Service Classes Research Articles

Klasterisasi Data Rekam Medis Pasien Pengguna Layanan BPJS Kesehatan Menggunakan Metode K-Means

Patient histories who use the services of Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan are stored in medical record data. Each medical record data contains important information that is very valuable and can be processed to explore new knowledge using a data mining approach. This study aims to help Prof. Dr. Tabrani hospital in classifying patient data who use BPJS Kesehatan, so that the pattern of disease spread is known based on class of service. The data used is patient medical record data in 2019 from October to December, the data will be processed using the K-Means Clustering algorithm with a total of 3 clusters. In cluster 0 (H0) there are 3 patients who are dominated by A09.9 disease (Diarrhea / Dysentery) in Class 2 and Class 3, for cluster 1 (H1) there are 5 patients with more diverse types of disease, while for cluster 2 (H2) there are 5 patients who are predominantly K30 disease (Dyspepsia) in Class 1.

Design and performance evaluation of contention-based transmission schemes for URLLC services

We investigate in this paper uplink transmission schemes for 5G Ultra-Reliable Low Latency Communications (URLLC) traffic. The URLLC class of services has been defined for applications requiring extremely stringent latency and reliability. We show that, in systems with episodic traffic and many users compared with the number of transmission resources, randomly transmitting multiple copies of a packet allows to meet the URLLC requirements. We first develop an analytical model for the packet loss rate for a contention based multiple transmission scheme where replica positions are assigned randomly and show that it allows to meet the performance requirements of URLLC for small enough user activity rate. We then propose an advanced replication scheme where positions for the different replicas are pre-allocated to users so that users have pairwise-distinct positions. We show that this advanced scheme achieves very high reliability with low resource consumption.

Optimized backoff scheme for prioritized data in wireless sensor networks: A class of service approach.

Data prioritization of heterogeneous data in wireless sensor networks gives meaning to mission-critical data that are time-sensitive as this may be a matter of life and death. However, the standard IEEE 802.15.4 does not consider the prioritization of data. Prioritization schemes proffered in the literature have not adequately addressed this issue as proposed schemes either uses a single or complex backoff algorithm to estimate backoff time-slots for prioritized data. Subsequently, the carrier sense multiple access with collision avoidance scheme exhibits an exponentially increasing range of backoff times. These approaches are not only inefficient but result in high latency and increased power consumption. In this article, the concept of class of service (CS) was adopted to prioritize heterogeneous data (real-time and non-real-time), resulting in an optimized prioritized backoff MAC scheme called Class of Service Traffic Priority-based Medium Access Control (CSTP-MAC). This scheme classifies data into high priority data (HPD) and low priority data (LPD) by computing backoff times with expressions peculiar to the data priority class. The improved scheme grants nodes the opportunity to access the shared medium in a timely and power-efficient manner. Benchmarked against contemporary schemes, CSTP-MAC attained a 99% packet delivery ratio with improved power saving capability, which translates to a longer operational lifetime.

A QoE-Aware Strategy for Supporting Service Continuity in an MCC Environment

Mobile cloud computing (MCC) enables ubiquitous access to a diverse range of Internet multimedia services in a pay-as-you-go economic model. In an MCC environment with highly mobile users, the migration of service requests from one cloud server to another due to user movement may frequently occur. We note that when the load offered to the cloud server is increased beyond the capacity limit, particularly when migrated traffic due to user movement suddenly appears, the probability to disrupt existing services gets higher, consequently resulting in the degradation of user quality of experience (QoE). To keep the service disruption probability at an acceptable level so as to maintain a high user-perceived QoE for different classes of multimedia services, this paper proposes a QoE-aware service continuity strategy for the cloud server in an MCC environment. The strategy is based on the buffer-occupancy threshold policy that differentiates newly arriving service requests coming from the mobile users and offers effective protection for migrated service requests against traffic fluctuation in newly arriving service requests. With the proposed strategy, the cloud server can dynamically change the buffer thresholds for different classes of service requests based on the offered traffic load and the user mobility to improve resource utilization, and, most importantly, to keep the service disruption probability at an acceptable level. Besides, by taking the effect of migrated traffic into account, we develop an analytical model to study the performance of the cloud server using the proposed strategy. With the analytical model, we propose an iterative method to determine the optimal buffer thresholds that maximize resource utilization while keeping an acceptable user QoE for different classes of services.

