The U.S. Army desires to improve safety during Intelligence, Surveillance, Reconnaissance (ISR) operations by removing Warfighters from direct line-of-fire by enhancing ISR operational capabilities with unmanned systems, also known as Robot-Aided ISR (RAISR) (DOD, 2013). Additionally, RAISR presents an opportunity to fulfill ISR capability requirements of modern combat environments including: detection of High-Value Individuals (HVI) from safer distances, identification of baseline behavior, and interpretation of adversarial intent (U.S. Army, 2008). Along with the demand and projected acquisition of RAISR technology, there is the added need to design training requirements for system operation and task execution instruction. While documentation identifying specific training standards and objectives for ISR tasks utilizing unmanned systems is limited (DOD, 2013), simulation-based training has been identified as a critical training medium for RAISR (U.S. Army, 2008). ISR analysts will primarily conduct RAISR tasks via Indirect Vision Displays (IVD) which transition well into multimodal simulations (Salcedo, Lackey, & Maraj, 2014). However, simulation alone may not fulfill the complex training needs of RAISR tasks, therefore, incorporating instructional support may improve the effectiveness of training (Oser, Gualtieri, Cannon-Bowers, & Salas, 1999). One method to accomplish this is to utilize a Scenario-Based Training (SBT) framework enhanced with instructional strategies to target specific training objectives. The purpose for the present experiment was to assess the effectiveness of SBT enhanced with selected instructional strategies for a PC-based RAISR training simulation. The specific task type was the identification of HVIs within a group through behavior cue analysis. The instructional strategies assessed in this experiment, Highlighting and Massed Exposure, have shown to improve attentional weighting, visual search, and pattern recognition skills, which are critical for successful behavior cue analysis. Training effectiveness was evaluated by analyzing the impact of the instructional strategies on performance outcomes, including detection accuracy, classification accuracy, and median response time, and perceptions of the level of engagement, immersion, and presence during training exercises. Performance results revealed that the Massed Exposure strategy produced significantly faster response times for one subtle and one familiar target behavior cue. Perception results indicated that Highlighting was the least challenging instructional strategy and the Control offered the preferred level of challenge. The relationships between performance and perception measures revealed that higher levels of engagement, immersion, and presence were associated with better performance in the Control, but this trend did not always hold for Massed Exposure and Highlighting. Furthermore, presence emerged as the primary predictor of performance for select target behavior cues in the Control and Massed Exposure conditions, while immersion and engagement predicted performance of select cues in the Highlighting condition. The findings of the present experiment point to the potential benefit of SBT instructional strategies to improve effectiveness of simulation-based training for behavior cue analysis during RAISR operations. Specifically, the findings suggest that the Massed Exposure strategy has the potential to improve response time when detecting both familiar and novel targets. The results also highlight directions for future research to investigate methods to alter instructional strategy design and delivery in order to improve trainee perceptions of the instruction.

Scenario based training (SBT) allows organizations to train the competencies necessary for effective performance in an environment that replicates critical aspects of the transfer or operational setting. One of the most salient training features that can be delivered during SBT is feedback. Task feedback may be provided to trainees either during a training scenario (immediately following actions) or between training scenarios (after action review). However, little is known regarding the effects of immediate versus delayed feedback given to teams. Prior research on training individuals suggests that immediate feedback improves performance as assessed immediately after training (acquisition performance), however delayed feedback improves performance after time has passed (retention performance). Moreover, several individual training studies have found that trainee goal orientation moderates the influence of instructional features such as goal difficulty and content organization. I hypothesized that team member goal orientation would also moderate the influence of feedback timing on team performance. Three facets of goal orientation were assessed. Learning goal orientation refers to the extent to which individuals strive towards the mastery of skills for the sake of continuous improvement. Prove goal orientation refers to the extent to which individuals strive to demonstrate their own competence to others. Finally, avoid goal orientation refers to the extent to which individuals seek to avoid demonstrating their incompetence to others. Participants were 160 undergraduate psychology students assigned to 80 two-person teams. These teams were trained and tested using a simulated military task called the Forward Observer Personal Computer-based Simulator. Teams received 36 minutes of training prior to performing a skill acquisition test on day one of the experiment. One week later teams returned to perform a skill retention test. Teams were randomly assigned to receive immediate feedback during their team training scenarios or delayed feedback following each training scenario. Results indicated that the timing of feedback had no impact on acquisition performance. As predicted, however, teams that had received delayed feedback outperformed those that had received immediate feedback on the retention test. Moreover, the positive impact of delayed feedback on retention performance was greatest for teams that scored higher on a measure of state learning goal orientation on the day of their training. This interaction was mediated by the team's perception of the instrumentality of the feedback provided to them. Theoretical and practical implications, as well as, limitations and directions for future research are discussed.

