The effect of ride experience on changing opinions toward autonomous vehicle safety

Autonomous vehicles (AVs) are a promising emerging technology that is likely to be widely deployed in the near future. People\u27s perception on AV safety is critical to the pace and success of deploying the AV technology. Existing studies found that people\u27s perceptions on emerging technologies might change as additional information was provided. To investigate this phenomenon in the AV technology context, this paper conducted real-world AV experiments and collected factors that may associate with people\u27s initial opinions without any AV riding experience and opinion change after a successful AV ride. A number of ordered probit and binary probit models considering data heterogeneity were employed to estimate the impact of these factors on people\u27s initial opinions and opinion change. The study found that people\u27s initial opinions toward AV safety are significantly associated with people\u27s age, personal income, monthly fuel cost, education experience, and previous AV experience. Further, the factors dominating people\u27s opinion change after a successful AV ride include people\u27s age, personal income, monthly fuel cost, daily commute time, driving alone indicator, willingness to pay for AV technology, and previous AV experience. These results provide important references for future implementations of the AV technology. Additionally, based on the inconsistent effects for variables across different models, suggestions for future transportation survey designs are provided

The management of socio‐political issues and environments::Toward a research agenda for corporate socio‐political engagement

Socio-political issues and environments are becoming more complex and challenging. In this introduction to the special issue on ‘The Management of Socio-Political Issues and Environments: Organizational and Strategic Perspectives’, we take stock of the burgeoning research on how firms interact with socio-political actors and environments over the last few decades, specifically research on Corporate Political Activity and Corporate Social Responsibility. We then argue that the socio-political environments and actors with which firms interact are in a state of flux, such that issues are more interrelated and dynamic, and actors are more diverse and demanding. As such, we propose a new concept of corporate socio-political engagement (CSPE), which represents a more holistic perspective to understanding complex interactions among firms and their social/political stakeholders, incorporating and transcending conventional notions and tactics documented in the extant nonmarket strategy literature. Using a two-dimensional framework that captures the identity of socio-political actor or the nature of socio-political issues (political, social, or both) as well as the relevant level of analysis at which the interactions unfold, we showcase the contributions of the special issue articles to this research agenda. Finally, we discuss and specify future research directions for revealing the multifaceted nature of CSPE

Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic

Coronavirus disease 2019 (COVID-19) has been spreading rapidly and is still threatening human health currently. A series of measures for restraining epidemic spreading has been adopted throughout the world, which seriously impacted the gross domestic product (GDP) globally. However, details of the changes in the GDP and its spatial heterogeneity characteristics on a fine scale worldwide during the pandemic are still uncertain. We designed a novel scheme to simulate a 0.1° × 0.1° resolution grid global GDP map during the COVID-19 pandemic. Simulated nighttime-light remotely sensed data (SNTL) was forecasted via a GM(1, 1) model under the assumption that there was no COVID-19 epidemic in 2020. We constructed a geographically weighted regression (GWR) model to determine the quantitative relationship between the variation of nighttime light (ΔNTL) and the variation of GDP (ΔGDP). The scheme can detect and explain the spatial heterogeneity of ΔGDP at the grid scale. It is found that a series of policies played an obvious role in affecting GDP. This work demonstrated that the global GDP, except for in a few countries, represented a remarkably decreasing trend, whereas the ΔGDP exhibited significant differences

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

Differences in Intrinsic Brain Abnormalities Between Patients With Left- and Right-Sided Long-Term Hearing Impairment

Unilateral hearing impairment is characterized by asymmetric hearing input, which causes bilateral unbalanced auditory afferents and tinnitus of varying degrees. Long-term hearing imbalance can cause functional reorganization in the brain. However, differences between intrinsic functional changes in the brains of patients with left- and those with right-sided long-term hearing impairments are incompletely understood. This study included 67 patients with unilateral hearing impairments (left-sided, 33 patients; right-sided, 34 patients) and 32 healthy controls. All study participants underwent blood oxygenation level dependent resting-state functional magnetic resonance imaging and T1-weighted imaging with three-dimensional fast spoiled gradient-echo sequences. After data preprocessing, fractional amplitude of low frequency (fALFF) and functional connectivity (FC) analyses were used to evaluate differences between patients and healthy controls. When compared with the right-sided hearing impairment group, the left-sided hearing impairment group showed significantly higher fALFF values in the left superior parietal gyrus, right inferior parietal lobule, and right superior frontal gyrus, whereas it showed significantly lower fALFF values in the left Heschl’s gyrus, right supramarginal gyrus, and left superior frontal gyrus. In the left-sided hearing impairment group, paired brain regions with enhanced FC were the left Heschl’s gyrus and right supramarginal gyrus, left Heschl’s gyrus and left superior parietal gyrus, left superior parietal gyrus and right inferior parietal lobule, right inferior parietal lobule and right superior frontal gyrus, and left and right superior frontal gyri. In the left-sided hearing impairment group, the FC of the paired brain regions correlated negatively with the duration and pure tone audiometry were in the left Heschl’s gyrus and right supramarginal gyrus. In the right-sided hearing impairment group, the FC of the paired brain regions correlated negatively with the duration was in the left Heschl’s gyrus and superior parietal gyrus, and with pure tone audiometry was right inferior parietal lobule and superior frontal gyrus. The intrinsic reintegration mechanisms of the brain appeared to differ between patients with left-sided hearing impairment and those with right-sided hearing impairment, and the severity of hearing impairment was associated with differences in functional integration in certain brain regions

Selection of hyperfunctional siRNAs with improved potency and specificity

One critical step in RNA interference (RNAi) experiments is to design small interfering RNAs (siRNAs) that can greatly reduce the expression of the target transcripts, but not of other unintended targets. Although various statistical and computational approaches have been attempted, this remains a challenge facing RNAi researchers. Here, we present a new experimentally validated method for siRNA design. By analyzing public siRNA data and focusing on hyperfunctional siRNAs, we identified a set of sequence features as potency selection criteria to build an siRNA design algorithm with support vector machines. Additional bioinformatics filters were also included in the algorithm to increase RNAi specificity by reducing potential sequence cross-hybridization or microRNA-like effects. Independent validation experiments were performed, which indicated that the newly designed siRNAs have significantly improved performance, and worked effectively even at low concentrations. Furthermore, our cell-based studies demonstrated that the siRNA off-target effects were significantly reduced when the siRNAs were delivered into cells at the 3 nM concentration compared to 30 nM. Thus, the capability of our new design program to select highly potent siRNAs also renders increased RNAi specificity because these siRNAs can be used at a much lower concentration. The siRNA design web server is available at http://www5.appliedbiosystems.com/tools/siDesign/

A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms

We describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms ( SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds ( a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines - in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases

DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches