Research on parking sharing strategies considering user overtime parking.
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ABSTRACT:
SUBMITTER: Huang X
PROVIDER: S-EPMC7286510 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This paper studies the pharmaceutical production supply chain system composed of upstream and downstream pharmaceutical enterprises, and explores the impact of government subsidy strategies on the new drug research and development (R&D) decision variables and profits of pharmaceutical enterprises as well as social welfare, when considering both the horizontal spillover effects within the industry and the vertical spillover effects between industries. Comparing and analyzing the impact of these strategies including non-government subsidy strategy, pharmaceutical enterprise innovation input subsidy strategy, pharmaceutical enterprise innovative product subsidy strategy, patient price subsidy strategy, and patient medical insurance subsidy strategy. By establishing a four-stage Cournot duopoly model incorporating spillover effects, the equilibrium solutions are obtained by backward induction, and the impact of spillover effects on decision variables is investigated accordingly. Studies have shown that: (a) Government subsidy strategies can promote pharmaceutical enterprises' R&D investment and have a positive incentive effect on the pharmaceutical enterprises' profits and social welfare. (b) The patient medical insurance subsidy strategy is the optimal subsidy strategy, which can generate higher profits for pharmaceutical enterprises and higher social welfare.
| S-EPMC8830673 | biostudies-literature
Project description:People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and semantic web technologies such as FOAF and microformats are valuable sources of personal information that could provide a fair understanding of the user, but profile information is scattered over different user models. Some researchers in the ubiquitous user modeling community envision the need to share user model's information from heterogeneous sources. In this paper, we address the syntactic and semantic heterogeneity of user models in order to enable user modeling interoperability. We present a dynamic user profile structure based in Simple Knowledge Organization for the Web (SKOS) to provide knowledge representation for ubiquitous user model. We propose a two-tier matching strategy for concept schemas alignment to enable user modeling interoperability. Our proposal is proved in the application scenario of sharing and reusing data in order to deal with overweight and obesity.
| S-EPMC3545566 | biostudies-literature
Project description:Review-based research is needed which can establish the psychosocial outcomes and mechanisms of "storytelling and sharing" interventions for people with stroke. This information will act to inform the value and development of such interventions. An integrative review was conducted in three stages: (a) a systematic search strategy was undertaken to focus on articles between 2009 until January 2020 to locate articles the considered storytelling and sharing interventions for people diagnosed with stroke; (b) critical appraisal was undertaken to assess study quality; and (c) synthesis within three stages including data reduction, data display and conclusion. Fourteen articles (including 727 participants) were identified that met the eligibility criteria. Five themes were identified that represented the outcome and mechanisms that appeared to be associated with a stroke intervention. These included introducing the concept of hope and learning to be positive, the enhanced ability to cope, the impact of loneliness and social interaction, impact on emotions, depression and related emotions such as fear. Storytelling interventions appear to impact loneliness, introduce positivity and hope and enable coping through knowledge exchange. The main mechanisms which appeared to influence these outcomes were social comparisons and social control.
