Explosive Expansion Research Articles

Revolutionizing Finance: Decentralized Finance as a Disruptive Challenge to Traditional Finance

This paper examines the emergence and impact of Decentralized Finance (DeFi), between 2019 and 2024, as a disruptive agent opposing traditional finance (TradFi). By using blockchain technology to offer decentralized, open, and readily available financial services, the DeFi industry has seen explosive expansion Emphasizing DeFi's competitive advantage over TradFi, this study aims to investigate notable trends, developments, and market drivers in the toolkit. The main conclusions are the fast and significant rise in Total Value Locked (TVL), the clear increase in user adoption, the expansion in the number of transactions and trading volume on decentralized exchanges (DEXs), and the development of creative financial products. DeFi constantly changes the financial scene despite regulatory oversight and market volatility, so posing major problems and opportunities for established financial institutions.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

Ties that Sever: Losing the Right to Belong in Denmark

AbstractIn 2018, the Danish Supreme Court revoked Adam Johansen’s citizenship in conjunction with his conviction for terrorism. Applying a proportionality test adapted from European Court of Human Rights (ECtHR) jurisprudence for naturalised, not natural, citizens, the Danish court determined that Johansen’s Muslim faith tied him to Tunisia, his father’s country, rather than to Denmark. In March 2022, the ECtHR unanimously upheld this judgment. In so doing, the ECtHR solidified an emerging standard in cases of citizenship revocation for natural citizens, which standard is weaker than the protections enjoyed by naturalised citizens. This article reviews the Danish and ECtHR jurisprudence to show how the explosive expansion of citizenship revocation in relation to terror crimes, combined with the ECtHR’s emerging jurisprudence rejecting substantive review for such revocation, demonstrates a significant, multidirectional weakening of rights protections in Europe.

Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security

In the age of information, the proverb "knowledge is power" has been shown to be true. admission to, which ultimately leads to knowledge acquisition. The relevance of the ability to glean knowledge from vast amounts of facts has increased. To describe the process of distributing, storing, and gathering enormous amounts of data for future analysis, researchers coined the term "big data analytics" (BDA). Data is generated at an alarming rate. The Internet of Things (IoT), the net's explosive expansion, and other technological advancements are the main forces behind this long-term growth. Since the information generated reflects the environment in which it is formed, the use of information gleaned from systems to understand the inner workings of those systems. The goal of protecting assets has been developed into a crucial component of cybersecurity. Additionally, big data now has the status of a high-value target due to the growing value of data. Current cybersecurity research in relation to big data has been reported here to explore big data security and its potential use as a cybersecurity tool. This gives trends, open research projects, and challenges along with a summary of current studies in the form of tables. In addition to current advancements and unanswered questions in this area of active research, this research work also provides readers a more thorough understanding of safety in the big data era.

Optimization of cross-border E-commerce (CBEC) supply chain management based on fuzzy logic and auction theory

People have benefited enormously from e-commerce’s explosive expansion in recent years. E-commerce, in contrast to the traditional business environment, is dynamic and complicated, which poses a number of challenges. The prediction market can create mixed intelligence for sales forecasting, which is essential for e-commerce enterprises, to handle this difficulty. Combining the usage of human analysts and machine learning algorithms can accomplish this. To accurately anticipate retailer volume and allot resources, a novel methodology for optimizing supply chain management at CBEC is proposed in this paper. The framework improves efficiency and profitability by using fuzzy logic and auction theory to make strategic decisions. Thanks to this creative strategy, managers can now make more informed decisions, ultimately enhancing the efficiency of CBEC’s supply chain. The results of this paper reveal that our proposed method is superior to previous comparable methods, with RMSE and MAE values of 22.31 and 18.76, respectively. This approach offers a promising solution to the challenges faced by e-commerce businesses, and can help them achieve greater success in the dynamic and complex world of online commerce.

Bigdata clustering and classification with improved fuzzy based deep architecture under MapReduce framework

The current state of economic, social ideas, and the advancement of cutting-edge technology are determined by the primary subjects of the contemporary information era, big data. People are immersed in a world of information, guided by the abundance of data that penetrates every element of their surroundings. Smart gadgets, the IoT, and other technologies are responsible for the data’s explosive expansion. Organisations have struggled to store data effectively throughout the past few decades. This disadvantage is related to outdated, expensive, and inadequately large storage technology. In the meanwhile, large data demands innovative storage techniques supported by strong technology. This paper proposes the bigdata clustering and classification model with improved fuzzy-based Deep Architecture under the Map Reduce framework. At first, the pre-processing phase involves data partitioning from the big dataset utilizing an improved C-Means clustering procedure. The pre-processed big data is then handled by the Map Reduce framework, which involves the mapper and reducer phases. In the mapper phase. Data normalization takes place, followed by the feature fusion approach that combines the extracted features like entropy-based features and correlation-based features. In the reduction phase, all the mappers are combined to produce an acceptable feature. Finally, a deep hybrid model, which is the combination of a DCNN and Bi-GRU is used for the classification process. The Improved score level fusion procedure is used in this case to obtain the final classification result. Moreover, the analysis of the proposed work has proved to be efficient in terms of classification accuracy, precision, recall, FNR, FPR, and other performance metrics.

