Drive-in torque for self-tapping screws into timber

Self-tapping screws have been widely used in timber constructions nowadays. Current practice considers self tapping screws perform best in connecting two members when they are fully threaded, however the drive-in resistance caused by the friction between woods and screws can potentially damage the screw and reduce the effectiveness of its applications. The relationship between their thread configuration and the drive-in torque force has not been investigated, and how would knots in the member affect the drive-in force remains in question. This study conducted a series of tests aiming to demonstrate the influence of thread configuration on the drive-in torque of screws. Two types of self-tapping screws and three different thread configurations were studied. The drive-in torque for partially threaded screws was found to be significantly slower than that of the fully threaded ones. The results showed knots can significantly influence the positioning of screw and increase the drive-in torque. The application of pre-drilled hole was found to be an effective way to minimise the influence of knots. This article points out that with appropriate consideration of thread configuration, partially threaded self-tapping screws can not only achieve the same efficiency with fully-threaded ones, they will also benefit from reduced drive-in torque force

Exfoliate cancer cell analysis in rectal cancer surgery: comparison of laparoscopic and transanal total mesorectal excision, a pilot study

Purpose Minimally invasive surgery (MIS) is currently the standard treatment for rectal cancer. However, its limitations include complications and incomplete total mesorectal excision (TME) due to anatomical features and technical difficulties. Transanal TME (TaTME) has been practiced since 2010 to improve this, but there is a risk of local recurrence and intra-abdominal contamination. We aimed to analyze samples obtained through lavage to compare laparoscopic TME (LapTME) and TaTME. Methods From June 2020 to January 2021, 20 patients with rectal cancer undergoing MIS were consecutively and prospectively recruited. Samples were collected at the start of surgery, immediately after TME, and after irrigation. The samples were analyzed for carcinoembryonic antigen (CEA) and cytokeratin 20 (CK20) through a quantitative real-time polymerase chain reaction. The primary outcome was to compare the detected amounts of CEA and CK20 immediately after TME between the surgical methods. Results Among the 20 patients, 13 underwent LapTME and 7 underwent TaTME. Tumor location was lower in TaTME (7.3 cm vs. 4.6 cm, P=0.012), and negative mesorectal fascia (MRF) was more in LapTME (76.9% vs. 28.6%, P=0.044). CEA and CK20 levels were high in 3 patients (42.9%) only in TaTME. There was 1 case of T4 with incomplete purse-string suture and 1 case of positive MRF with dissection failure. All patients were followed up for an average of 32.5 months without local recurrence. Conclusion CEA and CK20 levels were high only in TaTME and were related to tumor factors or intraoperative events. However, whether the detection amount is clinically related to local recurrence remains unclear

Stratification of rate of lymph node metastasis according to risk factors and oncologic outcomes in patients who underwent radical resection for rectal neuroendocrine tumors

Purpose Most predictive factors for lymph node metastasis in rectal neuroendocrine tumors (NETs) have been based on local and endoscopic resection. We aimed to evaluate the risk factors for lymph node metastasis in patients who underwent radical resection for rectal NETs and stratify the risk of lymph node metastasis. Methods Sixty-four patients who underwent radical resection for rectal NETs between January 2001 and January 2018 were included. We investigated the risk factors of lymph node metastasis using clinicopathologic data. We also performed a risk stratification for lymph node metastases using the number of previously known risk factors. For oncologic outcomes, the 5-year overall survival and recurrence-free survival were evaluated in both groups. Results Among the patients who underwent radical surgery, 32 (50.0%) had lymph node metastasis and 32 (50.0%) had non–lymph node metastasis. In the multivariable analysis, only the male sex was identified as a risk factor for lymph node metastasis (odds ratio, 3.695; 95% confidence interval, 1.128–12.105; P=0.031). When there were 2 or more known risk factors, the lymph node metastasis rate was significantly higher than when there were one or no risk factors (odds ratio, 3.667; 95% confidence interval, 1.023–13.143; P=0.046). There was also no statistical difference between the 2 groups in 5-year overall survival (P=0.431) and 5-year recurrence-free survival (P=0.144). Conclusion We found that the rate of lymph node metastasis increased significantly when the number of known risk factors is 2 or more

