Flexible Service Research Articles

Multi-UAV Enabled Aerial-Ground Integrated Networks: A Stochastic Geometry Analysis

Multiple unmanned aerial vehicles (UAVs) can function as aerial base stations to provide flexible and reliable communication services for massive ground devices (GDs). It is quite a challenging task to analyze such multi-UAV networks when considering practical mutually exclusive relationships among UAVs. Based on the tools of stochastic geometry, we in this paper develop a theoretical framework for modeling and analyzing aerial networks with UAVs following Matérn hard-core point process (MHCPP). As the tractable probability generating functional (PGFL) of repulsive point processes is unavailable, we employ an approximate approach based on the Poisson point process to analyze the cumulative interference and the signal-to-interference ratio (SIR) of a typical GD. By considering both line-of-sight (LOS) and none-line-of-sight (NLOS) communications, we obtain the approximation expressions of the network coverage probability and average rate. Finally, extensive simulation results are presented to validate the efficiency and accuracy of our proposed framework.

Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

Patients’ Reasons for “No Shows” and Premature Termination of Treatment at an Outpatient Mental Health Program

One in five Canadians currently experience mental health problems, and although effective psychotherapy treatment is currently available, patients’ decision to end therapy before completing treatment impacts limited resources. This qualitative study explored the experiences of 15 patients who either did not attend a scheduled intake appointment or had dropped out of service prematurely from an outpatient mental health clinic. Overall, the overarching themes identified were overall experience with service and access, multiple factors and external circumstances, experience with group structure, group processes and interactions, and age-related differences. Younger participants shared a need for flexible and spontaneous service.

Health service approaches to providing care for people who seek treatment for obesity: identifying challenges and ways forward

This paper aims to identify challenges in current health service approaches to providing optimal care for people who seek treatment for obesity. Type of program or service: Health service management of obesity in Australia Methods: Drawing on lived experience, clinical and academic perspectives, and available evidence, we reflect on the current state of the healthcare system to support people seeking treatment for obesity. We suggest actions to enable effective, acceptable and equitable care for this group of people. Identified challenges include the complexity of care required to adequately manage obesity, existing service capability and capacity, and high out-of-pocket patient costs. To address these challenges, a comprehensive response is required at all levels of the healthcare system. As a starting point, we propose eight areas of action: partner with people living with obesity; eliminate weight stigma; increase healthcare professional education, guidelines and resources; establish clear referral pathways and working partnerships; scale services to meet demand; ensure flexible and accessible service delivery; implement changes to the Medicare Benefits Schedule to increase service provision and pursue opportunities for subsidised medicines.

Prioritizing Flexibility, Human Connection, and Representation: Findings from the Qualitative Process Evaluation of the Special Projects of National Significance Black Men Who Have Sex with Men Initiative.

Previous research has identified significant unmet need for behavioral health care services for Black men who have sex with men (Black MSM); this challenge has been linked to poorer overall health and well-being. Health Resources and Services Administration (HRSA) funded a Special Projects of National Significance (SPNS) Initiative, Implementation of Evidence-Informed Behavioral Health Models to Improve HIV Health Outcomes for Black Men who have Sex with Men, with a goal to integrate behavioral health and clinical care services using four different evidence-informed models of care, ultimately improving HIV health outcomes. NORC at the University of Chicago conducted a multisite evaluation to assess the success of this Initiative, including a qualitative process evaluation that examined adaptations, services, integration activities, recruitment methods, and fidelity. The process evaluation described methods and processes used by demonstration sites to achieve their goals. This included challenges or barriers to implementation and the associated adaptations, notably due to the COVID-19 Public Health Emergency. Our study found key themes that indicated successful implementation were flexible service delivery, human connection, and client representation. We recommend future replicators apply these lessons learned in diverse health care and community settings that serve Black MSM. Additional information about the interventions can be found on TargetHIV.

An allocation model for emergency medical services based on integrated healthcare during an epidemic

Enhanced virus control and patient treatment outcomes during an epidemic requires flexible and efficient medical services. This research explores an epidemic medical service allocation model from an integrated healthcare perspective, aiming to enhance the overall regional emergency medical service capability based on collaborative medical treatment. The study is based on COVID-19 medical services and its main treatment stages, and resource allocation models are constructed for the allocation of multiple medical services. The model addresses the dispersed medical service resources and demands in a certain region, and realizes the division of labor and resource sharing among medical institutions from the perspectives of distance, cost, and fairness with the advantage of integrated healthcare. We design examples in the context of COVID-19 epidemic and verify the validity and applicability of the model under different phases. This paper provides reference to the research and practice of achieving collaborative medical treatment between multiple medical institutions.

