Multiple Services Research Articles

An Efficient Multi-Topology Construction Method for Scheduling Mobile Data Flows in Software-Defined Networking

In mobile networks, a content server can provide multiple services simultaneously to a mobile device, generating multiple data flows. As the device moves, the transmission path in the wired network may need to be switched to maintain service continuity. However, a single switching path may not be able to accommodate all the flows, potentially leading to congestion and a degraded user experience. To address this challenge, we propose a multi-topology routing-based mobile data scheduling method that dynamically switches flows across multiple paths to enhance flexibility and load balancing. The performance of this method is significantly influenced by the construction of logical topologies. Well-designed topologies provide high-bandwidth, low-latency paths to all possible destination nodes, while poorly designed topologies waste switch capacity and fail to achieve these goals. In this paper, we introduce an efficient multi-topology construction method for scheduling mobile data flows in software-defined networking (SDN). Our approach optimizes and balances transmission capacity for each destination node while adhering to the flow entry constraints of switches. Simulations demonstrate that our method consistently outperforms the single-path switching method and the other two multi-topology construction methods in terms of packet delay, packet loss rate, and network throughput, regardless of the device’s new location.

Hub-and-spoke network design considering multiple delivery requirements under uncertainties

To satisfy customers' personalized delivery demands and expand the application of hub location models, this paper introduces a hub location problem with multiple service levels and mixture uncertainties, in which two or more allowed delivery times are provided for shipment. In an expected cost-minimization context with delivery-time restrictions, the researched problem explicitly considers fixed hub number, single-assignment pattern from hubs to demand nodes, diversified transportation modes, and two types of real-life uncertainties (stochastic customer demand and fuzzy transportation cost). To help eliminate the mixture uncertainties in the proposed mathematical model, the expected value model and fuzzy chance constrained programming are utilized. By employing the existing fuzzy arithmetic and the newly-proposed operational law of γ-pessimistic value for functions of regular fuzzy numbers, two novel two-phase solution approaches are designed, respectively. The effectiveness of solution approaches and the necessity of uncertain parameters setting in the model are comprehensively illustrated by a series of numerical experiments based on Turkish data set.

Rice-fish co-culture promotes multiple ecosystem services supporting increased yields

Rice-fish co-culture, combining rice cultivation with managed fish populations, has been conducted in southeast Asia for more than 2000 years. This is both an extensive and integrated management system that has significant potential to enhance yield by promoting a range of ecosystem services benefiting soil health and natural pest control in the rice growing period. However, very little is known about the role of earthworms in this process once the crop has been harvested. Here, we conducted a field experiment over two years (2019 and 2021) comparing rice-fish co-culture vs. rice-monoculture (fish free) systems in eastern China. We evaluated rice yield, earthworm populations (supporting nutrient cycling and soil structure), soil quality (including nitrogen, phosphorus, potassium and organic matter), pest abundance and populations of arthropod predators. Results showed that rice-fish co-culture increased earthworm and predator populations, while also improving soil quality and decreasing insect pests. This led to increased yield. We showed that the spill-over effects of earthworms from surrounding paddy field ridges into rice paddy fields benefited soil quality acting to increase rice productivity, but that this acted in combination with other processes such as the activity of fish and increased natural pest control. Our study suggests that rice-fish co-culture farming practices support multiple ecosystem services through improved soil quality, earthworm abundance, arthropod predator natural pest control and rice productivity.

PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.

A novel elliptical MIMO antenna design for enhanced wireless connectivity in greenhouse applications

This paper presents a novel design and development of an elliptical antenna tailored for Greenhouse applications, providing support for multiple wireless services. The proposed antenna configuration is a quad-port multiple input multiple output (MIMO) system consisting of four identical elliptical antenna elements arranged in an orthogonal configuration on a Polycarbonate substrates. It operates effectively across the 2.4 GHz band and the 3–14.8 GHz band, enabling seamless connectivity for a range of Greenhouse and wireless applications, including Bluetooth, Wi-Fi, intelligent control systems (ICS), Greenhouse-to-infrastructure (G2I) communication, sensor networks (SN), and Greenhouse-to-network (G2N) connections. Rigorous analysis of antenna diversity performance is conducted, focusing on key parameters such as the envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and channel capacity loss (CCL). The antenna demonstrates exceptional performance, with an ECC of less than 0.005, DG exceeding 10 dB, TARC below − 25 dB, and CCL less than 0.25 bits/s/Hz. Detailed parametric analysis confirms the suitability of this design for 5G Greenhouse applications.

