Service Delay Research Articles

Transformasi Digital: Pengembangan Aplikasi Kasir untuk Meningkatkan Efisiensi Operasional di Cafe Casa n Co Sub

In the context of rapid digitalization, many cafes, including Cafe Casa n Co Sub, face challenges in managing transactions that are still carried out manually, which is at high risk of recording errors and service delays. This research aims to develop a digital cashier application that can improve operational efficiency and recording accuracy in the café. The research method used is a case study with a qualitative approach. This data was collected through in-depth interviews with café owners and staff, direct observation of the operational process, and analysis of related documents. Techniques in data analysis are carried out thematically to identify patterns and provide recommendations for improvement. The results of the study show that the application of information technology in the form of a cashier application can support the management of cafes more professionally and efficiently.

Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Time Will Tell: A Temporal Analysis of Victim-Survivor’s Formal Support-Seeking for Co-occurring Family Violence and Sexual Harm

Abstract Drawing on qualitative interviews with both victim-survivors (n = 11) and specialist service providers (n = 11) in Victoria (Australia), this article analyses formal support-seeking for co-occurring family violence and sexual harm through a temporal lens. Our findings focus on two main themes: (i) victim-survivor readiness and trauma-informed support; and (ii) service readiness, time constraints and delays in providing support. The analysis sheds light on the disconnection between crisis intervention for family violence and long-term therapeutic support for sexual harm. In response, we emphasize the unique contribution of a trauma-informed temporal lens to criminological knowledge and discuss implications for policy and practice.

Construction of a simulation model of the work of a transport and forwarding enterprise in organizing multimodal cargo transportation

The object of this study is the process of rendering transport and forwarding services when organizing multimodal cargo transportation in international traffic. The task to tackle was predetermined by the need to devise recommendations for choosing the optimal cargo transportation route, taking into account the individual requirements of the customer and performance indicators of the forwarders at the transport and forwarding company. A simulation model of the work of a transport and forwarding company in the organization of multimodal transportation of goods was built and implemented in the GPSS World simulation automation software. The model involves the optimization of the duration of service provision and makes it possible to determine the required number of transportation department specialists to enable effective cooperation with customers. When constructing the model, the duration of business processes in the organization of multimodal transportation was taken into account. In addition, the model accounts for the probability of errors and service delays, as well as the average delay time when errors occur, which will make it possible to take into account the additional work time of the forwarder to eliminate them. The application of the built model in practice will allow specialists at transport and forwarding companies to organize multimodal transportation according to several alternative routes that meet the individual needs of the customer. It will also make it possible to determine the optimal number of forwarders needed to work at the multimodal transportation department. In this case, the duration of rendering transport and forwarding services will be reduced by 12–16 %, the waiting time in queues for service will decrease by 9–13 %, and the efficiency of the work of specialists at the multimodal transportation department will increase by 9–14 %

A Study of Customer Satisfaction towards BFSI Sector

The BFSI (Banking, Financial Services, and Insurance) sector plays a pivotal role in the global economy by offering essential financial products and services. This study focuses on customer satisfaction within the BFSI sector, exploring key factors that influence customer perceptions and experiences. The research examines aspects such as service quality, accessibility, reliability, digitalization, and employee responsiveness, using both qualitative and quantitative methodologies. Primary data was collected through surveys and interviews with BFSI customers, while secondary data was gathered from industry reports and academic literature. Analytical tools were employed to identify trends and measure satisfaction levels across diverse customer segments. The study applies established frameworks, such as the SERVQUAL model, to evaluate service gaps and suggests actionable strategies for improvement. Findings indicate that digital innovations, personalized services, and efficient grievance redressal mechanisms significantly enhance customer satisfaction. Conversely, issues like delayed services and inadequate communication remain areas of concern. This report concludes with recommendations to strengthen customer relationships, enhance trust, and improve overall service delivery, ensuring long-term competitiveness in the BFSI sector. This study provides valuable insights for stakeholders aiming to foster customer-centric growth in a dynamic financial landscape. Customer satisfaction is a critical measure of success in the BFSI (Banking, Financial Services, and Insurance) sector, which serves as the backbone of the financial ecosystem. This study investigates the factors influencing customer satisfaction in the BFSI sector, focusing on key elements such as service quality, technological advancements, ease of access, and relationship management. The research employed a mixed-method approach, combining surveys, interviews, and secondary data analysis to evaluate customer experiences and expectations. Analytical tools such as the SERVQUAL model were utilized to assess gaps in service delivery. The study also explored the impact of digital transformation and personalized service offerings on customer loyalty and satisfaction. Findings reveal that while technological innovations and efficient grievance handling improve satisfaction, challenges such as service delays, lack of transparency, and limited financial literacy hinder the overall customer experience. Recommendations include enhancing digital platforms, offering tailored financial solutions, and fostering customer trust through effective communication and reliability.

