Variation In Quality Of Services Research Articles

Estimating Lost Dividends from Incomplete Energy Access Transitions

Abstract Energy access is often considered a catalyst for development. Yet, the binary classification of household electrification misses important variation in service quality and in how households use electricity. To examine the benefits of household electrification and illustrate the importance of using more nuanced classifications of energy access, this article develops a metric called the Energy Access Dividend (EAD), which quantifies the electrification benefits forgone due to slow and incomplete energy transitions. This framework is flexible, allowing for the estimation of a variety of electrification benefits such as reduced lighting and cell phone charging expenditures, environmental improvements, time use and asset ownership changes, and improvements associated with productive energy use. To demonstrate the applicability of this framework, we calculate the EAD for several proposed electrification trajectory alternatives in Honduras. We find that in Honduras, a country with high rates of basic electricity access, achieving immediate universal, high-quality electricity would generate nearly $697 million in benefits over the period leading up to 2050. We also estimate the EADs associated with more limited immediate electrification as well as geographically based electrification scenarios, demonstrating that these calculations can inform priorities for energy policy design.

MIAWM: MQTT based IoT Application for Weather Monitoring

The applications of IoT have increased manyfold owing to the declining prices of electronic devices and the growing internet. This study discusses at length the various characteristics, applications, merits, demerits, and problems associated with IoT. The current research work showcases the design and development of application coined as MIAWM: MQTT based IoT Application for Weather Monitoring. The application describes how the MQTT protocol can easily be used to perform efficient routing in IoT. The Application describes how to create a simulation for the MQTT Publisher Subscriber model which provides desired weather data to a remote subscriber anywhere in the world. The dataset used contains information about the temperature, precipitation, and wind speed in Berlin and Germany. This paper puts the various quality of service to experiment by comparing the time taken to service the request.

Institutional Framework as a Determinant of Variations in Service Quality Between Public and Private Hospitals in Tanzania

Purpose of the article: This study was set to determine the influence of institutional framework on service quality in both private and public hospitals. The paper sought to establish the perception of respondents regarding the institutional factors that explain the existence of variations in service quality between public and private hospitals. The institutional framework aspects that were considered in comparing health service quality in public and private hospitals were: institutional culture, control, stability and structure. A descriptive survey design was used because the study sought to know the perception of respondents regarding the factors that influence quality of service in hospitals. Research methods: A multistage sampling method was used to select three public hospitals and three private hospitals from the health service sector. The three public hospitals were Muhimbili referral hospital in Dar es Salaam City, Dodoma regional referral hospital and Geita referral hospital in Geita municipality. Private hospitals included in the study were Bugando referral hospital in Mwanza City, St Francis referral hospital in Ifakara town in Morogoro region and Nkinga referral hospital in Tabora region. Medical doctors, nurses and patients were selected using a systematic random sampling method and patients were selected using a convenience sampling method. The total population for the study comprised 10,650 people (i.e. 2,610 doctors and nurses and 8,040 patients) and the selected sample size, which was determined using McCall’s Table was 400 people. Both primary and secondary data collection sources were used. Analysis of the quantitative data was done using the Statistical Package for Social Sciences (SPSS) and qualitative data were transcribed verbatim, coded and analysed manually. All ethical considerations were observed. Main findings: The findings revealed that private hospitals were better in cleanliness compared to public hospitals and physical arrangement was user friendly in private hospitals compared to public hospitals. There was also more control in private hospitals compared to public hospitals in management of resources and this demonstrates an application of strong institutional framework in private hospitals compared to public hospitals in terms of control, structure and culture. Likewise, there was a more friendly atmosphere in private hospitals compared to public hospitals. However, there was more stability in public hospitals compared to private hospitals in terms of financial stability, affordability of service and medical supplies. It is concluded that quality of service provided in private hospitals is higher compared to quality of service in public hospitals. The possible explanation for the causes for differences in levels of service quality could be the existence of strong institutional framework in private hospitals. It is recommended that for higher service quality, organizations should practice the institutional framework aspects in terms of culture, control, stability and structure.

