Impact Of Transformation Research Articles

Adapting the Future VET Curriculum in Response to Emerging Challenges: A Model for Evaluating the Impact of AI Integration in VET for Armed Conflicts and Warfare

This article examines the profound implications and the transformative impact of artificial intelligence (AI) on vocational education and training (VET) within military contexts, underscoring the imperative for curriculum reform to address the evolving demands of contemporary warfare. As AI technologies progress, they present both significant opportunities and challenges, reshaping defense strategies and operational frameworks, fundamentally altering defense strategies and operational paradigms. The integration of AI into military applications necessitates a workforce proficient in specialized competencies, including technical expertise in AI ethics, system design, and human-centered methodologies. This paper delineates the essential competencies required for future VET professionals, emphasizing the necessity for ongoing upskilling and reskilling to effectively navigate the ethical and legal complexities associated with AI in military operations. By incorporating and embedding AI education into VET curricula, stakeholders can cultivate a new generation of defense professionals equipped to utilize AI technologies in a responsible and effective manner, ensuring adherence to and compliance with international humanitarian standards while enhancing operational efficacy. The findings highlight the critical role of VET in developing a skilled workforce prepared to confront the dynamic landscape of military technologies driven by AI.

Decorin as a key marker of desmoplastic cancer-associated fibroblasts mediating first-line immune checkpoint blockade resistance in metastatic gastric cancer.

Gastric cancer (GC) remains a significant cause of cancer-related mortality worldwide. Despite the transformative impact of immune checkpoint blockade (ICB) therapies across various cancers, only a minority of patients with metastatic GC (mGC) benefit, emphasizing the urgent need for precise biomarkers to predict therapeutic responses and optimize patient selection. In this multi-omics study, we conducted whole exome and transcriptome sequencing on 12 tumors from mGC patients treated with nivolumab as first-line therapy. To validate our findings, we performed whole transcriptome sequencing on 17 additional tumors and analyzed 45 tumors from public dataset (PRJEB25780) of patients who received ICB therapy as second or third-line treatment. Comprehensive multi-omics analyses were conducted using single-cell RNA sequencing (n = 5, GSE167297) and spatial transcriptome sequencing (n = 2, independent internal dataset). ICB-sensitive tumors exhibited robust activation of the interferon response pathway, while ICB-resistant tumors displayed epithelial-mesenchymal transition signatures. Intriguingly, at the single-cell level, genes associated with ICB sensitivity were predominantly expressed in immune cells, whereas genes associated with resistance were primarily found in cancer-associated fibroblasts (CAFs), particularly the desmoplastic CAF (dCAF) subtype. We identified DCN as a hallmark dCAF marker, and high DCN expression was inversely correlated with PD-L1 levels, ICB resistance, and poor prognosis in mGC (log-rank p = 0.027). This study elucidates the critical influence of the tumor microenvironment, specifically dCAFs, in mediating ICB resistance in mGC. Our findings highlight DCN as a representative marker for dCAF and a promising negative predictive biomarker for ICB response. These findings highlight the complex stromal-immune interactions and open avenues for personalized treatment for mGC.

The Transformative Impact of Artificial Intelligence and Machine Learning on Marketing Operations

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on modern marketing operations. The research delves into key areas where AI and ML are revolutionizing marketing strategies, including hyper-personalization, predictive analytics, and conversational AI. Through an analysis of recent developments and case studies, the article demonstrates how AI-driven personalization significantly enhances customer engagement and relevance, with some implementations showing up to 25% increase in revenue and 15% improvement in customer retention rates. The article also examines the role of predictive analytics in shifting marketing strategies from reactive to proactive approaches, enabling more accurate forecasting of customer behavior, campaign performance, and market trends. Furthermore, the evolution of chatbots and conversational AI is explored, highlighting their capacity to automate lead qualification, scale customer engagement, and gather real-time insights without increasing manual input. The integration of AI in marketing operations is shown to improve campaign management efficiency, enhance personalization capabilities, and facilitate future-focused campaign development. However, the research also addresses the challenges and ethical considerations associated with AI integration in marketing, including data privacy concerns, skill gaps, and the need to balance automation with human creativity. This article provides a comprehensive overview of how AI and ML are reshaping the marketing landscape, offering valuable insights for marketers, researchers, and business leaders navigating this technological revolution.

