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Storylines are introduced in climate science to provide unity of discourse, integrate the physical and socioeconomic components of phenomena, and make climate evolution more tangible. The use of this concept by multiple scholar communities and the novelty of some of its applications renders the concept ambiguous nonetheless, because the term hides behind a wide range of purposes, understandings, and methodologies. This semi‐systematic literature review identifies three approaches that use storylines as a keystone concept: scenarios—familiar for their use in IPCC reports—discourse‐analytical approaches, and physical climate storylines. After screening peer‐reviewed articles that mention climate and storylines, 270 articles are selected, with 158, 55, and 57 in each category. The results indicate that each scholarly community works with a finite and different set of methods and diverging understandings. Moreover, these approaches have received criticism in their assembly of storylines: either for lacking explicitness or for the homogeneity of expertise involved. This article proposes that cross‐pollination among the approaches can improve the usefulness and usability of climate‐related storylines. Among good practices are the involvement of a broader range of scientific disciplines and expertise, use of mixed‐methods, assessment of storylines against a wider set of quality criteria, and targeted stakeholder participation in key stages of the process. This article presents a semi‐systematic literature review that identifies three storyline‐reliant approaches in climate‐related science. The review highlights how the divergent approaches have a wide range of methodologies to assemble storylines, but currently exist independently of each other. The article discusses ways in which cross‐pollination based on good practices could benefit each.

Summary Security‐constrained unit commitment (SCUC) is a key component of operating electricity markets. By increasing the share of renewable energies on the generation side, and emergence and growth of new stochastic loads on the demand side, stochastic SCUC has become more important for secure‐optimal operation of the market. Scenario‐based techniques have been suggested widely for stochastic SCUC in the literature. However, they are usually very time‐consuming. This problem is escalated in large‐scale power systems with high penetration of stochastic generation and loads. To mitigate the computational burden of SCUC problem, this paper develops an algorithm, based on point estimation method and Bender's decomposition technique. The proposed approach breaks the probabilistic problem into a few deterministic points with much lower computation burden yet with minimal loss of accuracy. The proposed approach is implemented on a six‐bus system as the first numerical study, and on a modified IEEE 118‐bus system with 94 probabilistic variables as the second case study. The efficacy of proposed algorithm is confirmed, especially in the last test case with notable reduction in computational burden without considerable loss of precision. Copyright © 2015 John Wiley & Sons, Ltd.

The large‐scale penetration of electric vehicles (EVs) into the power system will provoke new challenges needed to be handled by distribution system operators (DSOs). Demand response (DR) strategies play a key role in facilitating the integration of each new asset into the power system. With the aid of the smart grid paradigm, a day‐ahead charging operation of large‐scale penetration of EVs in different regions that include different aggregators and various EV parking lots (EVPLs) is propounded in this study. Moreover, the uncertainty of the related EV owners, such as the initial state‐of‐energy and the arrival time to the related EVPL, is taken into account. The stochasticity of PV generation is also investigated by using a scenario‐based approach related to daily solar irradiation data. Last but not least, the operational flexibility is also taken into consideration by implementing peak load limitation (PLL) based DR strategies from the DSO point of view. To reveal the effectiveness of the devised scheduling model, it is performed under various case studies that have different levels of PLL, and for the cases with and without PV generation.

The insufficient loading of vehicles, which leads to a low logistics load factor is a common problem in the logistics industry. This study addresses this issue by utilizing actual shipment data from an automotive company. An effective method has been proposed to improve the company’s logistics efficiency through a scenario-based approach. Two real- world scenarios were developed to enhance vehicle loading performance. Machine learning algorithms were employed to evaluate the shipment performance of these scenarios. For the study, a dataset was generated from the company’s ERP system and real-world shipment data. Since this is a real-world problem, the dataset consisted of unlabeled data. To solve this problem, both supervised and unsupervised learning algorithms were applied. First, unsupervised clustering algorithms were used to group the shipment performance based on similarities. Then, supervised learning algorithms were utilized to classify the data within each group. The average cost was used to evaluate the clusters obtained through the unsupervised methods, while classification performance was measured using supervised machine learning techniques. The scenario-based approach has significantly improved the performance of the shipments as it shows the changes in load factor more clearly. In the actual case, only %25.7 of shipments were high performing, while this percentage gradually increased to %98.4 in the scenarios. The results show that optimizing the load factor makes the transports more efficient and balanced.

The applicability and efficiency of location-routing problems for relief commodities motivate several researchers to develop optimization models and algorithms with regards to real-world cases. This study by presenting a multiobjective mixed integer mathematical model for the location-routing of the medical relief problem at the time of a disaster, proposes a new extension to this applicable model with reliability considerations. The proposed model focuses on the location of temporary relief centers and delivers the pharmaceutical commodities to centers at the shortest possible time with reliability assurance. The model includes three simultaneous objectives of minimizing response time, minimizing operational costs, and maximizing the reliability of the transportation network. As far as we know, this study firstly optimizes these three objectives simultaneously. Another novelty is to add the uncertainty of the problem. In this regard, the inherent uncertainty is formulated by a scenario-based approach. Considering the multiobjectiveness of the proposed model, the Epsilon constraint method as a solution algorithm has been used to solve the model. Results are tested for numerical examples via different scenarios. The results represent the excellent performance of the model to minimize the costs and to increase the reliability for the proposed location-routing problem of relief commodities.

