End-user Services Research Articles

Lagrangian decomposition for stochastic TIMES energy system optimization model

<abstract><p>Energy system optimization models play an essential role in current decision support on topics including energy security, sustainable development and environmental protection from industrial, regional, national and even global perspective. One of the key energy system optimization models applied in international energy policy is TIMES. The article establishes two basic deterministic TIMES models which cover an energy commodity (coal or gas), a three-step supply curve and an end-use energy service demand. Then we convert the deterministic TIMES models into a stochastic optimization problem with multiple scenarios, and implement the Lagrangian decomposition approach in solving the stochastic programming models. The numerical experiment shows the feasibility of the Lagrangian decomposition algorithm to solve stochastic TIMES models with a small amount of scenarios, and analyze several reasons for non-convergence cases including the choice of step length and initial values of Lagrangian multipliers.</p></abstract>

RAMARL: Robustness Analysis with Multi-Agent Reinforcement Learning - Robust Reasoning in Autonomous Cyber-Physical Systems

A key driver to ofering smart services is an infrastructure of Cyber-Physical systems (CPS)s. By definition, CPSs are intertwined physical and computational components that integrate physical behaviour with computation. The reason is to autonomously execute a task or a set of tasks providing a service or a list of end-users services. In real-life applications, CPSs operate in dynamically changing surroundings characterized by unexpected or unpredictable situations. Such operations involve complex interactions between multiple intelligent agents in a highly non-stationary environment. For safety reasons, a CPS should withstand a certain amount of disruption and exert the operations in a stable and robust manner when performing complex tasks. Recent advances in reinforcement learning have proven suitable for enabling multi-agents to robustly adapt to their environment, yet they often depend on a massive amount of training data and experiences. In these cases, robustness analysis outlines necessary components and specifications in a framework, ensuring reliable and stable behaviour while considering the dynamicity of the environment.This paper presents a combination of multi-agent reinforcement learning with robustness analysis shaping a cyber-physical system infrastructure that reasons robustly in a dynamically changing environment. The combination strengthens the reinforcement learning, increasing the reliability and flexibility of the system by applying robustness analysis. Robustness analysis identifies vulnerability issues when the system interacts within a dynamically changing environment. Based on this identification, when incorporated into the system, robustness analysis suggests robust solutions and actions rather than optimal ones provided by reinforcement learning alone. Results from the combination show that this infrastructure can enable reliable operations with the flexibility to adapt to the changing environment dynamics.

Toward the Integration of UAVs' Services into the Cloud

Integrating unmanned aerial vehicles (UAV) into the cloud environment aims to put their on board equipment and capabilities at the service of end users at an affordable cost. Coupling both technologies will help leveraging them as a strong and novel service provider. This integration incolves service infrastructures, services offer and request protocols, and services selection techniques. In this article, we propose a new UAV-cloud architecture that enables UAVs to offer services allowing static as well as mobile users to request real-time and delay-tolerant UAV services using different communication technologies. This architecture comprises three layers. The middleware layer receives and processes requests from the end-user layer, then orchestrates activities where it plans missions and chooses from the infrastructure layer and the edge the appropriate UAV to satisfy consumer requests. The interaction between the different layers is made possible using two new protocols proposed to deal with the service request and consumption scenarios. Moreover, UAVs are highly extensible, and can advertise offers and receive requests in a centralized or distributed way by integrating a new module to their systems.

Implementing Co-Creation as a Policy Norm in Sweden — Steering Strategies for a Robust Municipal Organisation

Abstract Building upon findings from a strategically selected case study of a pioneering Swedish municipality, Jönköping, a participant in a Horizon2020 project on co-creating public service innovations in Europe (CoSIE), this paper illustrates how local municipalities may take a systemic approach to creating the necessary conditions for sustainable co-creation and assesses whether this illustrates an ongoing paradigmatic shift in service management and culture. The strategic change management efforts in adapting a public sector organisation to a co-creation culture, are assessed against a normative theoretical framework for such a transformative change (Finansdepartementet 2018; Torfing et al, 2016;). The study employs the concept of co-creation to highlight the paradigmatic shift in the approach to citizens as service end users from passive clients to active citizens with resources and capabilities to exert an impact on service design, delivery, and value creation. Th e article offers new insights into how a robust organisation may be moving towards a new public administration paradigm that accommodates co-creation, and especially how a new approach to management based on trust may be strategically implemented as a key factor in facilitating systemic change (Torfing et al, 2016). Against the background of a significant shortage of studies exploring how conditions for such a transformative change are created at different system levels, the article examines a longitudinal real-time study and illustrates new management approaches, strategies, and tools used. The study also contributes a normative framework to explore a shift to more trust-based steering and a more nuanced explanation of an ongoing managerial shift towards a ‘co-creation’ paradigm. It argues that achieving trust-based steering has major potential to facilitate a co-creation culture but that a paradigmatic shift towards such a culture requires congruence in discourses and actions on different system levels and a major transformation of approaches, roles, and relationship dynamics between senior and first-line management.

