Network Services Research Articles

Research on the Rural Financial Service System in China

With the in-depth implementation of the Rural Revitalization Strategy, the optimization of the rural financial service system is particularly critical, which is of great significance in promoting rural economic development and improving the living standards of farmers. At present, the rural economy and the overall economic strength are relatively weak, making it difficult to achieve the goal of promoting rural revitalization comprehensively only through rural finance. Based on this, this paper analyzes the current situation of the rural financial service system and puts forward strategic measures such as strengthening the construction of the service network, innovating financial products and services, and improving the risk prevention and control mechanism. These measures will help to improve the overall efficiency of the rural financial service system, provide strong financial support for rural revitalization, and promote the sustainable development of the rural economy.

Implementation of the Macao dementia policy: a scoping review for the way forward

BackgroundThe implementation of dementia policy is a complex process of translating policy goals to actions to address the changing needs of people living with dementia. Leveraging on others’ experiences would help policy decision-makers and actors better prepare for the challenges.PurposeThis study explored the development, the implementation and the impact of the dementia policy in Macao, a “role model” recognized by the Alzheimer’s Disease International.MethodsA scoping review of policies, strategies, and news articles, as well as scholarly work from 6 scientific databases dated till March 2023 was conducted under the guidance of the Health Policy Triangle Framework.ResultsAccording to 284 documents, the dementia policy in Macao, driven by government leadership and supported with public-private partnership, aimed to integrate health and social services to achieve the goals of “Early prevention, Early detection, Early diagnosis, Early treatment and Early support.” Promoting the preparedness according to the dementia burden trajectory, empowering the public and the service providers with training and education, and encouraging services-related research were among the key actions. With major changes in dementia care configuration, a dementia service network, a dementia-friendly community and a one-stop service model for disease screening, diagnosis, treatment and support have been developed.DiscussionReconfiguring existing resources in the health and social services to form an integrated service network at the community level could be considered a priority of action. Continuous engagement, collaboration and empowerment at different levels across these sectors is crucial for the sustainability of a dementia policy.

Improving primary care access for rural women Veterans: the Boost Team

ObjectivesTo improve healthcare access for rural cisgender women and gender diverse Veterans, we created the “Boost Team,” a clinician-driven telehealth outreach service to connect this population to Veterans Health Administration (VHA) services.MethodsBetween 9/2021 and 2/2022, we conducted a needs assessment in the Veterans Integrated Service Network (VISN) 21 and used those data to develop an outreach intervention. We piloted a clinician-led outreach intervention in 3/2022, and formally deployed an outreach team in 9/2022.ResultsThe needs assessment uncovered opportunities to educate Veterans, staff, and clinicians about available VHA women's health services, and a need for easily-accessible gender-sensitive services. During the pilot, 58% (7/12) rural cisgender women Veterans were successfully contacted, all reported positive experiences with the intervention. The formal outreach team launched in 9/2022 and consists of a nurse practitioner (NP), scheduler, Peer Support Specialist, and medical director. From 9/2022 to 12/2022 the Boost NP called 110 rural cisgender women and gender diverse Veterans and spoke to 65 (59%) of them. Common care needs identified and addressed included care coordination, new referrals, medication management, and diagnostics.DiscussionData from Boost show that clinician-led outreach can engage rural cisgender women and gender diverse Veterans in VHA services, there is a desire for more gender-sensitive services, and there is a need for systems-level improvements to allow for improved care coordination and decreased leakage outside of VHA. Using robust strategies grounded in implementation sciences, we will continue conducting a program evaluation to study the impact of Boost and scale and expand the program.

Cloud Network Function Virtualization (NFV)

Cloud Network Function Virtualization (NFV) is a groundbreaking technology that revolutionizes the way network services are delivered. By virtualizing network functions, NFV enables faster deployment, scalability, and management of network services. This article explores the benefits and use cases of NFV, including virtualized load balancers, firewalls, and intrusion detection devices. We also discuss the evolution of NFV to Cloud-Native Network Functions (CNF) and the challenges and solutions associated with NFV adoption. Discover how Cloud NFV is transforming network architecture and enabling organizations to achieve greater agility, flexibility, and cost savings.