Cross-Layer Optimization on Different Data Rates for Efficient Performance in Wireless Sensor Network

The traditional protocols used in wireless sensor networks adhere to stringent layering approaches, which decreases the performance of the quality of service (Quality of Service) metrics. As per specifications 802.15.4, wireless sensor networks are inexpensive and energy efficient. It is essential for evaluating the performance of WSNs. Researchers have looked into the fundamental aspects of a single physical layer and the medium access control (MAC) layer protocol using methodologies calculated using several mathematical models or experimental approaches, respectively. In this research, we offer an improved cross-layer analytical model that utilises a thorough combining and interacting of a Markov chain model of the MAC layer's propagation with a model of the PHY layer's propagation. This combination and interaction are described in detail. Various Quality of Service (quality of service) statistics are presented and evaluated, and a cross-layer effectiveness degradation study is conducted under different inputs of multi-parameter vectors. Other parameters, such as Average Wait Time, Reliability, Failure Probability, and Throughput, have been estimated from the simulation results and contrasted with standardised models. The cross-layer model provides a more thorough performance study with various cross-layer parameter sets, some of which comprise distance, power transmission, and offered loads, among other things.

Cost-Efficient Fault-Tolerant Workflow Scheduling for Deadline-Constrained Microservice-Based Applications in Clouds

Microservices are becoming increasingly popular in the construction of cloud applications. On the basis of containers, microservice instances can be implemented with high scalability and maintainability. Due to the need of ensuring various quality of service (QoS) requirements and the two-layer resource structure of containers and virtual machines (VMs), microservice workflow scheduling in clouds is a challenging problem to address. This paper proposes a heuristic algorithm GSMS to minimize execution cost of a microservice-based workflow application while satisfying deadline and reliability constraints. GSMS adopts a greedy fault-tolerant scheduling strategy for replicas of each task to select appropriate resources that meet the sub-deadline and minimize the cost until the sub-reliability is guaranteed. Furthermore, a resource adjustment strategy is incorporated into GSMS to further improve resource utilization. By conducting extensive experiments with several realistic workflow applications, in comparison with existing algorithms, the effectiveness and efficiency of GSMS in achieving lower execution cost and meeting deadline and reliability requirements are validated.

AIoTtalk: A SIP-Based Service Platform for Heterogeneous Artificial Intelligence of Things Applications

Recently, several Internet of Things (IoT) service platforms have been proposed to facilitate IoT application deployment. These platforms typically utilize the lightweight MQTT or CoAP application protocol, optimized for massive IoT applications. Unfortunately, these protocols are not suitable for the emerging, more sophisticated Artificial Intelligence of Things (AIoT). Session Initiation Protocol (SIP), in contrast, is a signaling and controlling protocol for real-time multimedia sessions, and has been viewed as a better candidate to provide a full range support of different broadband, critical, and industrial AIoT applications. However, there exists no generic SIP-based AIoT service platform that supports creations and operations of heterogeneous AIoT applications with various quality of service. This paper presents the first SIP-based AIoT service platform, AIoTtalk, that enables rapid development of scalar and multimedia AIoT applications. Moreover, we deploy an experimental testbed and two real SIP-based AIoT applications to demonstrate the applicability and the performance of our AIoTtalk under both the cloud and edge scenarios. The experimental results show that, together with accurate model predictions and edge-virtualization auto scaling, AIoTtalk guarantees low latency and high quality of experience for messaging and streaming-based AIoT applications.

Reliability-Aware and Energy-Efficient Workflow Scheduling in IaaS Clouds

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms. <i>Note to Practitioners</i>&#x2014;Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy. We conduct the experiments to evaluate the proposed REWS and the results demonstrate that REWS outperforms the state-of-the-art algorithms.

Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic

Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.

Minimizing energy consumption for NOMA multi-drone communications in automotive-industry 5.0

The forthcoming era of the automotive industry, known as Automotive-Industry 5.0, will leverage the latest advancements in 6G communications technology to enable reliable, computationally advanced, and energy-efficient exchange of data between diverse onboard sensors, drones and other vehicles. We propose a non-orthogonal multiple access (NOMA) multi-drone communications network in order to address the requirements of enormous connections, various quality of services (QoS), ultra-reliability, and low latency in upcoming sixth-generation (6G) drone communications. Through the use of a power optimization framework, one of our goals is to evaluate the energy efficiency of the system. In particular, we define a non-convex power optimization problem while considering the possibility of imperfect successive interference cancellation (SIC) detection. Therefore, the goal is to reduce the total energy consumption of NOMA drone communications while guaranteeing the lowest possible rate for wireless devices. We use a novel method based on iterative sequential quadratic programming (SQP) to get the best possible solution to the non-convex optimization problem so that we may move on to the next step and solve it. The standard OMA framework, the Karush–Kuhn–Tucker (KKT)-based NOMA framework, and the average power NOMA framework are compared with the newly proposed optimization framework. The results of the Monte Carlo simulation demonstrate the accuracy of our derivations. The results that have been presented also demonstrate that the optimization framework that has been proposed is superior to previous benchmark frameworks in terms of system-achievable energy efficiency.