A qualitative examination of substance use service needs among people who use drugs (PWUD) with treatment and service experience in Ontario, Canada

BackgroundPeople who use drugs (PWUD) often have complex health and social support needs related to substance use, yet face numerous barriers to service access, resulting in unmet treatment needs and a corresponding gap in treatment. While initiatives to scale up substance use services for PWUD in Canada - and Ontario - have been undertaken, these have excluded PWUD’ perspectives, and their needs have largely been defined by other actors. As end-users of services, PWUD’ perspectives are vital to understanding what services are required, and whether existent services are adequate, appropriate and effective. Thus, the present study aimed to elicit in-depth knowledge from PWUD with lived experience of accessing services to better understand their unmet treatment and service needs, towards closing the service and treatment gap in Ontario.MethodsThis qualitative study included one-on-one interviews conducted with a cohort of n = 45 adult PWUD with substance use and treatment experience in Ontario, Canada. Participants were recruited from substance use services based on ConnexOntario’s directory of all provincial addiction services, as well as by word-of-mouth. Questions focused on participants’ experiences and perspectives on substance use services towards understanding their service needs. Data underwent an inductive thematic analysis based on key themes that emerged.ResultsParticipants commonly engaged in polysubstance use, and identified a number of unmet substance use service needs including complex factors within the current service system that influenced access to available programs. Specifically, participants suggested the need to address stigmatization and system fragmentation, increase service provision and capacity, and scale up specific services and related supports such as harm reduction, counseling, treatment, and housing.ConclusionsThis study identified PWUD’ needs in relation to substance use service provision in Ontario, Canada, and highlighted important areas for policy change and program planning and implementation. Concrete recommendations include the development of a government-funded, low-barrier, comprehensive and integrated service delivery and referral models that include PWUD as collaborators and program facilitators to ensure that services are as accessible, effective, and cohesive as possible. Results from this study can be used to enhance provincial substance use treatment and service provision.

Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing.

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.

Does Congestion Always Hurt? Managing Discount Under Congestion in a Game-Theoretic Setting

Many firms buy cloud services from cloud vendors, such as Amazon Web Services to serve end users. One of the key factors that affect the quality of cloud services is congestion. Congestion leads to a potential loss of end users, resulting in lower demand for cloud services. Although discount can stimulate demand, its effect under congestion is ambiguous; a higher discount leads to higher demand, but it can further lead to higher congestion, thereby lowering demand. We explore how congestion moderates both cloud vendor pricing and the buyer’s fulfillment decisions. We seek to answer how the congestion sensitivity of the end users and the cost of technology impact buyer profitability and the cloud vendor’s choice of discount. We also examine how the cost of technology determines the buyer’s willingness to pass on savings to end users. Our results show that the buyer is not necessarily worse off even when the end users are more intolerant to congestion. In fact, when end users are more congestion sensitive, the demand for cloud services can sometimes increase, and the discount offered by the vendor can decrease. We also observe that a lower cost of technology can sometimes hurt the buyer, and the buyer can pass on lower benefits to end users.

An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques

Things or device under the Internet of Things, when connected to the internet, becomes a product, offers various applications and services for end-users. Existing methodologies offer sensor-based IoT based health-care services, to the end-users populated with lots of sensed values all at a time, making the health-care system with least robustness and inefficient due to unsystematic preview of patient’s health record. In order to address this issue, a novel semantic-based service framework is proposed in this paper which allows the end-user to subscribe for a specific physiological parameter, among all the available sensed data, making the health-care system more efficient. In the SeSem framework, Semantification rules and semantification relationship table are applied to the sensed JavaScript object notation format (JSON) data, in order to semantically separate the JSON data, into a meaningful format. To list the sensed data according to the significance of health issues, a priority is assigned to the sensors in the semantification relationship table. Hence, semantically separated data can be done along with assigned priority. Sensor-based sematic ontology is then applied to the semantically separated data, to transform the sensed data more relevant in terms of particular disease and sensor associated with it. The semantically separated sensed data are then published to the message queuing telemetry transport (MQTT) interface. Using MQTT subscribe, the end-user along with date and time, requests for a particular service, using a Semantic Similarity mapping algorithm, which compares the entire sensed data to that of requested data and responds with a particular physiological parameter request. To make the health care system deploy services in an intelligent way, deep learning algorithm Feedforward Recurrent Neural Networks are applied, which makes the prediction of sensed data based on the latest update when the end-user subscribes for a certain sensed data without specifying date and time. The proposed methodology is evaluated against IoT performance metrics which had shoed showing better results in terms of service-oriented IoT.

