Correct Decisions Research Articles

Time-varying connectedness between sport cryptocurrency and listed European football stocks: evidence from a LASSO-VAR approach

ABSTRACT Digital assets and the traditional financial sectors are increasingly interacting. In the field of cryptocurrency, the combination with the sports industry has received attention from fans and clubs, media, and academic circles. Based on the LASSO-VAR model which is suitable for large samples, this article investigates the connectivity between sports cryptocurrency Chiliz (CHZ) and listed European football club stocks. The results show that there is a certain interrelatedness among the selected assets, and it becomes closer with the outbreak of major emergencies. In addition, CHZ is a net recipient of spillovers, while club stocks show diversification. Our results are helpful for club fans, individual and institutional investors, market regulators, and policymakers to make correct decisions.

The dynamics of competition and decision-making

AbstractWe examine the underlying cognitive mechanisms that govern how competitions play out over time. We used cognitive modeling to examine the dynamic effects of time remaining and relative performance (whether the person is winning or losing) on effort and strategy. In this experiment, participants completed a competitive decision-making task with varying time limits and starting scores, in a repeated-measures design. Participants were tasked with scoring more points than their computerized opponent during a certain time frame, gaining and losing points for correct and incorrect decisions, respectively. The results showed that as the competition deadline approached and as participants drew ahead of their opponent within a competition, they increased effort and became more cautious. Furthermore, the effect of relative score on effort and caution changed over the course of a competition as the deadline approached. These results highlight the importance of considering dynamics when working to understand how competitions unfold as well as the underlying cognitive mechanisms that give rise to the dynamic behavior.

Support vector machine-based stock market prediction using long short-term memory and convolutional neural network with aquila circle inspired optimization

ABSTRACT Predicting the stock market is one of the significant chores and has a successful prediction of stock rates, and it helps in making correct decisions. The prediction of the stock market is the main challenge due to blaring, chaotic data as well as non-stationary data. In this research, the support vector machine (SVM) is devised for performing an effective stock market prediction. At first, the input time series data is considered and the pre-processing of data is done by employing a standard scalar. Then, the time intrinsic features are extracted and the suitable features are selected in the feature selection stage by eliminating other features using recursive feature elimination. Afterwards, the Long Short-Term Memory (LSTM) based prediction is done, wherein LSTM is trained to employ Aquila circle-inspired optimization (ACIO) that is newly introduced by merging Aquila optimizer (AO) with circle-inspired optimization algorithm (CIOA). On the other hand, delay-based matrix formation is conducted by considering input time series data. After that, convolutional neural network (CNN)-based prediction is performed, where CNN is tuned by the same ACIO. Finally, stock market prediction is executed utilizing SVM by fusing the predicted outputs attained from LSTM-based prediction and CNN-based prediction. Furthermore, the SVM attains better outcomes of minimum mean absolute percentage error; (MAPE) and normalized root-mean-square error (RMSE) with values about 0.378 and 0.294.

THE IMPACT OF BOARD GENDER DIVERSITY ON ENVIRONMENTAL SUSTAINABILITY

Purpose- The Board of Diversity helps companies make more democratic and correct management decisions. The contributions of gender diversity to the financial and non-financial activities of companies are significant. This research was conducted to show the positive contribution of women executives to the environment. Methodology- The study sample includes 108 American companies in the S&P500 index. The study was organized with panel data of USA companies between 2008 and 2021. OLS regression method was used to analyse the results. Findings- According to the results of the analyses, the environmental impact of women on the company board is positive and significant. Conclusion- This study figures out that women executives have higher sensitivity and responsibility towards the environment than men. According to these results, it is essential both in terms of dealing with global warming and in terms of achieving the UN's 2030 targets

Trigeminal neuralgia at the present stage: Pathophysiology, classification, diagnosis and treatment (literature review)

