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Organic light-emitting diodes (OLEDs) exhibiting thermally activated delayed fluorescence (TADF) demonstrate high quantum efficiencies. However, their drawbacks include a short device lifetime and low efficiencies in the high-luminance region. This study synthesizes a blue TADF emitter by modifying robust, stable 5,9-dioxa-13b-boranaphtho[3,2,1-de]anthracene (DOBNA). By introducing a benzonitrile group into DOBNA as an acceptor with extended π-conjugation and using a carbazole donor, a donor–acceptor–donor structure (DOB-1) is formed. To increase the lifetime and efficiency, carbazole derivative donors substituted with tert-butyl (DOB-2) or phenyl (DOB-3) are introduced at both the 3 and 6 positions to enhance the electron-donating characteristics, achieving a high photoluminescence quantum yield (PLQY), a superior horizontal transition dipole orientation (HTDO) ratio, and fast reverse intersystem crossing (RISC). As-synthesized DOB-2 and DOB-3 exhibit higher PLQYs (0.92 and 0.95) than DOB-1 (0.83), along with fast RISC (∼105 s−1) and better HTDO ratios (0.86 and 0.90) than DOB-1 (0.74). The TADF OLEDs employing DOB-2 and DOB-3 demonstrate 1.69 and 2.05 times higher external quantum efficiencies, respectively. Notably, the DOB-2 and DOB-3-based OLEDs exhibit operational lifetimes (LT50 at 2000 cd m−2) of 706 and 1377 h, which are 2.52 and 4.54 times longer than that of DOB-1 emitters. Our results will advance research on efficient, long-lifetime TADF materials for OLEDs. [ABSTRACT FROM AUTHOR]

Enhancing corporate accountability in areas such as environment, social, and governance (ESG) has solidified its role in the discussion on improving corporate resilience and growth. ESG management activities not only augment corporate sustainability and risk control but also influence the professional roles and personal lives of members through their perceived ESG. Historically, most ESG research has centered on the interrelation of corporate ESG endeavors and outcomes, while studies focusing on the influence of perceived ESG on members have been sparse. In this light, our investigation, rooted in the conservation of resources theory, aimed to delineate the mechanistic link between perceived ESG and members' psychological well-being. This study employed a stratified random sampling technique and collected data across three waves, each spaced four weeks apart. Our sample comprised 325 Korean employees working in administrative, technical, service, and sales roles. This study recruited 325 Korean employees across three time-lagged phases and found that ESG comprehension enhances job meaningfulness, subsequently amplifying psychological wellness. Intriguingly, as pay satisfaction escalates, the mediating role of job meaningfulness between perceived ESG and well-being intensifies. Our study underscores that for organizations to harness perceived ESG to boost psychological well-being via job meaningfulness, managing pay satisfaction is imperative. These findings highlight a crucial policy implication: policymakers must actively promote ESG awareness and incorporate it into employee compensation strategies. This integration is essential to cultivating a healthier, more engaged workforce and driving long-term organizational success. [ABSTRACT FROM AUTHOR]

Thermally activated delayed fluorescence (TADF) materials with multiple resonance (MR) effects have been widely studied owing to their narrow full width at half maximum (FWHM) and high efficiency. In particular, the MR effect is due to the separation of the distribution of the highest occupied molecular orbitals and lowest unoccupied molecular orbitals inside a molecule by the combination of boron and nitrogen, resulting in a low energy gap between the singlet and triplet energies (EST; < 0.3 eV). However, although TADF materials with MR effects have a low ΔEST, they typically exhibit a high oscillator strength. These features favour their use as the final emitting materials in hyperfluorescence (HF) devices. In this study, we synthesise a green TADF material, tBN-4BF, with an MR structure and evaluate its effectiveness in HF devices using known TADF materials, 2,4,5,6-tetrakis(9H-cabazol-9-yl)isophthalonitrile and 3,3′,3′′-(1,3,5-triazine-2,4,6-triyl)tris(4-(9H-carbazol-9-yl)benzonitrile) (TCz-4mCNTrz), as sensitizers. The evaluation shows an FWHM of 35 nm in a single device with an MR dopant in 2-biphenyl-4,6-bis(12-phenylindolo[2,3-a]carbazole-11-yl)-1,3,5-triazine as the host, achieving 29.3 h at LT50@5000 nit. In addition, an HF device utilising TCz-4mCNTrz as a sensitizer achieves 124 h at an LT50@5000 nit. These results demonstrate the potential of green TADF materials and the lifetime of the HF devices in which they are applied. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) refers to the self-reported persistent cognitive decline despite normal objective testing, increasing the risk of dementia compared to cognitively normal individuals. Objective: This study aims to investigate the attributes of SCD patients who demonstrated memory function improvement. Methods: In this prospective study of SCD, a total of 120 subjects were enrolled as part of a multicenter cohort study aimed at identifying predictors for the clinical progression to mild cognitive impairment or dementia (CoSCo study). All subjects underwent 18F-florbetaben PET and brain MRI scans at baseline and annual neuropsychological tests. At the 24-month follow-up, we classified SCD patients based on changes in memory function, the z-score of the Seoul verbal learning test delayed recall. Results: Of the 120 enrolled patients, 107 successfully completed the 24-month follow-up assessment. Among these, 80 patients (74.8%) with SCD exhibited memory function improvements. SCD patients with improved memory function had a lower prevalence of coronary artery disease at baseline and performed better in the trail-making test part B compared to those without improvement. Anatomical and biomarker analysis showed a lower frequency of amyloid PET positivity and larger volumes in the left and right superior parietal lobes in subjects with improved memory function. Conclusions: Our prospective study indicates that SCD patients experiencing memory improvement over a 24-month period had a lower amyloid burden, fewer cardiovascular risk factors, and superior executive cognitive function. Identifying these key factors associated with cognitive improvement may assist clinicians in predicting future memory function improvements in SCD patients. [ABSTRACT FROM AUTHOR]