Artificial Noise Assisted Secure Mobile Crowd Computing in Intelligently Connected Vehicular Networks

Growing computation requirements in emerging intelligently connected vehicle (ICV) networks has posed a big challenge for vehicles to process computation-intensive tasks on-board, and thus computation partition and offloading plays a critical role. Owing to broadcast nature of wireless channels, confidential messages without proper security protection in ICVs may be easily exposed to malicious users. In this paper, a mobile crowdsourcing (MCS) based mobile crowd computing framework for ICV networks is proposed, where multiple vehicles act as workers to provide computing services for end user (EU). In particular, artificial noise (AN) assisted physical (PHY) layer security approaches are used to enhance the security in offloading links. Ergodic secrecy rates in different offloading phases in time-varying channels are derived. In addition, an incentive-driven mechanism is introduced to encourage workers to share their idle computing resources, and an optimization problem is formulated to minimize the overall price paid for computing tasks, subject to energy consumption and delay constraints. Finally, Monte Carlo simulations verify the analysis on the ergodic secrecy rates.

Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

In order to compete for a prominent market share, network operators and service providers should retain and increase the verticals’ subscription, catering to their needs in order to differentiate themselves from competitors. In this scenario, verticals’ satisfaction arises of paramount importance. As such, user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end-to-end system functioning. To properly estimate end user satisfaction, operators and service providers require efficient means for quality monitoring and estimation at all layers, in conjunction with mechanisms able to maintain said quality at optimum levels. Given these factors, this paper proposes a mechanism for Quality of Perception (QoP) estimation in e-Health services, enabling the QoP-aware management of network slices fulfilling the requirements of supported services. To this end, the paper proposes a cognitive-based architecture which allows for the collection and monitoring of verticals’ data to estimate QoP and provides mechanisms to re-configure the underlying network slices according to the monitored quality levels. A machine learning (ML) model is introduced that aims to forecast any future degradation in the quality perceived by vertical users. In case of a predicted degradation, the proposed architecture reacts and triggers the necessary remedial actions, referred as actuations. In order to evaluate the developed ML model and to showcase the interaction between the different components of the proposed architecture, an experimental study is presented with real data extracted from a roaming ambulance. In addition, a Proof of Concept of the actuation mechanism is demonstrated through an experimental testbed emulating e-Health services.

A Dual Path Model of Work-Related Well-Being in Healthcare and Social Work Settings: The Interweaving Between Trait Emotional Intelligence, End-User Job Demands, Coworkers Related Job Resources, Burnout, and Work Engagement.

Framing the job demands-resources (JD-R) model, the present study deepened how trait emotional intelligence (TEI, i.e., perception about one's own emotional realm) contributes to the work-related well-being of healthcare professionals. A total of 302 healthcare professionals were involved in the study and completed an anonymous self-report questionnaire. The results of the structural equation modeling revealed that TEI was directly and indirectly—mediated by end-user job demands—negatively associated with burnout, and directly and indirectly—mediated by coworkers related job resources— associated with work engagement. According to the health impairment and motivational processes of JD-R, the present study highlights that TEI could targets burnout and work engagement through different paths. The first path revealed that TEI would reduce burnout protecting by the insurgence harmful relationships with service end-users and the second showed that TEI would support work engagement sustaining the development of positive relationship with coworkers.

Co-production in solid waste management: analyses of emerging cases and implications for circular economy in Nigeria.

Co-production is a paradigm shift from the traditional model of public policymaking and service delivery that advocates for the involvement and participation of end-users of services as co-partaker in the process. In this paper, we examined the emerging models of co-production in solid waste management in Nigeria using a case study methodology. Four cases were purposefully selected for detailed exploration. The results of the analysis show that the involvement of the plurality of the non-state actors in waste management co-production brought in innovation through ICT, financial resources through grants, and increased public awareness. And have also given the service receivers a change of orientation that makes them perceive waste as a source of income rather than all rubbish needed to be discarded. However, possible exploitation of informal waste pickers, unclear business models, and absence of prior arrangement for coming together of both state and non-state actors in designing the service production are challenges to the emerging co-production cases. The current study further shows that the emerging co-production efforts have huge potential in promoting circular economy as it creates a better avenue for the implementation of extended producer responsibility (EPR), the establishment of eco-industrial parks, and safe integration of informal waste recyclers.

