Vicious and virtuous relationships between procrastination and emotions: an investigation of the reciprocal relationship between academic procrastination and learning-related anxiety and hope

Although cross-sectional studies depict (negative) emotions as both antecedents and consequences of trait procrastination, longitudinal studies examining reciprocal relationships between procrastination and emotions are scant. Yet, investigating reciprocal relationships between procrastination and emotions within long-term frameworks can shed light on the mechanisms underlying these relationships. Additionally, the role of positive emotions concerning procrastination is largely unattended to in the procrastination–emotion research; albeit, this perspective can inform preventive and intervention measures against procrastination. In the present study, we explored reciprocal associations between trait academic procrastination on the one hand and trait-like learning-related anxiety and hope on the other hand over one semester. Overall, N = 789 students in German universities participated in a three-wave online panel study. Participants responded to questions on academic procrastination as well as learning-related anxiety and hope at the beginning (T1), middle (T2), and end (T3) of the lecture period of the semester in approximately 6-week measurement intervals. A latent cross-lagged panel model was used to test the hypotheses. After accounting for autoregressive effects, our results showed that academic procrastination at T1 positively predicted learning-related anxiety at T2. In contrast, academic procrastination at T1 negatively predicted learning-related hope at T2, which in turn negatively predicted academic procrastination at T3. Our results highlight positive emotions (e.g., hope) as also significant factors for procrastination and suggest them as possible “protective factors” against procrastination. Boosting positive emotions as part of interventions against procrastination could potentially help reduce the tendency to procrastinate

Study satisfaction among university students during the COVID-19 pandemic: longitudinal development and personal-contextual predictors

The COVID-19 pandemic challenges the well-being and academic success of many students. Yet, little is known about students’ study satisfaction during the COVID-19 pandemic, a multilayered construct which accounts for students’ subjective cognitive well-being and academic success. Besides, previous studies on study satisfaction are mostly cross-sectional and hardly consider the distinct subdimensions of this construct. Therefore, our main goal in this study was to shed light on the understudied development of the subdimensions of study satisfaction (i.e., satisfaction with study content, conditions of studying, and coping with study-related stress) in two semesters amid the COVID-19 pandemic. Additionally, we examined how particular personal (i.e., gender, age, GPA, intrinsic motivation, motivational cost, and academic procrastination) and contextual (i.e., loneliness) factors are related to these subdimensions. We conducted two panel studies with convenience and purposeful samples of university students in Germany (Nstudy1 = 837; Nstudy2 = 719). Participants responded online to questions on each of the subdimensions of study satisfaction at the beginning, middle, and end of each semester but responded to measures of personal and contextual factors only at the beginning of each semester. In both studies, manifest growth curve models indicated a decrease in all subdimensions of study satisfaction as the semester progressed. Generally, gender (male) and intrinsic motivation were positive predictors but age (younger students), motivational cost, and loneliness were negative predictors of different subdimensions of study satisfaction – particularly satisfaction with study content. Overall, motivational costs and loneliness were the most consistent predictors of all subdimensions of study satisfaction across both studies. Our findings provide support for the understanding that study satisfaction could diminish in the face of challenging situations such as in this pandemic. The present study also highlights certain personal and contextual factors that relate to study satisfaction and calls for intensive research into the multidimensional construct of study satisfaction

A Site Wide Perspective on Uranium Geochemistry at the Hanford Site

Uranium (U) is an important risk-driving contaminant at the Hanford Site. Over 200,000 kg have been released to the vadose zone over the course of site operations, and a number of vadose zone and groundwater plumes containing the uranyl cation [UO22+, U(VI)] have been identified. U is recognized to be of moderate-to-high mobility, conditions dependent. The site is currently making decisions on several of these plumes with long-lasting implications, and others are soon to come. Uranium is one of nature’s most intriguing and chemically complex elements. The fate and transport of U(VI) has been studied over the long lifetime of the Hanford Site by various contractors, along with the Pacific Northwest National Laboratory (PNNL) and its collaborators. Significant research has more recently been contributed by the national scientific community with support from the U.S. Department of Energy’s (DOE) Office of Science through its Environmental Remediation Sciences Division (ERSD). This report represents a first attempt to integrate these findings into a cohesive view of the subsurface geochemistry of U at the Hanford Site. The objective is to inform all interested Hanford parties about the in-ground inventory of U and its geochemical behavior. This report also comments on the prospects for the development of a robust generic model to more accurately forecast future U(VI) migration at different Hanford waste sites, along with further research necessary to reach this goal

The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

International audienceForest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (aGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. aGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. all plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities

SyML: Guiding Symbolic Execution Toward Vulnerable States Through Pattern Learning

Exploring many execution paths in a binary program is essential to discover new vulnerabilities. Dynamic Symbolic Execution (DSE) is useful to trigger complex input conditions and enables an accurate exploration of a program while providing extensive crash replayability and semantic insights. However, scaling this type of analysis to complex binaries is difficult. Current methods suffer from the path explosion problem, despite many attempts to mitigate this challenge (e.g., by merging paths when appropriate). Still, in general, this challenge is not yet surmounted, and most bugs discovered through such techniques are shallow. We propose a novel approach to address the path explosion problem: A smart triaging system that leverages supervised machine learning techniques to replicate human expertise, leading to vulnerable path discovery. Our approach monitors the execution traces in vulnerable programs and extracts relevant features - register and memory accesses, function complexity, system calls - to guide the symbolic exploration. We train models to learn the patterns of vulnerable paths from the extracted features, and we leverage their predictions to discover interesting execution paths in new programs. We implement our approach in a tool called SyML, and we evaluate it on the Cyber Grand Challenge (CGC) dataset - a well-known dataset of vulnerable programs - and on 3 real-world Linux binaries. We show that the knowledge collected from the analysis of vulnerable paths, without any explicit prior knowledge about vulnerability patterns, is transferrable to unseen binaries, and leads to outperforming prior work in path prioritization by triggering more, and different, unique vulnerabilities

Screening of Potential Remediation Methods for the 200-ZP-1 Operable Unit at the Hanford Site

A screening-level evaluation of potential remediation methods for application to the contaminants of concern (COC) in the 200-ZP-1 Operable Unit at the Hanford Site was conducted based on the methods outlined in the Guidance for Conducting Remedial Investigations and Feasibility Studies under CERCLA Interim Final. The scope of this screening was to identify the most promising remediation methods for use in the more detailed analysis of remediation alternatives that will be conducted as part of the full feasibility study. The screening evaluation was conducted for the primary COC (potential major risk drivers). COC with similar properties were grouped for the screening evaluation. The screening evaluation was conducted in two primary steps. The initial screening step evaluated potential remediation methods based on whether they can be effectively applied within the environmental setting of the 200-ZP-1 Operable Unit for the specified contaminants. In the second step, potential remediation methods were screened using scoping calculations to estimate the scale of infrastructure, overall quantities of reagents, and conceptual approach for applying the method for each defined grouping of COC. Based on these estimates, each method was screened with respect to effectiveness, implementability, and relative cost categories of the CERCLA feasibility study screening process defined in EPA guidance