Operation Process Research Articles

The Role and Forms of Financing from the Ebrd - Focusing on Kosovo During the Years 2013-2020

This paper analyzes the roles of European Bank for Reconstruction and Development (EBRD) strategic financing in Kosovo from 2013 to 2020, focusing on economic impacts and development progress. Using a quantitative and comparative approach, this study uses secondary data analysis, supported by statistical tools in Microsoft Excel, to assess the effectiveness of EBRD investments in different sectors within Kosovo. Statistical and comparative data shows the sectors and amounts of support from the EBRD for Kosovo over the years, both before and after its admission to the EBRD. A retrospective of the history of the establishment of the EBRD, the operation process of its banking system, the way the bank finances projects and its impact on the economic development of a country, namely Kosovo, was also discussed. The findings reveal significant EBRD investments, mainly in SMEs and infrastructure, contributing to competitive gains and sustainable development. The study details the volume of investments and their distribution across different sectors, highlighting key impacts on employment and green energy initiatives. This research enriches the literature on the roles of international financial institutions in post-conflict economic development by providing a nuanced understanding of the EBRD's strategic priorities and operational outcomes in transition economies. The practical implications of the study provide policy makers and development agencies with insights into practical strategies for leveraging international finance for economic growth and sustainability. Recommendations are given to increase the focus on renewable energy and infrastructure, in line with the global goals of sustainable development.

Intuitionistic fuzzy divergence for evaluating the mechanical stress state of steel plates subject to bi-axial loads

The uncertainty that characterizes the external mechanical loads to which any connection plate in steel structures is subjected determines the non-uniqueness of the isochoric deformation distributions. Since the eddy currents induced on the plates produce magnetic field maps with a high fuzziness content, similar to those of the isochoric deformations, their use can be exploited to evaluate the extent of the external load that determines a specific induced current map. Starting from an approach known in the literature, according to which the map-external load association is operated through fuzzy similarity computations, in this paper, we generalize this method by reformulating it in terms of intuitionistic fuzzy logic by proposing a classification based on divergence computations. Our approach, acting adaptively on the fuzzification of the maps, results in a better classification percentage, besides significantly reducing the presence of doubtful cases due to the uncertainty of each applied load. Furthermore, a FEM software tool was developed, which turned out to be, to a certain extent, a substitute for the experimental procedure, notoriously more expensive. Even if the procedure was applied on plates subjected to bi-axial loads, it could be used for other types of loads since the classification operator processes the eddy current maps exclusively, regardless of their cause.

Automated debt recovery systems: Harnessing AI for enhanced performance

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Trusting organizational law

AbstractDecentralized governance technologies like blockchains are often proposed as substitutes for private legal arrangements like those provided by company law or organizational law more generally. In established legal systems in developed countries, the costs of implementing such algorithmic mechanisms are likely to be greater than the agency or other costs that come from selecting and trusting an existing third party. But even if that were not true, attempting to achieve decentralized governance solely by algorithm is more complicated than it appears and incurs significant “formalism costs.” Such costs can be reduced by the wealth of experience represented by the operational processes of organizational law. Mixing those operational processes with algorithmic governance by “opting into” organizational law, thereby letting it govern the interface between the algorithms and the real‐world legal rights associated with an organization, is likely to be a governance model superior to the use of algorithmic governance alone.

Identification and Positioning Method of Bulk Cargo Terminal Unloading Hopper Based on Monocular Vision Three-Dimensional Measurement

Rapid identification and localization of dry bulk cargo hoppers are currently core issues in the automation control of gantry cranes at dry bulk terminals. The current conventional method relies on LiDAR systems for the identification and positioning of bulk unloading hoppers. However, this approach is complex and costly. In contrast, GPS-based positioning solutions for bulk unloading hoppers are prone to damage due to the vibrations generated during the operation process. Therefore, in this paper, a hopper localization system based on monocular camera vision is proposed to locate the position of the bulk unloading hopper. The hopper identification and localization process are divided into three stages. The first stage uses the improved YOLOv5 model to quickly and roughly locate the hopper target. The second stage uses morphological geometrical features to locate the corner points of the hopper target. The third stage determines the three-dimensional coordinates of the hopper target by solving the position of the corner points in the world coordinate system through the PnP (Perspective-n-Point) algorithm. The experimental results show that the average positioning accuracy of the coordinates of the method is above 93%, demonstrating the accuracy and effectiveness of the method.

