Showing papers by "University of North Carolina at Charlotte published in 2017"

A review of thin film solar cell technologies and challenges

Abstract: Thin film solar cells are favorable because of their minimum material usage and rising efficiencies. The three major thin film solar cell technologies include amorphous silicon (α-Si), copper indium gallium selenide (CIGS), and cadmium telluride (CdTe). In this paper, the evolution of each technology is discussed in both laboratory and commercial settings, and market share and reliability are equally explored. The module efficiencies of CIGS and CdTe technologies almost rival that of crystalline solar cells, which currently possess greater than 55% of the market share. α-Si is plagued with low efficiency and light-induced degradation, so it is almost extinct in terrestrial applications. CIGS and CdTe hold the greatest promise for the future of thin film. Longevity, reliability, consumer confidence and greater investments must be established before thin film solar cells are explored on building integrated photovoltaic systems.

Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering

Abstract: Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multimodal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multimodal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a ‘co-attention’ mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-theart performance on the real-world VQA dataset. Code available at https://github.com/yuzcccc/mfb.

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Abstract: Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

The Empirical Status of Deterrence Theory: A Meta-Analysis

Abstract: The literature on deterrence theory has undergone a number of changes in recent years. With the rise of new ways of thinking about rational decision-making and offending, four developments have changed the way criminologists view the deterrence perspective: the effectiveness of certain situational crime prevention strategies; the recognition of the importance of the "non-legal costs" of criminal behavior; the integration of deterrence theory with other criminological perspectives, such as social learning and self-control theories; and how the imposition of sanctions can actually lower individuals' perceived estimates of getting caught in the future, known as the "resetting effect". The body of "shaming" research points to the growing recognition of the complex effects that criminal sanctions have on individuals' future criminal behaviour. Even independent of shame, however, research has emerged indicating that individuals who have been punished end up being more inclined to commit future offenses than those who have not been punished.

What to Do Next: Modeling User Behaviors by Time-LSTM.

Abstract: Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users’ actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users’ actions are of significant importance in capturing the relations of users’ actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users’ sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users’ shortterm and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using TimeLSTM over the traditional methods.

Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium

Abstract: In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies.

The promise of organ and tissue preservation to transform medicine

Abstract: The ability to replace organs and tissues on demand could save or improve millions of lives each year globally and create public health benefits on par with curing cancer. Unmet needs for organ and tissue preservation place enormous logistical limitations on transplantation, regenerative medicine, drug discovery, and a variety of rapidly advancing areas spanning biomedicine. A growing coalition of researchers, clinicians, advocacy organizations, academic institutions, and other stakeholders has assembled to address the unmet need for preservation advances, outlining remaining challenges and identifying areas of underinvestment and untapped opportunities. Meanwhile, recent discoveries provide proofs of principle for breakthroughs in a family of research areas surrounding biopreservation. These developments indicate that a new paradigm, integrating multiple existing preservation approaches and new technologies that have flourished in the past 10 years, could transform preservation research. Capitalizing on these opportunities will require engagement across many research areas and stakeholder groups. A coordinated effort is needed to expedite preservation advances that can transform several areas of medicine and medical science.

iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

Abstract: To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.

Pathview Web: user friendly pathway visualization and data integration.

Abstract: Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/.

Back into the Wild – Apply Untapped Genetic Diversity of Wild Relatives for Crop Improvement

Abstract: Deleterious effects of climate change and human activities, as well as diverse environmental stresses, present critical challenges to food production and the maintenance of natural diversity. These challenges may be met by the development of novel crop varieties with increased biotic or abiotic resistance that enables them to thrive in marginal lands. However, considering the diverse interactions between crops and environmental factors, it is surprising that evolutionary principles have been underexploited in addressing these food and environmental challenges. Compared with domesticated cultivars, crop wild relatives (CWRs) have been challenged in natural environments for thousands of years and maintain a much higher level of genetic diversity. In this review, we highlight the significance of CWRs for crop improvement by providing examples of CWRs that have been used to increase biotic and abiotic stress resistance/tolerance and overall yield in various crop species. We also discuss the surge of advanced biotechnologies, such as next-generation sequencing technologies and omics, with particular emphasis on how they have facilitated gene discovery in CWRs. We end the review by discussing the available resources and conservation of CWRs, including the urgent need for CWR prioritization and collection to ensure continuous crop improvement for food sustainability.

Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts

Abstract: The majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a practical methodology to generate probabilistic load forecasts by performing quantile regression averaging on a set of sister point forecasts. There are two major benefits of the proposed approach. It can leverage the development in the point load forecasting literature over the past several decades and it does not rely so much on high-quality expert forecasts, which are rarely achievable in load forecasting practice. To demonstrate the effectiveness of the proposed approach and make the results reproducible to the load forecasting community, we construct a case study using the publicly available data from the Global Energy Forecasting Competition 2014. Compared with several benchmark methods, the proposed approach leads to dominantly better performance as measured by the pinball loss function and the Winkler score.

One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks.

Abstract: False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography–mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection ...

Epidemiology of Posttraumatic Osteoarthritis.

Abstract: Osteoarthritis is a leading cause of disability whose prevalence and incidence continue to increase. History of joint injury represents an important risk factor for posttraumatic osteoarthritis and is a significant contributor to the rapidly growing percentage of the population with osteoarthritis. This review will present the epidemiology associated with posttraumatic osteoarthritis, with particular emphasis on the knee and ankle joints. It is important to understand the effect of posttraumatic osteoarthritis on the population so that sufficient resources can be devoted to countering the disease and promoting optimal long-term health for patients after joint injury.

Entrepreneurial Team Composition Characteristics and New Venture Performance: A Meta‐Analysis

Abstract: Upper echelon theory highlights the importance of top management teams in large and established firms; however, effects are not always clear outside of this context. Due to the unique nature of new...

What mathematics education may prepare students for the society of the future

Abstract: This paper attempts to engage the field in a discussion about what mathematics is needed for students to engage in society, especially with an increase in technology and digitalization. In this respect, mathematics holds a special place in STEM as machines do most of the calculations that students are taught in K-12. We raise questions about what mathematical proficiency means in today’s world and what shifts need to be made in both content and pedagogy to prepare students for 21st Century Skills and mathematical reasoning.

A Systematic Review of the mHealth Interventions to Prevent Alcohol and Substance Abuse

Abstract: Substance abuse in young adults is a public health issue with costs to the individual and society. There is mounting evidence that the increased uses of mHealth approaches have promise as a way to facilitate reductions in substance use. This systematic review evaluated the recent body of research on mHealth-based interventions for substance use, with aims of (a) examining the functionality and effectiveness of these interventions, (b) evaluating the available research on the effectiveness of these interventions for substance use, and (c) evaluating the design, methodology, results, theoretical grounding, limitations, and implications of each study. We identified eligible studies by searching electronic databases using Boolean methods. The reviewed studies (N = 12) indicated that that a wide range of Internet-based, text messaging, and smartphone application interventions have been developed to address substance use. Interventions had an assortment of features; participants in each study highlighted the ease and convenience of the interventions; and the majority of studies provided support for the efficacy of mHealth in reducing substance use. Mobile technology is a promising tool for reducing substance use and warrants further development. Future practice including the use of mHealth interventions can be an integral part of reducing substance use.

Locoregional Effects of Microbiota in a Preclinical Model of Colon Carcinogenesis.

Abstract: Inflammation and microbiota are critical components of intestinal tumorigenesis. To dissect how the microbiota contributes to tumor distribution, we generated germ-free (GF) ApcMin/+and ApcMin/+;Il10−/− mice and exposed them to specific-pathogen-free (SPF) or colorectal cancer-associated bacteria. We found that colon tumorigenesis significantly correlated with inflammation in SPF-housed ApcMin/+;Il10−/−, but not in ApcMin/+mice. In contrast, small intestinal neoplasia development significantly correlated with age in both ApcMin/+;Il10−/− and ApcMin/+ mice. GF ApcMin/+;Il10−/− mice conventionalized by an SPF microbiota had significantly more colon tumors compared with GF mice. Gnotobiotic studies revealed that while Fusobacterium nucleatum clinical isolates with FadA and Fap2 adhesins failed to induce inflammation and tumorigenesis, pks+Escherichia coli promoted tumorigenesis in the ApcMin/+;Il10−/− model in a colibactin-dependent manner, suggesting colibactin is a driver of carcinogenesis. Our results suggest a distinct etiology of cancers in different locations of the gut, where colon cancer is primarily driven by inflammation and the microbiome, while age is a driving force for small intestine cancer. Cancer Res; 77(10); 2620–32. ©2017 AACR.

Optimal recharging scheduling for urban electric buses: A case study in Davis

Abstract: In this paper, a modeling framework to optimize electric bus recharging schedules is developed, which determines both the planning and operational decisions while minimizing total annual costs. The model is demonstrated using a real-world transit network based in Davis, California. The results showed that range anxiety can be eliminated by adopting certain recharging strategies. Sensitivity analyses revealed that the model could provide transit agencies with comprehensive guidance on the utilization of electric buses and development of a fast charging system. The comparative analyses showed that it was more economical and environmentally friendly to utilize electric buses than diesel buses.

