Hasil untuk "Recreation leadership. Administration of recreation services"

Zuzanna M. Swirad, Adam P. Young

Fatma Abdo, Amel Youssef, Abeer ElDeeb et al.

Background and purpose The prevalence of work-related spinal disorders (WRSDs) is higher among female physical therapists compared to their male counterparts, yet the physical and psychosocial risk factors are not well understood. This study aimed to determine the prevalence and associated risk factors of WRSDs among female physical therapy internship students in Egypt. Materials and Methods The study included 128 female internship students, all free from work-related spinal disorders at the start. Assessments for neck and lower back pain, physical activity levels, psychosocial factors, and spinal angles were conducted using the Nordic Musculoskeletal Questionnaire (NMQ), International Physical Activity Questionnaire (IPAQ), Copenhagen Psychosocial Questionnaire (COPSOQ), and inclinometers. These assessments were performed both before and after 12 months of the internship. Results The prevalence of WRSDs was found to be 73.44%, with 33% reporting neck and upper back pain, 33% low back pain (LBP), and 34% experiencing both. Students with work-related spinal disorders (group A) exhibited significant decreases (p<0.05) in Copenhagen Psychosocial Questionnaire scores and thoracic and lumbar angles compared to those without pain (group B) after 12 months. LBP scores positively correlated with changes in Copenhagen Psychosocial Questionnaire factors, including work pace, recognition, and work-life conflict. Changes in thoracic angles were also positively correlated with work pace and emotional demands. Conclusion The study concludes that the increased prevalence of work-related spinal disorders among female physical therapy internship students is linked to psychosocial elements such as fast work pace, recognition seeking, work-life conflict, and emotional demands, which serve as risk factors for work-related spinal disorders.

Xiaoyu Chu, Sacheendra Talluri, Qingxian Lu et al.

People and businesses increasingly rely on public LLM services, such as ChatGPT, DALLE, and Claude. Understanding their outages, and particularly measuring their failure-recovery processes, is becoming a stringent problem. However, only limited studies exist in this emerging area. Addressing this problem, in this work we conduct an empirical characterization of outages and failure-recovery in public LLM services. We collect and prepare datasets for 8 commonly used LLM services across 3 major LLM providers, including market-leads OpenAI and Anthropic. We conduct a detailed analysis of failure recovery statistical properties, temporal patterns, co-occurrence, and the impact range of outage-causing incidents. We make over 10 observations, among which: (1) Failures in OpenAI's ChatGPT take longer to resolve but occur less frequently than those in Anthropic's Claude;(2) OpenAI and Anthropic service failures exhibit strong weekly and monthly periodicity; and (3) OpenAI services offer better failure-isolation than Anthropic services. Our research explains LLM failure characteristics and thus enables optimization in building and using LLM systems. FAIR data and code are publicly available on https://zenodo.org/records/14018219 and https://github.com/atlarge-research/llm-service-analysis.

Nitika Chopra, Taher A. Ghaleb

Continuous Integration (CI) services, such as GitHub Actions and Travis CI, are widely adopted in open-source development to automate testing and deployment. Though existing research often examines individual services in isolation, it remains unclear how projects adopt and transition between multiple services over time. To understand how CI adoption is evolving across services, we present a preliminary study analyzing the historical CI adoption of 18,924 Java projects hosted on GitHub between January 2008 and December 2024, adopting at least one of eight CI services, namely Travis CI, AppVeyor, CircleCI, Azure Pipelines, GitHub Actions, Bitbucket, GitLab CI, and Cirrus CI. Specifically, we investigate: (1) how frequently CI services are co-adopted or replaced, and (2) how maintenance activity varies across different services. Our analysis shows that the use of multiple CI services within the same project is a recurring pattern observed in nearly one in five projects, often reflecting migration across CI services. Our study is among the first to examine multi-CI adoption in practice, offering new insights for future research and highlighting the need for strategies and tools to support service selection, coordination, and migration in evolving CI environments.

Diogo Landau, Gijs Blanken, Jorge Barbosa et al.