The Effect of Cognitive Impairment on the Association Between Social Network Properties and Mortality Among Older Korean Adults

Objectives: This study investigated the effect of cognitive impairment on the association between social network properties and mortality among older Korean adults. Methods: This study used data from the Korean Social Life, Health, and Aging Project. It obtained 814 older adults’ complete network maps across an entire village in 2011-2012. Participants’ deaths until December 31, 2020 were confirmed by cause-of-death statistics. A Cox proportional hazards model was used to assess the risks of poor social network properties (low degree centrality, perceived loneliness, social non-participation, group-level segregation, and lack of support) on mortality according to cognitive impairment. Results: In total, 675 participants (5510.4 person-years) were analyzed, excluding those with missing data and those whose deaths could not be verified. Along with cognitive impairment, all social network properties except loneliness were independently associated with mortality. When stratified by cognitive function, some variables indicating poor social relations had higher risks among older adults with cognitive impairment, with adjusted hazard ratios (HRs) of 2.12 (95% confidence interval [CI], 1.34 to 3.35) for social non-participation, 1.58 (95% CI, 0.94 to 2.65) for group-level segregation, and 3.44 (95% CI, 1.55 to 7.60) for lack of support. On the contrary, these effects were not observed among those with normal cognition, with adjusted HRs of 0.73 (95% CI, 0.31 to 1.71), 0.96 (95% CI, 0.42 to 2.21), and 0.95 (95% CI, 0.23 to 3.96), respectively. Conclusions: The effect of social network properties was more critical among the elderly with cognitive impairment. Older adults with poor cognitive function are particularly encouraged to participate in social activities to reduce the risk of mortality

Synergistic Effect of Crosslinked Organic-Inorganic Composite Protective Layer for High Performance Lithium Metal Batteries

Maintaining a stable interface of lithium metal anodes (LMAs) by implementing a protective layer is a promising approach in extending the cycle life of lithium metal batteries (LMBs). Nevertheless, designing a protective layer with desired physicochemical properties is still a challenging task. Herein, an inorganic-organic composite protective layer consisting of fluorinated graphene oxide (FGO) (inorganic part) and polyacrylic acid (PAA) (organic part) that are in situ crosslinked via poly(ethylene glycol) diglycidyl ether (PEGDE) into a robust network is reported. The mechanical strength of FGO and the elasticity of the polymeric network jointly suppress the unwanted dendritic Li growth while fluorine-functional groups in FGO induce an LiF-enriched interface. This balanced inorganic-organic composite protective layer facilitates charge transfer kinetics for enhanced lithium-ion diffusion at the interface. Utilizing this protective layer, LMB full-cells with LiFePO4 demonstrate negligible capacity loss for 100 cycles even under an extreme negative/positive capacity (N/P) ratio of 1.0. This study uncovers the possibility of highly robust, reliable LMBs by a sophisticatedly designed protective layer of widely used inorganic and organic components.N

Association of group-level segregation with cardiovascular health in older adults: an analysis of data from the Korean Social Life, Health, and Aging Project

