Multiple Services Research Articles

Preprint pointers from a long COVID scoping review: considerations for source selection and searching.

This paper describes the search approach for preprints for a post COVID-19 condition (i.e., long COVID) scoping review, including source selection, search strategy development, challenges, and insights throughout a project life cycle. With the growth of medical preprints since the COVID-19 pandemic, information professionals and researchers should be aware that preprints are possible sources of evidence and be prepared to manage them in evidence reviews for COVID-19 topics and beyond. Preprints are not peer-reviewed but can include important evidence about emerging topics. Because of the importance of preprints to the scoping review, a preprint search of Europe PubMed Central (PMC) was added. Europe PMC and similar aggregators combine multiple preprint servers and often have Boolean search, but sometimes limited search functionalities or few export options. Strategy translation encountered challenges such as varying and inconsistent terminology for post-COVID-19 condition, a complex search, and negotiating large numbers of preprints with resource constraints. Europe PMC identified additional preprints for inclusion due to additional preprint server coverage. It was helpful to limit the preprint search to the title and abstract fields, and to run an extra Internet search for publication of included study preprints. Challenges and potential solutions are summarized to support those conducting preprint searches for COVID-19 and other topics.

PEER CONNECT: a pragmatic feasibility randomised controlled trial of peer coaching for adults with long-term conditions

ObjectiveTo test the feasibility of a targeted peer coaching intervention on the health and well-being of people with long-term health conditions and low activation attending outpatient clinics at a UK...

Comparative analysis of federated learning algorithms under non-IID data

With the increasing global attention to data privacy and security issues, how to effectively utilize distributed data while protecting personal privacy has become an important research topic. Federated learning (FL) aims to address data silos and privacy issues by enabling multiple devices or servers to train shared models in collaboration without submitting raw data to a central server. Although a variety of federated learning algorithms have been proposed, there is still a gap in the research on their performance differences under the same model. The goal of this study is to compare and analyze the performance differences of different federated learning algorithms on the same model through experiments. Based on the Fashion-MNIST dataset, this paper compares four commonly used federated learning algorithms in detail: Federal Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), Stochastic Controlled Averaging for Federated Learning (SCAFFOLD), and Federated Proximal (FedProx). The experimental results show that FedProx performs best in all evaluation indicators, followed by SCAFFOLD and FedAvg, while FedSGD performs the worst. These insights into algorithm performance with non-IID data inform practical application suitability and guide future research.

Soil Fungal Pathogens in Pinus pinaster Mature Reforestation: Silvicultural Treatments Effects.

Soil fungal communities play a key role in multiple functions and ecosystem services within forest ecosystems. Today, forest ecosystems are subject to multiple environmental and anthropogenic disturbances (e.g., fire or forest management) that mainly lead to changes in vegetation as well as in plant-soil interactions. Soil pathogens play an important role in controlling plant diversity, ecosystem functions, and human and animal health. In this work we analyzed the response of soil plant pathogenic fungi to forest management in a Pinus pinaster reforestation. We started from an experimental design, in which forest thinning and gap cutting treatments were applied at different intensities and sizes, respectively. The fungal communities of plant pathogens in spring were described, and the effect of the silvicultural treatments was evaluated 5 years after their application, as were the possible relationships between soil plant pathogenic fungal communities and other environmental factors. Only a strong low thinning treatment (35% basal area) was able to generate homogeneous changes in soil pathogenic diversity. In the gaps, only the central position showed significant changes in the soil plant pathogenic fungi community.

Solid waste circulation in tourism: a close study of accommodation service establishments in Hoi An City, Vietnam

ABSTRACT Tourism in Vietnam has become an important sector of the economy. Its environmental impacts include the quantity of waste generated. This study aims to assess the compositions of solid waste generated within diverse accommodation service establishments (ASEs) in Hoi An City to understand the differences across accommodation types and generating areas. The analysis indicated that most of the ASEs provided multiple services and waste components differed among generating areas. Initiatives for optimising the recovered material flows from waste at the ASEs were proposed. A decentralised-centralised hybrid model was suggested as a suitable approach for waste circulation in the accommodation service sector. The training of staff in the ASEs and tourists, as well, is crucial. So is the participation of social entities in the city’s waste management.

Analyzing the Reasons for Prime Videos Better Performance

In 2021, Amazon successfully finalized its significant acquisition of Metro-Goldwyn-Mayer (MGM). This development strengthened Prime Video's position in the fiercely competitive sector of streaming video-on-demand (SVOD). This study aimed to investigate the factors that contributed to Prime Video's improved performance following its acquisition of MGM. The investigation determined that Prime Video effectively enhanced its market performance through the implementation of advertising services, regular updates to its original content library, and a vigorous expansion of its market reach. This paper provides concrete recommendations based on the future trajectory of the industry. Firstly, it suggests enhancing the cost-effectiveness of services to appeal to a wider range of users. Secondly, it recommends continuing to invest in original content and regular updates to maintain user engagement. Lastly, it proposes bolstering the development of the brand ecosystem by integrating multiple services within Amazon, thereby enhancing the user experience and fostering brand loyalty. This research not only addresses the existing scholarly deficiency but also serves as a valuable resource for guiding the future progress of other firms.

