An analysis of the impact of datacenter temperature on energy efficiency

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program; in conjunction with the SDM Fellows Program, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 65-66).The optimal air temperature for datacenters is one of ways to improve energy efficiency of datacenter cooling systems. Many datacenter owners have been interested in raising the room temperature as a quick and simple method to increase energy efficiency. The purpose of this paper is both to provide recommendations on maximizing the energy efficiency of datacenters by optimizing datacenter temperature setpoint, and to understand the drivers of datacenter costs. This optimization and the potential energy savings used in cooling system can drive higher energy use in IT equipment and may not be a good trade off. For this reason, this paper provided a detailed look at the overall effect on energy of temperature changes in order to figure out the optimal datacenter temperature setpoint. Since this optimal temperature range varies by equipment and other factors in the datacenter, each datacenter should identify its appropriate temperature based on the optimization calculation in this paper. Sensitivity analysis is used to identify the drivers of the cost of ownership in a datacenter and to identify opportunities for datacenter efficiency improvement. The model is also used to evaluate potential datacenter efficiency.by Heechang Lee.S.M.in Engineering and Managemen

Exploring the Behavioral Intention to Use Collaborative Commerce: A Case of Uber

The goal of our research study is to develop a hybrid instrument built on the revised Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, which is reliable in predicting the behavioral intention to use and subsequent use of the Uber ridesharing app. It focuses on extending the UTAUT2 in the area of collaborative consumption, particularly from a consumer and ridesharing-app perspective. Our proposed framework, UTAUT-CC, preserves existing UTAUT2 constructs – performance expectancy, effort expectancy, social expectancy, and facilitating conditions. It also retains demographic moderating variables of age and gender, while maintaining some of the key integral relationships depicted in those models. We integrated three new constructs deemed relevant in linking to collaborative consumption and a sharing economy – price, trust, and convenience. We incorporated elements of online services and offline services (O2O) together from respective perspectives of mobile technology and ridesharing. Our overall model explained 70.5% of the variance of behavioral intention of Uber. We conclude the paper by exploring actionable implications for practitioners and scholars

Full band ensemble Monte Carlo simulation of silicon devices

A Monte Carlo simulator for silicon devices has been developed. The band structure data for this self-consistent device simulator were computed using the empirical pseudopotential method. The ensemble Monte Carlo technique used in the simulations is described in detail. A homogeneous simulator, based on the same transport physics, is used to calibrate the device simulator as well as to indicate the shortcomings of more traditional simulators such as drift-diffusion based models, hydrodynamic and energy balance based models, and nonparabolic band approximation Monte Carlo models. A conventional metal-oxide-semiconductor field effect transistor (MOSFET) is simulated as a test case to validate the simulator. Finally, a floating gate memory element (non-volatile memory) is also examined. In this simulation, the Monte Carlo simulator is used as a post-processor to PISCES IIB in the interest of execution time. Despite the lack of self-consistency and rigor, the simulator is able to produce results which are in good agreement with experimental data.U of I OnlyETDs are only available to UIUC Users without author permissio

Experimental Study on Mechanical Properties of Single- and Dual-material 3D Printed Products

The recent increase in application of Additive Manufacturing (AM) products has resulted in new demands throughout the industry. Although FDM-based products are used in various fields, the mechanical properties of such products still tend to be weaker than that of the products manufactured through conventional manufacturing processes. Therefore, improving the mechanical properties of FDM-printed products is a key factor that can greatly contribute to the manufacturing industry. In this study, tensile tests are conducted on a single material specimen to analyze the influence of various experiment variables that may add up to the enhancement of the mechanical properties of 3D printed products. Additional experiments are conducted with respect to the structural arrangement and material ratio of dual material 3D printing in order to investigate the effectiveness of dual material printed products. Studies on improving such mechanical properties are expected to contribute to the enhancement of the strength for single material printed products, and provide some guidance when manufacturing dual material printed products by considering the optimum efficiency of each material

