Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles.

Volatile organic compounds (VOCs) are ubiquitous in vehicle cabin environments, which can significantly impact the health of drivers and passengers, whereas quick and intelligent prediction methods are lacking. In this study, we firstly analyzed the variations of environmental parameters, VOC levels and potential sources inside a new car during 7 summer workdays, indicating that formaldehyde had the highest concentration and about one third of the measurements exceeded the standard limit for in-cabin air quality. Feature importance analysis reveals that the most important factor affecting in-cabin VOC emission behaviors is the material surface temperature rather than the air temperature. By introducing the attention mechanism and ensemble strategy, we present an LSTM-A-E deep learning model to predict the concentrations of 12 observed typical VOCs, together with other five deep learning models for comparison. By comparing the prediction-observation discrepancies and five evaluation metrics, the LSTM-A-E model demonstrates better performance, which is more consistent with field measurements. Extension of the developed model for predicting the 10-day VOC concentrations in a realistic residence further illustrates its excellent environmental adaptation. This study probes the not-well-explored in-cabin VOC dynamics via observation and deep learning approaches, facilitating rapid prediction and exposure assessment of VOCs in the vehicle micro-environment.

Intention beyond desire: Spontaneous intentional commitment regulates conflicting desires.

The human mind is a mosaic composed of multiple selves with conflicting desires. How can coherent actions emerge from such conflicts? Classical desire theory argues that rational action depends on maximizing the expected utilities evaluated by all desires. In contrast, intention theory suggests that humans regulate conflicting desires with an intentional commitment that constrains action planning towards a fixed goal. Here, we designed a series of 2D navigation games in which participants were instructed to navigate to two equally desirable destinations. We focused on the critical moments in navigation to test whether humans spontaneously commit to an intention and take actions that would be qualitatively different from those of a purely desire-driven agent. Across four experiments, we found three distinctive signatures of intentional commitment that only exist in human actions: "goal perseverance" as the persistent pursuit of an original intention despite unexpected drift making the intention suboptimal; "self-binding" as the proactive binding of oneself to a committed future by avoiding a path that could lead to many futures; and "temporal leap" as the commitment to a distant future even before reaching the proximal one. These results suggest that humans spontaneously form an intention with a committed plan to quarantine conflicting desires from actions, supporting intention as a distinctive mental state beyond desire. Additionally, our findings shed light on the possible functions of intention, such as reducing computational load and making one's actions more predictable in the eyes of a third-party observer.

Sharing Rewards Undermines Coordinated Hunting.

Coordinated hunting is widely observed in animals, and sharing rewards is often considered a major incentive for its success. While current theories about the role played by sharing in coordinated hunting are based on correlational evidence, we reveal the causal roles of sharing rewards through computational modeling with a state-of-the-art Multi-agent Reinforcement Learning (MARL) algorithm. We show that counterintuitively, while selfish agents reach robust coordination, sharing rewards undermines coordination. Hunting coordination modeled through sharing rewards (1) suffers from the free-rider problem, (2) plateaus at a small group size, and (3) is not a Nash equilibrium. Moreover, individually rewarded predators outperform predators that share rewards, especially when the hunting is difficult, the group size is large, and the action cost is high. Our results shed new light on the actual importance of prosocial motives for successful coordination in nonhuman animals and suggest that sharing rewards might simply be a byproduct of hunting, instead of a design strategy aimed at facilitating group coordination. This also highlights that current artificial intelligence modeling of human-like coordination in a group setting that assumes rewards sharing as a motivator (e.g., MARL) might not be adequately capturing what is truly necessary for successful coordination.

Quick and label-free detection for Coumaphos by using surface plasmon resonance biochip.

Coumaphos is a common organophosphorus pesticide used in agricultural products. It is harmful to human health and has a strictly stipulated maximum residue limit (MRL) on fruits and vegetables. Currently existing methods for detection are complex in execution, require expensive tools and are time consuming and labor intensive. The surface plasmon resonance method has been widely used in biomedicine and many other fields. This study discusses a detection method based on surface plasmon resonance in organophosphorus pesticide residues. As an alternative solution, this study proposes a method to detect Coumaphos. The method, which is based on surface plasmon resonance (SPR) and immune reaction, belongs to the suppression method. A group of samples of Coumaphos was detected by this method. The concentrations of Coumaphos in the samples were 0 µg/L, 50 µg/L, 100 µg/L, 300 µg/L, 500 µg/L, 1000 µg/L, 3000 µg/L and 5000 µg/L, respectively. Through detecting a group of samples, the process of kinetic reactions was analyzed and the corresponding standard curve was obtained. The sensibility is less than 25 µg/L, conforming to the standard of the MRL of Coumaphos stipulated by China. This method is label-free, using an unpurified single antibody only and can continuously test at least 80 groups of samples continuously. It has high sensitivity and specificity. The required equipments are simple, environmental friendly and easy to control. So this method is promised for a large number of samples quick detection on spot and for application prospects.