Modern distributed systems rely on complex networks of interconnected services, creating direct or indirect dependencies that can propagate faults and cause cascading failures. To localize the root cause of performance degradation in these environments, constructing a service dependency graph is highly beneficial. However, building an accurate service dependency graph is impaired by complex routing techniques, such as Network Address Translation (NAT), an essential mechanism for connecting services across networks. NAT obfuscates the actual hosts running the services, causing existing run-time approaches that passively observe network metadata to fail in accurately inferring service dependencies. To this end, this paper introduces XXXX, a novel run-time system for constructing process-level service dependency graphs. It operates without source code instrumentation and remains resilient under complex network routing mechanisms, including NAT. XXXX implements a non-disruptive method of injecting metadata onto a TCP packet's header that maintains protocol correctness across host boundaries. In other words, if no receiving agent is present, the instrumentation leaves existing TCP connections unaffected, ensuring non-disruptive operation when it is partially deployed across hosts. We evaluated XXXX extensively against three state-of-the-art systems across nine scenarios, involving three network configurations (NAT-free, internal-NAT, external-NAT) and three microservice benchmarks. XXXX was the only approach that performed consistently across networking configurations. With regards to correctness, it performed on par with, or better than, the state-of-the-art with precision and recall values of 100% in the majority of the scenarios.

Yun Tang, Mengbang Zou, Udhaya Chandhar Srinivasan et al.

Efficient orchestration of AI services in 6G AI-RAN requires well-structured, ready-to-deploy AI service repositories combined with orchestration methods adaptive to diverse runtime contexts across radio access, edge, and cloud layers. Current literature lacks comprehensive frameworks for constructing such repositories and generally overlooks key practical orchestration factors. This paper systematically identifies and categorizes critical attributes influencing AI service orchestration in 6G networks and introduces an open-source, LLM-assisted toolchain that automates service packaging, deployment, and runtime profiling. We validate the proposed toolchain through the Cranfield AI Service repository case study, demonstrating significant automation benefits, reduced manual coding efforts, and the necessity of infrastructure-specific profiling, paving the way for more practical orchestration frameworks.

Maria Antónia Rodrigues , Maria Amélia Carvalho , Luciana Oliveira et al.

This paper aims to understand the effect of content and digital influencer characteristics on their persuasiveness ability and Generation Y's resulting brand attitudes and purchase intentions (Millennials). Based on a sample of 201 individuals and quantitative analysis, data was analysed using Smart PLS. The results show that the quality of the content is the characteristic with the highest impact on the influencer's persuasive power. In terms of influencer characteristics, trust is a critical determinant. The influencer's persuasiveness is essential for developing a positive attitude towards the brand and purchase intention. Our findings update previous studies, focusing on Generation Y and including trust as a variable while simultaneously revealing a fundamental determinant of influence power. For brands, this study confirms the importance of having digital influencers in their strategies. For influencers, it suggests that when targeting Generation Y, they should invest in the quality of their publications and increase trust, as this is strongly linked to their persuasiveness and, consequently, to the purchase intention of their followers.

Jiani Fan, Minrui Xu, Ziyao Liu et al.

Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV) network have numerous advantages over traditional cloud-based AIGC services, including enhanced network efficiency, better reconfigurability, and stronger data security and privacy. Nonetheless, AIGC service provisioning frequently demands significant resources. Consequently, resource-constrained roadside units (RSUs) face challenges in maintaining a heterogeneous pool of AIGC services and addressing all user service requests without degrading overall performance. Therefore, in this paper, we propose a decentralized incentive mechanism for mobile AIGC service allocation, employing multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context, optimizing user experience and minimizing transmission latency. Experimental results demonstrate that our approach achieves superior performance compared to other baseline models.

Siyuan Li, Xi Lin, Yaju Liu et al.

AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an ideal platform for providing powerful AIGC network services. At the same time, advanced AIGC techniques can also make future network services more intelligent, especially various online content generation services. However, the significant untrustworthiness concerns of current AIGC models, such as robustness, security, and fairness, greatly affect the credibility of intelligent network services, especially in ensuring secure AIGC services. This paper proposes TrustGAIN, a trustworthy AIGC framework that incorporates robust, secure, and fair network services. We first discuss the robustness to adversarial attacks faced by AIGC models in network systems and the corresponding protection issues. Subsequently, we emphasize the importance of avoiding unsafe and illegal services and ensuring the fairness of the AIGC network services. Then as a case study, we propose a novel sentiment analysis-based detection method to guide the robust detection of unsafe content in network services. We conduct our experiments on fake news, malicious code, and unsafe review datasets to represent LLM application scenarios. Our results indicate that TrustGAIN is an exploration of future networks that can support trustworthy AIGC network services.

Abstract The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

Daniela Melisa Gambarota, María Amalia Lorda, Silvia London

El turismo se encuentra en constante cambio al igual que las preferencias de los viajeros, por lo tanto, las localidades deben ser creativas e innovadoras en los productos turísticos diseñados, desarrollados y comercializados. El territorio que desee impulsar la utilización sustentable de sus recursos para el desarrollo del turismo, debe evaluar su potencialidad para tal fin e implementar una adecuada planificación. Bahía Blanca constituye una ciudad intermedia, donde se desarrolla el turismo de Congresos y Convenciones y posee atractivos promocionados desde el municipio, así como potenciales recursos, factibles de ser incorporados a la oferta turística contribuyendo como alternativa de desarrollo local. El objetivo del presente trabajo consiste evaluar la potencialidad turística de Bahía Blanca, mediante el análisis de la existencia de recursos, equipamiento turístico, instalaciones turísticas, infraestructura y mercado. Metodológicamente se realizó trabajo de campo, exploración bibliográfica y de internet, y se aplicó el método propuesto por la Secretaría de Turismo de México (2010) para evaluar la potencialidad turística. Los resultados obtenidos reflejan que, Bahía Blanca cuenta con desarrollo de actividad turística pues, se sitúa en cuadrante IV. Sin embargo, sería importante impulsar iniciativas turísticas involucrando a todos los actores locales para propiciar su fomento y desarrollo.

Guillermo Isaac González Rodríguez, Estela Mishelle Campos-Medel

Dentro de la conformación del patrimonio simbólico cultural, la gastronomía juega un papel fundamental, pues es el punto de encuentro entre la identidad y el arraigo histórico de una comunidad, pueblo o raza; generando con ello una mezcla distintiva representativa de su origen, cosmovisión y cosmogonía. En este sentido, el presente trabajo pretende describir la cultura, las tradiciones y los símbolos que existen dentro de los pueblos afromexicanos en la región de Cuajinicuilapa, México. Por tanto, mediante un diagnóstico descriptivo, se presentan sus principales elementos festivos como parte de su cultura, en conjunto con la gastronomía que acompaña a los procesos simbólicos y rituales. Se utiliza una metodología cualitativa con un método descriptivo secuencial y dos instrumentos aplicados. El estudio concluye con la obtención de un inventario de tradiciones que las personas afromexicanas conservan como parte de su historia cultural y replican como un símbolo de su identidad.

Brandon A. Knettel, Emily M. Cherenack, Conner Rougier-Chapman et al.

Mental health challenges and substance use are common among college athletes, yet few studies have been conducted to understand substance use as a coping strategy. The pressures of collegiate athletics - including commitments to training, travel, and competition - can contribute to maladaptive coping among college athletes, including alcohol and other substance use. An online survey was completed by 188 college athletes competing across NCAA/NJCAA divisions at six institutions in the United States to examine factors associated with substance use coping and whether specific strategies of coping were associated with risk of substance use. Alcohol and drug use were assessed using the CRAFFT Screening Test, NIDA-Modified ASSIST, and Alcohol Use Disorders Identification Test. Coping was assessed with the Coping Orientation to Problems Experienced Inventory, stress was assessed using an adapted Graduate Stress Inventory, athletics-related anxiety was assessed with the Sport Anxiety Scale, and perceived control of stress was assessed using the Perceived Control Questionnaire. Older athletes, men, and those with higher stress were more likely to use substances to cope. Higher behavioral disengagement, higher substance use coping, and lower religious coping were associated with increased likelihood of binge drinking and substance-related risk behaviors. These findings point to the importance of developing targeted interventions aimed at addressing stress and facilitating healthy coping to reduce problematic drinking and substance use among college athletes.

