Implementation Complexity Research Articles

ПІДВИЩЕННЯ ЧУТЛИВОСТІ ЕЛЕКТРОМАГНІТНО-АКУСТИЧНИХ ПЕРЕТВОРЮВАЧІВ ДЛЯ КОНТРОЛЮ, ВИМІРЮВАННЯ І ДІАГНОСТИКИ ФЕРОМАГНІТНИХ МЕТАЛОВИРОБІВ ЗА РАХУНОК ЗБІЛЬШЕННЯ ВЕЛИЧИНИ ІНДУКЦІЇ МАГНІТНОГО ПОЛЯ. ОГЛЯД

The analysis of information sources on the development and usage of electromagnetic-acoustic methods and means of ultrasonic inspection, measurements and diagnostics was performed. It is shown that electromagnetic-acoustic (EMA) transducers (EMAT) with permanent magnets have significant advantages compared to piezoelectric transducers, but also have disadvantages, especially in the inspection of ferromagnetic metal products, both in normal and hot conditions, especially when using them in portable ultrasonic devices. Many experts suggest eliminating the disadvantages of EMAT with permanent magnets by using pulsed magnetization. This direction of research is promising, especially when testing hot metal, because it allows for creation of pulsed magnetic fields with a peak value of 2...3 and even 10 T, making it possible to significantly increase the sensitivity of testing, since the coefficient of conversion of electromagnetic energy into ultrasonic and vice versa depends on the induction of magnetic fields squarely. But until now, data on the widespread use of pulsed magnetization in portable EMATs has not been established. This situation is caused by a small gap between the EMAT and the metal, insufficient development of the EMAT functioning technologies, the complexity of the hardware implementation, difficulties in dealing with interferences that occur during the excitation of pulsed magnetic field, etc. Therefore, for the implementation of such a promising direction of development of sensitive EMA transducers, theoretical and practical developments are to be carried out, which is to make it possible to introduce high-tech means of inspection, measurements and diagnostics into production. References 31, figures 9.

Development of carbon capture and storage technologies in natural gas facilities for clean energy production

Carbon capture and storage (CCS) technologies represent a critical strategic intervention in addressing global climate change challenges while maintaining energy security in natural gas infrastructure. This comprehensive review explores the multifaceted landscape of CCS technologies, examining technological innovations, economic frameworks, and environmental implications for clean energy production. By synthesizing current research and emerging approaches, the study provides a holistic assessment of CCS potential in mitigating carbon emissions from natural gas facilities. The review critically analyzes existing capture technologies, including post-combustion, pre-combustion, and oxy-fuel combustion methods, highlighting recent advances in material sciences and technological innovations. Economic considerations are examined through the lens of investment strategies, policy frameworks, and long-term viability. Environmental performance evaluation encompasses comprehensive emissions assessment, geological storage risks, and broader ecosystem implications. Key findings underscore the complexity of CCS implementation, revealing both significant challenges and promising opportunities for transforming natural gas infrastructure. The study emphasizes the need for interdisciplinary approaches, robust policy support, and continued technological innovation to realize the full potential of carbon capture technologies in achieving sustainable energy transitions.

Evaluating the potential of agile branding: opportunities and challenges in the start-up context

The necessity for companies to adopt an agile brand management approach to maintain relevance and competitiveness in our dynamic and fast-moving world is a key finding of recent studies. Nevertheless, no study has yet investigated the potential and applicability of the concept of agile branding. Concurrently, start-ups are confronted with the challenge of high failure rates due to shortcomings in brand management. The necessity for start-ups to adopt innovative and dynamic brand strategies arises from the constant internal and external changes that characterise the start-up environment. Previous studies on brand management, by contrast, have mainly focussed on established companies. Given the affinity and need for agility observed in start-ups, agile branding appears to be particularly relevant for start-ups. This study aims to address two research gaps: firstly, to explore the potential of the concept of agile branding and, secondly, to expand academic research of branding practices tailored to start-ups to assist them in overcoming the specific brand management challenges they face. The study draws on an extensive and structured literature review, followed by qualitative semi-structured interviews with eleven experts and 18 start-ups, to discuss the opportunities and challenges of agile branding in the context of start-ups. The results were analysed from the perspective of the five dimensions of agile branding and the dynamic capabilities framework. Agile branding offers start-ups the opportunity to adapt swiftly to market changes, allocate resources efficiently, and reduce risk through data-driven decision-making. Overcoming challenges requires a shift in organisational culture and employee mindset, optimisation of stakeholder feedback processes, and navigation of the complexities of implementation. In conclusion, the advantages of agile branding for start-ups are found to outweigh the disadvantages. The study extends the scarce research on agile branding and provides brand managers and start-up founders with an efficient approach to organising their brand management process with limited resources.

