Introduction: Femoral and tibial morphology posted as anatomical risk factors for ACL injuries. Samora et.al found out that a decreased BIA was associated with ACL rupture. Alentorn-Geli et al. found that the angle be...Introduction: Femoral and tibial morphology posted as anatomical risk factors for ACL injuries. Samora et.al found out that a decreased BIA was associated with ACL rupture. Alentorn-Geli et al. found that the angle between the Blumensaat line and the anterior tibial slope (BATS angle) was significantly greater in men with ACL injury. However, other authors were not able to reproduce the similar findings. Our study aimed to determine the Blumensaat inclination angle (BIA) and angle between Blumensaat line and tibial slope (BATS) in patients with or without anterior cruciate ligament injury. We also explored the factors influence them. Method: We elavuated 142 MRI knee done in Hospital Sultan Ismail from January 2017 to November 2020. Study group was patient with ACL injuries, with or without meniscus and cartilage injuries. Control group was patient with no ACL injuries. 57 patients with history of fracture around the knee joint, multiligamentous injuries, inflammatory arthritis and tumour were excluded from the study. We recorded their age, gender, BIA, and BATS angle. BIA and BATS angle were measured in sagittal plane MRI as described by Koji Iswasaki et al. and Alentorn-Geli et al. Result: 54 patients were in study group and 31 years in control group. The mean age for study group was 32.7 (8.95) year old, and for control group was 42.5 (14.54). The mean BIA for study group was 36.20 (4.542) degree, and control group was 37.25 (4.941). The mean BATS for study group was 36.33 (5.78) degree, and control group was 25.26 (6.047) degree. BIA and BATS angle did not differ in both groups, age and gender. Conclusion: Our study did not show BIA and BATS angle as an anatomical risk factor for ACL injuries. Age and gender did not affect these angles.展开更多
Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- a...Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.展开更多
Cyber threats and risks are increasing exponentially with time. For preventing and defense against these threats and risks, precise risk perception for effective mitigation is the first step. Risk perception is necess...Cyber threats and risks are increasing exponentially with time. For preventing and defense against these threats and risks, precise risk perception for effective mitigation is the first step. Risk perception is necessary requirement to mitigate risk as it drives the security strategy at the organizational level and human attitude at individual level. Sometime, individuals understand there is a risk that a negative event or incident can occur, but they do not believe there will be a personal impact if the risk comes to realization but instead, they believe that the negative event will impact others. This belief supports the common belief that individuals tend to think of themselves as invulnerable, i.e., optimistically bias about the situation, thus affecting their attitude for taking preventive measures due to inappropriate risk perception or overconfidence. The main motivation of this meta-analysis is to assess that how the cyber optimistic bias or cyber optimism bias affects individual’s cyber security risk perception and how it changes their decisions. Applying a meta-analysis, this study found that optimistic bias has an overall negative impact on the cyber security due to the inappropriate risk perception and considering themselves invulnerable by biasing that the threat will not occur to them. Due to the cyber optimism bias, the individual will sometimes share passwords by considering it will not be maliciously used, lack in adopting of preventive measures, ignore security incidents, wrong perception of cyber threats and overconfidence on themselves in the context of cyber security.展开更多
文摘Introduction: Femoral and tibial morphology posted as anatomical risk factors for ACL injuries. Samora et.al found out that a decreased BIA was associated with ACL rupture. Alentorn-Geli et al. found that the angle between the Blumensaat line and the anterior tibial slope (BATS angle) was significantly greater in men with ACL injury. However, other authors were not able to reproduce the similar findings. Our study aimed to determine the Blumensaat inclination angle (BIA) and angle between Blumensaat line and tibial slope (BATS) in patients with or without anterior cruciate ligament injury. We also explored the factors influence them. Method: We elavuated 142 MRI knee done in Hospital Sultan Ismail from January 2017 to November 2020. Study group was patient with ACL injuries, with or without meniscus and cartilage injuries. Control group was patient with no ACL injuries. 57 patients with history of fracture around the knee joint, multiligamentous injuries, inflammatory arthritis and tumour were excluded from the study. We recorded their age, gender, BIA, and BATS angle. BIA and BATS angle were measured in sagittal plane MRI as described by Koji Iswasaki et al. and Alentorn-Geli et al. Result: 54 patients were in study group and 31 years in control group. The mean age for study group was 32.7 (8.95) year old, and for control group was 42.5 (14.54). The mean BIA for study group was 36.20 (4.542) degree, and control group was 37.25 (4.941). The mean BATS for study group was 36.33 (5.78) degree, and control group was 25.26 (6.047) degree. BIA and BATS angle did not differ in both groups, age and gender. Conclusion: Our study did not show BIA and BATS angle as an anatomical risk factor for ACL injuries. Age and gender did not affect these angles.
基金supported by the National Key Research and Development Program of China[grant number 2020YFA0608000]the National Natural Science Foundation of China[grant number 42075141]+2 种基金the Meteorological Joint Funds of the National Natural Science Foundation of China[grant number U2142211]the Key Project Fund of the Shanghai 2020“Science and Technology Innovation Action Plan”for Social Development[grant number 20dz1200702]the first batch of Model Interdisciplinary Joint Research Projects of Tongji University in 2021[grant number YB-21-202110].
文摘Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.
文摘Cyber threats and risks are increasing exponentially with time. For preventing and defense against these threats and risks, precise risk perception for effective mitigation is the first step. Risk perception is necessary requirement to mitigate risk as it drives the security strategy at the organizational level and human attitude at individual level. Sometime, individuals understand there is a risk that a negative event or incident can occur, but they do not believe there will be a personal impact if the risk comes to realization but instead, they believe that the negative event will impact others. This belief supports the common belief that individuals tend to think of themselves as invulnerable, i.e., optimistically bias about the situation, thus affecting their attitude for taking preventive measures due to inappropriate risk perception or overconfidence. The main motivation of this meta-analysis is to assess that how the cyber optimistic bias or cyber optimism bias affects individual’s cyber security risk perception and how it changes their decisions. Applying a meta-analysis, this study found that optimistic bias has an overall negative impact on the cyber security due to the inappropriate risk perception and considering themselves invulnerable by biasing that the threat will not occur to them. Due to the cyber optimism bias, the individual will sometimes share passwords by considering it will not be maliciously used, lack in adopting of preventive measures, ignore security incidents, wrong perception of cyber threats and overconfidence on themselves in the context of cyber security.
