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Extreme weather events and global climate change have exacerbated the problem of evaporation rates. Thus, accurately predicting soil moisture evaporation rates affecting soil cracking becomes crucial. However, less is known about how novel feature engineering techniques and machine- learning predictions may account for estimating the soil moisture evaporation rate. This research focuses on predicting the evaporation rate of soil using machine learning (ML) models. The dataset comprised twenty-one ground-based parameters, including temperature, humidity, and soil-related features, used as features to predict evaporation potential. To tackle the high number of features and potential uncorrelated features, a novel guided backpropagation-based feature selection technique was developed to rank the most relevant features. The top-10 features, highly correlated with evaporation rate, were selected for ML model input, alongside the top-5 and all features. Several ML models, including multiple regression (MR), K-nearest neighbor (KNN), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg), random forest (RF), and a novel K-Nearest Oracles (KNORA) ensemble, were constructed for the purpose of forecasting the evaporation rate. The average error of these models was assessed using the root mean squared error (RMSE). Experimental results showed that the KNORA ensemble model performed the best, achieving a 7.54 mg/h RMSE in testing with the top-10 features. MLP was followed closely by a 25.1 mg/h RMSE in the same testing. An empirical model using all features showed a higher RMSE of 1319.1 mg/h, indicating the superiority of the ML models for accurate evaporation rate predictions. We highlight the implications of our results for climate-induced soil cracking in the real world. [ABSTRACT FROM AUTHOR]

Background To assess the effect of health information on immunisation uptake in rural India, we conducted an individually randomised controlled trial of health information messages targeting the mothers of unvaccinated or incompletely vaccinated children through home visits in rural Uttar Pradesh, India. Methods and findings The study tested a brief intervention that provided mothers face-to-face with information on the benefits of the tetanus vaccine. Participants were 722 mothers of children aged 0-36 months who had not received 3 doses of diphtheria-pertussis-tetanus (DPT) vaccine (DPT3). Mothers were randomly assigned in a ratio of 1:1:1 to 1 of 3 study arms: mothers in the first treatment group received information framed as a gain (e.g., the child is less likely to get tetanus and more likely to be healthy if vaccinated), mothers in the second treatment group received information framed in terms of a loss (e.g., the child is more likely to get tetanus and suffer ill health if not vaccinated), and the third arm acted as a control group, with no information given to the mother. Surveys were conducted at baseline (September 2015) and after the intervention (April 2016). The primary outcome was the proportion of children who had received DPT3 measured after 7 months of follow-up. The analysis was by intention to treat. A total of 16 (2.2%) participants were lost to follow-up. The coverage of DPT3 was 28% in the control group and 43% in the pooled information groups, giving a risk difference of 15 percentage points (95% CI: 7% to 22%, p < 0.001) and a relative risk of 1.52 (95% CI: 1.2 to 1.9, p < 0.001). The information intervention increased the rate of measles vaccination by 22 percentage points (risk difference: 22%, 95% CI: 14% to 30%, p < 0.001; relative risk: 1.53, 95% CI: 1.29 to 1.80) and the rate of full immunisation by 14 percentage points (risk difference: 14%, 95% CI: 8% to 21%, p < 0.001; relative risk: 1.72, 95% CI: 1.29 to 2.29). It had a large positive effect on knowledge of the causes, symptoms, and prevention of tetanus but no effect on perceptions of vaccine efficacy. There was no difference in the proportion of children with DPT3 between the group that received information framed as a loss and the group that received information framed as a gain (risk difference: 4%, 95% CI: -5% to 13%; p = 0.352; relative risk: 1.11, 95% CI: 0.90 to 1.36). The cost per disability-adjusted life year averted of providing information was US$186, making the intervention highly cost-effective with respect to the WHO-recommended threshold of once the gross domestic product per capita (US$793 in the case of Uttar Pradesh). Key study limitations include the modest sample size for this trial, limiting power to detect small differences in the framing of information, and the potential for contamination among households. Conclusions Providing mothers of unvaccinated/incompletely vaccinated children with information on tetanus and the benefits of DPT vaccination substantially increased immunisation coverage and was highly cost-effective. The framing of the health information message did not appear to matter. Trial registration The trial is registered with ISRCTN, number ISRCTN84560580., Author(s): Timothy Powell-Jackson 1,*, Camilla Fabbri 1, Varun Dutt 2, Sarah Tougher 1, Kultar Singh 2 Introduction An estimated 5.9 million children die each year globally, of which 1.2 million [...]