An Efficient Lightweight Crypto Security Module for Protecting Data Transmission Through IOT Based Electronic Sensors

The Internet of Things (IoT) devices are advanced nanoelectronics devices which has recently witnessed an explosive expansion in the field of communication and electronics, becoming ubiquitous in various applications. However, the rapid growth of IoT applications makes them prone to security threats and data breaches. Hence, cryptographic techniques are developed to ensure data confidentiality and integrity in IoT and many of the applications from optoelectronics. However, the existing cryptographic algorithms face challenges in securing the data from threats during transmission, as they lack effective key management. Therefore, we proposed a novel optimized lightweight cryptography (LWC) to resolve this challenge using the combined benefits of Grey Wolf Optimization and Hyper Elliptic Curve Cryptography (GW-HECC). The proposed LWC algorithm protects the data from attacks during data exchange by optimizing the key management process and aims to deliver greater Quality of Service (QoS) in IoT networks. An IoT network was initially created with multiple sensor devices, IoT gateways, and data aggregators. The proposed framework includes a Quantum Neural Network (QNN)-based attack prediction module to predict the malicious data entry in the IoT network. The QNN learns the attack patterns from the historical IoT data and prevents incoming malicious data entries, ensuring that only normal data is transmitted to the cloud. For secure data transmission, the sensed data from the IoT network are encrypted using the proposed GW-HECC. The presented work was designed and implemented in Python software; the experimental results demonstrate that the proposed method offers greater data confidentiality of 97.9%, improved attack prediction accuracy of 99.8%, and a reduced delay of 0.37 s. Furthermore, a comparative analysis was made with existing cryptographic algorithms, manifesting that the proposed algorithm acquired improved results.

A novel self-powered sensitive porous ZnO NWs/PDMS sponge capacitive pressure sensor

The demand for flexible sensors has increased due to the explosive expansion of flexible electronic applications, but there are concerns that the development of high-precision measurement is limited. Capacitive pressure sensors are extensively utilized among diverse sensors due to their simple structure, high resolution, and outstanding dynamic response characteristics. Therefore, by sandwiching a porous ZnO NWs PDMS (PZP) sponge dielectric layer between two ITO/PET electrodes, a flexible capacitive pressure sensor with high sensitivity, short response time, and wide working range was prepared. The porous PDMS sponge was prepared utilizing the salt particle as a template, then ZnO NWs in different precise proportions were added to it. The sensitivity of PZP sponge capacitive pressure sensors with different salt particle sizes and ZnO NWs dosages were compared. The research results indicate that when salt particle sizes less than 150 μm, and the amount of ZnO NWs in PZP sponge is increased to 200 mg, the sensor sensitivity is highest in the pressure range of 2.5-7.5 kPa, reaching 0.1279 kPa-1, response time less than 70 ms. When the PZP sponge sensor is used as a self-powered source, it achieves the best output of 1.2 V and 6.8 nA at 5.74 kPa pressure. It provides a new way to tackle the challenge of low sensitivity of flexible sensors to capacitive signal and lays a foundation for expanding the application scenarios of ZnO NWs.

Critical Analysis of OTT Platforms in the light of Censorship Laws in India

The goal of the study paper is to perform a thorough and critical analysis of Over-The-Top (OTT) platforms in India with a particular focus on their content and the difficulties presented by the changing censorship landscape. Concerns about content control have increased due to the explosive expansion of OTT platform usage and the consumption of digital media. The study starts out by giving a general overview of the OTT market in India, charting its development, and analyzing its effects on traditional media. The paper discusses the development of streaming services, content delivery systems, and the elements influencing their enduring appeal to a wide range of audiences. The research then examines the censoring laws and rules that currently apply to India's conventional media, movies, and broadcasting. It examines the issues given by the decentralized and international nature of OTT material as well as how these rules, developed before to the OTT era, relate to the digital medium.Analysis of OTT content's impact on the younger generation and prospective implications on social norm formation receives special focus.Finally, the research provides a critical assessment of the status quo and recommendations for policy makers, content creators and OTT platforms to balance creative freedom with responsible content curation.