The incidence of trigeminal neuralgia (TN) is 4–5 cases per 100,000 population per year, and the prevalence is about 20–50 cases per 100,000 population. According to the modern classification, the following types of TN are distinguished: classical, symptomatic and idiopathic. The cause of classical TN is a neurovascular conflict, of symptomatic TN – neoplasms in the petroclival area, vascular anomalies, multiple sclerosis, etc. In the absence of a reliable etiological factor, TN is considered idiopathic. TN pathogenesis remains one of the most challenging topics in neuroscience. Today, there are many theories and hypotheses regarding the peripheral and central mechanisms of trigeminal neuralgia. The most popular theory is the neurovascular conflict that occurs between the trigeminal nerve and the cerebral vessels, but this theory is not the only one. It is known that even after microvascular decompression, patients may continue to experience facial pain. Therefore, other pathogenetic mechanisms are discussed: short circuit theory, multineuronal mechanism, allergic and immune hypothesis, gate theory, bioresonance hypothesis, trigeminal convergence projection theory, “ignition” hypothesis and ion channel pathology. TN is a clinical diagnosis, and its establishing requires certain criteria proposed by the International Headache Society. Using instrumental research methods, namely magnetic resonance imaging of the brain, is necessary for the differential diagnosis of classical and symptomatic TN, and imaging results should always be interpreted in combination with clinical data to make correct decisions on further treatment tactics.

Оюутнуудын спорт тоглоомын төрлүүдийн техникийн гүйцэтгэлд хийсэн дүн шинжилгээ

Sports improve a person's physical development, affect their ability to respond to complex situations, and allow for the development of management skills to make correct decisions quickly. Therefore, some types of sports should dominate in the high school physical education curriculum. This research aims to study today's 17 to 18-year-old college students' abilities in sports in the area of (1) passing, dribbling & shooting a basketball, (2) the ability to pass and serve a volleyball, and (3) the ability to change direction and passing techniques in football. A complex set of exercises based on the minimum skills to be mastered by the physical education curriculum at the basic & secondary education levels was developed and used in the study of 204-216 students. The results find students' abilities in performing certain sports skills as follows: the ability to pass a basketball - above-average, the ability to shoot from dribbling - below-average, and female students performing at a lower level than their male counterparts. Statistically significant differences between gender and performance were observed during complex basketball drills. In football, the ability to change the direction of the ball was well below-average, the ability to pass the ball to be above-average; with 29% of male students and 45% of female students had low or insufficient ability to perform complex football exercises at higher speeds. In volleyball, passing and serving skills were all above-average. This study proves that the expected results of the proposed physical education program are not fully implemented in sports other than volleyball.(?)

PRICE RECALCULATION MODEL OF CONSTRUCTION CONTRACTS

In recent years, the world economy has experienced rapid economic change in the construction sector and record inflation. The rapidly rising prices of construction materials and machinery in the construction market are also pushing up the cost of construction services. The main problem is that construction projects have been drawn up and contracts between clients and contractors have been awarded at previous prices, and it is therefore necessary to look for opportunities to index the price of construction contracts. The aim of the study is to propose a mathematical model and a smartphone application for the price recalculation of construction projects. The success of a construction project depends on the correct decisions taken at the stage of preparation and implementation of the procurement documents for construction contracts, a complex process requiring a lot of legal and technological knowledge. The proposed mathematical model would help clients and contractors to carry out the calculation of the price according to the construction price index quickly and without the need for extensive specialist technical expertise. The mathematical model is adapted to smartphones with Android software.

Software development that determines IMSS quotas for SMEs

Currently, SMEs in Mexico do not have a high investment to include technology in their daily processes. Therefore, it leads to delays in their administrative obligations and therefore inadequate financial planning. In this sense, technological inclusion is required in SMEs to help them improve competitively with large companies. This study focuses on developing software that allows SMEs of the Juan C. Bonilla, Puebla in Mexico to calculate their administrative obligations in social security, which allows them to anticipate their financial situation and thereby determine correct decisions. The methodology used in this work has an application purpose because it adapts to the needs of SMEs, whit a qualitative approach because it is based on natural environments, in terms of its depth it is explanatory because it determines the causes and consequences, with respect to the obligations administrative, finally is experimental because the calculation variables can be manipulated in relation to the development of the software with which elements that were not useful for SMEs were detected. In the background, a flow diagram was prepared that determined the basic operations of calculating social security for that later the software was developed through the computer language C++. The results obtained from the software were through the preliminary implementation in the laboratory to small businesspersons from the Juan C. Bonilla, Puebla, making known through a usability list the functionality of the software with respect to the interface and the time to obtain the calculation of social security of which 90% seemed adequate to its daily operations.

Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.

S2k guideline for the treatment of hidradenitis suppurativa / acne inversa - Short version.

The S2k guideline on hidradenitis suppurativa/acne inversa (HS/AI) aims to provide an accepted decision aid for the selection/implementation of appropriate/sufficient therapy. HS/AI is a chronic recurrent, inflammatory, potentially mutilating skin disease of the terminal hair follicle-glandular apparatus, with painful, inflammatory lesions in the apocrine gland-rich regions of the body. Its point prevalence in Germany is 0.3%, it is diagnosed with a delay of 10.0 ±9.6 years. Abnormal differentiation of the keratinocytes of the hair follicle-gland apparatus and accompanying inflammation form the central pathogenetic basis. Primary HS/AI lesions are inflammatory nodules, abscesses and draining tunnels. Recurrences in the last 6 months with at least 2 lesions at the predilection sites point to HS/AI with a 97% accuracy. HS/AI patients suffer from a significant reduction in quality of life. For correct treatment decisions, classification and activity assessment should be done with a validated tool, such as the International Hidradenitis Suppurativa Severity Scoring System (IHS4). HS/AI is classified into two forms according to the degree of detectable inflammation: active, inflammatory (mild, moderate, and severe according to IHS4) and predominantly inactive, non-inflammatory (Hurley grade I, II and III) HS/AI. Oral tetracyclines or 5-day intravenous therapy with clindamycin are equal to the effectiveness of clindamycin/rifampicin. Subcutaneously administered adalimumab, secukinumab and bimekizumab are approved for the therapy of HS/AI. Various surgical procedures are available for the predominantly non-inflammatory disease form. Drug/surgical combinations are considered a holistic therapy method.

Hluboké krční záněty u dětí – přístup k diagnostice a léčbě

Summary Introduction: In the treatment of deep throat infections, it is necessary to make a correct decision about surgical treatment. The aim was to observe trends and characteristics of patients requiring surgical treatment and to identify factors that predict the type of treatment. Materials and methods: This is a retrospective study including pediatric patients hospitalized in the Children‘s Hospital of The University Hospital Brno with deep throat infections between 2010–2019. Studied parameters: age, sex, duration of problems before hospitalization, presence of dysphagia, odynophagia, impaired neck mobility, bulging of the pharyngeal wall, ankylostomia, respiratory problems, dysphonia, drooling, cervical lymphadenitis, sore throat, length of hospitalization, conservative or surgical approach, and well-being day. Laboratory investigations included CRP, neutrophil granulocyte count, and microbiological findings. Imaging examinations monitored the presence of phlegm or abscess, side, position relative to the great vessels and oppression of the great vessels, and size of abscess. Results: This study presents the clinical characteristics of 78 deep throat infections. Observed trends: patients with abscesses were younger, had higher CRP, and were hospitalized longer. Larger abscess statistically resulted in surgical treatment. Patients with phlegm had a shorter time to achieve comprehensive improvement. The incidence of an abscess is not related to gender. Boys had statistically significantly higher CRP and neutrophil granulocyte counts. The most common microbiological finding was Streptococcus pyogenes. Conclusion: Patients with abscesses were younger, had longer hospital stays, and had higher CRP. Key words children – deep neck infection – abscess

Cognitive Development and Decision Making in Basketball: A Comparison between Male Players with and without Intellectual Impairment and across Different Age-Groups.