Background: Currently positron emission tomography (PET) is used as the initial or sole biomarker of β‐amyloid (Aβ) brain pathology, which may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. Machine learning (ML)‐based EEG biomarkers may address these challenges. Previous studies have confirmed their ability to accurately discriminate between normal, mild cognitive impairment (MCI) and Alzheimer's dementia. However, few quantitative EEG (QEEG) biomarkers have been validated with Aβ PET. We developed a QEEG‐ML algorithm to predict brain Aβ pathology among subjective cognitive decline (SCD) and MCI patients, and validated it using Aβ PET. Method: EEG (19‐channel, eye‐closed, resting‐state) and Aβ PET data were collected from 160 human subjects with MCI (77 Aβ+, 83 Aβ‐). We excluded 45 data (23 Aβ+, 22 Aβ‐) for test verification. QEEG absolute power, relative power, power ratio, and connectivity between channels (iCoherence) comprised the input features, from which the most relevant predictive features were identified using four methods (Random Forest Importance (GBM, XGB), ElasticNet, Whitney‐Mann). We then trained six ML algorithms (SVM, Logistic, KNN, Naive Bayes, Random Forest(GBM/XGB)) using each relevant feature set, yielding 24 models (4 sets * 6 algorithms). The 45 test data sets were input into each model to compare their performance. Then, 111 MCI data (56 Aβ+, 55 Aβ‐) and 165 SCD data (31 Aβ+, 134 Aβ‐) were classified by the model for 3rd validation. Result: In test validation, the best‐performing model showed 77.8% accuracy, 81.8% sensitivity, 73.9% specificity in discriminating Aβ+ from Aβ‐. In 3rd data validation, the best‐performing model showed 72.1% accuracy, 71.4% sensitivity, 72.7% specificity in MCI. In SCD, it showed, 69.1% accuracy, 64.5% sensitivity, 70.2% specificity. Conclusion: These findings suggest that our novel ML‐based QEEG biomarker can accurately predict the presence of brain Aβ plaque. Additional benefits of such a biomarker include reduced expense, wide availability, and high‐throughput screening and response monitoring. Future studies will assess utility as primary AD screens, adjunctive use with PET, and as a support in clinical rationales for Aβ PET and treatment choice in AD. [ABSTRACT FROM AUTHOR]

This study explored the effects of trust in a supervisor (TIS) on social loafing behaviors of employees. In addition, this study examined the mediating effect of perceived organizational support (POS) on the relationship between trust in a supervisor and employees' social loafing behaviors. It also examined the moderating effects of perceived organizational politics (POP) on the relationship between TIS and POS, TIS and social loafing behaviors, and POS and social loafing behaviors. Data were collected from local government employees in Korea, and the final sample was 260. Our results indicate that trust in a supervisor has indirect negative effects on social loafing behaviors mediated by POS. In addition, it was found that the effects of TIS on POS and POS on social loafing behaviors were moderated by POP. The results of this study contribute to the extant literature on social loafing behaviors. Moreover, the findings imply that political behaviors in organizations might induce social loafing behaviors. [ABSTRACT FROM AUTHOR]

