Inclusion of an Introduction to Infrastructure Course in a Civil and Environmental Engineering Curriculum

Civil infrastructure refers to the built environment (sometimes referred to as public works) and consists of roads, bridges, buildings, dams, levees, drinking water treatment facilities, wastewater treatment facilities, power generation and transmission facilities, communications, solid waste facilities, hazardous waste facilities, and other sectors. Although there is a need to train engineers who have a holistic view of infrastructure, there is evidence that civil and environmental engineering (CEE) programs have not fully addressed this increasingly recognized need. One effective approach to address this educational gap is to incorporate a course related to infrastructure into the curriculum for first-year or second-year civil and environmental engineering students. Therefore, this study assesses the current status of teaching such courses in the United States and identifies the incentives for, and the barriers against, incorporating an introduction to infrastructure course into schools’ current CEE curricula. Two distinct activities enabled these objectives. First, a questionnaire was distributed to CEE programs across the United States, to which 33 responses were received. The results indicated that although the majority of participants believe that offering such a course will benefit students by increasing the breadth of the curriculum and by providing a holistic view of CEE, barriers such as the maximum allowable credits for graduation, the lack of motivation within a department—either because such a course did not have a champion or because the department had no plans to revise their curriculum—and a lack of expertise among faculty members inhibited inclusion of the course in curricula. Second, three case studies demonstrating successful inclusion of an introduction to infrastructure course into the CEE curriculum were evaluated. Cases were collected from Marquette University, University of Wisconsin-Platteville, and West Point CEE programs, and it was found that the key to success in including such a course is a motivated team of faculty members who are committed to educating students about different aspects of infrastructure. The results of the study can be used as a road map to help universities successfully incorporate an introduction to infrastructure course in their CEE programs

A trans-inverse coupled-inductor semi-SEPIC DC/DC converter with full control range

© 1986-2012 IEEE. This letter proposes a single switch magnetically coupled dc-dc converter with a high voltage gain. The unique features of the converter are summarized as follows: 1) voltage gain of the converters is raised by lowering its magnetic turn ratio; 2) wide control range (0< D< 1); 3) continuous current from the source that makes it a suitable candidate for renewable energy applications; and 4) there is no dc current saturation in the core due to the presence of capacitor in the primary winding of the inductor. The feasibility of the proposed converter is studied in details supported by circuit analysis and simulation results. Furthermore, the proposed converter is analyzed and compared with other converters with similar features. Finally the superior performance of the circuit is validated experimentally

An integrated geometric and hysteretic error model of a three axis machine tool and its identification with a 3D telescoping ball-bar

ABSTRACT: The ball-bar instrument is used to estimate a maximum number of hysteretic error sources. Machine error parameters include inter- and intra-axis errors as well as hysteresis effects. An error model containing cubic polynomial functions and modified qualitative variables, for hysteresis modeling, is proposed to identify such errors of the three nominally orthogonal linear axes machine. Such model has a total of 90 coefficients, not all of which being necessary. A numerical analysis is conducted to select a minimal but complete non-confounded set of error coefficients. Four different ball-bar test strategies to estimate the model coefficients are simulated and compared. The first one consists of circular trajectories on the primary planes XY, YZ, and XZ and the others use the XY plane, as an equator, and either four, five, or nine meridians. It is concluded that the five-meridian strategy can estimate the additional eight error coefficients: ECZ1, ECZ2, ECZ3, ECZb, EZY3, EZX3, ECX3, and ECXb. The Jacobian condition number is improved by increasing the number of meridians to 5. Further increasing the number of meridians from five to nine improves neither the number of estimable coefficients nor the conditioning, and so as it increases, the test time it was dismissed

ChatGPT: Applications, Opportunities, and Threats

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.Comment: 13 Pages, 1 Figure, Preprint accepted in IEEE Systems and Information Engineering Design Symposium (SIEDS) 202

A randomized controlled multimodal behavioral intervention trial for improving antiepileptic drug adherence

