Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments.

Humans are able to adapt to the fast-changing world by estimating statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the computational and neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across 2 experiments, computational modeling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modeling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.

Progress of Keystroke Dynamics in Deception Research.

Traditional polygraph techniques mostly rely on the changes of an individual's physiological indicators, such as electrodermal activity, heart rate, breath, eye movement and function of neural signals and other indicators. They are easily affected by individual physical conditions, counter-tests, external environment and other aspects, and it is difficult to conduct large-scale screening tests based on the traditional polygraph techniques. The application of keystroke dynamics to polygraph can overcome the shortcomings of the traditional polygraph techniques to a large extend, increase the reliability of polygraph results and promote the validity of legal evidence of polygraph results in forensic practice. This paper introduces keystroke dynamics and its application in deception research. Compared with the traditional polygraph techniques, keystroke dynamics can be used with a relatively wider application range, not only for deception research but also for identity identification, network screening and other large-scale tests. At the same time, the development direction of keystroke dynamics in the field of polygraph is prospected.

Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN's achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

Remediation of aniline-contaminated aquifer by combining in-well Rhizobium borbori and circulated groundwater electrolysis.

Aniline has become a common groundwater contaminant due to its wide use as a raw material in agriculture and pharmaceutical products. The current technologies for in situ remediation of aniline in groundwater are limited by the strains deficient in bacterial species, limited oxygen supply, excessive waste gas load and cost. Accordingly, we conducted a laboratory sand tank experiment to remediate groundwater contaminated with aniline by combining circulated groundwater electrolysis and in-well Rhizobium borbori, which was isolated from activated sludge. The results of the experiment indicated that the optimum concentration of aniline for Rhizobium borbori is about 5 mg/L, beyond which the maximum cell density and the highest specific growth rate decreases as the aniline concentration increases. The optimized duration for immobilizing the Rhizobium borbori into the bioreactor is 4-5 days. Though the Rhizobium borbori was strongly inhibited by the high-concentration of aniline, the immobilized bioreactor in the 350 mg/L aniline solution successfully formed biofilm. The aniline volatilization had limited influence on the observation of bioremediation performance, and the combination of circulated groundwater and in-well Rhizobium borbori supplied a steady dose of oxygen to the bioreactor efficiently degrading the entire region between the injection and extraction well. In addition, a numerical model for the sand tank remediation experiment was used to estimate the yield coefficient of oxygen to be 0.484 g/g, which indicates the presence of ammonia nitrogen as by-products; accordingly, a smaller wellbore size as well a higher circulation flow rate and intensity of current are recommended to improve the water quality. Despite the positive outcomes and potential of the newly developed technology to degrade subsurface aniline, parallel experiments should be conducted to estimate the environmental risk of the by-products and explore the controlling mechanisms of each component in this comprehensive system.

Hazards and influence factors of arsenic in the upper pleistocene aquifer, Hetao region, using machine learning modeling.

The Hetao region is one of the regions with the most serious problem of the greatest measured arsenic concentrations in China. The enrichment of arsenic in groundwater may poses a great risk to the health of local residents. A comprehensive understanding of the groundwater quality, spatial distribution characteristics and hazard of the high arsenic in groundwater is indispensable for the sustainable utilization of groundwater resources and resident health. This study selected six environmental factors, climate, human activity, sedimentary environment, hydrogeology, soil, and others, as the independent input variables to the model, compared three machine learning algorithms (support vector machine, extreme gradient boosting, and random forest), and mapped unsafe arsenic to estimate the population that may be exposed to unhealthy conditions in the Hetao region. The results show that nearly half the number of the 605 sampling wells for arsenic exceeded the WHO provisional guide value for drinking water, the water chemistry of groundwater are mainly Na-HCO3-Cl or Na-Mg-HCO3-Cl type water, and the groundwater with excessive arsenic concentration is mainly concentrated in the ancient stream channel influence zone and the Yellow River crevasse splay. The results of factor importance explanation revealed that the sedimentary environment was the key factor affecting the primary high arsenic groundwater concentration, followed by climate and human activities. The random forest algorithm produced the probability distribution of high arsenic groundwater that is consistent with the observed results. The estimated area of groundwater with excessive arsenic reached 38.81 %. An estimated 940,000 people could be exposed to high arsenic in groundwater.

Assessment of aquifer specific vulnerability to total nitrate contamination using ensemble learning and geochemical evidence.

Henan Province's plain area is the granary of China, yet its regional aquifer is being polluted by industrial wastewater, agricultural pesticide, fertilizer and domestic wastewater. In order to safeguard the security of food and drinking water, and in response to the problem of low prediction accuracy caused by the lack of samples and unevenly distributed groundwater monitoring data, we propose a new way to predict the aquifer vulnerability in large areas by rich small-scale data, so as to identify the pollution risks and to address the issue of sample shortage. In small regions with abundant nitrate data, we employed a Random Forest model to screen key impact indicators, using them as features and nitrate-N concentration as the target variable. Consequently, we established six machine learning prediction models, and then selected the best bagging model (R2 = 0.86) to predict the vulnerability of aquifers in larger regions lacking nitrate data. The predicted results showed that highly vulnerable areas accounted for 20 %, which were mainly affected by aquifer thickness (65.91 %). High nitrate-N concentration implies serious aquifer contamination. Therefore, a long series of groundwater nitrate-N concentration monitoring data in a large scale, the trend and slope of nitrate-N concentration showed a significant correlation with the model prediction results (Spearman's correlation coefficients are 0.75 and 0.58). This study can help identify the risk of aquifer contamination, solve the problem of sample shortage in large areas, thus contributing to the security of food and drinking water.

Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning.

Chronic exposure to elevated geogenic arsenic (As) and fluoride (F-) concentrations in groundwater poses a significant global health risk. In regions around the world where regular groundwater quality assessments are limited, the presence of harmful levels of As and F- in shallow groundwater extracted from specific wells remains uncertain. This study utilized an enhanced stacking ensemble learning model to predict the distributions of As and F- in shallow groundwater based on 4,393 available datasets of observed concentrations and forty relevant environmental factors. The enhanced model was obtained by fusing well-suited Extreme Gradient Boosting, Random Forest, and Support Vector Machine as the base learners and a structurally simple Linear Discriminant Analysis as the meta-learner. The model precisely captured the patchy distributions of groundwater As and F- with an AUC value of 0.836 and 0.853, respectively. The findings revealed that 9.0% of the study area was characterized by a high As risk in shallow groundwater, while 21.2% was at high F- risk identified as having a high risk of fluoride contamination. About 0.2% of the study area shows elevated levels of both of them. The affected populations are estimated at approximately 7.61 million, 34.1 million, and 0.2 million, respectively. Furthermore, sedimentary environment exerted the greatest influence on distribution of groundwater As, with human activities and climate following closely behind at 29.5%, 28.1%, and 21.9%, respectively. Likewise, sedimentary environment was the primary factor affecting groundwater F- distribution, followed by hydrogeology and soil physicochemical properties, contributing 27.8%, 24.0%, and 23.3%, respectively. This study contributed to the identification of health risks associated with shallow groundwater As and F-, and provided insights into evaluating health risks in regions with limited samples.