Network Services Research Articles

(OFC 2024) Generalized Quantum-Assisted Digital Signature Service in an SDN-controlled Quantum-Integrated Optical Network

(OFC 2024) Generalized Quantum-Assisted Digital Signature Service in an SDN-controlled Quantum-Integrated Optical Network

AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model

As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary.

Network embeddedness and service innovation: the mediating role of knowledge co-creation and the moderating role of digital transformation

Network embeddedness and service innovation: the mediating role of knowledge co-creation and the moderating role of digital transformation

An Iterative Exact Algorithm over a Time-Expanded Network for the Transportation of Biomedical Samples

In this article we propose an iterative algorithm to address the optimization problem of distributing a set of multiple highly perishable commodities in a healthcare network. In the biomedical sample transportation problem, numerous commodities with short lifespans presume multiple transportation requests at the same facility in a day and restrict the maximum time to reach their destination. These two characteristics create an interdependency between the routing and the pickup decisions in time that is highly complex. To address these timing issues, we model this problem as a service network design problem over a time-expanded network. Our solution method aggregates the network at two levels. First, the commodities are aggregated and artificially consolidated, reducing the symmetry arising when multiple transportation requests are solicited within a short period of time. Second, the space-time nodes in the network are constructed dynamically, thus reducing the size of the mathematical model to be solved at each iteration. Moreover, the method creates auxiliary networks to calculate good-quality primal bounds to the problem. Our algorithm proves to be efficient to solve a set of real-life instances from the Quebec laboratory network under the management of the Ministère de la Santé et des Services sociaux (Ministry of Health and Social Services) with a detailed network of up to 2,377 periods and 277 transportation requests. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants 2018-04609, 2020-06311]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0061 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0061 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Sovrappeso e obesità in età pediatrica: un intervento pragmatico nelle cure primarie

Paediatric clinics are in a unique position to detect chronic diseases, identify at-risk individuals, educate families and implement early preventive interventions. This article aims to provide evidence on how and with what strategies the AUSL of Reggio Emilia has built a programme to combat overweight and obesity in childhood by involving a network of services, starting with paediatricians. A variety of approaches have been taken to address the problem of promoting healthy lifestyles. The BMIF project (Very Fit Children) focuses on early detection and treatment of overweight children using motivational interviewing to help families change their lifestyles. First, second and third level interventions provide treatment for children with obesity according to defined care pathways. Anticipatory guides on responsive feeding and the BeBa (Children's Wellbeing) app promote healthy lifestyles. Effectiveness and equity were the guiding principles for the choices made. This required time and a significant effort, but it also resulted in greater support from both operators and decision-makers.

A Intervenção do Serviço de Atenção Domiciliar junto ao usuário e cuidador na rede de atenção à saúde

**Introduction**: Population aging drives the development of home care practices, expanding the role of healthcare teams. The Home Care Service (SAD) is characterized by comprehensive actions in health promotion, prevention, treatment, and rehabilitation performed at home, ensuring continuity of care and effective integration with healthcare networks and the user. **Objective**: To highlight the relationship between the Home Care Service (SAD) and healthcare networks. **Methodology**: This is a descriptive study in the form of a narrative literature review, which sought to highlight the relationship between the Home Care Service (SAD) and healthcare networks. The research was conducted through online access to the PubMed, Medline, and Scielo databases, using the descriptors "home care services," "SUS," and "healthcare networks." Inclusion criteria were: articles available in full text, in Portuguese and English, addressing the topic, and published between 2019 and 2023. **Results**: The results highlight the importance of home care services in assessing, identifying, and training caregivers, promoting self-care and user autonomy. The lack of professional guidance for caregivers presents an opportunity for improvement in care practices. Furthermore, SAD stands out as an innovative strategy in healthcare, surpassing primary care and emergency services, adopting a humanized approach aligned with SUS. By replacing or complementing hospital stays, it seeks equity in resource distribution for efficient access to home care, with active intervention in caregiver training, playing a crucial role in preventing complications, and consolidating itself as a key agent for a patient-centered approach within the SUS framework. **Conclusion**: The intervention of SAD with the user and caregiver is of utmost importance in training caregivers to assist users, avoiding unnecessary complications and ensuring continuity of care within SUS. Thus, the service complements the care provided in primary care and emergency services and substitutes or complements hospital stays.

Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies.

Cloud Network Collaborative SFC Deployment Under Heterogeneous Resource Of Power White Box Network

The construction of new electric power systems has led to a multiplication in the number of access nodes for electric power IoT information communication, accompanied by a deepening trend in business cloudification. However, due to the insufficient resource linkage and regulation capabilities of the electric power communication network on the cloud side, network side, and edge side, coupled with the lack of a convenient and unified cloud-network resource convergence and control mechanism, the existing electric power communication architecture is gradually finding it difficult to fully meet the demand for deterministic multi-service bearer. Therefore, it is necessary to evolve the original network equipment within the network toward a white box model, opening up interfaces for fine-grained sensing and full-area control, in order to provide more accurate and controllable network transmission services. In this paper, we first propose a cloud-network cooperative resource scheduling architecture for the electric power white box network. This architecture establishes a bearer network to achieve high consistency and cooperativeness of cloud-side end computing power through white box switches, uniformly schedules the global resources of the cloud-network convergence network, and improves the network service quality between cloud-side and side-side. Furthermore, we design a multi-dimensional re-source scheduling method for cloud-network synergy in the electric power white box network. This involves constructing a white box network virtualized heterogeneous resource (VHR) model and a heterogeneous resource control flow (HRCF) model. We transform the problem of merged deployment of service function chains (SFCs) into an SFC merger deployment problem by jointly controlling heterogeneous information, communication, storage, and arithmetic resources, along with the white box switch-specific pipelining resources in the white box network. We then carry out merger optimization of the same kind of function chains. Additionally, we propose a G-GS algorithm based on heuristic methods. Simulation results demonstrate that the method presented in this paper can significantly reduce the cost consumption of white box pipeline resources in the electric power white box network, thereby reducing the processing delay of SFC and improving the quality of electric power service bearing.

Gambaran Hasil Pemeriksaan Keton Urin Segera dan Ditunda Metode Dipstik

Laboratory testing is part of a medical process, both from the beginning to the monitoring of diagnostic development, often the urine tested experiences delays due to late delivery of samples or other factors. One element in the urine is the body of ketone (acetoacetate, 3-hydroxybutyrate, and acetone). Acetone includes the classification of ketones with easily evaporating properties due to temperature. Urine ketone is checked by dipstick methods. There's a 10-90% decrease in urine ketone if urine tests are delayed for 2 hours. Of the 30 urine samples with varying results, which are decreasing and increasing but still touch the normal value limit of urine ketone 0.5 mmol/L. Optimal handling and time of urine sampling for urine ketone testing should be observed to maintain medical protocol and ensure more accurate diagnostic results on the public health service network.

Serving Innovation: Exploring Patent Agency Service Networks in China

ABSTRACTPatent agency services are crucial for the codification of technical knowledge and inventions. Drawing on an inter‐firm network analysis of the patent agency service relationships between patent agencies and their innovation clients during the last two decades in China, this paper seeks to explore the structure of urban patent agency service networks and how it is shaped by China's unique institutional and market forces. The study found that although the patent agency service network generally follows the basic structure of China's innovative and economic landscapes, it is still deeply shaped by China's unique intellectual property institutions, administrative hierarchy factors, market forces driven by patent agency service demand, and multidimensional proximities in a complex manner. Analyzing how these factors function should be related to the characteristics of patent agencies and the actors of networks. The study will help us understand how city patent service networks might be shaped by institutional and spatial factors, and provide new evidence on how cities are interlinked within patent agency service networks.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Daily forecasting of tourism demand: An ST-LSTM model with social network service co-occurrence similarity

Daily forecasting of tourism demand: An ST-LSTM model with social network service co-occurrence similarity

Using generative neural networks when training digital engineers to improve the quality of their training

