Key Techniques Research Articles

Harnessing the Power of AI in Salesforce: An Empirical Study on Enhanced Data Insights and Organizational Efficiency

Abstract: This article examines the integration of Artificial Intelligence (AI) in Salesforce platforms to enhance business analytics and decision-making processes. We explore how AI-powered solutions are revolutionizing data analysis, providing organizations with actionable insights, and improving market responsiveness. The article investigates key AI techniques implemented in Salesforce, including machine learning algorithms for pattern recognition and natural language processing for unstructured data analysis. We analyze the impact of these technologies on identifying trends, forecasting, threat detection, and opportunity identification for sustainable growth. Through case studies and empirical analysis, we demonstrate the effectiveness of AI-driven Salesforce solutions in streamlining data processing, enabling real-time market adaptation, and enhancing organizational efficiency. The article also addresses the technological infrastructure supporting these AI solutions, discussing cloud-based computing resources, integration challenges, and data security considerations. Our findings suggest that AIpowered Salesforce solutions significantly improve business analytics capabilities, allowing companies to make more informed decisions and maintain a competitive edge in rapidly evolving markets. This article contributes to the growing body of knowledge on AI applications in customer relationship management and business intelligence systems

The role of nuclear medicine techniques; Tc-99m-labeled dimercaptosuccinic acid, diethylenetriaminepentaacetic acid, and mercaptoacetyltriglycine scintigraphies in pediatric surgery

The purpose of this review is to highlight the pivotal role of nuclear medicine in pediatric surgery, specifically in the diagnosis and management of nephro-urological conditions. This review focuses on the applications and advantages of three key nuclear medicine techniques: Tc-99m dimercaptosuccinic acid (DMSA), Tc-99m diethylenetriaminepentaacetic acid (DTPA), and Tc-99m mercaptoacetyltriglycine (MAG3) scintigraphies. These imaging modalities are crucial for providing detailed anatomical and functional information with minimal invasiveness, essential for evaluating congenital anomalies, urinary tract infections, and obstructive uropathies in children. DMSA Scintigraphy is established as the gold standard for detecting renal cortical scarring and assessing functional renal parenchymal damage. It offers superior sensitivity and specificity compared to other imaging techniques, making it indispensable for identifying renal scars from recurrent urinary tract infections or pyelonephritis. DTPA Scintigraphy is critical for measuring glomerular filtration rate (GFR) and analyzing renogram curves, providing quantitative assessments of renal function. It effectively evaluates renal perfusion and identifies perfusion defects and renal ischemia, essential for managing conditions like renal artery stenosis and unilateral kidney disease. MAG3 Scintigraphy excels in dynamic renal scintigraphy, particularly in patients with impaired renal function. It provides high-quality images and superior diagnostic capability, making it invaluable for assessing ureteropelvic junction obstruction and other obstructive uropathies in pediatric patients. The review concludes by emphasizing the integration of these scintigraphy techniques with SPECT/CT, enhancing diagnostic accuracy and providing comprehensive anatomical and functional insights. Despite concerns regarding radiation exposure, measures such as dose optimization and advanced imaging techniques ensure safety and efficacy in pediatric applications.

Pore design of POM@MOF hybrids for enhanced methylene blue capture

Abstract Adsorption of methylene blue from aqueous effluents is a key technique to treat wastewater discharge from chemical industries. Toward methylene blue adsorption, porous metal–organic frameworks have been promising owing to their high surface areas and tunable porosity. The incorporation of polyoxometalates (POMs) into metal–organic frameworks has been found to facilitate methylene blue adsorption due to electrostatic interactions between the anionic polyoxometalate and the cationic methylene blue. However, it remains unclear how the customizable pore of POM@MOF hybrids affects the capture of methylene blue. In this work, we study the effect of the size and environment of metal–organic frameworks on the property of methylene blue capture for [Zr6O4(OH)4(1,4-benzenedicarboxylate)6] (UiO-66) embedding an α-Keggin-type polyoxometalate [α-SiW12O40]4− (SiW12). Tuning pore size and environment based on ligand engineering of the metal–organic frameworks suggests that (i) lengthening the organic linkers and (ii) functionalization of the linker with −NO2 groups could enhance the methylene blue uptakes. Notably, an increase in the loading amounts of SiW12 on UiO-66 functionalized with −NO2 groups led to outstanding methylene blue capture, comparable to that of representative zeolites such as ZSM-5 and zeolite Y.

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

Development of an Ultrasensitive Competitive Double-Enzyme Cascade Fluorescence Signal Amplification Method for Factor XIa Inhibitor Screening.