Svitlana Karatieieva, Oleksandr Slobodian, Taras Lukashiv et al.

Purpose: to find out the features of hip girth (distal) of both limbs of young boys and young girls of Bukovynian higher educational institutions, depending on the sport type. Materials and methods: 115 students of Bukovynian higher education institutions aged from 16 to 21 years old participated in the study, 78 (67.82%) of them were young boys and 37 (32.18%) were young girls. The main group was 75 (65.22 %) students of the I-II courses of the Faculty of Physical Culture and Human Health (the Yuriy Fedkovich Chernivtsi National University), the control group - 40 (34.78%) college students and students of the Stomatological Faculty of the Bukovynian State Medical University, who underwent an anthropometric study, according to the method of V.V. Bunaka in the modification of P.P. Shaparenko (determination of body weight and hip girth distally). Results: a comparison of the length of the distal right and left hip girth of young boys and young girls of both groups shows that the length of the right hip girth of young boys and young girls is bigger than the left: the main group (right in young boys - 48.50±2.0 cm, left - 42.25 ±2.0 cm; right in young girls – 48.59±2.0 cm; left – 41.74±2.0 cm), control group (right in young boys – 49.19±2.0 cm, left – 44 ,42±2.0 cm; young girls' right - 46.57±2.0 cm; left - 41.52±2.0 cm). The conducted regression analysis shows that gender and weight are significant factors for hip girth distally on the right and left. Conclusions: the model for predicting the circumference of the distal right thigh has the equation: y = β1+β2 +0.318*x, where y is the distal right thigh circumference, x is weight. Coefficient β1 = 29.848 for young girls and β2 = 25.95 for young boys. The coefficient of determination is 0.994. On the left, it has the equation: y = β1+β2 +0.292*x, where y is the hip girth on the distal left, x is weight. Coefficient β1 = 29.848 for women and β2 = 21.901 for men. The coefficient of determination is 0.991.

Yike Xu, Mark S. Andersland

In this paper, we shed new light on a classical scheduling problem: given a slot-timed, constant-capacity server, what short-run scheduling decisions must be made to provide long-run service guarantees to competing flows of unit-sized tasks? We model each flow's long-run guarantee as a worst-case service that maps each queued arrival vector recording the flow's cumulative task arrivals, including those initially queued, to a worst-case acceptable departure vector lower-bounding its cumulative served tasks. We show that these maps are states that can be updated as tasks arrive and are served, introduce state-based scheduling, find the schedulability condition necessary and sufficient to maintain all flows' long-run guarantees, and use this condition to identify all short-run scheduling decisions that preserve schedulability. Our framework is general but computationally complex. To reduce complexity, we consider three specializations. First, we show that when satisfactory short-run scheduling decisions exist, at least one can be efficiently identified by maximizing the server's capacity slack, a generalization of the earliest-deadline-first rule. Second, we show that a special class of worst-case services, min-plus services, can be efficiently specified and updated using properties of the min-plus algebra. Finally, we show that efficiency can be further improved by restricting attention to a min-plus service subclass, dual-curve services. This last specialization turns out to be a dynamic extension of service curves that maintains all essential features of our framework while approaching near practical viability.

Alex Cummaudo, Scott Barnett, Rajesh Vasa et al.

Increased popularity of `intelligent' web services provides end-users with machine-learnt functionality at little effort to developers. However, these services require a decision threshold to be set which is dependent on problem-specific data. Developers lack a systematic approach for evaluating intelligent services and existing evaluation tools are predominantly targeted at data scientists for pre-development evaluation. This paper presents a workflow and supporting tool, Threshy, to help software developers select a decision threshold suited to their problem domain. Unlike existing tools, Threshy is designed to operate in multiple workflows including pre-development, pre-release, and support. Threshy is designed for tuning the confidence scores returned by intelligent web services and does not deal with hyper-parameter optimisation used in ML models. Additionally, it considers the financial impacts of false positives. Threshold configuration files exported by Threshy can be integrated into client applications and monitoring infrastructure. Demo: https://bit.ly/2YKeYhE.