Cost analysis of establishing and operating the first human milk bank at Da Nang Hospital for Women and Children in Vietnam: an activity-based costing ingredients study

BackgroundBreastfeeding is the biological norm for feeding infants and young children. When mothers’ breastmilk is unavailable, donor human milk (DHM) from a human milk bank (HMB) becomes the next option for small vulnerable newborns. A comprehensive cost analysis is essential for understanding the investments needed to establish, operate, and scale up HMBs. This study aims to estimate and analyze such costs at the first facility established in Vietnam.MethodsAn activity-based costing ingredients (ABC-I) approach was employed, with the cost perspective from service provision agencies (specifically, the project conducted at Da Nang Hospital for Women and Children and Development Partners). Estimated financial costs, based on actual expenditures, were measured in 2023 local currency and then converted to 2023 US dollars (USD). We examined three scenarios: 1) direct start-up costs + indirect start-up costs + implementation costs, 2) direct start-up costs + implementation costs, and 3) capital costs + implementation costs over the 6.5 years of operation.ResultsThe total start-up cost was USD 616,263, with total expenditure on direct activities at USD 228,131 and indirect activities at USD 388,132. Investment in equipment accounted for the largest proportion (USD 84,213). The monthly costs of Da Nang HMB were USD 25,217, 14,565, and 9,326, corresponding to scenarios 1, 2, and 3, respectively. Over HMB's 6.5 years of operation, on average, the unit costs were USD 166, USD 96, and USD 62 for DHM received and USD 201, USD 116, and USD 74 for pasteurized DHM meeting specified criteria in the corresponding scenarios. Unit costs were highest in the initial six months, decreased, and reached their lowest levels after a year. Then, the unit costs experienced an increase in late 2020 and early 2021.ConclusionAlthough the unit cost of DHM in Da Nang HMB is comparable to that in certain neighboring countries, intentional measures to reduce disposal rates, improve HMB efficiency, motivate more community-based donors, and establish an HMB service network should be implemented to lower costs.

Estimation of site-specific amplification function from quarter-wavelength velocity profile and its application to obtain local amplification maps

Quarter-wavelength (QWL) velocity refers to the S-wave velocity at a depth equal to one-fourth of the wavelength of the seismic waves. It provides valuable information about the characteristics of the subsurface material properties affecting seismic waves propagation. The Swiss Seismological Service (SED) network, with over 200 stations across different lithologies, offers a rich dataset to implement correlation between site properties and site amplification factors. The current study is based on a subset of 113 selected SED seismic stations for which shear-wave velocity (Vs) profiles from geophysical measurements are available. We first meticulously analyzed and adjusted them to accurately determine the bedrock depth of the sites they are located on. Using empirical spectral modelling (ESM), we computed amplification functions from recorded earthquakes, referenced to the Swiss standard rock profile with a VS30 of 1100 m/s. Then we performed a bivariate analysis between empirical amplification functions, QWL velocity and QWL velocity contrast. The resulting coefficients are used to predict elastic amplification at frequencies between 0.5 and 10 Hz, with predictions generally being within 20–28% of observed values. We also developed an empirical equation relating VS30 (time-averaged Vs to 30 m depth) and κ0 (high-frequency attenuation parameter), to incorporate the anelastic term in the amplification. Applying our method to a 3D geophysical model of Visp, a high seismic hazard zone in Switzerland, we found it effective in predicting 1D site amplification, but noted caution in areas susceptible of 2D/3D resonance effects. A calibration function based on observed vs. predicted amplification and the frequencies of estimation normalized by the expected 2D resonance frequency improved predictions for Visp. Our study demonstrates that QWL velocity profiles offer a straightforward approach for characterizing site effects, which can be used in seismic hazard assessment to evaluate the potential amplification of ground motions.

A cloud-fog distributed trust service for wireless sensor networks

A cloud-fog distributed trust service for wireless sensor networks

A Qualitative Analysis About Sexual Health of Foster Teenagers: What Concerns Do Their Caregivers Have?