Starvation resistance in the nematode Pristionchus pacificus requires a conserved supplementary nuclear receptor

Nuclear hormone receptors (NHRs) are a deeply-conserved superfamily of metazoan transcription factors, which fine-tune the expression of their regulatory target genes in response to a plethora of sensory inputs. In nematodes, NHRs underwent an explosive expansion and many species have hundreds of nhr genes, most of which remain functionally uncharacterized. However, recent studies have reported that two sister receptors, Ppa-NHR-1 and Ppa-NHR-40, are crucial regulators of feeding-structure morphogenesis in the diplogastrid model nematode Pristionchus pacificus. In the present study, we functionally characterize Ppa-NHR-10, the sister paralog of Ppa-NHR-1 and Ppa-NHR-40, aiming to reveal whether it too regulates aspects of feeding-structure development. We used CRISPR/CAS9-mediated mutagenesis to create small frameshift mutations of this nuclear receptor gene and applied a combination of geometric morphometrics and unsupervised clustering to characterize potential mutant phenotypes. However, we found that Ppa-nhr-10 mutants do not show aberrant feeding-structure morphologies. Instead, multiple RNA-seq experiments revealed that many of the target genes of this receptor are involved in lipid catabolic processes. We hypothesized that their mis-regulation could affect the survival of mutant worms during starvation, where lipid catabolism is often essential. Indeed, using novel survival assays, we found that mutant worms show drastically decreased starvation resistance, both as young adults and as dauer larvae. We also characterized genome-wide changes to the transcriptional landscape in P. pacificus when exposed to 24 h of acute starvation, and found that Ppa-NHR-10 partially regulates some of these responses. Taken together, these results demonstrate that Ppa-NHR-10 is broadly required for starvation resistance and regulates different biological processes than its closest paralogs Ppa-NHR-1 and Ppa-NHR-40.

Expansion Kinetics of Flexible Polymers upon Release from a Disk-Shaped Confinement.

A general theory is developed to explain the expansion kinetics of a polymer released from a confining cavity in a d-dimensional space. At beginning, the decompressed chain undergoes an explosive expansion while keeping the structure resembling a sphere. As the process continues, the chain transitions to a coil conformation, and the expansion significantly slows down. The kinetics are derived by applying Onsager's variational principle. Computer simulations are then conducted in a quasi-two-dimensional space to verify the theory. The average expansion of the chain size exhibits a distinctive sigmoidal variation on a logarithmic scale, characterized by two times and associated exponents that represent the fast and the slow dynamics, respectively. Through an analysis of the kinetic state diagrams, two important universal behaviors are discovered in the two expansion stages. The intersection of the expansion speed curves allows us to define the crossover point between the stages and study its properties. The scaling relations of the characteristic times and exponents are thoroughly investigated under different confining conditions, with the results strongly supporting the theory. Additional calculations conducted in a three-dimensional (3D) space demonstrate the robustness of the proposed theory in describing the kinetics of polymer expansion in both 2D and 3D spaces.

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Cyberbullying is a new form of online violence that has emerged as a result of the social media industry's explosive expansion since it allows for indirect communication. Despite the usefulness of digital media, it has been used by weak people to threaten and bully others online. In the last ten years, research has shown that children and teenagers are increasingly experiencing cyberbullying as a concern. This paper examines the research conducted during the previous ten years, categorizes it, and presents statistical analyses of the data collected during that time. A table is used to present various data, including the dataset that was used, its size (number of samples, posts, or messages), the methods that were employed, and the metrics that were gathered from the examined research that was taken from the publications that were looked into. This survey will be helpful to everyone who wants to advance their understanding of how machine learning may be used to identify cyberbullying, and it may help create a social media environment that is safe and relatively healthy by automatically identifying bullying communications.

Work life balance and work engagement on employee performance in the auto cutting department

The business world's explosive expansion and development have given rise to new industries and commercial prospects. Human resources in the company are said to be the main ones because human resources themselves determine the direction and goals of the company. Considering the critical role that human resources play, it makes sense for a business to retain its human capital by closely monitoring employee performance. The outcome of an individual's job, in relation to both number and quality, is their performance as an worker executing their assigned tasks in conformity with their responsibilities. This study aims to investigate the connection between employee engagement and work-life balance have on productivity in the PT. Shoetown Ligung Indonesia Auto Cutting Department. The researchers' analytical approach makes use of a descriptive verification method. With a sample size of 124, the study's population consisted of 225 workers from PT. Shoetown Ligung Indonesia's auto cutting department. Probability sampling combined with basic random sampling is the sample method utilised. To gather information for this research, Likert scale questionnaires were distributed. The multiple regression analysis, or the traditional assumption test, simultaneous analysis of the determination coefficient, and partial determination coefficient analysis are the analysis techniques that are employed. The study's findings show that employee performance and work-life balance are positively and dramatically impacted by engagement, partially and simultaneously. The study's conclusions can be used as a foundation for enhancing and preserving employee performance, work-life balance, and engagement at work.