Advancements in deep learning for visual object tracking

Abstract. Since contemporary information-retrieval systems rely heavily on the content of titles and abstracts to identify relevant articles in literature searches, great care should be taken in constructing both. This comprehensive review delves into the transformative impact of deep learning on the domain of visual object tracking. Since the inception of AlexNet in 2012, deep learning has revolutionized feature extraction, leading to significant advancements in tracking accuracy and robustness. The article explores the integration of deep learning with various tracking algorithms, including deep correlation filters, classification-based approaches, Siamese networks, gradient-based methods, and the innovative application of Transformer architectures. Moreover, the role of tracking datasets in fostering algorithm development and innovation is highlighted, with an emphasis on the expansion in scale, diversity, and annotation quality. Furthermore, the article also examines the multifaceted evaluation metrics for tracking algorithms, encompassing precision, robustness, efficiency, generalization, and real-time capabilities. Looking ahead, the review outlines future research directions, such as algorithm optimization for lightweight and accelerated performance, enhancing generalizability, leveraging multimodal data fusion, and refining Transformer models for improved temporal information processing. The challenges of long-term tracking and the growing importance of algorithm interpretability and transparency are also discussed. In summary, the article underscores the promising trajectory of deep learning in visual object tracking, with ongoing research poised to make tracking technologies smarter, more efficient, and robust, catering to a wide array of practical applications and environments.

The development of carbon nanostructured biosensors for glucose detection to enhance healthcare services: a review

In this review, the forefront of biosensor development has been marked by a profound exploration of carbon nanostructured materials for the specific application of glucose detection. Moreover, this progressive line of inquiry capitalizes on the distinctive attributes of carbon nanostructured materials such as carbon nanotubes, carbon quantum dots, and graphene which exhibit unique characteristics in the development of biosensor engineering design. It also enhanced analytical performances regarding the limit of detection, selectivity, sensitivity, and reproducibility towards glucose detection in biological samples. Most importantly, the strategic integration of carbon nanostructured-based biosensor architectures has played a significant role in advancements, characterized by heightened sensitivity, exquisite selectivity, and augmented stability in glucose detection processes. Furthermore, utilizing these advanced materials has engendered a transformative impact on electrochemical properties, propelling the biosensors to achieve rapid and precise glucose-sensing capabilities. The confluence of carbon nanostructures with biosensor technology has not only elevated the scientific understanding of glucose detection mechanisms. Still, it has also paved the way for miniaturized and portable biosensors. This transformative shift holds great promise for the realization of point-of-care diagnostics, representing a pivotal step towards durability and efficient glucose monitoring in health/medical care. These advancements emphasize the crucial role of carbon nanostructured-based biosensors in opening the way to a new avenue of superiority and effectiveness in diabetes management. Conclusively, the challenges and, in a forward-looking stance, the prospective futures of glucose biosensors anchored on carbon nanostructured frameworks were considered.

Teledentistry Integration: Revolutionizing Dental Insurance Through Technological Innovation

This article examines the transformative impact of teledentistry on the dental insurance industry, exploring how telecommunications technology is revolutionizing dental care delivery, access, and insurance. It discusses the expansion of dental care accessibility, streamlining of insurance processes, enhancement of preventive care, and the generation of data-driven insights through teledentistry. The article also addresses privacy and security considerations, presents case studies of successful implementations, and explores future prospects and innovations in the field. By analyzing the integration of teledentistry with artificial intelligence and wearable technology, the article highlights the potential for more proactive, personalized, and accessible dental care, while emphasizing the need for continued research and development to fully realize these benefits.

Artificial Intelligence in SAP S/4HANA: Transforming Enterprise Resource Planning through Intelligent Automation

