The impact of facial attractiveness and alleged personality traits on fairness decisions in the ultimatum game: Evidence from ERPs.

In the mind of the beholder the personality and facial attractiveness of others are interrelated. However, how these specific properties are processed in the neurocognitive system and interact with each other while economic decisions are made is not well understood. Here, we combined the ultimatum game with EEG technology, to investigate how alleged personality traits and the perceived facial attractiveness of proposers of fair and unfair offers influence their acceptance by the responders. As expected, acceptance rate was higher for fair than unfair allocations. Overall, responders were more likely to accept proposals from individuals with higher facial attractiveness and with more positive personality traits. In ERPs, words denoting negative personality traits elicited larger P2 components than positive trait words, and more attractive faces elicited larger LPC amplitudes. Replicating previous findings, FRN amplitudes were larger to unfair than to fair allocations. This effect was diminished if the proposer's faces were attractive or associated with positive personality traits. Hence, facial attractiveness and the valence of personality traits seem to be evaluated independently and at different time points. Subsequent decision making about unfair offers is similarly influenced by high attractiveness and positive personality of the proposer, diminishing the negative response normally elicited by "unfair" proposals, possibly due a "reward" effect. In the ERPs to the proposals the effect of positive personality and attractiveness were seen in the FRN and P300 components but for positive personality traits the effect even preceded the FRN effect. Altogether, the present results indicate that both high facial attractiveness and alleged positive personality mitigate the effects of unfair proposals, with temporally overlapping but independent neurocognitive correlates.

A premium for positive social interest and attractive voices in the acceptability of unfair offers? An ERP study.

Although the attractiveness of voices plays an important role in social interactions, it is unclear how voice attractiveness and social interest influence social decision-making. Here, we combined the ultimatum game with recording event-related brain potentials (ERPs) and examined the effect of attractive versus unattractive voices of the proposers, expressing positive versus negative social interest ("I like you" vs. "I don't like you"), on the acceptance of the proposal. Overall, fair offers were accepted at significantly higher rates than unfair offers, and high voice attractiveness increased acceptance rates for all proposals. In ERPs in response to the voices, their attractiveness and expressed social interests yielded early additive effects in the N1 component, followed by interactions in the subsequent P2, P3 and N400 components. More importantly, unfair offers elicited a larger Medial Frontal Negativity (MFN) than fair offers but only when the proposer's voice was unattractive or when the voice carried positive social interest. These results suggest that both voice attractiveness and social interest moderate social decision-making and there is a similar "beauty premium" for voices as for faces.

Arising 2D Perovskites for Ionizing Radiation Detection.

2D perovskites have greatly improved moisture stability owing to the large organic cations embedded in the inorganic octahedral structure, which also suppresses the ions migration and reduces the dark current. The suppression of ions migration by 2D perovskites effectively suppresses excessive device noise and baseline drift and shows excellent potential in the direct X-ray detection field. In addition, 2D perovskites have gradually emerged with many unique properties, such as anisotropy, tunable bandgap, high photoluminescence quantum yield, and wide range exciton binding energy, which continuously promote the development of 2D perovskites in ionizing radiation detection. This review aims to systematically summarize the advances and progress of 2D halide perovskite semiconductor and scintillator ionizing radiation detectors, including reported alpha (α) particle, beta (ß) particle, neutron, X-ray, and gamma (γ) ray detection. The unique structural features of 2D perovskites and their advantages in X-ray detection are discussed. Development directions are also proposed to overcome the limitations of 2D halide perovskite radiation detectors.

Parameters optimization for decontamination and fine physical regeneration pathways of polypropylene plastics from waste lunchboxes.

Due to the development of the food delivery industry, a large amount of waste lunchboxes made of homo polypropylene (PP) plastic have been generated. This study developed a new technological strategy to effectively regenerate PP from waste lunchboxes. Through response surface curve analysis, it was found that under the optimal process conditions of hot alkali washing at 80 â„ƒ, 30 min, and pH 13, the optimal contact angle was 65.55°, indicating a good oil stain removal effect. By identifying and analyzing the characteristics of impurities in waste lunchboxes, a physical sorting and granulation regeneration process was constructed. And through large-scale statistical analysis and data collection, it was further verified that recycled PP plastics maintained their physical stability and excellent processing performance. The quality stability of recycled PP plastics in terms of impurities content was also verified. By designing different formulations specifically, recycled PP was mixed with different virgin PP and antioxidants in appropriate proportions, and extruded into particles under 150-300 mesh filtration conditions to obtain modified recycled PP. Modified recycled PP was applied in textiles, clothing, and injection molded products. In conclusion, we achieve the up-cylcing of waste PP lunchboxes instead of down-cylcing.