Background: While the central location is a known adverse prognostic factor in lung cancer, a precise definition of central lung cancer has not yet emerged. Purpose: To determine the prognostic significance of central lung cancer (defined by location index) in resected T1-sized early-stage non-small cell lung cancer (NSCLC). Material and Methods: Patients with resected T1-sized early-stage NSCLC between 2010 and 2015 at a single tertiary cancer center were retrospectively reviewed. Central lung cancer was defined by a location index of the second tertile or less. Kaplan–Meier analysis with log-rank test and multivariable Cox regression analysis were performed to analyze the relationship between central lung cancer and the prognosis of relapse-free survival (RFS) and overall survival (OS). Inter-observer agreement was assessed using Cohen's kappa value and intraclass correlation coefficient (ICC). Results: Overall, 289 patients (169 men; median age 65 years; interquartile range 58–70 years) were evaluated. Central lung cancer (defined by location index) was adversely associated with RFS (P = 0.005) and OS (P = 0.01). Multivariable Cox regression analysis showed that central lung cancer was independently associated with poor RFS (adjusted hazard ratio 1.91; 95% confidence interval [CI] 1.12–3.24; P = 0.017) and OS (adjusted hazard ratio 1.69; 95% CI 1.04–2.74; P = 0.033). Location index demonstrated excellent inter-observer agreement (Cohen's kappa value 0.88; 95% CI 0.82–0.93) with a high ICC (0.98; 95% CI 0.97–0.98). Conclusion: Central lung cancer defined by a location index of the second tertile or lower is an independent adverse prognostic factor in resected T1-sized early-stage NSCLC. [ABSTRACT FROM AUTHOR]

Purpose: This study used quantitative electroencephalography (QEEG) to investigate the characteristics of attention-deficit/hyperactivity disorder (ADHD) subtypes in children. Patients and Methods: There were 69 subjects (42 with ADHD and 27 neurotypical (NT)) in this study. A semi-structured interview was conducted with each participant for psychiatric diagnostic evaluation. We measured the absolute and relative power in 19 channels and analyzed QEEG using the following frequency ranges: delta (1– 4 Hz), theta (4– 8 Hz), alpha 1 (8– 10 Hz), alpha 2 (10– 12 Hz), beta 1 (12– 15 Hz), beta 2 (15– 20 Hz), beta 3 (20– 30 Hz), and gamma (30– 45 Hz). Group analyses and EEG noise preprocessing were conducted using iSyncBrain, a cloud-based, artificial intelligence EEG analysis platform. Analysis of covariance adjusted for IQ, age, and sex was used. Results: QEEG analysis revealed three ADHD subtypes, characterized by (A) elevated relative fast alpha and beta power, (B) elevated absolute slow frequency (delta and theta power), or (C) elevated absolute and relative beta power. A significant difference was found in the Korean ADHD Rating Scale (K-ARS) among the four groups (df=3, F=8.004, p< 0.001); group C had the highest score (25.31± 11.16), followed by group A (21.67± 13.18). The score of group B (12.64± 7.84) was similar to that of the NT group (11.07± 6.12) and did not reach the cut-off point of the K-ARS. In the Wender–Utah Rating Scale (WURS), group B score (55.82± 23.17) was significantly higher than the NT group score (42.81± 13.26). Conclusion: These results indicate that children with ADHD do not constitute a neurophysiologically homogenous group. Children with QEEG subtype B (elevated slow frequency) may be difficult to distinguish from normal children using the K-ARS, which is the most common screening tool for ADHD. Moreover, parents of children with this subtype may be less sensitive to observing ADHD symptoms. [ABSTRACT FROM AUTHOR]

Modern organizational environments encounter serious competition and paradoxical situations. This study discusses the effect of paradoxical leadership on overcoming competitive and paradoxical situations happening in the Korean workplace. More specifically, it investigates the dynamic relationship between paradoxical leadership and involvement in creative tasks in a Korean context and examines whether creative self-efficacy positively mediates this relationship. Our research design addresses the moderating role and moderated mediating role of task complexity in the relationship between paradoxical leadership and the involvement in creative tasks via creative self-efficacy. The main hypotheses were tested by using a cross-sectional design and administering questionnaires to 268 employees working in Korean firms. Empirical analysis revealed that paradoxical leadership is positively related to involvement in creative tasks and creative self-efficacy and that creative self-efficacy positively mediated the relationship between paradoxical leadership and involvement in creative tasks. Importantly, as the relationship between paradoxical leadership and creative self-efficacy depends on task complexity, the mediated relationship was effective under high task complexity. Uncovering the relationship between paradoxical leadership and involvement in creative tasks with the mediating role of creative self-efficacy and the moderated mediating role of task complexity can provide useful theoretical and managerial implications. [ABSTRACT FROM AUTHOR]

Many previous studies on creativity have focused on discovering positive factors to improve creativity and innovation performance from leader, individual, and organizational perspectives. However, research on factors that hinder creative performance was relatively insufficient. This study examines leaders' behavior that hinders employees' creative performance by focusing on abusive supervision. Based on the Korean employee context, our research model draws upon constructs of abusive supervision, relational conflict, employee silence, and creative performance to hypothesize serial mediation mechanisms connecting abusive supervision to creative performance. Using survey data of 555 Korean employees, we find that abusive supervision is negatively related to creative performance. We also find that both relational conflict and employee silence mediate the relationship between abusive supervision and employee creative performance. More importantly, our empirical analysis indicates that a serial mediation effect testing a dual coordination effect was identified in the process of the leader's abusive supervision leading to employee's creative performance. Although many previous studies were focused on a single medium effect in the relationship between leadership types and employee creativity, this study applied the serial mediation effects in the relationship to test a dual medium effect. We further addressed a more complex process to explain the path of reducing creative performance by supervisor abusive supervision. We conclude by discussing both theoretical and practical implications. [ABSTRACT FROM AUTHOR]

