Forecasting blood glucose values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network, (...) which is optimized for sequence data, and RNN-based algorithms, stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm was applied to the five prediction models to optimize their weights through ensemble techniques and to yield the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08, 19.25, and 31.30 mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28, 19.99, and 33.13 mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance. (shrink)

BackgroundSelf-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths, self-report multiple choice questionnaires have considerable limitations in nature. With the rise of machine learning and Natural language processing, researchers in the field of psychology are widely adopting NLP to assess psychological construct to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that of psychology due to small data sets and (...) unvalidated modeling practices.AimsThe current article introduces the study method and procedure of phase II which includes the interview questions for the five-factor model of personality developed in phase I. This study aims to develop the interview and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology, and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress. The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction.MethodsPhase I study was conducted to fifty-nine native Korean adults to acquire the personality-related text data from the interview and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 300 Korean adults will be recruited using a convenience sampling method via online survey. The text data collected from interviews will be analyzed using the natural language processing. The results of the online survey including demographic data, depression, anxiety, and personality inventories will be analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress. (shrink)

Meaning in life has been widely recognized as a hallmark of psychological well-being and positive youth development. The goal of this study was to validate the Korean version of the Meaning in Life in Children Questionnaire utilizing the framework suggested by the Standards for Educational and Psychological Testing. Data were obtained from 277 fifth graders aged 10–11 in three elementary schools in Seoul and Gyeonggi through a paper-and-pencil survey. We translated the MIL-CQ, a 21-item self-report measure developed based on Frankl’s (...) “meaning triangle,” into Korean. Psychological well-being measures were also assessed. Validity and reliability data were collected. The content of domains and items was appropriate for measuring MIL among children. A three-factor model consisting of attitude, creativity, and experience pathways was extracted via exploratory factor analysis, and a three-factor hierarchical model including attitude, creativity, and experience as first-order factors and MIL as a second-order factor was confirmed via confirmatory factor analysis. Higher levels of MIL were related to higher levels of satisfaction with life, self-esteem, positive affectivity, and lower levels of negative affectivity. All item fit statistics were acceptable based on the Rasch model. The analysis of the measurement invariance of each item showed that the responses to one item varied by gender, suggesting that additional items might facilitate better measurement of MIL in children. This study provides validity and reliability evidence that K-MIL-CQ is appropriate for measuring MIL among South Korean elementary school students. (shrink)

The cognitive unbinding paradigm suggests that the synthesis of neural information is attenuated by general anesthesia. Here, we analyzed the functional organization of brain activities in the conscious and anesthetized states, based on functional segregation and integration. Electroencephalography recordings were obtained from 14 subjects undergoing induction of general anesthesia with propofol. We quantified changes in mean information integration capacity in each band of the EEG. After induction with propofol, mean information integration capacity was reduced most prominently in the γ band (...) of the EEG . Furthermore, we demonstrate that loss of consciousness is reflected by the breakdown of the spatiotemporal organization of γ waves. We conclude that induction of general anesthesia with propofol reduces the capacity for information integration in the brain. These data directly support the information integration theory of consciousness and the cognitive unbinding paradigm of general anesthesia. (shrink)

Frontoparietal connectivity has been suggested to be important in conscious processing and its interruption is thought to be one mechanism of general anesthesia. Data in animals demonstrate that feedforward processing of information may persist during the anesthetized state, while feedback processing is inhibited. We investigated the directionality and functional organization of frontoparietal connectivity in 10 human subjects anesthetized with propofol on two separate occasions. Multichannel electroencephalography and a computational method of assessing directed functional connectivity were employed. We demonstrate that directed (...) feedback connectivity is diminished with loss of consciousness and returns with responsiveness to verbal command. We also applied the Dendrogram classification method to assess the global organization of directed functional connectivity during consciousness and anesthesia. We demonstrate a state-specific hierarchy and subject-specific subhierarchy in functional organization. These data support the hypothesis that specific states of human consciousness are defined by specific states of frontoparietal connectivity. (shrink)

Alzheimer's disease (AD) is the most common cause of dementia, and there is currently no cure. The “β‐amyloid cascade hypothesis” of AD is the basis of current understanding of AD pathogenesis and drug discovery. However, no AD models have fully validated this hypothesis. We recently developed a human stem cell culture model of AD by cultivating genetically modified human neural stem cells in a three‐dimensional (3D) cell culture system. These cells were able to recapitulate key events of AD pathology including (...) β‐amyloid plaques and neurofibrillary tangles. In this review, we will discuss the progress and current limitations of AD mouse models and human stem cell models as well as explore the breakthroughs of 3D cell culture systems. We will also share our perspective on the potential of dish models of neurodegenerative diseases for studying pathogenic cascades and therapeutic drug discovery. (shrink)

This paper deals with two different problems in which infinity plays a central role. I first respond to a claim that infinity renders counting knowledge-level beliefs an infeasible approach to measuring and comparing how much we know. There are two methods of comparing sizes of infinite sets, using the one-to-one correspondence principle or the subset principle, and I argue that we should use the subset principle for measuring knowledge. I then turn to the normalizability and coarse tuning objections to fine-tuning (...) arguments for the existence of God or a multiverse. These objections center on the difficulty of talking about the epistemic probability of a physical constant falling within a finite life-permitting range when the possible range of that constant is infinite. Applying the lessons learned regarding infinity and the measurement of knowledge, I hope to blunt much of the force of these objections to fine-tuning arguments. (shrink)

Shin and DuBrow propose that a key principle driving event segmentation relates to causal analyses: specifically, that experiences that are attributed as having the same underlying cause are grouped together into an event. This offers an alternative to accounts of segmentation based on prediction error.

The primary purpose of this study is to examine the relationship between the dynamic capabilities embedded in ESG management, which are being pursued by global companies, and corporate performance amid increasing uncertainty. Furthermore, the secondary purpose is to examine the function of environmental uncertainty moderating the DCs-performance relationship. Concerning the analysis tool, this study employs topic modeling with Word2Vec embedding that analyzes unstructured data. This was employed as an alternative method beyond the limitations of the traditional approach, i.e., survey or (...) interview. A DCs dictionary was constructed by redesigning the 12 detailed dimensions of Teece’s DCs into 10 dimensions, and then time series scores of individual global companies were extracted by applying this dictionary to the sustainability reports of 97 companies. Sustainability reports of 153 companies among Fortune Global 500 companies announced in 2020 were originally collected, but in the process of collecting additional financial data about these companies from OSIRIS, a total of only 97 companies was selected in the end due to omission of data. A fixed effect panel analysis was conducted, and the main findings are as follows: First, the DCs embedded in ESG management have a positive or negative direct effect on corporate performance. In particular, a statistically significant relationship was not observed in the innovation oriented capabilities, whereas a statistically significant positive relationship was observed in the customer oriented capabilities. Second, uncertainty moderates the relationship between DCs and corporate performance positively or negatively. Interestingly, the moderating effect of uncertainty only appears in the function of the sensing and reconfiguring capabilities. From this, it can be seen that the function of DCs, which is embedded in the ESG management of global companies, is limited due to the imbalance between the sensing-seizing-reconfiguring capabilities. These findings imply that, despite the positive function of DCs embedded in ESG management, costs and benefits occur at the same time, and DCs can improve performance only if there is an organizational adaptation strategy suitable for uncertainty. Accordingly, business managers need to recognize the importance of pursuing sensing-seizing-reconfiguring capabilities in a balanced way to improve corporate performance through ESG management under uncertainty. (shrink)