This comprehensive article investigates the transformative impact of AI-enhanced SAP S/4HANA Finance across healthcare, manufacturing, scientific research, auto, food & Oil &Gas sectors, focusing on human-AI collaboration patterns and implementation outcomes. Through a mixed-methods approach analyzing 15 organizations over 18 months, the research examines how AI integration transforms traditional ERP functionalities into intelligent financial management systems. The article collected data from 450 end-users and 45 key stakeholders, employing both quantitative metrics and qualitative assessments to evaluate implementation patterns, challenges, and success factors. The findings reveal significant improvements across all sectors: healthcare organizations achieved 40% reduction in billing processing time and 15% improvement in collection rates; manufacturing entities realized 35% reduction in unplanned downtime and 22% decrease in working capital requirements; while research institutions demonstrated 45% faster grant processing and 35% improved budget forecasting accuracy. The article introduces the Adaptive Financial Intelligence Framework (AFIF) for conceptualizing human-AI collaboration in financial management, contributing to both theoretical understanding and practical implementation strategies. The article concludes that successful AI integration depends on industry-specific adaptations, comprehensive training programs, and robust governance frameworks while highlighting the critical role of human expertise in maximizing system benefits. These findings provide valuable insights for organizations pursuing AI-enhanced financial management solutions while offering a roadmap for future developments in human-AI collaboration within enterprise systems.

Recent Advances in controlled manipulation of micro/nano particles: A review

Abstract This review discusses the transformative impact of micro/nano particle manipulation techniques across scientific and technological disciplines. Emphasizing the pivotal role of precise control at the micro and nanoscale, the paper categorizes manipulation strategies into mechanical/surface force-based, field-control manipulation, and microfluidics manipulation. It addresses challenges specific to the submicrometer scale, highlighting the strengths and limitations of each approach. The unique behaviors exhibited by objects at the micro–nano scale influence the design and operation of manipulators, algorithms, and control systems, particularly in interactions with biological systems. The review covers dielectrophoresis and magnetic manipulation, showcasing their applications in particle manipulation and microfluidics. The evolution of optical tweezers, including holographic, surface plasmon-based, and optical fiber tweezers, is discussed, emphasizing their contributions in various scientific fields. Additionally, the paper also explores the manipulation of micro/nano particle in microfluidic platforms. The comprehensive review underscores the significance of understanding manipulation strategies in diverse environments, anticipating further advancements in science and technology.

Cloud-Based Machine Learning : Opportunities and Challenges

This comprehensive article explores the transformative impact of cloud-based machine learning (ML) on modern enterprises, examining both opportunities and challenges in implementation. The article investigates the rapidly growing cloud computing market and its ML segment, revolutionizing how organizations approach data analytics and business intelligence. Through detailed analysis of enterprise implementations, the article demonstrates how cloud ML solutions have democratized access to advanced analytics, significantly reducing operational costs while improving data processing efficiency. The article examines key aspects, including scalability advantages, cost efficiencies, and technical complexities, while providing evidence-based best practices for successful implementation. Drawing from multiple industry studies and real-world deployments, the article presents a framework for organizations to navigate challenges in data privacy, vendor dependencies, and skill requirements while maximizing the benefits of cloud-based ML solutions.

Research on the Impact of Digital Transformation on the Sustainable Development Capability of the Information Service Industry

Digital transformation is a trend in the information service industry that is gaining traction. Enterprises can benefit from the development of innovative business models as well as improved service quality and efficiency, thereby promoting sustainable development of the information services industry. A rapid advancement in technology has led to the introduction of new digital technologies such as cloud computing, big data, artificial intelligence, etc. Several fields of the information service industry are slowly becoming impacted by these technologies, creating new opportunities and challenges for its sustainable growth. Information services can be optimized through digital transformation and work efficiency can be improved. Digital technologies facilitate the transmission of information and facilitate the processing of information, which helps enterprises respond to market changes quickly and enhance their competitiveness. Furthermore, businesses can use digital technology to better understand and meet the needs of their customers. Using big data, companies can gain a deeper understanding of customer behavior and preferences, enabling them to adjust their service strategies accordingly. Despite the benefits of the digital transformation, it has brought some problems and challenges, such as privacy and security concerns, as well as an increased demand for employees with specialized skills. As a result, enterprises should pay attention to solutions to these issues during the digital transformation process, such as strengthening data security measures, providing employee training and skill upgrades.

