Why Do We Still Not Know How to Prevent Firefighter Entrapments?—Thoughts and Observations from a Few Perplexed Fire Practitioners

Wildland firefighters continue to die in the line of duty. Flammable landscapes intersect with bold but good-intentioned doers and trigger entrapment—a situation where personnel is unexpectedly caught in fire behaviour-related, life-threatening positions where planned escape routes or safety zones are absent, inadequate, or compromised. We often document, share and discuss these stories, but many are missed, especially when the situation is a near miss. Entrapment continues to be a significant cause of wildland firefighter deaths. Why do we still not know how to prevent them? We review a selection of entrapment reports courtesy of the Wildland Fire Lessons Learned Centre (WFLLC) and focus on human factors involved in entrapment rather than the specifics of fire behaviour and the environment. We found that in order for operational supervisors to make more informed strategic and tactical decisions, a more holistic and complete trend analysis is necessary of the existing database of entrapment incidents. Analysis of the entrapment data would allow training to include a more fulsome understanding of when suppression resources are applying strategies and tactics that might expose them to a higher likelihood of entrapment. Operational supervisors would make more informed decisions as to where and when to deploy resources in critical situations in order to reduce the exposure to unnecessary risk of entrapment.

Water management in wheat using non-traditional techniques

Water is one of the most limiting resources for agricultural production. Due to uneven distribution of rainfall, supplemental irrigation is often required to produce sustainable yield level. Timing and frequency of irrigation is one of the most important tactical decisions, which a farmer has to make to maximize profit from limited water availability. Computer based dynamic simulation models have the capability to assess management options under different environments to help in decision making. In this study, CRESS-Wheat Model V-3.5 has been utilized to quantify the optimum utilization of limited water for popular wheat genotypes of NW India for operational use in Agrometeorological Advisory services with routinely measured weather parameters.

Validation of CERES-Rice v3.5 under the climate of Andhra Pradesh state, India

Crop growth simulation models, properly validated against experimental data have the potential for tactical and strategic decision making in agriculture. Such validated models can also take the information generated through site specific experiments and trials to other sites and years. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, field experiments were conducted at Rajendranagar (17°19' N, 78°23' E; 542.3 m amsl) during 1994-97 for three popular varieties of rice viz. Sambamasuri, Rajavadlu and Tellahamsa under irrigated conditions and data collected. Genetic coefficients required for running the CERES-Rice v3.5 model were calculated and the performance of the model under the climate of the area was evaluated. The results of the study show that the model simulations of date of flowering for Sambamasuri, Rajavadlu and Tellahamsa were within an average error of 6.2, 5.7 and 6.7 days respectively. Similar errors in predictions of physiological maturity dates were 7.6, 6.7 and 7.2 days. The error in grain yield predictions by the model averaged at 7.9%, 8.3%, and 5.7% respectively for the three crop varieties. These results indicate that the CERES Rice v3.5 model is capable of prediction of grain yield and phenological development of the crop in the climatic conditions of Andhra Pradesh with reasonable accuracy and hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions

Predictable operations are the basis of efficient air traffic management. In this context, accurately estimating the arrival time to the destination airport is fundamental to make tactical decisions about an optimal schedule of landing and take-off operations. In this paper, we evaluate different deep learning models based on LSTM architectures for predicting estimated time of arrival of commercial flights, mainly using surveillance data from OpenSky Network. We observed that the number of previous states of the flight used to make the prediction have great influence on the accuracy of the estimation, independently of the architecture. The best model, with an input sequence length of 50, has reported a MAE of 3.33 min and a RMSE of 5.42 min on the test set, with MAE values of 5.67 and 2.13 min 90 and 15 min before the end of the flight, respectively.

Researching the Process of Using Special Knowledge in Criminal Activities as a Condition for the Objectivity of the Cognition and Qualification of Criminal Realitys Manifestations

The author researches the process of using special knowledge in criminal activities while taking into account the contents of the object of criminalistics, and the essence of the criminalistic culture of law enforcement. Examples are presented to illustrate the features of using specialized knowledge in the criminal sphere. The author analyzes the conditions for defining the role of a concrete person, acting as a specialist, in preparing and committing a crime. The specific features of the theoretical platform of criminal law sciences are singled out. Priorities in the classification and qualification spheres of criminal law knowledge are established. The author draws attention to the identification, prediction and diagnostic essence of criminal knowledge and describes the connection between the nature of a lawyer’s cognitive activities and the qualification boundaries of criminal law actions. It is noted that the process of cognizing the criminal reality is dialectic. The author shows the primacy of the substantive understanding of the act of crime while taking into account the criminal law and procedure benchmarks for working out methodological and tactical decisions in the process of crime investigation.