Abstract Introduction The social environment of children in foster care is one of the factors that impact on their sexual and reproductive development. Understanding these aspects is essential for the development of effective prevention strategies informed by research. Our aim is to explore the primary concerns of caregivers regarding the sexual development of teenagers in foster care. Methods The participants, 24 teenage foster caregivers, aged 32 to 55 years, were recruited through a social service network collaborating with foster families.Three focus groups’ discussion was conducted with foster caregivers using a semi-structured interview between April and June 2023. A qualitative reflexive thematic analysis was employed, focusing on themes related to foster caregivers’ concerns and challenges regarding the sexual development of the foster teenagers. Researchers conducted line-by-line coding inductively using NVIVO 14 software. Results The findings revealed that caregivers are concerned about teenagers’ exposure to sexual risks, challenges in establishing healthy relationship boundaries, dominant behaviors, and early expressions of sexuality. However, certain inaccuracies in attribution and interpretation were identified, which may be attributed to a lack of knowledge and information about normative sexual development, attachment, resilience, and insufficient training as an affective-sexual educator. The participating foster caregivers often linked sexual behaviors and distorted thoughts of teenagers with past childhood experiences. Conclusions The gathered information contributes to a deeper understanding of the needs and deficiencies of this group. Policy Implications This data lays the groundwork for designing more targeted interventions tailored to the needs identified by caregivers, which demand an immediate response.

A privacy-preserving federated graph learning framework for threat detection in IoT trigger-action programming

Trigger-Action Programming (TAP) is a common user-programming paradigm in Internet of Things (IoT) smart home platforms, allowing users to create customized automation rules to match IoT devices and network services. However, the potential security threats associated with TAP rules are often overlooked or underestimated by users. To address this issue, we propose PFTAP, a novel federated graph learning framework for threat detection of TAP rules while simultaneously protecting user data and privacy. First, we propose a hierarchical graph attention network. This network comprises intra-rule attention and inter-rule attention modules, which enable the learning of comprehensive feature representations for triggers and actions. By capturing the intricate relationships between different rules, the network enhances the detection accuracy of risky TAP rules. Moreover, our framework is based on federated learning and integrates symmetric encryption and local differential privacy techniques, aiming to safeguard user privacy from unauthorized access or tampering. To evaluate the effectiveness of our framework, we conduct experiments using an extensive dataset of IFTTT rules. The experimental results convincingly demonstrate that PFTAP outperforms state-of-the-art methods in terms of threat detection performance.

End-to-End Delay Performance Analysis for Delay-Sensitive Services in Polymorphic Networks

End-to-End Delay Performance Analysis for Delay-Sensitive Services in Polymorphic Networks

Prospects for Applying Artificial Intelligence Technologies by Public Libraries of Ukraine

The aim of the article is to study prospects for the artificial intelligence technologies usage by public libraries of Ukraine in conditions of digital transformation of society. Research methods. General scientific research methods are used, in particular, analysis and generalisation of possibilities of language prognostication models based on neural networks in the activity of Ukrainian public libraries. The scientific novelty of the obtained results consists in actualising the problem of using ChatGPT 3.5, Gemini, Microsoft Bing and Grok technologies in the library sphere, and outlining the key possibilities of using these language models of artificial intelligence among public libraries. The usage of new generation chatbots for interacting with users of public libraries, searching the information and generating content are actively gaining popularity, moving from theory to practical level. Linguistic models of neural networks are used to create marketing campaigns, analysis and familiarisation with the content of works and archival materials, interaction with library users, and finding answers to professional questions. Main conclusions. The potential of language prognostication models based on neural networks in the practical activity of public libraries is analysed. By means of the analysis of ChatGPT 3.5, Gemini, Bing Chat and Grok technologies, and their usage in the library sphere, the main potential possibilities of interaction of information institution employees with these technologies are determined. In particular, the integration of ChatGPT functions through API into the system of public libraries will allow to optimise searching the information in the database and and feedback opportunities with library users. Using the ChatGPT 3.5 neural network service will speed up the development of a marketing content plan, instructions, as well as automate book recommendations based on the analysis of the readers’ preferences and books read. It is found out that the methods of implementing innovative models can allow not only to qualitatively improve the service of public libraries users, but also to automate their work in order to optimise the performance of everyday tasks, preserve cultural heritage and access to digital resources. In the future, the level of deepening the involvement of these technologies will inevitably grow. The sooner library institutions start using neural networks in everyday practice, the sooner they will be able to facilitate the work of library sphere workers, provide improved users’ service, and increase the effectiveness of library marketing support. It is emphasised that experiments on the use of artificial intelligence language models still need an empirical research method. Particularly, they require attention not only from the point of view of usage for personal purposes, but also in the professional activity of the library sphere.

