Integration Capability Research Articles

Coopetition strategy and innovation performance: a fuzzy-set qualitative comparative analysis

ABSTRACT Coopetition harbours the potential to drive innovation. However, our understanding of how innovation is impacted by various coopetition strategies, and how this interacts with firms’ external governance mechanisms and internal organisational capabilities, remains limited. This study employs the fsQCA method to investigate how the interplay among coopetition strategies, governance mechanisms and knowledge integration capability (KIC) – the three antecedent conditions – propels innovation. Through an examination of 413 high-tech firms, we identified four distinct configurations and unveiled two primary findings. First, when firms embrace an unbalanced coopetition strategy, it should be matched with relational governance; Conversely, for a balanced-strong coopetition strategy, transactional governance is deemed more suitable. Second, within any configuration characterised by high-innovation performance, KIC emerges as a core condition. This research contributes a new lens to the deeper understanding of complexity theory in firm innovation.

System Center Virtual Machine Manager 2022: Enhanced Cloud Integration and Management Capabilities

System Center Virtual Machine Manager (SCVMM) 2022 represents a transformative advancement in private cloud management, offering enhanced capabilities across virtualization, deployment, and cloud integration domains. This comprehensive platform introduces sophisticated hardware-level security protocols, dynamic resource allocation mechanisms, and intelligent orchestration capabilities that significantly improve operational efficiency. The integration with Azure Arc and support for Azure Kubernetes Service demonstrates SCVMM's evolution in hybrid cloud management, while enhanced data protection solutions ensure robust security and recovery capabilities. The platform's implementation of energy-aware management protocols and low-carbon operational methodologies showcases Microsoft's commitment to environmental sustainability without compromising performance. Through advanced predictive analytics, automated workflow management, and streamlined deployment architectures, SCVMM 2022 addresses the growing demands of modern enterprise environments. This article examines the platform's core enhancements, deployment methodologies, cloud integration capabilities, and impact on enterprise operations while providing implementation best practices derived from extensive research and real-world deployments.

Lab-on-Fiber Operando Deciphering of a MOF Electrocatalyst.

Despite the great success in deploying metal-organic frameworks (MOFs) as efficient electrocatalysts, the low adoption of operando methods hinders the understanding of underlying mechanism. By leveraging the subtle refractive index evolution, including both the real and the imaginary parts, an entirely new concept of a lab-on-fiber operando method and its feasibility for "pristine-immersion-operando-post analysis" of electrocatalyts are demonstrated. Concurrent collection of absorption spectra and surface plasmon resonance is achieved by engineering fiber-optic waveguidesto simultaneously induce guided light attenuation and plasmonic coupling. In situ-formed Co hydroxide and oxide reactive intermediates in zeolitic imidazolate framework-67 (ZIF-67) electrocatalyst are optically identified, whichshows its underlying self-reconstruction conversions at different stages during electrocatalytic oxygen evolution reactions, and address the gap in knowledge concerning whether ZIF-67 is a precatalyst for real catalyst production. This illuminating operando method offers intriguing opportunities to collect all the observables in a single fiber, provides an exciting potential of a new class of device with long-sought integration and miniaturization capability, and is expected to enable the electrocatalysis community to conquer challenges with conducting multimodal operando experiments outside the laboratory.

Is effective coordination the key to sustainable performance? A probe into integrated e-agri supply chains in India

PurposeThe study aims to assess how e-agri supply chain coordination (SCC), supply chain integration (SCI) and competitive capabilities (COC) impact both market performance (MO) and operational performance (OP). It particularly emphasizes the critical role of information flow for the benefit of farmers, intermediaries and end-consumers, shedding light on the broader implications of these factors within the agricultural supply chain.Design/methodology/approachData are collected online from farmers, intermediaries engaged in buying/supplying agri-products and consumers using a semi-structured questionnaire of 25 items adapted from the extant literature measuring SCC, SCI, COC, OP and MP. The survey instrument is validated for its reliability, convergent and discriminant validity tests. Purposive and convenient sampling is used for data collection. Finally, 280 responses were analyzed using SEM to conclude the study.FindingsFindings underscore information flow’s significance in the e-agri supply chain, addressing various stakeholders’ needs. Technical excellence, featuring robust transaction capabilities and cost-effective maintenance is pivotal. Enhanced supply chain coordination fosters integration and efficient information sharing, enhancing agricultural market performance and sectoral efficiency.Research limitations/implicationsThe study explores technology adoption, understands information flow and coordination impact, enhances efficiencies, empowers farmers and promotes transparency, sustainability and consumer benefits.Practical implicationsThe study promotes efficient information flow, digital adoption, collaborative planning, tech investment and enhanced responsiveness for agricultural sector managers.Social implicationsThe study bridges the rural-urban divide, empowering farmers, providing fair pricing, sustainable practices, transparency, informed consumers, responsible consumption and income distribution.Originality/valueThe novelty of this study lies in its comprehensive information flow study, new theoretical model and e-agri-specific sub-constructs that define coordination and integration, aiding efficiency and competitiveness.