This study aimed to develop and evaluate a scenario-based nutrition literacy (NL) online programme for Taiwanese college students. A randomised pilot trial design was used in this study. The study was conducted at a university in Taiwan. The intervention consisted of a five-unit web-based NL programme including videos of real-life scenario-based stories, situational analysis teaching and after-unit quizzes. Theme-related website information and smartphone apps (both iOS and Android systems) were offered for reference in every unit. The NL measure consisted of a self-rated scale, a scenario-based test and a healthy eating behaviour survey. Paired sample t-tests and ANCOVA were performed to test the effects on NL and healthy eating behaviour. Participants were ninety-eight students, with a retention rate of 98 %. The ratio of men to women was 0·2:1. Most students were freshmen (48 %). Compared with the control group, the experimental group showed significant post-intervention improvements in the NL and healthy eating behaviours after controlling for pretest scores. This pilot study offers preliminary evidence of the potential positive effects of implementing a scenario-based NL online programme for college students. It offers a possibly novel strategy to enhance health-promoting behaviours in Taiwanese universities. Further research with larger sample sizes and more rigorous designs is warranted to confirm and build upon these initial findings.

Natural disasters threaten human life in various ways, and a better understanding of their components reduces environmental and societal ramifications. This study seeks a pedagogical approach to contribute to such knowledge at a junior landscape architecture studio by concentrating on research-based experience. Assigning five disaster categories, including flood, drought and food, climate change, earthquake, and disaster-resilient society, the students obtained enriched knowledge on the studio process while they had difficulty in applying theoretical aspects of natural disasters to their projects, mainly gearing from analysis (upper scale) to design (lower scale) solutions. The study results propose that natural disaster education and awareness efforts should be integrated into design- and planning-related disciplines sooner rather than later.

Background This paper explores the feasibility of establishing a large-scale population-based cohort and biobank in Switzerland by assessing potential participants’ needs, expectations, and concerns about such an infrastructure providing information on health, lifestyle, and exposure trajectories, the development of disease, and risk factors over time. Methods We utilized a scenario-based questionnaire in the Swiss Health Study pilot phase (2020–2021), involving 1349 adults aged 20–69 from the cantons Vaud and Bern. We conducted descriptive statistics supported by R and qualitative content analysis of n  = 374 open responses related to attitudes towards research. Results We highlight the benefits and challenges of the scenario-based approach, discuss the sample represented in the pilot phase, and present implications for building a full cohort. We also report on participants’ attitudes towards and previous experience with health research. We analyze references regarding informed consent and feedback, attitudes towards the Swiss Health Study, and recommendations on improving its scope, design, and instruments. Results indicate a high interest (90%) in participating in a national health study, with 85% of a random population sample willing to join a long-term cohort. Only 43% were familiar with biobanks, and 44% preferred general consent. Trust was high for Swiss-based public research but lower for researchers from other countries or private sector. Over 95% expressed willingness to complete online questionnaires, undergo physical examination, and donate biosamples. Almost all participants wanted to know the outcomes of the medical tests (99.5%) and the exposure to environmental stressors (95%) from their study center visit. Preferred tools for monitoring sleep, physical activity, and diet were known smartphone apps with automatic data management. Conclusion Overall, the study reveals a positive attitude towards personalized health research, with a strong willingness to share data and samples. Key insights focus the meaning of informed consent for participation, the relevance of sampling and representativeness, as well as the significance and challenges of personalized feedback, especially regarding environmental health concerns. Findings emphasize participants’ supportive yet reflexive stances, underscoring the importance of aligning research values with individual values in personalized health research. These insights contribute valuable considerations for refining the scope, design, and instruments of future cohort studies.

Recent accounts of predictive processing in posttraumatic stress disorder (PTSD) suggest that trauma-exposed individuals struggle to update trauma-related hypotheses predicting danger, which may be involved in the etiology and maintenance of this disorder. Initial research supports this account, documenting an association between trauma-exposure, impaired expectation updating, and PTSD symptoms. Yet, no study to date has examined biased belief updating in PTSD using a scenario-based approach. Here, we examined the predictive processing account among trauma-exposed and non-trauma-exposed individuals using a modified Trauma-Related version of the Bias Against Disconfirmatory Evidence task. The task presents both danger-and safety-related scenarios highly relevant for trauma-exposed individuals. For each scenario, participants viewed several explanations and rated their plausibility. Their ability to update their initial interpretation following new-contradictory information was assessed. Preregistered analyses did not reveal any significant findings. Based on indications that our sample may not have been sufficiently powered, we conducted exploratory analyses in an extended sample of participants. These analyses yielded a significant association between reduced belief updating and PTSD symptoms which was evident for disconfirming both safety and danger scenarios. However, the effect sizes we found were in the small-to-medium range. Although preliminary, our current findings support initial evidence that individuals with higher PTSD symptoms show a higher resistance to update their beliefs upon new disconfirmatory evidence. Our results should be interpreted cautiously in light of the extended sample and the limitations of the current study.

The purpose of this article is to pay attention to the coverage distance of stores, as one of the key features of the consumer market. This factor can have many effects on economic and environmental issues as well as on the service level. In this paper, two models are proposed. In the first model, the location and allocation of stores are discussed using the maximum coverage method, and in the second model, the optimal number of products is calculated and the network costs and pollution are minimized. GAMS software with CPLEX solver was used to solve mixed linear integer programming under the uncertainty of demand parameters, maximum coverage distance, and carbon trade price in different scenarios. Due to the lack of law to minimize the amount of pollution caused by transportation in Iran, the cap-and-trade policy was used in the supply chain of the Bistoon sugar factory in Kermanshah. The results showed that increasing the coverage distance, in addition to reducing the level of customer service, will also increase the overall costs of the manufacturer and network pollution. Also, despite the costly implementation of environmental policies, by replacement of green vehicles, the overall costs of the network have not increased.