Application of computer vision and deep learning models to automatically classify medically important mosquitoes in North Borneo, Malaysia.

Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic identification require well-trained personnel, are time-consuming and labour-intensive. In this study, we aim to use a deep leaning model (DL) to develop an application capable of automatically detecting mosquito vectors collected from urban and suburban areas in North Borneo, Malaysia. Specifically, a DL model called MobileNetV2 was developed using a total of 4880 images of Aedes aegypti, Aedes albopictus and Culex quinquefasciatus mosquitoes, which are widely distributed in Malaysia. More importantly, the model was deployed as an application that can be used in the field. The model was fine-tuned with hyperparameters of learning rate 0.0001, 0.0005, 0.001, 0.01 and the performance of the model was tested for accuracy, precision, recall and F1 score. Inference time was also considered during development to assess the feasibility of the model as an app in the real world. The model showed an accuracy of at least 97%, a precision of 96% and a recall of 97% on the test set. When used as an app in the field to detect mosquitoes with the elements of different background environments, the model was able to achieve an accuracy of 76% with an inference time of 47.33 ms. Our result demonstrates the practicality of computer vision and DL in the real world of vector and pest surveillance programmes. In the future, more image data and robust DL architecture can be explored to improve the prediction result.

High-Sensitivity Photoelectrochemical Ultraviolet Photodetector with Stable pH-Universal Adaptability Based on Whole Single-Crystal Integrated Self-Supporting 4H-SiC Nanoarrays.

Emerging photoelectrochemical (PEC) photodetectors (PDs) have notable advantages over conventional PDs and have attracted extensive attention. However, harsh liquid environments, such as those with high corrosivity and attenuation, substantially restrict their widespread application. Moreover, most PEC PDs are constructed by assembling numerous nanostructures on current collector substrates, which inevitably contain abundant interfaces and defects, thus greatly weakening the properties of PDs. To address these challenges, a high-performance pH-universal PEC ultraviolet (UV) PD based on a whole single-crystal integrated self-supporting 4H-SiC nanopore array photoelectrode is constructed, which is fabricated using a two-step anodic oxidation approach. The PD exhibits excellent photodetection behavior, with high responsivity (218.77 mA W-1 ), detectivity (6.64 × 1013  Jones), external quantum efficiency (72.47%), and rapid rise/decay times (17/48 ms) under 375 nm light illumination with a low intensity of 0.15 mW cm-2 and a bias voltage of 0.6 V, which is fall in the state-of-the-art of the wide-bandgap semiconductor-based PDs reported thus far. Furthermore, the SiC PEC PD exhibits excellent photoresponse and long-term operational stability in pH-universal liquid environments. The improved photodetection performance of the SiC PEC PD is primarily attributed to the synergistic effect of the nanopore array structure, integrated self-supporting configuration, and single-crystal structure of the whole photoelectrode.

Quantification and Characterization of Fine Plastic Particles as Considerable Components in Atmospheric Fine Particles.

The negative effects of air pollution, especially fine particulate matter (PM2.5, particles with an aerodynamic diameter of ≤2.5 µm), on human health, climate, and ecosystems are causing significant concern. Nevertheless, little is known about the contributions of emerging pollutants such as plastic particles to PM2.5 due to the lack of continuous measurements and characterization methods for atmospheric plastic particles. Here, we investigated the levels of fine plastic particles (FPPs) in PM2.5 collected in urban Shanghai at a 2 h resolution by using a novel versatile aerosol concentration enrichment system that concentrates ambient aerosols up to 10-fold. The FPPs were analyzed offline using the combination of spectroscopic and microscopic techniques that distinguished FPPs from other carbon-containing particles. The average FPP concentrations of 5.6 µg/m3 were observed, and the ratio of FPPs to PM2.5 was 13.2% in this study. The FPP sources were closely related to anthropogenic activities, which pose a potential threat to ecosystems and human health. Given the dramatic increase in plastic production over the past 70 years, this study calls for better quantification and control of FPP pollution in the atmosphere.

