Service Level Research Articles

The Classification Method is Used for Sentiment Analysis in My Telkomsel

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.

Основные факторы конкурентоспособности услуг курортных отелей в современных условиях РФ

The hotel business, as well as other sectors of the Russian economy, is experiencing significant economic problems in the modern conditions of the Russian Federation. At the same time, certain segments of the hospitality industry are characterized by a high level of competition. To increase the competitiveness of the hotel, it is necessary to ensure the growth of the competitiveness of the services offered by it, which depends on the degree of customer satisfaction with their quality and level of service. This work is devoted to the identification of the main factors determining the competitiveness of resort hotel services. The results of the study can be further used to identify strategies for the functioning of relevant economic entities.

The Implementation of Integrated Youth Services in Canada: Planning and Costing of a Pan-Canadian Model: La mise en œuvre des services intégrés pour les jeunes au Canada : planification et établissement des coûts d'un modèle pancanadien.

The implementation of Integrated Youth Services (IYS) can help ensure that youth are adequately supported. The objective of this analysis was to provide a model for the planning and costing of IYS throughout Canada over a 15-year period. To estimate resource allocation for IYS, we determined the number of hubs and hub staffing requirements by service level and jurisdiction, backbone support and infrastructure requirements by jurisdiction. A needs-based analytic framework for planning was employed to estimate the number of hubs required. The optimal mix of hub staffing requirements was determined based on prior literature. The costs of running each hub were estimated using publicly available data and internal documents from existing IYS agencies. Finally, the cost of setting up IYS hubs, IYS virtual care and respective backbone support throughout Canada was estimated and projected over 15 years and the cost-savings of IYS were calculated. At maturity, it was estimated that 399 hubs-188 small, 43 medium, 168 large-across Canada would be required to address youth mental health and substance use needs. The cost of implementing IYS initiatives across Canada would vary between $4,349,126 (for less populous jurisdictions) and $248,950,524 (for more populous jurisdictions), for a total annual cost of $676,633,388 (excluding costs of infrastructure). It was estimated that the implementation of IYS hubs would lead to cost-savings of $2.1 billion annually and have the potential to be cost-effective. The implementation of IYS hubs can provide good value for money, in the form of high client satisfaction, earlier supports with improved youth outcomes and decreased health care costs. Future work should address gaps in data availability on mental health and substance use-related needs of youth with neurodevelopmental disorders, youth experiencing homelessness, youth in congregate living and foster care, and Indigenous youth.

Energy-efficient joint performance optimization of cloud data centre users/operator using memetic algorithm

ABSTRACT The use of cloud computing as a cost-effective and flexible method has currently drawn the attention of cloud service providers. When offering various cloud services to users, a certain level of Service Level Agreement (SLA) must be guaranteed by the cloud operator, based on the service received at the user level. Considering the non-linear dependency of network users’ Quality of Experience (QoE) on cloud resources (RAM, disk memory, network bandwidth, and CPU core usage) as well as the non-linear and time-varying dependency of cloud operator efficiency on the level of resources consumed by users, joint optimization of user experience and cloud operator efficiency poses significant computational challenges. This paper focuses on the green optimization of both user experience and the benefits of cloud data centre operators, employing a metaheuristic algorithm, which essentially combines a genetic algorithm with an innovative approach. In this approach, the efficiency of the cloud data centre operator, energy consumption of data centres, and user experience quality are jointly optimized. Simulation results demonstrate the improvement of the combined user/operator utility function compared to traditional methods, considering energy consumption constraints.

National census of UK endoscopy services in 2023

BackgroundThe Joint Advisory Group on Gastrointestinal Endoscopy (JAG) conducts a biennial census of UK endoscopy services. The 2023 census aimed to assess the current status of endoscopy services and compare...

