Scientific Research Articles

Unraveling the Immune Web: Advances in SMI Capsular Fibrosis from Molecular Insights to Preclinical Breakthroughs

Breast implant surgery has evolved significantly, yet challenges such as capsular contracture remain a persistent concern. This review presents an in-depth analysis of recent advancements in understanding the immune mechanisms and clinical implications associated with silicone mammary implants (SMIs). The article systematically examines the complex interplay between immune responses and capsular fibrosis, emphasizing the pathophysiological mechanisms of inflammation in the etiology of this fibrotic response. It discusses innovations in biomaterial science, including the development of novel anti-biofilm coatings and immunomodulatory surfaces designed to enhance implant integration and minimize complications. Emphasis is placed on personalized risk assessment strategies, leveraging molecular insights to tailor interventions and improve patient outcomes. Emerging therapeutic targets, advancements in surgical techniques, and the refinement of post-operative care are also explored. Despite notable progress, challenges such as the variability in immune responses, the long-term efficacy of new interventions, and ethical considerations remain. Future research directions are identified, focusing on personalized medicine, advanced biomaterials, and bridging preclinical findings with clinical applications. As we advance from bench to bedside, this review illuminates the path forward, where interdisciplinary collaboration and continued inquiry weave together to enhance the art and science of breast implant surgery, transforming patient care into a realm of precision and excellence.

Retraction: Role of scientific and technological innovations on industrial upgradation in China: A spatial econometric analysis.

Retraction: Role of scientific and technological innovations on industrial upgradation in China: A spatial econometric analysis.

A Comparative Analysis of the Sustainable Development of Urban Agriculture in Chengdu and Singapore

With the deepening global concern for sustainable development, urban agriculture has received more and more attention as an important combination of urban development and ecological environmental protection. This paper provides a comprehensive evaluation and comparison of two areas, Chengdu and Singapore, in terms of their capacity for sustainable development of urban agriculture through an empirical analysis method. To make a multidimensional evaluation index system, the study uses the hierarchical analysis method (AHP) and the fuzzy comprehensive evaluation method. The index covers important areas like the level of agricultural development, the intensity of applied inputs, the rate of energy use, the rate of resource utilization, and the level of agricultural factors. The results show that Chengdu performs significantly in terms of resource utilization rate and agricultural factor level, while Singapore performs better in terms of energy utilization rate. The study provides targeted recommendations, including strengthening scientific and technological innovation, policy support, and ecological and environmental protection, with the aim of providing references and lessons for the sustainable development of urban agriculture in the two places and other areas around the world.

Progress in Radical Fluorosulfonyl Reagents

AbstractSulfur(VI) fluoride exchange (SuFEx) chemistry that relies on the unique reactivity–stability balance of high valent organosulfur has emerged as a promising topic for the next-generation click reaction. Sulfonyl fluorides are the most widely used connective hubs of the SuFEx click reaction with widespread applications in the fields of chemical biology, drug discovery, and materials science. Compared with current methods, direct fluorosulfonylation with fluorosulfonyl radicals has emerged as a concise and efficient approach for the production of sulfonyl fluorides. The highly active SO2F radical was an unstable and inaccessible precursor until it was observed in the decomposition of fluorosulfonyl azide, which inspired efforts towards the production of fluorosulfonyl radical precursors for direct radical fluorosulfonylation processes. This review presents and discusses current breakthroughs in the generation of fluorosulfonyl radicals from various radical precursors, as well as their application in the synthesis of diverse functionalized sulfonyl fluorides.1 Introduction2 FSO2Cl as a Radical Precursor3 Benzimidazolium Fluorosulfonates as Radical Precursors4 Alk-1-ynylsulfonyl Fluorides as Radical Precursors5 (Diarylmethylene)sulfamoyl Fluorides as Radical Precursors6 Vinyl Fluorosulfates as Radical Precursors7 Conclusion and Outlook

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists.

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

Persianate Dialectics

ABSTRACT In this short reflection on the responses to The Making of Persianate Modernity, the author offers thoughts on the methodology of literary history, on the relationship between history and literature, and on the politics of historical research in general and Persianate studies in particular.