Provider Ownership and Indicators of Service Quality: Evidence from Swedish Residential Care Homes

Abstract The provision of public services by for-profit and non-profit organizations is widespread in OECD countries, but the jury is still out on whether outsourcing has improved service quality. This article seeks to nuance existing debate by bringing to the fore variation in service quality between different types of non-public providers. Building on theories of dimensional publicness and incomplete contracts, we argue that different forms of non-public ownership are associated with varying intensity of incentives for profit maximization, ultimately affecting service quality. Using residential elder care homes in Sweden as our universe of cases, we leverage novel panel data for 2,639 facilities from 2012 to 2019, capturing the ownership type of the care home operators, against a set of indicators pertaining to inputs, processes, and outcomes. The results suggest that non-public providers with high-powered incentives to make profit, such as those owned by private equity firms and publicly traded companies, perform worse on most of the selected indicators compared to private limited liability companies and nonprofits. Our findings that the intensity of quality-shading incentives is not the same for all non-public providers have important implications for government contracting and contract management.

A Cluster-Based V2V Approach for Mixed Data Dissemination in Urban Scenario of IoVs

Data dissemination in Internet of Vehicles (IoVs) is attracting more and more attention. However, few efforts have been made on mixed data dissemination in urban scenario. The rigid Quality of Service (QoS) requirements of Emergency Messages (EMs), the varied QoS tolerant degrees of Service Messages (SMs), and the complex transmission environment in urban scenario pose great challenges. In this paper, a Cluster-based Vehicle-to-Vehicle (V2V) approach for Mixed Data Dissemination (CMDD) is proposed to tackle the aforementioned challenges in urban scenario of IoVs. The proposed approach consists of two main algorithms: the bus-based clustering and the mixed data scheduling. To deal with the complex and harsh transmission environment in urban scenario, we propose to use buses, which have fixed driving routes and schedules as well as sufficient resources, to act as cluster heads. Meanwhile, multiple characteristics are deeply analyzed and artfully utilized in the design of the clustering algorithm, to achieve efficient data dissemination from buses to ordinary vehicles. To meet the rigid and personalized QoS requirements of EMs/SMs under stringent resources, a new priority calculation method is proposed and mixed data are scheduled accordingly. Simulation results show that the proposed approach achieves remarkable performance advantage in terms of data download delay, emergency warning ratio and service response ratio.

Intelligent Random Access for Massive-Machine Type Communications in Sliced Mobile Networks

With the emerging Internet of Things paradigm, massive Machine-Type Communication (mMTC) has been identified as one of the prominent services that enables a broad range of applications with various Quality of Service (QoS) requirements for 5G-and-beyond networks. However, it is very difficult to employ a monolithic physical network to support various mMTC applications with differentiated QoS requirements. Moreover, in ultra-dense mobile networks, the scarcity of the preamble and Physical Downlink Control CHannel (PDCCH) resources may easily lead to resource collisions when a large number of devices access the network simultaneously. To tackle these issues, in this paper, we propose a network slicing-enabled intelligent random access framework for mMTC. First, by tailoring a gigantic physical network into multiple lightweight network slices, fine-grained QoS provisioning can be accomplished, and the collision domain of Random Access (RA) can be effectively reduced. In addition, we propose a novel concept of sliced preambles (sPreambles), based on which the transitional RA procedure is optimized, and the issue of preamble shortage is effectively relieved. Furthermore, with the aim of alleviating PDCCH resource shortage and improving transmission efficiency, we propose a learning-based resource-sharing scheme that can intelligently multiplex the PDCCH resources in the naturally dynamic environment. Simulation results show that the proposed framework can efficiently allocate resources to individual mMTC devices while guaranteeing their QoS requirements in random access processes.