Purpose: Medication nonadherence is one of the most important reasons for treatment failure in patients with epilepsy. The present study investigated the effectiveness of a multicomponent intervention to improve adherence to antiepileptic drug (AED) medication in patients with epilepsy. Methods: In a prospective, randomizedmulticenter trial, three sessions of face-to-facemotivational interviewing (MI) in combination with complementary behavior change techniques were compared with standard care.Motivational interviewing prompted change talk and self-motivated statements from the patients, planning their own medication intake regimen and also identifying and overcoming barriers thatmay prevent adherence. Participants were provided with calendars to self-monitor their medication taking behavior. A family member and the health-care teamwere invited to attend the last session ofMI in order to improve the collaboration and communication between patients, their caregiver or family member, and their health-care provider. At baseline and 6-month follow-up, psychosocial variables and medical adherence were assessed. Results: In total, 275 participantswere included in the study. Comparedwith the active control group, patients in the intervention group reported significantly highermedication adherence, aswell as stronger intention and perceptions of control for taking medication regularly. The intervention group also reported higher levels of action planning, coping planning, self-monitoring, and lower medication concerns. Conclusions: This study shows that MI can be effective in clinical practice to improvemedication adherence in patientswith epilepsy. It also provides evidence that combining volitional interventions, including action planning, coping planning, and self-monitoring withmotivational interviewing can promote the effectiveness of the medical treatments for epilepsy by improving adherenc

Comparative effects of manipulated beaker's yeast and Lansy PZ on fatty acid composition of adults in Artemia urmiana and A. franciscana

Recently, due to the high costs and a decrease in producing of Lansy PZ, various researches have been conducted to the baker's yeast (Saccharomyces cerevisiae) as a substitute for Lansy PZ in Artemia culture technologies. In this study, the effects of six feeding regimes: Lansy PZ (as control), enriched yeast with HUFA, enriched yeast with HUFA and without mannoproteins in wall cells, yeast without mannoproteins in wall cells, industrial yeast 100 %, and industrial yeast 50 % replaced with alga were respectively examined on the fatty acid composition of two Artemia species (Artemia urmiana and A. franciscana) at a salinity of 80 ppt and a density of 500 nauplii per liter in culture conditions. Results showed that the enrichment of baker’s yeast with HUFA had increasing trend on the EPA and DHA contents of baker yeast (19.11 and 34.51%, respectively). The yeast type had significant effect on the fatty acid composition of the two species of Artemia. The highest content of HUFA obtained when Artemia fed the Lansy PZ. Our results recommended that the Artemia fed with HUFA enriched yeast and enriched yeast with HUFA without mannoproteins in wall cells induced higher contents of essential fatty acid (especially DHA) compared to other treatments. On the basis of the present investigation, the enrichment of Artemia with yeast enriched HUFA can be substitute to Artemia fed with Lanzy PZ

Quantification of perineural satellitosis in pretreatment glioblastoma with structural MRI and a diffusion tensor imaging template

Background Survival outcomes for glioblastoma (GBM) patients remain unfavorable, and tumor recurrence is often observed. Understanding the radiological growth patterns of GBM could aid in improving outcomes. This study aimed to examine the relationship between contrast-enhancing tumor growth direction and white matter, using an image registration and deformation strategy. Methods In GBM patients 2 pretreatment scans (diagnostic and neuronavigation) were gathered retrospectively, and coregistered to a template and diffusion tensor imaging (DTI) atlas. The GBM lesions were segmented and coregistered to the same space. Growth vectors were derived and divided into vector populations parallel (Φ = 0–20°) and perpendicular (Φ = 70–90°) to white matter. To test for statistical significance between parallel and perpendicular groups, a paired samples Student’s t-test was performed. O6-methylguanine-DNA methyltransferase (MGMT) methylation status and its correlation to growth rate were also tested using a one-way ANOVA test. Results For 78 GBM patients (mean age 61 years ± 13 SD, 32 men), the included GBM lesions showed a predominant preference for perineural satellitosis (P < .001), with a mean percentile growth of 30.8% (95% CI: 29.6–32.0%) parallel (0° < |Φ| < 20°) to white matter. Perpendicular tumor growth with respect to white matter microstructure (70° < |Φ| < 90°) showed to be 22.7% (95% CI: 21.3–24.1%) of total tumor growth direction. Conclusions The presented strategy showed that tumor growth direction in pretreatment GBM patients correlated with white matter architecture. Future studies with patient-specific DTI data are required to verify the accuracy of this method prospectively to identify its usefulness as a clinical metric in pre and posttreatment settings.publishedVersio