In the era of a graying workforce, individuals and their employers are concerned with the impact of the graying workforce on the level of engagement at work. Contrary to the myths about older workers being less engaged, statistics have shown that the level of engagement is higher as people age. Within the broad framework of conservation of resources theory in the area of work engagement, the current study aims to clarify how older workers are more engaged at work than younger workers. Building upon socioemotional selectivity theory and continuity theory from a life-span perspective, we explain how individuals gain more resources as they age, including the abilities to regulate emotion and to maintain a career identity. We argue that this positive relationship between age and resources explains why older people have high levels of work engagement. We tested our predictions with a sample of 613 workers in the United States. The study demonstrates that well-regulated emotion and strong commitment to career mediate the positive relationship between age and work engagement simultaneously. These findings contribute to the understanding of an aging workforce's engagement in the workplace. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

With today's increasingly dynamic and competitive business environment, creativity is critical for enterprises to enhance their competitiveness. Companies today invest and seek new ways to enhance creativity of employees within the organization. Our study describes the effects of servant leadership, psychological safety, and employee well-being on creativity under the conservation of resources theory. We used a sample of 252 full-time employees in the United Kingdom who had been recruited online and collected their data for analysis. We conducted confirmatory factor analyses to test the validity of the measurement model and regression to evaluate the direct effects. Subsequently, we used bootstrapping to confirm mediation and serial mediation effects. The results showed that servant leadership was positively related to creativity and that psychological safety and employee well-being were serial mediators between them. [ABSTRACT FROM AUTHOR]

We describe the utility of a standardized index (Z-score) in quantitative EEG (QEEG) capable of when referenced to a resting-state, sex- and age-differentiated QEEG normative database (ISB-NormDB). Our ISB-NormDB comprises data for 1,289 subjects (553 males, 736 females) ages 4.5 to 81 years that met strict normative data criteria. A de-noising process allowed stratification based on QEEG variability between normal healthy men and women at various age ranges. The ISB-NormDB data set that is stratified by sex provides a unique, highly accurate ISB-NormDB model (ISB-NormDB: ISB-NormDB-Male, ISB-NormDB-Female). To evaluate the trends and accuracy of the ISB-NormDB, we used actual data to compare Z-scores obtained through the ISB-NormDB with those obtained through a traditional QEEG normative database to confirm that basic trends are maintained in most bands and are sensitive to abnormal test data. Finally, we demonstrate the value of our standardized index of QEEG, and highlight it's capacity to minimize the confounding variables of sex and age in any analysis. [ABSTRACT FROM AUTHOR]

The use of positron emission tomography (PET) as the initial or sole biomarker of β-amyloid (Aβ) brain pathology may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aβ pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aβ PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1–4 Hz), theta (4–8 Hz), alpha 1 (8–10 Hz), alpha 2 (10–12 Hz), beta 1 (12–15 Hz), beta 2 (15–20 Hz), beta 3 (20–30 Hz), and gamma (30–45 Hz) calculated by FFT and denoised by iSyncBrain®. The resulting 152 features were analyzed using a genetic algorithm strategy to identify optimal feature combinations and maximize classification accuracy. Guided by gene modeling methods, we treated each channel and frequency band of EEG power as a gene and modeled it with every possible combination within a given dimension. We then collected the models that showed the best performance and identified the genes that appeared most frequently in the superior models. By repeating this process, we converged on a model that approximates the optimum. We found that the average performance increased as this iterative development of the genetic algorithm progressed. We ultimately achieved 85.7% sensitivity, 89.3% specificity, and 88.6% accuracy in SCD amyloid positive/negative classification, and 83.3% sensitivity, 85.7% specificity, and 84.6% accuracy in MCI amyloid positive/negative classification. [ABSTRACT FROM AUTHOR]

This study is an investigation of the relationships among job meaningfulness, work engagement, and performance, including testing for a possible mediation effect of work engagement on the relationship between job meaningfulness and performance. We examine task interdependence as a boundary condition that facilitates employee engagement using two-stage multiple-source respondent data drawn from a sample of 183 Uzbek employees from public organizations and their 47 supervisors to test the hypotheses. The research findings confirm a positive association between job meaningfulness and engagement and the relationship between work engagement and performance. Mediation analysis using bootstrapping indicated that work engagement explained the influence of meaningfulness on performance. Furthermore, task interdependence negatively moderated the relationship between meaningfulness and engagement. This study responds to calls for researchers to identify the key and situational drivers of work engagement as well as examine the importance of meaningfulness in the public sector. It also increases the external validity of the findings by examining the relationship between engagement and performance in a non-Western context, namely, Islamic Uzbekistan. Despite the limitations of this research, the empirical findings contribute to the growing body of research on work engagement and meaningfulness in public organizations. [ABSTRACT FROM AUTHOR]

