Service Classes Research Articles

Comparison of Actual Deformations of Historic Wooden Structures with Values Obtained from Static Calculations

In historic wooden structures, deformations can be influenced by factors that are negligible within the first 50 years of the building's existence. In attics where the roof covering is made of metal sheets, the air and structural elements heat up during the day and cool down rapidly at night. This phenomenon, over a long period, can cause micro-cracks and surface material degradation. For the study, historic buildings meeting the criteria for service class II according to PN-EN 1995-1-1 were selected. Measurements of deflections, humidity, and modulus of elasticity of the wood were conducted. The actual deflections of the examined structures were found to be 28% to 37% higher than those obtained from numerical calculations, indicating ongoing rheological processes in the wooden structures.

Dynamic wavelength and bandwidth allocation using service class prioritization for upstream in 100 Gb/s NG-EPON

A passive optical network (PON) is an auspicious access technology that keeps track of emerging application diversity. When it comes to the point of handling multiple service types together in the same network with resource limitation, there comes the important term of prioritization as all of the services do not need the same quality of service. In this paper, an efficient resource allocation scheme based on service prioritization is proposed for 100 Gb/s next-generation Ethernet passive optical network (NG-EPON) upstream. Higher-priority service quality is ensured despite load fluctuation. The proposed prioritization scheme by accessing the multiple wavelengths for higher-priority service classes also enhances bandwidth utilization. Higher-priority classes can work suitably using excess bandwidth occupied by the low-priority services during increasing demand. Further increment can preempt low-priority bandwidth for higher-priority services. We performed extensive simulation and got that at 11 % overloaded network case, the highest priority service delay of 379 msec decreased to 5 msec from the non-priority scheme to the priority scheme. The jitter of 1.06 μsec decreased to 0.035 μsec. This prioritization does not decrease the bandwidth utilization which was found near 99.26 % excluding the overhead. The proposed scheme achieves stable performance of the higher-priority service classes.

Служилые тарханы Западного Приуралья в XVI–XVII вв.

Research objectives: To analyze the evolution of the status of the institution of serving tarkhans of the Western Cis-Urals during the period of this territory’s entry into the Moscow state (second half of the 16th–17th centuries). Research materials: The empirical basis of the study is unpublished documents of the 17th century from the “Ufa Prikaznaya Izba” collection of the Russian State Archive of Ancient Acts, act materials from the Kazan Khanate period introduced into scientific circulation, as well as the works of scientists who touched upon the problem of the functioning of the institution of serving tarkhans in the Turkic-Tatar states and the Moscow principality in their works. Results and scientific novelty: The data revealing the legal status of the serving tarkhans in the eastern uluses of the Kazan Khanate were studied and summarized, and the evolution of the position of representatives of this stratum of service people of the Moscow state on the territory of the Ufa district in the 17th century was shown. Based on the studied materials, it was concluded that the tarkhans were an integral part of the Tatar service class who had significant patrimonial estates in the border regions of the Kazan Khanate. After the Urals became part of the Muscovite kingdom, the Russian administration maintained the previous rights of the tarkhans, including in the area of land ownership. Significant changes in the status of the tarkhans were noted in the second half of the 17th century, when a group of “new” tarkhans began to form from among the yasak population (Bashkirs) who did not have tax immunity, which contributed to the rapprochement of the tarkhans with representatives of the Bashkir class.

NeSt: A QoS differentiating end-to-end networked storage simulator

The emerging high-speed storage technologies increasingly use Nonvolatile Memory Express (NVMe) protocol to meet their high throughput and low latency needs. In a datacenter environment, applications accessing multiple such devices over the fabric (i.e. the network) tend to have Quality of Service (QoS) requirements pertaining to offered throughput and experienced latency. In this paper we describe a networked storage system simulator called NeSt that supports end-to-end (E2E) QoS differentiation across multiple classes of service. This is done by conveying the class designation end to end and using it to consistently but independently apply the differentiation in each segment of the path. We demonstrate the ability of NeSt to provide end-to-end QoS differentiation under a variety of situations. To the best of our knowledge, NeSt is the first simulator of networked storage (consisting of multiple NVMe SSDs) that supports E2E QoS differentiation.