Generative Adversarial Network and Its Application in Energy Internet

Energy Internet (EI) can provide consumers with flexible energy-sharing services. Recently, artificial intelligence (AI) technology has been widely used in the field of EI. Generative adversarial network (GAN) is one of the hottest research directions in the field of AI in recent years, and its excellent data generation ability has attracted wide attention. First, this paper introduces the framework, advantages, disadvantages, and improvement of classic GAN. Then, the application of GAN in the field of EI is reviewed. Finally, the paper is summarized, and the possible application of GAN in EI is prospected.

Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers

The efficient use of energy resources is profoundly changing power grid regulation and policy. New forms of power generation coupled with storage and the presence of new, increasingly flexible loads such as electric vehicles enable the development of multi-agent planning systems based on new forms of interaction. For instance, consumers can take advantage of flexibility by interacting directly with the grid or through aggregators that bridge the gap between these end-users and traditional centralised markets. This paper aims to provide insight into the benefits for aggregators and end-users from a financial perspective by proposing a methodology that can be applied to different scenarios. End-users may provide flexibility services related to private vehicle charging stations or battery storage systems. The paper will analyse different remuneration levels for end-users by highlighting the most beneficial scenarios for aggregators and end-users and providing evidence on potential conflict of interests. The numerical results show that some consumers may benefit more from aggregation. This is because if taken individually, consumption habits do not allow the same flexibility when considering clusters of consumers with different behaviour. It is also shown that there are cases in which consumers do not seem to benefit from the presence of intermediate parties. We provide extensive numerical results to gain insight for better decision making.

IoT-fog-based healthcare 4.0 system using blockchain technology.

Real-time tracking and surveillance of patients' health has become ubiquitous in the healthcare sector as a result of the development of fog, cloud computing, and Internet of Things (IoT) technologies. Medical IoT (MIoT) equipment often transfers health data to a pharmaceutical data center, where it is saved, evaluated, and made available to relevant stakeholders or users. Fog layers have been utilized to increase the scalability and flexibility of IoT-based healthcare services, by providing quick response times and low latency. Our proposed solution focuses on an electronic healthcare system that manages both critical and non-critical patients simultaneously. Fog layer is distributed into two halves: critical fog cluster and non-critical fog cluster. Critical patients are handled at critical fog clusters for quick response, while non-critical patients are handled using blockchain technology at non-critical fog cluster, which protects the privacy of patient health records. The suggested solution requires little modification to the current IoT ecosystem while decrease the response time for critical messages and offloading the cloud infrastructure. Reduced storage requirements for cloud data centers benefit users in addition to saving money on construction and operating expenses. In addition, we examined the proposed work for recall, accuracy, precision, and F-score. The results show that the suggested approach is successful in protecting privacy while retaining standard network settings. Moreover, suggested system and benchmark are evaluated in terms of system response time, drop rate, throughput, fog, and cloud utilization. Evaluated results clearly indicate the performance of proposed system is better than benchmark.

A Robust Possibilistic Programming Approach for a Road-Rail Intermodal Routing Problem with Multiple Time Windows and Truck Operations Optimization under Carbon Cap-and-Trade Policy and Uncertainty

This study investigates a road-rail intermodal routing problem in a hub-and-spoke network. Carbon cap-and-trade policy is accommodated with the routing to reduce carbon dioxide emissions. Multiple time windows are employed to enhance customer flexibility and achieve on-time pickup and delivery services. Road service flexibility and resulting truck operations optimization are explored by combining truck departure time planning under traffic restrictions and speed optimization with the routing. To enhance the feasibility and optimality of the problem optimization, the routing problem is formulated in a fuzzy environment where capacity and carbon trading price rate are trapezoidal fuzzy parameters. Based on the customer-centric objective setting, a fuzzy nonlinear optimization model and its linear reformation are given to formulate the proposed routing problem that combines distribution route design, time window selection and truck operations optimization. A robust possibilistic programming approach is developed to optimize the routing problem by obtaining its robust solutions. A case study is presented to demonstrate the feasibility of the proposed approaches. The results show that the multiple time windows and truck operations optimization can lower the total costs, enhance the optimality robustness and reduce carbon dioxide emissions of the routing optimization. The sensitivity analysis finds that increasing the lower bound of the confidence level in the robust possibilistic programming model improve the robustness and environmental sustainability; however, worsen the economy of the routing optimization.

