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Three-dimensional photoluminescence imaging of threading dislocations in GaN by sub-band optical excitation
Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning
Upconverting microgauges reveal intraluminal force dynamics in vivo
Measuring four facets of emotion beliefs in Germany: A German-language adaptation of the EBQ and its comparability across gender and different emotion abilities
Adaptive emotion regulation, involving the modulation of positive and negative emotions based on goals, is a crucial function for a person’s mental health and general well-being. Factors influencing successful emotion regulation include beliefs about emotions, such as the controllability and usefulness of emotions. The Emotion Beliefs Questionnaire (EBQ) was developed to assess these beliefs and has shown promise in predicting emotion regulation and psychopathology across different countries. This study aims to advance EBQ’s generalizability in measuring emotion beliefs by examining the scale’s different validities for a developed German version. In a German sample of 348 respondents, we show the scale’s factorial and broader construct validity as well as its factors’ reliability. Notably, we demonstrate that the German EBQ is mostly strictly measurement invariant across central sociodemographic variables like age and gender. Interestingly, we also find the scale to be robust across different levels of other psychological constructs such as emotional reactivity and efficacy.
Comparison of the growth, fruit quality and physiological characteristics of cucumber fertigated by three different nutrient solutions in soil culture and soilless culture systems
The hidden role of heterotrophic bacteria in early carbonate diagenesis
AbstractMicrobial impacts on early carbonate diagenesis, particularly the formation of Mg-carbonates at low temperatures, have long eluded scientists. Our breakthrough laboratory experiments with two species of halophilic aerobic bacteria and marine carbonate grains reveal that these bacteria created a distinctive protodolomite (disordered dolomite) rim around the grains. Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) confirmed the protodolomite formation, while solid-state nuclear magnetic resonance (NMR) revealed bacterial interactions with carboxylated organic matter, such as extracellular polymeric substances (EPS). We observed a significant carbon isotope fractionation (average δ13C = 11.3‰) and notable changes in Mg/Ca ratios throughout the experiments. Initial medium δ13C was − 18‰, sterile sediments were at 2‰ (n = 12), bacterial-altered sediments were − 6.8‰ (n = 12), and final medium δ13C was − 4.7‰. These results highlight the role of bacteria in driving organic carbon sequestration into Mg-rich carbonates and demonstrate the utility of NMR as a tool for detecting microbial biosignatures. This has significant implications for understanding carbonate diagenesis (dissolution and reprecipitation), climate science, and extraterrestrial research.
Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau
Direct fabrication of lasers on silicon suggests solution to chip-production problem
Global prioritization schemes vary in their impact on the placement of protected areas
In response to global declines in biodiversity, many global conservation prioritization schemes were developed to guide effective protected area establishment. Protected area coverage has grown dramatically since the introduction of several high-profile biodiversity prioritization schemes, but the impact of such schemes on protected area establishment has not been evaluated. We used matching methods and a Before-After Control-Impact causal analysis to evaluate the impact of two key prioritization schemes—Biodiversity Hotspots and Last of the Wild—representing examples of the reactive and proactive ends of the prioritization spectrum. We found that Last of the Wild had a positive impact on the rate of protection in its identified priority areas, but Biodiversity Hotspots did not. Because Biodiversity Hotspots are in or near human-dominated landscapes, this scheme may have been unable to overcome biases towards protecting areas with little human pressure. In contrast, Last of the Wild aligned with the tendency to protect areas far from high human use and thus with lower implementation costs, and so received greater uptake. Stronger links between large-scale prioritizations and more locally driven implementation of area-based conservation, as well as other forms of conservation action, are needed to overcome practical constraints and effectively protect biodiversity on an increasingly human-dominated planet.
Recovery of carbon fiber from carbon fiber reinforced plastics using alkali molten hydroxide
Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development
Transaxillary vs. Transsubclavian Gasless endoscopic thyroidectomy approaches for papillary thyroid cancer
A pulsar-like polarization angle swing from a nearby fast radio burst
An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks
The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks. This study proposed a novel approach using the AWID 3 dataset for cyberattack detection. The retained features were analyzed to assess their transferability, creating a lightweight and cost-effective model. A decision tree with a recursive feature elimination method was implemented for the extraction of the reduced features subset, and an additional feature wlan_radio.signal_dbm was used in combination with the extracted feature subset. Several deep learning and machine learning models were implemented, where DT and CNN achieved promising classification results. Further, feature transferability and generalizability were evaluated, and their detection performance was analyzed across different network versions where CNN outperformed other classification models. The practical implications of this research are crucial for the secure automation of wireless intrusion detection frameworks and tools in personal and enterprise paradigms.