Integrating Wetlands as Nature-Based Solutions for Sustainable Built Environments

Wetlands are ecosystems that can provide numerous services critical for sustainable development, especially in urban areas, by ensuring environmental stability. The wetlands receive increasing recognition as Nature-Based Solutions (NBSs) to environmental challenges. This review synthesizes the numerous roles of wetlands as NBSs for promoting sustainability in both rural and urban environments and highlights the potential contributions of multiple wetland services and benefits towards sustainable built environments. The review methodology involved an article search from various databases with the utilization of specific keywords in an organized framework to understand the contribution of wetlands as NBSs. The articles were reviewed to provide a comprehensive analysis of the existing research on the associated topics, focusing on specific sub titles and pre-selected themes. The findings of this review identify various parameters through which wetlands contribute to sustainable built environments, including ecological resilience, storm water management, climate adaptation, biodiversity enhancement, recreational opportunities, pollution control, and cultural values. The review also encompasses case studies of different types of wetland features such as riparian buffer zones, retention ponds, reed beds, bio swales, rain gardens, constructed wetlands, etc. in the urban environment and their contribution as NBSs. These contributions are discussed in terms of integration in urban development planning in different segments. Future work recommendations consist of a holistic integration of wetlands into urban planning and design considerations to promote more resilient, healthy, and sustainable built environments for present and future generations.

Aurel_AI: Automating an Institutional Help Desk Using an LLM Chatbot

The Aurel_AI research project was born from the need to implement a virtual help desk for a university, providing accurate organizational information to both internal and external clients. The information includes details about academic programs, regulations, processes, and personnel. Aurel_AI is part of a broader research program on the use of AI in academia. Traditional solutions for a help desk, such as telephone call centers, present quality and efficiency issues that are difficult to solve. Call center staff generally lack comprehensive knowledge about the institution, rely on specific information that is sometimes outdated, require additional systems for information retrieval, and experience high turnover rates. This leads to associated costs and issues related with outdated information, resulting in inaccurate responses and long waiting times. Generative artificial intelligence models, known as Large Language Models (LLMs), offer an interesting alternative for an automated virtual help desk. These models can understand even vague and poorly structured questions and generate reasonably appropriate answers. However, they are not without flaws, as they tend to present issues like "hallucinations" when the required information is not present in their training data. To minimize this problem, it is crucial to ensure that the model has precise and comprehensive information, which needs a specific methodology for information collection, validation, and updating. Base models require an adaptation process to be used for specific cases, for which techniques like Fine-Tuning and Retrieval Augmented Generation (RAG) exist. Fine-tuning retrains a model's weights with new specific information, while RAG uses both proprietary information—in this case, from the university—and publicly available internet data. Both techniques have pros and cons that need to be evaluated to select the most suitable option. They also demand appropriate and specialized infrastructure, which is often expensive. Thus, another challenge is to find a balance between suitable equipment and reasonable costs. The final system, from the user's perspective, must be accurate, flexible, and adaptable to deliver a satisfactory experience. As the results show, Aurel_AI represents an advance in the digitalization of educational services, standing out for its ability to generate accurate and personalized responses. However, its current limitations, such as handling concurrent queries and hallucinations, underscore the need for adjustments to both infrastructure and data processing methodology. With strategic improvements, the system has the potential to consolidate itself as a replicable model for multiple university digital services.

Efficiency Analysis of a Simulation Model of Cloud Computing in the Case of Multiple Server Failures

Efficiency Analysis of a Simulation Model of Cloud Computing in the Case of Multiple Server Failures

A faulty simulation model guided Ramanujan Digital twin architecture for rotating machine health monitoring.

