Service Decisions Research Articles

Robotics Classification of Domain Knowledge Based on a Knowledge Graph for Home Service Robot Applications

The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment recognition tasks and the small scale of indoor scene data, a method and model for rapid classification of household environment domain knowledge is proposed, which can achieve high recognition accuracy by using a small-scale indoor scene and tool dataset. This paper uses a knowledge graph to associate data for home service robots. The application requirements of knowledge graphs for home service robots are analyzed to establish a rule base for the system. A domain ontology of the home environment is constructed for use in the knowledge graph system, and the interior functional areas and functional tools are classified. This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. This proposal adopts a combination of MobileNetV3 and transfer learning, integrating large-scale pre-training with fine-tuning for the home environment to address the challenge of limited data for home robots. The results show that the proposed model achieves higher recognition accuracy and recognition speed than other common methods, meeting the work requirements of service robots. With the Scene15 dataset, the proposed scheme has the highest recognition accuracy of 0.8815 and the fastest recognition speed of 63.11 microseconds per sheet.

Autonomous artificial intelligence for sorting the results of preventive radiological studies on the example of mammography

The purpose of research. Radiation diagnostics is central to the detection of malignant neoplasms. Recently, the implementation of screening programs has faced a number of obstacles, including staff shortages and limited funding. The introduction of artificial intelligence (AI)-based systems capable of absolutely accurate sorting of research into two categories - "normal" and "not normal", seems to be a promising solution to these problems. However, before they are widely used, it is critically important to verify their ability to guarantee the safety and high quality of the screening process. The aim of the study is to evaluate the possibility of using autonomous sorting of mammographic examination results in real clinical conditions. Methods. The study was carried out in 2 stages. At the first stage, 25,892 mammographic studies processed by the AI service were retrospectively analyzed. A ROC analysis of these results was carried out in order to assess the possibility of configuring the AI service for 100% sensitivity. At the prospective stage, the results of 82,372 mammograms were analyzed. All studies were processed by AI services configured for 100% sensitivity. The tasks of the AI services included the sorting of mammography results into the categories "normal" and "not normal". Next, the decisions of AI services and radiologists on categorization were compared. Results. According to the results of a retrospective study, when configuring the AI service for 100% sensitivity, the specificity was 39%. In the course of a prospective study, the proportion of defects (false attribution of research results to the "norm" category) was 0.08%, the specific weight of clinically significant defects in AI services was 0.02%, which is significantly lower than that of a radiologist. Conclusion. The use of autonomous sorting of mammographic research results in clinical practice is possible in order to optimize the diagnostic process during preventive measures, as well as under the condition of monitoring the quality of artificial intelligence technologies. Keywords: artificial intelligence, mammography, preventive examinations, radiation diagnostics. Conflict of interest: The author declares the absence of obvious and potential conflicts of interest related to the publication of this article.

Competition and organizational structure co-optimization of OEMs in a product-service supply chain

Smart Connected Products (SCPs) and Digital Services (DSs) play pivotal roles in facilitating the digital and intelligent transformation of the manufacturing industry. In the context of the service-oriented transformation of manufacturing, SCPs are sold by Original Equipment Manufacturers (OEMs) via a retailer, whereas DSs are either provided directly by OEMs or indirectly through the retailer, thereby forming a product-service supply chain. This paper constructs a three-stage dynamic game model and analyzes two DS strategies, i.e., integrated and separated DS strategies, for competing OEMs by integrating and separating manufacturing and DS departments. These strategies encompass four scenarios: both OEMs integrate them, both separate them, and one OEM integrates while the other separates them. We analyze the equilibrium DS levels, the prices of SCPs and DSs, demands, and profits in four scenarios, and explore the service and pricing decisions of supply chain members at equilibrium. The results indicate that OEMs can achieve higher profitability by separating manufacturing and DS departments as competition intensity increases within a certain range. Secondly, when both OEMs adopt the same strategy, integration of departments is more profitable if competition is low, whereas separation yields higher profits when competition is intense. Interestingly, two Nash equilibria emerge: an OEM achieves the highest profit by adopting an integrated DS strategy in an asymmetric scenario when competition is weak and digital R&D is challenging. Conversely, both OEMs can reach a Nash equilibrium by separating their departments when competition is intense and digital R&D is relatively simple. Thirdly, from the perspective of the retailer and the entire supply chain, integrating manufacturing-service at both OEMs provides the highest return when competition is low. However, under intense competition, the highest profits are achieved when OEMs adopt different DS strategies. Finally, in a highly competitive market without a retailer, an asymmetric scenario proves most profitable when digital R&D investment is low, whereas separating manufacturing-service yields the highest profits when digital R&D investment is high.