Aerial-ground collaborative routing with time constraints

The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage. To reach a balance between green transportation and competitive edge, the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery, where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services. In this paper, we propose the Vehicle Routing Problem with Drones under Time constraints (VRPD-T) and focus on the time constraints involved in realistic scenarios during the delivery. A mixed-integer linear programming model has been developed to minimize the total delivery completion time. Furthermore, to overcome the limitations of standard solvers in handling large-scale complex issues, a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution. The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.

Remunerating space–time, load-shifting flexibility from data centers in electricity markets

We establish economic properties for an electricity market clearing formulation that seeks to remunerate spatio-temporal, load-shifting flexibility provided by data centers (DaCes). Load-shifting flexibility is a key asset for power grid operators as they aim to integrate larger amounts of intermittent renewable power and to decarbonize the grid. Central to our study is the concept of virtual links, which provide non-physical pathways that can be used by DaCes to shift power loads (by shifting computing loads) across space and time. We use virtual links to show that the clearing formulation treats DaCes as prosumers that simultaneously request load and provide a load-shifting flexibility service. Our analysis also reveals that DaCes are remunerated for the provision of load-shifting flexibility based on nodal price differences (across space and time). We also show that DaCe flexibility helps relieve space–time price volatility and show that the clearing formulation satisfies fundamental properties that are expected from coordinated markets (e.g., provides a competitive equilibrium and achieves revenue adequacy and cost recovery). The concepts presented are applicable to other key market players that can offer space–time shifting flexibility such as distributed manufacturing facilities and storage systems. Case studies are presented to demonstrate these properties.

SPM: A Novel Hierarchical Model for Evaluating the Effectiveness of Combined ACDs in a Blockchain-Based Cloud Environment

Cloud computing provides blockchain a flexible and cost-effective service by on-demand resource sharing, which also introduces additional security risks. Adaptive Cyber Defense (ACD) provides a solution that continuously changes the attack surface according to the cloud environments. The dynamic characteristics of ACDs give defenders a tactical advantage against threats. However, when assessing the effectiveness of ACDs, the structure of traditional security evaluation methods becomes unstable, especially when combining multiple ACD techniques. Therefore, there is still a lack of standard methods to quantitatively evaluate the effectiveness of ACDs. In this paper, we conducted a thorough evaluation with a hierarchical model named SPM. The proposed model is made up of three layers integrating Stochastic Reward net (SRN), Poisson process, and Martingale theory incorporated in the Markov chain. SPM provides two main advantages: (1) it allows explicit quantification of the security with a straightforward computation; (2) it helps obtain the effectiveness metrics of interest. Moreover, the hierarchical architecture of SPM allows each layer to be used independently to evaluate the effectiveness of each adopted ACD method. The simulation results show that SPM is efficient in evaluating various ACDs and the synergy effect of their combination, which thus helps improve the system configuration accordingly.

Reinforcement-Learning Based Handover Optimization for Cellular UAVs Connectivity

The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learningbased algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Digital service flexibility: a conceptual framework and roadmap for digital business transformation

ABSTRACT Due to the growing need to continually generate customer value, firms must be flexible to address changing business environments and customer expectations. Although research has examined Information Technology (IT) flexibility, the nuances of IT flexibility in services have received limited attention. This study uses a three-stage roadmap to build digital service flexibility. First, using prior research on services and IT flexibility, we articulate three dimensions for digital services flexibility (DSF): core DSF, transaction DSF, and customer DSF. Second, we propose a theoretical customer-service value framework as a blueprint for service firms to align their strategies along these three DSF dimensions using four propositions. Third, for DSF to be applied in practice, we discuss contextual and situational embeddedness to support digital transformation for services. We use a practical application of DSF using a multi-year case study that aligns service innovations inside and outside a patient room in a hospital setting. The main contributions of this study lie in a) conceptualizing digital service flexibility and identifying its three dimensions, b) proposing a conceptual framework highlighting how firms can use DSF in alignment with their business strategy, and c) illustrating how the concept of DSF applies in a hospital setting.

Multi-objective optimization of coal-fired power units considering deep peaking regulation in China.

