Factors affecting post-traumatic growth in South Korean police officers by age group

Introduction: Police officers are exposed to a variety of traumatic events, which can be physical or psychological. This study aimed to identify factors that influence Post-traumatic growth (PTG) in South Korean police officers, according to age group. Methods: Raw data were collected from September 26 to October 9, 2017, for 269 police officers who are employed at 10 police offices in Seoul. PTG was affected significantly by age, marital status, monthly income, and police rank in pain perception and social support variables among general characteristics. Results: Factors that affected PTG in the ‘20~29’ age group were resilience and pain perception, but in the ‘30~39’ age group, only Pain perception was significant, and in the ‘over 50’ age group, social support and pain perception were significant. Conclusions: The development of mental health programs for police should consider the age group of the patients. Mental health care should also be continuous

When I feel that I am better off, science seems to make the world better off too: inequality, perceived standard of living and perceptions toward science

The COVID-19 pandemic has underscored that divisive views on science and technology span both the Global North and South. This study posits that an individual’s perception of their current living standard acts as a mediating factor linking income inequality to attitudes towards scientific and technological advancements. It contends that rising income disparities shape perceptions, making individuals feel their current living conditions have not surpassed those of prior generations. Consequently, such perceptions diminish the likelihood of recognizing the positive impacts of science and technology on societal progress and future prospects. This paper sheds light on how escalating inequality fosters societal rifts concerning science and technology

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.Comment: 3 figure

Disorder-dependent Li diffusion in Li6PS5Cl\mathrm{Li_6PS_5Cl} investigated by machine learning potential

Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$ at 300 K using large-scale, long-term MD simulations empowered by machine learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a $5\times5\times5$ supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites, rather than at 50% where the disorder is maximized. This phenomenon is explained by the interplay between inter-cage and intra-cage jumps. By elucidating the key factors affecting Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$, this work paves the way for optimizing ionic conductivity in the argyrodite family.Comment: 34 pages, 6 figure

Framework for the validation of simulation-based productivity analysis: focused on curtain wall construction process

 As construction projects have become more complicated in design and construction, it is necessary to establish the construction operational plans in advance. However, there were some limitations in analyzing construction produc­tivity due to the difficulty of collecting accurate data. To address this challenge, this study aimed to develop the frame­work for the validation of simulation-based productivity analysis, which consisted of three measures: (i) validation of the measured productivity data as target variable; (ii) validation of the measured duration data as input variable; and (iii) validation of the simulation model compared to the actual construction process. To verify the feasibility of the proposed framework, this study focused on the curtain wall construction project of “S” office building as a case study. The T-test was applied to investigate the statistical difference between the measure and simulated productivity. It was determined that the significance level α in the T-test for the unloading process was 0.136 with 95% confidence interval; the lifting process, 0.106; and the installing process, 0.311. As a result, there was no significant difference between the measured and simulated productivity. The proposed framework could enable executives and managers in charge of project plan­ning and scheduling to accurately predict the simulation-based productivity. First published online: 13 Jul 201

Blood pressure and dementia risk by physical frailty in the elderly: a nationwide cohort study

Background Midlife hypertension has been recognized as a modifiable risk factor for dementia, but association between blood pressure (BP) in late life and dementia has been inconclusive. In addition, few studies have investigated effects of BP control on dementia incidence in the frail elderly. Thus, this study aimed to investigate the association of BP and dementia incidence with concomitant consideration of physical frailty in the young elderly population. Methods Using the Korean National Health Information Database, we identified 804,024 subjects without history of dementia at age 66. Dementia diagnosis was defined with prescription records of anti-dementia drugs and dementia-related diagnostic codes. Physical frailty was measured using the Timed Up and Go test. Association of BP and dementia incidence with concomitant consideration of physical frailty was investigated using Cox hazards analyses. Results The risks of Alzheimers and vascular dementia increased from systolic BP ≥ 160 and 130–139mmHg, respectively; a significant association of dementia incidence with low BP was not observed. In the analyses stratified by the physical frailty status, low BP was not associated with increased risks of dementia within the groups both with and without physical frailty. Conclusions High BP was associated with increased risks of dementia, especially for vascular dementia, while low BP was not associated with increased risks of any type of dementia in young elderly people, even in those with physical frailty. This study suggests the need for tight BP control in young elderly people, irrespective of frailty status, to prevent dementia and supports the current clinical guidelines of hypertension treatment