The relationship between co-occurring traumatic experiences and co-occurring mental health domains for veterans resident in Northern Ireland

IntroductionVeterans residing in Northern Ireland (NI) likely experience higher levels of co-occurring lifetime trauma exposure and associated co-occurring mental health symptoms, due to the Troubles. As NI veterans have been subject to little exploration it is difficult to know how to support them. This exploratory study explored the co-occurrence of mental health symptoms as a function of co-occurring traumatic experiences.MethodsTwo latent class analyses (LCA) were conducted on NI veteran data (n = 609). One LCA factored endorsements of 16-lifetime traumatic events, with a separate LCA exploring the co-occurrence of symptoms across six mental health domains. Mental health symptom classes were considered as a function of trauma classes, military-specific variables, gender and age.ResultsThree trauma classes were identified: High Multi Trauma (10.84%); High Combat/Conflict (47.62%); Moderate Combat Conflict (41.54%), and three mental health symptom classes: High Co-occurring Mental Health (19.38%); High Depression Moderate Anxiety/Alcohol (24.63%); Moderate Alcohol/Normative (55.99%). Significant predictors of High Co-occurring Mental Health class membership were UDR service, ‘Worst’ military trauma and High Multi Trauma class membership. Both combat classes had a negative relationship with the High Co-occurring Mental Health. Predictors of the High Depression Moderate Anxiety/Alcohol class were High Multi Trauma class membership and UDR service, with Age and Moderate Combat Class membership having a negative relationship.DiscussionNI veterans could be labelled as ‘traumatised’ due to high levels of combat/conflict exposure, yet the two combat classes seemed unrelated to membership of poorer mental health classes. UDR membership indicated that living in one’s theatre of war could have mental health implications. It was concerning that 45% experienced some co-occurring mental health symptoms with 19% experiencing the poorest symptoms. Hazardous alcohol use appeared unrelated to poor mental health. Further research is needed, utilising robust methods. No clinical inferences are to be made from this exploratory study.

Stuck in the middle with you: Conceptualizing data intermediaries and data intermediation services

Data intermediation services are a novel class of services mitigating various aspects of inter-organizational data sharing. Facilitating data intermediation services is a pivotal activity that leverages the benefits of the data economy. These services are usually provided by so-called data intermediaries, who organize data sharing between one or multiple data provider(s) and data consumer(s). Against the background of prominent legislation, these data intermediaries (e.g., data marketplaces or data trusts) receive increasing attention in research, practice, and politics. For instance, the Data Governance Act regulates data intermediation services, prompting organizations sharing data to comply with various regulations, including registration with government bodies or acquiring a trusted label. However, to date, little research has been done about the nature of data intermediation services, motivating us to tackle this issue in a two-fold strategy. First, we derive a visualization of a data intermediary and concept subsuming its potential functions. Second, we explore archetypical configurations of co-existing services of data intermediaries. With this, we contribute to understanding the data intermediaries as a concept and enrich this understanding with more detailed service configurations.

Social class and lunch: differences in midday meal format in Santiago, Chile

PurposeThe social sciences have extensively studied meals; nonetheless, a few have investigated the menu format, with all the data originating from European countries. Within this framework, the novelty of this research is that it analyses the relationship between social class and lunch structure among adults in a Global South city: Santiago, Chile.Design/methodology/approachThe study worked with data from the Survey of Commensality in Adults (>18) of the Metropolitan Region, which used a questionnaire and a self-administered eating event diary. The analysis unit was lunches (n = 3,595). The dependent variable was the structure of the lunches (single course, starter with a main course, a main course with dessert or a full-course menu with starter, main course and dessert). The independent variable was the individual’s social class (either the working, intermediate or service class).FindingsThe data showed that lunches are mostly semi- or fully structured (only 44.5% of the lunches reported by the participants contained a single course). The odds of eating a single course were lower in the service class than the working one and the odds of eating a full-course meal were higher in the service class than the working one.Originality/valueThe results provide new quantitative evidence from a representative sample of a Global South city about the relevance of social class as a differentiating factor in food, specifically regarding the existence of simpler meals among the lower classes.