China states to build new power system dominated by new energy power to promote the targets for peaking carbon emissions by 2030 and achieve carbon neutrality by 2060. Peaking regulation ancillary services provided by coal-fired power units is an essential solution to mitigate the volatility and instability of large-scale renewable energy for China's specific power mix. However, when the coal-fired power units operate at a low power output, the intensity of both coal consumption and carbon emissions gradually rises with the falling output rate. Moreover, cutting down the power output of coal-fired units frequently will damage the technical life. Given the impacts of power market reform and carbon mitigation targets, whether to participate in the energy market or the peaking regulation ancillary service market is an urgent issue for coal-fired power units. Considering the discrepancy in costs and benefits of various units at different output rate, this paper proposes a multi-objective optimization model to solve the issue from the perspective of the coal-fired power generators, in which both economic profit and carbon reduction goals are coordinated. Sequential quadratic programming is adopted to solve the nonlinear optimization problem. In order to study the difference in the decisions made by varied technical units, 7 different types of units are analyzed in the case study. The scenarios analysis indicates that large-capacity and new coal-fired power units are better to participate in energy market since it can give full play to the advantage of higher generation efficiency, while the small-capacity ones are suitable to provide flexible service in the peaking regulation ancillary service market. Besides, simple low-carbon objective will burden the cost of coal-fired power units and challenge the sustainable transition of power system. Hence, the power system should balance both economic profit of generators and national carbon mitigation targets during the low-carbon transition.

Characteristics of the ideal healthcare services to meet adolescents' mental health needs: A qualitative study of adolescents' perspectives.

Despite increased focus on development of mental health services worldwide, many adolescents still hesitate to reach out to the services when they need them. This might be linked to the lack of adolescent involvement in the development of services. This study aimed to explore adolescents' perspectives on the ideal healthcare services to meet their mental health needs. A two-stage qualitative study was carried out to explore the perspectives of adolescents on the healthcare services to support their mental health. In the first stage, data from 295 adolescents attending a mental health seminar were analysed using qualitative content analysis. In the second stage, in-depth perspectives of 10 adolescent coresearchers were gathered through self-reflection and group discussions to further explore the categories developed in the first stage. Thematic analysis was used to develop the themes answering the research question. Ten adolescent coresearchers planned the study, collected and analysed data and authored the manuscript, with the support of a senior researcher. Five themes describe adolescents' perspectives on the ideal healthcare services to meet their mental health needs: (1) Culturally Sensitive and Responsive; (2) Communication of Information; (3) Easy Access; (4) Variety of Support; and (5) Consistency. Culturally Sensitive and Responsive services influenced all other themes. The themes describe suggestions for mental health service improvement, including how to manage the barriers that adolescents face to receive help from the mental health services. This study highlights the importance of culturally sensitive and responsive services. It raises the need for an expanded definition of culture going beyond nationality and ethnic background. Adolescents need flexible services that meet their individual mental health needs. This has implications for practitioners, educators, system organizers and researchers, who should also involve adolescents in planning, implementing and assessing the services. There is a need for a self-learning system to continuously adapt to user feedback. This study provides insight into adolescents' perspectives on the ideal mental health services. It offers suggestions for ways to improve services to better meet the individual mental health needs of adolescents. Additional research is needed to further develop and implement service changes, as well as to assess their acceptability, effectiveness, cost-effectiveness and safety. This is a study lead by adolescents. Adolescent coresearchers have, with the support of a senior researcher, planned and carried out the study, collected and analysed data and authored the manuscript.

Barriers and facilitators for obtaining support services among underserved families with an autistic child: A systematic qualitative review.

Families from underrepresented ethnic or racial groups and those with limited financial resources could experience more difficulty in accessing support services for their autistic child due to certain types of barriers. We searched academic journals, websites, and other sources for studies which looked at what barriers might be present for such families and what might help families access support services for their autistic child. The search found 18 studies. Results from each study were examined and coded into themes. Parents reported that accessibility, diversity of support services, and stigma influenced their experiences with support services. We discuss what these findings might mean for future research and for service delivery.

Scenario-Based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers.

Hydrogen Technologies to Provide Flexibility to the Electric System: A Review

Nowadays, the electric system is evolving towards a paradigm shift, with a constantly higher penetration of renewable generation, as well as the electrification of the transport and different industry sectors. This new scenario requires flexibility services to be provided to transmission and distribution grids, to allow their safe and reliable operation. From amongst the various options currently investigated, hydrogen technologies are demonstrating great potential in terms of lower energy costs for consumers/prosumers, lower emissions, and better levels of quality and security of supply. This paper provides a review of the latest state-of-the-art flexibility services that hydrogen-based technologies can provide to both DSOs and end-users. This review considers contributions from both academia and industry.