Thiazolothiazole Fluorophores Exhibiting Strong Fluorescence and Viologen-Like Reversible Electrochromism

Abstract: The synthesis, electrochemical, and photophysical characterization of N,N′-dialkylated and N,N′-dibenzylated dipyridinium thiazolo[5,4-d]thiazole derivatives are reported. The thiazolothiazole viologens exhibit strong blue fluorescence with high quantum yields between 0.8–0.96. The dioctyl, dimethyl, and dibenzyl derivatives also show distinctive and reversible yellow to dark blue electrochromism at low reduction potentials. The fused bicyclic thiazolo[5,4-d]thiazole heterocycle allows the alkylated pyridinium groups to remain planar, strongly affecting their electrochemical properties. The singlet quantum yield is greatly enhanced with quaternarization of the peripheral 4-pyridyl groups (ΦF increases from 0.22 to 0.96) while long-lived fluorescence lifetimes were observed between 1.8–2.4 ns. The thiazolothiazole viologens have been characterized using cyclic voltammetry, UV–visible absorbance and fluorescence spectroscopy, spectroelectrochemistry, and time-resolved photoluminescence. The electrochromic p...

The Posttraumatic Growth Inventory: A Revision Integrating Existential and Spiritual Change.

Abstract: Spiritual Change (SC) is one of 5 domains of posttraumatic growth (PTG). The current Posttraumatic Growth Inventory (PTGI) assesses this area of growth with only 2 items, one focusing on religiosity and the other focusing on spiritual understanding. The addition of 4 newly developed spiritual-existential change (SEC) items, creating an expanded PTGI (Posttraumatic Growth Inventory-X), reflects a diversity of perspectives on spiritual-existential experiences that are represented in different cultures. Samples were obtained from 3 countries: the United States (n = 250), Turkey (n = 502), and Japan (n = 314). Analyses indicated that the newly added items capture additional experiences of growth outside traditional religious concepts, yet still are correlated with the original SC items, especially in the U.S. and Turkish samples. Relationships of the PTGI-X to established predictors of PTG, event-related rumination, and core beliefs, were as predicted in all 3 countries. The new 6-item SEC factor demonstrated high internal reliability, and the 5-factor structure of the expanded scale was supported by confirmatory factor analysis. The resulting 25-item PTGI-X can be used as a validated instrument in a wide range of samples in which traditional religious beliefs are less dominant.

The State of the Art in Integrating Machine Learning into Visual Analytics

Abstract: Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

Latent Profile Analysis: Understanding Family Firm Profiles

Abstract: We demonstrate how latent profile analysis (LPA) can be applied to generate profiles (i.e., homogenous subgroups) in a sample of family firms. In doing so, we highlight how LPA can provide additional insight into family firm phenomena when used in conjunction with other methodological approaches (i.e., regression). We compare LPA with other techniques (i.e., cluster analysis and qualitative comparative analysis) and show LPA’s superior ability to capture complex patterns of important family firm characteristics. We demonstrate how profiles can be linked to differences in dependent variables, providing family firm scholars with a tool to assess heterogeneity and its consequences among family firms.

Mechanical weathering and rock erosion by climate-dependent subcritical cracking

Abstract: This work constructs a fracture mechanics framework for conceptualizing mechanical rock breakdown and consequent regolith production and erosion on the surface of Earth and other terrestrial bodies. Here our analysis of fracture mechanics literature explicitly establishes for the first time that all mechanical weathering in most rock types likely progresses by climate-dependent subcritical cracking under virtually all Earth surface and near-surface environmental conditions. We substantiate and quantify this finding through development of physically based subcritical cracking and rock erosion models founded in well-vetted fracture mechanics and mechanical weathering, theory, and observation. The models show that subcritical cracking can culminate in significant rock fracture and erosion under commonly experienced environmental stress magnitudes that are significantly lower than rock critical strength. Our calculations also indicate that climate strongly influences subcritical cracking—and thus rock weathering rates—irrespective of the source of the stress (e.g., freezing, thermal cycling, and unloading). The climate dependence of subcritical cracking rates is due to the chemophysical processes acting to break bonds at crack tips experiencing these low stresses. We find that for any stress or combination of stresses lower than a rock's critical strength, linear increases in humidity lead to exponential acceleration of subcritical cracking and associated rock erosion. Our modeling also shows that these rates are sensitive to numerous other environment, rock, and mineral properties that are currently not well characterized. We propose that confining pressure from overlying soil or rock may serve to suppress subcritical cracking in near-surface environments. These results are applicable to all weathering processes.