Rice phenology and growth simulation using CERES-Rice model under the agro-climate of upper Brahmaputra valley of Assam

Crop growth simulation models, properly validated against experimental data have the potential for facilitating strategic decision making in agriculture. Such validated models can also make use of the information generated for site specific experiments and trials to other sites and for different time durations. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, data from seven field experiments conducted at Jorhat (26° 47' N, 94°12' E; 87 m amsl) during 1998-2005 for long duration rice cultivar Ranjit grown under rainfed conditions were collected. Genetic coefficients required for running the CERES-Rice v4.5 model were derived and the performance of the model under the climate of upper Brahmaputra valley was evaluated. These results indicate that the CERES Rice v4.5 model is capable of estimating growth stages and grain yield of rice cultivar Ranjit in the climatic conditions of upper Brahmaputra valley with reasonable accuracy. Hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

Incorporating Environmental Perspective in Integrated Strategic-Tactical Economic Optimization Model of Biomass-to-Biofuel Supply Chain—A Real Case Study in Ethiopia

Several optimization models, which consider economic and environmental perspectives, have been developed recently to support the sustainable biomass-to-biofuel supply chain (BBSC) design. All of the economic-environmental optimization models rely on solving long-term planning problems with a conventional hierarchical approach, where tactical decisions are made based on the optimal strategic decisions from the strategic-level model, despite it arousing non-optimal solutions. Moreover, almost all of them have used non-monetary-based environmental indicators, which result in difficulties with clarity when comparing with economic objectives. Therefore, in this work, an effort is made to develop a more reliable planning strategy that offers optimal strategic and tactical decisions simultaneously and maximizes the economic and environmental benefits. Furthermore, the environmental performance of the BBSC has been assessed in terms of monetary value by adopting an ecocost approach after performing an LCA on the system. The integrated model is applied in the real biofuel sector of Ethiopia to optimize the country’s bioethanol and biodiesel supply chain over a 20-year horizon. Despite the abrupt rise in the model size, with it being a real countrywide case with many variables and large quantities of data, an alternative semi-heuristic method that offers a feasible solution to the multi-objective problem is provided.

Do I stay or do I go? Shifts in perch use by lizards during morning twilight suggest anticipatory behaviour

Anticipatory behaviour is the expectation of a near-future event based on information processed in the past and influences an animal's tactical decisions, particularly when there are significant fitness consequences. The grass lizard ( Takydromus viridipunctatus ) perches on blades of grass at night which likely reduces the probability of predation by terrestrial predators such as snakes, rodents and shrews. During twilight (starting 30 min before sunrise), they move from above the grass to within grass clumps and this is thought to afford the lizard protection while reducing detection by avian predators. Here, we examined how lizards shift their behaviour as a function of visual detectability to their primary predator, the cattle egret ( Bubulcus ibis ). We show that the lizards shift from their perch site during twilight at the earliest time at which egrets depart communal roosts. At the same time, visual modelling shows a dramatic increase in the detectability of the lizards to the visual system of egrets. Therefore, anticipatory behaviour in response to environmental cues acts to reduce predation risk as lizards become more conspicuous and predators become more active. Grass lizard anticipatory behaviour appears to be finely tuned by natural selection to adjust to temporal changes in predation risk.

Integrating maintenance strategies in autonomous production control using a cost-based model

<p class="Abstract">Autonomous production control (APC) is able to deal with challenges, inter alia, high delivery accuracy, shorter planning horizons, increasing product and process complexity, and frequent changes. However, several state-of-the-art approaches do not consider maintenance factors contributing to operational and tactical decisions in production planning and control. The incomprehensiveness of the decision models and related decision support tools cause inefficiency in production planning and thus lead to a low acceptance in the manufacturing enterprises. To overcome this challenge, this paper presents a conceptual cost-based model for integrating different maintenance strategies in autonomous production control. The model provides relevant decision aspects and a cost function for different maintenance strategies using on a market-based approach. The present work thus makes a positive contribution to cope with the high demands on flexibility and response times in planning while at the same time ensuring high plant productivity.</p>

Competitive Rationales: Beneath the Surface of Competitive Behavior

Competitive dynamics research has focused on studying whether rivals are able and likely to carry out competitive actions, typically by examining indirect reasons such as characteristics of the actions themselves, the firms involved, or the competitive context. We explore why rivals initiate a specific competitive action at a particular time and situation. Drawing from the philosophy of action literature, we introduce the concept of competitive rationales to examine the primary reasons that cause tactical actions. Given the rapid exchanges characterizing tactical competitive dynamics, we conducted an inductive, multicase study to explore the reasons behind over 800 discrete tactical decisions carried out by 9 professional basketball coaches during 15 basketball games. To garner insight, we develop a conceptual framework revealing their types and scope. Even during intense head-to-head rivalry, most rationales were not rivalrous but were instead organizational—to optimize resource use, strategic consistency, and reputation—or social—to manage relationships. Moreover, the three main types of rationales varied in scope, extending beyond immediate competitive situations and rivals to address longer term, strategic outcomes, and assorted stakeholders. Thus, our analysis reveals these rationales to be complex and potentially difficult for rivals to decipher. It also recasts each component of the dominant awareness-motivation-capability (AMC) model of rivalry, suggesting that awareness is challenged by subtle rationales, motivation drives not only action but also forbearance, and capability is both a requirement and product of action.