Satellite Long-Term Monitoring of Wetland Ecosystem Functioning in Ramsar Sites for Their Sustainable Management

The long-term monitoring of wetland ecosystem functioning is critical because wetlands, which provide multiple services, can be affected by human activities and climate change. The aim of this study was to monitor wetland ecosystem functioning in the long term using the Landsat archive. Four contrasting, Ramsar wetlands were selected in boreal, temperate, arid, and tropical areas. First, the annual sum of the normalized difference vegetation index (NDVI-I) was calculated as an indicator of annual net primary productivity for the period 1984–2021 using the continuous change detection and classification (CCDC) algorithm. Next, the influence of the number of Landsat images and class of land use and land cover (LULC) on the accuracy of the CCDC was investigated. Finally, correlations between annual NDVI-I and climate were analyzed. The results revealed that NDVI-I accuracy was influenced mainly by the LULC class and to a lesser extent by the number of cloud-free Landsat observations. Infra- and inter-site variations in NDVI-I were high and showed an overall increasing trend. NDVI-I was positively correlated with the mean temperature. This study shows that this approach applied in contrasting sites is robust for the long-term monitoring of wetland ecosystem functioning and can be used to improve the implementation of international biodiversity conservation policies.

Effects of Surface Area and Water Level Variability on Fisheries Production in Lake Naivasha, Kenya

Lake Naivasha is a freshwater, a Ramsar site and an Important Bird Area. It provides multiple services to the surrounding communities such as Fishing, Tourism and Agriculture. Lake Naivasha has been experiencing a water level variation cycle over the years as a result of it being shallow and experiencing high evaporation. Variation in water levels leads to flooding, and receding which disrupts the Lake ecosystem thus causing direct and indirect effects on fishing. The study was conducted to establish the relationship between Surface area and water level variability and fish yield and revenue in Lake Naivasha. Primary data was collected using a semi-structured questionnaire, and Key informant interviews. Secondary data was collected using Landsat images that provided the temporal and spatial water level variability and documentary analysis between 1989 to 2022. Both descriptive statistics and inferential statistics were used for data analysis. The results show a positive correlation between Surface area Variability and fish yield (r= 0.6261, R2= 0.3920) and fish revenue (r= 0.5219, R2= 0.2724). Surface area variability accounts for 39% of the variation in total fish yield, and 27% of the fish revenue. The study recommends continuous monitoring and surveillance of the lake level, and water quality by the relevant government agencies, as well as watershed management and conservation measures for the sustainability of the Lake’s fishery.

Seamless group handover authentication protocol for vehicle networks: Services continuity

The advantages of low latency, high reliability and high bandwidth of dense cellular network support real-time sensing, decision making and regulation of vehicular network. Network densification demands smaller coverage areas for base stations that are strategically deployed near consumers to support multiple services. This results in more frequently triggered handover processes. To reduce the latency induced by the handover process and ensure service continuity, this paper proposes a seamless vehicle group handover authentication protocol named SVGHP. The proposed protocol utilizes lightweight algorithms such as quadratic residual and symmetric encryption, and generates and distributes keys for group members based on the Chinese Residual Theorem to achieve handover authentication. Secondly, SVGHP includes a re-authentication phase, specifically, the vehicle group and the subsequent base stations receive the updated keys distributed by the previous RSUs for simplified authentication. And the involvement of the cloud server is avoided, eliminating the network communication delay to the core network. Detailed performance evaluation and comparison with existing protocols demonstrates that the proposed protocol is efficient in terms of computational cost and communication overhead. And in general, our scheme reduces the handover delay by at least 67.91% over the previous authentication schemes. We also provide formal and informal security proofs to prove that our protocol meets common security requirements.

Deep reinforcement learning based task offloading and resource allocation strategy across multiple edge servers

Deep reinforcement learning based task offloading and resource allocation strategy across multiple edge servers

Preventive Health Services Offered in a Sampling of US Emergency Departments, 2022-2023.