Liposuction Through an Axillary Fold Incision Combined with a Small Areola Incision to Treat Gynecomastia.

Gynecomastia is a progressive disease characterized by enlarged breasts, affecting a significant proportion of men. Persistent gynecomastia negatively affects the psychological and emotional development of patients; therefore, surgical intervention is required. In this article, we describe a surgical technique, where liposuction through an axillary incision is used in combination with a single small periareolar incision, to obtain the most minimal scars in the treatment of gynecomastia. Between June 2021 and June 2023, 125 patients with different Simon grades of gynecomastia were enrolled. The patients' basic conditions and operation processes were recorded. Following surgery, a score was assigned according to the five main aesthetic aspects of the surgical procedure. In total, 125 patients with gynecomastia were treated with a pre-axillary fold incision combined with a small areolar incision. There were 17 cases of Simon grade I, 46 grade IIA, 42 grade IIB, and 20 grade III. The average operation time was 45.8min, the average liposuction volume was 250.5mL, the average glandular tissue volume was 50.5g, intraoperative blood loss ranged from 15 to 60mL, and the average hospital stay was 3.2days. Regarding the postoperative aesthetic effect, doctors scored > 4 points, and the patient satisfaction score was > 7.5, which fully affirmed the aesthetic effect of this method. Treatment of gynecomastia through an anterior axillary fold incision combined with a small areolar incision is safe and feasible, involving a simple procedure, short operation time, and few complications. Its efficacy and cosmetic effects could lead to its use as a primary surgical method to treat gynecomastia. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine Ratings, please refer to Table of Contents or online Instructions to Authors www.springer.com/00266 .

Research on Information Fusion Method for Human Computer Interaction Interface of Command and Control System in MR Environment

In response to the challenge of high cognitive pressure and difficulty for commanders in cross-layer operations during the transition from traditional two-dimensional command and control system interfaces to mixed reality human-computer interaction interfaces, this paper conducts research on the information fusion display method for the human-computer interaction interface of the mixed reality command and control system. First, based on the findings of previous research, we developed a five-layer human-computer interaction interface for a mixed reality command and control system in typical combat scenarios. An evaluation experiment was designed and the resulting data analyzed. This analysis revealed that there are problems with low work efficiency and high cognitive load in the information acquisition process in cross-layer interaction operations. Consequently, we conducted research into the methods of integrating and displaying interactive interface information, integrating and displaying interactive and display interface information, and integrating and displaying complex situation interface information. The research findings indicate that: (1) The optimal display ratio for mixed reality 2D interface integration is 3:2. (2) The optimal display scheme for mixed reality 2D interface fusion is a combination of the main and secondary interfaces, with the main interface table displaying global information and the secondary interface displaying accurate information. (3) The optimal display scheme for mixed reality 2D and 3D interface fusion is the display of overall class information in 2D and the accurate identification of class information in 3D. The findings of this research can be applied to the design of human-computer interaction interfaces for mixed reality command and control systems in complex battlefield environments.

Division Errors Frequently Made by Students

Problems in mathematical problems are common across children of all ages and of all places. This study looks at the common problems students generally face during the mathematical operation of Division and attempts to provide solutions to prevent such problems from taking place further. The study surveyed and analyzed the mistakes of 50 Secondary school students from the district of Cuttack, Odisha in order to isolate the problems students faced during Division. The data was collected through a simple test consisting of a simple long division question and the participants were allowed as much time as they wanted to solve the question. After collecting the responses, the data was analyzed and the responses separated into four different categories. The results showed that a majority I.e. 56% of the students made a mistake in their responses which indicated a lacunae in the base understanding of the process of Division. The mistakes showed a variation in their types, each relating to a different lack of understanding of the process of the mathematical operation.