OBJECTIVES The adverse health effects of individual-level social isolation (e.g., perceived loneliness) have been well documented in older adults. However, little is known about the impact of collective-level social isolation on health outcomes. We sought to examine the association of group-level segregation with cardiovascular health (CVH) in older adults. METHODS From the prospective Korean Social Life, Health, and Aging Project database, we identified 528 community-dwelling older adults who were aged ≥60 years or were married to those aged ≥60 years. Participants who belonged to smaller social groups separate from the major social group were defined as group-level-segregated. The CVH score was calculated as the number of ideal non-dietary CVH metrics (0-6), as modified from the American Heart Association’s Life’s Simple 7. Using ordinal logistic regression models, we assessed cross-sectional and longitudinal associations between group-level segregation and CVH. RESULTS Of the 528 participants (mean age, 71.7 years; 60.0% female), 108 (20.5%) were segregated at baseline. In the cross-sectional analysis, group-level segregation was significantly associated with lower odds of having a higher CVH score at baseline after adjusting for socio-demographic factors and cognitive function (odds ratio [OR], 0.64; 95% confidence interval [CI], 0.43 to 0.95). Among 274 participants who completed an 8-year follow-up, group-level segregation at baseline was marginally associated with lower odds of having a higher CVH score at 8 years (OR, 0.49; 95% CI, 0.24 to 1.02). CONCLUSIONS Group-level segregation was associated with worse CVH. These findings imply that the social network structure of a community may influence its members’ health status

An Agent-Based Approach in Demographic Research: Preliminary Study on Korean Population Dynamics

In this paper, an agent-based modeling and simulation of the demographic research is presented. Different from the demographic research using microsimulation, the agentbased approach to demographic simulation is expected to provide a more scalable and flexible framework to analyze the population dynamics. While microsimulation, which is based on past statistical, is widely used in demographic study of analyzing the macroscopic changes of population, it lacks prescriptive capabilities of analyzing interactions among individuals (e.g., kinships, person-person relations) and environs (e.g., job status, incomes, education levels, cultural aspects, etc.) which may cause the population changes. The agent-based demographic simulation with Korean data conducted in this paper is expected to be the initial model for the agent-based demographic simulation, which may provide a variety of perspectives on the demographic research in the future. © Research India Publications.open

Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals

This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on road surface classification has not yet been conducted. If road surface classification and recognition are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, road surface classification is essential. To achieve this, tire-pavement interaction noise (TPIN) is adopted as a data source for road surface classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimensional image through time–frequency analysis. Converted images were used together with a CNN architecture to examine the feasibility of the road surface classification system

Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals

This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on road surface classification has not yet been conducted. If road surface classification and recognition are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, road surface classification is essential. To achieve this, tire-pavement interaction noise (TPIN) is adopted as a data source for road surface classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimensional image through time–frequency analysis. Converted images were used together with a CNN architecture to examine the feasibility of the road surface classification system

Unique profile of ordered arrangements of repetitive elements in the C57BL/6J mouse genome implicating their functional roles.

The entirety of all protein coding sequences is reported to represent a small fraction (~2%) of the mouse and human genomes; the vast majority of the rest of the genome is presumed to be repetitive elements (REs). In this study, the C57BL/6J mouse reference genome was subjected to an unbiased RE mining to establish a whole-genome profile of RE occurrence and arrangement. The C57BL/6J mouse genome was fragmented into an initial set of 5,321 units of 0.5 Mb, and surveyed for REs using unbiased self-alignment and dot-matrix protocols. The survey revealed that individual chromosomes had unique profiles of RE arrangement structures, named RE arrays. The RE populations in certain genomic regions were arranged into various forms of complexly organized structures using combinations of direct and/or inverse repeats. Some of these RE arrays spanned stretches of over 2 Mb, which may contribute to the structural configuration of the respective genomic regions. There were substantial differences in RE density among the 21 chromosomes, with chromosome Y being the most densely populated. In addition, the RE array population in the mouse chromosomes X and Y was substantially different from those of the reference human chromosomes. Conversion of the dot-matrix data pertaining to a tandem 13-repeat structure within the Ch7.032 genome unit into a line map of known REs revealed a repeat unit of ~11.3 Kb as a mosaic of six different RE types. The data obtained from this study allowed for a comprehensive RE profiling, including the establishment of a library of RE arrays, of the reference mouse genome. Some of these RE arrays may participate in a spectrum of normal and disease biology that are specific for mice