The problem and the aim of the study. Modernized higher education provides great prospects for artificial intelligence technologies to be introduced when training engineers for knowledge-intensive and high-tech industries. Applying generative neural networks makes it possible to meet the requirements both for programs to train relevant specialists and for the level of developed skills key to Industry 4.0 and for further Industry 5.0. The purpose of the study is to identify the possibilities of using generative neural networks when training digital engineers to improve the quality of their professional training. Research methods. The authors consider the generative neural network as an open resource with which it is possible to design, individualize and fill educational content in accordance with the professional interests of the participants in the interaction. The choice of service is justified by the type of information being processed and the engineer's work function being implemented. 68 bachelors of Vyatka State University are involved (Russian Federation). Training program: 08.03.01 – Construction. Focus: hydraulic engineering, industrial and civil engineering, Digital technologies for the examination of construction sites and real estate management. The assessment of the quality (level) of training is carried out with the help of materials from the fund of complex qualification tasks. For statistical processing of data, Pearson's chi-squared test is used. Results. The directions of work of students of the experimental group studying the discipline "Information Technology", including interactive communication supported by neural networks: generation of images and 3D-models, search for original titles, selection of a list of references, construction of diagrams and algorithms, etc. Statistically significant differences were revealed in the changes that occurred in the pedagogical system (χ2 = 9.095; p < 0.05). Conclusion. The summary are formulated that neural network services contribute to improving the quality of training due to the following didactic capabilities: presentation of information in various forms, automation of calculations, analysis of large amounts of data, decision support, etc. Difficulties are also highlighted: constantly updated features of services, the English-language interface, compliance with copyright and ethics of interaction on the network

대학생의 디지털 리터러시 변화에 대한 종단연구: 잠재성장모형을 적용하여

The purpose of this study was to examine the change patten of college students’ digital literacy by using the data of ‘Korean Media Panel Data for the 12th(2021), 13the(2021), 14th(2023) year conducted by Korea Information Society Development Institute(KISDI). Four sub-competencies of information utilization, information acquisition, media utilization, and personal information recognition were selected as digital literacy competencies. The latent growth model was utilized to analyze longitudinal change with influence of independent variables. The major findings of this research were as followings. First, college student’s digital literacy showed linearly increased. Second, it was confirmed that the major(engineering sciences), SNS(social network service) and OTT(over-the-top media service) consumption behavior had a positive effect on the initial level and change in digital literacy, while gender had no significant differences in digital literacy. Third, the predictors(major, SNS and OTT consumption) had significant effects on information utilization, information acquisition. Based on the results of study, we suggest that it is necessary to care of non-science and Engineering Students. Also, it is necessary to provide opportunities and programs for individual student to use digital platforms and online contents.

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

Analysis of feature extraction techniques for sentiment analysis of tweets

Over the past few years, sentiment analysis has moved from social networking services like LinkedIn, Facebook, YouTube, Twitter, and online product-based reviews to determine public opinion or emotion using social media textual contents. The methodology includes data selection, text pre-processing, feature extraction, classification model, and result analysis. Text pre-processing is an important stage in structuring data for improved performance of our methodology. The feature extraction technique (FET) is a crucial step in sentiment analysis as it is difficult to obtain effective and useful information from highly unstructured social media data. A number of feature extraction techniques are available to extract useful features. In this work, popular feature extraction techniques including bag of words (BOW), term frequency and inverse document frequency (TF-IDF), and Word2vec are compared and analyzed for the sentiment analysis of social media contents. A method is proposed for processing text data from social media networks for sentiment analysis that uses support vector machine as a classifier. The experiments are carried on three datasets of different context namely US Airline, Movie Review, and News from Twitter. The results show that TF-IDF consistently outperformed other techniques with best accuracy of 82.33%, 92.31%, and 99.10% for Airline, Movie Review, and News datasets respectively. It is also found that the proposed method performed better than some existing methods.

How the Struggle for Public Health in the Jim Crow South Reflected and Reinforced Systemic Racial Health Inequality

