Image Driven Hydrological Components Based Fish Habitability Modeling in Riparian Wetlands Triggered by Damming

Assessing fish habitability in pursuance of damming for some selected fishes in wetland of Indo-Bangladesh barind tract using hydrological ingredients like hydro-period, water depth, and water presence consistency is major focus of the present study. Rule based decision tree modeling has been applied for integrating aforesaid hydrological parameters to find out habitat suitability for some selected fishes like carp fishes, shrimps, tilapia and cat fishes both for pre-dam and post-dam periods. From this work it is highlighted that damming has accelerated the rate of wetland deterioration in forms of hydrological flow alteration i.e. inconsistency in water presence has increased, hydro-duration became shortened and water depth has attenuated. From the model it is very clear that a small proportion area was considered to be good fish habitat (16.54–39.90%) in pre-dam period, but after damming almost all parts have become least suitable for fish habitability. Field survey has confirmed that fishing quantity, growing rate of fishes was higher in pre-dam situation but it is reduced gradually during post-dam period. Image driven hydrological parameters to model fish habitability is a new approach but important parameters like food availability, water quality parameters could also be incorporated in order to get better result.

Nighttime sleep benefits the prospective component of prospective memory

Studies suggest that sleep benefits event-based prospective memory, which involves carrying out intentions when particular events occur. Prospective memory has a prospective component (remembering that one has an intention), and a retrospective component (remembering when to carry it out). As effects of sleep on retrospective memory are well established, the effect of sleep on prospective memory may thus be due exclusively to an effect of sleep on its retrospective component. Therefore, the authors investigated whether nighttime sleep improves the prospective component of prospective memory, or a retrospective component, or both. In a first session, participants performed an event-based prospective-memory task (that was embedded in an ongoing task) 3 minutes after forming an intention and, in a second session, 12 hours after forming an intention. The sessions were separated by either nighttime sleep or daytime wakefulness. The authors disentangled prospective-memory performance into its retrospective and prospective components via multinomial processing tree modeling. There was no effect of sleep on the retrospective component, which may have been due to a time-of-day effect. The prospective component, which is the component unique to prospective memory, declined less strongly after a retention interval filled with sleep as compared with a retention interval filled with wakefulness. A hybrid interaction suggested that refreshed attention after sleep may account for this effect, but did not support the consolidation of the association between the intention and its appropriate context as a mechanism driving the effect.

Inerrogating Variables Affecting Consumers' EV Purchasing Decision

This paper takes a multi-step approach to answer the research question “What are the factors that affect the consumers’ EV purchasing decision-making process and how do they affect it?” In order to answer this question, this paper studies consumer data from the last 15 years. Using Hierarchical cluster analysis, this paper shows how the importance of the factors changes over time. A predictive model has been developed using Ethnographic Decision tree Modeling (EDTM) for the decision-making process of the owners of the 4 top selling EV. The top selling EVs includes models of Nissan Leaf, Tesla, Chevy Volt, and Toyota Prius, from year 2009 to 2014. This EDTM model indicates that while consumers prefer variables such as gas requirement, performance and mile coverage over other variables when deciding to purchase an EV, when given several options of EV they consider other variable such as the environment, brand and country of vehicle production to be more important.

Inerrogating Variables Affecting Consumers' EV Purchasing Decision

This paper takes a multi-step approach to answer the research question “What are the factors that affect the consumers’ EV purchasing decision-making process and how do they affect it?” In order to answer this question, this paper studies consumer data from the last 15 years. Using Hierarchical cluster analysis, this paper shows how the importance of the factors changes over time. A predictive model has been developed using Ethnographic Decision tree Modeling (EDTM) for the decision-making process of the owners of the 4 top selling EV. The top selling EVs includes models of Nissan Leaf, Tesla, Chevy Volt, and Toyota Prius, from year 2009 to 2014. This EDTM model indicates that while consumers prefer variables such as gas requirement, performance and mile coverage over other variables when deciding to purchase an EV, when given several options of EV they consider other variable such as the environment, brand and country of vehicle production to be more important.

Factors Associated With Calling 911 for an Overdose: An Ethnographic Decision Tree Modeling Approach

Objectives. To identify factors that influence when people who use drugs (PWUDs) call 911 for an overdose. Methods. We conducted 45 qualitative interviews and 180 surveys with PWUDs who had recently witnessed overdoses in Southern California from 2017 to 2019. We used conditional inference tree and random forest models to generate and validate a model to predict whether 911 would be called. Results. Our model had good in- (83%) and out-of-sample (84%) predictive accuracy. Three aspects of the social and policy environment influenced calling 911 for an overdose: the effectiveness of response strategies employed, the behavior of other bystanders, and whether the responder believes it is their responsibility to call. Conclusions. Even in the presence of policies that provide some protections, PWUDs are faced with difficult decisions about calling 911 and must weigh their own safety against that of an overdose victim. Potential interventions include strengthening training and safety planning for PWUDs, bolstering protections for PWUDs when they call 911, and separating law enforcement response from emergency medical response to overdoses. (Am J Public Health. Published online ahead of print May 20, 2021: e1–e3. https://doi.org/10.2105/AJPH.2021.306261 )

FRUIT VIBRATION HARVESTING TECHNOLOGY AND ITS DAMAGE. A REVIEW

Manual harvesting of large area fruits is inefficient, which consumes manpower and resources. Mechanized harvesting is the inevitable trend of fruit harvest. Vibration harvesting is one of the important forms in terms of fruit mechanized harvesting. According to the different striking parts of fruit trees, the vibration modes were classified as trunk, crown, and branch types. The harvesting efficiency of fruit is an important index to measure the quality of all fruit harvesting machines. The reduction of fruit damage is considered in the harvesting of vulnerable fruits. In this study, the development of vibration harvesting technologies were studied in terms of vibration mode. The development of fruit damage, harvesting efficiency, and fruit tree modeling were discussed. Finally, the development direction of fruit vibration mechanized harvesting was looked forward. Machinery instead of manpower, fully mechanized harvesting is the inevitable development direction of fruit harvesting.

Faking Detection Improved: Adopting a Likert Item Response Process Tree Model

With the increasing popularity of noncognitive inventories in personnel selection, organizations typically wish to be able to tell when a job applicant purposefully manufactures a favorable impression. Past faking research has primarily focused on how to reduce faking via instrument design, warnings, and statistical corrections for faking. This article took a new approach by examining the effects of faking (experimentally manipulated and contextually driven) on response processes. We modified a recently introduced item response theory tree modeling procedure, the three-process model, to identify faking in two studies. Study 1 examined self-reported vocational interest assessment responses using an induced faking experimental design. Study 2 examined self-reported personality assessment responses when some people were in a high-stakes situation (i.e., selection). Across the two studies, individuals instructed or expected to fake were found to engage in more extreme responding. By identifying the underlying differences between fakers and honest respondents, the new approach improves our understanding of faking. Percentage cutoffs based on extreme responding produced a faker classification precision of 85% on average.