Unlocking Potential: Digital Transformation and Decision Support in Automotive Repair - A Case Study

In today's rapidly evolving business landscape, the significance of digital transformation for Small and Medium-sized Enterprises (SMEs) cannot be overstated. CV Karya Makmur, an Indonesian automotive repair shop, stands at the forefront of this change, recognizing the urgent need to incorporate advanced Information and Communication Technologies (ICTs) into its operations. This transformation is not just about adopting new technologies; it is a comprehensive shift that redefines how the business interacts with customers, optimizes operational processes and improves its overall financial health. The journey of CV Karya Makmur highlights the broader implications of digital transformation in this sector. This case study delves into the multifaceted process of integrating technologies such as augmented reality (AR) and artificial intelligence (AI) to enhance service efficiency, tackle logistical hurdles, and improve access to vital information. Yet, the transition from conventional practices to a digital framework is fraught with obstacles, primarily due to resistance to change. Effective change management, focusing on the human elements of organizational transformation and employee engagement, is pivotal. CV Karya Makmur's experience underscores the critical need for digital systems that promote data-driven decisions, standardize operations, and facilitate better communication. Despite the initial challenges, the potential benefits—increased productivity, reduced operational costs, and a competitive edge in the market—are compelling. This study suggests that considering an organization's unique context and objectives, a tailored digital transformation strategy is essential for leveraging technology to enhance business outcomes in developing countries like Indonesia.

Towards a process selection method for embedded analytics

AbstractDriven by technological progress, business analytics is gaining momentum while paving the path for next-generation business process management. Especially, embedded real-time analytics offers new opportunities for business process intelligence and value creation. However, there are several obstacles that organizations face in their adoption process. A key challenge is to identify business processes that are suitable for embedded analytics and hold relevant value potential. Our research addresses this need by introducing an exploratory BPM method, namely a process selection method. Applying action design research and situational method engineering, we iteratively built, used, evaluated, and refined the theory-ingrained method artifact. The method provides organizations with guidance in selecting operational business processes, for which a reengineering project should be initiated.

Design of political classroom interaction system in social virtual reality entertainment environment based on convolutional neural network

With the continuous progress of AI intelligent technology, the internet industry has undergone tremendous changes. The application of virtual reality and other digital entertainment technology has changed the classroom mode, and at the same time, current political classrooms place more emphasis on interaction with society. Therefore, this article constructs a political classroom AI intelligent system based on CNN network. This system studies the political classroom from the perspective of AI intelligent construction, using a combination of theory and practice. This article selects some research materials from both domestic and international sources. Firstly, it summarizes and analyzes relevant concepts such as convolutional neural networks, summarizes the importance of political classroom education, and analyzes the problems faced by political classrooms in the context of AI intelligence. Then, experimental analysis was conducted on the AI intelligent system in the political classroom. The experiment proved that the AI intelligent system in the political classroom has good working performance in the actual operation process, and the overall performance is also better than other systems. Implementing a network-based AI intelligent system for the political classroom and applying AI intelligent technology in practical applications can improve the personalization and accuracy of the political classroom.

Enterprise value creation effects of artificial intelligence technology from the perspective of digital agility: evidence from China

ABSTRACT As a pivotal technology driving the latest wave of technological revolution and industry transformation, artificial intelligence (AI) is permeating the processes of business operations widely. Grounded in the dynamic capability theory, the research use Word2Vec and Python to extract metrics for AI technology from annual reports of Chinese A-share listed companies from 2012 to 2022. This research empirically investigates the impact of AI technology on the level of enterprise value creation. The findings reveal that the application of AI technology by listed companies significantly enhances their value creation, a conclusion that remains robust following various endogeneity and robustness tests. AI technology empowers enterprise value creation through the mechanisms of sensing, integrating, and restructuring digital agility. Furthermore, the value-creation effect of AI technology is more significant in manufacturing and the eastern region. The results elucidate the logic behind the influence of AI technology on enterprise value creation and provide practical support for organisations in implementing AI strategic planning and decision-making.

Utilization of local corn (Zea Mays) wastes for bioethanol production by separate hydrolysis and fermentation

Maize is the second most-common crop globally. Thus, enormous maize cobs are generated annually from maize processing activities which could serve as a potential and non-edible source for biofuel production. The primary aim of this research was to assess the practicality of producing bioethanol from discarded maize cob through a distinct two-step process referred to as separate hydrolysis and co-fermentation. After a 72-hour fermentation period, the greatest ethanol yield of 66.23 ± 8.35 mL/kg was obtained. This was followed by 54.33 ± 7.27 35 mL/kg after 48 h, and 21.68 ± 2.97 35 mL/kg after 24 h. Importantly, all ethanol yields at different time points exhibited statistical significance at p < 0.05. Moreover, the study revealed a robust positive correlation (r = 0.99, p < 0.01) between glucose and Total reducing Sugars (TRS) yields, and negative correlations were observed between ethanol yield and glucose (r = -0.97, p < 0.05) as well as ethanol and TRS (r = -0.98, p < 0.05). The results indicate the potential of maize cob waste as a valuable resource for bioethanol production. Significant enhancements in operational processes are necessary to enhance the economic feasibility of producing ethanol from maize cobs. Nigeria's utilization of waste for biofuel production is bolstered by substantial policy and financial backing for renewable fuels. The economic viability of ethanol production from maize cobs relies heavily on its competitiveness relative to other waste treatment methods and the effectiveness of policy measures.