The conventional widely-used health monitoring methods for rotating machines have shortcomings such as the reliance on the selection of the preset parameters. Also, the strong noise interference caused by factors such as transmission path hinders the practical application of many fault feature extraction methods. To overcome these gaps, the digital twin notion is introduced and a new digital twin architecture called the Ramanujan Digital Twin (RDT) is designed. The Ramanujan Periodic Transform (RPT) model is employed to isolate the potential fault feature. For each frame in the whole life cycle of the rotating machine, the high-fidelity simulation model is constructed. Once the high-fidelity simulation-induced virtual sample is obtained, the RPT will be used to provide guidance information about the potential fault. With this information, the potential fault feature can be extracted without preset parameter selection. A health indicator (HI) can be constructed to perform multiple service end tasks including health monitoring and early fault prediction. Two case studies are carried out and the results show the proposed method can not only extract the potential fault feature more effectively with less noise interference but also monitor and predict the potential early fault earlier than fault log.

Study Protocol for a Randomized Health and Social Literacy Intervention to Improve Maternal Health in Pakistan

Many poor and illiterate women in reproductive years in Pakistan live in underdeveloped areas and are solely dependent on free primary health services provided by the state through the Lady Health Worker (LHW) Programme. These women need higher health and social literacy to make informed health decisions and receive inferior services from overburdened LHWs. This project aims to deliver a health and social literacy intervention by integrating Community Social Workers (CSWs) at the primary level to promote maternal health in the country. A 24-month intervention with six health and social literacy modules will be delivered by trained CSWs. Multistage randomized sampling will be used to sample 6 BHUs across six cities from six districts of Punjab, Pakistan. A total of 360 women will be sampled and assigned randomly to the experiment and control groups. Pakistan needs to catch up on its sustainable development goals for maternal health, not just due to the limitations of the existing services, but also due to the low health and social literacy of women, which prevents effective uptake. Each LHW is expected to have an estimated 1,500 women clients for multiple services, which makes support for women’s literacy and awareness difficult. Integrating a partner workforce or CSWs to support disadvantaged women for improved maternal health outcomes is critically needed. The results of this intervention will advise policymakers and stakeholders about long-term plans for literacy support of women and the integration of CSWs in the primary healthcare setup.

Stakeholder perspectives on ecosystem services in agricultural landscapes: A case study in the North China Plain

CONTEXTIntensive agriculture is under pressure from changing demands from society, prompting the need to redesign agricultural landscapes to provide multiple ecosystem services (ESs). However, implementation of changed practices requires positive engagement from stakeholders. Therefore, their perspective on ecosystem services needs to be known. OBJECTIVEThis study investigates stakeholders' perspectives on multiple ESs in Quzhou County, an area in the North China Plain used for intensified cereal production. We aim to elucidate perspectives within and across diverse stakeholder groups (farmers, companies, citizens, academics, village and township heads, and county government staff). METHODSEmploying the Q methodology, we identified differences in perspectives within stakeholder groups and we compared the similarities and differences of those perspectives across stakeholder groups. We also investigated how farmers' personal and household characteristics were related to the perspectives they held. RESULTS AND CONCLUSIONSSignificant differences in preference emerged among stakeholder groups. Academics assigned higher importance to regulating and supporting services than other stakeholder groups and companies assigned less importance to cultural services. We identified 18 distinct perspectives across seven stakeholder groups. These perspectives showed a combination of preferences for at least two different ES categories. Most of the perspectives prioritize provisioning services whereas only few perspectives prioritize supporting services. SIGNIFICANCEThis study exemplifies a bottom-up approach for systematically analyzing stakeholder perspectives on the relative importance of ESs derived from agricultural landscapes. The revealed differences and complexity of stakeholder perspectives can inform decision-making on the redesign of agricultural landscapes with stakeholder engagement. Recognizing areas of consensus and conflict can guide efforts to promote agroecologically sound practices and policies.

Addressing Barriers in the University Campus Environment for Neurodivergent Students.