Virtual Router Design and Modeling for Future Networks with QoS Guarantees

A virtual router model with a static and dynamic resource reconfiguration for future internet networking was developed. This technique allows us to create efficient virtual devices with optimal parameters (queue length, queue overflow management discipline, number of serving devices, mode of serving devices) to ensure the required level of quality of service (QoS). An analytical model of a network device with virtual routers is proposed. By means of the mentioned mathematical representation, it is possible to determine the main parameters of the virtual queue system, which are based on the first in, first out (FIFO) algorithm, in order to analyze the efficiency of network resources utilization, as well as to determine the parameters of QoS flows, for a given intensity of packets arrival at the input interface of the network element. In order to research the guaranteed level of QoS in future telecommunications networks, a simulation model of a packet router with resource virtualization was developed. This model will allow designers to choose the optimal parameters of network equipment for the organization of virtual routers, which, in contrast to the existing principle of service, will provide the necessary quality of service provision to end users in the future network. It is shown that the use of standard static network device virtualization technology is not able to fully provide a guaranteed level of QoS to all present flows in the network by the criterion of minimum delay. An approach for dynamic reconfiguration of network device resources for virtual routers has been proposed, which allows more flexible resource management at certain points in time depending on the input load. Based on the results of the study, it is shown that the dynamic virtualization of the network device provides a guaranteed level of QoS for all transmitted flows. Thus, the obtained results confirm the feasibility of using dynamic reconfiguration of network device resources to improve the quality of service for end users.

Needs-appropriate services for people with young onset dementia: The perspectives of healthcare professionals

Young onset dementia has become a growing challenge in national healthcare systems. Concerns about the situation have already been expressed; however, they mostly report the views of services end-users. Aimed at finding out useful further improvement, interviews documenting the working experiences of professionals addressing healthcare services to people with young onset dementia (YOD) were analysed qualitatively. The interviews resulted in four themes and twelve categories. The themes regarded services' complexity and responsiveness, the levels of education and knowledge on YOD, the impact that serving people with YOD reflects on staff working experience and quality of life, and a series of proposals aimed at improving services by giving people with YOD and families the provision of care they deserve. Although some of the themes recapped the ones reported in the previous literature, the mixture of internally and externally driven instances represented in the themes depict the complexity of care delivery in the services. Findings are discussed in the light of a pragmatical framework capable of suggesting what changes services should implement to be timely responsive.

Enhancing suicide prevention outreach via mobile-enabled website design.

Online outreach campaigns are a potential public health method for reaching service members at risk for suicide. The Real Warriors Campaign website underwent a full redesign in 2019 to enhance its ability to provide crisis resources and increase engagement by adopting a mobile-first strategy and implementing a responsive framework, meaning the site renders properly on all devices, including desktop, tablets, and mobile. Usability testing with end-user service members led to several innovations on the website, including a one-click banner that directly linked users on their mobile phones to crisis resources, the redesign of menus and content to better display on mobile devices, and promoting use of website resources through gateway topics. Comparing the 6months before and after the mobile redesign showed significant increases in new mobile users, pages viewed on mobile devices, and new users coming to the site through social media. There was also a significant increase in specific help-seeking actions by users, including use of referral links and live chat, as well as 200 individuals accessing crisis phone lines through new one-click dialing banners. Suicide prevention campaigns should continue to optimize their online presence to reach groups at risk. This study of the website redesign from the Real Warriors Campaign illustrates several best practices in digital outreach as applied to suicide prevention, including leveraging usability testing, synching outreach material with social media, and ensuring mobile compatibility.

A comprehensive set of global scenarios of housing, mobility, and material efficiency for material cycles and energy systems modeling