Research on the Operation Mechanism of Science Popularization Bases Based on Actor-Network Theory

Government-led, multi-stakeholder participation in social mobilization mechanisms and market-oriented operational models are important characteristics of the development of science popularization in the new era. Based on Actor-Network Theory, this paper employs methods such as questionnaires and structured interviews to analyze and sort out the relationships between various actors through the four steps of problem definition, interest allocation, recruitment, and mobilization in the translation process. The study examines the role of the Science and Technology Association (STA) as the core actor in the operational process, identifies the interests and obstacles of various actors, and proposes suggestions to better improve the operational mechanism of science popularization bases from aspects such as strengthening policy guidance, establishing alliances of science popularization base interests, and enriching the forms of activities at science popularization bases.

Digital transformation for safer circular lithium-ion battery supply chains: a blockchain ecosystem-data perspective

Lithium-ion battery (LIB) circular supply chains (CSCs) present unique safety challenges among operation processes. Blockchain technology can be a promising solution for addressing these challenges, by enabling effective tracking and verification of safety-related information throughout the supply chain. However, how blockchain can mitigate safety issues from a supply chain perspective is poorly understood. This study proposes a theory-supported framework through intervention-based research (IBR) to provide guidance for LIB CSC safety management. The conceptual framework and theoretical propositions set the foundation for research on a comprehensive blockchain ecosystem specifically designed for CSC safety management. The framework includes the design of a blockchain architecture with capabilities tailored to address safety concerns, the involvement of multiple stakeholders, and the development of a safety measurement matrix. This study suggests research and practical directions which lay the groundwork for leveraging blockchain technology to improve safety management in LIB CSC. This research contributes to sustainable supply chains by proposing a conceptual framework for mitigating safety concerns in LIB CSC, paving the way for effective blockchain implementation. In addition, this study contributes to advancing the theoretical design understanding and application of blockchain technology in CSC safety management.

The impact of physical activity on patient-reported outcomes following deep inferior epigastric perforator flap breast reconstruction

IntroductionWhile higher pre-operative physical activity levels have been shown to be beneficial to post-operative recovery at large, their effect on patient reported outcomes after DIEP flap breast reconstruction has yet to be investigated. This study aims to correlate patient physical activity levels with patient-reported outcome measures. MethodsA prospectively maintained database of patients who underwent DIEP flap breast reconstruction between July 2021 and June 2022 was analysed. Physical activity levels were assessed using the Global Physical Activity Questionnaire (GPAQ) and patient-reported outcomes were measured using the BREAST-Q questionnaire, both pre-operatively and one year post-operatively. Patients were stratified into low (<1000 METs) and high (>1000 METs) physical activity groups. ResultsOf the 136 patients who underwent surgery, 51 completed both questionnaires, with 34 responses meeting completeness criteria for statistical analysis. The low-MET group (n=19) and high-MET group (n=15) were comparable in age and co-morbidities. Post-operatively, the low-MET group exhibited a 54% increase in physical activity levels, while the high-MET group showed a 17% decrease (p<0.01). The low-MET group experienced significant declines in psychosocial well-being (9.3%, p=0.04) and sexual well-being (14%, p=0.02), whereas the high-MET group did not show significant changes in these domains. Satisfaction with the unclothed abdomen increased significantly in the low-MET group (2.9 from 2, p=0.001), while the high-MET group showed a non-significant decrease (3.2 from 2.9, p=0.43). However, abdominal well-being significantly decreased in both groups, with a 56% decline in the high-MET group (p=0.006) and a 51% decline in the low-MET group (p=0.0002). The latter reflecting the perceived difference between aesthetic appearance and wellbeing. ConclusionsPatients with lower pre-operative physical activity levels showed improved activity post-surgery, potentially due to motivational factors from the reconstructive process. Conversely, those with higher pre-operative activity levels faced greater challenges in maintaining their activity levels post-surgery, potentially arising from disruptions in daily routines and donor site symptoms, indicating a need for targeted interventions to support this group. Concurrently, patients with higher baseline exercise levels did not show a decrease in psychosocial and sexual wellbeing, possibly reflecting a higher resilience to the operative process. These findings underscore the importance of integrating physical activity assessments into pre-operative evaluations to inform patient-centred care and optimize recovery outcomes.