Exploring the Behavioral Intention to Use Collaborative Commerce: A Case of Uber

The goal of our research study is to develop a hybrid instrument built on the revised Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, which is reliable in predicting the behavioral intention to use the Uber ridesharing app. It focuses on extending the UTAUT2 in collaborative consumption, particularly from a consumer and ridesharing-app perspective. Our proposed framework, UTAUT-CC, preserves existing UTAUT2 constructs – performance expectancy, effort expectancy, social expectancy, and facilitating conditions. It also retains demographic moderating variables of age and gender, while maintaining some of the key integral relationships depicted in those models. We integrated three new constructs deemed relevant in linking collaborative consumption and a sharing economy – price, trust, and convenience. We incorporated elements of online and offline services (O2O) together from respective perspectives of mobile technology and ridesharing. Our overall model explained 70.5% of the variance of behavioral intention of Uber. We concluded the paper by exploring actionable implications for practitioners and scholars

Rapid Screening of Effective Dopants for Fe 2 O 3 Photocatalysts with Scanning Electrochemical Microscopy and Investigation of Their Photoelectrochemical Properties Rapid Screening of Effective Dopants for Fe 2 O 3 Photocatalysts with Scanning Electrochem

Scanning electrochemical microscopy in the photoelectrochemical (PEC) mode was used to search for more efficient doped iron oxide photocatalysts under visible light irradiation (λ g 420 nm). Iron oxide doped with one or two different metal cations was investigated to improve its PEC performance. Among various dopants, Sn or Ti as single dopants and Be or Al as codopants showed an improved photocurrent response of Fe 2 O 3 under visible light irradiation (λ g 420 nm). Fe 2 O 3 doped with 4% Sn(IV) and 6% Be(II) showed the highest photocurrent as well as a good photosensitivity and stability in alkali solution (0.2 M NaOH) under UV and visible light irradiation

Deep Learning based Diagnostics of Orbit Patterns in rotating machinery

Vibration-based orbit analysis has been employed as a powerful tool in diagnosing the operating state for rotating machinery in power plants. However, due to the difficulties of extracting mathematical features for data-driven approaches in the orbit analysis, it heavily depends on the expert knowledge or experience. In this paper, the deep learning algorithm in machine learning is used to develop autonomous orbit pattern recognition. In details, the convolutional neural network is implemented to build up weights between convolution kernels and pixels, and to construct the entire structure of the neural networks. Finally, the trained network enables us to classify the shapes of the orbit via orbit shape images and its result can estimate fault modes of the rotating machinery. The proposed framework is demonstrated with a rotating testbed

Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features

The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm

Comparison of the Complications in Vertical Rectus Abdominis Musculocutaneous Flap with Non-Reconstructed Cases after Pelvic Exenteration

Background Perineal reconstruction following pelvic exenteration is a challenging area in plastic surgery. Its advantages include preventing complications by obliterating the pelvic dead space and minimizing the scar by using the previous abdominal incision and a vertical rectus abdominis musculocutaneous (VRAM) flap. However, only a few studies have compared the complications and the outcomes following pelvic exenteration between cases with and without a VRAM flap. In this study, we aimed to compare the complications and the outcomes following pelvic exenteration with or without VRAM flap coverage. Methods We retrospectively reviewed the cases of nine patients for whom transpelvic VRAM flaps were created following pelvic exenteration due to pelvic malignancy. The complications and outcomes in these patients were compared with those of another nine patients who did not undergo such reconstruction. Results Flap reconstruction was successful in eight cases, with minor complications such as wound infection and dehiscence. In all cases in the reconstructed group (n=9), structural integrity was maintained and major complications including bowel obstruction and infection were prevented by obliterating the pelvic dead space. In contrast, in the control group (n=9), peritonitis and bowel obstruction occurred in 1 case (11%). Conclusions Despite the possibility of flap failure and minor complications, a VRAM flap can result in adequate perineal reconstruction to prevent major complications of pelvic exenteration