Corporate Da’wa Based on Spiritual Capital: Internalizing the Concept of Ihsan in Corporate Work Culture in Indonesia

The Internet of Things (IoT) has emerged with an ultimate goal of automating human life by providing its services. This incredible technology has a huge potential by which it can make human life simpler and easier. To increase the popularity of any type of services, first, their quality metrics need to be defined clearly. In IoT also, Quality of Service (QoS) metrics are needed to be defined first then only user will be able to understand and express their requirement using these metrics. This work is a step in this direction. This work identifies and describes various QoS metrics of IoT keeping the fact in mind that Computing, Communication and Things are three pillars of IoT. This work helps IoT providers to describe their services, users to describe their needs and researchers and professionals to develop the models in IoT considering the importance of QoS metrics.

In Data Stream Management Systems (DSMSs), data do not appear in the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying streams. Achieving a good performance in these systems is still the main challenge. Minimizing run-time memory usage and response time are the most important performance issues. Choosing a better scheduling algorithm implies a better performance. Variety of schedulers and customized Quality of Service (QoS) metrics (related to the amount of user's satisfaction), motivated us to find an approach for choosing the best scheduler per case. In this paper, a new static periodic scheduler called Meta-Scheduler is proposed. Our concentration is on non-real-time DSMSs dealing with semi-regular streams (not too bursty) using complex QoS metrics. We have used coloured Petri net models to choose the best scheduling algorithm for each period regarding complex QoS metrics and varying system statistics. We have studied our scheduler in the context of our new DSMS prototype. Finally, we showed that how Meta-Scheduler outperforms simple schedulers when a user defines a complex QoS metric.

Background: Rising demand for efficient chemotherapy services calls for improved hospital processes. Lean Hospital applies Lean principles to reduce waste and enhance service quality. Objective: This study aims to assess the available quantitative evidence regarding the impact of lean methodologies on key performance indicators (KPIs) such as cost efficiency, service quality, and patient satisfaction in the chemotherapy unit. Design: A systematic literature review methodology was adopted to collate evidence related to the implementation of lean hospital practices in chemotherapy units using PRISMA guidelines for Scopus, Web of Science, and PubMed searches. Data Sources: This systematic review synthesizes peer-reviewed studies published between 2015 and 2023, retrieved from Scopus, Web of Science, and PubMed databases. Review Methods: A systematic literature search was conducted in Scopus, Web of Science, and PubMed using a combination of keywords and MeSH terms related to lean hospital practices and chemotherapy services. Boolean operators and truncation were applied to refine results, with the search limited to peer-reviewed articles published between 2015 and 2023. Title, abstract, and full-text screenings were conducted based on predefined inclusion criteria. Results: This study included a total of 48 full-text studies for analysis. The findings indicate statistically significant improvements in process efficiency and service quality metrics following lean interventions, with cost savings ranging from 10% to 35% observed across studies. Conclusion: This systematic review shows that Lean Hospital practices effectively improve chemotherapy unit services. Lean interventions significantly reduce costs, enhance service quality, and boost patient satisfaction. Despite some methodological variations, the evidence supports lean methods as a cost-effective strategy for optimizing care delivery.

The focus of this study delves into the intricate dynamics among service innovation, service quality metrics, customer satisfaction levels, and the resultant loyalty patterns observed within the Indonesian Classification Bureau. The study investigates the direct impact of service innovation on service quality, customer satisfaction, and loyalty, while also exploring how service quality and satisfaction act as mediators in these relationships. Through rigorous regression and mediation analyses, the research reveals that service innovation enhances service quality but does not directly affect satisfaction or loyalty. Notably, improved service quality significantly correlates with higher levels of satisfaction and loyalty, with satisfaction playing a pivotal role in strengthening loyalty. These findings underscore the mediating roles of service quality and satisfaction in shaping the impact of service innovation on customer loyalty. The study advocates prioritizing initiatives to enhance service quality and implement effective loyalty programs. Furthermore, it recommends future investigations into specific types of service innovations and additional mediating factors that influence customer loyalty dynamics.