Cybersecurity Awareness In HR: Protecting Employee Data in the Digital Era

Cybersecurity in team member data management is critical to corporate operations, especially in the e-commerce industry, which faces continuously evolving digital threats. This study evaluates cybersecurity awareness in Tokopedia's Human Resources (HR) department and the effectiveness of security policies implemented following the 2020 data breach incident. Using a qualitative case study approach, data were collected through in-depth interviews, observations, and document analysis. The findings indicate that while Tokopedia has strengthened its data security policies, significant challenges remain in team member compliance with security protocols, policy implementation complexity, and limited understanding of cyber threats. To address these challenges, this study recommends an approach based on interactive training programs, adopting Zero Trust Architecture (ZTA) and using Artificial Intelligence (AI) for cybersecurity threat detection. Furthermore, strengthening cybersecurity culture through management involvement and team member incentive systems is also identified as a strategic measure to enhance awareness and compliance with security policies. With proper implementation, Tokopedia can enhance team member data protection, reduce cybersecurity risks, and ensure long-term information security sustainability within the company.

The Importance of Cost Accounting in Medium-Sized Businesses

This research explores the significance of cost accounting in medium-sized businesses, highlighting its role in financial management, operational efficiency, and strategic decision-making. Medium-sized enterprises operate in a unique space between small businesses and large corporations, requiring cost accounting systems that are both robust and adaptable. The study examines the benefits of cost accounting, including improved profitability, better cost control, and enhanced decision-making, while also addressing challenges such as implementation costs and complexity. Findings indicate that businesses leveraging cost accounting experience increased profit margins, better resource allocation, and operational efficiency. Additionally, technological advancements in cost accounting software are identified as key enablers of improved financial performance. The research concludes that effective cost accounting practices are essential for sustaining growth and competitiveness in medium-sized businesses.

Automated Group Discussion Feedback and Scoring with AI

The proposed AI-driven group discussion platform addresses several key challenges identified in 15 studies, such as dominance by certain participants, insufficient feedback, lack of structure, and unethical analysis difficulties. It also tackles issues like network variability, ML implementation complexity, security concerns, data biases, low student engagement, and privacy issues. The platform provides solutions by leveraging AI and machine learning for real-time monitoring and personalized feedback, ensuring balanced and productive discussions. Emotion recognition algorithms analyze micro-expressions, while multi-language support and gamification elements enhance accessibility and motivation. Integration with Learning Management Systems (LMS) streamlines resource access and performance tracking, and an advanced analytics dashboard offers insights into group dynamics. Efficient video delivery and scalable multipoint videoconferencing mitigate network issues. Robust security measures protect user data, and continuous assessment features enable personalized feedback. Future enhancements like virtual and augmented reality aim to create immersive discussion experiences and facilitate teamwork, addressing the limitations of existing systems and fostering an inclusive, engaging, and productive environment for all participants.

User Scheduling With Limited Feedback for Multi‐Cell MU‐Massive MIMO FDD Networks Deployment: From Performance Tradeoff Perspective

