Integration Capability Research Articles

A hypergraph transformer method for brain disease diagnosis

ObjectiveTo address the high-order correlation modeling and fusion challenges between functional and structural brain networks.MethodThis 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.ResultsThe 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.ConclusionThe 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.

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

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.

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.

Adaptive generation of optical single-sideband signal with dually modulated EML

The optical single sideband (SSB) transmitter based on dual modulation of an electro-absorption modulation laser (D-EML) has attracted considerable attention for its capability of monolithic integration and high output power. A model-based modulation method has been developed recently for generating high-quality optical SSB signals with this D-EML scheme. However, this method requires accurate characterization of the EML’s chirps and pre-compensation for frequency responses of all-optical/electrical components, as well as the path difference between two driving signals. This imposes notable requirements on the transmitter characterization in practical applications. In this paper, we propose an adaptive method to approach the required responses of the pre-compensation filters for this optical SSB transmitter. This method avoids cumbersome device characterization and shows great resilience to the variation of system parameters. By using the proposed adaptive method, we generate a 56 Gb/s optical SSB orthogonal frequency-division multiplexed signal with the sideband suppression power ratio exceeding 21 dB. It is convenient with this method to switch the devices, i.e., directly modulated laser (DML) or electro-absorption modulator (EAM), to be pre-compensated for the optical SSB signal generation. Moreover, this method exhibits good tolerance to the path delay (±15 ps) between DML and EAM, as well as modulation depth. We also successfully transmit this signal over 80 km long standard single-mode fiber.

Learning, Integration, and Re-Configuration: Dynamic Capabilities as Drivers of Data-Driven Insights and Decision Quality

Purpose The study investigates the role of the dynamic capabilities of learning, integration, and re-configuration on the quality of decision-making mediated by data-driven insights. It particularly examines how these dynamic capabilities individually contribute to offering efficient and effective decision-making in the hospitality sector. Design/methodology/approach This study employs a quantitative research approach harnessed through surveys and the application of partial least square structural equation modelling using SMART PLS 4. The hospitality sector being a highly competitive industry was selected for data collection. Findings The findings reveal that learning and reconfiguration capabilities have a significant positive individual impact on generating data-driven insights hence improving the effectiveness and efficiency of decision-making. Contrarily, the integration capabilities standalone did not influence in pointing out the statistical significance of the relationship with data-driven insight or decision-making quality, an indication that integration has to be combined with other dynamic capabilities for it to be effective. Originality/Value This study adds novelty to the literature by examining the individual influences of dynamic capabilities—learning, integration, and reconfiguration on data-driven insights and decision-making quality. Unlike previous studies that have generally explored bundles of capabilities, this study isolates each to answer the more nuanced question of how organizations can, in a strategic manner, invest in certain dynamic capabilities that can improve decision-making.

Supply Chain Integration Capability, Intra-Cluster Co-Opetition Strategy, and Breakthrough Innovation: The Moderating Effect of Environmental Turbulence

Under the frequent occurrence of external environmental risks and in the context of breakthrough innovations driving new quality productivity, this study explores the intrinsic mechanisms by which supply chain integration affects enterprise breakthrough innovation. Grounded in supply chain integration and breakthrough innovation theories, we used statistical methods to analyze data from a sample of 209 valid enterprises. The study systematically interprets these mechanisms from the perspective of competition and examines the moderating effect of external environmental turbulence on competitive strategy. The results demonstrate that supply chain integration capability significantly and positively influences breakthrough innovation, with vertical and horizontal competing strategies acting as mediators. Additionally, environmental turbulence positively moderates the relationship between supply chain integration capability and horizontal competing strategies. The results of the study are of great theoretical and practical significance in promoting the integration of enterprises’ supply chains and enhancing their sustainable innovation capabilities.

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

Integration of a dose management system in the public hospitals of Cyprus: Key areas and initial outcomes.