Background: Our everyday actions and decisions involve a complex coordination of various cognitive functions. Severe deterioration of such functions, commonly referred to as dementia, adversely affects quality of our lives. However, through early screening of the disease at its pre‐clinical stage and appropriate treatment, they can be reverted. Current diagnostic criteria for dementia utilize questionnaire‐based cognitive function assessments, such as the mini‐mental state examination. However, it is statistically less robust, where further tests such as positron emission tomography are required for accurate diagnosis. This is laborious, expensive and expose patients to harmful radiation. Therefore, we propose a quantitative electroencephalography (QEEG) based approach that distinguishes Alzheimer's disease dementia (ADD) from a non‐ADD group that include mild cognitive impairment and subjective cognitive decline data. Method: 19 channel EEG data employed in the present study were clinically acquired from Chung‐Ang university hospital, in eyes‐closed resting state. Artefacts were eliminated through independent component analysis and bad epoch rejection. Through spectral analysis, the powers of a frequency spectrum (1‐45Hz) were computed with 0.25Hz resolution. Henceforth, the Z‐score of each value were calculated through iMediSync's age‐ and sex‐differentiated normative database and were mapped into a rectangular arrangement in accordance with channel locations on the scalp. The rearranged Z‐score matrices were visualized, clearly exhibiting both spectral and spatial information. Gamma spectrum (30‐45Hz) was excluded due to its vulnerability to contaminations induced by external noises and muscle movements. The final dataset (N = 732; 137 ADD, 595 non‐ADD) was established, where the 75% of non‐ADD were first excluded as test data due to data imbalance. The remaining data were split into 2 to 8 ratio for testing (N = 503; 27 ADD; 476 non‐ADD) and training (N = 229; 110 ADD; 119 non‐ADD). Result: The final 18‐layer Residual network model showed test accuracy at 88.5% with ADD sensitivity at 88.9% and specificity at 88.4%. Explainable artificial intelligence algorithm was utilized for the inference and verification of classification criteria. Conclusion: The outstanding classification results of the established model upholds QEEG utility in the distinguishment of dementia from its pre‐clinical stages. Continuous refinement will bolster its potential in the diagnosis several other neurological diseases. [ABSTRACT FROM AUTHOR]