ABSTRACTFeedback of channel state information (CSI) from users to base station (BS) is very vital for efficient user scheduling (US) in frequency division duplexing (FDD)–based massive multiple‐input multiple‐output (mMIMO) systems. The feedback overhead is one of the major bottlenecks for multi‐user (MU) mMIMO systems as it increases with number of antennas and users. In the multicellular scenario, each and every BS schedules appropriate set of users based on the limited CSI feedback received from the users for achieving better throughput and fairness which are the prime objectives for such communication systems. To achieve the higher throughput, better fairness, and better scheduling gain with the least CSI, in this article, user grouping–based scheduling (UGS) strategy for multi‐cell MU mMIMO FDD system is considered. The proposed UGS scheme achieves a better trade‐off between the two prime objectives, that is, throughput and fairness. Moreover, hybrid precoding architecture is also used for this system as practically radio frequency (RF) chains are usually less in number to support less implementation complexity and budget. Our approach utilizes the angle of departure (AoD)–based adaptive codebook for controlling the amount of feedback. The variation of AoD is slow in nature as compared to path gains of the channel, which controls the feedback overhead. The proposed UGS using AoD codebook with limited channel feedback involves determining the beamspace path co‐ordinates of each user from the AoD information fed back by users and then group the users into sets based on the AoD CSI. Specifically, UGS scheme is applied to reduce inter‐user and inter‐group interferences. By integrating an AoD adaptive subspace codebook with a hybrid precoding architecture and focusing on the dual objectives of maximizing throughput and fairness, we offer a novel solution that addresses the key challenges of inter‐cell interference and limited CSI feedback. The numerical experiments show the effectiveness of the proposed UGS scheme for fifth generation and beyond wireless networks deployment.

Implementation of Income Tax (Pph) and Value Added Tax (PPN) in Personnel Operations of the Organizational Bureau of the Regional Secretary of North Sumatra Province

This research analyzes the implementation of Income Tax (PPh) and Value Added Tax (PPN) in the personnel operations of the Organization Bureau of the Regional Secretary of North Sumatra Province. Using a descriptive qualitative literature study method, the research evaluates four key dimensions: regulation, technology, human resources, and internal control systems. The research findings reveal the complexity of tax implementation, including regulatory limitations, digital transformation of e-SPT, human resource competency challenges, and the importance of proactive control systems. The study found that 42% of agencies experience regulation interpretation difficulties, 60% of technological infrastructure is available, but 35% of employees require capacity development. The research generates comprehensive recommendations for tax implementation optimization, including human resource development, technological system improvement, regulatory synchronization, and strengthening internal control mechanisms.

A Comparative Study of Oracle ERP Netsuite and Microsoft Dynamics 365 Contributions to Contemporary Business Development in India

ERP systems centrally provide support to a company's business operations and are capable of providing solutions in the area of resource management, process streamlining, and making data-driven decisions. Acceptance of ERP systems is growing in India as companies begin to become more efficient and competitive. Among the most prominent players are Oracle ERP Netsuite and Microsoft Dynamics 365, both holding unique attributes which pose a challenge for businesses when choosing the right system. The aim of this paper of study is to compare between Oracle ERP Netsuite and Microsoft Dynamics 365 by exploring their contribution to business development in India. With respect to the implementation complexity, user satisfaction, ROI, and overall business impact, this research study weighs the merits and demerits of each platform according to India. Although high-end multinationals prefer it for its strength and integrated features, the SME prefers Microsoft Dynamics 365 because of its flexibility and smooth integration with other products of Microsoft. Basing the conclusion drawn from the afore findings, the study does provide recommendations to the Indian business in the choice of the most appropriate ERP system. Thus, valuable inputs into the understanding of the adoption of ERP in India are expected from this article.

On Order Elevation for Unstructured Finite Volume Solvers using Defect Correction

Abstract Much of the effort in improving the accuracy of CFD simulations is focused on mesh refinement and adaptation although studies have shown that the use of high-order methods are more efficient in improving accuracy. Stability issues, complexity of implementation and demand of computational resources are some of the key factors hindering the use of high-order methods in commercial CFD solvers. This paper demonstrates an improvement in the order of accuracy of finite volume solutions on unstructured meshes without using a high-order solver. Defect correction and the error transport equation method are the techniques discussed, along with the method for obtaining an appropriate estimate of the truncation error, which is crucial in both these techniques. Methods to obtain high-order interpolation of the control volume averages and high-order integral functionals along curved boundaries are also discussed. Third-order accurate results are obtained for a variety of problems, including the 2-D Euler equations, without using a third-order discretization scheme.