Coagulation factor XIa (FXIa) is associated with a low risk of bleeding and has been identified as an effective and safe target for the development of novel anticoagulant drugs. In this study, we established an ultrasensitive competitive dual-enzyme cascade signal amplification method for the quantitative analysis and screening of FXIa inhibitors. Due to the specific recognition of FXIa's active site by the aptamer AptE40, the AptE40-QDs-EK recognition probe modified with enterokinase (EK) and the aptamer AptE40, was attached to the MNPs-FXIa capture probe. When FXIa inhibitor was present, it competed with AptE40 for binding to FXIa, resulting in the detachment of AptE40-QDs-EK from MNPs-FXIa. After magnetic separation, the enterokinase of AptE40-QDs-EK in the supernatant hydrolyzed N-terminal hexapeptide of trypsinogen, leading to the production of a large amount of trypsin as part of the first-stage signal cascade amplification. Next, trypsin could hydrolyze the hexameric arginine peptide (RRRRRR, R6), leading to the dissociation of RQDs from the R6-RQDs signal probe; this resulted in a dramatic increase in the fluorescence intensity of the supernatant as the second-stage signal cascade was amplified. The feasibility of the method was investigated using the FXIa inhibitor aptamer FELIAP as a positive model drug. Furthermore, the method was applied to screen the FXIa inhibitors in Eupolyphaga sinensis Walker. Two fractions with more active anticoagulated ingredients were successfully identified and validated via the conventional method, and the results were consistent. The established method provides a key technique for the sensitive detection, high-throughput analysis, and screening of the FXIa inhibitors.

Survey on Unmanned Aerial Vehicle Communications for 6G

Although the application of the fifth-Generation (5G) mobile communication has brought tremendous innovations to the daily life of human beings, e.g., autonomous vehicles and internet of everything, the upcoming huger data requirement leads to the emergence of the sixth-Generation (6G) mobile communication. Compared to 5G, the transmission rate, time delay, and wireless coverage need to be improved significantly. Thus, in this paper the applications of Unmanned Aerial Vehicles (UAVs) to the ubiquitous, intelligent and coupling 6G network are surveyed. First, the utilization of UAVs in the framework of space-air-ground-sea integrated network is demonstrated, and the roles and functions of UAVs in different scenarios are emphasized, e.g., the swarm base stations, the deployment for holographic projection, the long-distance relaying and the data collection. Then, the potential 6G key techniques of terahertz, ultra-massive multiple-input and multiple-output, endogenous artificial intelligence, Intelligent Reflecting Surface (IRS), intelligent edge computing, blockchain and integrated sensing and communication for UAV communications are investigated. Finally, the future challenges of UAV communications for 6G, including the limited duration, integration of networks, compatibility of IRS, development of THz communications, and user security are discussed.

Results of Indoor Hockey Training on Mastery of Dribble Techniques in Hockey Games

The purpose of the study. Mastery of fundamental techniques in hockey is essential for players. Mastery enables player cooperation to achieve the game's objective. Key techniques include dribbling and pushing the ball, applicable in both indoor and field hockey. These techniques are vital for effective gameplay. In indoor hockey, only dribbling techniques are permitted according to regulations. Practice enhances dribbling skills, requiring repetitive training. This study aims to compare indoor and field hockey training effectiveness on dribbling mastery. Materials and methods. An experimental method was employed, utilizing 24 athletes from Sambas Hockey Club, divided into two groups. Results. The results indicate a significant improvement in dribbling technique mastery in the indoor training group. Conclusions. The research concludes that indoor hockey training significantly enhances basic dribbling technique mastery compared to field hockey training.

Introductory Review of Soft Implantable Bioelectronics Using Conductive and Functional Hydrogels and Hydrogel Nanocomposites.

Interfaces between implantable bioelectrodes and tissues provide critical insights into the biological and pathological conditions of targeted organs, aiding diagnosis and treatment. While conventional bioelectronics, made from rigid materials like metals and silicon, have been essential for recording signals and delivering electric stimulation, they face limitations due to the mechanical mismatch between rigid devices and soft tissues. Recently, focus has shifted toward soft conductive materials, such as conductive hydrogels and hydrogel nanocomposites, known for their tissue-like softness, biocompatibility, and potential for functionalization. This review introduces these materials and provides an overview of recent advances in soft hydrogel nanocomposites for implantable electronics. It covers material strategies for conductive hydrogels, including both intrinsically conductive hydrogels and hydrogel nanocomposites, and explores key functionalization techniques like biodegradation, bioadhesiveness, injectability, and self-healing. Practical applications of these materials in implantable electronics are also highlighted, showcasing their effectiveness in real-world scenarios. Finally, we discuss emerging technologies and future needs for chronically implantable bioelectronics, offering insights into the evolving landscape of this field.