Research on the Impact of Digital Transformation on Innovation Performance in Manufacturing Enterprises

Research on the Impact of Digital Transformation on Innovation Performance in Manufacturing Enterprises

The Self-Improving Customer Service System: How AI Learns from Every Interaction

This article explores the transformative impact of self-improving AI systems in customer service, examining the parallels between AI and human learning processes, the mechanics of AI self-improvement, and the advantages of AI's learning at scale. It delves into real-world applications and impacts across various industries, highlighting improvements in personalization, efficiency, accuracy, customer satisfaction, and cost reduction. The article also addresses ethical considerations such as data privacy, transparency, and bias mitigation, while offering a future outlook on advanced AI capabilities in emotional intelligence, predictive service, and seamless human-AI collaboration. Through a comprehensive analysis of current technologies and future possibilities, this study provides insights into how self-improving AI is revolutionizing customer service, promising more efficient, personalized, and satisfying experiences for customers.

The impact of corporate digital transformation on stock market activity: evidence from the Chinese listed manufacturing companies

Purpose This research aims to investigate the impact of corporate digital transformation (CDT) on stock market activity. Design/methodology/approach A data set of Chinese listed manufacturing enterprises from 2012–2021 is used as a research sample. Findings This research finds that CDT can promote stock market activity. This study validates two potential mechanisms: increasing financial performance and mitigating information asymmetry. This study further suggests that internal control and analyst coverage can strengthen the impact of CDT on stock market activity. Research limitations/implications The research exhibits certain limitations that should be considered in future research. Because the findings are based on the Chinese context, the applicability and generalizability of the findings to other environments may be limited. This research enriches the literature on the determinants of stock market activity from a technological perspective and incrementally contributes to understanding the impact of CDT on stock markets. After analyzing two opposing perspectives on the economic consequences of CDT, the favorable effect of CDT on stock market activity is proven based on the resource-based view and agency theory. This research extends the literature on the relationship between CDT and investor behavior, demonstrating that investors perceive CDT as beneficial. The results provide evidence that CDT can increase financial performance and improve the information environment, leading to increased investor attention and enhanced trading activity. Practical implications This research has incremental practical implications for enterprises and regulatory authorities to comprehend the economic consequences of CDT in developing countries. First, enterprises should increase their digital investments to improve their performance and decrease information asymmetry. Furthermore, enterprise managers should strengthen information systems to adapt to the process of CDT and train employees on digital skills. Second, regulatory authorities should provide comprehensive digital policies and programs supported by tax incentives, subsidies and digital infrastructure projects (Wang et al., 2023). Originality/value This research strengthens the debate on the market impact of CDT. Unlike prior literature, this study explores the influence of CDT on stock market activity for the first time, enriching the literature on CDT and stock market activity. Furthermore, the outcomes guide regulatory authorities to actively support CDT and expedite the digital upgrading of manufacturing industries to promote stock market activity.

The Divergent Reactivity of Acid Chlorides Under Transition Metal Catalysis.

The power and ability of catalysis to build multiple C-C bonds in a single step has had a transformative impact on organic synthesis. While the reactivity of organohalides with metal catalysts is widely appreciated, the related and more intricate reactivity of acid chlorides is less so, despite their use as common reagents in synthesis. Here, we review the transformations of acid chlorides in combination with unsaturated C-C bonds catalyzed by palladium, rhodium, or iridium and provide an outlook for future research opportunities.

Economic transformation as a component of societal transformation: an integrative vision

The article is devoted to the problem of assessing the impact of economic transformation on the development of society. Particular attention is paid to the well-received drivers of economic change in the current decade – widespread digitalization and the gradual establishment of a “green”, carbon-free economy. The interpretation of sustainable societal transformation as a universal systemic process is substantiated. According to the argumentation presented, it is reasonable to assess the results of the transformation of the national economy based on indicators of the degree of its approach to a sustainable trajectory. The final part of the article reveals the role of economic transformation as a driver of future general social progress, which becomes achievable provided that the optimistic consolidated scenario of global development is fulfilled.

Investigating Digital Workspaces: Ethnographic Obstacles and Insights

Purpose- The current study investigates the intricacies of conducting digital workplace ethnography within professional environments, focusing on the critical challenges posed by technological constraints, increased digital workloads, the complexities of navigating virtual ecosystems, and limitations in remote collaboration tools and employee digital literacy. Design/methodology/approach- A mixed-methods research framework encompassed qualitative and quantitative methods. The study involved a pilot sample of 120 participants from a private IT firm, representing diverse roles including employees, managers, and IT professionals. To capture the breadth of digital challenges, structured interviews were administered, probing the participants' interactions with digital tools and assessing the organization’s broader digital culture. Statistical analyses, including correlation and chi-square tests, were utilized to evaluate the relationships between the impact of digital transformation on workplace collaboration and organizational adaptability. Findings-The research findings deliver a detailed exploration of these technological and cultural barriers and their broader consequences for the organization's operational efficacy. Research limitations/implications- Strategic implications are provided, which emphasize the enhancement of digital infrastructure, workforce training, and leadership development initiatives to foster a more resilient and adaptive digital work environment. Originality/value- This study explored workplace challenges within the realm of digital innovation. Drawing from existing research, it presents a solution to the existing problem in the form of Adopting Flexible Work Practices: and enhancing Digital Literacy with Training.