The Role of Artificial Intelligence in Website Encryption

The rapid advancement of digital services and the increasing reliance on internet-based transactions have highlighted the crucial need for robust cybersecurity measures. Encryption is a fundamental technique used to secure sensitive data, preventing unauthorized access and data manipulation. However, traditional encryption methods face limitations in adapting to evolving cyber threats. Artificial Intelligence (AI) has emerged as a transformative force in enhancing encryption techniques, leveraging deep learning, neural networks, and predictive analytics to provide dynamic and adaptive encryption solutions. This paper explores the role of AI in website encryption, focusing on its contributions to smart encryption, real-time threat detection, and the enhancement of encryption protocols such as TLS and SSL. Additionally, the study examines the challenges associated with AI-driven encryption, including high computational resource requirements, the risks of AI-powered attacks, and implementation complexities. As AI continues to evolve, its integration into encryption methodologies is expected to play a pivotal role in strengthening cybersecurity frameworks and mitigating emerging cyber threats.

Data processing method of silicon semiconductor vertex chamber based on impact point coordinate

This paper presents a comprehensive study on the reconstruction of vertex positions and particle tracks using heavy ion collision data from the Large Hadron Collider (LHC). Nonlinear particle trajectories, background noise, and scattering effects complicate accurate vertex prediction and trajectory reconstruction in real particle detectors. The solution proposed in this paper is integrating geometric methods with the DBSCAN density clustering algorithm to overcome these issues. This method effectively improves the precision of vertex finding and particle track reconstruction. In the meantime, a geometrical technique and probabilistic quantization are employed to predict the motion radius of the particle more accurately. This framework has been independently confirmed in an analysis of >10,000 collision points, which proves the frameworks robustness. Compared with the benchmark method, the framework only relies on the coordinates of the hit point recorded by the detector, which not only improves the universality of the algorithm application but also reduces the requirements on the equipment and the complexity of implementation and effectively improves the precision of vertex and trajectory reconstruction. The results are for the vertexing accuracy (0.005 cm) and average tracking search precision (95.40%) with a radius determination error of 0.5 cm. The proposed method also stands valid when these results are evaluated.

Community Mobilisation Through Translation: A Sustainable Framework for Participatory Planning

Participatory planning in neighbourhood regeneration faces challenges, including engagement difficulties, consensus-building, implementation complexities, and expectation management. This article investigates participatory planning processes aimed at addressing the aforementioned challenges in Bijiang Village, China. Using the framework of translation, it explores how this approach facilitates community mobilisation and engagement to achieve the United Nations Sustainable Development Goals (SDGs), specifically focusing on fostering sustainable communities. Translation theory comprises four moments: problematisation, interessement, enrolment, and mobilisation. The empirical studies demonstrate that these moments are dynamic and iterative. Initial problem framing, focused on historical landscapes, was unclear at first but became more defined through interest assignment, recruitment, and mobilisation. The interessement phase identifies stakeholders with shared concerns and values, empowering them early in the decision-making process. Enrolment effectively expanded participation by mobilising key stakeholders, such as clan elders and parents, through context-specific social networks and social ecology. This approach ensures that planning outcomes reflect community values and priorities. Mobilisation in Bijiang expanded participation, turned consensus into action, and fostered collective ownership and unity. Workshops, exhibitions, and focus groups translated public issues into defined community planning problems, facilitating the co-construction of solutions. These participatory methods made complex planning terms accessible, fostering deeper community involvement. The cyclical nature of problem framing and consensus-building in Bijiang Village underscores the importance of local socio-cultural context in rural regeneration. Translation theory offers a robust framework for managing complexities in participatory community planning. It demonstrates how continuous negotiation and realignment of interests through translation address immediate concerns and foster long-term engagement, contributing to sustainable development.

