Impact Of Transformation Research Articles

The impact of digital transformation on ESG performance

This paper constructs an analytical framework of digital transformation affecting enterprise ESG performance based on the investment perspective. And empirical analyses are conducted using firm-level panel data of Chinese A-share listed companies from 2009 to 2020 in the China's Stock Markets. It is found that digital transformation significantly improves enterprise ESG performance. Digital transformation improves enterprise ESG performance by enhancing risk-taking capacity, mitigating agency conflicts, and suppressing management overconfidence. Heterogeneity analyses show that the effects of digital transformation on capital investment efficiency and firms' ESG performance are more pronounced among firms in mature and declining stages. Digital transformation has a significant impact on capital investment efficiency and corporate ESG performance in both technology-intensive and capital-intensive firms. These findings are crucial for understanding the key role of digital transformation in shaping long-term economic growth potential and sustainability from an investment perspective.

Sectoral dynamics and environmental quality in Côte d‘Ivoire: assessing the impact of structural transformation

Sectoral dynamics and environmental quality in Côte d‘Ivoire: assessing the impact of structural transformation

From past to future: digital methods towards artefact analysis

Abstract Over the past two decades, Digital Humanities has transformed the landscape of humanities and social sciences, enabling advanced computational analysis and interpretation of extensive datasets. Notably, recent initiatives in Southeast Asia, particularly in Singapore, focus on categorizing and archiving historical data such as artwork, literature and, most notably archaeological artefacts. This study illustrates the profound potential of Digital Humanities through the application of statistical methods on two distinct artefact datasets. Specifically, we present the results of an automated minting study of mid-first millennium CE struck and cast ‘Rising Sun’ coinage from mainland Southeast Asia, while subsequently utilizing unsupervised statistical methods on 2D images of 13th–14th-century earthenware ceramics excavated from the precolonial St. Andrew’s Cathedral site in central Singapore. This research offers a comparative assessment showcasing the transformative impact of statistics-based approaches on the interpretation and analysis of diverse archaeological materials and within Digital Humanities overall.

The integral role of high‐entropy alloys in advancing solid‐state hydrogen storage

AbstractHigh‐entropy alloys (HEAs) have emerged as a groundbreaking class of materials poised to revolutionize solid‐state hydrogen storage technology. This comprehensive review delves into the intricate interplay between the unique compositional and structural attributes of HEAs and their remarkable hydrogen storage performance. By meticulously exploring the design strategies and synthesis techniques, encompassing experimental procedures, thermodynamic calculations, and machine learning approaches, this work illuminates the vast potential of HEAs in surmounting the challenges faced by conventional hydrogen storage materials. The review underscores the pivotal role of HEAs' diverse elemental landscape and phase dynamics in tailoring their hydrogen storage properties. It elucidates the complex mechanisms governing hydrogen absorption, diffusion, and desorption within these novel alloys, offering insights into enhancing their reversibility, cycling stability, and safety characteristics. Moreover, it highlights the transformative impact of advanced characterization techniques and computational modeling in unraveling the structure–property relationships and guiding the rational design of high‐performance HEAs for hydrogen storage applications. By bridging the gap between fundamental science and practical implementation, this review sets the stage for the development of next‐generation solid‐state hydrogen storage solutions. It identifies key research directions and strategies to accelerate the deployment of HEAs in hydrogen storage systems, including the optimization of synthesis routes, the integration of multiscale characterization, and the harnessing of data‐driven approaches. Ultimately, this comprehensive analysis serves as a roadmap for the scientific community, paving the way for the widespread adoption of HEAs as a disruptive technology in the pursuit of sustainable and efficient hydrogen storage for a clean energy future.

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Current Applications of the Three-Dimensional Printing Technology in Neurosurgery: A Review.

