Providentia

Predicting Alzheimers

Alzheimer’s, or so-called dementia, is one of the types of diseases that affects brain cells and causes memory loss, difficulty in thinking, and forgetfulness. Thus far, there is no effective treatment for AD, but treatment could be helpful in impeding the progression of the disease. Therefore, early AD diagnosis is effective in limiting the disease from progressing to advanced and dangerous stages. Physicians and radiologists face difficulties in diagnosing healthy nerve cells from soft tissue, and it requires substantial expertise and a long time to decipher the MRI images. Thus, artificial intelligence techniques can play a key role in diagnosing MRI images for early detection of AD. In a study published in Connection Science, four proposed systems with different methodologies and materials for tracking the stages of AD development are presented. The first proposed system is to classify a data set using artificial neural networks (ANNs) and feed-forward neural networks (FFNN) based on the features extracted in a hybrid manner by using a combination of Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) algorithms. The second proposed system is to classify the data set using two deep learning models—ResNet-18 and AlexNet—that are pre-trained based on deep feature map extraction. The third proposed system is to diagnose the data set using a hybrid technology between ResNet-18 and AlexNet models to extract feature maps and machine learning (SVM) to classify feature maps. The fourth proposed system diagnoses the data set using ANN and FFNN algorithms based on the hybrid features of ResNet-18 and AlexNet deep learning models and traditional algorithms (LBP, DWT, and GLCM). All the proposed techniques achieved superior results in the diagnosis of MRI images for early detection of AD. The FFNN algorithms based on the hybrid features extracted by ResNet-18 with features extracted using traditional algorithms achieved an accuracy of 99.8%, precision of 99.9%, sensitivity of 99.75%, specificity of 100%, and AUC of 99.94%. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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What Influences Social Networks?

In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual environment and become aware of others’ opinions and interests. Among them, influential users play an important role in disseminating information on social networks. Identifying such influencers is important in designing techniques to increase the speed of information dissemination. Such techniques are applicable in various fields including viral marketing, preventing the dissemination of harmful information, providing specialized recommendations, etc. Various approaches have been used to detect influencers on social networks, mostly based on the individual’s position in the network structure and their interactions. Considering the strengths and weaknesses of the previous methods, a study presented in the journal Applied Artificial Intelligence presents a novel method based on the content of the users’ posts without considering the network structure. This is done using a combination of high-level features extracted from images to measure the individual’s influence. Users’ images are investigated from three aspects: (1) color scheme, (2) advertising nature, (3) images’ semantics. To describe each of these aspects, feature extraction methods were used with acceptable accuracy in recognizing influential users. Finally, to achieve greater efficiency and precision, feature-combination methods have been investigated to provide an integrated classifier. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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Online music-assisted rehabilitation system for depressed people based on deep learning.

The processing of negative emotions is closely related to the occurrence of depression, and improving the mood of patients with depression has an important effect on improving symptoms.  An article recently published in Progress in Neuro-Psychopharmacology & Biological Psychiatry applies deep learning to the diagnosis and treatment of depression patients, combined with music-assisted rehabilitation to help depression patients recover. Moreover, this paper strengthens deep learning, combines the psychological characteristics of depression to improve the algorithm, and analyzes the algorithm with music-assisted rehabilitation, and obtains an intelligent algorithm that can be applied to the music-assisted rehabilitation system. Finally, this paper constructs an online music-assisted rehabilitation system for depression based on deep learning. The results of the experimental research show that the online music-assisted rehabilitation system for depression based on deep learning can play an important role in the treatment of depression. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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Living with Parkinson's Disease

 Symptoms and medication use in patients with Parkinson’s disease (PD) affect the quality of life of patients and caregivers, yet prior research seldom focused on their experiences with medications. A study published in International Journal of Qualitative Studies on Health and Well-being explored comprehensive living and medication experience from patients with PD and their caregivers. Methods: Patients diagnosed with PD for ≥2 years, with or without their caregivers, were recruited from an outpatient clinic in Taiwan. Semi-structured in-depth interviews were conducted based on the Common Sense Model. A qualitative content analysis was used to identify salient themes from verbatim transcripts. Results: In total, 15 patients and eight caregivers were interviewed. Five themes were derived: (1) symptoms and help-seeking behaviours before a diagnosis, (2) emotional impacts and life adaptations after a PD diagnosis, (3) life affected by medications, (4) experiences of caregivers in taking care of PD patients, and (5) communication between doctors and patients. Conclusions: Patients frequently adjusted their daily schedules to live with PD and the medication side effects. Caregivers struggle to overcome caring burdens and to stay positive to support patients. More attention on providing medication information, mental support, and communication between stakeholders is needed to improve the quality of life of patients and caregivers. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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Crystal Cube: Forecasting disruptive events.