This study aims to examine the effect of human resource development (HRD), work culture, and motivation on employee performance at the Secretariat General (Setjen) of the DPR RI, with job satisfaction as an intervening variable. The methodology used in this research is quantitative, involving 95 respondents consisting of all State Civil Apparatus (ASN) functional officials under the guidance of the Secretariat General of the DPR RI in 2022. This research applies a saturated sample technique, where the entire population is used as a sample. For data analysis, the Structural Equation Modeling (SEM) method was used. The results showed that HR development had no effect on employee performance, and no effect on job satisfaction. On the other hand, work culture also has no direct effect on performance, but has a positive and significant effect on job satisfaction. Motivation is proven to have a positive and significant impact on employee performance, although it has no effect on job satisfaction. In addition, job satisfaction is shown to have a positive and significant influence on performance. An important finding of this study is that HR development does not affect employee performance, even with job satisfaction as a mediator. In contrast, work culture shows a positive and significant influence on employee performance when job satisfaction is used as a mediator, while motivation does not show the same effect. This study is the first of its kind conducted at the Secretariat General of the House of Representatives and the results may serve as an impetus for researchers to provide further analysis to improve employee performance.

Service-Oriented Architectures (SOAs) successfully evolve over time to update existing exposed features to the users and fix possible bugs. This evolution process may have a negative impact on the design quality of Web services. Recent studies addressed the problem of Web service antipatterns detection (bad design practices). To the best of our knowledge, these studies focused only on the use of metrics extracted from the implementation details (source code) of the interface and the services. However, the quality of service (QoS) metrics, widely used to evaluate the overall performance, are never used in the context of Web service antipatterns detection. We start, in this work, from the hypothesis that these bad design practices may impact several QoS metrics such as the response time. Furthermore, the source code metrics of services may not be always available. Without the consideration of these QoS metrics, the current detection processes of antipatterns will still lack the integration of symptoms that could be extracted from the usage of services. In this paper, we propose an automated approach to generate Web service defect detection rules that consider not only the code/interface level metrics but also the quality of service attributes. Through multi-objective optimization, the proposed approach generates solutions (detection rules) that maximize the coverage of antipattern examples and minimize the coverage of well-designed service examples. An empirical validation is performed with eight different common types of Web design defects to evaluate our approach. We compared our results with three other state of the art techniques which are not using QoS metrics. The statistical analysis of the obtained results confirm that our approach outperforms other techniques and generates detection rules that are more meaningful from the services' user perspective.

This work aims to develop a secure and efficient process in crowdsourcing-based systems, such as mobile cloud computing. This project is developed to address the rising concern about security vulnerability and the degradation in such environments. The primary objective included enhancing the Quality of Service (QoS) metrics. This process includes developing a secured task management distribution and acceptance system, this additionally involves real-time monitoring of potential risks associated with task distribution and users. The system would be integrating advanced encryption mechanism protocols to protect sensitive task information which also provides real-time notifications in the administrators’ dashboard, for early detection of potential risk identification and allows admins to action on such risk for mitigation. Additionally, the platforms would be offering the QoS metrics on the admin dashboard for Admin users to monitor, this would include task completion time, acceptance delay, and the count of failed task counts. This metric's purpose is to allow admins to maintain a high performance on user satisfaction. The report will highlight the project structure, design, security, implementation, and how the system was developed according to the proposal while addressing the challenges that are associated with crowdsourcing-based mobile cloud computing. The paper covers the system’s architecture, security features, and real-world implementation, along with the technical challenges faced and how they were resolved to ensure a secure and reliable system.

The main focus of this paper is to explicitly characterize the disparity of quality of service (QoS) metrics between base stations in large heterogeneous wireless cellular networks. The considered QoS metrics are cell load, users’ number, and user throughput. The spatial disparity of these metrics is due to the irregularity of the cells’ geometry. In order to consider these irregularities, we assume a Poisson point process of base station locations, random transmission powers, and log-normal shadowing. The interdependency between the performances of the base stations is characterized by a system of load equations. The typical cell simulation model consists in resolving this system in order to find the loads and then deduce the remaining characteristics for each cell of the network. Using stochastic geometric and queueing theoretic techniques, we define the QoS averages, variances, and distributions. Inspired by the analysis of the typical cell model, several investigations lead us to propose a fully analytic approach, called mean cell model , that approximates the averages, variances, and distributions of these QoS metrics. Numerical experiments show a good agreement between the proposed approximations, simulation results, and real-life network measurements.