An EPR study of the cognitive processes underlying the impact of self-relevant information on emotional word processing.

Using the event-related potentials (ERPs) technique, this study successively presented names (in either a supra- or subthreshold manner) and emotional words to examine how self-relevant cue (self-name) affects emotional word processing in word class judgment task (to determine whether an emotional word is a noun or adjective) and valence judgment task (to determine whether an emotional word is positive or negative). At the suprathreshold condition, self-relevant positive words elicited a more significant Early posterior negativity (EPN) than negative words only in the valence judgment task. In contrast, at the subthreshold condition, self-relevant negative words elicited an enhanced Late positive potential (LPP) than positive words only in the word class judgment task. These results indicate that self-relevant cue affects emotional word processing at both suprathreshold and subthreshold conditions; nevertheless, the effect manifests as self-positive bias at the suprathreshold condition and self-negative bias at the subthreshold condition. The experimental task modulates these dynamics.

Deciphering DOM-metal binding using EEM-PARAFAC: Mechanisms, challenges, and perspectives.

Dissolved organic matter (DOM) is a pivotal component of the biogeochemical cycles and can combine with metal ions through chelation or complexation. Understanding this process is crucial for tracing metal solubility, mobility, and bioavailability. Fluorescence excitation emission matrix (EEM) and parallel factor analysis (PARAFAC) has emerged as a popular tool in deciphering DOM-metal interactions. In this review, we primarily discuss the advantages of EEM-PARAFAC compared with other algorithms and its main limitations in studying DOM-metal binding, including restrictions in spectral considerations, mathematical assumptions, and experimental procedures, as well as how to overcome these constraints and shortcomings. We summarize the principles of EEM to uncover DOM-metal association, including why fluorescence gets quenched and some potential mechanisms that affect the accuracy of fluorescence quenching. Lastly, we review some significant and innovative research, including the application of 2D-COS in DOM-metal binding analysis, hoping to provide a fresh perspective for possible future hotspots of study. We argue the expansion of EEM applications to a broader range of areas related to natural organic matter. This extension would facilitate our exploration of the mobility and fate of metals in the environment.

Differential perovskite hemispherical photodetector for intelligent imaging and location tracking.

Advanced photodetectors with intelligent functions are expected to take an important role in future technology. However, completing complex detection tasks within a limited number of pixels is still challenging. Here, we report a differential perovskite hemispherical photodetector serving as a smart locator for intelligent imaging and location tracking. The high external quantum efficiency (~1000%) and low noise (10-13 A Hz-0.5) of perovskite hemispherical photodetector enable stable and large variations in signal response. Analysing the differential light response of only 8 pixels with the computer algorithm can realize the capability of colorful imaging and a computational spectral resolution of 4.7 nm in a low-cost and lensless device geometry. Through machine learning to mimic the differential current signal under different applied biases, one more dimensional detection information can be recorded, for dynamically tracking the running trajectory of an object in a three-dimensional space or two-dimensional plane with a color classification function.

Cation-π interactions enabled water-stable perovskite X-ray flat mini-panel imager.

Sensitive and stable perovskite X-ray detectors are attractive in low-dosage medical examinations. The high sensitivity, tunable chemical compositions, electronic dimensions, and low-cost raw materials make perovskites promising next-generation semiconductors. However, their ionic nature brings serious concerns about their chemical and water stability, limiting their applications in well-established technologies like crystal polishing, micro-processing, photolithography, etc. Herein we report a one-dimensional tryptamine lead iodide perovskite, which is stable in water for several months as the strong cation-π interactions between organic cations. The one-dimensional and two-dimensional tryptamine lead iodide perovskite tablets are switchable through thermal-annealing or water-soaking treatments to relax microstrains. The water-stable and microstrain-free one-dimensional perovskite tablets yield a large sensitivity of 2.5 × 106 µC Gyair-1 cm-2 with the lowest detectable dose rate of 5 nGyair s-1. Microelectrode arrays are realized by surface photolithography to construct high-performance X-ray flat mini-panels with good X-ray imaging capability, and a record spatial resolution of 17.2 lp mm-1 is demonstrated.