Background: Early screening of dementia at its pre‐clinical stage is crucial, seeing as how deteriorated cognitive functions can be recovered through appropriate medicinal practices. The pre‐clinical stage, which we refer to as mild cognitive impairment (MCI) is known to affect the alpha rhythm of the brain activity. Hence, our study aims to establish quantitative EEG (qEEG)‐based alpha asymmetry biomarker that aids the diagnoses of MCI. Method: The qEEG dataset were composed of age‐ and sex‐ matched subjects (N = 634, 317 healthy control (HC), 317 MCI), acquired in accordance with the 10‐20 system and under resting state with their eyes being closed. Subjects were recruited by Chung‐Ang University (153 HC, 111 MCI), iMediSync(164 HC, 76 MCI), Dong‐a Medical Center(84 MCI) and Pusan Medical Center(46 MCI). The features were classified into four types: the absolute and relative region alpha power (AAP/RAP) of six regions (Prefrontal, Frontal, Temporal, Central, Parietal, Occipital); alpha power asymmetry (APA) between regions that align in anterior‐posterior and lateral planes; occipital alpha peak frequency (OPF) that analyzes peak frequencies. In order to highlight the alpha variability, EEG signals were bandpass filtered (6.5‐12Hz). Filtered signal at each channel was categorized into slow (6.5‐8Hz), middle (8‐10Hz) and fast alpha (10‐12Hz). The mean and standard deviation of their FFT were computed through moving time window. Henceforth, the feature set were used to train machine learning algorithms. Data were split into 8(training N = 507) to 2(test N = 127) ratio. 101 subjects were randomly selected and used as validation set. The algorithm that exhibited the best classification performance were determined through 5‐fold cross validation (CV) results. Result: Support vector machine (SVM) showed the best 5‐fold CV results – 84.61% average accuracy, 74.02% sensitivity, 95.25% specificity, 94.15% precision and F1‐Score of 0.8273. The RAP decreased for all regions and OPF were slower in MCI group when compared to HC. APA patterns of the right intra‐hemisphere exhibited most significant differences. Conclusion: This study illustrates the potential of APA as a biomarker for MCI. Current applications of APA are confined to emotional‐related studies, such as depressive disorder. The reported cases of MCI‐associated depression also uphold the validity of APA as a biomarker for MCI. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease dementia (ADD) is a predominant neurodegenerative disorder that results severe abnormalities in cognitive functions. However, most diagnoses rely on assessments made by individual clinical experts. Hence, we aim to establish a novel biomarker which can provide both accuracy and objectivity. Electroencephalogram (EEG) data were utilized for the training of artificial intelligence (AI) models, making use of its cost‐advantage and simple utility. Method: 19‐channel EEG data were recorded at Chung‐Ang University hospital in accordance with international 10‐20 system at resting state with eyes being closed. We first converted 19 channels into 68 regions of interest (ROIs) using standardized low resolution brain electromagnetic tomography (sLORETA). Henceforth, imaginary part of coherence (iCoh) between each ROI were calculated, reflecting functional connectivity between the ROIs. The iCoh values were then utilized in the calculation of the brain's functional brain network. The subjects were divided into non‐ADD and ADD group. The resulting training dataset consisted of 118 non‐ADD and 110 ADD subjects, where the number of non‐ADD training data were adjusted in order to account for data imbalance. The test dataset included 476 non‐ADD and 27 ADD subjects. The classification model was trained using 68 ROI source powers and functional brain network features. Only statistically significant features were selected for feature reduction purposes. Through primary classification via light gradient boosting method (LGBM), top 30 features with high importance values were selected and were employed in the training of our final LGBM classification model. Result: Through appropriate hyperparameter tuning, the LGBM classification model resulted in 87.67% accuracy, 88.89% sensitivity and 87.61% specificity. From the receiver operating characteristic (ROC) curve, 0.9506 area under ROC curve (AUC) was achieved. Conclusion: The LGBM model resulted in promising classification performance, solely through EEG features. In addition, functional brain network features had higher importance in comparison with source power features, indicating high potential of EEG brain network features in distinguishment of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Background: The pre‐clinical stage of dementia, mild cognitive impairment (MCI) carries various pathological pathways and respective prognoses. The two foremost causes of MCI and dementia that have been identified through autopsy diagnoses are Amyloid plaques and Lewy body, commonly referred to as Alzheimer's disease (AD) and Lewy body disease (LBD) respectively. Although correct identification of pathological pathways plays a crucial role in effective designing of treatment methods, MCI subtypes are underexploited in the current diagnostic paradigm due to multiple reasons, mainly cost‐effectiveness. Hence, we utilized quantitative electroencephalography (QEEG) that are cheap and convenient to record in the distinguishment of MCI subtypes. Method: A total of 151 MCI patients' EEG data were aggregated into 3 groups, pure AD (n = 29), pure LBD (n = 88), and mixed (n = 34). The mixed type was further subdivided into main AD (n = 31) and main LBD (n = 3) groups, in accordance with AD/LBD tendency exhibited by the patients. The clinical labelling was brought by the experienced experts of Yonsei Severance hospital, South Korea. 1D SE‐ResNet‐based classification model was established for quantitative investigation of AD and LBD propensity. The power spectrum density (PSD) in dB/Hz scale were computed from the EEG data and were used to train the model. Due to the small number of data, augmentation was applied to the training data. The final dataset was split into 8 to 1 to 1 ratio (Train n = 7092: 2274 pure AD + main AD; 3694 pure LBD + main LBD, Validation n = 14: 6 pure AD + main AD; 8 Pure LBD + main LBD, Test n = 15: 6 pure AD + main AD; 9 pure LBD + main LBD). Result: The classification model showed validation results of 85.7% accuracy, 83.3% AD sensitivity and 87.5% LBD sensitivity. Test results were at 92.8% accuracy, 83.3% AD sensitivity and 88.9% LBD sensitivity. Conclusion: QEEG‐based deep learning classifier developed in this study successfully distinguished the two main causes of cognitive degeneration in MCI patients. Our model can help identify subtype‐specific spectral trends, which could also make contributions in the establishment of effective treatment methods for MCI. [ABSTRACT FROM AUTHOR]