Transformative Impact of AI and Cloud Technologies: A Comparative Analysis across Healthcare, Retail, and Mobile Financial Services

This article examines the transformative impact of artificial intelligence (AI) and cloud technologies across healthcare, retail, and mobile financial services sectors. Through a comprehensive analysis of industry-specific applications, we explore how these technologies are revolutionizing operational efficiency, customer experiences, and innovation in each domain. In healthcare, we investigate the role of CI/CD automation and scalable cloud infrastructure in improving system reliability and patient care. The retail sector analysis focuses on AI-driven customer insights and supply chain optimization. For mobile financial services, we examine AI applications in fraud detection and real-time analytics. The findings reveal significant improvements in operational efficiency, decision-making processes, and customer satisfaction across all three industries. However, we also identify unique challenges and opportunities specific to each sector. This article contributes to the growing body of literature on digital transformation and provides valuable insights for industry professionals and policymakers navigating the integration of AI and cloud technologies in their respective fields.

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

Machine Vision AI in Railroad Safety: Advanced Inspection Techniques

This article explores the transformative impact of machine vision AI in railroad safety, focusing on advanced inspection techniques. It examines the integration of technologies such as line scan cameras, LiDAR, and IoT sensors in enabling comprehensive 360-degree inspections of railway infrastructure and rolling stock. The article discusses key innovations in continuous monitoring, data processing, and AI-driven analysis, highlighting their potential to significantly enhance defect detection accuracy, reduce maintenance costs, and prevent accidents. Real-world applications in structural integrity assessment and overhead line inspection are presented, along with an analysis of current challenges and future developments in the field. The article underscores the paradigm shift from reactive to proactive maintenance strategies, promising a safer and more efficient future for rail transport.

Unlocking sustainability: exploring the nexus of green banking, digital transformation, and financial performance with foreign ownership moderation

The purpose of this research is to empirically test and analyze the impact of green banking, digital transformation, and asset quality on the financial performance of commercial banks with foreign ownership as a moderator. Additionally, it aims to test and analyze the financial performance of sustainable business practices for commercial banks. This study was conducted on commercial banks, specifically focusing on bank groups categorized by core capital levels 3 and 4, listed on the Indonesia Stock Exchange, covering the period from 2017 to 2022. This research employed a judgment sampling technique, encompassing a total of 47 conventional commercial banks. Panel data regression analysis, a data analysis technique, used three variables: the dependent variable, the independent variable, and the moderation variable. The research unveiled that green banking does not appear to have a significant positive impact on financial performance, whereas digital transformation shows a clear positive effect. Non-performing loans, a measure of asset quality, also affect financial performance. Additionally, foreign ownership strengthens the impact of green banking on the financial performance of commercial banks in Indonesia. However, foreign ownership does not enhance the effect of digital transformation. Finally, foreign ownership plays a moderating role in the relationship between asset quality and financial performance in these banks. Ultimately, financial performance has a significant positive influence on sustainable business, suggesting that stronger financial performance contributes to the long-term sustainability of a bank’s operations. The implications of these findings are, first and foremost, that financial institutions should prioritize incorporating sustainable and environmentally friendly practices into their operations to improve their overall financial performance and emphasize the critical role of digital transformation in enhancing financial performance. Consequently, financial institutions are required to allocate resources towards information technology infrastructure and cultivate digital capabilities to adapt to evolving customer demands, such as offering 24/7 banking services from any location. Secondly, financial institutions should not stay far away from digital disruption, but rather embrace it and adapt their strategies accordingly, which includes allocating an adequate budget for IT infrastructure investment and accessing digital services that meet the banking industry’s demands, resulting in effective communication of sustainable practices.