Disruptive events within a country can have global repercussions, creating a need for the anticipation and planning of these events. Crystal Cube (CC) is a novel approach to forecasting disruptive political events at least one month into the future. The system uses a recurrent neural network and a novel measure of event similarity between past and current events. We also introduce the innovative Thermometer of Irregular Leadership Change (ILC). We present an evaluation of CC in predicting ILC for 167 countries and show promising results in forecasting events one to twelve months in advance. We compare CC results with results using a random forest as well as previous work. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

 

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Michael Scott and authentic leadership: What we learn about leadership from The Office.

A new study published in the Journal of Leadership Studies examines the actions of Michael Scott, self‐proclaimed “World's Best Boss”, to determine how he communicates leadership on The Office. Using Northouse's (2020) definition of leadership, Scott's behavior is content analyzed to establish his particular message transmission tendencies. Further, given Scott's unique management style, George's (2003) dimensions of authentic leadership are used to code Scott's behavior as Scranton's regional manager. Based on a quantitative coding of shows, two main findings are advanced. First, Scott's leadership prioritizes verbal over nonverbal messages. Second, Scott's approach to leadership emphasizes the authentic dimensions of close relationships, clear purpose, and a caring heart most often with his employees. Scenes from The Office are identified that validate these findings and feature authentic leadership theory in action. Pragmatically, the current research offers trainers, managers, and teachers a resource for educating trainees and students on the role of verbal and nonverbal messages in leadership as well as a tool for developing authentic leadership in others. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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5GSS: A framework for 5G-secure-smart healthcare monitoring

Currently, the main challenges of the frameworks for healthcare monitoring are as follows: minimising latency, especially for delay-sensitive diseases such as sudden heart disease; identifying health situation in a timely and accurate manner when correlating physiological indicators and context information; reducing the risk of exposure because health data are highly private. In response to the above, a new paper published in the journal Connection Science proposes a framework for 5G-secure-smart healthcare monitoring (5GSS) to achieve the following goals: fast and accurate identification of context-aware health situation, blockchain-based secure data sharing, and low-latency services for emergent patients. The framework consists of a data acquisition layer, a diagnosis and security layer (edge cloud), and a health service layer. The proposed framework adopts the following key technologies: a 5G-IPv6 communication network, context-aware health situation identification-based similarity measure, and blockchain-based secure data sharing mechanism. Finally, a prototype system has been implemented to monitor hypertensive heart disease, confirming its effectiveness with respect to a real scenario. Combined with the data of 45 patients, the prototype system can identify health situations with an accuracy of 96.34% at a sensitivity of 92.46% and a specificity of 93.62%, while significantly reducing the latency and improving the data sharing security. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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Identifying individuals with subtle cognitive decline

Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In a new study published in the journal Neuropsychology, researchers used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline. Method: Individual MoCA item data from the Alzheimer’s Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression. Results: A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others. Conclusions: We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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Is Life Getting Better?

National-level differences in individuals’ ratings of their recollected past, current, and anticipated future life satisfaction (LS) were examined in a new study published in the Journal of Personality and Social Psychology using results from two pioneering projects comprising national-level results for 14 countries (Cantril, 1965) and 15 regions of the world (Gallup International Research Institutes & Charles F. Kettering Foundation, 1976; Study 1), as well as sequential results from the Gallup World Poll based on 137 countries representing a broad range of nations from around the world surveyed from 2005 to 2018 (Study 2). Results from both studies revealed a robust belief that “life gets better” over time (i.e., recollected past < current < anticipated future LS) in nations around the world. Such beliefs were examined in relation to objective and subjective indicators of societal-level functioning. Results replicated across studies in showing that nations with less positive societal functioning and prosperity were characterized by less recollected past improvements in LS, and yet greater anticipated future improvements in LS. Results from Study 2 also revealed that such expectations were positively biased compared to changes over time in national levels of LS; further, greater bias was related to less positive societal-level functioning. In conclusion, examining national-level differences in LS from a subjective temporal perspective provides valuable new insights concerning human development and prosperity across countries, over time, and around the world. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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Mapping principal dimensions of prejudice in the United States.

Research is often guided by maps of elementary dimensions, such as core traits, foundations of morality, and principal stereotype dimensions. Yet, there is no comprehensive map of prejudice dimensions. A major limiter of developing a prejudice map is the ad hoc sampling of target groups. In a new study published in the Journal of Personality and Social Psychology, researchers used a broad and largely theory-agnostic selection of groups to derive a map of principal dimensions of expressed prejudice in contemporary American society. Across a series of exploratory and confirmatory studies, they found three principal factors: Prejudice against marginalized groups, prejudice against privileged/conservative groups, and prejudice against unconventional groups (with some inverse loadings for conservative groups). The authors documented distinct correlates for each factor, in terms of social identifications, perceived threats, personality, and behavioral manifestations. They then discuss how the current map integrates several lines of research, and point to novel and underexplored insights about prejudice. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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