The Convergence of Big Data and Generative AI: A Paradigm Shift in Data Analytics

The convergence of big data and generative artificial intelligence represents a transformative paradigm shift in data analytics and processing capabilities. This comprehensive article review examines the evolution, current state, and future implications of this technological integration across multiple sectors. The article explores advanced learning paradigms, including few-shot and zero-shot learning, which have revolutionized traditional machine learning approaches. The article delves into multimodal integration capabilities that enable simultaneous processing of diverse data types, enhancing analytical accuracy and decision-making processes. The article addresses critical technical implementation challenges, focusing on computational infrastructure demands, environmental impact, and sustainability considerations. Furthermore, the article examines crucial aspects of data privacy and ethical considerations, including security protocols, bias mitigation, and regulatory compliance. The article analysis extends to practical industry applications across healthcare, financial services, and manufacturing sectors, demonstrating substantial improvements in operational efficiency and decision-making capabilities. This article synthesizes current developments while providing insights into future directions and opportunities, emphasizing the increasingly critical role of responsible AI development and implementation strategies.

"Adoption of Artificial Intelligence in the Construction Industry in Saudi Arabia: Challenges and Proposed Solutions"

This paper examines the integration of Artificial Intelligence (AI) within the Saudi Arabian construction industry, addressing key challenges, potential solutions, and its transformative potential. With Saudi Arabia’s Vision 2030 emphasizing technological innovation and economic diversification, the construction sector stands to benefit significantly from AI-driven advancements that improve productivity and enhance project management. Key challenges identified include technical barriers, such as data quality issues, complex system integration, and the limited availability of specialized AI tools. Additionally, regulatory hurdles related to compliance and data privacy, high implementation costs, limited ROI, and social resistance driven by job displacement fears and lack of AI familiarity pose significant obstacles. A survey conducted among industry professionals confirmed these challenges, highlighting difficulties in AI system integration, economic concerns about costs and talent shortages, and regulatory uncertainties. Social resistance to AI adoption also emerged as a critical barrier, underscoring the need for workforce training and change management strategies to foster acceptance. The paper proposes targeted solutions, including improving data integration capabilities, establishing comprehensive regulatory frameworks, providing financial incentives, and offering training programs to build AI-related expertise. Case studies and hypothetical scenarios illustrate AI’s potential benefits, such as optimizing resource allocation, enhancing safety, reducing costs, and streamlining project timelines. Government-backed initiatives like King Salman Energy Park (SPARK) emphasize the importance of infrastructure and state support in facilitating AI adoption across industries. These efforts align with national goals to transform construction practices and drive innovation. The findings demonstrate AI’s potential to revolutionize the construction sector, but overcoming existing challenges requires continuous collaboration between industry stakeholders, policymakers, and technology providers. Further research is necessary to validate these assumptions and explore practical strategies for AI integration, ultimately paving the way for a more efficient, innovative, and sustainable construction industry.

Ecosystem-related digital capabilities and radical service innovation: evidence from the Chinese manufacturing industry

PurposeThis study aims to explore the impact of ecosystem-related digital decoupling capability (DDC) and digital integrative capability (DIC) on manufacturing firms’ radical service innovation (RSI) and the moderating effect of strategic flexibility, specifically resource flexibility (RF) and culture flexibility (CF).Design/methodology/approachThis study employed empirical survey data collected from 238 Chinese manufacturing firms to test all hypotheses through hierarchical multiple regression analyses.FindingsDDC and DIC have positive effects on RSI, with DDC exerting a stronger impact. RF and CF strengthen the positive effect of DIC on RSI, whereas CF diminishes DDC’s positive effect on RSI, and RF does not moderate this effect.Practical implicationsManufacturing firms should prioritize developing ecosystem-related DDC and DIC to drive RSI, especially DDC. Managers should improve firms’ RF and CF when leveraging DIC to drive RSI. However, they should minimize the negative impact of CF when leveraging DDC to drive RSI.Originality/valueThis study shifts the service innovation capabilities from the firm to the ecosystem level, opening a new perspective for exploring RSI’s driving mechanism. With RF and CF as the moderators, the findings reveal the contingent impact mechanism of ecosystem-related digital capabilities on RSI, which provides profound insights into the complex role of strategic flexibility in the new contexts.