Prospects for Applying Artificial Intelligence Technologies by Public Libraries of Ukraine

The aim of the article is to study prospects for the artificial intelligence technologies usage by public libraries of Ukraine in conditions of digital transformation of society. Research methods. General scientific research methods are used, in particular, analysis and generalisation of possibilities of language prognostication models based on neural networks in the activity of Ukrainian public libraries. The scientific novelty of the obtained results consists in actualising the problem of using ChatGPT 3.5, Gemini, Microsoft Bing and Grok technologies in the library sphere, and outlining the key possibilities of using these language models of artificial intelligence among public libraries. The usage of new generation chatbots for interacting with users of public libraries, searching the information and generating content are actively gaining popularity, moving from theory to practical level. Linguistic models of neural networks are used to create marketing campaigns, analysis and familiarisation with the content of works and archival materials, interaction with library users, and finding answers to professional questions. Main conclusions. The potential of language prognostication models based on neural networks in the practical activity of public libraries is analysed. By means of the analysis of ChatGPT 3.5, Gemini, Bing Chat and Grok technologies, and their usage in the library sphere, the main potential possibilities of interaction of information institution employees with these technologies are determined. In particular, the integration of ChatGPT functions through API into the system of public libraries will allow to optimise searching the information in the database and and feedback opportunities with library users. Using the ChatGPT 3.5 neural network service will speed up the development of a marketing content plan, instructions, as well as automate book recommendations based on the analysis of the readers’ preferences and books read. It is found out that the methods of implementing innovative models can allow not only to qualitatively improve the service of public libraries users, but also to automate their work in order to optimise the performance of everyday tasks, preserve cultural heritage and access to digital resources. In the future, the level of deepening the involvement of these technologies will inevitably grow. The sooner library institutions start using neural networks in everyday practice, the sooner they will be able to facilitate the work of library sphere workers, provide improved users’ service, and increase the effectiveness of library marketing support. It is emphasised that experiments on the use of artificial intelligence language models still need an empirical research method. Particularly, they require attention not only from the point of view of usage for personal purposes, but also in the professional activity of the library sphere.

An efficient multi-objective UAV assisted RSU deployment (MOURD) scheme for VANET

A Roadside Unit (RSU) serves as essential infrastructure in Vehicular Ad Hoc Networks (VANETs) that supports the goals of Intelligent Transportation Systems (ITS) by providing safety services, shared storage, and enhanced internet connectivity to vehicular users, drivers, and pedestrians. Additionally, the efficiency of VANETs, concerning network service utility and latency, depends on the relative positioning of these RSUs within the network topology. Most existing RSU deployment approaches deal with a single objective, either enhancing network service utility or minimizing the latency. For instance, some studies suggest deploying RSUs in high-traffic road segments that enhance network service utility but lead to higher latency. Conversely, some suggest deploying the RSUs in low-traffic road segments that minimize the network latency, but there will be low network service utility. Hence, there exists a trade-off between these two conflicting objectives in VANETs, and none of the studies address both objectives simultaneously. To achieve the balance between these two objectives, this paper proposes a Multi-Objective UAV assisted RSU Deployment (MOURD) scheme that leverages the Unmanned Aerial Vehicles (UAVs) for VANET efficiency. The MOURD scheme statically places RSUs in high-traffic road segments and dynamically dispatches the UAVs in low-traffic road segments to facilitate seamless network coverage and minimize the overall network latency. The simulation results on the road network of Delhi, India, demonstrate the effectiveness of the proposed MOURD scheme compared to other benchmark RSU & UAV deployment approaches. MOURD scheme outperforms on an average of 17.42%, 13.29%, 15.67% and 6.23% in terms of vehicle connection time, packet delivery ratio, throughput, and latency, respectively.

ECONOMIC BENEFITS OF DEPLOYING FOLDABLE CONTAINERS: REDUCING BUNKER AND CONTAINER MANAGEMENT COSTS IN A MULTI-PORT SHIPPING NETWORK

This study seeks to explore the effectiveness of employing foldable containers (FLDs) in liner shipping to reduce relocation and the empty containers and bunker costs (BCs) associated with ship operations. This resolves a minimum-cost multi-commodity network flow problem by optimizing container fleet size and empty container relocation in a multi-port shipping service network. Port handling time and sailing speed provided by obtained optimal solutions enable the determination of ship BCs as a secondary step. The numerical experiments demonstrate the comparative effect of FLDs against standard ones on the reduction of the costs of empty containers and containership bunker oil.