Optimal resource management for multi-access edge computing without using cross-layer communication

We consider a Multi-access Edge Computing (MEC) system with a set of users, a base station (BS) with an attached MEC server, and a cloud server. The users can serve the service requests locally or can offload them to the BS which in turn can serve a subset of the offloaded requests at the MEC and can forward the requests to the cloud server. The user devices and the MEC server can be dynamically configured to serve different classes of services. The service requests offloaded to the BS incur offloading costs and those forwarded to the cloud incur additional costs; the costs could represent service charges or delays. Aggregate cost minimization subject to stability warrants optimal service scheduling and offloading at the users and the MEC server, at their application layers, and optimal uplink packet scheduling at the users’ MAC layers. Classical back-pressure (BP) based solutions entail cross-layer message exchange, and hence are not viable. We propose virtual queue-based drift-plus-penalty algorithms that are throughput optimal, and achieve the optimal delay arbitrarily closely but do not require cross-layer communication. We first consider an MEC system without local computation, and subsequently, extend our framework to incorporate local computation also. We demonstrate that the proposed algorithms offer almost the same performance as BP based algorithms. These algorithms contain tuneable parameters that offer a trade off between queue lengths at the users and the BS and the offloading costs.

Heterogeneity in public health service utilization and its relationship with social integration among older adult migrants in China: a latent class analysis.

The older adult migrant population in China is on the rise, which presents challenges for the national public health service system. However, the heterogeneity of public health service utilization and its relationship with social integration among the older adult migrant population remains unclear. This study aims to explore the heterogeneity the public health service utilization and how it relates to their social integration. A total of 6,178 older adult migrants from the China Migrants Dynamic Survey (CMDS) in 2017 were included in this study. Exploratory factor analysis was used to categorize social integration into four dimensions. Latent class analysis (LCA) was used to identify different sub-groups of public health service utilization. ANOVA and multivariate logistic regression were used to determine the characteristics of different sub-groups. Three potential classes of public health service utilization were identified: low utilization of basic public health services class (N = 3,264,52.756%), medium utilization of basic public health services class (N = 1,743,28.172%), and high utilization of basic public health services class (N = 1,180,19.072%). Gender, education, extent of mobility, and move alone or not, flow time were all predictors of the class of public health service utilization. There were significant differences in social integration across potential categories (p<0.0001). The utilization of public health services of the older adult migrants is affected by many aspects. Social integration deserves attention as a significant influencing factor in the utilization of public health services. The government should pay attention to the characteristics of the older adult migrants and formulate relevant policies in a targeted manner in order to improve the utilization of public health services of the older adult migrants.

Certified Community Behavioral Health Clinic Services for Clients With Co-occurring Disorders: A Latent Class Approach.

Certified community behavioral health clinics (CCBHCs) are designed to provide comprehensive care for individuals with co-occurring mental and substance use disorders. The authors classified outpatient mental health treatment facilities on the basis of provision of services for clients with co-occurring disorders and assessed whether CCBHCs differed from other outpatient mental health facilities in services provided. The authors used latent class analysis to identify distinct services for clients with co-occurring disorders in 5,692 outpatient mental health facilities in the 2021 National Substance Use and Mental Health Services Survey. Nine indicators were included: treatment for clients with substance or alcohol use disorder co-occurring with serious mental illness or serious emotional disturbance, specialized programs or groups for such clients, medication-assisted treatment (MAT) for alcohol use disorder, MAT for opioid use disorder, detoxification, individual counseling, group counseling, case management, and 12-step groups. A multinomial logistic regression was used to estimate whether CCBHCs were associated with any identified classes after analyses controlled for facility characteristics. A four-class solution provided a model with the best fit, comprising comprehensive services (23.4%), case management services (17.7%), counseling and self-help services (58.6%), and professional services (4.3%). Regressing class membership on facility type and covariates, the authors found that compared with community mental health clinics (CMHCs), CCBHCs were more likely to belong to the comprehensive services class than to the case management services, counseling and self-help services, and professional services classes. CCBHCs were more likely than other outpatient programs to offer comprehensive care, and CCBHC status of a CMHC facilitated enhanced service provisions.