Why is the risk of industrial non-routine activities high and how can it be evaluated? An integrated approach.

Non-routine activities such as startup, shutdown, maintenance, and operation commissioning require increased human interaction with the corresponding process. Owing to operator or procedural violations, the risk of accidents can be high during non-routine activities, even though they are performed less frequently. To identify and evaluate the hazards of non-routine processes, an integrated method combining job hazard analysis (JHA), hazard and operability analysis (HAZOP), and deviation degrees is proposed. JHA is applied to break down an operational process into steps, which are further defined as nodes in HAZOP for hazard scenario analysis. The concept of deviation degree is defined by integrating the operational and control function deviations to quantify the deviation analysis. Finally, the heating-furnace startup process in an oil and gas gathering and transmission station was selected to illustrate the proposed integrated method. The results show that this method constitutes a systematical and intuitive approach to identify hazard scenarios and evaluate risks, as well as to establish preventive measures for non-routine processes.

Sulphur-induced structural rearrangement in the self-sensitization photo-oxidation behaviour of phenothiazine-imidazole molecules.

A green photo-oxidation reaction was discovered in which the imidazole ring is oxidized and rearranged to generate imidazolinone derivatives with thermal activation delayed fluorescence properties. The excellent photo-oxidation properties of p-PTZ-PIM can be utilized for detecting trace oxygen in the water phase and the sealing of food packaging bags.

Evaluation of pelvic structural abnormalities in primiparous women with stress urinary incontinence.

INTRODUCTION AND HYPOTHESIS: The objective was to evaluate the morphological characteristics of pelvic floor structure specific to de novo stress urinary incontinence (SUI) in primiparous women using three-dimensional (3D) reconstruction fusion technology based on static MRI combined with dynamic MRI. METHODS: Eighty-one primiparous women after the first vaginal delivery were studied, 40 with SUI and 41 without SUI. 3D reconstruction models based on static MRI were used to describe the anatomical abnormalities of pelvic floor tissues. Dynamic MRI was used to describe segmental activities of the urethra and vagina. The relationship between the morphometry and postpartum SUI was evaluated by logistic regression analysis and receiver operator characteristic curve. RESULTS: The differences in the distance from the bladder neck to the pubic symphysis (BSD), the angle between the posterior wall of the urethra and the anterior wall of the vagina, the width of the distal region of the vagina, urethral length, urethral compression muscle volume (CUV), and pubovisceral muscle volume, puborectal muscle volume, were measured, and except for the extremity of the anterior urethral wall, the total displacements (TDs) of the other sites between the two groups were statistically significant (p < 0.05). Logistic regression analysis showed that the BSD decreased, the CUV decreased, the TDs of the first site and the eighth site increment correlated significantly with postpartum SUI occurrence (p < 0.05). CONCLUSIONS: 3D reconstruction fusion technology provides an important support for a precise assessment of the pelvic floor dysfunction. The BSD, CUV, and iliococcygeus muscle volume have certain values in predicting de novo SUI after first vaginal birth.

A survey on few-shot class-incremental learning.

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

Music training affects listeners' processing of different types of accentuation information: Evidence from ERPs.

Previous studies found that prolonged musical training can promote language processing, but few studies have examined whether and how musical training affects the processing of accentuation in spoken language. In this study, a vocabulary detection task was conducted, with Chinese single sentences as materials, to investigate how musicians and non-musicians process corrective accent and information accent in the sentence-middle and sentence-final positions. In the sentence-middle position, results of the cluster-based permutation t-tests showed significant differences in the 574-714 ms time window for the control group. In the sentence-final position, the cluster-based permutation t-tests revealed significant differences in the 612-810 ms time window for the music group and in the 616-812 ms time window for the control group. These significant positive effects were induced by the processing of information accent relative to that of corrective accent. These results suggest that both groups were able to distinguish corrective accent from information accent, but they processed the two accent types differently in the sentence-middle position. These findings show that musical training has a cross-domain effect on spoken language processing and that the accent position also affects its processing.