In the recent years, three-dimensional (3D) printing technology has emerged as a transformative tool, particularly in health care, offering unprecedented possibilities in neurosurgery. This review explores the diverse applications of 3D printing in neurosurgery, assessing its impact on precision, customization, surgical planning, and education. A literature review was conducted using PubMed, Web of Science, Embase, and Scopus, identifying 84 relevant articles. These were categorized into spine applications, neurovascular applications, neuro-oncology applications, neuroendoscopy applications, cranioplasty applications, and modulation/stimulation applications. 3D printing applications in spine surgery showcased advancements in guide devices, prosthetics, and neurosurgical planning, with patient-specific models enhancing precision and minimizing complications. Neurovascular applications demonstrated the utility of 3D-printed guide devices in intracranial hemorrhage and enhanced surgical planning for cerebrovascular diseases. Neuro-oncology applications highlighted the role of 3D printing in guide devices for tumor surgery and improved surgical planning through realistic models. Neuroendoscopy applications emphasized the benefits of 3D-printed guide devices, anatomical models, and educational tools. Cranioplasty applications showed promising outcomes in patient-specific implants, addressing biomechanical considerations. The integration of 3D printing into neurosurgery has significantly advanced precision, customization, and surgical planning. Challenges include standardization, material considerations, and ethical issues. Future directions involve integrating artificial intelligence, multimodal imaging fusion, biofabrication, and global collaboration. 3D printing has revolutionized neurosurgery, offering tailored solutions, enhanced surgical planning, and invaluable educational tools. Addressing challenges and exploring future innovations will further solidify the transformative impact of 3D printing in neurosurgical care. This review serves as a comprehensive guide for researchers, clinicians, and policymakers navigating the dynamic landscape of 3D printing in neurosurgery.

LLM-generated competence-based e-assessment items for higher education mathematics: methodology and evaluation

In this article, we explore the transformative impact of advanced, parameter-rich Large Language Models (LLMs) on the production of instructional materials in higher education, with a focus on the automated generation of both formative and summative assessments for learners in the field of mathematics. We introduce a novel LLM-driven process and application, called ItemForge, tailored specifically for the automatic generation of e-assessment items in mathematics. The approach is thoroughly aligned with the levels and hierarchy of cognitive learning objectives as developed by Anderson and Krathwohl, and takes specific mathematical concepts from the considered courses into consideration. The quality of the generated free-text items, along with their corresponding answers (sample solutions), as well as their appropriateness to the designated cognitive level and subject matter, were evaluated in a small-scale study. In this study, three mathematical experts reviewed a total of 240 generated items, providing a comprehensive analysis of their effectiveness and relevance. Our findings demonstrate that the tool is proficient in producing high-quality items that align with the chosen concepts and targeted cognitive levels, indicating its potential suitability for educational purposes. However, it was observed that the provided answers (sample solutions) occasionally exhibited inaccuracies or were not entirely complete, signalling a necessity for additional refinement of the tool's processes.

A Review on Balancing Genomic Selection Efforts for Allogamous Plant Breeding

Genomic selection (GS) represents a paradigm shift in allogamous plant breeding, leveraging genome-wide marker data to predict the genetic potential of breeding candidates with unprecedented accuracy. This comprehensive review explores the evolution, principles, and applications of GS, highlighting its transformative impact on breeding efficiency and crop improvement. GS uses dense SNP markers to capture the genetic architecture of complex traits, surpassing traditional marker-assisted selection in predictive power. The integration of GS in breeding programs for crops like maize and perennial ryegrass has demonstrated significant gains in traits such as yield, disease resistance, and stress tolerance. Despite its advantages, GS faces challenges including high genotyping costs, the need for large training populations, and the complexity of genetic architectures. Economic constraints and ethical concerns about genetic diversity and data access also pose barriers. Emerging technologies such as AI, machine learning, high-throughput phenotyping, and genome editing hold promise for enhancing GS's accuracy and efficiency. Future strategies should focus on optimizing resource allocation, integrating GS with conventional breeding methods, and fostering collaborative efforts for data sharing and capacity building. The long-term impact of GS is profound, potentially accelerating breeding cycles, enhancing genetic diversity, and developing climate-resilient crops. Collaborative initiatives and open-access genomic resources will be crucial in overcoming current limitations and ensuring that GS benefits global agriculture. As GS continues to evolve, it promises to drive sustainable agricultural practices and improve food security, meeting the challenges posed by climate change and a growing global population. This review underscores the critical role of GS in modern plant breeding and its potential to revolutionize crop improvement.