Objective: We aim to learn about the cognitive mechanisms governing the decisions of attackers and defenders in cybersecurity involving intrusion detection systems (IDSs). Background: Prior research has experimentally studied the role of the presence and accuracy of IDS alerts on attacker's and defender's decisions using a game-theoretic approach. However, little is known about the cognitive mechanisms that govern these decisions. Method: To investigate the cognitive mechanisms governing the attacker's and defender's decisions in the presence of IDSs of different accuracies, instance-based learning (IBL) models were developed. One model (NIDS) disregarded the IDS alerts and one model (IDS) considered them in the instance structure. Both the IDS and NIDS models were trained in an existing dataset where IDSs were either absent or present and they possessed different accuracies. The calibrated IDS model was tested in a newly collected test dataset where IDSs were present 50% of the time and they possessed different accuracies. Results: Both the IDS and NIDS models were able to account for human decisions in the training dataset, where IDS was absent or present and it possessed different accuracies. However, the IDS model could accurately predict the decision-making in only one of the several IDS accuracy conditions in the test dataset. Conclusions: Cognitive models like IBL may provide some insights regarding the cognitive mechanisms governing the decisions of attackers and defenders in conditions not involving IDSs or IDSs of different accuracies. Application: IBL models may be helpful for penetration testing exercises in scenarios involving IDSs of different accuracies. [ABSTRACT FROM AUTHOR]

To investigate how differing amounts of experiential feedback and feedback's availability in an interactive simulation tool influences people's decision-making against landslide risks. Feedback via simulation tools is likely to help people improve their decisions against disasters; however, currently little is known on how differing amounts of experiential feedback and feedback's availability in simulation tools influences people's decisions against landslides. We tested the influence of differing amounts of experiential feedback and feedback's availability on people's decisions against landslide risks in an Interactive Landslide Simulation (ILS) tool. In an experiment, in high-damage conditions, the probabilities of damages to life and property due to landslides were 10-times higher than those in the low-damage conditions. In feedback-present condition, experiential feedback was provided in numeric, text, and graphical formats in ILS. In feedback-absent conditions, the probabilities of damages were described; however, there was no experiential feedback present. Investments were greater in conditions where experiential feedback was present and damages were high compared to conditions where experiential feedback was absent and damages were low. Furthermore, only high-damage feedback produced learning in ILS. Experience gained in ILS enables people to improve their decision-making against landslide risks. Simulation tools seem appropriate for landslide risk communication and for performing what-if analyses. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine how monetary motivations influence decision making of humans performing as security analysts and hackers in a cybersecurity game.Background: Cyberattacks are increasing at an alarming rate. As cyberattacks often cause damage to existing cyber infrastructures, it is important to understand how monetary rewards may influence decision making of hackers and analysts in the cyber world. Currently, only limited attention has been given to this area.Method: In an experiment, participants were randomly assigned to three between-subjects conditions ( n = 26 for each condition): equal payoff, where the magnitude of monetary rewards for hackers and defenders was the same; rewarding hacker, where the magnitude of monetary reward for hacker's successful attack was 10 times the reward for analyst's successful defense; and rewarding analyst, where the magnitude of monetary reward for analyst's successful defense was 10 times the reward for hacker's successful attack. In all conditions, half of the participants were human hackers playing against Nash analysts and half were human analysts playing against Nash hackers.Results: Results revealed that monetary rewards for human hackers and analysts caused a decrease in attack and defend actions compared with the baseline. Furthermore, rewarding human hackers for undetected attacks made analysts deviate significantly from their optimal behavior.Conclusions: If hackers are rewarded for their undetected attack actions, then this causes analysts to deviate from optimal defend proportions. Thus, analysts need to be trained not become overenthusiastic in defending networks.Application: Applications of our results are to networks where the influence of monetary rewards may cause information theft and system damage. [ABSTRACT FROM AUTHOR]