Growth Mechanism of Carbon Nanotubes Revealed by in situ Transmission Electron Microscopy.

Elucidating the growth mechanism of carbon nanotubes (CNTs) is critical to obtaining CNTs with desired structures and tailored properties for their practical applications. With atomic resolution imaging, in situ transmission electron microscopy (TEM) has been a key technique to reveal the microstructure and dynamics of CNTs in real time. In this review, recent advances in the development of in situ TEM with different types of environmental reactors will be introduced. The catalytic growth mechanisms of CNTs revealed by in situ TEM under realistic conditions are discussed from fundamental thermodynamics and kinetics to the detailed nucleation, growth, and termination mechanisms, including the state and phase of active catalysts, interfacial connections between catalyst and growing CNTs, and catalyst-related growth kinetics of CNTs. Great progresses have been made on how a CNT nucleates, grows and terminates, focusing on the interface dynamics and kinetic fluctuations. Finally, challenges and future directions for understanding the atomic dynamics under the real growth conditions are proposed. It is expected that breakthroughs in the fundamental growth mechanisms will pave the way to the ultimate goal of designing and controlling the atomic structures of CNTs for their applications in various devices.

Unveiling the competitive mechanism between SO2 and PCDD/Fs on activated carbon adsorption

In municipal solid waste incineration (MSWI) plants, activated carbon (AC) adsorption is the key technique for eliminating Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from flue gases. This research thoroughly investigates the potential competitive adsorption between SO2 and PCDD/Fs and examines how adsorption at the center and the edge of the AC layer impacts the adsorption process. The findings show a decline in the removal efficiency of PCDD/Fs from 86.8 % to 84.2 % and further to 74.4 % when using SO2 pre-treated (AC-A3) and H2SO4-impregnated (AC-B2) activated carbon, respectively. Multiple characterization methods reveal that sulfur elements occupy active sites within the inner pores of the activated carbon, reducing the availability of its pore structure, particularly affecting microporous more than mesoporous structures. DFT calculations suggest that the π-π EDA effect facilitates the adsorption of 2,3,7,8-Tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD), whereas dispersion force drive SO2 adsorption. Comparisons among various oxygenated functional groups show that the organic acid anhydride (CO-CO) has better adsorption selectivity toward TCDD and less adsorption to SO2. This study provides a novel perspective on the adsorption mechanisms of PCDD/Fs on AC and the competitive dynamics of sulfur in the flue gas.

Extraction of Attenuation and Backscattering Coefficient along Hollow-Core Fiber Length Using Two-Way Optical Time Domain Backscattering.

Optical time domain reflectometry (OTDR) is a key technique to characterize fabricated and installed optical fibers. It is also widely used in distributed sensing. OTDR of emerging hollow-core fibers (HCFs) has been demonstrated only very recently, being almost 30 dB weaker than that in the glass-core optical fibers. However, it has been challenging to extract useful data from the OTDR traces of HCFs, as the longitudinal variation in the fiber's geometry, notably the core size or the longitudinal variations of the air pressure within the core, results in commensurate changes of the backscattering strength. This is, however, necessary for continuous improvement of HCF fabrication and subsequent improvement in their performance such as minimum achievable loss, potentially enabling the use of HCF in a significantly broader range of applications than used today. Here, we demonstrate, for the first time, that the distributed loss and backscattering coefficient in antiresonant HCFs can be separated, obtaining key data about fiber distributed loss and uniformity. This is enabled by using OTDR traces obtained from both ends of the HCF.

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

A review of research on secret sharing

This article explores the importance and challenges of secret sharing technology in the context of rapid advancements in information technology. With increasing attention on data security and privacy protection, secret sharing serves as a key cryptographic technique that ensures secure data transmission, storage, and multi-party computation by dividing secrets into multiple shares. However, existing solutions encounter performance bottlenecks when dealing with large-scale datasets, managing share updates in dynamic environments, and facing quantum computing threats. This article systematically summarizes the basic concepts, theories, classifications, and characteristics of secret sharing while analyzing its applications in financial security, medical privacy, and other fields. Furthermore, it presents recent research progress on dynamic secret sharing and anti-quantum secret sharing. The findings indicate that dynamic secret sharing schemes improve long-term key storage security, while anti-quantum secret sharing provides a solution to counteract quantum computing threats. This study not only deepens understanding of secret sharing technology but also offers insights for future research directions, significantly contributing to enhancing data protection levels.

