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Epistemic virtues are character traits conducive to principled ways of thinking, leading to a life of flourishing. Recent years have witnessed an emergence of theoretical accounts describing how they develop. However, few if any studies have conducted rigorous empirical investigation into the mechanisms of intellectual virtue development. In this study, we review several significant frameworks before utilizing a randomized, pretest/posttest control trial design to understand the impacts of a novel thinking disposition intervention on intellectual virtue growth. We ascertain the direct and indirect effects of the intervention on four key intellectual virtues: curiosity, humility, integrity, and tenacity. Additionally, we test theoretical mediators of virtue learning. Clear evidence favoring a theory-inspired mediator is observed, though we observe weaker signals of direct effects, with nuances across the virtues. For instance, tenacity and curiosity variables appear to respond more to the intervention than do humility and integrity. Findings are discussed in light of contemporary theoretical perspectives.

This paper introduces a developed method of an artificial neural networks (ANN) architecture for estimating the residual strength of clay soil. To implement this purpose, a database of input soil parameters is built, including liquid limit, plasticity index, A-line value, clay fraction, massive minerals, mica, kaolinite, and smectite, in which the output is the residual friction angle. The ANN model was developed by extensively analyzing a number of hidden layers and number of neurons in every layer, incorporating a statistical investigation of the model performance. The obtained results indicate that the ANN model is an outperformed and promising method based on various well-known indicators such as correlation coefficient, mean absolute error, and root mean square error. The achieved ANN model also gives higher estimation accuracy than those results in the literature. Finally, partial dependence plot 2-D was used for sensitivity analysis within the ANN algorithm to investigate the effect of coupled input variables on the estimated residual friction angle of the soil. It was found that A-line value, clay fraction, and massive minerals are the most important input parameters influencing the residual friction angle.

In this work, a theoretical approach is proposed for estimating the minimum and maximum reinforcement ratios for hybrid glass fiber reinforced polymer (GFRP)/steel-reinforced concrete beams to prevent sudden and brittle failure as well as the compression failure of concrete before the tension failure of reinforcements. Equilibrium equations were used to develop a method for determining the minimum hybrid GFRP/steel reinforcement ratio. A method for determining the maximum hybrid GFRP/steel reinforcement ratio was also developed based on the equilibrium of forces of the balanced failure mode. For estimating the load-carrying capacity of concrete beams reinforced with hybrid GFRP/steel, less than the minimum and more than the maximum reinforcement ratio is recommended. Comparisons between the proposed expressions, experimental data, and available test results in the literature shows good agreement between the theoretical and experimental data, with a maximum discrepancy of 7%.

Background Hepatocellular carcinoma (HCC) is a common cancer, especially in the Association of Southeast Asian Nations (ASEAN) region, where the prevalence of hepatitis virus infection is high. Liver resection is a potentially curative and popular therapy for HCC. Laparoscopic surgery using minimally invasive techniques potentially brings benefits to patients who need liver resection for HCC. This study aimed to evaluate the effectiveness, safety, and benefits of laparoscopic liver resection for HCC with long-term follow-up evaluation. Methods This cohort study with 5-year results of total laparoscopic hepatectomy for HCC was conducted in one center. Patients with HCC were selected for laparoscopic liver resection by the same team. The operation also was performed by one team of surgeons. The follow-up protocol was similar to that for open surgery. The patients were scheduled to return for examination every 2 months after the operation. The data for the patients were collected and analyzed using SPSS software. Results From January 2008 to December 2012, 173 enrolled patients with HCC underwent laparoscopic liver resection. The male-to-female ratio was 3:1. The mean age of the patients was 56 years (range 16–83 years). The follow-up period for 130 patients was 21.6 ± 16.0 months (range 0–60 months). The mean tumor size was 3.73 cm (range 2–10 cm). The stages of HCC according to the Barcelona Clinic Liver Cancer (BCLC) categorization were as follows: 0 (6 %), A1 (59.5 %), A2 (6.9 %), A4 (2.9 %), and B (27.2 %). Four patients required conversion to other techniques (2.3 %) because of the potential for major bleeding and tumor perforation. The types of resection were resection of one segment (segments 2, 3, 4, 5, 6, 7, and 8; 43.8 %), resection of two segments (posterior sector, anterior sector, segments 5 and 6, and left lateral sector; 47.9 %), resection of three segments (left and central liver; 4.7 %), and four segments (right liver; 3.6 %). The mean operation time was 112 ± 56 min (range 30–345 min), and the median blood loss was 100 ml (range 20–1,200 ml). The mean hospital stay was 6.5 ± 2.0 days (range, 3 – 19 days). No perioperative mortality occurred. The overall survival rates were 94.2 % at 1 year, 87 % at 2 years, 72.9 % at 3 years, 72.9 % at 4 years, and 72.9 % at 5 years. The mean overall survival time was 49.7 ± 2.1 months (range 45.5–53.9 months). The disease-free survival rates were 79.1 % at 1 year, 60 % at 2 years, 57 % at 3 years, 52 % at 4 years, and 26.3 % at 5 years. The mean disease-free survival time was 38.9 ± 2.6 months (range 33.9–44.0 months). Conclusion Laparoscopic liver resection for HCC is feasible, safe, and effective, with good oncologic results. Major and anatomic hepatectomy are possible with improved skill and experience. Laparoscopic liver resection is a promising treatment option with minimally invasive benefits for HCC patients.