In the United States, more chronic and preventive healthcare is being delivered in the emergency department (ED) setting. Understanding the availability of preventive health services in the ED setting is crucial. Our goal was to understand the availability of a subset of preventive health services in US EDs and explore how that has changed over time. In 2022-2023, using the National Emergency Department Inventory (NEDI)-USA, we surveyed a random 20% (1,064) sampling of all 5,613 US EDs. We asked directors of these EDs about the availability of and preference for 12 preventive health services, social worker availability, self-reported percentage of uninsured ED patients, and measures of ED crowding. We also asked about perceptions of barriers to implementing preventive health services in the ED. We used unadjusted and multivariable logistic regression models to compare service frequency in 2022-2023 to prior findings from 2008-2009 that represented a 5.7% random sampling of all EDs. Among 302 responders to the 2022-2023 survey (5.4% random sampling, 28.4% response rate), 94% reported offering at least one preventive health service, with a median of five services. The most common service offered was intimate partner violence screening (83%), while the least common was routine HIV screening (19%). Seven services (eg, intimate partner violence, alcohol risk, and smoking cessation screening) had a higher odds of being offered in 2022-2023 than in 2008-2009; findings were unchanged in sensitivity analyses. A small proportion of directors opposed offering preventive health services. However, many expressed concerns that preventive health services in the ED would lead to longer lengths of stay (56%), increased costs to their ED (58%), a diversion of staff time from providing acute care (50%), or that their patients would not have access to adequate follow-up (49%). Nearly all EDs offer at least one preventive health service. Many offer multiple services; rates were higher than those identified in 2008-2009, in both unadjusted and multivariable models. Although limited by the response rate, this work provides the most recent and comprehensive snapshot of the type and frequency of a subset of preventive health services currently offered in US EDs.

Leveraging plant functional traits in the design of nature‐based solutions: A research agenda

Abstract Nature‐based solutions (NbS) involve interventions that modify ecosystems to enhance multiple ecosystem services (ES). Yet, despite our increasing knowledge of the key role that functional traits play in determining ES, certain types of NbS often do not consider a functional trait perspective. A reason for this lack of translation may be the range of NbS approaches and levels of knowledge of trait–ES relationships. Functional traits may be most useful as targets for NbS design when knowledge on trait–ES mapping is established and the NbS involves a medium or high level of management intervention. A richer understanding of causality in trait–ES and trait–resilience linkages, and context dependency in those linkages, is needed to broaden the application of functional traits in NbS. An improved understanding of trait linkages with cultural ES and how traits interact with stakeholder values to shape NbS uptake would also improve application. Synthesis: A greater consideration of functional traits will increase the efficacy of NbS projects, and could be accelerated by tackling gaps in knowledge about functional traits. Here, we envision a research agenda that both advances NbS design and leverages the varied objectives of NbS to robustly test our knowledge about functional traits.

Distributed Transactions in Microservice Architecture: Informed Decision-making Strategies

The emergence of microservice architecture has revolutionized software development practices by decentralizing components, facilitating scalability, and enabling agility in system design and deployment. There are some benefits of incorporating microservices instead of a single server, however, distributed components introduce extra constraints and complexities in maintaining data consistency as well. As microservices interact independently, coordinating data updates across multiple services becomes challenging, particularly in scenarios where transactional integrity is required. Distributed transactions are one of the solutions for ensuring data consistency across services. Regardless of effectiveness distributed transactions entail different trade-offs and performance implications. Those trade-offs are not always justified. This study highlights the need for a nuanced understanding of distributed transactions in microservices by revisiting challenges in managing distributed transactions within data storage systems. It also represents existing solutions to the different distributed transaction methods. In this paper, through experiments comparing microservices and monolithic systems, the impact of distributed transactions on system performance is evaluated, giving intuition about consequences when a single data source transaction is migrated to the distributed environment. This research also contributes to enhancing understanding and decision-making regarding the utilization of distributed transactions in a microservices architecture. Ultimately, this paper presents an optimized decision-framework for the application of distributed transactions in microservices architecture, aiming to simplify and expedite processes of software architecture for software engineers, solution architects, and developers.

Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain

AbstractAirport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.

Multiple shared mobility services under competition: Empirical evidence for public acceptance and policy insights to sustainable transport

Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers’ decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies.

Managing ecosystem services in oleaginous forests for bioenergy provision and climate change mitigation

Oleaginous forests provide diverse ecosystem services, including timber, seed yield (a vital feedstock for biodiesel production), and substantial carbon savings. These carbon savings encompass carbon sequestration related to timber growth and carbon savings resulting from substituting fossil fuel with biodiesel. However, oleaginous forests are vulnerable to seed wasp attacks (disservice), which significantly threaten both seed yield and carbon savings. Using an integrated ecological-economic model that includes Faustmann's Land Expectation Value model and a pest damage control model, we aim to understand the intricate relationship among multiple ecosystem services and disservices of oleaginous forests. The results reveal four distinct phases contingent on varying pesticide application rates: the pesticide under-use phase, substitution phase, complementary phase, and over-use phase. Notably, a potential avenue to minimize pest damage is identified during the complementary phase by reducing the optimal rotation age at the expense of decreased carbon sequestration and bioenergy provision, posing a challenge to climate change mitigation. These findings have implications for formulating policies to manage conflicting ecosystem services of energy forests, offering valuable insights into the intersection of sustainable forest management and climate policy.

