189 research outputs found
Greening Seoul : A New Toolkit for Adaptive Reuse
As cities continue to develop and become denser, carbon emissions are increasing, causing the Earth\u27s temperature to rise and accelerating climate change. Construction and buildings account for about 40%-50% of the causes of carbon emissions, in addition to the embodied energy of the materials used. This thesis proposes a sustainable toolkit of greening strategies that can be applied to countless existing buildings, consisting of elements to create a green facades, green walls, and energy efficiency systems. Because the majority of carbon emissions occur during the demolition of old and construction of new buildings, the top priority should be to avoid demolition altogether and instead to improve the energy efficiency of old buildings. As a test case for applying the sustainable tool kit of greening strategy I have chosen one wing of the Nakwon Arcade complex in Seoul, South Korea. The Nakwon Arcade is the first generation of musical instrument shopping malls. Because this building is a typical Korean arcade style complex, a my sustainable toolkit strategies can be applied and installed in numerous buildings of the same or related construction systems. The project aims to demonstrate that sustainable design is not only possible but also essential for the future of our planet. The sustainable components I propose to install on the exterior will reduce carbon emissions and increase the efficiency of older buildings. This will have a great direct impact on the city\u27s microclimate, and together these modified buildings can reduce carbon emissions to a greater extent, creating a solution that can slow down climate change
Effect of rotator cuff muscle fatigue on shoulder muscle activation and posture during driving
Rotator cuff (RC) muscle dysfunction impacts the ability to perform daily functional tasks, such as driving. It has been suggested that RC muscle fatigue can mimic rotator cuff tears (RCT) during sudden steering in terms of kinematics. It has also been found that two RC muscles (infraspinatus and supraspinatus) are highly active during driving. However, it is unknown whether fatigue of these muscles would change the kinematic strategy during driving. The aim of this research was to analyze changes in joint angle and electromyography (EMG) signals of the upper extremity in simulated driving to identify compensatory mechanism of rotator cuff muscles.
Mean, maximum, standard deviation, and range of motion (ROM) of joint angles for four degrees of freedom (shoulder plane, shoulder elevation, shoulder rotation, and elbow flexion) were examined for four steering patterns (straight, left, right, and complex) and compared between before and after fatigue. Along with kinematic analyses, EMG signals of four muscles (deltoid, supraspinatus, infraspinatus, and biceps) were measured to analyze the relationship between kinematics and muscle usage before and after fatigue.
In straight and left turns, usage of the right deltoid significantly increased (pā¤0.05) in all three measurements (mean, standard deviation, and maximum) whereas in complex turn, the right bicep was used more (pā¤0.05). However, kinematics in corresponding muscles did not show significant change, which indicates change in muscle usage did not impact driver's kinematic strategy. The results suggest that in simple steering, the deltoid compensates for fatigue of RC muscles while in more dynamic steering, the biceps compensate for fatigue of RC muscles. However, the extent of this compensation was minimal as activation level of infraspinatus reached close to its maximum contraction (~96.5% MVC) while non-RC muscles were generally below 30% MVC in all turns
A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques
OBJECTIVES: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)\u27s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.
METHODS: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.
RESULTS: Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).
CONCLUSIONS: With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction
Switchable Ļ-electronic network of bis(Ī±-oligothienyl)-substituted hexaphyrins between helical versus rectangular circuit
The switching phenomena of conformation with Ļ-electronic network through deprotonation-protonation processes were investigated by employing a series of 5, 20-bis(Ī±-oligothienyl) substituted hexaphyrins(1.1.1.1.1.1). They showed significant changes in the absorption and emission spectra with deprotonation, and returned to the initial state with protonation. Through NMR measurements and single crystal X-ray diffraction analysis, we found that the 5, 20-bis(Ī±-oligothienyl) substituted hexaphyrins, which possess acyclic, helical electronic networks involving oligothienyl chains in dumbbell conformations (type-I) in a neutral form, underwent effective deprotonation upon treatment with tetrabutylammonium fluoride (TBAF) to generate the corresponding dianions, which display cyclic electronic networks with enhanced aromaticity in rectangular conformations (type-II). Our quantum calculation results provide an unambiguous description for the switchable conformation and Ļ-conjugation, which revealed that a deprotonation-induced enhanced aromatic conjugation pathway is involved in the switchable Ļ-electronic network
Controlling the Manifold of Polariton States Through Molecular Disorder
Exciton polaritons, arising from the interaction of electronic transitions
with confined electromagnetic fields, have emerged as a powerful tool to
manipulate the properties of organic materials. However, standard experimental
and theoretical approaches overlook the significant energetic disorder present
in most materials now studied. Using the conjugated polymer P3HT as a model
platform, we systematically tune the degree of energetic disorder and observe a
corresponding redistribution of photonic character within the polariton
manifold. Based on these subtle spectral features, we develop a more
generalized approach to describe strong light-matter coupling in disordered
systems that captures the key spectroscopic observables and provides a
description of the rich manifold of states intermediate between bright and
dark. Applied to a wide range of organic systems, our method challenges
prevailing notions about ultrastrong coupling and whether it can be achieved
with broad, disordered absorbers
- ā¦