Sentiment Analysis and Marketing Mix in Twitter Conversations About Mixue

This study delves into the rapid growth and public reception of Mixue, a Chinese beverage and ice cream franchise, in post-pandemic Indonesia. Employing Twitter sentiment analysis through the lens of the 7P marketing mix framework (Product, Price, Promotion, Place, People, Process, Physical Evidence), the research evaluates consumer attitudes towards Mixue amidst its explosive expansion and recent Halal certification. Data was collected through Twitter API and Scrapy, encompassing 7087 Indonesian-language tweets related to Mixue between January and February 2023. Sentiment analysis revealed an overall positive reception towards Mixue, particularly regarding its products and affordability. However, concerns emerged surrounding limited flavor variety, perceived high prices, promotional saturation, and queuing difficulties. Implications of these findings highlight the importance of franchise businesses adapting to evolving consumer preferences in the post-pandemic market. Mixue could benefit from diversifying its menu, addressing queuing issues, and tailoring promotions to enhance the customer experience. Gaining insights into public sentiment allows Mixue and other franchises to refine their strategies and maintain long-term success in the ever-changing Indonesian market. This research serves as a valuable stepping stone for further exploration into consumer preferences for specific flavor variations, conducting price comparisons, evaluating promotional effectiveness, and optimizing outlet placement and customer service.Addressing these aspects can fuel Mixue's continued growth and strengthen its position within the thriving Indonesian food and beverage landscape.

Kinetic energy distributions of atomic ions from disintegration of argon containing nanoclusters in moderately intense nanosecond laser fields: Coulomb explosion or hydrodynamic expansion.

We report kinetic energies (KE) of multiply charged atomic ions (MCAI) from interactions of moderately intense nanosecond lasers at 532 nm with argon containing clusters, including neat and doped clusters with a trace amount of trichlorobenzene. We develop a mathematical method to retrieve speed and thereby kinetic energy information from analyzing the time-of-flight profiles of the MCAI. This method should be generally applicable in detections of energetic charged particles with high velocities, a realm where velocity map imaging is inadequate. From this analysis, we discover that the KE of MCAI from doped clusters demonstrates a quadratic dependence on the charge of the atomic ions, while for neat clusters, the dependence is cubic. This result confirms the nature of the cluster disintegration process to be dominated by Coulomb explosion. This result bears more similarity to reports from extreme vacuum ultraviolet (EUV) fields with similar intensities, than to reports from near infrared (NIR) intense laser fields. However, the charge state distribution from our experiment is the opposite: we observe more higher charge state ions than reported in EUV fields, and our charge state distribution is actually similar to those reported in NIR fields. We also report a significant effect of the external electric field on the charge state distribution of the atomic ions: the presence of an electric field can significantly increase the charge from the atomic ions, as shown by a three-fold reduction in the average kinetic energy per charge. Although molecular dynamics simulations have been implemented for experiments in the EUV and NIR, our results allude to the need of a concerted effort in this regime of moderately intense nanosecond laser fields. The significant decrease in charge state distribution and the significant increase in KE from doped clusters, compared with neat clusters, is a telltale sign that the true interaction time between the laser field and the cluster may be substantially shorter than the duration of the laser, a welcome relief for molecular dynamics simulations.

Cipher Craft: Design and Analysis of Advanced Cryptographic Techniques for Secure Communication Systems

The name “cryptography” comes from a Greek word that refers to the art of securing data by organizing it in a disorganized and unintelligible way. It combines software engineering with math. The explosive expansion of the Internet has led to a greater awareness of intriguing uncertainty concerns. Although security is the biggest concern when it comes to the internet, many apps have been developed and designed without taking confidentiality, authentication, and protection—the three essential components of data security—into account. Knowing these kinds of security problems and challenges is going to be more important as our daily activities rely more and more on data networks. Cryptography is necessary to prevent some unwanted customers or persons from gaining access to the data. This study presents a novel hybrid security cipher that combines the three most significant ciphers, such as the Caesar, Rail Fence and Vigenère ciphers. When compared to traditional ciphers, this hybrid encryption cipher offers more security.