| S-EPMC8234102 | biostudies-literature
Project description:BACKGROUND:Advances in medicine rely to a great extent on people's willingness to share their data with researchers. With increasingly widespread use of digital technologies, several Web-based communities have emerged aiming to enable their users to share large amounts of data, some of which can possibly be employed for research purposes by scientists, or to conduct participant-led research (PLR). Scholarship has recently addressed the necessity of interrogating how existing ethical standards can and should be applied and adapted in view of the specificities of such Web-based activities. So far, no study has explored participants' beliefs about and attitudes toward ethical oversight when it comes to platforms that involve medical data sharing. OBJECTIVE:This paper presents the protocol for a survey study aimed at understanding users' beliefs about Web-based data-sharing platforms regarding how research ethics principles should be applied in such a setting. Furthermore, the study aims at quantitatively assessing the relationship between participants' perspectives on ethical oversight and other variables such as previous participation in research, beliefs about data sharing, and attitudes toward self-experimentation. METHODS:We are conducting a Web-based survey with users of a popular Web-based data-sharing platform, Open Humans. The survey has been sent to approximately 4640 users registered for the Open Humans newsletter. To fill out the survey, participants need to have an account on Open Humans. We expect a 5%-10% response rate (between 200 and 400 completed surveys out of approximately 4000 survey invitations sent). Independent variables include past data-sharing behavior and intention, beliefs about data sharing, past participation in research, attitudes toward self-experimentation, perceived knowledge of the platform's guidelines and terms, perceived importance of having transparent guidelines, and governance-related beliefs. The main dependent variable is participants' expectations regarding who should ensure that ethical requirements are met within research projects conducted on open data-sharing platforms, based on Emanuel et al's ethical framework. We will use chi-square tests to assess the relationship between participants' expectations regarding ethical oversight and their past behavior, future intentions, beliefs, attitudes, and knowledge. RESULTS:Data collection started on June 13, 2018. A reminder to fill out the survey was sent to participants in mid-July. We expect to gain insights on users' perspectives on the ethical oversight of Web-based data-sharing platforms and on the associated experiences, beliefs, and sociodemographic characteristics. CONCLUSIONS:When digital tools allow people to engage in PLR including medical data, understanding how people interpret and envision the ethical oversight of their data-sharing practices is crucial. This will be the first study to explore users' perspectives on ethical oversight of Web-based data-sharing platforms. The results will help inform the development of a framework that can be employed for platforms hosting various kinds of research projects to accommodate participants' ethical oversight needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR1-10.2196/10939.
| S-EPMC6291678 | biostudies-other
Project description:Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science 'badges', funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories.
| S-EPMC8808319 | biostudies-literature
Project description:Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71×10 -9 ). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10 -07 ). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.
| S-EPMC10441317 | biostudies-literature
Project description:In June 2015, the National Institutes of Health (NIH) released a Guide notice (NOT-OD-15-102) that highlighted the expectation of the NIH that the possible role of sex as a biologic variable be factored into research design, analyses, and reporting of vertebrate animal and human studies. Anticipating these guidelines, the NIH Office of Research on Women's Health, in October 2014, convened key stakeholders to discuss methods and techniques for integrating sex as a biologic variable in preclinical research. The workshop focused on practical methods, experimental design, and approaches to statistical analyses in the use of both male and female animals, cells, and tissues in preclinical research. Workshop participants also considered gender as a modifier of biology. This article builds on the workshop and is meant as a guide to preclinical investigators as they consider methods and techniques for inclusion of both sexes in preclinical research and is not intended to prescribe exhaustive/specific approaches for compliance with the new NIH policy.-Miller, L. R., Marks, C., Becker, J. B., Hurn, P. D., Chen, W.-J., Woodruff, T., McCarthy, M. M., Sohrabji, F., Schiebinger, L., Wetherington, C. L., Makris, S., Arnold, A. P., Einstein, G., Miller, V. M., Sandberg, K., Maier, S., Cornelison, T. L., Clayton, J. A. Considering sex as a biological variable in preclinical research.
| S-EPMC6191005 | biostudies-literature
Project description: Not available
| S-EPMC11344768 | biostudies-literature
Project description:Car sharing has become a new mode of transport during the past two decades in the world. Its rapid growth in China has attracted a wide range of users and posed some problems. The main focus is on service efficiency and user satisfaction. To explore possible service enhancement and management intervention, this study aims at capturing the user characteristics according to different user types and scrutinizing their satisfaction with station-based one-way car sharing service. The study firstly illustrates descriptive statistics of user profile. This is followed by a study of user satisfaction influenced by user rates on staffs, the efficiency of rental process, vehicle situation, the use of credit card and their familiarity towards rental station. Furthermore, by clustering users according to the total travel time and distance during one rent, two different types of users are identified and defined as User Group A (UGA) and User Group B (UGB). To examine how fully do users utilize the shared cars, ANOVA was conducted implying family car ownership, total travel distance and main travel purpose have strong impact on total rental time for UGB, while for UGA, travel purpose and age have strong impact. Finally, ordinal logistic regression was introduced to find that for UGB, "shopping" is the main travel purpose with longer rental time, whereas for UGA, "out for business", "shopping", "visit friends" or "pick up others" are the main travel purposes with longer total travel time. Based on the findings, advices for operators on how to improve service quality and suggestions for government management strategy are discussed, respectively.
| S-EPMC8809597 | biostudies-literature
Project description:It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.
| S-EPMC5007429 | biostudies-literature