Abdallah Moubayed, Abdallah Shami, Parisa Heidari et al.

Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit computation resources available at edge nodes. To that end, this work formulates the problem of optimal V2X service placement (OVSP) in a hybrid core/edge environment as a binary integer linear programming problem. To the best of our knowledge, no previous work considered the V2X service placement problem while taking into consideration the computational resource availability at the nodes. Moreover, a low-complexity greedy-based heuristic algorithm named "Greedy V2X Service Placement Algorithm" (G-VSPA) was developed to solve this problem. Simulation results show that the OVSP model successfully guarantees and maintains the QoS requirements of all the different V2X services. Additionally, it is observed that the proposed G-VSPA algorithm achieves close to optimal performance while having lower complexity.

JaeYoung Hwang, Lionel Nkenyereye, NakMyoung Sung et al.

With the advancement of IoT technology, various domains such as smart factories, smart cities and smart cars use the IoT to provide value-added services. In addition, technologies such as MEC and network slicing provide another opportunity for the IoT to support more advanced and real-time services that could not have been previously supported. However, the simple integration of such technologies into the IoT does not take full advantage of MEC and network slicing or the reduction of latency and traffic prioritization, respectively. Therefore, there is a strong need for an efficient integration mechanism for IoT platforms to maximize the benefit of using such technologies. In this article, we introduce a novel architectural framework that enables the virtualization of an IoT platform with minimum functions to support specific IoT services and host the instance in an edge node, close to the end-user. As the instance provides its service at the edge node where the MEC node and network slice are located, the traffic for the end-user does not need to traverse back to the cloud. This architecture guarantees not only low latency but also efficient management of IoT services at the edge node. To show the feasibility of the proposed architecture, we conduct an experimental evaluation by comparing the transmission time of both IoT services running on the central cloud and those using sliced IoT functions in the edge gateway. The results show that the proposed architecture provides two times faster transmission time than that from the conventional cloud-based IoT platform.

Paul Diac

The automatic composition of web services refers to how services can be used in a complex and aggregate manner, to serve a specific and known functionality. Given a list of services described by the input and output parameters, and a request of a similar structure: the initially known and required parameters; a solution can be designed to automatically search for the set of web services that satisfy the request, under certain constraints. We first propose two very efficient algorithms that solve the problem of the automatic composition of the web services as it was formulated in the competitions organized in 2005 and 2008. The algorithms obtain much better results than the rest of the participants with respect to execution time and even composition size. Evaluation consists of running the previous and the proposed solutions on given benchmarks and generated tests. Further, we design two new models to match service's parameters, extending the semantic expressiveness of the 2008 challenge. The initial goal is to resolve some simple and practical use-cases that cannot be expressed in the previous models. We also adhere to modern service description languages, like OpenAPI and especially schema.org. Algorithms for the new models can solve instances of significant size. Addressing a wider and more realistic perspective, we define the online version of the composition problem. In this regard, we consider that web services and compositions requests can be added and removed in real-time, and the system must handle such operations on the fly. It is necessary to maintain the workflows for users who actively run the compositions over time. As for the new semantic models, we propose new algorithms and provide comprehensive evaluation by generating test cases that simulate all corner cases.

Soheila Sadeghiram, Hui MA, Gang Chen

Distributed computing which uses Web services as fundamental elements, enables high-speed development of software applications through composing many interoperating, distributed, re-usable, and autonomous services. As a fundamental challenge for service developers, service composition must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. On the other hand, huge amounts of data have been created by advances in technologies, which may be exchanged between services. Data-intensive Web services are of great interest to implement data-intensive processes. However, current approaches to Web service composition have omitted either the effect of data, or the distribution of services. Evolutionary Computing (EC) techniques allow for the creation of compositions that meet all the above factors. In this paper, we will develop Genetic Algorithm (GA)-based approach for solving the problem of distributed data-intensive Web service composition (DWSC). In particular, we will introduce two new heuristics, i.e. Longest Common Subsequence(LCS) distance of services, in designing crossover operators. Additionally, a new local search technique incorporating distance of services will be proposed.