The Church and Violence During Multiparty Elections in Kenya from 1992 to 2017

Violence fastened with the passion of religion predominantly continues cloaking the world at an alarming rate because violence does not only belong to everyone but it is also at the heart of the consecrated. It is no surprise therefore that violence in Kenya recurs virtually during every election resulting into polarization and tensions along religious and ethnic affiliations or both. The purpose of this paper was to examine the relationship between the Church and violence during multiparty politics in Kenya. This paper analyzed the role the church played in specific violent events during multiparty politics since 1992 up to 2017 in Kenya. In this regard the study responded to the question; what was the role of the church in the violent events during elections in multiparty politics since 1992 in Kenya? To answer this question the study investigated the role of the church in the specific violence which occurred during elections in 1992, 1997, 2002, 2007, 2013 and 2017 election years in Kenya inferring that their roles influenced the occurrence of violence from time to time. The study was conducted through qualitative research method using historical research designs. The study established that the churches in Kenya played various roles in the violence; some of the churches kept quiet as violence flourished in their areas of jurisdiction because they conceived it as punitive and ungodly and in that way, they were in coordinated efforts; while some other churches condoned and extenuated circumstances that led to violent acts because they had a passive attitude towards the government authority having been part and parcel of it in calling for social changes; whereas some churches were not only complicit but endorsed and exhorted violence particularly by blessing youth warriors before going to fight in ethnic violence, believing that it was their religious duty to extirpate injustices and subdue evil in the sinful world using strategic acts of violence as necessary means of deterring large acts of violence and that they had the divine authority to legitimize violence so as to uproot the political evils bedeviling Kenya. In view of all these, the study concluded that the churches were metaphysically, morally, politically and criminally culpable for the violence during multiparty politics in Kenya. Consequently the study maintains that the church in Kenya, like it is with religion in general, is intrinsically violent in the version of a raging cosmic battle between “order” and “disorder” akin to the cosmic war theory as advanced by Mark Juergensmeyer. Often it is the boundary between ‘this world’ and the ‘other world’ that is blurred by church during electioneering period so that the supposedly cosmic battle becomes a real war when people shed real blood and die. The study recommends that it is important to understand the dynamics of the roles played in violence by other religious organization in multi religious nations like Kenya. The degree to which other faiths influence political views in Kenya with regard to election violence is critical. The issue of how the church can mitigate against the cyclic political violence in Kenya kept on evocating in the study. It is important to understand the remedies that the church can put in place to alleviate violence during elections in Kenya.

Selective synthesis of fluorescent metal nanoclusters over metal nanoparticles.

Metal nanoparticles and nanoclusters are pivotal in nanomaterial science, each offering unique properties for diverse applications. Nanoclusters, typically smaller than 2nm, exhibit distinct optical and electronic characteristics due to quantum confinement, resulting in fluorescence emission. In contrast, metal nanoparticles, sized between 2 and 100nm, exhibit absorption spectra. Both are synthesized by reducing metal precursors in the presence of a suitable stabilizing agent. While nanoparticles have been the historical research focus, recent attention has shifted to nanoclusters for their exceptional properties and their synthesis has evolved significantly over the past few decades. This review discusses the selective synthesis of nanoclusters over nanoparticles, emphasizing the role of various factors such as ligand concentration(metal-to-ligand ratio), reducing agents, pH,reactiontime and temperature, solvents, and assistant reagents. Higher ligand concentrations stabilize smaller nanoclusters by preventing aggregation, while lower concentrations lead to larger nanoparticles. Stronger reducing agents produce smaller, more uniform particles, whereas weaker reducingagents yield larger ones. pH affects nanocluster size and emission properties. Solvents and assistant reagents influence reaction kinetics and material properties. Temperature and reaction time also play critical roles in controlling nanocluster size and properties. These insights guide the optimized synthesis of metal nanoclusters, for their specific applications.