Assessing the Impact of Digital Transformation on Business Operations: A Case Study Analysis

This study assesses the impact of digital transformation on business operations through a case study of Zivame, an Indian SME in the retail sector. The research aims to explore how digital technologies improve operational efficiency, customer engagement, and financial performance. Using qualitative research methods, semi-structured interviews were conducted with key decision-makers at Zivame. Thematic analysis was employed to analyze the data. The results indicate significant operational improvements, including a reduction in order fulfillment time and increased inventory turnover. Customer satisfaction and repeat purchase rates also improved substantially following the implementation of Customer Relationship Management (CRM) systems and other digital tools. Zivame faced challenges such as employee resistance to change and high initial investment costs, which were mitigated through targeted employee training programs. The financial analysis revealed a growing return on investment (ROI) from 31% in 2020 to 44% in 2023. The study’s findings underscore the importance of digital transformation for Indian SMEs, showing that, with strategic planning and investment, businesses can improve their competitiveness and achieve long-term success. These findings contribute to the broader literature on digital transformation in emerging markets and offer practical insights for SMEs embarking on similar journeys.

Has digital transformation enhanced the resilience of manufacturing enterprises?

As environmental uncertainty and market competition intensify, the impact of digital transformation on the survival and development of enterprises has garnered increasing attention. This paper empirically examines the influence of digital transformation on the resilience of manufacturing firms, based on data from Chinese-listed manufacturing companies from 2013 to 2022. Additionally, the study also analyzes the role of dynamic capabilities in this process. It is found that digital transformation significantly enhances the resilience of manufacturing enterprises, primarily through improving their stability and growth. Extended regression results indicate that the breadth of digitalization significantly boosts firm resilience, while the impact of the depth of digitalization on resilience varies with the type of technology implemented. Mechanism tests from the perspective of dynamic capabilities reveal that digital transformation enhances organizational resilience by improving adaptive and absorptive capacities. Further analysis shows that in the face of significant unexpected shocks, enterprises that have implemented digital transformation exhibit significantly greater resilience compared to those that have not. These findings provide theoretical insights and practical references for leveraging digital potential, enhancing organizational resilience, and achieving high-quality economic and social development.

The impact of digital transformation on enterprise organizational structure

The impact of digital transformation on enterprise organizational structure

Occurrence and Fate of Fluoroalkyl Sulfonamide-Based Copolymers in Earthworms-Bioavailability, Transformation, and Potential Impact of Sludge Application.

To date, considerable knowledge and data gaps regarding the occurrence, environmental levels, and fate of polymeric perfluoroalkyl and polyfluoroalkyl substances (PFAS) exist. In the present study availability, accumulation, and transformation of C4- and C8-fluoroalkylsulfonamide (FASA)-based copolymers were assessed in laboratory-grown earthworms (Eisenia fetida, triplicate of exposure tests and control). Further, a field study on earthworms (18 pooled samples) in sludge-amended soil was conducted to assess the environmental impact of sludge-amended soil with regard to the FASA-based copolymers, together with the applied sludge (n = 3), and the field soils during the period (n = 4). In the laboratory study, the FASA-based copolymers were taken up by the earthworms in concentrations between 19 and 33 ng/g of dw for the C8- and between 767 and 1735 ng/g of dw for the C4-FASA-based copolymer. Higher biota soil accumulation factors (BAFs) were observed for the copolymer with a longer perfluorinated side-chain length (C8, average BAF value of 0.7) compared to the copolymer with a shorter side-chain length (C4, average BAF value of 0.02). Perfluorooctane sulfonamidoacetates (FOSAAs) and perfluorooctane sulfonamide (FOSA), including both branched and linear isomers, were detected after exposure to the C8-FASA-based copolymer. Two metabolites were detected in the earthworms exposed to the C4-FASA-based copolymer: perfluorobutanesulfonamide (FBSA) and perfluorobutanesulfonic acid (PFBS). Although the presence of other monomers or impurities in the copolymer formulation cannot be ruled out, the present laboratory study suggests that the FASA-based copolymers may be an indirect source of lower molecular weight PFAS in the environment through transformation. Elevated levels of C8-FASA-based copolymer were found in the field sludge-amended soil compared to nontreated soil (32 versus 11 ng/g d.w.), and higher concentrations of PFAS in earthworms living in sludge-amended soil compared to nontreated soil (566 versus 103 ng/g d.w.) were observed. These findings imply that the application of sludge is a potential pathway of PFAS to the environment.

