Yun-Cheng Wang

Two kinds of probability expressions, verbal and numerical, have been used to characterize the uncertainty that people face. However, the question of whether verbal and numerical probabilities are cognitively processed in a similar manner remains unresolved. From a levels-of-processing perspective, verbal and numerical probabilities may be processed differently during early sensory processing but similarly in later semantic-associated operations. This event-related potential (ERP) study investigated the neural processing of verbal and numerical probabilities in risky choices. The results showed that verbal probability and numerical probability elicited different N1 amplitudes but that verbal and numerical probabilities elicited similar N2 and P3 waveforms in response to different levels of probability (high to low). These results were consistent with a level-of-processing framework and suggest some internal consistency between the cognitive processing of verbal and numerical probabilities in risky choices. Our findings shed light on possible mechanism underlying probability expression and may provide the neural evidence to support the translation of verbal to numerical probabilities (or vice versa).

Purpose As an enabler of environmental sustainability, Green information technology has become an emerging topic of interest in both academic and business communities. Despite its importance, confusions exist in the content and scope of Green IT practice. The purpose of this paper is to provide an overview of the current state of Green IT practice. Design/methodology/approach First 14 widely accepted Green IT practice topics were identified from prior research and a taxonomy was developed to categorize them. Using the content analysis method, these topics were examined in the sustainability reports of 30 IT companies in 2014 Fortune 500. A quantity–quality portfolio framework was developed and applied to measure and assess the Green IT practices of the selected samples. Findings Currently, the Green IT practice is still in its infancy. Both research and practice attention are now focusing on IT’s direct impacts and enabling impacts, while overlooking the systemic impacts, on natural environment. The possible reasons for the current state and the recommendations for future research and practice are provided. Originality/value Theoretically, this paper identified 14 widely accepted Green IT practice topics and developed a taxonomy for categorizing them. The taxonomy and topics provide a theoretical basis for future examination on Green IT practice-related issues. Practically, the findings of this paper provide guidelines for Green IT practice and directions for both Green IT developers and adopters in their decision-making.

Diffusion MRI is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification of the dMRI volumes or focus on detecting a single type of artifact. In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive sub-volumes or “slabs” extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development and 4,226 from the Healthy Brain Network dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy and participant age, to test whether the use of our model improves the brain-age association.

For the western region to deepen reform of rural credit cooperatives, rural credit cooperatives reform of property rights in the direction of selection, selection and organization of the western rural finance related reforms have been analyzed, and pointed out that the reform should be guided by the mechanisms of capital flows in western rural areas, timely promote agricultural development bank reform, the establishment of rural credit cooperatives and other measures of deposit protection mechanism related to the rural financial sector reform. It has been a year since the rural credit cooperative pilot reform was carried out after the issue of the policies of the reform in the pilot provinces, but there is still shortage of the study on the further reform of the rural credit cooperative in the western regions . On the basis of the combination with theory and practice, this paper analyzes some relative issues including the direction choice of the property rights reform and the organization form choice of the rural credit cooperative in the western regions, and the coordinated financial reform in the western countryside, then offers the solutions to some issues