Legal Issues of Ensuring Technological Sovereignty

Objective: to identify the legal issues of ensuring technological sovereignty and to determine scientifically grounded vectors of their solution.Methods: the study is based on formal-legal, historical-legal, comparative legal methods, as well as the methodology of soft systematicity, legal forecasting, and legal modeling. Results: the article presents a theoretical and legal approach to understanding sovereignty and differentiating its types. Under modern conditions, a significant role is given to the independence and autonomy of the state in the technological sphere. The correlation of digital and technological sovereignty is considered; the latter notion is outlined taking into account the gaining popularity of the Western concept of digital (technological) solidarity. The regulatory foundation of the state strategic autonomy is legal regulation, in which the concept of technology-centrism has been firmly established in recent years. The technological paradigm of modern legal regulations was identified. It consists in strategizing the scientific and technological innovations in strategic planning documents, as well as in sovereignization and cyclization of the legal sphere, digital transformation of the culture of lawmaking and law enforcement, technologization of the legal language, expansion of the scope of legislative regulation and the volume of subordinate legislation. The analysis of the correlation between the legislative and subordinate law levels of technological positioning of the Russian Federation in strategic areas has allowed to emphasize the important systemic interrelation of the involved traditional and innovative law-making tools as they ensure technological development. The author also identifies the risks of expanding legal experimentation in the digital area of public relations, which should exclude the possibility of circumventing the established critical limitations.Scientific novelty: the work forms a theoretical and legal model of ensuring technological sovereignty, which is of strategic importance for the preservation of the Russian Federation sovereignty in its classical understanding as the main and most important feature of the state.Practical significance: the results can be used in law-making activities of public authorities to create legal mechanisms for research, development and implementation of critical and end-to-end technologies and the production of high-tech products based on them in order to ensure national security of the Russian Federation.

DEVELOPMENT OF HIGHER MEDICAL EDUCATION ABROAD IN THE STUDIES OF UKRAINIAN COMPARATIVISTS

The article presents a synthesized historiographical analysis of Ukrainian pedagogical comparativistsʼ works on the development of higher medical education in foreign countries, identifying trends, achievements, bottlenecks, and prospects for studying this problem. The presented sample of dissertations, monographs, and textbooks demonstrated their narrow country-specific vector and similarity in formulating the subject of research. Historiographical and comparative analysis revealed similar approaches of scientists to determining trends in the development and functioning of national systems of professional training for doctors. These concern the preservation and expansion of decentralization in higher medical education management; strengthening the interaction of its various links; shifting emphasis in determining the prerequisites for the development of national higher medical education systems from “traditional” factors (political, socio-economic, cultural situation of the country, etc.) to clarifying the influences of international medical organizations and documents in the field of healthcare that substantiate general strategies for training doctors in higher medical education institutions, etc. Scientists also propose original approaches to studying the functioning of national systems of higher medical education. These are manifested in the development of authors periodizations and classifications of the specified process; thorough characteristics of university environments for training future doctors in different countries; comprehensive understanding of the determinants of their formation and development, which, in addition to social, economic, and cultural factors of the country's development, relate to the level of education of the population, its multiculturalism, the specifics of educational systems, etc. In these perspectives, the analyzed studies mark two main country-specific vectors of research in higher medical education: “overseas” (USA and Canada) and “European” (mainly Great Britain, Poland, and German-speaking countries).

Arguments against Federalism in Nepal: A Discussion on State Structure Reform

Aims: This study critically examines the evolution of federalism in Nepal and its challenges within the country's diverse political, social, and ethnic landscape. The objective is to explore how political parties, ethnic groups, and indigenous communities differ in their perspectives on state restructuring and national unity. Study Design: Qualitative study. Place and Duration of Study: This study utilizes secondary data sources from library archives, academic articles, party manifestos, public speeches, and reports published over the past decade. Methodology: The study applies thematic analysis to secondary data, identifying recurring themes such as nationalism, ethnic identity, governance structures, and critiques of federalism. Sources include academic literature and public documents on political discourse in Nepal. Results: The study reveals that the debate on federalism is highly contested, particularly in terms of ethnic fragmentation, socio-economic risks, and national disintegration. Political opposition to federalism raises concerns about governance, ethnic relations, and national cohesion. Conclusion: Federalism in Nepal remains a complex and contested issue, with significant political and ethnic implications. The study highlights the need for more historical and cultural research to support federal restructuring based on identity, while addressing concerns about the risks posed to national unity and economic stability.