Last train schedule optimization for metro systems considering minimum adjustment cost under elastic passenger demand

ABSTRACT Optimizing the last train schedule is challenged by the elastic nature of passenger flow and the immutability of the schedule once announced to the public. The elastic passenger demand is first mathematically described, and an optimization method is proposed to minimize the adjustment to the last trains while maximizing the accessible passengers within the metro network. Linearization techniques are then employed to transform it into a linear programming form. The proposed method is verified through a real-world case study, which indicate an 8.4% increase in accessible passenger flow on a weekday with adjustments required for only 3 lines and 10 stations’ last train times. In a special event scenario, a 9.8% is achieved with adjustments to 2 lines and 8 stations’ last train times. The results suggest that for existing metro systems, judiciously adjusting the last train times can effectively enhance the overall level of nighttime operational service.

Enablers to high vaccination uptake among a disadvantaged minority population: a qualitative study of the Arab population of Israel

BackgroundOver 1.9 million Arabs live in Israel and constitute 21% of the total population. Despite being a disadvantaged minority population with wide gaps in health indicators, Arabs have higher Human Papillomavirus (HPV) and Measles, Mumps, and Rubella (MMR) vaccination rates compared with the general Jewish population.MethodsIn-depth interviews with 21 health care providers, 16 Arab mothers, and 16 teenagers were conducted to collect information about health system enablers to HPV and MMR vaccination. All interviews were conducted in Arabic by an Arab researcher, audio-recorded, transcribed, and analysed using thematic analysis of the transcripts. Themes were mapped according to the WHO Health Systems Building Block Framework.ResultsWe identified several health system enablers. On the service delivery level, accessible and availability of vaccination services, delivery of vaccines through the school system and mother child clinics, and framing vaccinations as the norm were themes. Personable characteristics of the health workforce, the health care providers belonging to the same cultural group, and high levels of trust towards healthcare professionals were reported. Vaccination provided at no cost was also identified. On the leadership/governance level, the Arab community felt equal treatment and distribution of services, which was also an enabler reported. Despite high vaccine uptakes, parents and teenagers had limited knowledge regarding vaccination, particularly HPV.ConclusionsThis study highlights that a combination of good access and delivery to vaccination, delivered by a culturally competent, available, accessible respectful workforce can enable disadvantaged minorities to achieve high vaccine coverage, in particular in a cultural context where the population trusts and follows medical advice. Such evidence can serve as a basis for developing policies, interventions, and guidance to improve vaccine uptake among other underserved minority communities.

Proposing and Analyzing the Effects of a Road Work Zone Mobile Application with User Interface and Rerouting on Level of Service

Work zones are common on roadways either for construction or maintenance purposes. They usually cause a drop in the capacity, as well as the level of service (LOS) of the road, and result into traffic congestion. In this paper, a free-flow segment of 222 m along the eastbound movement of Sheikh Jaber Al Ahmad Al Sabah (SJAS) Highway was studied as it went under road construction. The acceleration lane was closed which resulted in a drop of capacity from 2472 pc/h/ln to 2396 pc/h/ln and dramatic drop in LOS from A to D. The paper proposes a mobile application to improve LOS along roadways with workzones. The proposed application will provide information to the road user along with alternative routes. A survey was done which covered 1025 vehicle users in Bahrain. It aimed to understand and analyze traveler’s willingness to reroute in case of previous knowledge about work zones along their route. The survey showed that 96% of the sample was willing to reroute. Calculations were made based on certain assumptions to get LOS at work zone back to A. It showed that to improve the LOS to A, 50% to 54% of the vehicles along the road should reroute, which is way below 96% and gives us a huge margin of error. In conclusion, it could be said that the proposed application could improve the work zone LOS, when developed and implemented according to the recommendations of this research.