Identification of peripheral inflammatory markers between normal control and Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Multiple pathogenic factors may contribute to the pathophysiology of Alzheimer's disease (AD). Peripheral blood markers have been used to assess biochemical changes associated with AD and mild cognitive impairment (MCI) and involved in their pathophysiology.</p> <p>Methods</p> <p>Plasma samples and clinical data were obtained from participants in the Ansan Geriatric Study (AGE study). Plasma concentrations of four candidate biomarkers were measured in the normal control (NC), MCI, and AD group: interleukin-8 (IL-8), IL-10, monocyte chemoattractant protein-1 (MCP-1), and tumor necrosis factor-α (TNF-α).</p> <p>Body mass index (BMI), MMSE (Mini Mental State Examination), CDR(Clinical Dementia Rating) score and homocystein level were recorded with social and demographic information.</p> <p>Results</p> <p>Total of 59 subjects were randomly selected for this analysis [NC (n = 21), MCI(n = 20) and AD(n = 18)]. In demographic data, educational year was correlated with the diagnosis states (<b><it>p </it></b>< 0.0001). No significant differences in cardiovascular disease, BMI and use of NSAIDs were found in MCI or AD group compared with NC group, respectively. The involvement of inflammatory illness or conditions in subjects, WBC count, fibrinogen and homocystein of the three groups, but no significant differences were found in each groups. The plasma IL-8 level was lower in MCI and AD patients compared with the normal control group (respectively, <it>p </it>< 0.0001). The MCI and AD patients had similar MCP-1, IL-10, and TNF-α level.</p> <p>Conclusions</p> <p>Our study suggests the existence of an independent and negative relationship between plasma IL-8 levels and functional status in MCI and AD patients.</p

Myotis rufoniger genome sequence and analyses: M-rufoniger&apos;s genomic feature and the decreasing effective population size of Myotis bats

Myotis rufoniger is a vesper bat in the genus Myotis. Here we report the whole genome sequence and analyses of the M. rufoniger. We generated 124 Gb of short-read DNA sequences with an estimated genome size of 1.88 Gb at a sequencing depth of 66x fold. The sequences were aligned to M. brandtii bat reference genome at a mapping rate of 96.50% covering 95.71% coding sequence region at 10x coverage. The divergence time of Myotis bat family is estimated to be 11.5 million years, and the divergence time between M. rufoniger and its closest species M. davidii is estimated to be 10.4 million years. We found 1,239 function-altering M. rufoniger specific amino acid sequences from 929 genes compared to other Myotis bat and mammalian genomes. The functional enrichment test of the 929 genes detected amino acid changes in melanin associated DCT, SLC45A2, TYRP1, and OCA2 genes possibly responsible for the M. rufoniger&apos;s red fur color and a general coloration in Myotis. N6AMT1 gene, associated with arsenic resistance, showed a high degree of function alteration in M. rufoniger. We further confirmed that the M. rufoniger also has batspecific sequences within FSHB, GHR, IGF1R, TP53, MDM2, SLC45A2, RGS7BP, RHO, OPN1SW, and CNGB3 genes that have already been published to be related to bat&apos;s reproduction, lifespan, flight, low vision, and echolocation. Additionally, our demographic history analysis found that the effective population size of Myotis clade has been consistently decreasing since similar to 30k years ago. M. rufoniger&apos;s effective population size was the lowest in Myotis bats, confirming its relatively low genetic diversity