A Study on Customer Satisfaction towards Automobile Industry in Pune

The research paper aims to study Customer Satisfaction in the Automobile Industry in Pune. The study seeks to understand the key factors influencing customer satisfaction, such as product quality, sales experience, after-sales services, and overall customer support. It employs a quantitative research approach, using surveys to gather primary data from 135 respondents, mostly existing automobile industry customers. Key findings indicate high satisfaction with the quality and performance of the Automobile Industry. However, some customers raised concerns about the availability of spare parts and service delays. The study also highlights the importance of personalized customer service in improving satisfaction levels. Suggestions include enhancing after-sales service efficiency, improving communication regarding repairs and warranties, and offering customization options to attract a broader customer base. Ultimately, the study concludes that customer satisfaction is a crucial driver for brand loyalty and future growth for the Automobile Industry.

A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges

Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service quality. However, there has been some controversy over privacy violations in caching content at the edge network. On the one hand, the multi-access open edge network provides an ideal entrance or interface for external attackers to obtain private data from edge caches by extracting sensitive information. On the other hand, privacy can be infringed on by curious edge caching providers through caching trace analysis targeting the achievement of better caching performance or higher profits. Therefore, an in-depth understanding of privacy issues in edge caching networks is vital and indispensable for creating a privacy-preserving caching service at the edge network. In this article, we are among the first to fill this gap by examining privacy-preserving techniques for caching content at the edge network. Firstly, we provide an introduction to the background of privacy-preserving edge caching (PPEC). Next, we summarize the key privacy issues and present a taxonomy for caching at the edge network from the perspective of private information. Additionally, we conduct a retrospective review of the state-of-the-art countermeasures against privacy leakage from content caching at the edge network. Finally, we conclude the survey and envision challenges for future research.

Evaluating access to National Health Insurance Scheme Services

Access to health insurance is vital for achieving Universal Health Coverage, yet barriers such as high costs, inadequate coverage, and systemic inefficiencies often impede access to necessary health care. This study evaluated access to the National Health Insurance Scheme-provided services among enrollees at the outpatient department (OPD) of Levy Mwanawasa University Teaching Hospital (LMUTH) in Lusaka, Zambia, chosen for its role as a pioneering NHIS-accredited facility and its significance as a major publicly funded referral hospital. Data were collected in May 2024 from 270 systematically sampled NHIS enrollees at the OPD of LMUTH using an interviewer-administered questionnaire. Descriptive analysis revealed a balanced gender distribution, with most respondents aged 35 and above, married, holding tertiary education, and earning a monthly Zambian Kwacha (ZMW) of 20,000 or less. The thematic analysis using NVivo uncovered positive and negative experiences with NHIS services, highlighting issues such as medical service availability, drug shortages, service delays, technological problems, communication challenges, equity in service delivery, and coverage gaps. The key policy implications for improving access to NHIS services include enhancing supply chains, digital infrastructure, coverage to rural areas, staff training, public awareness of the NHIS, and information technology systems.