Navigating hidden borderlands: the role of cultural heritage for black African refugee students in American higher education

ABSTRACT In this conceptual paper, we advance a scholarly conversation about Black African refugee students in the United States. Although agreeing on a universal definition of a refugee is conceptually challenging, for the sake of focus, we define refugees as: individuals who have encountered forced migration, who have lived in the U.S. for less than five years, and who are not yet eligible for U.S. citizenship. While there are also Asian, European, Latin American, and South American refugee groups living in the United States, our paper focuses on Black African refugee students because the anti-Black racism of U.S. society positions them distinctly. Overall, we observe that Black African refugees exist in a metaphorical borderland that hides and (dis)locates their existence. While living within this borderland, Black African refugees engage in different mobilities and tap into their social network capital through cultural heritage. Cultural heritage, which includes both tangible and intangible artifacts, can be a source of psychological and social resilience, buttressing resettlement and reconstruction of identity. We also argue that Black African refugee students exhibit agentic, encouraged, and necessitated hiddenness. We conclude with a closer look into how this population may help higher education scholars arrive at liberatory possibilities that have implications for higher education research. Specifically, we recommend explicitly focusing on refugee student needs, crafting a culturally responsive curriculum and instructions, and facilitating relationships with refugee-supporting community organizations.

Analysis of Accelerating Ideation Process with Large Language Model

Abstract. Large Language Models (LLMs) have rapidly advanced, demonstrating near-human intelligence in language comprehension, problem-solving, and autonomous decision-making, which can be leveraged in scientific discovery. Previous research of LLM for science often focuses on experiment execution part and the first stage of research, i.e., idea generation with large language models is lack of research. This paper fills this gap by surveying the use of LLM-based agent systems in research idea generation. This research first proposes three stages in research idea generation pipeline, pre-ideation (knowledge preparation), ideation (generation and iteration), post-ideation (evaluation), and provide detailed summary in different methods in each stage, then it summarizes different metrics and angles of evaluating generated research ideas, finally it discusses limitations and ethical concerns of existing works and suggest potential solutions and future directions. These results aim to provide a solid foundation for future research in improving LLM-based ideation systems and fostering responsible AI usage in scientific discovery.

A study of machine learning applications in healthcare

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.

Landscape Histories and the Search for Early Settlements along Louisiana's Bayous

Abstract Historical changes from shifting land use, the natural meandering of waterways, and the aftereffects of erosion complicate modern environments and obfuscate precontact landscapes. Although archaeologists can create stratified sampling models or employ systematic surveys, traditional field methodologies are often not suitable for site discovery, thereby limiting knowledge of ancient cultural landscapes. Many water systems in southern Louisiana, and in many parts of the world, have been covered or concealed in backswamps by natural geomorphological processes, development, or environmental degradation. Investigation standards that do not account for these changes will not be effective at identifying archaeological sites in such transformed landscapes. Discoveries made during ongoing archaeological research in Iberville Parish, Louisiana, provide examples of what can be missed and offer solutions through changes in archaeological field methods. This article advocates for a mixed-methodology approach, drawing from historical research and shallow geophysics to look at landforms and landscape changes. Strictly following state survey guidelines can muddle the archaeological record, particularly in places subject to significant landscape change from historical land-use alteration. By applying these approaches, we offer a way to reconstruct ancient landscapes and landforms that are culturally significant but often missed given the nature of modern environmental conditions.

Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data

Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data augmentation methods that tackle issues like data scarcity and task variability in HAR. In this work, we investigate and expand the use of mixup and cutout data augmentation methods to sensor-based and skeleton-based HAR datasets. These methods were first widely used in Computer Vision and Natural Language Processing. We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. Specifically, we customize mixup data augmentation for sensor-based datasets and cutout data augmentation for skeleton-based datasets with the goal of improving model accuracy without adding more data. Our results show that using mixup and cutout techniques improves the accuracy and generalization of activity recognition models on both sensor-based and skeleton-based human activity datasets. This work showcases the potential of data augmentation techniques on transformers and Graph Neural Networks by offering a novel method for enhancing time series and skeletal HAR tasks.