Customer Trust Mediates Word of Mouth Impact on Queen Decoration Service Decisions

Purpose: This study examines the effect of Word of Mouth (WOM) on Customer Trust. To investigate the impact of Customer Trus on the Decision to Use Decoration Services. To examine the impact of Word of Mouth (WOM) on the Decision to Use Decoration Services. Moreover, to explore the impact of Word of Mouth (WOM) on the Decision to Use Decoration Services through Customer Trust. Research Design and Methodology: This study is a quantitative descriptive research study. All customers who had ordered decoration services at Queen Dekorasi, including 140 respondents. Data was collected using field research techniques. The data analysis method used Instrument Tests and Structural Equation Modeling (SEM) Analysis. Findings and Discussion: The results showed that Word Of Mouth positively and significantly affects Customer Trust. Customer Trust has a positive and significant effect on Decisions to Use Services. Word Of Mouth does not significantly affect the decision to use services. Moreover, Customer Trust can mediate the influence of Word of Mouth on the Decision to Use Services. Implications: This research can be considered and evaluated so that service business actors continue paying attention to the word-of-mouth and customer trust factors influencing decisions to use services and increasing public interest in the services offered. Hopefully, this research can add to the knowledge for further research to develop what factors can influence service decisions.

Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool

The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients' risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery.

Census Tract and Neighborhood Racial Change and the Locational Decisions of Financial Services in Metro Detroit

Scholarship on the geography of US retail financial services consistently finds racialized patterns in the locations of traditional (e.g. banks, credit unions) and higher-cost or alternative (e.g. payday lenders, check cashers) financial services. While scholars tend to offer explanations based on spatial void and market segmentation hypotheses, we theorize these racialized patterns as consistent with a social reproduction of investment and divestment. We use a novel, longitudinal dataset that follows census tract- and neighborhood-level changes in racial demographics and financial services in Detroit during the pre- and post-Recession periods. Changes in the locations of financial services follow changes in racial demographics, which occur in addition to the well-documented practice of targeting or avoiding tracts and neighborhoods based on their racial majority. These racialized patterns are heightened in the post-Great Recession period.

Pricing and Service Decisions of Products in a Dual-Channel Supply Chain Introducing Ship-to-Store

This study investigates the optimal pricing and service decisions when implementing ship-to-store (STS) in a dual-channel supply chain comprising one BM store and one manufacturer. Homogeneous products within the market are categorized into mainstream and niche products. The effects of consumer service sensitivity, channel preference, and degree of personalization on optimal decisions and profitability are analyzed, followed by numerical simulation and analysis. It is found that (1) the supply chain should charge high prices for mainstream products and low prices for niche products to maximize profits. (2) Implementing STS is conducive to expanding niche products in the market when facing highly service-sensitive consumer groups. (3) Based on highly service-sensitive consumer segments, supply chains are more profitable implementing STS strategies in a centralized decision-making model than in a traditional dual-channel one; however, under decentralized decision-making, it is only profitable to implement an STS strategy if consumers have a low level of personalization and have a particularly pronounced preference for online direct marketing channels.

Fast 3D solvers for interactive computational mechanics

While interactive simulations have been mostly limited to Computer Graphics applications, new generations of Graphics Processing Units (GPUs) allow the realization of industrial-grade interactive 3D physics simulations. By combining an immersed boundary method with efficient GPU-based MINRES and CG solvers using a GPU-based geometric multigrid preconditioner, we demonstrate a fast industrial 3D computational mechanics solver. The various implementation aspects - specifically how they differ from similar concepts used in the Computer Graphics community - are discussed in detail. The proposed concept opens up new classes of industrial simulation applications allowing a democratization beyond today’s expert users, from designer centric simulation to operational and service decisions based on 3D simulations. To support this, we provide various benchmark cases including a real-world study of a simulation-based service decision for a damaged gear-box mount.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Spatial Analysis with Detailed Indoor Building Models for Emergency Services

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations.