Statistical Delay and Error-Rate Bounded QoS Provisioning Over mmWave Cell-Free M-MIMO and FBC-HARQ-IR Based 6G Wireless Networks

As a new and dominating 6G mobile-networks’ service class for time-sensitive traffics, massive ultra-reliable and low latency communications (mURLLC) has received tremendous attention. One of key 6G enabling-techniques for achieving mURLLC lies in how to efficiently support statistical delay and error-rate bounded quality-of-services (QoS) provisioning for real-time data-transmissions over time-varying wireless networks. Towards this end, several emerging wireless techniques, including finite blocklength coding (FBC), hybrid automatic repeat request with incremental redundancy (HARQ-IR) protocol, millimeter wave (mmWave), cell-free (CF) massive multiple-input multiple-output (m-MIMO), etc., have been shown to be 6G promising enablers to significantly improve various QoS performances. However, integrating these techniques with the statistical delay and error-rate bounded QoS provisioning theory for mURLLC has imposed many new difficulties not encountered before. To overcome these challenges, in this paper we propose the statistical delay-and-error-rate-bounded QoS provisioning system architecture over mmWave user-centric CF m-MIMO and FBC-HARQ-IR based 6G wireless networks. First, we establish the comprehensive system models by accurately characterizing the integrations of above-described 6G promising techniques with statistical QoS provisioning theory. Then, we integrate FBC with HARQ-IR protocol to derive the channel capacity as a function of error probability. Finally, we obtain the closed-form expressions for effective capacities under our proposed schemes. We also conduct a set of simulations to validate and evaluate our proposed FBC-HARQ-IR based mmWave user-centric CF m-MIMO schemes.

MaxHadoop: An Efficient Scalable Emulation Tool to Test SDN Protocols in Emulated Hadoop Environments

This paper presents MaxHadoop, a flexible and scalable emulation tool, which allows the efficient and accurate emulation of Hadoop environments over Software Defined Networks (SDNs). Hadoop has been designed to manage endless data-streams over networks, making it a tailored candidate to support the new class of network services belonging to Big Data. The development of Hadoop is contemporary with the evolution of networks towards the new architectures “Software Defined.” To create our emulation environment, tailored to SDNs, we employ MaxiNet, given its capability of emulating large-scale SDNs. We make it possible to emulate realistic Hadoop scenarios on large-scale SDNs using low-cost commodity hardware, by resolving a few key limitations of MaxiNet through appropriate configuration settings. We validate the MaxHadoop emulator by executing two benchmarks, namely WordCount and TeraSort, to evaluate a set of Key Performance Indicators. The tests’ outcomes evidence that MaxHadoop outperforms other existing emulation tools running over commodity hardware. Finally, we show the potentiality of MaxHadoop by utilizing it to perform a comparison of SDN-based network protocols.

Degrees of ‘eliteness’ in higher education systems: A comparison between Germany and France

AbstractEducation systems worldwide differ in their degree of eliteness. They range from being non‐elitist with dominant patterns of horizontal differentiation (e.g., Germany) to being strongly elitist and having established elite education sectors (e.g., France). Quantitatively, it remains relatively unclear how the eliteness of education systems can be assessed and evaluated. We identify several theoretical mechanisms that are assumed to increase or decrease vertical differentiation between education institutions. For the analyses, we use two large secondary datasets of student beginners’ higher education choices in Germany and France and test different measures of inequality and concentration (Gini index, Duncan’s D, inverse Pareto coefficient, percentile ratios) to describe the degree of eliteness. We estimated the shares of students from higher status groups (high achievers, upper‐tier service class academic milieu) in higher education institutions and fields of study and compared their distribution. Our findings indicate that analyses of percentiles, percentile ratios and inverse Pareto coefficient confirm an expected stronger concentration of high achieving students in the top of the distribution of higher education institutions and fields of study in France than in Germany. In comparison to students’ achievement, social origin proved to be less important for country‐specific differences in concentration.