About 15% of the global population is considered neurodivergent (having different ways of sensory processing than what are perceived as neurotypical persons). Persons with neurodivergence typically include those with autism, attention-deficit hyperactivity disorder (ADHD), and Down Syndrome. Studies have shown that many neurodivergent persons experience sensory processing disorder (SPD). Noise, lighting, temperature, and aesthetics are some factors that can significantly impact the quality of interaction with the built environment for these individuals. A significantly lower proportion of youths with SPD enter higher educational institutions (HEI), hindered in part by physical design on university campuses. Universities in Canada are now mandated to address barriers in the campus environments for persons with disability. However, space design needs of neurodivergent students are often overlooked. We interviewed eight neurodivergent persons with SPD (NPSPD) about their experiences as students navigating a university campus located in Halifax, Canada. We asked what specific spaces on campus pose barriers to them (to enter, traverse, and use), and how the design should be improved. The participant responses revealed many elements on campus that act as barriers-largely consistent with existing literature. However, the participants' comments illustrated more complex dynamics of these factors, which can exacerbate their stress and anxiety. Oftentimes, barriers are more to do with lack of information about the characteristics of the space prior to using it, which would otherwise allow students to plan ahead their journey to destinations and use of given spaces. A wayfinding aid that informs what to expect in spaces or pathways on campus would be a potential area for innovation, along with multiple services to comprehensively and flexibly cater to individual needs to alleviate sensory overload. Ongoing communications about barriers across campus by all users of the university campus would facilitate implementation of pragmatic solutions needed to address diverse needs existing in HEI.

Service Operations for Justice-on-Time: A Data-Driven Queueing Approach

Problem definition: Limited resources in the judicial system can lead to costly delays, stunted economic development, and even failure to deliver justice. Using the Supreme Court of India as an exemplar for such resource-constrained settings, we apply ideas from service operations to study delay. Specifically, court dynamics constitute a case-management queue, whereby each case may experience multiple service encounters spread across time, but all are necessarily with the same server. Our goal is to elucidate the drivers of congestion, focusing on metrics such as the expected case-disposition time (delay) and expected number of cases awaiting adjudication (pendency), and leverage this understanding to recommend operational interventions. Methodology/results: We employ data-driven calibrated simulations to model the analytically intractable case-management queue. The life cycle of a case comprises two stages: preadmission (before determining its merit for detailed hearings) and postadmission. Our methodology allows us to capture the queueing dynamics in which the judges are shared resources across the two stages. It also permits modeling of holiday capacity, which is flexibly tailored to address any surplus work that spills over from the regular year. We find that the second stage of this judicial queue is overloaded, but holiday capacity creates a perception of stability by steadying performance metrics. Managerial implications: The sources of inefficiency that drive congestion include a misalignment between scheduling guidelines and judicial capacity, coupled with the requirement to schedule hearings in advance. Together, these factors inhibit utilization of shared capacity across the two-stage judicial queue. We demonstrate how interventions that account for these inefficiencies can successfully tackle judicial delay. In particular, scheduling to improve the allocation of time across preadmission and postadmission cases can cut down the expected delay by as much as 65%. Funding: This study is (partially) supported by a Korea University Business School Research Grant. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0530 .

Optimized Profit Allocation Model for Service Alliance Transactions Considering Risk

In service alliances, where multiple service providers collaborate to complete service transactions, the equitable allocation of profits based on their respective contributions and risk-bearing capacities is paramount. This paper introduces an optimized profit allocation game model that integrates risk considerations into the Nash bargaining framework. Initially, the study established a service alliance transaction model that considered the interactions among multiple participants, providing a robust theoretical foundation for cooperation. Subsequently, the concept of marginal risk was introduced, and a unique calculation method based on the Shapley value was devised to quantify risk contributions. Finally, an improved Nash bargaining model was proposed, which introduced a risk adjustment factor, explicitly addressing the impact of each participant’s risk on profit allocation. Through computational cases and result analyses, this model demonstrated its ability to balance profit and risk and to optimize outcomes for all participants, and it validated the fairness and rationality of the proposed allocation method.

Optimizing multiple ecosystem services in Eucalyptus plantations: Dual effects of intercropping and restoration

