The spirit of giving: Mennonite narratives about charitable contributions

Many charitable contributions in the USA are religiously motivated. Based on an analysis of the discussions about charitable contributions among three Mennonite groups in south-central Pennsylvania, this article examines members' complex decision-making processes about giving. Most recent studies of such donations among Christians emphasise the importance of a sense of sacrifice and the demonstration of one's religious commitment through giving. This article, however, suggests that giving decisions cannot be fully understood without considering members' pragmatic, but also sometimes conflictive views on appropriate religious contributions. The three groups in this article differ considerably in the ways in which their beliefs affect their decisions about contributions. While one group prioritises their immediate church community, another group emphasises systematic monetary contributions for evangelical activities and the third stresses the relationship between their financial decisions and the broader social and economic context in which church members are situated.     

Social loafing vs. social enhancement: Public goods provisioning in real-time with irrevocable commitments

Whereas most experimental studies of public goods provisioning require that all players make their decisions simultaneously, in most organizational settings contribution decisions are made in real time. To account for this aspect of the decision process, we introduce a real-time protocol of play in which, at any point in time, players can either withhold or contribute their entire endowment to a step-level public good. Once contributed, the individual endowments—that in the present experiment differ from one group member to another—cannot be withdrawn. Our results show that contribution levels under the real-time protocol with irrevocable commitments significantly exceed those observed in previous studies under the more common simultaneous protocol of play, thereby considerably reducing social loafing (free riding). Consistent with our equilibrium analysis, over multiple iterations of the game play converges to an equilibrium set of players who maximize the sum of their individual benefit-to-contribution ratios.     

Psychological correlates of early onset of ischemic heart disease in a sample drawn from a Pakistani population

Even in the presence of substantial empirical evidence which proves that psychological risk factors play a significant role in onset of ischemic heart disease (IHD), in Pakistan researchers have not paid much attention to exploring these factors. This research was mainly undertaken to investigate whether psychological factors such as stress, anxiety, depression, anger, and hostility in their intense states are prevalent within the indigenous patients with IHD. It was hypothesized that: High levels of perceived stress will significantly increase risk for IHD versus lower levels of perceived stress; high levels of anxiety will significantly increase the risk for IHD versus lower levels of anxiety; high levels of depression will increase the chances of IHD versus lower levels. Likewise, it was proposed that elevated trait anger will significantly increase risk for IHD versus lower levels of trait anger and that higher levels of hostility significantly increase risk for IHD versus lower levels. A case–control research design was employed to conduct this study. To investigate the association of the abovementioned factors with IHD and to find whether these factors differ between cases and controls, we solicited a sample of 190 patients with confirmed diagnosis of IHD and 380 age‐ and gender‐matched community controls, who were free of IHD, aged 35 to 55 years. Standardized tools to measure psychological factors were translated and semistandardized into the national language and their psychometric properties were predetermined before use in this study. To infer the proposed hypotheses, multivariate binary logistic regression analysis was carried out. Results highlight significant association between stress, depression, anxiety, anger, and IHD. Implications for the implementation of routine screening for psychological factors, particularly stress, depression and anger, are proposed.     

Binary choice under instructions to select versus reject

Two experiments examine differences in binary choice under select versus reject instructions. Three aspects of the choice process are examined: commitment to the chosen alternative, absolute magnitude of attribute evaluations, and information distortion during the choice process. Although the findings support previously hypothesized causes (Study 1), these results are reversed when the decision alternatives are uniformly negative (Study 2a). Accompanying verbal protocols (Study 2b) provide additional insights into the underlying decision process. The results consistently support a compatibility effect. Whenever there is a match between the valences of the alternatives and of the decision strategy, namely selecting a positive alternative or rejecting a negative one, there is greater accentuation of attribute differences, higher certainty in the final choice, and more information distortion. Metaphorically, the choice process seems to flow more smoothly in the compatible conditions.     

Efficiency investigation from shallow to deep neural network techniques in human activity recognition

In the last years, several researchers measured different recognition rates with different artificial neural network (ANN) techniques on public data sets in the human activity recognition (HAR) problem. However an overall investigation does not exist in the literature and the efficiency of complex and deeper ANNs over shallow networks is not clear. The purpose of this paper is to investigate the recognition rate and time requirement of different kinds of ANN approaches in HAR. This work examines the performance of shallow ANN architectures with different hyper-parameters, ANN ensembles, binary ANN classifier groups, and convolutional neural networks on two public databases. Although the popularity of binary classifiers, classifier ensembles and deep learning have been significantly increasing, this study shows that shallow ANNs with appropriate hyper-parameters in combination with extracted features can reach similar or higher recognition rate in less time than other artificial neural network methods in HAR. With a well-tuned ANN we outperformed all previous results on two public databases. Consequently, instead of the more complex ANN techniques, the usage of simple ANN with two or three layers can be an appropriate choice for activity recognition.     

Pedestrian compliance and cross walking speed adaptation due to countdown timer installations: A self report study

Although they are aware of the possible risk, a high number of pedestrians still violate the red light indication and cross the road illegally. This hazardous behaviour may cause incidents between them and the road vehicles. In order to reduce this illegal behaviour, the traffic signals are equipped with countdown timers, in order to provide more information and decrease pedestrians’ noncompliant behaviour. The main purpose of the present paper is to investigate the influence of countdown timers on pedestrians’ compliance regarding their crossing behaviour at intersections as well as to examine the parameters affecting walking speed adaptation. In the context of this analysis two regression models were developed. The first model is a binary logistic regression model which examines pedestrians’ self reported compliance. The results showed that the gender, the age, the perceived comfort and the seconds remaining for the onset of red light are the main parameters that affect compliance. The second model is an ordinal regression model which examines the factors that make pedestrians adapt their walking speed as they are crossing the road and are informed by the countdown timers about the second remaining for the termination of the green phase. The results of the second model revealed that the age, the compliance and the perceived assistance that the countdown timer provides for the walking speed adaptation affect the minimum remaining time before a pedestrian accelerates.     

Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.