Exploring the impact of information technology integration capability on project management performance in Chinese construction industry: a moderated mediation analysis

PurposeThe purpose of this study is to explore whether information technology (IT) integration capability and data sharing can improve project management performance in China’s construction industry under the background of global informatization. Moreover, the authors explore the moderating role of relational governance between IT integration capability and data sharing.Design/methodology/approachA theoretical model based on the research hypotheses proposed in this study was developed, and a questionnaire survey was conducted with 205 professionals. The data collected were analyzed by the structural equation modeling (SEM) technique.FindingsThe results indicate that IT integration capability has a significant and positive impact on project management performance and data sharing. Moreover, data sharing has a significant and positive impact on project management performance, and it plays a mediating role between IT integration capability and project management performance. In addition, relational governance significantly influences the mediating effect of data sharing.Research limitations/implicationsThe data used in this study is from Chinese scenarios, so the research conclusions and application effects based on this are bound to have certain regional limitations. So, a larger sample size from other countries could be selected to test the model. Besides, there are many factors that affect project management performance improving, and the theoretical model proposed in this study may not be fully considered. Therefore, follow-up researchers can consider bringing more suitable variables into their research studies, so that the theoretical research studies can be more in line with the actual project management practice.Originality/valueThis research’s value is as follows: Firstly, this paper broadens the understanding of how IT integration capability, data sharing and relational governance affects project management performance and enriches the literature in the construction management field under the background of global informatization. Secondly, this research provides a detailed governance solution to improve project management performance.

Bi-MOF-based point-of-care testing paper for dual-mode detection of H2S

Hydrogen sulfide (H2S) is noxious gas and significantly threatens human health. Herein, a Bi-based metal-organic frameworks (Bi-MOFs) based paper analysis device (PAD) has been developed for rapid and effective dual-mode detection of H2S. The dominant H2S sensing mechanism is explored. The H2S can react with Bi-MOF to form bismuth sulfide (Bi2S3) MOF (Bi2S3-MOF), changing the color of PAD from white to black. The distinguish color change makes it very convenience for visible detection. Meanwhile, the generated Bi2S3-MOF exhibits excellent photothermal property, endowing it possible for photothermal imaging detection. In this regard, the PAD boasts a wide linear detection range of 0–40 μM for H2S with an impressively low limit of detection (LOD) (0.23 μM). The constructed sensing system is demonstrated with outstanding selectivity and storage stability. Satisfied recovery is reached for H2S detection in diverse real samples. Interestingly, the PAD can be used as intelligent label for assessing meat freshness by monitor H2S release from meat under different storage temperatures. With integration capabilities of smartphones, the portable and user-friendly PAD offers a simple and rapid solution for on-the-go detection of H2S in food and environment. The investigation provides an instructive way for environment monitoring and food safety guarding.

Global Hunger and Food Waste Tracking Platform: A Technical Implementation Study

The Global Hunger and Food Waste Tracking Platform represents an innovative technological solution to address the critical challenge of global food distribution inefficiency. This article presents a comprehensive technical analysis of a system that processes 2.5 petabytes of supply chain data annually to optimize food distribution networks. The platform connects 25,000 farms, 3,500 food banks, and 12,000 distribution centers in a unified ecosystem, utilizing cloud computing, artificial intelligence, and real-time analytics. First-year implementation results demonstrate a 22% reduction in food waste, translating to 145,000 tons of food redirected and $432M in total economic benefits. The system achieves 89% accuracy in shortage prediction and 93% in surplus identification, while reducing delivery times by 35%. This paper details the platform's architecture, security framework, and future enhancements, including blockchain integration and advanced analytics capabilities, establishing a model for humanitarian technology initiatives.