The Development of a Named Entity Recognizer for Detecting Personal Information Using a Korean Pretrained Language Model

Social network services and chatbots are susceptible to personal information leakage while facilitating language learning without time or space constraints. Accurate detection of personal information is paramount in avoiding such leaks. Conventionally named entity recognizers commonly used for this purpose often fail owing to errors of unrecognition and misrecognition. Research in named entity recognition predominantly focuses on English, which poses challenges for non-English languages. By specifying procedures for the development of Korean-based tag sets, data collection, and preprocessing, we formulated directions on the application of entity recognition research to non-English languages. Such research could significantly benefit artificial intelligence (AI)-based natural language processing globally. We developed a personal information tag set comprising 33 items and established guidelines for dataset creation, later converting it into JSON format for AI learning. State-of-the-art AI models, BERT and ELECTRA, were employed to implement and evaluate the named entity recognition (NER) model, which achieved an 0.943 F1-score and outperformed conventional recognizers in detecting personal information. This advancement suggests that the proposed NER model can effectively prevent personal information leakage in systems processing interactive text data, marking a significant stride in safeguarding privacy across digital platforms.

Building trust in cybernetic payment network: Insights from an emerging economy

This study investigates customers' trust and behavioural intentions regarding the use of cybernetic payment network services in an emerging country, focusing on perceived quality, system quality, and perceived image. Data from 324 respondents in Bangladesh were collected via a self-administered Google survey form distributed through email, WhatsApp, and Messenger using purposive sampling. The SmartPLS-4 software was used to assess construct validity and hypothesized path coefficients. Results indicate that perceived quality, system quality, and image significantly influence perceived trust in cybernetic payment network services. Additionally, perceived trust and system quality positively impact behavioural intention to use these services. Furthermore, perceived trust mediates the relationship between behavioural intention and system quality, perceived quality, and image. The study underscores the importance for cybernetic payment network service providers to enhance perceived quality, system quality, and perceived image to foster trust and promote adoption. It highlights the pivotal role of trust in driving customers' intention to adopt these services, urging providers to prioritize efforts in building trust among users. This study contributes to the limited literature on the impact of perceived quality, system quality, and perceived image on customers' inclination to use cybernetic payment network services, offering valuable insights for bank managers and policymakers to formulate effective strategies for sustaining innovation and enhancing adoption rates.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Embedding sustainability in local welfare systems: bottom-up contributions from social workers and care professionals in public and third sector organisations

ABSTRACT Sustainability, and how it may be embedded in welfare systems and services, is a much-debated topic in social work studies, with implications for the role that social workers and other care professionals play in this process. Analysing a local welfare system, this paper investigates professionals’ experiences of the organisational dimension of social services, and potential factors supporting a service redesign informed to sustainability. Professionals’ perspectives on sustainability, and their views on structural factors, barriers and methodological tools influencing the establishment of sustainable service networks, are explored. Data is drawn from a study exploring views on sustainability in care professionals in Italy (Veneto region). Eight focus groups, involving n = 26 social workers and n = 12 care professionals in Public and Third Sector Organisations were conducted using a photo-elicitation technique. Interviews were thematically analysed and interpreted through Boetto’s ‘transformative eco-social model’ (2017). Results describe the structuring of the eco-social intervention network and the (un)sustainable system aspects. Participants highlighted the benefits of participatory planning approaches, community work, new economic models, and organisational cultures fostering meaningful work. We conclude that social care professionals have a key role in catalysing grassroots change, and local services are privileged settings for building a sustainable, generative welfare.

Microsoft Azure Networking: Empowering Cloud Connectivity and Security

In the current era dominated by cloud computing, networking infrastructure serves as the backbone of digital operations. Among the top cloud service providers, Microsoft Azure offers a robust suite of networking solutions tailored to meet the evolving needs of modern businesses. This document aims to provide a comprehensive overview of Azure networking, examining its key components, deployment models, best practices, and practical applications. By exploring Azure Virtual Network, Load Balancer, VPN Gateway, ExpressRoute, Firewall, and other services in detail, the goal is to equip organizations with the necessary knowledge and insights to effectively leverage Azure networking for seamless cloud connectivity and enhanced security.

Цифровая экономика и экономика данных

Whether we are talking about the digital economy, the data economy, or big data, it is always meant to rep-resent information (be it signals, commands, or data) in digital format or, more precisely, in binary code. Under-standing this is the key to understanding many events and facts. In particular, this concerns the rapid develop-ment of network services based on digital technologies and many other realities of the modern economy. This is shown in the article using specific examples from the past of leading companies and specific individuals who have become symbols of the new economy.