A Survey on Deep Learning for Human Mobility

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

Reframing whistleblowing intention: an analysis of individual and situational factors

Purpose This paper aims to reframe the whistleblowing process by examining the individual and situational factors that have been overlooked by prior studies. Ethical climate, public service motivation (PSM), organisation identification and psychological safety are inquired. Design/methodology/approach The present study sample was drawn from a population of Indonesian local governments located in east Java, Indonesia. Particularly, self-administered questionnaires were hand-distributed to the employees in the four local governments. Of 2,169 questionnaires distributed to the employees, 1,687 questionnaires were returned to the researcher. However, the researcher removed 33 returned questionnaires because of poor data quality, such as incomplete answers. Thus, only 1,654 questionnaires were analysed in this study. Findings The findings support the idea of an ethical climate that can encourage the individual to blow the whistle. However, its effect is indirect. The predictive power of ethical climate on the individual’s whistleblowing intentions depends on the meditating roles of PSM, psychological safety and organisation identification. Interestingly, the mediating effects of PSM, psychological safety and organisation identification are extremely acknowledged when individuals have an opportunity to choose internal or external disclosures. Originality/value This study produces a different approach to understanding people’s intentions to report any wrongdoings. This study is dissimilar from prior studies in terms of the theoretical paradigm and research design. Previous studies mostly used students as their experiments. In contrast, the current study recruited employees who work in local governments. This situation fundamentally affects the understanding of the impact of an ethical climate on the individual intention to blow the whistle.

Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis

Somatic mutations are one of the most important factors in tumorigenesis and are the focus of most cancer-sequencing efforts. The co-occurrence of multiple mutations in one tumor has gained increasing attention as a means of identifying cooperating mutations or pathways that contribute to cancer. Using multi-omics, phenotypical, and clinical data from 29,559 cancer subjects and 1747 cancer cell lines covering 78 distinct cancer types, we show that co-mutations are associated with prognosis, drug sensitivity, and disparities in sex, age, and race. Some co-mutation combinations displayed stronger effects than their corresponding single mutations. For example, co-mutation TP53:KRAS in pancreatic adenocarcinoma is significantly associated with disease specific survival (hazard ratio = 2.87, adjusted p-value = 0.0003) and its prognostic predictive power is greater than either TP53 or KRAS as individually mutated genes. Functional analyses revealed that co-mutations with higher prognostic values have higher potential impact and cause greater dysregulation of gene expression. Furthermore, many of the prognostically significant co-mutations caused gains or losses of binding sequences of RNA binding proteins or micro RNAs with known cancer associations. Thus, detailed analyses of co-mutations can identify mechanisms that cooperate in tumorigenesis.

Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

The study’s primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying “high-risk MCL” was evaluated by receiver operating characteristics (ROC). The four radiomic features, “Uniformity”, “Entropy”, “Skewness” and “Difference Entropy” showed predictive significance for relapse (p < 0.05)—in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature “Uniformity” (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter “Short Axis,” were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.

German Laypeople’s Willingness to Donate Toward Insect Conservation: Application of an Extended Protection Motivation Theory

It is essential to engage the public in conservation measures to conserve insects. We investigate the Protection Motivation Theory (PMT), as well as knowledge, attitudes, and sociodemographic variables (gender, age, education level, and income) as predictors of willingness to donate (WTD) and actual donations to insect conservation for a representative German sample (N = 515; MAge = 49.36, SD = 16.73; female = 50.1%). The PMT subcomponents severity, self-efficacy, and response efficacy, as well as attitudes toward insects, income, and education level, significantly predicted WTD. In contrast, severity, response barriers, age, gender, and the WTD significantly influenced actual donations. Overall, components of the PMT have high predictive power for both dependent variables. Our results suggest that an intention-behavior gap exists between the intention to donate and the actual donation toward insect conservation. Measures to increase WTD and actual donations for insect conservation are discussed.

Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour

PurposeGiven the increasing industry interest in blockchain technologies for supply chain management and product traceability, this paper aims to investigate consumer purchasing intentions for blockchain traceable coffee and their psychosocial antecedents, utilising an extended model of the theory of planned behaviour (TPB).Design/methodology/approachAn online questionnaire study of 123 participants was deployed, using two traceability systems (one based on blockchain and one on a more established traceability certification) for organic coffee.FindingsAdding variables such as environmental protections, trust and habits significantly increased the predictive power of TPB. The results suggest that attitude, perceived behavioural control and environmental protections drive intentions to purchase blockchain traceable coffee.Research limitations/implicationsApart from establishing the factors affecting consumer intentions for blockchain traceable coffee, this study validates the TPB as a model of explaining coffee purchasing intentions and provides evidence of new variables that can significantly increase the model's predictive power.Practical implicationsThe proposed format of presenting traceability information along with the significant variables revealed in our study can function as a guide for designing product features and marketing strategies for blockchain traceable organic coffee. Increasing consumer awareness on product traceability will also play a crucial role in the success of these products.Originality/valueThis study is the first to explore consumer purchasing intentions for blockchain traceable coffee and establish the psychosocial variables behind them contributing, in that way, to an understudied area in academic literature as well as providing insights for a more consumer-centric design of such products.

Sun, Sleep, and Satisfaction: Mediating Role of Depression and Source of Endogeneity among Middle-aged and Older Adults in China

Purpose To examine: (i) depression as a mediator in effects of sleep duration and quality on life satisfaction (LS), (ii) source of endogeneity in self-reported data on sleep, and (iii) predictive power of sleep duration and quality on LS. Methods Panel data of 22,674 observations from the China Health and Retirement Longitudinal Survey (2015 & 2018) was used. Sleep was assessed with self-reported duration and quality. Depression was measured by the 10-question version of the Center for Epidemiological Survey - Depression. LS was rated by five scales. Fixed-effects ordered logit models were used to determine the effect of sleep duration and quality on life satisfaction and the mediating role of depression. We used instrumental variable strategy to explore the source of endogeneity. Information value and random forest model were used to examine the predictive power of sleep measures duration and quality. Results Sleep duration and quality were found to improve life satisfaction via lower depression score. Non-agricultural employed population with urban hukou (household registration) accounted for the endogeneity, but the instrument variable sunset failed the weak instrument test. Sleep measures were found to predict life satisfaction, especially for the lower life satisfaction groups. Conclusion Our findings suggest the importance of sleep and the study of the associations between solar cues, social schedules, and sleep. Policy makers of social care of older adults might consider sleep intervention among this population.

Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.