Leader–follower identification with vehicle-following calibration for non-lane-based traffic

Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based traffic. However, traffic conditions with varying vehicle dimensions, intermittent following, and multiple leaders often occur, making subjective Leader–Follower (LF) pair identification challenging. In this study, we analyze Vehicle Following (VF) behavior in traffic with a lack of lane discipline using high-resolution microscopic trajectory data collected in Chennai, India. The paper’s main contributions are threefold. Firstly, three criteria are used to identify LF pairs from the driver’s perspective, taking into account the intermittent following, lack of lane discipline due to consideration of lateral separation, and the presence of in-between vehicles. Secondly, the psycho-physical concept of the regime in the Wiedemann-99 model is leveraged to determine the traffic-dependent “influence zone” for LF identification. Thirdly, a joint and consistent framework is proposed for identifying LF pairs and estimating VF parameters. The proposed methodology outperforms other heuristic-based LF identification methods from the literature in terms of quantitative and qualitative performance measures. The proposed approach can enable robust and more realistic LF identification and VF parameter calibration with practical applications such as level of service (LOS) analysis, capacity, and travel time estimation.

Enhancing supply chain resilience with recovery strategies under electricity shortages

Electricity shortages triggered by extreme events have profoundly disrupted supply chains (SCs) in recent years. As SC operations heavily depend on electricity, simultaneous uncertainties may occur in supply and production capacities, demand, and transportation capacity if electric trucks are used. To enhance SC resilience, it is imperative to combine new recovery strategies (i.e. alternative energy sources and alternative transportation vehicles (ATVs)) with traditional ones to restore SC performance. However, these strategies have not been investigated quantitatively. Besides, only partial probability distribution information of uncertainties may be available under electricity shortages. Thus, this work improves SC resilience from a worst-case perspective, where the moment information is known. For the problem, a bi-objective distributionally robust chance-constrained optimisation model is constructed to balance the total cost and the service level. Then, an effective ϵ-model-based constructive heuristic (ϵ-MBCH) is developed. Compared with the ϵ-constraint method, the efficiency of ϵ-MBCH is improved by 99.81%. Based on stress tests and sensitivity analyses on a real case, key managerial insights include: (i) the ATV strategy is indispensable under varying severities of electricity shortages; (ii) the inventory management strategy helps reduce reliance on power generators and diesel trucks; and (iii) mitigating demand variability is essential for reducing costs.

Shared automated mobility-on-demand system with different response, pricing and dispatching strategies

ABSTRACT The shared automated mobility-on-demand system can offer convenient ‘point-to-point’ services for travelers. However, most previous studies have not combined pricing strategies with dispatching strategies to investigate their impact on system operation results. This paper proposes two demand response strategies for the shared automated mobility-on-demand system. From the perspective of time and space, eight ridesharing pricing strategies are proposed. The preliminary vehicle matching and routing are obtained through the integer linear programming considering system profit and service level, and the vehicle status is updated by four dispatching strategies that consider whether the matching scheme and route are updated over time. The results show that the factors taken into account in the ridesharing pricing strategy are restricted by the system to maximize profit, and the most flexible dispatching strategy is not the most profitable. These detailed analyses can provide a basis for formulating an effective operation mode of the automated mobility-on-demand system.

Hospital system market share and commercial prices: a cross-sectional approach using price transparency data

BackgroundThe goal of this study is to estimate the association between hospital system market share and negotiated prices.Hospital system consolidation has led to many highly concentrated markets where systems can leverage their market share to negotiate higher commercial prices. Recently, the Centers for Medicare & Medicaid Services, under its Transparency in Coverage initiative, required health insurers to release all negotiated commercial prices, providing, for the first time, publicly available, nationally representative data on commercial rates. We utilize this newly available data on negotiated prices of healthcare services to show that a hospital with 10% higher market share charges 880-1,180 more per admission.Study designWe used commercial price data for national networks of three large, national insurers and performed a linear regression based on more than 1.3 million negotiated rates across 1,784 hospitals to estimate the association between a hospital’s system-level market share and commercial negotiated rates, adjusting for service (DRG), health system, and area level time-invariant characteristics.ResultsWe find that a one percentage point increase in hospital system market share is associated with an $88 to $118 higher negotiated rate per admission. All else equal, a hospital that is part of a system with a 10-percentage point higher market share can expect from $880 to $1,180 more per admission relative to a hospital with lower system market share (5.4% to 6.2% of the median price).ConclusionThese findings confirm that higher hospital system market share is strongly associated with higher commercial negotiated prices and should aid policymakers and decisionmakers in assessing the impact of various policy options aimed at reducing provider consolidation in the healthcare market.Trial registrationNot applicable.