Scenario-based assessments are a useful tool to explore unknown futures and inform decision makers and the public of the consequences of different courses of action. Scenario developments in industrial ecology have focused on disparate components of the socioeconomic metabolism and case studies, and few efforts of comprehensive and cumulative scenario formulation are documented. Many important, empirically derived relationships between material cycles, end-use services, and energy use are relevant to global scenario modeling efforts, for example, of integrated assessment models (IAMs), which do not routinely describe material cycles or the life-cycle impacts of various technology shifts. These inconsistent depictions of material cycles and their environmental impacts hinder the assessment of sustainable development strategies such as demand-side sufficiency, material efficiency, and energy efficiency. We developed three highly detailed scenarios covering 20 global regions to 2060 for the service provisioning of dwelling area and personal transport grounded in salient building and vehicle operation parameters. Our scenarios are based on, and interface with, the Low Energy Demand (LED) and Shared Socioeconomic Pathways (SSP1 and SSP2) narratives. The results comprise scenario-, region-, and period-specific narratives and corresponding parameter values, including per-capita floor space and vehicle stocks, building and vehicle archetype mixes, passenger-km, vehicle-km, vehicle occupancy rates, and implementation potentials of nine material efficiency strategies. The explicit storyline extension approach presented here is an alternative to the aggregate GDP-driven or historical trend extrapolations of service or energy demands. We describe the scenario formulation processes, resulting parameters, their applications, and offer an outlook for prospective sustainability models. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

Architectures, frameworks, and applications in IoT-based smart environment: a review

We are in a time when a large number of electronic devices influence human interaction with the environment. The physical place where these various devices interact with each other to provide useful services for end-users gives birth to the concept of a Smart Environment. The interaction of the multiple devices that make up a smart environment produces a large amount of information or Big Data problems, so the insertion of the Internet of Things (IoT) technology in the environment is essential. IoT integration and cloud computing technology are needed as the impact of evolution on the next generation of smart environments. Trends in research related to IoT and Smart Environments are increasing rapidly beyond the trend of Smart City that first emerged. This paper reviewed current trends about smart environments, architectural concepts, frameworks, and applications of IoT-based Smart Environments.

Blockchain: a framework for membership and access

Blockchain: a framework for membership and access

Configurations, Power Topologies and Applications of Hybrid Distribution Transformers

Distribution systems are under constant stress due to their highly variable operating conditions, which jeopardize distribution transformers and lines, degrading the end-user service. Due to transformer regulation, variable loads can generate voltage profiles out of the acceptable bands recommended by grid codes, affecting the quality of service. At the same time, nonlinear loads, such as diode bridge rectifiers without power factor correction systems, generate nonlinear currents that affect the distribution transformer operation, reducing its lifetime. Variable loads can be commonly found at domiciliary levels due to the random operation of home appliances, but recently also due to electric vehicle charging stations, where the distribution transformer can cyclically vary between no-load, rated and overrated load. Thus, the distribution transformer can not safely operate under highly-dynamic and stressful conditions, requiring the support of alternative systems. Among the existing solutions, hybrid transformers, which are composed of a conventional transformer and a power converter, are an interesting alternative to cope with several power quality problems. This article is a review of the available literature about hybrid distribution transformers.

Multiple Narrowband Interferences Characterization, Detection and Mitigation Using Simplified Welch Algorithm and Notch Filtering

By increasing the demand for radio frequency (RF) and access of hackers and spoofers to low price hardware and software defined radios (SDR), radio frequency interference (RFI) became a more frequent and serious problem. In order to increase the security of satellite communication (Satcom) and guarantee the quality of service (QoS) of end users, it is crucial to detect the RFI in the desired bandwidth and protect the receiver with a proper mitigation mechanism. Digital narrowband signals are so sensitive into the interference and because of their special power spectrum shape, it is hard to detect and eliminate the RFI from their bandwidth. Thus, a proper detector requires a high precision and smooth estimation of input signal power spectral density (PSD). By utilizing the presented power spectrum by the simplified Welch method, this article proposes a solid and effective algorithm that can find all necessary interference parameters in the frequency domain while targeting practical implantation for the embedded system with minimum complexity. The proposed detector can detect several multi narrowband interferences and estimate their center frequency, bandwidth, power, start, and end of each interference individually. To remove multiple interferences, a chain of several infinite impulse response (IIR) notch filters with multiplexers is proposed. To minimize damage to the original signal, the bandwidth of each notch is adjusted in a way that maximizes the received signal to noise ratio (SNR) by the receiver. Multiple carrier wave interferences (MCWI) is utilized as a jamming attack to the Digital Video Broadcasting-Satellite-Second Generation (DVB-S2) receiver and performance of a new detector and mitigation system is investigated and validated in both simulation and practical tests. Based on the obtained results, the proposed detector can detect a weak power interference down to −25 dB and track a hopping frequency interference with center frequency variation speed up to 3 kHz. Bit error ratio (BER) performance shows 3 dB improvement by utilizing new adaptive mitigation scenario compared to non-adaptive one. Finally, the protected DVB-S2 can receive the data with SNR close to the normal situation while it is under the attack of the MCWI jammer.