Previous research has shown that multitasking can have a positive or a negative influence on driving performance. The aim of this study was to determine how the interaction between driving circumstances and cognitive requirements of secondary tasks affect a driver's ability to control a car. We created a driving simulator paradigm where participants had to perform one of two scenarios: one with no traffic in the driver's lane, and one with substantial traffic in both lanes, some of which had to be overtaken. Four different secondary task conditions were combined with these driving scenarios. In both driving scenarios, using a tablet resulted in the worst, most dangerous, performance, while passively listening to the radio or answering questions for a radio quiz led to the best driving performance. Interestingly, driving as a single task did not produce better performance than driving in combination with one of the radio tasks, and even tended to be slightly worse. These results suggest that drivers switch to internally focused secondary tasks when nothing else is available during monotonous or repetitive driving environments. This mind wandering potentially has a stronger interference effect with driving than non-visual secondary tasks. [ABSTRACT FROM AUTHOR]

It is well-known that even toddlers are able to manipulate tools in an appropriate manner according to their physical properties. The ability of children to make novel tools in order to solve problems is, however, surprisingly limited. In adults, mechanical problem solving (MPS) has been proposed to be supported by "technical reasoning skills," which are thought to be involved in every situation requiring the use of a tool (whether conventional or unusual). The aim of this study was to investigate the typical development of real tool use (RTU) skills and its link with technical reasoning abilities in healthy children. Three experimental tasks were adapted from those used with adults: MPS (three different apparatus), RTU (10 familiar tool-object pairs), and functional knowledge (FK; 10 functional picture matching with familiar tools previously used). The tasks were administered to 85 healthy children divided into six age groups (from 6 to 14 years of age). The results revealed that RTU (p = 0.01) and MPS skills improve with age, even if this improvement differs according to the apparatus for the latter (p < 0.01 for the Hook task and p < 0.05 for the Sloping task). Results also showed that MPS is a better predictor of RTU than FK, with a significant and greater weight (importance weight: 0.65; Estimate ± Standard Error: 0.27 ± 0.08). Ours findings suggest that RTU and technical reasoning develop jointly in children, independently from development of FK. In addition, technical reasoning appears partially operative from the age of six onward, even though the outcome of these skills depends of the context in which they are applied (i.e., the type of apparatus). [ABSTRACT FROM AUTHOR]

Systematicity is a property of cognition where capacity for certain cognitive abilities implies capacity for certain other (structurally related) cognitive abilities. This property is thought to derive from a capacity to represent/process common structural relations between constituents of cognizable entities, however, systematicity may not always materialize in such admissible contexts. A theoretical challenge is to explain why systematicity fails to materialize in contexts that allow the realization (e.g., by induction) of common structure (universal construction). We hypothesize that one cause of failure arises when the potential gain afforded by induction of common structure is overshadowed by the immediate benefit of learning the task as independent stimulus-response associations. This hypothesis was tested in an experiment that required learning two series of pair maps that involved products (universal construction), or non-products (control) of varied size: the number of unique cue/target elements (three to six) constituting pairs. Each series was learned in either ascending or descending order of size. Only performance on the product series was affected by order: systematicity was obtained universally in the descend group, but only on large sets in the ascend group, as revealed by the significant order size interaction for errors in the product condition, F(3, 87) = 3.38, p < 0.05. Smaller maps are more easily learned without inducing the common product structure, which is more readily observable with larger maps: larger maps provide more evidence for relationships between stimulus dimensions that facilitate the discovery of the common structure. The new challenge, then, is to explain the systematic learnability of stimulus-response maps, i.e., second-order systematicity. [ABSTRACT FROM AUTHOR]