Exploring Key Factors Influencing Blockchain Adoption in E-Government: Pilot Study

E-government systems face numerous challenges, including trust, privacy, transparency, security, traceability, and service delays. Blockchain technology holds promise for revolutionizing these systems by addressing their long-standing vulnerabilities. Despite the acknowledged potential of blockchain in enhancing e-government systems via improved security and transparency, empirical research on the factors influencing its adoption within government remains scarce. This pilot study addresses this gap by constructing and validating a theoretical model and a corresponding questionnaire. The development of the model and questionnaire followed a four-step methodology. Initially, potential influencing factors were identified and collected. These factors were then filtered and categorized into four main groups: Technological Specific Factors (TSFs), Organizational Specific Factors (OSFs), Individual Specific Factors (ISFs), and Environmental Specific Factors (ESFs). The Analytic Hierarchy Process (AHP) was applied to rank these factors based on their relative importance. The fifteen top-ranked factors were then used to construct the model and develop the questionnaire. Finally, Structural Equation Modelling (SEM) was utilized to assess the reliability and validity of the constructs. The SEM results confirmed the reliability and validity of all model constructs, including the items. Based on these findings, a validated questionnaire has been formulated for future research. This questionnaire is designed to gather data to test hypotheses and identify statistically significant factors that influence the adoption of blockchain technology in e-government.

Patient satisfaction survey in a public hospital: Remera Rukoma District Hospital, Rwanda, 2023

BackgroundPatient satisfaction is a crucial indicator of healthcare quality, influencing outcomes and providing insights for improvement. This study aims to assess patient satisfaction levels, and associated factors, and identify areas for enhancement at Remera Rukoma District Hospital (RRDH) in Rwanda, where such data was previously lacking.MethodsA cross-sectional study using a mixed approach of data collection was conducted, involving 384 participants (164 inpatients, 220 outpatients). Quantitative surveys and qualitative interviews were employed. Patient satisfaction was categorized as Very Satisfied (> 70%), Satisfied (50–69.9%), or Not Satisfied (≤ 49.9%). Data analysis included descriptive statistics, bivariate logistic regression, and thematic analysis of qualitative data.ResultsThe study comprised 37.8% male and 62.2% female participants. Overall, 52.6% were highly satisfied, 29% satisfied, and 18% not satisfied. Among outpatients, 35.9% were dissatisfied with cashier services, and 46.9% with pharmacy wait times. Inpatients showed 74.4% satisfaction with the admission process, but 69.5% reported delayed test result feedback. Educational level significantly influenced satisfaction, with uneducated participants showing lower odds of satisfaction compared to university-educated ones (cOR = 0.409, 95% CI: 0.186–0.897, p = 0.026). Qualitative findings highlighted issues with wait times, communication, and service consistency.ConclusionsWhile overall satisfaction at RRDH was positive, the study identified areas needing improvement, particularly in communication, wait times, and service delays. Education level was significantly associated with satisfaction level. Addressing these factors, beyond operational efficiency, may significantly impact patient satisfaction. Enhancing communication, managing expectations, and optimizing service delivery are crucial for maintaining satisfaction and improving service quality.

Intervention design for artificial intelligence-enabled macular service implementation: a primary qualitative study