Despite the growing use of dose management systems (DMSs), there is limited guidance on their optimal setup. This study aims to fill this gap by outlining the process followed to integrate "DOSE" (Qaelum NV, Belgium) DMS within the Cypriot public hospitals. DOSE was connected to the available central PACS, followed by a thorough data and functioning validation. A preliminary study was also conducted to assess the DOSE's ability to establish typical dose values in diagnostic radiography. Overall, over 70 x-ray units were configured. For some connections, it was necessary to first adjust the unit's DICOM attributes or to utilise the DICOM coercion technique. The DICOM image header was sometimes preferred over the DICOM RDSR, as the chosen data source. This study underscored the importance of understanding the equipment's technical capabilities for a successful DMS integration, while personnel with expertise in diagnostic radiology and the IT aspects of medical physics proved to have a vital role in the process. The pilot study showed that DOSE is an effective tool for establishing typical dose values and assessing compliance with relevant dose levels.

New paradigms of 2D layered material self-driven photodetectors.

By virtue of the high carrier mobility, diverse electronic band structures, excellent electrostatic tunability, easy integration, and strong light-harvesting capability, 2D layered materials (2DLMs) have emerged as compelling contenders in the realm of photodetection and ushered in a new era of optoelectronic industry. In contrast to powered devices, self-driven photodetectors boast a wealth of advantages, notably low dark current, superior signal-to-noise ratio, low energy consumption, and exceptional compactness. Nevertheless, the construction of self-driven 2DLM photodetectors based on traditional p-n, homo-type, or Schottky heterojunctions, predominantly adopting a vertical configuration, confronts insurmountable dilemmas such as intricate fabrication procedures, sophisticated equipment, and formidable interface issues. In recent years, worldwide researchers have been devoted to pursuing exceptional strategies aimed at achieving the self-driven characteristics. This comprehensive review offers a methodical survey of the emergent paradigms toward self-driven photodetectors constructed from 2DLMs. Firstly, the burgeoning approaches employed to realize diverse self-driven 2DLM photodetectors are compiled, encompassing strategies such as strain modulation, thickness tailoring, structural engineering, asymmetric ferroelectric gating, asymmetric contacts (including work function, contact length, and contact area), ferroelectricity-enabled bulk photovoltaic effect, asymmetric optical antennas, among others, with a keen eye on the fundamental physical mechanisms that underpin them. Subsequently, the prevalent challenges within this research landscape are outlined, and the corresponding potential approaches for overcoming these obstacles are proposed. On the whole, this review highlights new device engineering avenues for the implementation of bias-free, high-performance, and highly integrated 2DLM optoelectronic devices.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

Modal-nexus auto-encoder for multi-modality cellular data integration and imputation

Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there’s a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E’s accuracy and robustness in multi-modality cellular data integration and imputation.

Congruence and Discrepancy: Matching Effect of Searching and Integration on Green Product Development Performance

The development of green products in manufacturing enterprises is challenged by limited independent innovation capabilities and insufficient green technology. Based on knowledge-based theory, this study investigates the influence of boundary-spanning green technology search (BGTS) and technology integration capability (TIC) on green product development performance (GPDP). Using polynomial regression analysis with response surface methodology (PRA with RSM) on data from 341 companies, the following findings were obtained: (1) Higher levels of BGTS and TIC congruence are associated with higher GPDP compared to lower levels of congruence. (2) The degree of discrepancy between the BGTS and TIC has an inverted U-shaped relationship with GPDP. (3) GPDP is higher in the “low BGTS–high TIC” combination than in the “high BGTS–low TIC” combination. (4) Green technology innovation capability mediates the relationship between BGTS-TIC congruence and GPDP. (5) Strategic foresight positively moderates the relationship between the BGTS-TIC congruence and green technology innovation capability. These findings enrich the research content of organizational search theory, deepen the research of green technology innovation, expand research on the development of green products, and provide a decision-making reference for manufacturing enterprises’ green transition practices.