Robust Matrix Completion with Heavy-Tailed Noise

This article studies noisy low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of theoretical understanding when the observations are contaminated by heavy-tailed noises. Prior theory falls short of explaining the empirical results and is unable to capture the optimal dependence of the estimation error on the noise level. In this article, we adopt an adaptive Huber loss to accommodate heavy-tailed noise, which is robust against large and possibly asymmetric errors when the parameter in the Huber loss function is carefully designed to balance the Huberization biases and robustness to outliers. Then, we propose an efficient nonconvex algorithm via a balanced low-rank Burer-Monteiro matrix factorization and gradient descent with robust spectral initialization. We prove that under merely a bounded second-moment condition on the error distributions, rather than the sub-Gaussian assumption, the Euclidean errors of the iterates generated by the proposed algorithm decrease geometrically fast until achieving a minimax-optimal statistical estimation error, which has the same order as that in the sub-Gaussian case. The key technique behind this significant advancement is a powerful leave-one-out analysis framework. The theoretical results are corroborated by our numerical studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Development of autonomous navigation system based on neural network and visual servoing for row-crop tracking in vegetable greenhouses

Accurate navigation line localization strategy and robust path tracking control scheme are two key techniques for advancing precision agriculture and enhancing the sustainability of smart farms. In this paper, a superior and cost-effective autonomous navigation system for a visual servoing-based agricultural vehicle in the greenhouse scenario with a distinct layout of two crop rows in each ridge is presented. First, based on image segmentation techniques, a neural network model named DGLNet integrating recognition of crop rows and prediction of the effective area of the navigation line is designed. The linear equation of the navigation line we required is obtained through post-processing operations of linear fitting completed in the effective area. According to experimental results, directly teaching the neural network to learn navigation line localization features utilizes the semantic and spatial information in the feature map to establish a stronger relationship between crop rows and the navigation line, which greatly improves the generalization of the navigation line localization in the case of large-angle yaw scenarios without additional training datasets. In addition, the performance of the network is significantly enhanced by adding an attention mechanism in the backbone based on U-Net. Subsequently, a dataset of cabbage vegetable images covering a wide range of complexities in the greenhouse scenario is created, and is available to support future academic research now. Further, a path tracking control scheme based on point-feature and line-feature is proposed, which can control the agricultural vehicle using the information from camera images without the need to perform explicit localization or maintain a global map. Finally, extensive field experiments have been carried out to show that this autonomous navigation system can preserve the stability of the vehicle and ensure timely convergence of deviations in different complex scenarios, which indicates that this control scheme is reliable and robust. Meanwhile, benefit from the efficiency of this recognition algorithm and the unnecessity of the complete representation of the path in the control scheme, the system has high real-time performance in every scenario.

Machine Learning for Actionable Warning Identification: A Comprehensive Survey

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based AWI approaches, benefiting from ML’s strong ability to learn subtle and previously unseen patterns from historical data, have demonstrated superior performance. However, a comprehensive overview of these approaches is missing, which could hinder researchers and practitioners from understanding the current process and discovering potential for future improvement in the ML-based AWI community. In this paper, we systematically review the state-of-the-art ML-based AWI approaches. First, we employ a meticulous survey methodology and gather 51 primary studies from 2000/01/01 to 2023/09/01. Then, we outline a typical ML-based AWI workflow, including warning dataset preparation, preprocessing, AWI model construction, and evaluation stages. In such a workflow, we categorize ML-based AWI approaches based on the warning output format. Besides, we analyze the key techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future ML-based AWI approaches, focusing on aspects like data improvement (e.g., enhancing the warning labeling strategy) and model exploration (e.g., exploring large language models for AWI).

Navigating Complex Process and Design Challenges in Hybrid Bonding for Advanced Semiconductor Integration: A Comprehensive Analysis