Feasibility and Acceptability of a Physical Activity Group Program Using telerehabilitation during the COVID-19 Pandemic in Multiple early Intervention for Psychosis Services

BackgroundAlthough physical activity (PA) is beneficial to young people with early psychosis (YEP) to improve physical health and psychiatric symptoms, few YEP initiate and maintain PA. The sports group interventions offered in early psychosis services had to be suspended due to the COVID-19 pandemic. Telehealth has shown promising results in different fields of health services including for patients with mental health disorders. MethodsDescriptive retrospective study aiming to determine the feasibility and acceptability of a telekinesiology intervention among YEP and to describe its multicenter implementation. The PA sessions were delivered to YEP by a kinesiologist and peer support workers. Feasibility was measured by the number of programs approached which referred participants, and the proportion of referred YEP who participated to at least one PA session. Acceptability was measured by the proportion of participants who attended more than one PA session, the number of sessions attended per participant and by surveys on patient satisfaction. ResultsOf the 35 clinics approached, 150 YEP (of 214 referred) from 13 clinics participated to at least one of the 204 telekinesiology sessions (offered 2–3 times/week from May 2020 to May 2022) The mean number per participant was 5.5 sessions. 106 YEP engaged in more than one session (mean of 7.3 sessions per persistent participant). The mean number of participants per session was 4 (1−12). 99 % of the survey respondents were very satisfied/or satisfied with the sessions. ConclusionTelekinesiology appears to be an acceptable and feasible option to be implemented simultaneously in multiple early intervention services.

Prevalence and predictors of multiple, concurrent public early childhood education utilization among head start children

With growing public investment in early childhood services, families enrolled in Head Start (HS) have increasing opportunities to participate in other public early care and education (ECE) programs. Using integrated administrative data from across Iowa’s service systems, we examined the prevalence and predictors of HS children’s multiple concurrent ECE service utilization the year before entering kindergarten, focusing on state pre-K and child care subsidy (CCS). Results showed that 61 % of children participating in HS additionally utilized other public ECE programs, with 44 % participating in state pre-K, 8.3 % in CCS, and 8.7 % in both. Utilization patterns differed based on child and family characteristics. Children who were Black, living in urban areas, or born to single mothers were more likely to participate in multiple, concurrent services, especially a combination of services that included CCS. Results imply that improving coordination and collaboration across various ECE public programs may support HS families’ needs.

Socio Connect: Bridging Citizens and Government Initiatives with Intelligent Systems

The digital divide in India presents significant barriers for economically disadvantaged and illiterate citizens, obstructing their access to essential government services and information. Despite various efforts to bridge this divide, such as the UMANG (Unified Mobile Application for New-age Governance) app, which consolidates multiple government services into a single platform, these solutions primarily cater to literate users with access to modern technology. The UMANG app, while comprehensive, remains inaccessible to those who are illiterate, semi-literate, or lack advanced devices [1][2]. Research highlights that a large segment of the Indian population continues to rely on basic mobile phones, which limits their ability to access digital services [3][4]. Our proposed solution, SOCIO CONNECT, addresses this gap by utilizing basic mobile technology to deliver government services through voice calls, making it accessible to both literate and illiterate users. By integrating voice-based interaction with government schemes, SOCIO CONNECT aims to enhance inclusivity and empower marginalized citizens who are otherwise excluded from digital solutions [5][6][7].

Joint Optimization Algorithm for Latency-Energy and Privacy Combining Discrete Differential Evolution and Convex Optimization

A privacy protection task offloading algorithm combining discrete differential evolution and convex optimization is proposed to resolve the new position privacy leakage problem for mobile terminal due to transferring tasks to multiple edge servers for computation via wireless networks. First, the privacy cost of offloading decision is defined for the characteristics of the position privacy problem combined with the standard deviation theory. Second, the objective problem is modeled as a joint optimization of position privacy protection, offloading decision and transmission power, and the total cost of task offloading is an optimization objective. Finally, to handle the objective problem efficiently, offloading decision variable is decoupled from the transmission power variable to decrease the computational complexity of the problem; the discrete differential evolution algorithm is designed to accumulate the approximate optimal offloading decision, and the optimal transmission power is efficiently solved by variable substitution method and subgradient method. Simulations reveal that the algorithm has excellent convergence; it improves the position privacy protection level of mobile terminal and reduces the total cost of task offloading while maintaining lower energy consumption and latency.