Analyzing Feedback <i>M</i>/<i>G</i>/1 Double Retrial Orbit with Two-Phase Optional Service and Repair under Working Vacation Policy

This research presents a comprehensive analysis of the M/G/1 double retrial queue model with a two-phase optional service and repair, under a working vacation policy. The model addresses scenarios where patients are willing to pay more for enhanced comfort and care. To accommodate this, an optional service phase is introduced, allowing patients to choose between ordinary and premium service levels. The system is further refined by a two-phase service mechanism: in the first phase, patients receive an initial level of service, and depending on their satisfaction or specific needs, they may opt for an enhanced second phase of service. Additionally, the model incorporates feedback and repair processes, reflecting a more realistic representation of queue dynamics. Patients who are dissatisfied with the service or encounter issues can re-enter the system for further attention. The non-Markovian model equations are solved using the probability-generating function approach, which facilitates the analysis of complex queue behaviors and the calculation of key performance indicators, such as expected system length and queue length. The paper includes detailed numerical results with test data to demonstrate the accuracy of the model’s predictions and validate the proposed system design’s effectiveness.

Integrated Performance Assessment of Prefabricated Component Suppliers Based on a Hybrid Method Using Analytic Hierarchy Process–Entropy Weight and Cloud Model

Prefabricated construction has garnered widespread attention worldwide. As an important part in purchasing management, prefabricated component suppliers (PCSs) undertake the bulk of the work and responsibility in the supply chain from on-site to off-site. Therefore, it is crucial and necessary to establish a decision-making framework to comprehensively evaluate the performance of PCSs. This study proposed a set of performance indicators for PCSs, including component quality, cost, delivery capability, service level, enterprise development potential, and enterprise cooperation potential. A hybrid method was established to evaluate the integrated performance of PCSs based on Analytic Hierarchy Process (AHP)–entropy weight and cloud model. It integrated the AHP and entropy weight method to calculate indicator weights, while the cloud model was employed to transform qualitative characteristics into quantitative ones. To verify the feasibility of this method, an empirical study was subsequently conducted using a typical case from China. The obtained results demonstrate that the overall performance of Supplier A lies at the “good” level, with the similarity index between the comprehensive cloud model and the standard cloud model within the good range, at 0.4045. Among the six primary indicators, quality performance scored the highest at 0.65, meeting the “excellent” standard. It can be seen that the hybrid approach of AHP–entropy weight and cloud model accurately and effectively demonstrates the integrated performance of PCSs. The main aim of this study was to establish a comprehensive system and develop a novel approach for evaluating the performance of PCSs within a hesitant fuzzy environment. The findings of this study can provide guidelines for researchers and the public to evaluate PCS performance, contributing significantly to the fields of supply chain management and construction engineering. Moreover, it provided a practical tool for professionals in the industry to enhance the supplier selection processes.