Effect of Path QoS on Throughput Aggregation Capability of the MPT Network Layer Multipath Communication Library

An increase in the use of smart and portable devices like smartphones, laptops, and tablets has led to a rise in the number of network interfaces and thus the number of possible channels for communication. However, the current approach over the Internet only employs a single path for a communication session. As an innovative and promising method for real-time transmission based on GRE-in-UDP encapsulation, which provides an IPv4 or IPv6 tunneling mechanism, this paper presents multipath throughput testing for the MPT network layer multipath communication library. We investigated the effectiveness of MPT's channel capacity aggregation while dealing with wired channels and examined scenarios in symmetric and asymmetric paths. Our network throughput measurements showed that MPT can efficiently aggregate the capacities of both symmetric and asymmetric paths. In this paper, we established a network topology that included a server, which we used for generating various quality of service (QoS) metrics. We measured how latency, transmission speed, packet loss rate, jitter, and the setting of the path weights influence throughput aggregation capability of the MPT communication library.

Quality of Municipal Long-Term Care in Different Models of Care: A Cross-Sectional Study From Norway.

The quality of care remains a critical concern for health systems around the globe, especially in an era of unprecedented financial challenges and rising demands. Previous research indicates large variation in several indicators of quality in the long-term care setting, highlighting the need for further investigation into the factors contributing to such disparities. As different ways of delivering long-term care services likely affect quality of care, the objectives of our study is to investigate (1) variation in structure, process and outcome quality between municipalities, and (2) to what extent variation in quality is associated with municipal models of care and structural characteristics. The study had a cross-sectional approach and we utilized data on the municipal level from 3 sources: (1) a survey for models of care (2) Statistics Norway for municipal structural characteristics and (3) the National Health Care Quality Indicator System. Descriptive statistics showed that the Norwegian long-term care sector performs better (measured as percentage or probability) on structure (85.53) and outcome (84.86) quality than process (37.85) quality. Hierarchical linear regressions indicated that municipal structural characteristics and model of care had very limited effect on the quality of long-term care. A deeper understanding of variation in service quality may be found at the micro level in healthcare workers' day-to-day practice.

Medical interview in the system of dental care providing to patients with special needs

Among the leading trends in the field of medicine – improving the quality of various services; focus on individually targeted care, experience and needs of patients; creating equal opportunities and favorable treatment conditions. Relevant in this context is the “Cambridge guide to the medical interview” designed to identify effective communication skills between physician and patient and to provide a sound framework for their analysis. To solve the two leading tasks, an adapted interview model for dentists was developed and tested, taking into account the communicative needs of patients with severe hearing impairment (to the general tasks of “providing structure”, “building the relationship”), which are cross-cutting for each stage of medical interview, “ensuring a universal design of the environment” included, before the stage of “initiating the session” an additional “selection of priority means of communication/necessary adaptations”, etc.) was proposed. Based on the described modern approaches to building trusting relationships in medical practice, taking into account the adapted version of the interview, the key components of working with patients with special needs (hearing impairment) in dentistry are identified.

Mediation Effect of Service Quality on the Relationship of Healthcare Service Innovation and Outpatient Experience

Healthcare organizations play an important role in people's well-being all over the world; good healthcare facilities are extremely beneficial in combating many diseases worldwide. Innovation is one of the most important factors influencing service quality in healthcare organizations worldwide, particularly in the UAE. The pandemic, changing human needs and globalization have made organizations more aware of the importance of improving patients' experiences through improved service quality and innovative services, which has become a winning strategy and provides organizations witha comparative advantage over others through customer confidence building. This research assessed mediating effect of service quality on health services innovation and outpatient experience. The model was developed with the help of SmartPLS software package and the data was gathered through questionnaire survey. Analysis of 397 questionnaire forms were used to analyze direct relations between service quality and service innovation with out-patient experience as well as indirect effect of service innovation on out-patient experience through service quality. The developed model was confirmed through convergent validity and discirminant validity. The R-square reveals that service innovation accounted for approximately 47% of the variation in service quality. While service innovation and service quality explained approximately 80% of the change in outpatient experience. This means that a unit change in both service innovation and service quality improves the outpatient experience by 80%.

Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

The increase in the number of services in the power distribution grid leads to a massive increase in task data. Power distribution internet of things (PDIoT) is the specific application of internet of things (IoT) in the power distribution grid. By deploying a large number of PDIoT devices, the voltage, active power, reactive power, and harmonic parameters are collected to support distribution grid services such as fault identification and status detection. Therefore, PDIoT utilizes massive devices to collect and offload tasks to the edge server through 5G network for real-time data processing. However, how to dynamically select edge servers and channels to meet the energy-efficient and low-latency task offloading requirements of PDIoT devices still faces several technical challenges such as task offloading decisions coupling among devices, unobtainable global state information, as well as interrelation of various quality of service (QoS) metrics such as energy efficiency and delay. To this end, we firstly construct a joint optimization problem to maximize the weighted difference between energy efficiency and delay of devices in PDIoT. Then, the joint optimization problem is decomposed into a large-timescale server selection problem and a small-timescale channel selection problem. Next, we propose an ML-based two-stage task offloading algorithm, where the large-timescale problem is solved by two-side matching in the first stage, and the small-timescale problem is solved by adaptive ε -greedy learning in the second stage. Finally, simulation results show that compared with the task offloading delay-first matching algorithm and the matching theory-based task offloading strategy, the proposed algorithm performs superior in terms of energy efficiency and delay.

An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches

Wireless sensor network (WSN) is widely acceptable communication network where human-intervention is less. Another prominent factors are cheap in cost and covers huge area of field for communication. WSN as name suggests sensor nodes are present which communicate to the neighboring node to form a network. These nodes are communicate via radio signals and equipped with battery which is one of most challenge in these networks. The battery consumption is depend on weather where sensors are deployed, routing protocols etc. To reduce the battery at routing level various quality of services (QoS) parameters are available to measure the performance of the network. To overcome this problem, many routing protocol has been proposed. In this paper, we considered two energy efficient protocols i.e. LEACH and Sub-cluster LEACH protocols. For provision of better performance of network Levenberg-Marquardt neural network (LMNN) and Moth-Flame optimisation both are implemented one by one. QoS parameters considered to measure the performance are energy efficiency, end-to-end delay, Throughput and Packet delivery ratio (PDR). After implementation, simulation results show that Sub-cluster LEACH with MFO is outperforms among other algorithms.Along with this, second part of paper considered to anomaly detection based on machine learning algorithms such as SVM, KNN and LR. NSLKDD dataset is considered and than proposed the anomaly detection method.Simulation results shows that proposed method with SVM provide better results among others.

Human Capital as A Determinant of Service Quality in Accredited Universities in Kenya

The purpose of this study is to determine the effect of human capital on the service quality of accredited universities in Kenya. The study was guided by the positivist domain which is a major doctrine or theory in social sciences largely used in survey types of research. The study employed both a crosssectional research design and an explanatory research design. The target population comprised 74 public and private universities in Kenya. The sample size was 222 respondents. The main data collection tool was a questionnaire. Both descriptive and inferential statistics were used to analyze the data. Findings established that human capital had a significantly positive relationship with service quality. Human capital explained about 38% of the variation in service quality of accredited universities in Kenya. The practical implications for the study are that the human capital such as strategic leaders in universities should be aligned and increase their adoption of strategic leadership practices in order to inspire good managerial practices in universities. Additionally, the finding that human capital affects service quality is consistent with the Upper Echelons theory. This theory offers a framework for viewing leaders as wise, experienced, and educated change agents who serve as critical assets capable of enhancing service quality in their institutions.

Determination of the effect of human capital on service quality of accredited universities in Kenya.

Design/Methodology- this study was guided by the positivist domain which is a major doctrine or theory in social sciences largely used in survey types of research. The study employed both cross-sectional research design and explanatory research design. The target population comprised the 74 public and private universities in Kenya. The sample size was 222 respondents. The main data collection tool was a questionnaire. Both descriptive and inferential statistics were used to analyze the data Findings- the study established that human capital had a significantly positive relationship with service quality. Human capital explained about 38% of the variation in service quality of accredited universities in Kenya. Practical Implications- The human capital such as strategic leaders in universities should be aligned and increase their adoption of strategic leadership practices in order to inspire good managerial practices in universities. Additionally, the finding that human capital affects service quality is consistent with the Upper Echelons theory. This theory offers a framework for viewing leaders as wise, experienced and educated change agents who serve as a critical asset capable of enhancing service quality in their institutions.&#x0D;