Mixed plantations of Eucalyptus with native tree species can effectively improve forest ecosystem services. However, the long-term effects and ratios of Eucalyptus-native tree species mixture on plant diversity in different plant layer, multiple ecosystem services, multiservice and the underlying mechanisms remain unclear. To fill these gaps, a four-level intercropping gradient of Eucalyptus urophylla planted with eight native tree species (NS) was set up (i.e., 20%NS, 30%NS, 40%NS, 50%NS), monitored and compared with a monoculture E. urophylla plantation (EU) and a randomly mixed plantation (98%NS) in southern China. We calculated the multiple ecosystem services of the plantation and the multidimensional diversity (taxonomic diversity (TD), functional diversity (FD), and phylogenetic diversity (PD)) of different plant layers. The results showed that the long-term (13–15a) and short-term (1a) plantation communities were significantly different in multidimensional diversity in all three vertical plant layers. In the long-term, intercropping with more than 20% native trees significantly increased the multidimensional diversity in the tree layer, but not the shrub and herb layers, compared to the EU. Intercropping with 50% native trees significantly promoted biodiversity conservation service, ecosystem multiservice and synergistic relationships among ecosystem services over a decade. Both initial mixing ratio and naturally restored multidimensional diversity in shrub and herb layers influenced tree layer's multidimensional diversity, contributing to long-term multiple ecosystem services of mixed Eucalyptus-native tree species plantations. Our results highlighted the risk of applying short-term based diversity results in long-term strategy making for E. urophylla plantation, quantitatively explored the high explanatory power of plant functional and phylogenetic diversities on multiservice, and expanded understanding of the mechanisms of the triad, biodiversity-ecosystem service-ecosystem stability. To practice, intercropping 50% native trees was recommend for long-term optimizing multiservice restoration, including but not limited to biodiversity conservation, and the syngeneic development among ecosystem services of Eucalyptus plantations.

Multi-timescale hierarchical dispatch strategy of hybrid energy storage for multiple auxiliary service markets

As a flexible regulatory resource, hybrid energy storage system (HESS) is capable of providing multiple reliable ancillary services, which improves the adaptability of the distribution system to large-scale grid connection of the distributed generation (DG) and alleviate the pressure of peak load and frequency response. In this context, this paper proposes an optimal dispatch strategy of a HESS for DG electricity production and multiple auxiliary service markets to create stackable benefits for HESS operators. Firstly, different types of energy storage system (ESS) (energy-based and power-based) are unified to the joint optimal framework of peak shaving (PS), frequency containment reserves (FCR), and secondary frequency regulation (SFR). By constructing a virtual ESS model based on the idle-time reuse response, an optimal bidding strategy for HESS under this revenue combination is proposed to achieve the optimal utilization of resource allocation. Furthermore, an “hourly–minute–secondly” progressive time series is introduced, and a multi-timescale hierarchical dispatch model named “daily baseline–regulation basepoint–real-time regulation” is constructed. Specifically, the PS capacity is allocated day-ahead and a two-stage capacity allocation method for FCR and SFR is proposed in the intraday, which realizes the parallel optimal of HESS at the scale of full clearance in the auxiliary service markets. Results show that compared to the combined benefits of two types of ESS, the proposed method achieved the comprehensive income increased by 4.87 % and the auxiliary services income increased by 15.2 %. Under this revenue combination, the SFR market can create an additional 60 %–90 % economic value for HESS operators. The proposed hierarchical optimal model can better adapt to the trading rules of different auxiliary service markets and provide guidance for HESS and DG to further participate in the electricity market.

Impact of pine wilt disease infection on multiple ecosystem services and their trade-offs in subtropical Masson pine forests

Pine wilt disease (PWD) severely damages the health, stability, and functions of pine forests. However, empirical evidence regarding the impact of PWD on multiple ecosystem services in these forest ecosystems remains limited. This study investigated five ecosystem services, namely carbon sequestration, water conservation, soil nutrient accumulation, biomass nutrient accumulation and understory plant diversity in subtropical Masson pine (Pinus massoniana) forests, and quantified their trade-offs along varying ages of PWD infection (uninfected (0 years), 6, 10, and 16 years). The results showed that PWD infection significantly affected ecosystem services in Masson pine forests, with decreased carbon sequestration, water conservation, and biomass nutrient accumulation in 6 years of PWD infection forests. As the duration of PWD infection increased, the composite score of ecosystem services initially decreased, then increased, and finally decreased again. In contrast, soil conservation and understory plant diversity showed an initial increase, followed by a decline. Moreover, PWD infection increased the trade-offs among ecosystem services, with the highest trade-offs for 10 years of infected forests. PWD infection altered the trade-offs between understory plant diversity and other ecosystem services from low to high levels. Our results suggest that forest management should be strengthened to accelerate the recovery of ecosystem services while controlling PWD infection in these disturbed forests.

Incorporating Ecosystem Services into Solar Energy Siting to Enhance Sustainable Energy Transitions.

Solar energy is expected to play a large role in decarbonization of the energy sector globally. In the United States, solar energy is forecasted to generate roughly 45% of the electricity by 2050. Although solar energy mitigates the negative effects of climate change by providing electricity without releasing greenhouse gases, little is known about the implications of solar energy development for ecosystem services. In this study, we developed a spatially explicit, techno-ecological solar suitability model consisting of six scenarios designed to evaluate the trade-offs between ground-mounted solar energy generation and multiple ecosystem services. By incorporating solar suitability modeling with ecosystem service evaluation, we develop a method that provides a comprehensive understanding of potential techno-ecological trade-offs. To test our methodology, we used Connecticut (USA) as a study site for analyzing the potential trade-offs of future solar energy facilities, but the methods can be widely applied. Our results suggest that well-sited solar energy development can decrease sediment and nutrient export while offsetting carbon emissions from power plants. This study provides a holistic assessment of incorporating ecosystem services in future solar energy development decision-making and presents an approach for minimizing trade-offs and maximizing sustainable outcomes.

Study on the co-occurrence of multiple health service needs throughout the lifecourse of rural residents in China based on association rules.

To understand the multiple health service needs of rural residents in China and explore the co-occurrence patterns of these needs throughout the entire life course, providing a basis for the formulation and optimization of health service packaging policies. This study utilized a stratified random sampling method, resulting in a final sample size of 15,125 individuals. The R statistical software was employed to apply the Apriori algorithm to mine the co-occurrence relationships among multiple health service needs across the life course and to explore the packaging model of these services. The health service needs rate among rural residents in China is 86.76%, with a multiple health service needs rate of 78.72%. The most needed services are health exercise guidance (17.10%), Traditional Chinese Medicine health care (15.53%), and internet health information services (14.40%). The highest combined health service need is for "exercise guidance need + internet health information need + Traditional Chinese Medicine health care need," followed by "exercise guidance need + internet information need." There are significant differences in the content and strength of associations in the co-occurrence structure of multiple health service needs across different age groups. During the life preparation stage, the need for multiple health services is high, with modern medical care and child management having the highest support. In the life protection stage, the focus shifts to preventive health needs, with strong associations among co-occurring needs (the highest support rule being Traditional Chinese Medicine health care + exercise guidance, support = 21.12%). The co-occurrence of medical and preventive health service needs among the older adult is diverse, with the strongest association being between chronic disease management services and rehabilitation services (support = 31.24%). The multiple health service needs rate among rural residents in China is high, with the greatest needs being for exercise guidance, Traditional Chinese Medicine health care, and internet health information services. There are significant differences in health service needs across different life stages. It is essential to enhance the integration and packaging of health service resources to promote diversity in health services and meet the multiple health service needs of residents throughout their life courses.

Agile C-states: A Core C-state Architecture for Latency Critical Applications Optimizing both Transition and Cold-Start Latency

Latency-critical applications running in modern datacenters exhibit irregular request arrival patterns and are implemented using multiple services with strict latency requirements (30–250μs). These characteristics render existing energy-saving idle CPU sleep states ineffective due to the performance overhead caused by the state’s transition latency. Besides the state transition latency, another important contributor to the performance overhead of sleep states is the cold-start latency, or in other words, the time required to warm up the microarchitectural state (e.g., cache contents, branch predictor metadata) that is flushed or discarded when transitioning to a lower-power state. Both the transition latency and cold-start latency can be particularly detrimental to the performance of latency critical applications with short execution times. While prior work focuses on mitigating the effects of transition and cold-start latency by optimizing request scheduling, in this work we propose a redesign of the core C-state architecture for latency-critical applications. In particular, we introduce C6Awarm, a new Agile core C-state that drastically reduces the performance overhead caused by idle sleep state transition latency and cold-start latency while maintaining significant energy savings. C6Awarm achieves its goals by (1) implementing medium-grained power gating, (2) preserving the microarchitectural state of the core, and (3) keeping the clock generator and PLL active and locked. Our analysis for a set of microservices based on an Intel Skylake server shows that C6Awarm manages to reduce the energy consumption by up to 70% with limited performance degradation (at most 2%).