Facilitating remote and virtual access provision by European research infrastructures - requirements, issues, and recommendations.

Research Infrastructures (RIs) are strategic assets facilitating innovation and knowledge advancement across all scientific disciplines. They provide researchers with advanced tools and resources that go beyond individual or institutional capacities and promote collaboration, community-building and the application of scientific standards. Remote and virtual access to RIs enables scientists to use these essential resources without the necessity of being physically present. The COVID-19 pandemic restrictions where a catalyst for the expansion and further development of remote and virtual access models, particularly in fields where physical access had been the predominant model. The eRImote project explores pathways for digital and remote RI access through targeted surveys, stakeholder workshops, expert groups discussions, and the analysis of specific use cases. This paper provides a definition of remote and virtual access and remote training and explores their implementation across various RIs, highlighting the implications for their operational processes and the dynamics of interaction between RIs and their user communities. It presents the identified advantages, obstacles, and best-practices, alongside strategies and recommendations to navigate and mitigate challenges effectively. Key issues and recommendations are summed up separately for remote access, virtual access, and remote training, complemented by general recommendations for facilitating remote and virtual access to RIs. These relate to budgeting and funding, the balancing of RI access models, the need for regulatory frameworks for sample shipments, collaboration among RIs, impact assessment of remote and virtual access on user interactions, operational efficiency and the environment footprint of RIs, and the adaption of data sharing policies. Stakeholders are broadly invited to give their feedback on the paper's findings and conclusions, which will be integrated into improved versions of this paper.

Joint optimization of preventive maintenance and triggering mechanism for k-out-of-n: F systems with protective devices based on periodic inspection

Systems with protective devices (PDs) have attracted increasing research interests due to their extensive engineering applications. In practice, the PDs are triggered to function under certain conditions. However, existing studies focus on the reliability modelling and analysis for such systems, and the joint optimization of maintenance and PD's triggering mechanism has not been investigated. Consequently, this paper explores the joint optimization of maintenance and triggering mechanism for k-out-of-n: F systems with PDs. A new triggering mechanism of the PD is proposed based on the number of failed or unhealthy components. The working PD can reduce the impacts of shocks on components. Based on the detected system status through periodic inspections, corresponding actions including corrective maintenance for components or the PD, preventive maintenance for components or shutting down the PD can be decided to improve the system reliability. The operation process of the established system is modelled by Markov decision process, and optimal actions under diverse system states are obtained via value iteration algorithm. The optimal inspection interval and triggering mechanism are derived by solving the optimization model aiming to minimize the expected total cost. Finally, the applicability of the proposed model is validated by abundant numerical examples.

Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief

Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster relief operations. However, UAVs are limited by their battery capacity, which makes it hard for them to perform both large-scale information gathering and data processing at the same time. Nowadays, smartphones (SPs), which have become portable devices for people, have the characteristics of strong computing power, rich communication means and wide distribution. Therefore, in this study, we developed SPs to assist UAVs in computation incentive-based task execution. To balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem was established, and the Multi-Objective Mutation-Immune Bat (MOMIB) algorithm was developed to optimize the proposed problem. Additionally, considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to ensure the performance requirements of different types of tasks. A large number of simulation experiments show that the task-offloading scheme that we propose is effective.

Implementing Pega Collections Command Center for Real-Time Monitoring and Decision Support

The Pega Collections Command Center (PCCC) represents a significant advancement in financial institutions' capabilities for real-time monitoring and decision support. By integrating dynamic dashboards, alerting mechanisms, and predictive analytics, PCCC offers a comprehensive solution for managing collections operations. This technical paper explores the implementation of PCCC, detailing its features and the benefits it provides to financial institutions. The study discusses the rationale behind this technological adoption, the methodologies employed, the results observed, and the broader implications of this implementation. The findings contribute to the existing knowledge base by demonstrating the effectiveness of PCCC in enhancing operational efficiency and decision-making processes.