BackgroundNeovascular age-related macular degeneration (nAMD) is one of the largest single-disease contributors to hospital outpatient appointments. Challenges in finding the clinical capacity to meet this demand can lead to sight-threatening delays in the macular services that provide treatment. Clinical artificial intelligence (AI) technologies pose one opportunity to rebalance demand and capacity in macular services. However, there is a lack of evidence to guide early-adopters seeking to use AI as a solution to demand-capacity imbalance. This study aims to provide guidance for these early adopters on how AI-enabled macular services may best be implemented by exploring what will influence the outcome of AI implementation and why.MethodsThirty-six semi-structured interviews were conducted with participants. Data were analysed with the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework to identify factors likely to influence implementation outcomes. These factors and the primary data then underwent a secondary analysis using the Fit between Individuals, Technology and Task (FITT) framework to propose an actionable intervention.ResultsnAMD treatment should be initiated at face-to-face appointments with clinicians who recommend year-long periods of AI-enabled scheduling of treatments. This aims to maintain or enhance the quality of patient communication, whilst reducing consultation frequency. Appropriately trained photographers should take on the additional roles of inputting retinal imaging into the AI device and overseeing its communication to clinical colleagues, while ophthalmologists assume clinical oversight and consultation roles. Interoperability to facilitate this intervention would best be served by imaging equipment that can send images to the cloud securely for analysis by AI tools. Picture Archiving and Communication Software (PACS) should have the capability to output directly into electronic medical records (EMR) familiar to clinical and administrative staff.ConclusionThere are many enablers to implementation and few of the remaining barriers relate directly to the AI technology itself. The proposed intervention requires local tailoring and prospective evaluation but can support early adopters in optimising the chances of success from initial efforts to implement AI-enabled macular services.Protocol registrationHogg HDJ, Brittain K, Teare D, Talks J, Balaskas K, Keane P, Maniatopoulos G. Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study. BMJ Open. 2023 Feb 1;13(2):e069443. https://doi.org/10.1136/bmjopen-2022-069443. PMID: 36725098; PMCID: PMC9896175.

An automatic IoT application placement using an improved equilibrium optimizer algorithm in fog computing

PurposeThe rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approachIn this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.FindingsSimulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.Research limitations/implicationsOne of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implicationsFrom a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.Social implicationsOur proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.Originality/valueExisting resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

Does distance to hospital and interhospital transfer negatively impact time to definitive fixation and outcomes in patients with fractured neck of femur in a rural setting?

This study aims to investigate in patients over 65 with neck of femur (NOF) fractures in Rural Australia, does initial presentation to a peripheral hospital result in a delay to surgery? Retrospective cohort study. Dubba Base Hospital, Trauma Hospital Rural Australia(Modified Monash Model (MMM) 3) and catchment area (MMM 3-7), NSW, Australia. The study includes 350 patients over 65, presenting with closed, unilateral NOF fractures who underwent operative management at the operating centre, 203 from peripheral hospitals. Primary outcomes include time to surgery and adherence to recommended timeframes for NOF fixation. Secondary outcomes encompass complications, hospital length of stay and a subgroup analysis to identify causes of surgery delay. Patients transferred from peripheral hospitals experienced a statistically significant delay in time from presentation to surgery compared to those presenting directly to the operating centre (42 h vs. 24 h, p < 0.001) and were more likely to be outside of current guidelines for NOF fixation within 36 h of presentation (OR 5.1, p < 0.001). There were no differences in mortality at 1 year between the two groups (15% vs. 18%, p = 0.5). On subgroup analysis, distance from the operating centre, time to x-ray and after-hours presentation were associated with increased likelihood of surgery outside of 36 h in the peripheral hospital group. This study underscores an inequity in service delivery for rural patients with NOF fractures, particularly those requiring transfer. Pre-arrival delays necessitate targeted interventions to address diagnostic service delays, logistical challenges and transport issues in rural health care.

Increased adverse pregnancy outcomes among decreased assisted reproductions during the COVID-19 pandemic: insights from a birth cohort study in Southwest China