Insights into decision‐maker's perceptions of good versus bad decisions in emergency services—A modified Delphi study

AbstractDecision‐making in emergency situations, such as those faced by fire, police, and health service personnel, presents unique challenges due to the high‐stakes and time‐pressured environment. Here we aim to better understand what emergency responders regard as constituting ‘good’ and ‘bad’ decisions in emergency situations. We administered a modified Delphi study, eliciting opinions from decision‐makers across all these sectors towards consensus around the key elements of good and bad decision‐making. Participants were first asked to define what makes a ‘good’ and a ‘bad’ decision, and subsequently to identify the top five most important elements of each. While consensus was not found, important insights were identified that can assist improve the standard of decision making at all levels of emergency response and management. We observed (i) a lack of a common understanding between participants of what a decision is, and how a decision differs from pre‐decision and postdecision components; (ii) responses varied according to whether a free text description or the identification of separate elements was requested; (iii) respondents valued ‘goodness’ across different and at times unrelated components a scaled measure of decision quality as opposed to a binary evaluation of ‘rightness’; and (iv) pre‐ and postdecision elements are considered more important than the decision itself when determining the quality of a decision. To address the issues highlighted by the study we recommend improvements in training, and improvements to organization doctrine related to decision making, risk tolerance, assessment of decision quality and development of intent‐based or principle‐based operational guidance. Ultimately, incident controllers from all services must remain cognizant that they will face considerable scrutiny if they cannot explain how they arrived at the decision they made.

A discrete-event simulation study for pre-hospital emergency systems

ABSTRACT Ambulance dispatch, deployment, and relocation are crucial decisions in pre-hospital emergency medical services (EMS) that significantly impact response times. In metropolitan areas, the rapid movement of people and complex traffic conditions create a challenging environment for effective decision-making. This paper presents a discrete-event simulation framework designed to evaluate and improve pre-hospital emergency systems by capturing core EMS workflows and incorporating comprehensive, dynamic data. To demonstrate the practical application of this framework, a simulation instance is implemented and validated in the context of downtown Shanghai, with an analysis of key performance indicators for EMS in this area. Additionally, we propose a dynamic ambulance relocation model to improve the alignment between ambulance deployment plans and emergency calls. Simulation experiments show that dynamic ambulance relocation effectively reduces response times. Due to its ability to integrate real-time data, this framework has the potential to support immediate decision-making and evaluate proposed strategies in the real world.

Transcarotid artery revascularization outperforms transfemoral carotid artery stenting regardless of aortic arch type or degree of atherosclerosis

ObjectiveThe Centers for Medicare and Medicaid Services now approve reimbursement for transfemoral carotid artery stenting (TFCAS) in the treatment of standard-risk patients with carotid artery occlusive disease. TFCAS in patients with complex aortic arch anatomy is known to be challenging with worse outcomes. Transcarotid artery revascularization (TCAR) could be a preferable alternative in these patients owing to avoiding the aortic arch and using flow reversal during stent deployment. We aim to compare the outcomes of TCAR vs TFCAS across all aortic arch types and degrees of arch atherosclerosis. MethodsAll patients undergoing carotid artery stenting between September 2016 and October 2023 were identified in the Vascular Quality Initiative database. Patients were stratified into four groups: Group A (mild atherosclerosis and type I/II arch), Group B (mild atherosclerosis and type III arch), Group C (moderate/severe atherosclerosis and type I/II arch), and Group D (moderate/severe atherosclerosis and type III arch). The primary outcome was in-hospital composite stroke or death. Analysis of variance and χ2 tests analyzed differences for baseline characteristics. Logistic regression models were adjusted for potential confounders, and backward stepwise selection was implemented to identify significant variables for inclusion in the final models. Kaplan-Meier survival estimates, log rank test, and multivariable Cox regression models analyzed hazard ratios for 1-year mortality. ResultsA total of 20,114 patients were included (Group A: 12,980 [64.53%]; Group B: 1175 [5.84%]; Group C: 5124 [25.47%]; and Group D: 835 [4.15%]). TCAR was more commonly performed across the four groups (72.21%, 67.06%, 74.94%, and 69.22%; P < .001). Compared with patients with mild arch atherosclerosis, patients with advanced arch atherosclerosis in Group C and Group D were more likely to be female, hypertensive, smokers, and have chronic kidney disease. Patients with type III arch in Group B and Group D were more likely to present with stroke preoperatively. On multivariable analysis, TCAR had less than one-half the risk of stroke/death and 1-year mortality compared with TFCAS in the patients with the mildest atherosclerosis and simple arch anatomy (Group A) (odds ratio [OR], 0.43; 95% confidence interval [CI], 0.31-0.61; P < .001; hazard ratio, 0.42; 95% CI, 0.32-0.57; P < .001). Group B patients with similar atherosclerosis but more complex arch anatomy had 70% lower odds of stroke/death with TCAR compared with TFCAS (OR, 0.30; 95% CI, 0.12-0.75; P = .01). Similar findings were also evident in patients with more severe atherosclerosis and simple arch anatomy (OR, 0.66; 95% CI, 0.44-0.97; P = .037). There was no significant difference in odds of stroke/death in patients with advanced arch atherosclerosis and complex arch (Group D) (OR, 0.91; 95% CI, 0.39-2.16; P = .834). ConclusionsTCAR is safer than TFCAS in patients with simple and advanced arch anatomy. This could be related to the efficiency of flow reversal vs distal embolic protection. The current Centers for Medicare and Medicaid Services decision will likely increase stroke and death outcomes of carotid stenting nationally if multidisciplinary approach and appropriate patient selection are not implemented.