Evaluation of Trans Metro Pekanbaru public transport bus performance and services for sustainable mobility

Abstract The city of Pekanbaru is the capital of Riau Province with a relatively high rate of population growth, migration and urbanization. Population growth based on BPS data from Pekanbaru City in 2023 is 1.30%, where the growth that increases every year is directly proportional to the condition of increasing use of private transportation has exceeded the capacity of the road. Based on data from the Pekanbaru Transportation Agency in 2023, the number of daily users of Trans Metro Pekanbaru public transportation is only 0.44% of the total population, this shows that interest in using public transportation in Pekanbaru City is still very low, while on the other hand, congestion and air pollution are increasing due to the use of private transportation. The purpose of this study is to determine the performance level of Trans Metro Pekanbaru with a performance evaluation method that is guided by the technical assessment standards of bus performance in accordance with SK.687/AJ.206/DRJ/2002 concerning the implementation of public transportation issued by the land transportation office. The results of the evaluation of the performance evaluation assessment of Trans Metro Pekanbaru obtained a range weight value for weekends and weekdays of 14-16 including the currently category. In addition to the level of performance to increase interest and the public in using Trans Metro Pekanbaru, a survey and analysis of service levels were also carried out using the Customer Satisfaction Index (CSI) method. Meanwhile, the level of service was obtained through the results of a passenger questionnaire survey in the field which was then analyzed using the Customer Satisfaction Index (CSI) method. The results of the service level with the CSI method obtained for Trans Metro Pekanbaru are 86% which can be interpreted as very satisfied with the service level of Trans Metro Pekanbaru.

Design model for shear keys used as thermal break systems in balcony-slab joints under combined shear and bending loads

Balcony-slab joints (BSs) are common in residential buildings. Integrating thermal break systems (TBSs) can reduce the energy loss resulting from thermal bridging. However, the mechanical performance of BSs equipped with TBSs against shear and bending remains hardly explored in design and experimentation. The aim of this study is to evaluate the mechanical behavior of BS joints under combined shear and bending actions, taking into account the presence of TBSs. Seven large-scale balcony-slab specimens were fabricated and tested under vertical loading with different moment-shear ratios (M/V). The BSs are equipped with TBSs comprising steel bars and shear profiles. A simplified finite element (FE) model was then developed using the ABAQUS software. The FE model employs spring-like elements to simulate the bond and contact behavior between concrete and steel, reproducing both the resistance and stiffness of the tested specimens. On the basis of the experimental and FE results, a resistance model consistent with the Eurocode design guidelines was drawn. Finally, moment-shear (M-V) interaction curves for balcony-slab joints were generated at the ultimate (ULS) and serviceability (SLS) levels, considering the impact of horizontal loading to achieve comprehensive design insights. The tests and FE results showed that the moment-shear ratio (M/V) significantly influences the shear capacity of the BS joints. The steel profiles effectively acted as shear keys connecting the balcony and the slab. A strength reduction coefficient of 0.701 was introduced for the design.

Spatial variation of bus stop crime response to changes in the surrounding environment and transit level of service

Increasing concerns over the significant number of crimes occurring at and around bus stops have motivated the analysis of intervening factors to propose solutions to this problem. However, models found so far overlook important features of crime data: spatial dependence and spatial heterogeneity. In addition, crime predictor data has shown to be multicollinear in previous studies. To tackle these issues, this paper analyzes mobile phone thefts and robberies at 19,329 bus stops in São Paulo (Brazil) based on Geographically Weighted Regression (GWR), using components retained from a Principal Component Analysis (PCA) as explanatory variables. A comparison is carried out between GWR and a non-spatial Transformed Linear Regression (TLR), and a Negative Binomial Regression (NBR) with uncorrelated predictors. Effects on crime from 9 PCs, representing central areas, bus transit level of service, transport infrastructure, land use and sociodemographic features, were proven to have high spatial variability. Changes in the surrounding environment can cause higher or lower increases in mobile phone thefts and robberies at stops according to their spatial location. Results showed that GWR performs better than NBR and TLR in predicting bus stop crime, thus compensating for a loss of information associated with PCA. In addition, GWR was able to completely incorporate the spatial dependence found in the non-spatial model, which covered the nearest 96 neighboring stops. The proposed method can effectively detect critical bus stops and regions, contributing to crime prevention. GWR coupled with PCA can also help identify the best locations to install new bus stops.