ObjectivesWe elucidate the impact of the coronavirus disease 2019 (COVID-19) pandemic on assisted reproductive technology (ART) services and birth outcomes and establish an evidence-based framework to maintain the high quality of ART healthcare services and ensure continuous improvement of birth outcomes.MethodsA total of 19,170 pregnant women from Sichuan, Guizhou and Chongqing in Southwest China between 2018 and 2021 were included in this study. The log-binomial regression model was employed to analyse the changes in the probability of adverse birth outcomes, such as low birth weight (LBW), preterm birth (PTB), Apgar score < 7 at 1 min and congenital anomalies (CAs) and their relationship with ART before and after the pandemic. In this analysis, confounding factors such as family annual income, maternal ethnicity, delivery age, subjective prenatal health status, vitamin or mineral supplementation during pregnancy and level of prenatal care provided by the hospital were controlled.ResultsART mothers had the highest probability of giving birth to LBW babies (relative risk (RR): 2.82, 95% confidence interval (CI): 2.32–3.41), experiencing PTB (RR: 2.72, 95% CI: 2.78–3.22) and delivering babies with an Apgar score < 7 at 1 min (RR: 1.73, 95% CI: 1.05–2.69). Before the pandemic, the ART rate increased from 4.42% in 2018 to 6.71% in 2019 (rate difference of 2.29%, P < 0.001). After the pandemic, the ART rate decreased from 6.71% in 2019 to 6.55% in 2020 (rate difference of − 0.16%, P = 0.752). Compared with the pre-pandemic period, the rate difference for LBW decreased from − 0.21% (P = 0.646) in 2018–2019 to an increase of + 0.89% (P = 0.030) in 2019–2020. Similarly, PTB showed an increase in rate difference from + 0.20% (P = 0.623) before the pandemic to + 0.53% (P = 0.256) afterwards. Apgar score < 7 at 1 min had a negative rate difference of − 0.50% (P = 0.012), which changed to a positive value of + 0.20% (P = 0.340). For CAs, the rate difference increased from + 0.34% (P = 0.089) prior to the outbreak to + 0.59% (P = 0.102) at post-outbreak. In 2018 (pre-pandemic), ART was the most significant predictor of LBW, exhibiting an RR of 3.45 (95% CI: 2.57–4.53). Furthermore, in 2020, its RR was 2.49 (95% CI: 1.78–3.42). Prior to the onset of the pandemic (2018), ART (RR: 3.17, 95% CI: 2.42–4.08) was the most robust predictor of PTB. In 2020, its RR was 2.23 (95% CI: 1.65–2.97).ConclusionART services have been significantly impacted by the COVID-19 pandemic, and the resulting delays in ART services have had notable implications for maternal birth outcomes.

CRUISE TOURIST EXPERIENCE: AN ANALYSIS FROM TRIPADVISOR REVIEWS

Cruise tourism has grown in popularity, making it essential to understand the experiences of tourists who choose this unique mode of travel. This study examines the cruise tourist experience through the analysis of TripAdvisor reviews, focusing on key factors that shape customer satisfaction and overall travel experience. Utilizing content analysis, a comprehensive dataset of cruise reviews was analyzed to identify recurring themes such as service quality, onboard activity, and perceived value The results indicate that positive cruise experiences are heavily influenced by personalized service, high-quality food, and diverse entertainment offerings, while negative experiences often stem from overcrowding, service delays, and itinerary changes. This research provides valuable insights into the aspects of cruise tourism that enhance or diminish customer satisfaction. These findings offer practical implications for cruise operators looking to improve service quality and enhance the overall travel experience for cruise passengers. Further research recommendations are provided to explore variations in experience across different demographics and cruise types.

Vulnerability Comparisons of Various Complex Urban Metro Networks Under Multiple Failure Scenarios