Estimating the lives that could be saved by expanded access to weight-loss drugs

Obesity is a major public health crisis in the United States (US) affecting 42% of the population, exacerbating a spectrum of other diseases and contributing significantly to morbidity and mortality overall. Recent advances in pharmaceutical interventions, particularly glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, liraglutide) and dual gastric inhibitory polypeptide and GLP-1 receptor agonists (e.g., tirzepatide), have shown remarkable efficacy in weight-loss. However, limited access to these medications due to high costs and insurance coverage issues restricts their utility in mitigating the obesity epidemic. We quantify the annual mortality burden directly attributable to limited access to these medications in the US. By integrating hazard ratios of mortality across body mass index categories with current obesity prevalence data, combined with healthcare access, willingness to take the medication, and observed adherence to and efficacy of the medications, we estimate the impact of making these medications accessible to all those eligible. Specifically, we project that with expanded access, over 42,000 deaths could be averted annually, including more than 11,000 deaths among people with type 2 diabetes. These findings underscore the urgent need to address barriers to access and highlight the transformative public health impact that could be achieved by expanding access to these novel treatments.

Telemedicine dan Kesehatan: Memahami Dampak Teknologi Kedokteran di Era Digital dalam Konteks Ajaran Islam

In the digital era, telemedicine has revolutionized healthcare by providing faster and more accessible remote medical services. This article explores the transformative impact of this technology on the quality and accessibility of healthcare services, while also examining its compatibility with Islamic values. From an Islamic perspective, telemedicine is reviewed in relation to ethical principles such as the preservation of life, the ease of access to treatment, and respect for patient privacy. The article aims to highlight how telemedicine can serve as an innovative solution that not only supports modern healthcare advancements but also aligns with ethical guidelines within Islamic teachings. This study offers fresh insights into the opportunities and challenges of telemedicine in healthcare, particularly within Muslim communities, and its implications for ethical medical decision-making.

Immersive Decision-Making : A Quantitative Analysis of Metaverse Applications in Tourism Marketing

This article investigates the transformative impact of metaverse-based virtualization on tourism marketing and consumer experiences. Through quantitative analysis of data collected via standardized questionnaires, we examine how virtual reality tours, 3D representations, and simulated experiences in the metaverse influence tourist decision-making processes and pre-travel engagement. Our findings reveal that metaverse applications significantly enhance tourists' ability to make informed decisions, reduce travel anxiety, and engage with personalized marketing content. The article demonstrates a positive correlation between virtual destination previews and increased consumer confidence, with 78% of respondents reporting higher satisfaction in their travel choices. Moreover, the study identifies a 35% reduction in pre-travel uncertainty among first-time visitors to unfamiliar destinations who utilized metaverse-based virtual tours. These results underscore the potential of metaverse technology to revolutionize tourism marketing strategies, offering implications for industry practitioners and paving the way for future research in digital tourism experiences.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Modelling and optimisation of the structural performance of lightweight polypropylene fibre-reinforced LECA concrete

