More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.

Child conduct problems (CP) reflect a heterogeneous collection of oppositional, aggressive, norm-violating, and sometimes violent behaviors, whereas child callous–unemotional (CU) behaviors reflect interpersonal styles of interactions reflecting a lack of guilt and empathy as well as uncaring and shallow emotional responses to others. Taken together, high levels of child CP and CU behaviors are thought to identify a relatively homogenous group of children at elevated risk for persistent and more severe problem behaviors across childhood and into adulthood. Although a large body of research has examined the developmental etiology of CP behaviors, only recently has a developmental psychopathology approach been applied to early CU behaviors. The current study examines multiple levels of contextual influences during the first years of life, including family socioeconomic status, household chaos, and parenting behaviors, on CP and CU behaviors assessed during the first-grade year. Whereas previous studies found associations between parenting behaviors and child problem behaviors moderated by household chaos, the current study found no evidence of moderation. However, path analyses suggest that the associations between child CP and CU behaviors and the contextual variables of socioeconomic status (family income and parental education) and household chaos (disorganization and instability) were mediated by maternal sensitive and harsh–intrusive parenting behavior. Analyses are presented, interpreted, and discussed with respect to both bioecological and family stress models of development.

While it is well-known that religiosity measures inform modern political alignments and voting behavior, less is known about how people of various religious orthodoxies think about the role of religion in society. To learn more about this veritable “black box” with respect to whether and why people connect their spiritual life to the political world, we conducted several focus groups in randomly selected Christian congregations in a mid-sized Midwestern city. Our analysis offers confirmatory, amplifying, and challenging evidence with respect to the “Three Bs” (believing, behaving, and belonging) perspective on how religion affects politics. Specifically, we show that while contemporary measures of religious traditionalism accurately reflect individuals’ partisan, ideological, and issue preferences, attitudes regarding the broad intersection of faith and politics are perhaps best understood via the presence (or absence) of denominational guidance on questions of the role of religion in society. We conclude by offering suggestions for future survey research seeking to explain the relationship between religion and politics.