Hybrid and optical packet switching supporting different service classes in data center network

Optical packet switching is a prominent technology proposing not only a reduction in the energy consumption by the elimination of numerous optical–electrical–optical conversions in electronic switches, but also a decrease in network latencies due to the cut-through nature of packet transmission. However, it is adversely affected by packet contention, preventing its deployment. Solutions have been proposed to tackle the problem: addition of shared electronic buffers to optical switches (then called hybrid opto-electronic switches), customization of TCP protocols, and use of different service classes of packets with distinct switching criteria. In the context of data center networks, we investigate a combination of said solutions and show that the hybrid switch, compared to the optical switch, boosts the performance of the data center network. Furthermore, we show that introducing a “Reliable” service class improves performance for this class not only in the case of the hybrid switch, but also brings the optical switch to performance levels comparable to that of the hybrid switch, all the while keeping other classes’ performance on the same level.

Streaming Video Classification Using Machine Learning

This paper presents a method using neural networks and Markov Decision Process (MDP) to identify the source and class of video streaming services. The paper presents the design and implementation of an end-to-end pipeline for training and classifying a machine learning system that can take in packets collected over a network interface and classify the data stream as belonging to one of five streaming video services: You Tube, You Tube TV, Netflix, Amazon Prime, or HBO

THE RELATIONSHIP BETWEEN TEACHERS’ PERCEPTION OF ORGANIZATIONAL CYNICISM AND SCHOOL ADMINISTRATORS’ BEHAVIOURS OF FAVOURITISM

The purpose of this study is to determine the relationship between teachers’ perception of organizational cynicism and school administrators’ behaviours of favouritism. Since the research is a study that was carried out in order to detect the relationship between favouritism and cynicism, it is in correlational survey model. The sample of the research is constituted by 242 teachers who work in formal elementary schools in Van province. “Favouritism Scale in School Management” and “Organizational Cynicism Scale” were used to collect data for the study. Average, standard deviation, t-test, variance analysis, and regression, which are from descriptive statistics, were used in the analysis of the data. As a result of the study, it was observed that teachers’ levels related to their perception of behaviours of favouritism of school administrators and their perception of organizational cynicism are low. Female teachers perform more cynical behaviours compared to male teachers. It has been observed that teachers working in schools which have 40 or more teachers have less cynicism and favouritism perceptions compared to other groups. It has been observed that there is not a significant difference between favouritism and cynicism perceptions of teachers by class, branch, or period of service. It has been observed that there is a significant, positive, and medium level of relationship between favouritism and organizational cynicism. In the scope of this study, it has been observed that the perception of favouritism in schools is a significant predictor of organizational cynicism level. Article visualizations:

Flow Management with Differentiated Classes of Service and Quality of Experience

Recently, mobile networks have been overloaded with a considerable amount of data traffic. This paper proposes a brokerage service for mobile environments, using policies and quality metrics and ensuring better usage of network resources with more fine-grained management based on flows, and expands previous work (Silva, et al.) with the novel addition of differentiated classes of service. A closed innovative control loop included in a flexible brokerage service in the network, as well as agents at the mobile terminals, detects and counteracts an abnormal data load in any access technology. This allows the terminals to make well-informed decisions about their connections to improve the number of connected flows per technology and the individual service level offered to each flow. Simulation results indicate that the current proposed management solution optimizes the usage of available 4G/5G network resources among a high number of flows in several usage scenarios where the technologies are extremely overloaded, while at the same time, protecting the higher classes of service.

Proposed Fuzzy Real-Time HaPticS Protocol Carrying Haptic Data and Multisensory Streams

Sensory and haptic data transfers to critical real-time applications over the Internet require better than best effort transport, strict timely and reliable ordered deliveries. Multi-sensory applications usually include video and audio streams with real-time control and sensory data, which aggravate and compress within real-time flows. Such real-time are vulnerable to synchronization to synchronization problems, if combined with poor Internet links. Apart from the use of differentiated QoS and MPLS services, several haptic transport protocols have been proposed to confront such issues, focusing on minimizing flows rate disruption while maintaining a steady transmission rate at the sender. Nevertheless, these protocols fail to cope with network variations and queuing delays posed by the Internet routers.
 This paper proposes a new haptic protocol that tries to alleviate such inadequacies using three different metrics: mean frame delay, jitter and frame loss calculated at the receiver end and propagated to the sender. In order to dynamically adjust flow rate in a fuzzy controlled manners, the proposed protocol includes a fuzzy controller to its protocol structure. The proposed FRTPS protocol (Fuzzy Real-Time haPticS protocol), utilizes crisp inputs into a fuzzification process followed by fuzzy control rules in order to calculate a crisp level output service class, denoted as Service Rate Level (SRL). The experimental results of FRTPS over RTP show that FRTPS outperforms RTP in cases of congestion incidents, out of order deliveries and goodput.