The aim of this study was to investigate the role of age and intellectual impairment (II) in decision-making in basketball. The current study investigated differences in decision making between equally well-trained adult basketball male players with intellectual impairment (players with II) (n = 93), adults without II (senior) (n = 44) and youth basketball players (under-14, n = 31; under-16, n = 25; under-18, n = 30). A computer test was developed composed by 20 photographs displaying various basketball game-situations, and participants had to decide as fast as possible what the player in ball possession should do: dribble, pass or shoot. Decision time and accuracy were recorded for every situation. Players with II had slower decision time (3.8 ± 1.8 s vs. 1.5 ± 0.5 s, p < 0.001) and less decision-making accuracy (15.7 ± 2.8 correct decisions vs. 17.9 ± 1.2 correct decisions, p < 0.001) compared to senior players without II. Discriminant analysis with speed and accuracy as independent variables classified 91.2% (CCA = 0.769) of the players correctly into their group: players with II or players without II. A Spearman correlation revealed that age correlated significantly (p < 0.001) with the number of correct decisions (rs = 0.269) and mean decision time (rs = -0.331). Our findings support that decision making in basketball develops with age and experience, but is significantly deteriorated in experienced adult players who have II. Decision-making should be considered as an important eligibility criterion to participate in competitive basketball events for male players with II.

Pathogenicity Prediction of Gene Fusion in Structural Variations: A Knowledge Graph-Infused Explainable Artificial Intelligence (XAI) Framework.

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.

Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.

Investment Decisions of the Individual Investors in the Capital Market of Bangladesh

Investment decisions of the individual investors in the capital market are influenced by various factors. Main objective of this study is to find out the influences of the factors on investment decisions of individual investors in the capital market in Bangladesh. A highly structured questionnaire has been used to collect primary data from 384 individual investors who are traded in the Dhaka Stock Exchange (DSE). The study respondents have been selected at a simple random method. Analysis of variance (ANOVA), multiple regression model, graphs, charts and tabular analysis have been used to analyze collected data and to test hypotheses of the study. The results showed that EPS has positive influence (β=0.0682, t=1.2439 and p=0.2143, test of hypothesis 2) on investment decisions and rank of EPS is 4 with mean score of 3.8099 and S.D=0.5087 (table 3). It has also found that dividend has positive relationship (β=0.0590, t=1.1054 and p=0.2697, test of hypothesis 3) with investment decisions and dividend is ranked 6 with mean score of 3.5755 and S.D=0.5593 (table 3). Accordingly, P/E ratio has a negative influence (β =-0.0069, t=-0.1341 and p=0.8934) on investment decisions of the individual investors. Therefore, this study will be very helpful to the individual investors to make correct investment decisions by analysing EPS, dividend trends and P/E ratio of the company.

Utilizing Data from Electrical Impedance Spectroscopy Significantly Improves the Decision to Biopsy Pigmented Skin Lesions Beyond Clinical Evaluation and Dermoscopy

Background: Even the most experienced dermatologists may forego a biopsy on as many as one-third of malignant melanomas (MMs). Electrical impedance spectroscopy (EIS) is a noninvasive technology that send a painless, very low voltage electrical current through a pigmented lesion to determine if it is benign or malignant. This study aimed to determine if EIS data can improve the decision to biopsy a pigmented lesion even beyond dermoscopy. Methods: A survey with 49 images of MMs, severe dysplastic nevi (SDNs), and benign pigmented skin lesions (PSLs) was shown to dermatologists at a national conference. They were asked if they would biopsy the lesion after first seeing the clinical image, then again after seeing the dermoscopic image, and again after receiving the EIS score. Results: 151 dermatologists completed the survey. Respondents significantly increased correct biopsy decisions (biopsy MMs and SDNs and forego biopsy of benign PSLs) with the addition of dermoscopy versus clinical image alone for MM (78.5% vs. 56.2%, p&lt;0.01) and SDN (62.7% vs. 43.8%, p&lt;0.01). Participants also demonstrated a statistically significant increase in correct biopsy decisions beyond the dermoscopic evaluation when integrating the EIS score for MM (86.2% vs. 78.9%, p&lt;0.01), SDN (68.1% vs. 62.7%, p&lt;0.05) and benign lesions (58.7% vs. 48.0% vs, p&lt;0.01). Conclusion: EIS was able to further improve the rate of correct biopsy choice for MMs and SDNs even beyond dermoscopic evaluation. While dermoscopy worsened diagnostic accuracy for benign PSLs, EIS results were able to significantly improve decision making for these lesions as well. This study demonstrates the clinical utility of EIS technology for improving melanoma diagnosis.