Abstract In advanced electronic systems, achieving top-tier interconnect interfaces with fine-pitch integration is of paramount importance. Among interconnect options, hybrid bonding is the preeminent choice given its exceptional capacity for accommodating a high input/output (I/O) count, which facilitates high-density memory integration, increased power delivery, and enhanced signal speed. One key technique for ensuring utmost quality in hybrid bonding involves embedding Cu interconnects within the dielectric layer. Equally pivotal is surface planarization, accomplished through chemical mechanical polishing (CMP), which encompasses a meticulous two-step procedure, commencing with copper bulk CMP and culminating in barrier CMP. The latter step is particularly critical, yielding the indispensable surface finish required for a successful hybrid bonding process. Several crucial surface properties significantly influence overall bond yield, including a copper recess (“dishing”) in the vias, erosion and roughness of the dielectric layer, and surface topography changes from high-density to low-density copper vias. To optimize these parameters, a comprehensive understanding of the interconnect layer's design is essential. Here, we delve into the ramifications of via scaling, ranging from 5 to 1 µm, on dishing and roll-off, and the effects of copper via density variation, spanning from 16% to 13%, on topography. Furthermore, we explore how incorporation of dummy vias impacts the final surface finish, potentially leading to improved bond yie

Tropical Refined Curve Counting with Descendants

We prove a q-refined tropical correspondence theorem for higher genus descendant logarithmic Gromov–Witten invariants with a λg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda _g$$\\end{document} class in toric surfaces. Specifically, a generating series of such logarithmic Gromov–Witten invariants agrees with a q-refined count of rational tropical curves satisfying higher valency conditions. As a corollary, we obtain a geometric proof of the deformation invariance of this tropical count. In particular, our results give an algebro-geometric meaning to the tropical count defined by Blechman and Shustin. Our strategy is to use the logarithmic degeneration formula, and the key new technique is to reduce to computing integrals against double ramification cycles and connect these integrals to the non-commutative KdV hierarchy.

Probing Defects in Covalent Organic Frameworks.

Defects in covalent organic frameworks (COFs) play a pivotal role in determining their properties and performance, significantly influencing interactions with adsorbates, guest molecules, and substrates as well as affecting charge carrier dynamics and light absorption characteristics. The present review focuses on the diverse array of techniques employed for characterizing and quantifying defects in COFs, addressing a critical need in the field of materials science. As will be discussed in this review, there are basically two types of defects referring either to missing organic moieties leaving free binding groups in the material or structural imperfections resulting in lower crystallinity, grain boundary defects, and incomplete stacking. The review summarizes an in-depth analysis of state-of-the-art characterization techniques, elucidating their specific strengths and limitations for each defect type. Key techniques examined in this review include powder X-ray diffraction (PXRD), infrared spectroscopy (IR), thermogravimetric analysis (TGA), nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), scanning transmission electron microscopy (STEM), scanning tunneling microscope (STM), high resolution transmission electron microcoe (HRTEM), gas adsorption, acid-base titration, advanced electron microscopy methods, and computational calculations. We critically assess the capability of each technique to provide qualitative and quantitative information about COF defects, offering insights into their complementary nature and potential for synergistic use. The last section summarizes the main concepts of the review and provides perspectives for future development to overcome the existing challenges.

Research on the Aesthetic Value of Costume Creation in the Song Dynasty

The objective of the study is to trace the evolution of costume design in the Song Dynasty, highlighting key aesthetic influences, materials, and techniques employed in clothing creation and to explore the underlying aesthetic principles and thoughts that guided costume creation in the Song Dynasty, including notions of beauty, harmony, and symbolism. This study try to investigate the specific materials and techniques used in creating Song Dynasty costumes and their impact on the overall aesthetic value, considering aspects such as fabric, embroidery, dyeing, and tailoring methods. In the course of the aesthetic study, this study looked very closely at artworks from the Song Dynasty in an effort to comprehend the aesthetic concepts such as beauty, harmony, and symbolism that influenced costume design during that time period. During the materials and techniques investigation, this study used scientific approaches to get a better understanding of the materials that were used in the production of the costumes. Additionally, this study worked with specialists to get a more in-depth examination of the processes used for dyeing , embroidery, and tailoring. According to the findings, the aesthetic ideals that guided costume production underwent a process of evolution during the course of the Song Dynasty. During the early decades of the 20th century, there was an emphasis placed on elegance and harmony with nature. This is shown in the usage of delicate silks and subtle floral designs at the time. When Neo-Confucianism became more popular, ideas of integrity and symmetry began to influence costume design (Table 1 and Table 2). This resulted in color combinations that were more harmonious and the integration of yin-yang symbols. . Pseudo-evidence on silk, sophisticated needlework, state of the art natural coloring procedures and ahead tailoring techniques demonstrate how such components played their particular part towards the attractiveness of cloth products made under Song Dynasty. Firstly this research gives an understanding of the interplay diverse influences such as changing circumstances from historical, cultural and artistic perspectives that fused together to form a rich legacy in beauty and elegance that adds immensely to the understanding of the aesthetic nature of costume design throughout Song Dynasty.