Effortless Student Attendance: A Smart Human-Computer Interactive System Using Real Time Facial Recognition

Objective: Recording attendance is a critical process in academic institutions due to its significant impact on student performance and engagement. Current methods for recording attendance are often time-consuming and labour-intensive for lecturers and administrative staff, necessitating the development of more efficient and flexible solutions. While various automated attendance systems have been proposed, they often encounter challenges related to cost, implementation complexity, or reliability, hindering widespread adoption in educational settings. Method: This paper introduces a novel approach to automating attendance registration using face recognition technology. Our method integrates multiple feature extraction algorithms within a user-friendly graphical interface, specifically designed in English to enhance usability. By using existing security cameras commonly found in academic institutions, our approach addresses both cost and time inefficiencies. The attendance registration process involves capturing a video of the classroom, which is then processed to identify and log student attendance in a CSV file. A significant aspect of our study is using a comprehensive dataset comprising 2,170 images collected from 31 students at Mustansiriyah University. This extensive dataset enhances the robustness and reliability of our system, providing a diverse range of facial expressions, angles, and lighting conditions that improve the accuracy and generalizability of our model. Result: The system demonstrated accuracy of up to 100%, with deep learning algorithms outperforming machine learning methods. Conclusion: These promising results suggest that face recognition technology can effectively streamline and automate attendance tracking, offering a viable solution for educational institutions seeking to improve operational efficiency and accuracy

Super-Resolution Angle Estimation Algorithm using Low Complexity MUSIC-Based RELAX for MIMO FMCW Radar

A novel super-resolution angle estimation algorithm using low complexity-multiple signal classification (LC-MUSIC)-based relaxation (RELAX) for multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar is proposed in this paper. FMCW radar is widely used to estimate target location information in various fields, including autonomous driving, defense, and robotics. However, FMCW radar struggles to provide high-resolution direction of arrival (DOA) data. To obtain precise target positions, especially for multiple targets, FMCW radar with high-resolution DOA capability is needed. Conventional FMCW radar employs MIMO-based technology to improve angular resolution because of its low complexity and ease of implementation, but MIMO-based radar still struggles to provide precise angular resolution. To improve the angle resolution, we propose a novel super-resolution DOA algorithm in a MIMO scheme. The new method consists of RELAX-based CLEAN and LC-MUSIC. Combining the MIMO technique with the new super-resolution algorithm enables FMCW radar with high angular resolution to be obtained. To analyze the performance of the proposed estimation, Monte Carlo simulations are performed, and root-mean-square error results are analyzed. Using the FMCW radar module, experiments comparing the proposed and conventional algorithms were performed in an indoor environment.

The Relevance of Trust in the Implementation of AI-Driven Clinical Decision Support Systems by Healthcare Professionals: An Extended UTAUT Model

Background. In the healthcare sector clinical decision support powered by artificial intelligence is rapidly expanding. It can help medical personnel make better-informed decisions while saving time. The opposition of healthcare professionals to AI initiatives in the healthcare industry might prove to be a significant barrier, due to the combination of optimism and fear related to the technology. Professionals in the sector may be apprehensive about AI due to job security worries and the general public's lack of faith in the technology. Objective. A primary objective of this study is to examine how trust influences healthcare professionals' willingness to use AI-driven CDSS, particularly at well-known tertiary hospitals in Delhi-NCR region India, as well as to expand the Unified theory of acceptance and use of technology (UTAUT) by including constructs such as perceived risk and self-efficacy. Method. The UTAUT model provided the basis for the construction of a new model, which was developed by integrating the variables of perceived risk and self-efficacy. The model had eight components which were assessed using 31 survey questions. Two hundred and twenty participants filled out the surveys for the study. Results. The findings indicate that there are significant relationships between (PE) and (AI), (EE) and(AI), (PR) and (AI) and (SE) and (AI). This study reveals that trust fully mediates the relationship between AI which means that fostering trust in AI technology is essential for the successful adoption and implementation of AI-CDSS technology. There was no significant relationship between social influence and adoption of AI-powered CDSS nor between intention to adopt and actual use. Conclusion. The result of this study shed light on the factors influencing healthcare professionals to adopt AI-CDSS. The outcomes of this study provide insight into how individuals' performance expectancy, effort expectancy, social influence, perceived risk, and self-efficacy determine whether they embrace AI-CDSS. This study highlights the importance of trust in the deployment of these technologies in the healthcare sector. Furthermore, it highlights the significance of addressing perceived risk and self-efficacy. Although social influence plays a crucial role in technology adoption its impact may be limited in the case of AI-CDSS deployment in India. This could be due to the complexities of AI-CDSS implementation, the requirement for training, or the fact that healthcare personnel are unaware of the benefits of AI-CDSS or have limited exposure to it.