Extending mechanical thrombectomy service provision to 24/7: a break-even analysis

BackgroundComprehensive stroke centres across England have developed investment proposals, showing the estimated increases in mechanical thrombectomy (MT) treatment volume that would justify extending the standard hours to a 24/7 service provision. These investment proposals have been developed taking a financial accounting perspective, that is by considering the financial revenues from tariff income. However, given the pressure put on local health authorities to provide value for money services, an affordability question emerges. That is, at what additional MT treatment volume the additional treatment costs are offset by the additional health economic benefits, that is quality-adjusted life years (QALYs) and societal cost savings, generated by administering MT compared to standard care.MethodsA break-even analysis was conducted to identify the additional MT treatment volume required. The incremental hospital-related costs associated with the 24/7 MT extension were estimated using information and parameters from four relevant business cases. The additional societal cost savings and health benefits were estimated by adapting a previously developed Markov chain-based model.ResultsThe additional hospital-related annual costs for extending MT to a 24/7 service were estimated at a mean of £3,756,818 (range £1,847,387 to £5,092,788). On average, 750 (range 246 to 1,571) additional eligible stroke patients are required to be treated with MT yearly for the proposed 24/7 service extension to be affordable from a health economic perspective. Overall, the additional facility and equipment costs associated with the 24/7 extension would affect this estimate by 20%.ConclusionsThese findings support the ongoing debate regarding the optimal levels of MT treatment required for a 24/7 extension and respective changes in hospital organisational activities. They also highlight a need for a regional-level coordination between local authorities and hospital administrations to ensure equity provision in that stroke patients can benefit from MT and that the optimal MT treatment volume is reached. Future studies should contemplate reproducing the presented analysis for different health service provision settings and decision making contexts.

The impact of intergovernmental fiscal tools on local service decision: the Nebraska case for water and wastewater systems

ABSTRACT This study examines the impact of federal funding on local spending for water and wastewater infrastructure (hereafter ‘W&WI’) in Nebraska. The recent deterioration of W&WI creates potential risks to citizens’ lives. The federal government operates several programmes to assist with W&WI improvements. However, funding is limited, while there are substantial demands for funding. To understand the fiscal impact of intergovernmental aid on local expenditures, this study utilised panel data from 2010 to 2018 for 129 Nebraska municipalities with population over 800 that have water and wastewater systems. This study finds some evidence of the stimulating (‘flypaper’) effect of federal aid on local expenditures. However, in disaggregated models, the aid has crowd-out effects on specific types of spending (e.g. operating expenses). The findings can contribute to policymakers’ understanding of the impact of federal funding programmes and suggest directions for improvement of these tools.

Service Performance in Banks: The Effects of Customer Relationship Management Systems Extended Usage

With the rapid development of information technology, customer relationship management systems (CRMS) have been widely adopted by banks and play a crucial role in customer development and retention. However, there are also numerous cases of CRMS implementation failures. Further, employees are enabled by CRMS to make service decisions with the support of leaders. The role of leaders in employees’ CRMS usage is understudied. Thus, based on the information systems diffusion model and the theory of leader substitutes, a research model is proposed and empirically tested. The results show that employee CRMS explorative usage has a positive impact on their service performance, while employee CRMS exploitative usage has a significant negative impact on service performance. Besides, significant leadership substitution effects are discussed in CRMS usage.