Studying the performance of pavement defects at different road slopes using the vibration-based method and deep machine learning

Road networks are the backbone of urban life and significantly impact the sustainability of any country's infrastructure sector. Therefore, it is necessary to maintain the condition of roads and pavements through continuous monitoring and periodic maintenance in order to achieve the highest levels of service for road users and the sustainability of their use. Pavement is the main component of road networks, providing the highest degree of comfort to drivers and roadway users when it is appropriately designed and free from defects and cracks. More clearly, defects are one of the most important factors that reduce the operational life of roads and cause economic losses to road users by causing damage to their vehicles; moreover, the damaged pavement needs frequent and long maintenance that may also drain the resources of government institutions and transport agencies. Therefore, there is a crucial need for a monitoring and follow-up system for the condition of the roads in order to identify and treat defects quickly. This study used a vibration-based system to monitor pavement conditions on several roads with different gradients. A fully electric car was used to determine the vibration values, which indicate the degree of driving comfort, to determine the spread and behaviour of defects on the pavement at multiple locations on roads with different gradients. Also, a machine learning model was applied using a "decision tree" model to identify, classify and predict defects on the pavements. The results of this study indicated that pavement defects were more prevalent in the first and last quadrants of the high-slope roads compared to the low-slope roads. The prediction model achieved accuracy in predicting the performance of defects with a rate of 94% for roads with low gradients and 90% and 86% for roads with medium and high gradients, respectively.

A multi-core makespan model for parallel scientific workflow execution in cloud computational framework

Researchers have shown a lot of potential in optimizing cloud-based workload-scheduling over the past few years. However, executing scientific workloads inside the cloud is time-consuming and costly, making it inefficient from both a financial and productivity standpoint. As a result, there are many investigations conducted, with the general trend being to speed up the rate of processing and establish a cost-effective system, whereby customers are billed according to their actual use. In addition, energy-consumption is capable of being reduced, especially if the available resources are heterogeneous; however, few investigations have optimized multi-core with analyzing makespan parameters collectively to fulfill the quality of service (QoS) and service level agreement (SLA) of the workload task. In this research, we introduce an optimal scheduling for a heterogeneous distributed cloud computing environment called task aware makespan optimized scheduler (TAMOS) that guarantees requirements across the task levels of scientific workflows. The energy and time required to carry out specific workflows are significantly reduced by using this TAMOS strategy. The TAMOS framework was studied using the scientific workflows namely, inspiral and sipht. When compared to the conventional method of scheduling work, our methodology used less energy and makespan.

ESTIMATING OPERATIONAL COSTS FOR ENHANCED CONTINUITY IN WATER SUPPLY AND SANITATION SERVICES: AN INVERSE DATA ENVELOPMENT ANALYSIS APPROACH IN CHILEAN UTILITIES

Study regionThe largest 23 water utilities in Chile providing water and sanitation services to 97.9% of total urban customers. Study focusThis study introduces an innovative methodological framework based on inverse data envelopment analysis technique to quantify the additional operational costs required for water utilities to enhance their water supply and sanitation continuity while maintaining their techno-economic efficiency. This framework is pivotal for setting realistic continuity targets and assessing potential impacts on water and sanitation tariffs, thereby aiding in policy and operational planning within the water sector. New hydrological insights for the regionThe analysis was performed across four distinct continuity scenarios revealing substantial cost variability contingent on continuity targets, current service levels, and the efficiency of each utility. Results showed a median operational cost increase that varied from 28.41% in the most stringent scenario to just 0.27% in the most lenient. The financial implications of meeting these costs through water and sanitation tariffs would necessitate a median monthly increase in water bills, ranging from $17.24 per household in the most demanding scenarios to only $0.01 per household in the more lenient scenarios. This research provides a valuable tool for water sector regulators and managers, enabling informed decision-making by balancing service improvement objectives with economic considerations and customer willingness to pay for enhanced service continuity.