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal–Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.

Research on Optimization of Financial Performance Evaluation of Energy Enterprises under the Background of Low-Carbon Economy

The development of human society and the production and operation activities of enterprises have brought about global warming, resulting in frequent natural disasters. It has become the consensus of all countries in the world to develop a green and low-carbon economy. Under this background, enterprises, as the main body of economic activities, especially energy industry enterprises, should optimize and upgrade the traditional production and operation mode with high pollution, high consumption, and low output to a high-efficiency and low-pollution mode, and pay attention to the co-ordinated development of economic benefits, social benefits, and ecological benefits. Financial performance evaluation indicators have become the main basis for senior leaders of energy industry enterprises to make decisions and evaluate the low-carbon economic benefits of enterprises. This paper constructs a set of financial evaluation index systems of energy industry enterprises under the background of a low-carbon economy from four dimensions: profitability, asset quality, debt risk, and business growth. The analytic hierarchy process (AHP) is used to measure the comprehensive contribution of financial indicators of low-carbon production and operation. The purpose of this study is to provide scientific financial management decisions for energy enterprises to reduce costs and increase the efficiency and low-carbon operation under the background of a low-carbon economy. The research results show that the comprehensive evaluation index system after the traditional financial evaluation index of energy industry enterprises is integrated with the evaluation index of a low-carbon economy can help enterprises make more correct and effective financial decisions in the process of survival, development, and growth, and, at the same time, the financial evaluation index of a low-carbon economy should pay more attention to the indicators with a higher comprehensive contribution, so as to effectively promote the low-carbon operation efficiency of enterprise production, management, and sales. Compared with other research results, this paper innovatively constructs a financial management decision-making index system for measuring the low-carbon operation of energy enterprises in theory, which has important value in guiding energy enterprises to reduce costs and increase the efficiency and low-carbon operation in practice.

Robotics Perception: Intention Recognition to Determine the Handball Occurrence during a Football or Soccer Match

In football or soccer, a referee controls the game based on the set rules. The decisions made by the referee are final and can’t be appealed. Some of the decisions, especially after a handball event, whether to award a penalty kick or a yellow/red card can greatly affect the final results of a game. It is therefore necessary that the referee does not make an error. The objective is therefore to create a system that can accurately recognize such events and make the correct decision. This study chose handball, an event that occurs in a football game (Not to be confused with the game of Handball). We define a handball event using object detection and robotic perception and decide whether it is intentional or not. Intention recognition is a robotic perception of emotion recognition. To define handball, we trained a model to detect the hand and ball which are primary objects. We then determined the intention using gaze recognition and finally combined the results to recognize a handball event. On our dataset, the results of the hand and the ball object detection were 96% and 100% respectively. With the gaze recognition at 100%, if all objects were recognized, then the intention and handball event recognition were at 100%.

Improved Perception of Aggression Under (un)Related Threat of Shock.

Anxiety shifts visual attention and perceptual mechanisms, preparing oneself to detect potentially threatening information more rapidly. Despite being demonstrated for threat-related social stimuli, such as fearful expressions, it remains unexplored if these effects encompass other social cues of danger, such as aggressive gestures/actions. To this end, we recruited a total of 65 participants and asked them to identify, as quickly and accurately as possible, potentially aggressive actions depicted by an agent. By introducing and manipulating the occurrence of electric shocks, we induced safe and threatening conditions. In addition, the association between electric shocks and aggression was also manipulated. Our result showed that participants have improved sensitivity, with no changes to criterion, when detecting aggressive gestures during threat compared to safe conditions. Furthermore, drift diffusion model analysis showed that under threat participants exhibited faster evidence accumulation toward the correct perceptual decision. Lastly, the relationship between threat source and aggression appeared to not impact any of the effects described above. Overall, our results indicate that the benefits gained from states of anxiety, such as increased sensitivity toward threat and greater evidence accumulation, are transposable to social stimuli capable of signaling danger other than facial expressions.