Lightweight fibre-reinforced concrete integrates the advantages of lightweight aggregates with the strength-enhancing properties of fibres, resulting in a lighter composite with enhanced impact and mechanical performance. However, achieving an optimal balance between structural weight, and performance remains a challenging endeavour. This study investigates the mechanical properties, impact energy absorptions, flexural toughness, and crack resistance of lightweight fibre-reinforced concrete with the coarse aggregate entirely replaced with lightweight expanded clay aggregate (LECA). Concrete mixes containing 0 %, 0.5 %, 0.75 %, and 1.0 % Polypropylene fibre (PPF) and 10 % micro-silica were experimentally investigated. Predictions for concrete mixes with up to 2 % PPF were made using regression models developed from experimental data. The experimental and predicted results were analysed using response surface methodology. The findings reveal significant enhancements of up to 300 % and 570 % in toughness indices I5 and I10 at 1 % PPF, coupled with a 55.4 % increase in residual strength. Furthermore, an optimised slab thickness of 47 mm containing 1.73 % PPF yielded optimal impact energy absorption of 680 J and 2384 J and crack resistance of 3823 MPa and 16279 MPa at service and ultimate loading, respectively. These metrics represent improvements of 4.8, 15.2, 37, and 56 times, respectively, compared to the control samples. These substantial advancements highlight the potential of lightweight fibre-reinforced LECA concrete in engineering applications where balancing impact energy absorption, crack resistance, and structural weight is crucial. This innovative approach promises a transformative impact on the construction industry, paving the way for more efficient and resilient infrastructure.

Precision Design of Sequence‐Defined Polyurethanes: Exploring Controlled Folding Through Computational Design

AbstractThis study presents the exploration of sequence‐defined polyurethanes (PUs) as a new class of heteropolymers capable of precise conformational control. Utilizing molecular dynamics simulations, the folding behavior of polyurethane chains is investigated of varying lengths (11, 20, and 50 monomers) in both vacuum and aqueous environments. The simulations reveal that the heterogeneous chains systematically refold to approach the designed target structures better than non‐designed chains or chains with artificially disrupted hydrogen‐bond networks. The subsequent synthesis of an optimized 11‐mer sequence (P1) is achieved through solid‐phase chemistry, with thorough characterization via NMR, MS, and SEC confirming the accuracy of the predicted sequence and its controlled chain length. Solubility tests showed favorable results across multiple solvents, highlighting the versatility of the designed polymer. This research underscores the potential of sequence‐defined polyurethanes to emulate the structural and functional attributes of biological macromolecules, opening new pathways for their application in catalysis, drug delivery, and advanced material design. The findings illustrate a promising direction for the development of synthetic polymers with tailored properties, emphasizing the transformative impact of sequence control in polymer chemistry.

Targeting esophageal carcinoma: molecular mechanisms and clinical studies.

Esophageal cancer (EC)is identified as a predominant health threat worldwide, with its highest incidence and mortality rates reported in China. The complex molecular mechanisms underlying EC, coupled with the differential incidence of esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) across various regions, highlight the necessity for in-depth research targeting molecular pathogenesis and innovative treatment strategies. Despite recent progress in targeted therapy and immunotherapy, challenges such as drug resistance and the lack of effective biomarkers for patient selection persist, impeding the optimization of therapeutic outcomes. Our review delves into the molecular pathology of EC, emphasizing genetic and epigenetic alterations, aberrant signaling pathways, tumor microenvironment factors, and the mechanisms of metastasis and immune evasion. We further scrutinize the current landscape of targeted therapies, including the roles of EGFR, HER2, and VEGFR, alongside the transformative impact of ICIs. The discussion extends to evaluating combination therapies, spotlighting the synergy between targeted and immune-mediated treatments, and introduces the burgeoning domain of antibody-drug conjugates, bispecific antibodies, and multitarget-directed ligands. This review lies in its holistic synthesis of EC's molecular underpinnings and therapeutic interventions, fused with an outlook on future directions including overcoming resistance mechanisms, biomarker discovery, and the potential of novel drug formulations.

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.