Advancing bone biology: The mutual promotion of biology and pioneering technologies

<p>Bone is a mineralized tissue that provides fundamental support for body posture and biomechanical forces. Bones have also been meticulously used by humans as fine tools for at least over 25,000 years, perpetually captivating our curiosity and driving exploration into its mysteries. In this perspective, we revisit the milestones in the history of bone biology since the 17<sup>th</sup> century, and highlighted the transformative impact of pioneering technologies. From macroanatomical observations to microscopic cellular analyses and molecular signaling pathway explorations, each milestone has heralded groundbreaking discoveries. These include the elucidation of Wollf’s Law, the regulatory mechanisms of calcium metabolism (via osteotropic parathyroid hormone analogs and calcitonin), as well as the revelation of critical cell interactions and signaling pathways in bone remodeling. These foundational insights deepen our understanding of bone as an enigmatic and underexplored tissue, but also yield significant clinical advancements. Bone biology stands poised for an explosive expansion of knowledge as we usher in the era of systems biology, propelled by cutting-edge multi-omics approaches, including single cell RNA sequencing (scRNA-seq) and spatial transcriptomics, as well as artificial intelligence. Therefore, challenges and future directions are discussed to navigate the complexities of integrating emerging technologies and to foster interdisciplinary collaborations that will propel bone biology research towards innovative breakthroughs and holistic solutions.</p>

Autocomplete recommendation plugin and Summarizing Text using Natural Language Processing

Expert-caliber documents, reports, letters, and resumes can be easily developed using Microsoft Office. Microsoft Office offers capabilities such as grammar check, text and font checking & formatting, HTML compatibility, advanced page layout, image support, and more in contrast to a plain text editor, however, it does not have the autocomplete abbreviations feature. The paper proposes an Autocomplete abbreviation Recommendation System that will integrate the benefits of getting automatic suggestions of either full forms, abbreviations, or both by clicking on the option that is being suggested. This will provide more flexibility to the user using existing Microsoft Office platforms. To create this feature, we have examined the JavaScript JQuery functions to implement a basic autocomplete feature. Information overloading is also one of the most important problems brought on by the Internet's explosive expansion. Massive quantities of text are difficult for people to manually summarise. Thus, there is now a greater need for summarizers that are more sophisticated and potent. Hence, Python's packages, methods, and NLP are used in this work to implement Text Summarization. By using this technique, the phrase's overall meaning is enhanced and the reader's comprehension is enhanced.

Nanomaterial Application for Healthy Environment

Nanotechnology is very effective at removing pollutants in the field of air, water, and wastewater. The techniques for treating pollution detection that have been created using nanotechnology include nanoadsorbents, nanofiltration, nanocatalysts, nanoparticles, and nanosensors. Nanoparticles can be used to synthesize several types of nanomaterials with a range of benefits. Nanomaterials applications have advanced significantly as a result of the disciplines of materials in explosive expansion. Novel nanomaterials can be designed and synthesized to provide improved performance in applications related to the environment. The intensified human activities are shaking the environment balance and human health by nourishing with large quantities of anthropogenic unsafe toxic elements which pollute water, atmosphere, and soil and subsequently intimidate public health of human beings. Therefore, nanostructured materials can be used to handle any environmental issue, such as extremes in temperature or high wear, severe corrosive conditions, and environmental pollution. In order to handle the most recent developments in nanomaterials and their applications, knowledge distribution is crucial.

Prediction of Narcissistic Behavior on Indonesian Twitter Using Machine Learning Methods

Social media's explosive expansion in recent years has changed people's behavior, leading to an increase in narcissistic tendencies. Arrogance, the need to flaunt one's accomplishments, the urge to get approval from others, and excessive dreams about success, power, intelligence, attractiveness, and other attributes are characteristics of narcissistic behavior. Social media has evolved into a platform for showcasing accomplishments, particularly for people with narcissistic tendencies. Moreover, narcissistic trait is one of the three characteristics of the dark triad personality type. Several research have demonstrated that a variety of machine learning techniques can be used to predict dark triad personality traits and narcissism from social media posts. As some studies have suggested, this narcissistic behavior can further increase the level of cyberbullying in social media, while also have strong correlation with the use of chatbot for academic cheating. This research aims to build a prediction model for behavioral symptoms of narcissism based on posts on Twitter in Indonesian language, using the natural language processing technique and several basic machine learning methods (Nearest Neighbors, Naïve Bayes, Decision Tree, and Support Vector Machine), and then compare the results. We concluded that SVM model achieved the best performance, with Accuracy = 0.72 and F1 Score = 0.725.