A hypergraph transformer method for brain disease diagnosis.

To address the high-order correlation modeling and fusion challenges between functional and structural brain networks. This paper proposes a hypergraph transformer method for modeling high-order correlations between functional and structural brain networks. By utilizing hypergraphs, we can effectively capture the high-order correlations within brain networks. The Transformer model provides robust feature extraction and integration capabilities that are capable of handling complex multimodal brain imaging. The proposed method is evaluated on the ABIDE and ADNI datasets. It outperforms all the comparison methods, including traditional and graph-based methods, in diagnosing different types of brain diseases. The experimental results demonstrate its potential and application prospects in clinical practice. The proposed method provides new tools and insights for brain disease diagnosis, improving accuracy and aiding in understanding complex brain network relationships, thus laying a foundation for future brain science research.

Functional Organic Electrochemical Transistor-Based Biosensors for Biomedical Applications

Organic electrochemical transistors (OECTs), as an emerging device for the development of novel biosensors, have attracted more and more attention in recent years, demonstrating their promising prospects and commercial potential. Functional OECTs have been widely applied in the field of biosensors due to their decisive advantages, such as high transconductance, easy functionalization, and high integration capability. Therefore, this review aims to provide a comprehensive summary of the most recent advances in the application of functional OECT-based biosensors in biomedicine, especially focusing on those biosensors for the detection of physiological and biochemical parameters that are critical for the health of human beings. First, the main components and basic working principles of OECTs will be briefly introduced. In the following, the strategies and key technologies for the preparation of functional OECT-based biosensors will be outlined and discussed with regard to the applications of the detection of various targets, including metabolites, ions, neurotransmitters, electrophysiological parameters, and immunological molecules. Finally, the current main issues and future development trends of functional OECT-based biosensors will be proposed and discussed. The breakthrough in functional OECT-based biosensors is believed to enable such devices to achieve higher performance, and thus, this technology could provide new insight into the future field of medical and life sciences.

Cybersecurity Challenges in the Hashemite Kingdom of Jordan

In an era marked by rapid advancements in technology and the digital revolution, cyber-security has become one of the most significant technical issues across countries, including nation-states like the Hashemite Kingdom of Jordan. This paper examines the very complex cyber security issues that Jordan experiences and evaluates how the legal structure established in Cyber Security Act No. 16 of 2019 can be used to face these challenges? Comparatively analyzed with global standards, the research identifies substantial shortcomings in Jordan’s cybersecurity enforcement, coordination and integration capabilities. The research also discusses how international cooperation and public awareness can help improve national cybersecurity. The study appears to indicate that while significant steps have been taken Jordan’s legal infrastructure, there is a need for continued modernization in line with a rapidly evolving cyber threat landscape. Finally, the paper ends by suggesting policy interventions that can bolster cybersecurity in Jordan, and it also draws attention to a number of issues regarding risk-based approaches for managing cyber threats regulation across different regulatory authorities; as well the necessity of more inclusive digital public engagement efforts. This is necessary to safeguard our vital structures in Jordan and provide a stable, secure cyberspace.

Blockchain-as-a-Service (BaaS) Empowered Vendor-Managed Inventory (VMI): A Paradigm Shift in Supply Chain Efficiency

This article explores how Blockchain-as-a-Service (BaaS) can revolutionise Vendor-Managed Inventory (VMI) models. VMI is a supply chain approach where vendors manage customer inventory levels. However, traditional VMI models face challenges in data sharing, integration, real-time access, security, and enforcing service agreements. To overcome these limitations, this paper suggests using BaaS to implement VMI with blockchain technology. The utilisation of BaaS in VMI processes allows organisations to utilise blockchain’s attributes, including trustless collaboration, real-time data access, heightened security, data integrity, immutability, and automation through smart contracts. The article explains how BaaS platforms offer streamlined provisioning, easy configuration, rapid member onboarding, and seamless integration with existing systems. Furthermore, it highlights the benefits of using BaaS to enhance supply chain efficiency by facilitating smooth collaboration among VMI stakeholders. In addition to these benefits, the proposed solution emphasises the use of built-in functions within each partner’s ERP/supply chain/enterprise applications, combined with blockchain integration capability, to establish a comprehensive end-to-end VMI solution. Moreover, the article provides a conceptual model of the solution that covers an end-to-end VMI process. It also provides guidelines for selecting a suitable BaaS provider tailored to VMI implementation. Through these contributions, the article proposes an innovative VMI model built upon BaaS foundations, effectively addressing the inefficiencies of traditional VMI, and ushering in a new era of optimised supply chain performance.

