.