Organizational Structure, Public-Private Relationships, and Operational Performance of Large-Scale Stadiums: Evidence from Local Governments in China

The financial sustainability of large-scale sport stadiums has become a challenging issue for sport organizations in China due to increasing market competition, lack of professional sport franchises to tenant the facilities, and gradual slow-down in Chinese GDP growth. Previous findings about operational performance of sport organizations identify organizational structure and public-private partnership (PPP) as important predictors. The aim of the current study was (a) to propose a predictive model for operational performance of large-scale stadiums in China and (b) to examine the relationships among organizational structure, PPP, and operational performance. We conducted a literature review to establish a theoretical framework for the proposed model, selected Yangzhou Sports Park and Xuzhou Olympic Sports Center to examine the relationships, and conducted expert interviews to examine the research questions. We found that Xuzhou’s operational performance was more effective due to several mechanisms related to both organizational structure and PPP: incentive, supervision, and assessment. Notably, using built-in benchmark monitoring procedures, Xuzhou managers identified a variety of constraints early on to address onsite problems while maintaining efficient communication among key PPP stakeholders.

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The Potential Correlation Between Organizational Structure and Financial Sustainability in Turkish Local Government

Advances in Electronic Government, Digital Divide, and Regional Development - Financial Sustainability and Intergenerational Equity in Local Governments ◽

10.4018/978-1-5225-3713-7.ch010 ◽

2018 ◽

pp. 224-247 ◽

Cited By ~ 2

Author(s):

Ceray Aldemir ◽

Tuğba Uçma Uysal

Keyword(s):

Local Government ◽

Organizational Structure ◽

Public Administration ◽

Local Governments ◽

Financial Sustainability ◽

Global Level ◽

Voluntary Reporting ◽

Non Profit ◽

Social Environmental ◽

The Eu

Throughout the membership process of the EU, Turkey has undergone various transformations in public administration structure. For this reason, the financial and public transformation experienced by Turkey in the Europeanization process must be evaluated. Financial localization and financial sustainability (FS) in local governments are one of the reflections of this transformation. Despite being a non-profit structure, financial sustainability seems to be extremely important in terms of local government units. Financial and economic crises, especially those at global level, have highlighted the need to address FS in non-profit bodies. In the light of the above-mentioned explanations, this chapter analyses the potential correlation between organizational structure and FS in Turkish Local Government—focusing on 14 municipalities in Muğla City—by conducting in-debt interviews. Therefore, the main aim of this chapter is to show the interest of local governments to use voluntary reporting, in terms of ethical-social-environmental, as a tool for financial sustainability.

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Do Amalgamations Make a Difference? What We can Learn from Evaluating the Policy Success of a Large Scale Forced Amalgamation of Local Government

10.37808/paq.45.3.4 ◽

2021 ◽

Vol 45 (3) ◽

pp. 278-298

Author(s):

Dana McQuestin ◽

Joseph Drew ◽

Masato Miyazaki

Keyword(s):

Decision Making ◽

Learning Process ◽

Local Governments ◽

Large Scale ◽

Financial Sustainability ◽

Total Expenditure ◽

Potential Solution ◽

Policy Success ◽

Ex Post ◽

The Impact

Deteriorating financial sustainability of local governments internationally has resulted in increased implementation of structural reform programs as a potential solution. However, the lack of a coherent framework to evaluate policy success has resulted in a myriad of approaches being applied by scholars, sometimes with conflicting results. This inconsistency is problematic given the importance of ex post analyses to the learning process, needed to ensure better decision-making and more efficacious interventions in the future. To address this gap in the literature, we employed the policy success framework along with a number of difference-in-difference analyses to assess the impact of amalgamation following a recent large-scale program. Moreover, in cognisance of the policy success literature, we also introduced a new innovation whereby we conducted empirical estimations on the disaggregated elements of total expenditure. We conclude with an enumeration of important lessons for policymaking and scholarly analysis.

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Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

Information and Control Systems ◽

10.31799/1684-8853-2018-4-45-51 ◽

2018 ◽

pp. 45-51

Author(s):

A. V. Ponomarev

Keyword(s):

Large Scale ◽

Wide Spectrum ◽

Preference Relation ◽

Pairwise Comparison ◽

Ground Truth ◽

Comparison Method ◽

Characteristic Matrix ◽

Image Tagging ◽

Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems).

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Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

10.2196/preprints.22865 ◽

2020 ◽

Author(s):

Anusha Ampavathi ◽

Vijaya Saradhi T

Keyword(s):

Feature Extraction ◽

Big Data ◽

Deep Learning ◽

Weight Function ◽

Optimization Algorithm ◽

Large Scale ◽

Heuristic Algorithms ◽

Disease Prediction ◽

Health Care Decisions ◽

Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽

10.3390/electronics10141670 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1670

Author(s):

Waheeb Abu-Ulbeh ◽

Maryam Altalhi ◽

Laith Abualigah ◽

Abdulwahab Ali Almazroi ◽

Putra Sumari ◽

...