Background: Early screening and prevention of dementia are widely discussed and studied in the current healthcare paradigm. However, about 70 percent of dementia diagnoses are categorized into Alzheimer's disease (AD), neglecting the presence and influence of Lewy body. The present study aims to distinguish Alzheimer's disease dementia (pure AD) and Lewy body dementia (pure LBD) through quantitative electroencephalogram (QEEG)‐based classification algorithm. The mixed dementia that exhibits both AD and LBD related propensities were further tested for the verification of the established classification model. Method: 19 channel EEG data were recorded in accordance with the 10‐20 system in eyes‐closed resting state. The acquired data were bandpass filtered into the range of interest (1‐45Hz) and re‐referenced into common average reference. Artifacts were removed by bad epoch rejection and independent component analysis (ICA) through an automated, cloud‐based QEEG analysis platform iSyncBrain®. Henceforth, sensor‐level features were extracted and computed for the training of TabNet structure. Due to the data imbalance (N = 146, Pure AD = 48, Pure LBD = 102), time window‐based augmentation has been applied to establish the final dataset (N = 7292, Pure AD = 3376, Pure LBD = 3916), which were then split into 8 to 2 ratio for the training and testing of the classification model. In addition, mixed dementia (N = 136) data were used to further validate the model's performance. Result: The best classification model yielded test accuracy of 80% with pure AD sensitivity at 80% and pure LBD sensitivity 80%. Moreover, the predicted probability distribution for the mixed dementia data were prominently concentrated around 0.5, which reflect that both pure AD and LBD properties exist. Conclusion: Along with the promising classification performance of the established classification model, the centered probability distribution of the mixed dementia data reflect that the model can well distinguish the characteristics that are specific to pure AD and LBD. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the most common cause of dementia. However, accumulation of beta amyloid plague in the brain which is main pathology of AD could show the various spectrum of cognitive functional stage from preclinical level to overt dementia. It is important to understand functional differences between dementia and non‐dementia of AD to predict progression forward to dementia at prodromal or preclinical stage of AD. Method: Nineteen channels of resting state EEGs were measured from 45 Alzheimer's disease subjects who were all confirmed by brain amyloid‐beta PET. Neurocognitive test, activity of daily living (ADL) and MRI were conducted for clinical diagnosis. EEG power spectrum analysis and band‐power ratio, cortical source current density estimated by standardized low resolution brain electromagnetic tomography(sLORETA), functional connectivity between 68 region of interest (ROI) based on imaginary coherence(iCoh) and complex brain networks were analyzed. Result: 20 AD subjects (age: 76.8±7.54) were assessed as dementia, and the other 25 (age: 72.1±6.89) as mild cognitive impairment (MCI) or subjective cognitive decline (SCD) depending on delayed recall memory score. Average CDR was 0.23 in non‐dementia and 1.15 in dementia group. There was no significant difference at delta, but theta power of AD dementia group was significantly increased at all 19 scalp channels. Mid frontal channel (Fz) showed the most distinctive enhancement of theta power. In other hands, beta1 (12∼15Hz) was significantly increased at both temporoparietal area in non‐dementia AD group. These patterns about theta increase and beta 1 decrease in AD dementia group were observed very similarly in cortical source current density. iCoh among ROIs of default mode network (DMN) was significantly decreased at alpha2 band (10∼12Hz). At bilateral frontal pole, rostral middle frontal, anterior cingulate, parahippocampus and precuneus, the degree centrality and characteristic path length significant increased at theta band. In contrast, theta of cluster coefficient and closeness centrality significantly decreased. Conclusion: AD dementia showed increase of theta and decrease of bet1 both at scalp and cortical level comparing to non‐dementia AD subjects. Both global and local network efficiencies via theta wave were significantly deteriorated in AD dementia. QEEG could well discriminate the differences between dementia and non‐dementia in ADs. [ABSTRACT FROM AUTHOR]

Background: As existence of alpha synucleniopathy could affect progression of cognitive impairment in Alzheimer's disease (AD), it's very important to differentiate comorbidity of alpha synucleniopathy in AD to predict progression. To develop affordable EEG‐based discriminating machine‐learning(ML) algorithm for existence of alpha synucleniopathy, we explore differences between AD with vs without it. Method: Based on pattern of cognitive impairment and 3 types of PET scan [18F‐Florbetaben brain amyloid‐beta, FDG, DAT‐PET] , dementia due to Alzheimer's disease [pure ADD] or Lewy Body (pure LBD) or mixed type (AD with LB disease) were clinically classified. We measured 19ch resting state EEG based on international 10‐20. Quantitative analysis of EEG was done by iSyncBrain®. Result: Pure ADD showed the characteristics that general EEG slowing with low total power, relative delta enhancement and desynchronized alpha with low amplitude. Pure LBD showed the pattern of slow alpha peak frequency with intact synchronization, relatively higher EEG total power and frontotemporal theta enhancement. Compared to pure ADD, ADD with LB showed mixed pattern of ADD and LDB. Total power was higher, alpha wave showed intact synchronization but theta power was rather enhanced at frontal(Fz; p‐value < 0.05) and bilateral temporal(T3,5, T4,6; p‐value < 0.05) area that was reported as characteristic pattern of progressive type of AD by previous studies. Conclusion: These findings imply that ADD could have different EEG oscillation characteristics according to the Lewybody disease. Next step, we will develop the machine‐learning algorithm to discriminate the Lewybody disease based on specific EEG features and validate it. [ABSTRACT FROM AUTHOR]