Discovering Mathematical Expressions Through DeepSymNet: A Classification-Based Symbolic Regression Framework.

Symbolic regression (SR) is the process of finding an unknown mathematical expression given the input and output and has important applications in interpretable machine learning and knowledge discovery. The major difficulty of SR is that finding the expression structure is an NP-hard problem, which makes the entire process time-consuming. In this study, the solution of expression structures was regarded as a classification problem and solved by supervised learning such that SR can be solved quickly by using the solving experience. Techniques for classification tasks, such as equivalent label merging and sample balance, were used to enhance the robustness of the algorithm. We proposed a symbolic network called DeepSymNet to represent symbolic expressions to improve the performance of the algorithm. DeepSymNet has been proven to have a strong representation ability with a shorter label compared to the current popular representation methods, reducing the search space when predicting. Moreover, DeepSymNet conveniently decomposes SR into two smaller subproblems, which makes solving the problem easier. The proposed algorithm was tested on artificially generated expressions and public datasets and compared with other algorithms. The results demonstrate the effectiveness of the proposed algorithm.

Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions.

Incomplete combustion of fossil fuels and biomass burning emit large amounts of soot particles into the troposphere. The condensation process is considered to influence the size (Dp) and mixing state of soot particles, which affects their solar absorption efficiency and lifetimes. However, quantifying aging evolution of soot remains hampered in the real world because of complicated sources and observation technologies. In the Himalayas, we isolated soot sourced from transboundary transport of biomass burning and revealed soot aging mechanisms through microscopic observations. Most of coated soot particles stabilized one soot core under Dp < 400 nm, but 34.8% of them contained multi-soot cores (nsoot ≥ 2) and nsoot increased 3-9 times with increasing Dp. We established the soot mixing models to quantify transformation from condensation- to coagulation-dominant regime at Dp ≈ 400 nm. Studies provide essential references for adopting mixing rules and quantifying the optical absorption of soot in atmospheric models.

Soluble polymer facilely self-grown in situ on conducting substrates at room temperature towards electrochromic applications.

Electrochromic polymer film preparation methods such as spin coating, spray coating, and electrochemical polymerization, are commonly used. At present, developing new film preparation technology is an important aspect in the field of electrochromics. Herein, a continuous in situ self-growing method based on the chemical reaction occurring on the surface of an ITO glass between a metal oxide and organic acid groups was successfully applied to prepare electrochromic polymer films at a mild room temperature. SEM, FT-IR spectroscopy, XPS, and XRD characterization methods were combined to reveal the process and mechanism of film formation. The following notable electrochromic properties were observed: switching time within 6 s, contrast reached 35%, and minimal decrease of stability after 600 cycles. Finally, the patterned films were obtained through the directional growth of polymers in solution. This study provides an effective strategy for designing and preparing electrochromic films by self-growing methods in future applications.

SNR: Symbolic network-based rectifiable learning framework for symbolic regression.

Symbolic regression (SR) can be utilized to unveil the underlying mathematical expressions that describe a given set of observed data. At present, SR can be categorized into two methods: learning-from-scratch and learning-with-experience. Compared to learning-from-scratch, learning-with-experience yields results that are comparable to those of several benchmarks and incurs significantly lower time costs for obtaining expressions. However, the learning-with-experience model performs poorly in terms of unseen data distributions and lacks a rectification tool, apart from constant optimization, which exhibits limited performance. In this study, we propose a Symbolic Network-based Rectifiable Learning Framework (SNR) that possesses the ability to correct errors. SNR adopts Symbolic Network (SymNet) to represent an expression, and the encoding of SymNet is designed to provide supervised information, with numerous self-generated expressions, to train a policy net (PolicyNet). The training of PolicyNet can offer prior knowledge to guide effective searches. Subsequently, the incorrectly predicted expressions are revised via a rectification mechanism. This rectification mechanism endows SNR with broader applicability. Experimental results demonstrate that our proposed method achieves the highest averaged coefficient of determination on self-generated datasets when compared with other state-of-the-art methods and yields more accurate results in public datasets.