AI-driven Test Automation for Salesforce and System Integration

Integrating artificial intelligence into test automation frameworks has transformed quality assurance practices in Salesforce environments and system integrations. AI-driven solutions have revolutionized testing approaches through smart test selection, risk-based analysis, and dynamic element identification capabilities. These advancements enable organizations to detect defects earlier, reduce false positives, and significantly decrease test maintenance efforts. Self-healing locators and context-aware selection mechanisms have enhanced test stability across dynamic web applications, while pattern recognition and anomaly detection capabilities proactively identify potential issues. Real-world implementations demonstrate substantial improvements in testing efficiency, reliability, and cost-effectiveness. Despite the challenges of data requirements and implementation complexity, AI-powered testing solutions have proven particularly effective in handling complex Salesforce configurations and multi-system integrations. The continuous evolution of these technologies promises enhanced predictive capabilities, improved integration support, and more sophisticated automated testing approaches, marking a significant shift in how organizations approach quality assurance in modern software development.

Flexible working arrangement scenario planning for the social security agency for employment

This study investigates the readiness of the Social Security Agency for Employment (BPJS Ketenagakerjaan) to implement flexible working arrangements (FWAs) in response to the evolving work landscape post-COVID-19. With the Indonesian government's reclassification of the pandemic, organizations are reconsidering their work models, making it crucial to assess their preparedness for hybrid work systems. The research employs a case study design with a descriptive approach, utilizing the TAIDA method to develop planning scenarios. Data collection includes primary data from in-depth interviews with experts and secondary data from relevant literature and internal documents. Findings reveal that BPJS Ketenagakerjaan demonstrates a very good level of readiness across various dimensions, including technology and management, with an overall readiness index of 82.58. However, the mobility policy dimension indicated potential improvement areas. The study highlights the importance of establishing clear policies and standard operating procedures to support remote work, particularly for employees with specific health needs. The implications of this research are significant for BPJS Ketenagakerjaan as it provides a structured framework for enhancing organizational readiness for FWAs, ensuring employee engagement and satisfaction. This study contributes to the broader discourse on flexible work arrangements, offering practical guidance for public service organizations navigating the complexities of hybrid work implementation.

Leveraging Asynchronous Processing Tools in Salesforce: A Comprehensive Analysis

This article provides a comprehensive analysis of asynchronous processing tools within the Salesforce platform, exploring their significance in enhancing system performance, scalability, and user experience. It delves into a wide array of server-side and client-side asynchronous processing options, including Asynchronous Apex, Platform Events, Asynchronous Flows, and Lightning Actions, offering detailed insights into their functionalities, use cases, and implementation considerations. The article presents a comparative analysis of these tools, discussing their performance metrics, implementation complexities, and scalability factors. It also outlines best practices for implementing asynchronous processing in Salesforce, addressing key aspects such as resource optimization, platform limit mitigation, and data integrity. Furthermore, the article examines the challenges and limitations associated with asynchronous processing in Salesforce, and explores future trends and developments in this domain. By providing a thorough examination of Salesforce's asynchronous processing capabilities, this article serves as a valuable resource for developers, architects, and decision-makers seeking to leverage these tools effectively in building robust, efficient, and scalable Salesforce applications.

Privacy-Preserving in Machine Learning: Bridging Security and Performance in Modern Applications

Privacy-preserving machine learning (PPML) has emerged as a critical paradigm in the era of data-driven applications, addressing the fundamental tension between leveraging large-scale datasets and protecting individual privacy. This technical article examines recent advances in PPML techniques, focusing on three key approaches: federated learning, which enables distributed model training while keeping data localized; homomorphic encryption, allowing computation on encrypted data; and secure multi-party computation (MPC) for privacy-conscious collaborative learning. Through detailed architectural analysis and real-world case studies in mobile device personalization and healthcare analytics, this article demonstrates how these techniques can be effectively implemented while navigating computational overhead and implementation complexity. This article reveals current PPML approaches successfully preserve privacy in production environments, but they face significant challenges in computational efficiency and system integration. This article concludes by presenting optimization strategies and emerging research directions aimed at making PPML more practical for large-scale deployments.