Enhancing precision in coastal dunes vegetation mapping: ultra-high resolution hierarchical classification at the individual plant level

ABSTRACT The classification of ultra-high-resolution (UHR) imagery, characterized by spatial resolutions exceeding 10 cm, presents opportunities and challenges distinct from lower-resolution counterparts. Particularly, challenges are pronounced in some scenarios, such as mapping plant species in coastal environments, where similar vegetation responses and small plant sizes pose additional difficulties. The present work addressed such issues by developing a UHR vegetation cover classification model at the single plant level using data from uncrewed aerial systems (UASs) equipped with a multispectral optical sensor. The model was tested across the San Rossore Regional Park (Italy), where three pilot areas were defined as training-test-validation sites. The proposed solution consists of a hierarchical two-level-of-detail machine learning model based on object-based image analysis (OBIA) and random forest. This model considers spectral features and indices, elevation, and texture and can classify twelve plant species and two service classes (debris and sand) within the study areas. Train and test were carried out utilizing UAS flight data collected during two specific phenological periods and precise field data derived from in-situ vegetation surveys, which provided 937 herbaceous and shrub samples. The model performance was evaluated based on the error matrix and 50-fold stratified cross-validation method, obtaining an overall accuracy (OA) of 0.76 and a standard deviation of 0.08. Such assessment underscored the crucial role of texture information, in addition to radiometric and elevation. Finally, the model was tested against an unseen dataset, proving its transferability (OA equal to 0.62). Although the discussion highlights some aspects to be further improved and claims for future research, the first version of this hierarchical classification model demonstrated its potential for mapping and monitoring coastal sand dune ecosystems, providing data for understanding and, eventually, modeling ecological and biogeomorphological dynamics.

Universal Ideational Warfare: Preemptive Ideocide in the Global South

Abstract The world is entering a post-universal historical conjuncture marked by a “great divergence” triggered by a paroxysm of crises exacerbated by the Western world’s reluctance to accept its diminishing global role. In a panic response, the West is consolidating itself into a Euro-American Confederal Empire, engaging in a form of universal ideational warfare against emerging civilization-states in an effort to maintain its civilizational hegemony. This has resulted in an ideological engineering contest within the global public sphere pursued through the practice of “preemptive ideocide,” which seeks to perpetuate a missionary universalism that imposes Western political-cultural norms upon the world’s divergent cultural and self-governing modes of existence. This ideational warfare is situated within an emergent Cold War Two. The protagonists of this ideational warfare represent a service class of ideocrats devoted to the preservation of Western global primacy. The article highlights the key themes addressed by members of this service class who act as an elite posse of a global thought police. In conclusion, the article argues that humanity is irreversibly divided into non-convergent but non-conflictual cultural identities and proposes the adoption in the Global South of a cultural polygenesis covenant as an exit strategy from the prevailing dependency on missionary universalism’s ideational framework.