On the use of big data frameworks for big service composition

Over the last years, big data has emerged as a new paradigm for the processing and analysis of massive volumes of data. Big data processing has been combined with service and cloud computing, leading to a new class of services called “Big Services”. In this new model, services can be seen as an abstract layer that hides the complexity of the processed big data. To meet users' complex and heterogeneous needs in the era of big data, service reuse is a natural and efficient means that helps orchestrating available services' operations, to provide customer on-demand big services. However different from traditional Web service composition, composing big services refers to the reuse of, not only existing high-quality services, but also high-quality data sources, while taking into account their security constraints (e.g., data provenance, threat level and data leakage). Moreover, composing heterogeneous and large-scale data-centric services faces several challenges, apart from security risks, such as the big services' high execution time and the incompatibility between providers' policies across multiple domains and clouds. Aiming to solve the above issues, we propose a scalable approach for big service composition, which considers not only the quality of reused services (QoS), but also the quality of their consumed data sources (QoD). Since the correct representation of big services requirements is the first step towards an effective composition, we first propose a quality model for big services and we quantify the data breaches using L-Severity metrics. Then to facilitate processing and mining big services' related information during composition, we exploit the strong mathematical foundation of fuzzy Relational Concept Analysis (fuzzy RCA) to build the big services' repository as a lattice family. We also used fuzzy RCA to cluster services and data sources based on various criteria, including their quality levels, their domains, and the relationships between them. Finally, we define algorithms that parse the lattice family to select and compose high-quality and secure big services in a parallel fashion. The proposed method, which is implemented on top of Spark big data framework, is compared with two existing approaches, and experimental studies proved the effectiveness of our big service composition approach in terms of QoD-aware composition, scalability, and security breaches.

Social origin and compensation patterns over the occupational career in Italy

This paper studies dynamically the direct effect of social origin on occupational destinations among men in Italy over the career. It aims at investigating the existence, the pattern over time and the heterogeneity of differences in occupational achievement related to social origins, net of education (DESO) and occupational allocation at first job. It also analyses if the change of the DESO over the career is related to the effect of specific job change episodes (voluntary job change, involuntary job change, internal career move). Results based on growth curve models show the relevance of first job in shaping the DESO, which also slightly increases over the career. The DESO is stronger among highly educated individuals, confirming a boosting pattern primarily driven by a better allocation at first job. The (smaller) DESO among the low-educated, increasing over the career, depends from the higher probabilities to benefit from voluntary and internal career job changes for the children of the service class. The (stronger) DESO among the highly educated is driven by the higher probabilities of experiencing internal career mobility for the children of the service class as well as by their ability to benefit also from an involuntary job change (e.g. dismissal).

Stochastic data-driven optimization for multi-class dynamic pricing and capacity allocation in the passenger railroad transportation

As for any passenger transportation service provider, pricing and capacity management are two critical tools for the profitability of a passenger railroad service provider: pricing affects the demand for the services and the capacity management sets the availability of the services in advance. In this study, an expert system is developed as a decision support tool for a passenger railroad service provider’s integrated pricing and capacity management problem, which has great significance for the success of the service provider. Considering the demand uncertainty, we first formulate the integrated pricing and capacity management problem as a stochastic nonlinear integer programming (SNLIP) model. This model includes dynamic pricing and dynamic capacity allocation decisions for multiple service classes over a planning horizon in order to maximize profit. Also, several key characteristics of the passenger railroad service operations are captured in the model. Due to inherent demand uncertainty as well as the dynamic nature of the problem, a fast and efficient solution approach is needed. Therefore, a simulation-based procedure embedded in a simulated annealing method is proposed to solve the model. Several real-life cases from Fadak Five-Star Trains (an Iranian luxurious passenger railroad service provider) are presented to demonstrate the model and the solution approach. The results of the case studies show the operational and profitability impacts of using the proposed decision support tool as well as its potential capabilities for practical use by other service providers.