Data-Driven Revenue Cycle Management: Optimizing claims denial resolution and under payment for healthcare providers through SaaS Solution

The healthcare industry is increasingly under financial pressure due to high rates of claims denials, leading to substantial revenue loss and operational inefficiencies. Claims denials result from various factors, including coding errors, incomplete documentation, and regulatory non-compliance, all of which contribute to revenue leakage that often reaches 5–10% annually for healthcare providers. This paper explores a proprietary Software-as-a-Service (SaaS) solution that leverages data analytics, machine learning, and real-time reporting to enhance the claims denial management process. Through predictive and prescriptive analytics, the proposed solution enables healthcare providers to identify denial trends, reduce denial rates, optimize claims resolution, and increase revenue retention. The paper includes system architecture, data security protocols, pilot implementation findings, and a comparative analysis of performance metrics. The paper concludes with a discussion on future developments, including real- time denial prediction, automated appeals generation, and enhanced integration capabilities with existing Electronic Health Record (EHR) systems. Keywords: Revenue Cycle Management, healthcare claims denial, claim under payment, SaaS, data analytics, machine learning, real-time reporting, financial sustainability, healthcare providers

Knowledge-embedded multi-layer collaborative adaptive fusion network: Addressing challenges in foggy conditions and complex imaging

Infrared and visible image fusion aims at generating high-quality images that serve both human and machine visual perception under extreme imaging conditions. However, current fusion methods primarily rely on datasets comprising infrared and visible images captured under clear weather conditions. When applied to real-world scenarios, image fusion tasks inevitably encounter challenges posed by adverse weather conditions such as heavy fog, resulting in difficulties in obtaining effective information and inferior visual perception. To address these challenges, this paper proposes a Mean Teacher-based Self-supervised Image Restoration and multimodal Image Fusion joint learning network (SIRIFN), which enhances the robustness of the fusion network in adverse weather conditions by employing deep supervision from a guiding network to the learning network. Furthermore, to enhance the network’s information extraction and integration capabilities, our Multi-level Feature Collaborative adaptive Reconstruction Network (MFCRNet) is introduced, which adopts a multi-branch, multi-scale design, with differentiated processing strategies for different features. This approach preserves rich texture information while maintaining semantic consistency from the source images. Extensive experiments demonstrate that SIRIFN outperforms current state-of-the-art algorithms in both visual quality and quantitative evaluation. Specifically, the joint implementation of image restoration and multimodal fusion provides more effective information for visual tasks under extreme weather conditions, thereby facilitating downstream visual tasks.

Enhancing ambidextrous green innovation: the role of green supply chain integration, knowledge capabilities and technology dynamism in manufacturing

PurposeIn the context of the environmental degradation challenge in manufacturing firms, greening the supply chain (SC) is the most widely endorsed method to mitigate the adverse repercussions of climate change. Based on organizational learning and resource dependence theories, the aim of this research is to know how green supply chain external integration (GSCEI) and green supply chain internal integration (GSCII) influence ambidextrous green innovation (AGI). It also examines the mediating roles of green absorptive capacity (GAC) and green knowledge integration capability (GKIC), as well as the moderating role of green technology dynamism (GTD).Design/methodology/approachTo assess the hypothesized model, data were obtained with 386 questionnaires from managers employed in manufacturing firms in Pakistan applying a cross-sectional approach. A partial least square structural equation modeling technique was implemented to evaluate the data.FindingsThe results revealed that GSCEI and GSCII substantially impact AGI. Moreover, GAC and GKIC serve as mediators between GSCEI and AGI. GAC and GKIC also intervene in the relationship between GSCII and AGI. GTD was significant as a moderator for the correlation between GSCEI and AGI. However, it does not moderate the relationship between GSCII and AGI.Practical implicationsThis research offers significant comprehension and an innovative approach for manufacturing organizations to curb environmental corrosion by stimulating AGI through green SC integration. It suggests to practitioners that integrating internal knowledge with external partners expands communication and collaboration to ensure that resources connected with environmental preservation flow smoothly.Originality/valueThis research is a valuable addition to the field, as it explores for the first time the missing link among the studied constructs. It opened the black box of how knowledge-related capabilities facilitate knowledge resources to elicit AGI, an area that has not yet been explored.