Mobility-aware SFC migration in dynamic 5G-Edge networks

The fifth-generation mobile networks bring an evolution in the development of novel classes of applications, increasing the demand for performance and flexibility to support new classes of services. Edge Computing and Network Function Virtualization are two key technologies that bring data processing closer to the user and allow the replacement of traditional hardware-based network functions with virtualized counterparts, which reduces costs and permits the provision of low-latency services. In this scenario, services are often implemented as a Service Function Chain (SFC), consisting of a set of ordered Virtual Network Functions (VNFs) that provide the required service functionality. During service execution, users can move from one location to another, which impacts service performance. An approach to reduce the impact of user mobility is to migrate services as users move to different places. However, the SFC migration problem imposes several challenges that require complex decision-making. VNFs should be migrated to nodes that have the necessary resources to execute the VNF instances. Also, the migration should be fast and effective, using as few resources as possible to reduce migration costs. In this paper, we propose a proactive and iterative SFC migration strategy to address the performance impact caused by user mobility. Our proposed solution anticipates the movement of users and estimates the impact of their mobility on service delay, proactively triggering service migrations when needed. The proposed algorithm decides which VNFs of the SFC should be migrated and which compute nodes will host the migrating VNFs, fulfilling service, resource, and migration constraints while maintaining a low migration overhead. Our algorithm also considers the dynamic nature of the edge environment in which other SFC placement and migrations might be occurring when an SFC is being migrated, which also introduces new challenges related to the availability and concurrency of resources required for migration.

Impact of childhood traumatic brain injury on fitness for service class, length of service period, and cognitive performance during military service in Finland from 1998 to 2018: A retrospective register-based nationwide cohort study.

Traumatic brain injury (TBI) can cause neuronal damage and cerebrovascular dysfunction, leading to acute brain dysfunction and considerable physical and mental impairment long after initial injury. Our goal was to assess the impact of pediatric TBI (pTBI) on military service, completed by 65-70% of men in Finland. We conducted a retrospective register-based nationwide cohort study. All patients aged 0 to 17 years at the time of TBI, between 1998 and 2018, were included. Operatively and conservatively treated patients with pTBI were analyzed separately. The reference group was comprised of individuals with upper and lower extremity fractures. Information on length of service time, service completion, fitness for service class, and cognitive performance in a basic cognitive test (b-test) was gathered from the Finnish Military Records for both groups. Linear and logistic regression with 95% CI were used in comparisons. Our study group comprised 12 281 patients with pTBI and 20 338 reference group patients who participated in conscription. A total of 8 507 (66.5%) men in the pTBI group and 14 953 (71.2%) men in the reference group completed military service during the follow-up period. Men in the reference group were more likely to complete military service (OR 1.26, CI 1.18-1.34). A total of 31 (23.3%) men with operatively treated pTBI completed the military service. Men with conservatively treated pTBI had a much higher service rate (OR 7.20, CI 4.73-11.1). In the pTBI group, men (OR 1.26, CI 1.18-1.34) and women (OR 2.05, CI 1.27-3.36) were more likely to interrupt military service than the reference group. The PTBI group scored 0.15 points (CI 0.10-0.20) less than the reference group in cognitive b-test. PTBI groups had slightly shorter military service periods and higher interruption rate than our reference-group. There were only minor differences between groups in cognitive b-test.

Frictional Behavior of Chestnut (Castanea sativa Mill.) Sawn Timber for Carpentry and Mechanical Joints in Service Class 2

Wood is poised to become a material of choice for future construction. When appropriately managed, it is a renewable material with unique mechanical properties. Thus, there has been a growing demand for hardwoods, including Castanea sativa Mill., the focal point of this investigation, for structural applications. Albeit in a limited capacity, Eurocode 5-2 offers friction coefficients for softwoods, but it falls short for hardwoods. These coefficients play a critical role in numerical simulations involving friction, enabling the optimization of joints and, by extension, the overall structural integrity. Test samples were evaluated at 15% and 18% moisture content (Service Class 2) for various orientations of timber-to-timber and timber-to-steel friction. The results provide an experimental database for numerical simulations and highlight the influence of moisture on the stick–slip phenomenon, which was absent for the timber-to-timber tests, as well as on the rising friction values. At 18%, the static and kinetic coefficients were 0.70 and 0.48 for timber-to-timber and 0.5 and 0.50 for timber-to-steel. The increase was around 50% for timber-to-timber friction and over 170% for timber-to-steel pairs. Moreover, the findings proved a relationship between both coefficients and the validity of the linear estimation approach within the 12–18% moisture commonly applied to softwoods.