Public education is on the brink of a potential crisis attempting to significantly increase student enrollment while maintaining quality of education. Online courses have been proposed and debated among members of the UC regents, numerous college administrators, faculty, and students. On one hand, online education can reduce overhead while enrolling more students. Directly translating the classroom lectures and materials to an online environment does not necessarily produce equivalent student performance and satisfaction from the course compared to an in-class environment. Since there is no universal standard for online education, erratic and inconsistent results have been achieved in terms of student performance and costs to students as well as administration. A hybrid scalable teaching and learning methodology is required by both educators and students to achieve the greatest advantages of using today’s technology and to apply it toward improving student performance and participation. This dissertation presents a methodology and system to provide a more individualized and responsive learning environment for students in large hybrid and online university courses while keeping overall costs and time commitment down as well as improve overall student performance. The Active Learning Personal Advisor, the implemented learning design tool of this research, is developed based on multi-disciplinary metrics and studies from the fields of Psychology, Education, and Engineering. A primary limiting resource for both students and instructors is time. By automating some basic key interactions that may occur between students and instructors, hours of each individual’s time can be saved, maximizing the quality of the available in-person interactions to occur during a course while allowing for a more scalable sized classroom environment.

Online courses have been proposed and debated among universities, numerous college administrators, faculty, and students. On one hand, online education can reduce overhead while enrolling more students. Administration wants to enroll more students while incurring minimal overhead costs. However, simply translating the classroom lectures and materials to an online environment does not necessarily produce equivalent student performance and satisfaction from the course compared to an in-class environment. Since there is no standard in online education, it has produced erratic results in terms of student performance and costs to students as well as administration. A change in teaching and learning methodology by both teachers and students is required to take the most advantage from today's technology and apply it toward improving student performance and participation. This article overviews plans to research and create an automated learning tool to provide an individualized learning experience for students in large hybrid or online university courses while keeping overall costs and time commitment down as well as improve overall student performance. This way, we get a 1-to-1 experience where the tool studies student weaknesses, reviews their progress, and provides initial guidance without the need for direct intervention from instructors or teaching assistants. A new methodology and system will be introduced to simulate the basic day-to-day interaction a student may have with an instructor. Software algorithms and interdisciplinary concepts will be pioneered in increasing the effectiveness of existing teaching and learning methodologies by utilizing an automated tool to proactively assess students' abilities and individually refer them to other examples or modules where they can find additional help.

Online courses have been debated among the UC regents, numerous college administrators, faculty, and students. On one hand, online education can reduce overhead while enrolling more students. Administration wants to enroll more students while incurring minimal overhead costs. However, simply translating the classroom lectures and materials to an online environment does not necessarily produce equivalent student performance and satisfaction from the course compared to an in-class environment. Since there is no standard in online education, it has produced erratic results in terms of student performance and costs to students. A change in teaching and learning methodology by both teachers and students is required to get the most from today 's technology and apply it toward improving student performance and participation. This paper presents a management tool and methodology to provide a more individualized learning experience for students in large hybrid and online courses while keeping overall costs and time commitment down as well as improve overall student performance.

Online courses have been debated by various sources, including Governor Jerry Brown, the UC regents, and numerous college administrators, faculty, and students. The motivation behind such discussion is varied. Since there is no standard in online education, it has produced erratic results in terms of student performance and costs to students as well as administration. Online tools, if managed and prepared for properly, have the opportunity to provide the highly regarded individualized learning experience of the small classroom with the lower costs of large lecture halls, while decreasing overall overhead costs. This article presents tools and a methodology to provide the small classroom experience in a much larger setting, while keeping overall costs and time commitment down as well as improve overall student performance.

In the context of the industrial revolution 4.0 that is firmly taking place globally, the digital transformation process is considered a revolution that changes the operating and business model. In Vietnam, logistics is one of eight areas that need to be prioritized in the national conversion program to 2025, the orientation towards 2030 by the Prime Minister under Decision No. 749/QĐ-TTg. Digital transformation is an essential solution that helps businesses improve their competitiveness, increase labor productivity, sustainably develop businesses, and integrate with the global economy. This study analyzes the influencing factors of digital transformation and the situation in Vietnam’s logistics enterprises. This paper used a qualitative research method carried out through direct interviews with 20 digital transformation experts in the field of logistics about the current situation, adjusting models and scales, and discussing research results. Quantitative research was conducted online through 258 survey questionnaires of logistics enterprises in the country. The authors performed descriptive statistics, tested the scale, analyzed EFA using SPSS software, and tested the research hypotheses. Research results indicate that five factors—managers, digital transformation human resources, information technology, investment cost, and support services for digital transformation—affect the digital conversion activity in logistics enterprises. Afterward, the research team proposed solutions to promote this operation in Vietnam’s logistics enterprises, contributing to implementing critical tasks of the government’s digital transformation.

Early diagnosis, precise antimicrobial treatment and subsequent patient stratification can improve sepsis outcomes. Circulating biomarkers such as plasma microRNAs (miRNAs) have proven to be surrogates for diagnosis, severity and case management of infections. The expression of four selected miRNAs (miR-146-3p, miR-147b, miR-155 and miR-223) was validated for their prognostic and diagnostic potential in a clinically defined cohort of patients with sepsis and septic shock. The expression of plasma miRNAs was quantified by quantitative PCR (qPCR) in patients with bacterial sepsis (n = 78), in patients with septic shock (n = 52) and in patients with dengue haemorrhagic fever (DHF; n = 69) and in healthy controls (n = 82). The expression of studied miRNA was significantly increased in patients with bacterial sepsis and septic shock. The plasma miR-147b was able to differentiate bacterial sepsis from non-sepsis and septic shock (AUC = 0.77 and 0.8, respectively, p≤ 0.05), while the combination of plasma miR-147b and procalcitonin (PCT) predicted septic shock (AUC = 0.86, p≤ 0.05). The plasma miR-147b may be an useful biomarker independently or in combination with PCT to support clinical diagnosis of sepsis and equally prognosis of patients with septic shock.