Urban metro networks, characterized by their complex systems of interdependent components, are susceptible to a wide range of operational disturbances and threats. Such disruptions can cascade through the system, leading to service delays, operational inefficiencies, and substantial economic losses. Consequently, assessing and understanding network vulnerabilities have become crucial to ensuring resilient metro operations. While many studies focus on single-failure scenarios, comparative vulnerability analyses of various urban metro networks under multiple or simultaneous failures remain limited. To address this gap, our study introduces a comprehensive analytical framework comprising three key components: quantitative indices operating at both network and node levels, methodological approaches to assess the importance of network components (nodes, edges, and lines), and systematic protocols for evaluating vulnerabilities across multiple failure scenarios (stations, tunnels, lines, and areas). A comparative analysis of the Shenzhen Metro Network (SZMN) and the Zhengzhou Metro Network (ZZMN) validates the proposed methods. The results indicate that the SZMN demonstrates higher connectivity and accessibility than the ZZMN, despite a lower network density. Both networks are disassortative and heterogeneous, with edges connecting multiline transfer stations showing significantly higher edge betweenness centrality compared to those connecting general stations. In the SZMN, 6.63% of node failures and 4.74% of tunnel failures exceed a vulnerability threshold of 0.03, compared to 13.74% and 11.27% in the ZZMN. Failures across different lines and areas yield varying impacts on network performance and vulnerability. This study provides essential theoretical and practical insights, helping metro safety managers identify vulnerable points and strengthen the sustainable development of urban metro systems.

A qualitative study investigating fatigue among Indonesian freight-train drivers

Fatigue among Indonesian coal freight train drivers is a risk factor that has never been investigated. This study evaluated fatigue among these train drivers and sought to determine work- and non work-related factors contributing to fatigue. The objectives were achieved by conducting a field observation and semi-structured interviews (SSI) with 21 freight train drivers. Using a thematic analysis, four groups of factors (themes) were identified: 1) work demand, 2) management support, 3) quality of work and rest facilities, and 4) organizational issues. Findings of this study demonstrated that fatigue among the drivers was unnecessarily excessive. Several important causes of fatigue included extended period of duty duration (exceeding company policy), adverse working physical environment, and the difficulty in obtaining good quality of rest, particularly at the destination (transit) station. Rest period between shifts was deemed inadequate, and back-to-back duties (separated with less than 8 h of rest) were often unavoidable. Satisfactory personnel scheduling and assignments were challenging, due to the complex nature of the external coal company operating environment, in addition to the freight train lowest track priority that resulted in services backlogs, delays, and frequent stops along the routes. While further studies were needed, short-term interventions could be done, including more ergonomic locomotive cabin and improving rest facilities at the transit station. Long-term initiatives might include better coordination and communication with the coal company, especially in developing more accurate departure and arrival estimates and train timetables.

Analysis Of Waiter Performance In Improving Service Quality At Azure Bar Hotel Conrad Bali

Purpose: To improve the performance of waiters, this research is based on frequent errors in serving food and drinks, which cause delays in service. This error occurs because the waiter does not always repeat the order or ask about the guest's allergies, and is not precise in serving drinks. In addition, checking guest satisfaction based on order of arrival is also a problem. Waiters should be more proactive in meeting guests' needs.Methods: The research method used is descriptive qualitative with data collection through observation, interviews and literature study.Implikasi: The research showed that the waiters' performance was good but not optimal, with 56.25% of the 16 observation points carried out well, while 43.75% were still rarely carried out. From the interviews, it was concluded that the waiters at Azure Bar were friendly, responsive and professional, and played an important role in creating a satisfying experience for customers and maintaining the reputation of the place. In conclusion, the waiter's performance is quite good but can still be improved to ensure guest satisfaction and comfort.

The ABC Laundry Service Information System Based on Web using SDLC Method

In this modern era, the laundry service business has experienced rapid development along with the increasing public need for practical and instant services. Laundry ABC, a laundry business located in the Kimpulan area near the UII campus, faces significant challenges in managing customer data and transactions because it still uses a manual system. This manual system often causes recording errors, service delays, and potential fraud that impact financial reports and customer satisfaction. This study aims to design and develop a web-based laundry service information system using the System Development Life Cycle (SDLC) method with a waterfall approach. The proposed information system will automate the management of customer data, transactions, and financial reports, as well as improve the operational efficiency of Laundry ABC. This system is web-based and integrated with a database to ensure accurate recording, speed up the service process, and provide better data management. With this system, Laundry ABC is expected to improve the quality of service and gain a competitive advantage in the market.

Efficient and secure service function chain deployment method for delay optimization in air traffic information network

The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.