ADAPTING SYSTEMS ENGINEERING TO EVALUATE TECH STARTUPS: AN INNOVATIVE FRAMEWORK BASED ON OMG ESSENCE

The adaptation of system engineering to evaluate technical startups through an innovative framework based on the OMG Essence standard is a modern approach to simplify the process of analyzing and managing startups at various stages of their development. In this study, a universal framework was proposed that allows evaluating technical startups from the point of view of system engineering. This approach takes into account key aspects of startup development, such as requirements, stakeholders, technology, and team, which makes it an important tool for evaluating innovative projects. The main purpose of the proposed framework is to apply the principles of systems engineering to the evaluation of startups, focusing on technical aspects such as system architecture, integration capabilities, and the ability of the team to solve complex tasks. Traditional methods of evaluating startups, often focusing on business aspects, may not always take into account all the technical difficulties and risks that arise when developing software products. The OMG Essence-based framework fills this gap by providing a more comprehensive tool for analyzing and managing startup development. OMG Essence, as a standardized method, acts as a basis for formalizing practices and facilitating interaction between various project participants. The framework based on it offers the possibility of modular adaptation, which allows you to evaluate startups regardless of their complexity and current stage of development. An important feature of OMG Essence is the ability to integrate with other methodologies, which makes it universal for various fields. The application of this standard in the evaluation of startups allows you to obtain more objective results and improve the decision-making process.

Dynamic Capabilities and Competitive Advantage among Listed Commercial Banks in Kenya

Purpose: This study sought to evaluate how dynamic capabilities affect competitive advantage among listed commercial banks, in Kenya. Specifically, the study sought to ascertain how Sensing capability, learning capability, reconfiguration capability and integration capability affects competitive advantage among listed commercial banks, in Kenya. Methodology: This study used Cross-Sectional Research Design since the study focused on quantitative data. The study was carried out in Kenya, covering all the 39 solvent commercial banks in Kenya (Source, CBK 2020),(as of 31st March 2024.The respondents were 1404 functional managers which comprise of five (5) from Human Resource ,five (5) from information Technology ,Ten (10) from Sales and marketing ,five (5) from procurement, and five(5) from risk and compliance. The study used 10% of the target population as the study sample size hence the sample size was 140 respondents comprising of management employees. Data was collected using a self-administered structured questionnaire. The Statistical Package for Social Sciences (SPSS) version 25 software was used to analyze the data. Findings: The study concludes that sensing capability has a significant effect on competitive advantage among listed commercial banks, in Kenya. In addition, the study concludes that learning capability has a significant effect on competitive advantage among listed commercial banks, in Kenya. Further, the study concludes that reconfiguration capability has a significant effect on competitive advantage among listed commercial banks, in Kenya. The study also concludes that integration capability has a significant effect on competitive advantage among listed commercial banks, in Kenya. Unique Contribution to Theory, Policy and Practice: Based on the findings, the study recommends that the management of commercial banks in Kenya should implement a comprehensive knowledge management system that encourages continuous professional development and collaboration among employees. By establishing structured training programs, mentorship initiatives, and cross-departmental knowledge sharing, banks can foster a culture of innovation and adaptability.

Enhanced voltage management method based on hybrid transformer for wind power integration applications

The rapid voltage fluctuations and even exceedances caused by power variability of wind generations significantly impede their application within the power system. This paper presents a method for replacing the electromagnetic transformer at the Point of Common Coupling of wind power delivery systems with a hybrid transformer (HT) to enhance its voltage management and grid integration capabilities. Considering the operation voltage in wind farm and scalability, a cascaded H-bridge based HT model has been established. The operation modes and a set of control strategies groups have been analyzed and proposed. The HTs operate by default in reactive power compensation mode to manage the dual-side voltage coordinately. In case of reactive power contention and circulation, this paper proposes an additional reactive power circulation suppression method. Once it exceeds the reactive voltage regulation limits due to the grid faults or changes of gird operation modes, HT adopts ratio fast regulation mode, maintaining the wind-farm side node voltage and preventing wind turbine tripping. The influence of HT leakage reactance has also been studied here. Finally, the effectiveness of the proposed method is validated in simulation, which provides a new paradigm for the operational application of HTs.