EFFICIENCY OF INPATIENT LAYOUT IN PRIVATE HOSPITAL (Case Study: Bhakti Asih Hospital, Brebes Central Java)

In the context of hospital building, inpatient care has large portion in comparasion of the other facility areas within hospitals related to efficiency, such as outpatient services, emergency room, diagnostic and especially inpatient function group services. Even though inpatient care group do not have specific requirements for detailed design and building equipment, it requires efficiency considerations related to correlation with room layout. It is expected that by considering the level of efficiency of service to patients, design can fulfill technical requirements of health and medical aspects. Regarding designs for district-level private hospitals, demand optimization of placement and layout of inpatient care became main topic in this research. This is related to the value of investment in building area development and types of services provided according to inpatient services class. The method used is comparative study of two (2) private hospital design to find the mind factors that most influence of optimization of inpatient layout. The results of study can be used as a guide in architectural design process for designing hospital buildings especially related to design efficiency of inpatient layout so that the building can function sustainability because of optimal service.

Performance Evaluation of 5G Access Technologies and SDN Transport Network on an NS3 Simulator

In this article, we deal with the enhanced Mobile Broadband (eMBB) service class, defined within the new 5G communication paradigm, to evaluate the impact of the transition from 4G to 5G access technology on the Radio Access Network and on the Transport Network. Simulation results are obtained with ns3 and performance analyses are focused on 6 GHz radio scenarios for the Radio Access Network, where an Non-Standalone 5G configuration has been assumed, and on SDN-based scenarios for the Transport Network. Inspired by the 5G Transformer model, we describe and simulate each single element of the three main functional plains of the proposed architecture to aim a preliminary evaluation of the end-to-end system performances.

An investigation of the DOL effect of wood in tension perpendicular to grain

In this study, both short-term and long-term tests in tension perpendicular to grain on Northeast China Larch small clear specimens taken from glulam were conducted, in which the specimens for short-term test and the specimens for long-term test were treated as matched samples that followed the same strength distribution. The DOL effect was determined as the ratio of the applied stress of a failed specimen from the long-term test to the strength of the matching specimen of the same percentile from the short-term test. It was found that the DOL effect was more severe than that predicted by the Madison Curve for the duration shorter than one hour or so and less severe for the duration between one hour and one year by the end of the long-term test. Also, the test result showed that the MOE perpendicular to grain decreased slightly after long-term loading, but the decrease was less severe than the loss of the strength. The deformation factor of creep perpendicular to grain was found to be 2.17, which is close to the deformation factor of OSB in Service Class 1 as specified in Eurocode 5. Furthermore, the Foschi and Yao DOL model and the Nielsen DOL model were calibrated with the test data and the long-term strength can thus be predicted by the calibrated DOL models.

Rapid sensing-based emergency detection: A sequential approach

The upsurge of smart devices has enabled the realization of safe, efficient smart cities that improve the quality of life of their citizens. A prevalent class of smart city services that are attracting increasing attention are Smart Emergency Response and Management (SERM) systems, where sensing paradigms such as crowd sensing and IoT-centric sensing are employed to facilitate the detection of, and response to a crisis situation. In this paper, we study the detection of an abnormal change in a monitored variable through crowd sensed and heterogeneous data, where the change is suggestive of an emergency situation. We formulate our problem as a sequential change-point detection problem, where the underlying distribution of the variable changes at an unknown time. We aim to detect the change-point with minimal delay, subject to a false alarm constraint. We utilize Shiryaev’s test to construct two variants of the solution depending on the structure of the received data contributions and mobility of participating sensing elements. We conduct simulations experiments to show the performance of these variants in terms of the delay-false alarm trade-off in different scenarios.

To the question on the origins of regional cultural policy of Yakutia (XVIII – early XX centuries)

This article is firs to discuss an early stage of origination of the regional cultural policy of Yakutia in the Russian Empire of the XVIII – early XX centuries. Emphasis is made on the regional community: the representatives of traditional cultures – peoples of Yakutia and representatives of Russian culture (service class, government officials, taxed estates). The subject of this research is the historical prerequisites of such policy in the region, as well as the government structures that realized its key trends. The research is carried out in the all-Russian context, namely the context of transformations that took place during the Governorate Reform of 1775, as well as further evolution of the local administrative authorities that carried out cultural policy in the region. The questions is raised on the dynamic development of cultural processes in this suburb of the Empire, where the traditional cultures influencing the representatives of Russian provincial community, simultaneously became familiarized with the cultural trends from Russia. Despite the previous perceptions on the cultural backwardness of Yakutia as an imperial suburb, the conclusion is made on the relatively successful actions of imperial authorities in this field.