A molecular video-derived foundation model for scientific drug discovery

Accurate molecular representation of compounds is a fundamental challenge for prediction of drug targets and molecular properties. In this study, we present a molecular video-based foundation model, named VideoMol, pretrained on 120 million frames of 2 million unlabeled drug-like and bioactive molecules. VideoMol renders each molecule as a video with 60-frame and designs three self-supervised learning strategies on molecular videos to capture molecular representation. We show high performance of VideoMol in predicting molecular targets and properties across 43 drug discovery benchmark datasets. VideoMol achieves high accuracy in identifying antiviral molecules against common diverse disease-specific drug targets (i.e., BACE1 and EP4). Drugs screened by VideoMol show better binding affinity than molecular docking, revealing the effectiveness in understanding the three-dimensional structure of molecules. We further illustrate interpretability of VideoMol using key chemical substructures.

Emulating Multiparticle Emitters with Pair-Sources: Digital Discovery of a Quantum Optics Building Block

Abstract Linear quantum optics is advancing quickly, driven by sources of correlated photon pairs. Multi-photon sources beyond pairs would be a powerful resource, but are a difficult technology to implement. We have discovered a way in which we can combine multiple pair-sources to act analogous to sources of four, six or even eight correlated photons for the creation of highly entangled quantum states and other quantum information tasks. The existence of such setups is interesting from a conceptual perspective, but also offers a useful abstraction for the construction of more complicated photonic experiments, ranging from state generation to complex quantum networks. We show that even just going from probabilistic two-photon sources to effective four-photon sources allows conceptually new experiments for which no other building principles were known before. The setups which inspired the formulation of these abstract building blocks were discovered by a computer algorithm that can efficiently design quantum optics experiments. Our manuscript demonstrates how artificial intelligence can act as a source of inspiration for the scientific discoveries of new ideas and concepts in physics.
intelligence can act as a source of inspiration for the scientific discoveries of new ideas and concepts in physics.

Preface: 8th International Conference on Education, Management and Social Science (EMSS 2024)

At the end of 2024, we would like to present an international proceeding that containing of the selected papers from the 2024 8th International Conference on Education, Management and Social Science (EMSS 2024). This conference is an annual important event to publish of research results. We are hoping that the event will become a foothold of thinking in welcoming of global developments. In addition, publications packaged in this seminar will further strengthening existence and reputation as an educational institution that excels in scientific publication at national and international levels. EMSS 2024 is a leading international conference for researchers and practitioners to share their new ideas, original research results and practical development experiences from education research, management engineering, historical research, humanities, sociology, and social science. This conference was held in Manila, Philippines during September 21-22, 2024 with the aim to act as a forum for exchange and dissemination of knowledge and ideas on the latest developments. We hope the international proceedings will be able to encourage students, teachers, lecturers, practitioners, researchers, and higher education community in developing scientific research and publications to support the nation's developments. EMSS Organizing Committees Manila, Philippines

Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning.

Classifications of fluids using miniaturized sensors are of substantial importance for various fields of application. Modified with functional nanomaterials, a moisture-enabled electricity generation (MEG) device can execute a dual-purpose operation as both a self-powered framework and a fluid detection platform. In this study, a novel intelligent self-sustained sensing approach was implemented by integrating MEG with deep learning in microfluidics. Following a multilayer design, the MEG device including three individual units for power generation/fluid classification was fabricated in this study by using nonwoven fabrics, hydroxylated carbon nanotubes, poly(vinyl alcohol)-mixed gels, and indium tin bismuth liquid alloy. A composite configuration utilizing hydrophobic microfluidic channels and hydrophilic porous substrates was conducive to self-regulation of the on-chip flow. As a generator, the MEG device was capable of maintaining a continuous and stable power output for at least 6 h. As a sensor, the on-chip units synchronously measured the voltage (V), current (C), and resistance (R) signals as functions of time, whose transitions were completed using relays. These signals can serve as straightforward indicators of a fluid presence, such as the distinctive "fingerprint". After normalization and Fourier transform of raw V/C/R signals, a lightweight deep learning model (wide-kernel deep convolutional neural network, WDCNN) was employed for classifying pure water, kiwifruit, clementine, and lemon juices. In particular, the accuracy of the sample distinction using the WDCNN model was 100% within 15 s. The proposed integration of MEG, microfluidics, and deep learning provides a novel paradigm for the development of sustainable intelligent environmental perception, as well as new prospects for innovations in analytical science and smart instruments.