Abstract The twenty-first century COVID-19 epidemic revealed a U.S. public health system that countenanced health inequities and a U.S. public that resisted disease containment policies. This crisis, however, was only the most recent chapter in a longer struggle in the United States to institutionalize public health. We focus on two early twentieth-century public health campaigns in the American South, the unhealthiest U.S. region at the time. Black southerners—denied basic health, political, economic, and social rights under a rising Jim Crow regime—self-organized health services networks, including through the Tuskegee Woman’s Club, the Negro Organization Society of Virginia, and the Moveable School (1890s–1915). Around the same time, a philanthropic project, the Rockefeller Sanitary Commission (RSC, 1909–1914), seeded state-level public health agencies in eleven southern states, thereby installing public health in a top-down manner. We use archival data sources to explore key similarities and differences in the public health concerns and coalition-building approaches of each campaign and southern resistance to their efforts. We find Black-led campaigns often blurred the color line to form coalitions that provided services to the underserved while tackling environmental health risks at the community level. In contrast, RSC affiliates in southern states, as directed by RSC administrators, provided health services as short-term public dispensaries. Services reached Black and White communities willing to participate but in a manner that did not overtly challenge Jim Crow-era practices. Southern resistance to public health expansion persisted under each approach. The legacies of these struggles remain; the political-economic and ideological forces that limited public health expansion while marginalizing Black community health efforts reverberate in public health inequities today.

Streaming and Elastic Traffic Service in 5G-Sliced Wireless Networks and Mutual Utilization of Guaranteed Resource Units

Researchers of 5G-sliced Wireless Networks are faced with the task of achieving, at the same time, (i) mandatory isolation among network slices and (ii) the effective utilization of resource at Fifth Generation Base Station (gNB). This article proposes the second version of the Preemption-based service Prioritization (PP2) scheme that merges the capabilities of the classical Resource Reservation (RR) and Service Prioritization (SP) schemes and realizes the coexistence of non-TCP streaming and TCP elastic traffic at gNB. Analytical methods are given for Dynamic Resource Sharing (DRS) and Dynamic Resource Reallocation (DRR) with data rate reduction and service preemption. As main results, under some baseline scenario, the PP2 scheme, in comparison with RR, does increase the admission probability up to 99.999% at the system while ensuring 100% in system capacity utilization.

Optical Networking Gym: an open-source toolkit for resource assignment problems in optical networks

The dynamic provisioning of optical network services requires algorithms to find a suitable solution given the specific service requirements and the current network state. These algorithms are usually evaluated using a software simulator developed ad hoc, which may require different levels of detail depending on the problem addressed and how realistic the evaluation needs to be. Moreover, to demonstrate they are a significant contribution to the field, these new algorithms must be benchmarked against the best-performing previously proposed solutions. Due to the large set of parameters and their wide range of possible values, benchmarking algorithms from the literature is not straightforward and can quickly become challenging and time-consuming. This work introduces the Optical Networking Gym, an open-source toolkit that simplifies implementing optical resource assignment simulations and benchmarking new solutions against previously published algorithms. The toolkit provides environments modeling relevant optical networking scenarios, common algorithms for solving problems related to these scenarios, and a set of scripts to prepare and execute simulations for various use cases. Currently, four environments are available, with the possibility of increasing this number through contributions from the co-authors and the community. This paper describes the architecture, interface, environments, and scripts included with the toolkit. We adopt the quality of transmission (QoT)-aware dynamic resource allocation of optical services as the network scenario under examination. Three use cases highlight the toolkit’s modularity, flexibility, and performance. The toolkit allows researchers to streamline the process of developing simulation scenarios and algorithms, enhancing their ability to benchmark their algorithms.

Why Are Foodies Active on Social Network Services? An Exploratory Study on Foodies' Influence on Social Media.

As social media continues to gain traction in the food industry, this study explored how cooking and dining-out behaviors and social media use differ depending on the foodie tendencies of consumers, particularly Korean Millennial and Gen Z consumers. Additionally, this study considered the effect of foodie tendencies on active social media usage. Based on the Foodie Index, two groups were identified: high and low. The high group scored higher than the low group on foodie knowledge and spending, food interest, and time commitment. Comparison of the food, cooking, and dining-out behaviors of both groups revealed a significantly greater frequency of cooking and a higher proportion of using their own ingredients, even when dining alone, in the high group. Analysis of social media use characteristics also demonstrated significantly higher scores for the high group than the low group in terms of the degree of use, average daily usage time, checking frequency, and use of social media recommendations when cooking and purchasing food. Furthermore, foodie inclination significantly influenced active usage behavior, as users shared informative content and frequently posted articles and photos. This study found that foodies play a leading role in producing food-related information by actively using and sharing social media. Considering this ripple effect, consumers' foodie tendencies can be used as an important measure in food marketing-related research.