Keyword(s):

Data Analysis ◽

Structural Equation ◽

Large Scale ◽

Review Paper ◽

Essential Element ◽

Routine Activities ◽

Criminological Theory ◽

Equation Modeling ◽

Future Research ◽

Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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A National Evaluation of State and Territory Roles in Hazard Mitigation: Building Local Capacity to Implement FEMA Hazard Mitigation Assistance Grants

Sustainability ◽

10.3390/su122310013 ◽

2020 ◽

Vol 12 (23) ◽

pp. 13

Author(s):

Gavin Smith ◽

Olivia Vila

Keyword(s):

Emergency Management ◽

National Survey ◽

Local Governments ◽

Technical Assistance ◽

Hazard Mitigation ◽

Grant Applications ◽

Research Questions ◽

Low Levels ◽

Resilient Infrastructure ◽

Survey Responses

This article describes the findings of a national survey of State Hazard Mitigation Officers (SHMOs) in U.S. states and territories in order to gain a greater understanding of the roles that they play in assisting local governments to build the capacity required to successfully develop and implement Federal Emergency Management Agency (FEMA)-funded Hazard Mitigation Assistance (HMA) grants, an important but understudied aspect of hazard mitigation governance. The research questions focus on: (1) How states and territories enable local governments to develop and implement HMA grants and (2) SHMOs’ opinions regarding their perceived capacity and effectiveness in assisting local governments to develop and implement HMA grants. Results show that while states and territories are relatively well-equipped to perform general administrative duties required by FEMA, SHMOs expressed wide variation in their capacity to assist local governments to develop and implement HMA grants. This was particularly evident with regard to the delivery of specific technical assistance measures required to develop HMA grants. Survey responses also highlight modest levels of participation in FEMA-designed efforts to delegate responsibility to states and territories and low levels of participation in programs that offer pre-application funding to local governments to help them develop HMA grant applications. These findings should concern FEMA as the agency embarks on the implementation of the Building Resilient Infrastructure and Communities program, an ambitious pre-disaster hazard mitigation grant initiative.

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Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Electronics ◽

10.3390/electronics10040461 ◽

2021 ◽

Vol 10 (4) ◽

pp. 461

Author(s):

Yongbin Yim ◽

Euisin Lee ◽

Seungmin Oh

Keyword(s):

Energy Efficient ◽

Large Scale ◽

Future Research ◽

Technological Evolution ◽

Node Density ◽

Mobile Objects ◽

Research Challenges ◽

Energy Efficient Communication ◽

Efficient Communication ◽

Architectural Principles

Recently, the demand for monitoring a certain object covering large and dynamic scopes such as wildfires, glaciers, and radioactive contaminations, called large-scale fluid objects (LFOs), is coming to the fore due to disasters and catastrophes that lately happened. This article provides an analytic comparison of such LFOs and typical individual mobile objects (IMOs), namely animals, humans, vehicles, etc., to figure out inherent characteristics of LFOs. Since energy-efficient monitoring of IMOs has been intensively researched so far, but such inherent properties of LFOs hinder the direct adaptation of legacy technologies for IMOs, this article surveys technological evolution and advances of LFOs along with ones of IMOs. Based on the communication cost perspective correlated to energy efficiency, three technological phases, namely concentration, integration, and abbreviation, are defined in this article. By reviewing various methods and strategies employed by existing works with the three phases, this article concludes that LFO monitoring should achieve not only decoupling from node density and network structure but also trading off quantitative reduction against qualitative loss as architectural principles of energy-efficient communication to break through inherent properties of LFOs. Future research challenges related to this topic are also discussed.

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Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00218-2 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Junshu Wang ◽

Guoming Zhang ◽

Wei Wang ◽

Ka Zhang ◽

Yehua Sheng

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Medical Service ◽

Rapid Development ◽

Medical Knowledge ◽

Language Models ◽

Computing Environment ◽

Computing Power ◽

Cloud Computing Environment ◽

Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

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Assessing the Response of Snow Avalanche Runout Altitudes to Climate Fluctuations Using Hierarchical Modeling: Application to 61 Winters of Data in France

Journal of Climate ◽

10.1175/2010jcli3312.1 ◽

2010 ◽

Vol 23 (12) ◽

pp. 3157-3180 ◽

Cited By ~ 42

Author(s):

N. Eckert ◽

H. Baya ◽

M. Deschatres

Keyword(s):

Climate Change ◽

Large Scale ◽

Hierarchical Modeling ◽

Control Measures ◽

Level Shift ◽

Snow Avalanches ◽

Winter Climate ◽

Climate Fluctuations ◽

High Magnitude ◽

Proposed Model

Abstract Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes.

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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽

10.3390/info11020079 ◽

2020 ◽

Vol 11 (2) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Xiaoyu Han ◽

Yue Zhang ◽

Wenkai Zhang ◽

Tinglei Huang

Keyword(s):

Language Processing ◽

Large Scale ◽

Named Entity Recognition ◽

Relation Extraction ◽

Entity Recognition ◽

Additional Information ◽

Named Entity ◽

Proposed Model ◽

The Relationship ◽

Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

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