Burst traffic: Congestion management and performance optimization strategies in heterogeneous cognitive radio networks

AbstractIn order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion.

Enhanced Clustering MAC Protocol Based on Learning Automata for UV Networks

Ultraviolet (UV) networks are widely applied in complex electromagnetic environments. Designing an efficient multi-node medium access control (MAC) protocol for these networks is important. In this study, we proposed an enhanced clustering time division multiple access (TDMA) MAC protocol based on clustering and learning automata (LA). Subsequently, the effects of the network topology, class of service, and number of cluster nodes on the network performance under the proposed protocol were analyzed. Then, the protocol was compared with the TDMA protocol and clustering system. Results revealed that it obtained a better network performance, proving its suitability for multi-node UV networking.

Internet Users' Willingness to Disclose Biometric Data for Continuous Online Account Protection: An Empirical Investigation

Continuous authentication has emerged as a promising approach to increase user account security for online services. Unlike traditional authentication methods, continuous authentication provides ongoing security throughout the session, protecting against session takeover attacks due to illegitimate access. The effectiveness of continuous authentication systems relies on the continuous processing of users' sensitive biometric data. To balance security and privacy trade-offs, it's crucial to understand when users are willing to disclose biometric data for enhanced account security, addressing inevitable privacy concerns and user acceptance. To address this knowledge gap, we conducted an online study with 830 participants from the U.S., aiming to investigate user perceptions towards continuous authentication across different classes of online services. Our analysis identified four groups of biometric traits that directly reflect users' willingness to disclose them. Our findings demonstrate that willingness to disclose is influenced by both the specific biometric traits and the type of online service involved. User perceptions are strongly shaped by factors such as response efficacy, perceived privacy risks associated with the biometric traits, and concerns about the service providers' handling of such data. Our results emphasize the inadequacy of one-size-fits-all solutions and provide valuable insights for the design and implementation of continuous authentication systems.

Designing Service Menus for Bipartite Queueing Systems

Designing Fair and Incentive-Compatible Matching Systems Many service systems face the challenge of dynamically matching customers who have heterogeneous preferences with an available pool of service providers in a fair and equitable manner. The first come, first served (FCFS) service discipline offers a straightforward and fair approach to allocate arriving customers to available servers and manage congestion. However, FCFS may limit the ability of service providers to reach an efficient allocation based on the individual preferences of customers and the characteristics of servers. In “Designing Service Menus for Bipartite Queuing Systems,” Caldentey, Hillas, and Gupta develop a queueing framework to address this dynamic matching problem. Their proposed methodology enables service providers to offer a menu of different service classes, each served by a distinct pool of servers. By thoughtfully designing this menu, a service provider can encourage self-interested customers to select service classes in a way that optimizes the trade-off between quality of matching and waiting time delays.

Multi-angle perception and convolutional neural network for service quality evaluation of cross-border e-commerce logistics enterprise.

The development of cross-border e-commerce logistics services has injected new vitality into the development of international trade, and therefore has become a new hot spot in theoretical research. In order to ensure the healthy development of cross-border e-commerce, it is urgent to build a set of scientific and effective evaluation mechanisms to scientifically evaluate the logistics service quality of cross-border e-commerce. Multi-angle perceptual convolutional neural network is a framework for service scene identification of cross-border e-commerce logistics enterprises based on deep convolutional neural network and multi-angle perceptual width learning. In this article, both shallow features and deep features were input into the deep perception model (DPM) to obtain a set of distinguishable features with causal structure, which was used to completely describe the high-level semantic information of cross-border e-commerce logistics enterprise services. Among them, DPM mainly adopts the fusion strategy of shallow feature and deep feature. Meanwhile, the feature representation is input into the width learning pattern recognition system for training and classification, so as to evaluate the service quality of cross-border e-commerce logistics enterprises. The multi-angle perceptual convolutional neural network can effectively solve the problems of high similarity between service classes of cross-border e-commerce logistics enterprises and large differences within the class, and achieve better generalization performance and algorithm complexity than support vector machine, random forest and convolutional neural network.