Our previous study demonstrated that phlorotannin supplement had a sleep-promoting effect in rodents. In the present study, we investigated whether the phlorotannin supplement could improve sleep in subjects with self-reported sleep disturbances. In a randomized, double-blind, placebo-controlled trial, 24 subjects consumed either a placebo or phlorotannin supplement (500 mg/day) for 1 week, 30-60 min prior to bedtime. Sleep parameters were assessed at baseline and at 1 week with sleep questionnaires and polysomnography. At the end of the treatment period, the complete sets of sleep parameters from 20 subjects. Phlorotannin resulted in a significant increase in "Sleep duration" scores compared to the placebo (p = .044), although there were no significant differences on the total PSQI scores. Polysomnography revealed that wakefulness after sleep onset was significantly lower in the phlorotannin group compared to the placebo group (phlorotannin vs. placebo, -25.5 ± 30.5 vs. -1.7 ± 14.9; p = .045) as well as total wake time (phlorotannin vs. placebo, -0.9 ± 3.0 vs. -6.1 ± 6.8; p = .048). Additionally, the respiratory disturbance index during supine rapid eye movement sleep was significantly lower in the phlorotannin group (p = .035). There were no serious adverse effects in either group. Our data suggest that the phlorotannin supplement improved sleep maintenance (WHO ICTRP: KCT0001892). [ABSTRACT FROM AUTHOR]

The precedents of creativity comprise an important research topic that could help organisations survive fierce competition. To contribute to the literature on creativity, the authors examined the roles of competence and perceived organisational support (POS) in the relationship between servant leadership and follower creative behaviour. The authors proposed and tested a moderated mediation model with data from leader-follower dyads collected in a Vietnamese engineering firm. The results showed that follower competence is a significant mediator of the relationship between servant leadership and follower creativity. Furthermore, POS has a moderated mediating role, such that the mediated relationship (i.e., servant leadership, competence, and creativity) is more salient under high POS than under low POS. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

This study explored the effects of inclusive leadership on employee well-being and innovative behavior. We also investigated the mediating role of person-job fit in these relationships. We tested these effects on a sample of 207 employees in five telecommunication companies in Vietnam, using a questionnaire survey. The results showed that inclusive leadership is positively related to employee well-being and innovative behavior, and that person-job fit mediates these relationships. The study makes theoretical contributions to the literature of leadership and organizational psychology, and suggests useful managerial implications for organizations to boost employee well-being and innovative behavior. Taking a cultural approach, this study provides empirical cross-cultural validity of the effect of inclusive leadership on employee well-being. [ABSTRACT FROM AUTHOR]

We examine the influence of supervisors' close monitoring on employees' creativity and knowledge sharing, both of which are important to the enhanced performance and survival of organizations in this era of uncertainty and change. We also identify the mechanism through which supervisors' close monitoring affects employees' creativity and knowledge sharing. A survey was conducted with military officers in South Korea, among whom supervisor-employee interactions occur daily. A regression analysis of 163 supervisor-employee dyads shows that supervisors' close monitoring has a negative impact on employees' creativity and knowledge sharing; a mediation test using a bootstrapping methodology shows that supervisors' close monitoring has a significantly negative indirect impact on employees' creativity and knowledge sharing with leader-member exchange (LMX) as the mediator. Our theoretical contribution is to provide an improved understanding of the relationships among the variables. We also offer a practical implication because our findings show that supervisors' close monitoring may hinder employees' creativity and knowledge sharing by undermining LMX. [ABSTRACT FROM AUTHOR]

Purpose: Recent study suggests that psychological issues and eating habits are closely related. In this study, we aimed to find the association between eating habits and intakes of artificial sweeteners with emotional states of schoolchildren using quantitatively analyzing objective biosignals.Methods: The study was conducted at the National Standard Reference Data Center for Korean EEG as a cross-sectional study. Three hundred eighteen healthy children who have not been diagnosed with neurologic or psychiatric disorders were evaluated (168 girls and 150 boys; mean age of 11.8 ± 3.6 years). Analysis indicators were a dietary intake checklist for children's nutrition-related behavior score (NBS), consisting of 19 items; food frequency questionnaires (FFQs), consisting of 76 items; the Child Depression Inventory (CDI); State-Trait Anxiety Inventory-State (STAI-S); State-Trait Anxiety Inventory-Trait (STAI-T); electroencephalograph (EEG); and heart rate variability (HRV).Results: Higher scores on the CDI, STAI-S, and STAI-T indicate negative emotions, and these scores were significantly decreased from the first to the fourth quartiles. The HRV results showed that the standard deviation of all normal-to-normal (SDNN) intervals was significantly higher in the first quartile than in the fourth quartile (p < 0.05). The intakes of artificial sweeteners and processed foods such as hamburgers correlate with higher theta/beta ratios, and intakes of natural foods such as legumes and fruits correlate with lower theta/beta ratios (p < 0.05).Conclusions and Implications: From this result we confirmed a link between overall nutritional behavior, food additive intakes, and emotion in apparently healthy children and adolescents. [ABSTRACT FROM AUTHOR]