Digital servitization in digital enterprise: Leveraging digital platform capabilities to unlock data value

Existing research has not clearly defined digital platform capabilities or explained how they unlock data value, limiting the understanding of digital servitization in digital enterprises. Therefore, our study draws upon the Dynamic Capability Theory and employs an exploratory, in-depth single case study approach to bridge this research gap. We identify three key digital platform capabilities: data integration capability, data analytics capability, and data productization capability. Our results demonstrate that these capabilities function as “reservoirs,” “catalysts,” and “pipelines” in a specific iterative sequence to unlock data value, thereby facilitating the successful implementation of digital servitization in digital enterprises. Furthermore, these capabilities are interconnected, collectively forming a dynamic capability—digital platform capability—that unlocks data value by integrating and reconfiguring data resources. Additionally, we delineate the differences in digital servitization between digital enterprises and traditional manufacturers across three dimensions: motivation, key service design activities, and benefits. Our findings enrich the existing literature on digital platform capabilities and data value while expanding the research scope of digital servitization from the perspective of digital enterprises.

Empirical Analysis of Widely Used Website Automated Testing Tools

In today's software development, achieving product quality while minimising cost and time is critical. Automated testing is crucial to attaining these goals by lowering inspection efforts and discovering faults more effectively. This paper compares widely used automated testing tools, such as Selenium, Appium, Java Unit (JUnit), Test Next Generation (TestNG), Jenkins, Cucumber, LoadRunner, Katalon Studio, Simple Object Access Protocol User Interface (SoapUI), and TestComplete, based on functionality, ease of use, platform compatibility, and integration capabilities. Our findings show that no single tool is inherently superior, with each excelling in certain areas such as online, mobile, Application Programming Interface (API), or performance testing. While Selenium and Appium are the dominant online and mobile testing frameworks, TestComplete and Katalon Studio offer complete, user-friendly cross-platform testing solutions. Despite the benefits of automation, obstacles such as tool maintenance, scalability, and cost issues remain. The report finishes with advice for picking the best tool for the project and offers potential approaches for enhancing testing frameworks, such as AI-driven optimisation, cloud-based testing, and greater Continuous Integration/ Continuous Deployment (CI/CD) integration. This study offers useful information for developers and testers looking to optimise their testing methods and increase software quality.

Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy

Multi-area integrated energy systems (MA-IES) with a high proportion of renewable energy have become a trend in social development. The access to a large number of distributed energy sources makes the safe, stable, and economic operation of MA-IES suffer serious challenges. To reduce the frequency deviation and area control error (ACE) of MA-IES and to improve the economy of operation, this study proposed a hybrid modeling with data enhanced driven learning algorithm, which combines model-driven and data-driven algorithms, named combined proportion-integral-derivative and deep reinforcement learning (CPIDDRL). Firstly, the frequency deviation signals are collected and judged, and the smaller frequency deviation signals, which are sent to the model-driven part, are regulated by proportion-integral-derivative (PID) controllers. Then, the ACE and large frequency deviation signals are collected and transmitted to the data-driven part, and the Transformer deep learning network is applied to predict the multi-feature timeseries, which are applied to strategy selection for state-action-reward-state-action reinforcement learning. Finally, the model-driven part and data-driven part work together to generate adjustment commands every 4 s. The control effects of CPIDDRL, PID, Q-learning, and sliding model control algorithms are compared in two and four areas integrated energy systems. The results show that, compared to the comparison algorithms, the CPIDDRL reduces the mean values of frequency deviation and ACEs by at least 46.78% and 6.83%, respectively, and the total generation cost by at least 8.20%. CPIDDRL has practical significance in improving system stability, enhancing renewable energy integration capabilities, and promoting the development of smart grids.

Dividing Global Business Process: Safeguarding Confidential Data

This study shows that manufacturing unit process dispersion serves as an operational-level variable that allows businesses to modify their information protection strategy when it comes to sourcing business services overseas to suit the regulatory framework of the chosen host nation as well as the potential for using internal controls over activities carried out overseas. Our hypothesis is that businesses are more inclined to split up their processes among several foreign production facilities rather of concentrating every procedure duties in one unit when the aforementioned mechanisms are unavailable. Businesses may take advantage of the synergies between the scattered pieces of a process while lowering the risk of individual fragment theft because of IT-enabled integration capabilities. Robustness tests and empirical findings closely align with these theories. Additionally, we discover that the likelihood of using the defense mechanism against process fragmentation increases with the firm's expertise in the host nation as well as the alternative worth of the activity's unique knowledge that is outsourced.