The Manchester Scoring System for predicting BRCA1/2 mutations underperforms in Arabic Omani breast cancer patients.

Risk assessment models that are applied to assess the lifetime risk of cancer and pathogenic variant risk are more commonly used in Western populations. Using these models, without validation, for non-Western populations has been questioned. This study aimed to evaluate the use and consistency of the Manchester Scoring System as a risk assessment model for the Omani population. A retrospective, file-based analysis was performed on breast cancer patients seen in a genomics department over a two-year period. Personal cancer history and family history were used to analyze the Manchester scores of 409 breast and/or cancer patients. The results show that, overall, the Manchester scores were low. If this risk assessment model had been used to determine eligibility for a priori service and genetic testing decisions, 12 BRCA pathogenic cases would have been missed. At this time, the Manchester Scoring System does not seem to be the best risk assessment model for use in the Omani population, unless the eligibility threshold of ≥6 is used, which could provide a better sensitivity for the Omani population. We propose using concepts of the Manchester Scoring model to create a scoring system that is more suitable for the Omani and Arabic population.

Logistics service decision for e-commerce supply chain with fairness concern under uncertain environment

In this paper, we construct a two-echelon supply chain composed of an e-commerce platform and a manufacturer. We investigate the impact of manufacturer’s fairness concern (MFC) on e-commerce supply chain logistics service decisions under an uncertain environment. Given the uncertainty of the market environment, we depict unit cost of product and logistics service elasticity as uncertain variables. We construct four game-theoretical models, i.e. the manufacturer or the platform undertakes logistics service without and with considering the MFC, and compare the equilibrium solutions through numerical analysis. The results demonstrate that providing logistics services through an e-commerce platform increases the profits of all supply chain participants, regardless of whether the MFC is considered or not. Furthermore, we discover that the manufacturer will not always benefit from the MFC, the MFC can decrease the expected profits of supply chain participants. Interestingly, an increase in the level of environmental uncertainty does not necessarily imply a threat to the interests of supply chain participants. Under an uncertain environment, every supply chain participant needs to consider its fairness concern behavior and coordinate with logistics service strategies.

Health Professionals' Experiences with Health-Promoting Dialogues for Older Home-Dwellers-A Qualitative Study.

Home-dwelling older people without healthcare services might develop vulnerability and health-related issues that should be detected proactively by service providers. Health-promoting measures directed towards the target group could facilitate living longer and better at home, as well as delay the need for healthcare services. One approach is through health-promoting dialogues between the municipality and healthcare professionals. This study aims to explore the experiences of healthcare professionals involved in health-promoting dialogues with home-dwellers aged over 75 years without health service decisions in Norway. Data were collected through three focus groups. Thematic analysis was applied to the data resulting in the emergence of one major theme, "challenging dialogues", comprising three sub-themes: "promote the individual's perspectives", "uncovering vulnerability", and "ambiguity of the dialogues". The health-promoting dialogue uses a resource perspective for the elderly to remain independent in old age and can reveal vulnerability and underlying needs. The purpose of the dialogue appears ambiguous for the target group, which leads to unclear service expectations and frequent rejections of the offer. Nevertheless, this health-promoting service has a clear purpose of identifying and meeting the needs of the target group in a broader sense during the ageing process.

Efficient and Reliable Estimation of Knowledge Graph Accuracy

Data accuracy is a central dimension of data quality, especially when dealing with Knowledge Graphs (KGs). Auditing the accuracy of KGs is essential to make informed decisions in entity-oriented services or applications. However, manually evaluating the accuracy of large-scale KGs is prohibitively expensive, and research is focused on developing efficient sampling techniques for estimating KG accuracy. This work addresses the limitations of current KG accuracy estimation methods, which rely on the Wald method to build confidence intervals, addressing reliability issues such as zero-width and overshooting intervals. Our solution, rooted in the Wilson method and tailored for complex sampling designs, overcomes these limitations and ensures applicability across various evaluation scenarios. We show that the presented methods increase the reliability of accuracy estimates by up to two times when compared to the state-of-the-art while preserving or enhancing efficiency. Additionally, this consistency holds regardless of the KG size or topology.