Important applications, like e-commerce, online stock trading, traffic control demand real-time data services. Conventional database perform poor at these applications. A database for real-time data services has to support timing constraints and temporal consistency in addition to supporting characteristics of a conventional database system. In other words, it is desirable to execute transactions within its deadline using updated data reflecting the current world status. In order to achieve these objectives, the Quality of Service (QoS) metrics like miss ratio, data freshness, perceived freshness and Quality of Data (QoD) management schemes along with architectures for effective QoS management are introduced in Literature. These architectures accommodate the unpredictable change in user transactions and maintain QoS metrics by changing the update pattern of data items, which are periodically updated to reflect the real world status. In this study, we present architecture with an improved update policy and propose an algorithm to change the update pattern in such a manner that the system is able to maintain QoS metrics even under busty traffic conditions. The simulation results show that the proposed model can keep QoS metrics, like miss ratio and perceived freshness within limits under busty traffic conditions [1].

Context-aware end-to-end QoS qualitative diagnosis and quantitative guarantee based on Bayesian network

Cloud systems are transforming the Information Technology trade by facultative the companies to provide admission to their structure and also software products to the membership foundation.Because of the vast range within the delivered Cloud solutions, from the customer's perspective of an aspect, it's emerged as troublesome to decide whose providers they need to utilize and then what's the thought of his or her option.Especially, employing suitable metrics is vital in assessing practices.Nevertheless, to the most popular of our knowledge, there's no methodical explanation relating to metrics for estimating Cloud products and services.QoS (Quality of Service) metrics playing an important role in selecting Cloud providers and also optimizing resource utilization efficiency.While many reports have got to devote to exploitation QoS metrics, relatively not much equipment supports the remark and investigation of QoS metrics of Cloud programs.To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential necessity.So, this text suggests various QoS metrics for service vendors, especially thinking about the consumer's worry.This article provides the metrics list may stand to help the future study and also assessment within the field of Cloud service's evaluation.

With the IT services industry heading towards commoditization of services, there has been a lot of recent interest in defining standard Quality of Service (QoS) metrics and implementing Quality of Service Management (QoSM) systems and solutions. Some of the QoS solutions focus on continuously monitoring and predicting the state of operational parameters in IT service delivery settings, and some others focus on understanding customer survey data to build QoS metrics that affect customer satisfaction. The relationship between these two seemingly disparate approaches to QoSM is not well understood. While continuous monitoring of operational QoS metrics is very important to improving operational efficiency, the impact of improving operational efficiency itself on the customer's assessment of QoS is not very well known. This paper provides a new framework and methodology to study the effect of improving operational QoS on customer satisfaction. Taking examples from a global service delivery organization, the paper also applies the new framework to show that incremental improvements to operational efficiency do not always contribute very significantly to customer satisfaction. There may also be other aspects of operational performance not captured in the usual Key Performance Indicators (KPIs), such as adaptability of operational parameters to changing customer scenarios, that are more relevant to customer perception of QoS.

This article focuses on technical issues related to quality of service provisioning at routing layer in ad hoc networks. It describes the design and implementation of a unified support for quality of service (QoS) metrics within the routing protocol OLSR. This is achieved by the extension of both signalling messages and route calculation process. Major benefits of the proposed approach are to allow dynamic enforcing and adaptation of QoS metrics according to policies defined into the network. QoS metrics information are inserted in a generic way within OLSR signalling messages taking advantage of Linux kernel plugins. These messages are used by the routing process in order to compute routes with respect to the chosen QoS metrics.

Applications such as voice and video require network paths that satisfy several different quality of service (QoS) metrics, such as delay, jitter, packet loss rate and availability. The calculation of paths under multiple QoS metrics, such as the above four metrics, is a difficult problem since these metrics are in general incompatible. The authors propose a simple method for combining the above four QoS metrics into a single composite QoS metric which can be used as a link cost in Dijkstra's algorithm in order to calculate a path. The authors evaluated the proposed method in a multi-domain routing environment where domain reachability information is available through a service oriented architecture paradigm, and they show that it outperforms two commonly used methods. The results are also applicable to routing within a single domain.

Enhancing functional safety of industrial Wireless Sensor Network (WSN) becomes critical especially when systems operate to carryout critical tasks in autonomous modes. Industrial functional safety of WSN can be augmented by leveraging Quality of Service (QoS) metrics. In this paper we will be discussing a statistical approach evolved for real time compliance assessment of WSN to industrial functional safety integrity levels (SIL) using the QoS metrics. The safety assessment approach is illustrated by simulating QoS metrics such as Packet Delivery Ratio (PDR) and Packet Receive Ratio (PRR). The assessment methodology evolved is flexible and can be suitably modified to serve multiple sectors including but not limited to industrial, automotive, and aerospace.