Climate change is projected to increase the intensity and frequency of storm events that would increase flooding hazards. Urbanization associated with land use and land cover change has altered hydrological cycles by increasing stormwater runoff, reducing baseflow and increasing flooding hazards. Combined urbanization and climate change impacts on long-term riparian flooding during future growth are likely to affect more socially vulnerable populations. Growth strategies and green infrastructure are critical planning interventions for minimizing urbanization impacts and mitigating flooding hazards. Within the social-ecological systems planning framework, this empirical research evaluated the effects of planning interventions (infill development and stormwater detention) through a risk assessment in three studies. First, a climate sensitivity study using SWAT modeling was conducted for building a long-term flooding hazard index (HI) and determining climate change impact scenarios. A Social Vulnerability Index (SoVI) was constructed using socio-economic variables and statistical methods. Subsequently, the long-term climate change-induced flooding risk index (RI) was formulated by multiplying HI and SoVI. Second, growth strategies in four future growth scenarios developed through the BMA ULTRA-ex project were evaluated through land use change input in SWAT modeling and under climate change impact scenarios for the effects on the risk indices. Third, detention under climate sensitivity study using SWAT modeling was investigated in relation to long-term flooding hazard indices. The results illustrated that increasing temperature decreases HI while increasing precipitation change and land use change would increase HI. In addition, there is a relationship between climate change and growth scenarios which illustrates a potential threshold when the impacts from land use and land cover change diminished under the High impact climate change scenario. Moreover, spatial analysis revealed no correlation between HI and SoVI in their current conditions. Nevertheless, the Current Trends scenario has planned to allocate more people living in the long-term climate change-induced flooding risk hotspots. Finally, the results of using 3% of the watershed area currently available for detention in the model revealed that a projected range of 0 to 8% watershed area would be required to mitigate climate change-induced flooding hazards to the current climate conditions. This research has demonstrated the value of using empirical study on a local scale in order to understand the place-based and watershed-specific flooding risks under linked social-ecological dynamics. The outcomes of evaluating planning interventions are critical to inform policy-makers and practitioners for setting climate change parameters in seeking innovations in planning policy and practices through a transdisciplinary participatory planning process. Subsequently, communities are able to set priorities for allocating resources in order to enhance people's livelihoods and invest in green infrastructure for building communities toward resilience and sustainability

Объектом исследования является анализ накопленного российскими event-агентствами опыта составления программ и сценариев специальных мероприятий и раскрытие стратегии и технологии их планирования. Методологической основой исследования выступают теории event-менеджмента. Они позволяют раскрыть его специфику относительно процесса формирования механизмов нематериального стимулирования персонала и поддержания имиджа компаний посредством организации различных event'ов и, в частности, совершенствования их сценарной составляющей. / The objectives of the study are to analyze the experience gained by Russian event agencies in compiling programs and scenarios for special events and to reveal strategies and technologies for their planning. The methodological basis of the study is the theory of event management. They allow revealing its specifics regarding the process of formation of mechanisms of non-material incentives for personnel and maintaining the image of companies by organizing various events and, in particular, improving their scenario component. The research methods include document analysis, observation included, methods of comparison, analysis and synthesis.

Municipalities globally are recognizing their role in mitigating climate change and are actively working to reduce carbon emissions. This complex challenge is heightened in areas like Northern Sweden, where municipalities are adapting to accommodate new industries essential for meeting global climate targets, subsequently changing the energy landscape. The local administration must not only decarbonize existing energy use but also develop infrastructure for the new industries, all while fostering sustainable and appealing cities where residents aspire to live. However, the trajectory of these changes and the subsequent future energy requirements remain uncertain. This study aims to assist the local administration in navigating through these uncertainties and setting ambitious climate and energy targets aligned with the goals of the Paris Agreement and sustainable developments. The research explores how model based scenario analysis can be improved to identify a set of relevant pathways that the municipalities can adopt by employing system analysis, energy system optimization, and scenario analysis. The study focuses on Gällivare municipality in Northern Sweden and employs the TIMES-City model to develop the energy system model of the municipality (RQ1). To identify relevant scenarios for local energy transition a framework for developing "Glocal" scenarios has been established (RQ2). These glocal scenarios incorporate global, national, and local socioeconomic trends into a coherent narrative and provide a more holistic and realistic view of potential future pathways (Paper 2). Additionally, a set of SDG indicators for evaluating the sustainability of different scenarios has been developed and applied in the model (RQ3, Paper 3). While the study focuses on Gällivare, the "glocal" scenario framework and SDG indicators developed in this research can be utilized by municipalities across the globe for identifying their climate and energy targets.

The global increase in demand for wood products, calls for a more sustainable management of forests to optimize both the production of wood and the conservation of forest biodiversity. In this paper, we evaluate the status and future trends of forest birds in Central European forests, assuming different forest management scenarios that to a varying degree respond to the demand for wood production. To this end, we use niche models (Boosted Regression Trees and Generalized Linear Models) to model the responses of 15 forest bird species to predictors related to forest stand (e.g., stand volume of specific tree species) and landscape structure (e.g., percentage cover), and to climate (bioclimatic variables). We then define five distinct forest management scenarios, ranging from set-aside to productivity-driven scenarios, project them 100 years into the future, and apply our niche models into these scenarios to assess the birds’ responses to different forest management alternatives. Our models show that the species’ responses to management vary reflecting differences in their ecological niches, and consequently, no single management practice can benefit all species if applied across the whole landscape. Thus, we conclude that in order to promote the overall forest bird species richness in the study region, it is necessary to manage the forests in a multi-functional way, e.g., by spatially optimizing the management practices in the landscape.

Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs.

Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorical accident and traffic observation databases. Hereby, the fusion is demonstrated in two use cases, each featuring several fusion methods. In use case 1, a synthetic database was generated out of two accident data samples, whereby 78.7% of the original values could be estimated correctly by a random forest classifier. The same fusion using distance-hot-deck reproduced only 67% of the original values, but better preserved the marginal distributions. A real-world application is illustrated in use case 2, where accident data was fused with over 23,000 car trajectories at one intersection in Germany. We could show that SM is applicable to fuse categorical traffic databases. In future research, the combination of hotdeck- methods and machine learning classifiers needs to be further investigated.

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