A new fuzzy additive model for determining the common set of weights in Data Envelopment Analysis

The main advantage of Data Envelopment Analysis (DEA) is that it does not require any priori weights for inputs and outputs and allows individual DMUs to evaluate their efficiencies with the input and output weights that are only most favorable weights for calculating their efficiency. It can be argued that if DMUs are experiencing similar circumstances, then the pricing of inputs and outputs should apply uniformly across all DMUs. That is using of different weights for DMUs makes their efficiencies unable to be compared and not possible to rank them on the same basis. This is a significant drawback of DEA; however literature observed many solutions including the use of common set of weights (CSW). Besides, the conventional DEA methods require accurate measurement of both the inputs and outputs; however, crisp input and output data may not relevant be available in real world applications. This paper develops a new model for the calculation of CSW in fuzzy environments using fuzzy DEA. Further, a numerical example is used to show the validity and efficacy of the proposed model and to compare the results with previous models available in the literature.

Wireless power and data transfer via a common inductive link using frequency division multiplexing

For wireless power transfer (WPT) systems, communication between the primary side and the pickup side is a challenge because of the large air gap and magnetic interferences. A novel method, which integrates bidirectional data communication into a high-power WPT system, is proposed in this paper. The power and data transfer share the same inductive link between coreless coils. Power/data frequency division multiplexing technique is applied, and the power and data are transmitted by employing different frequency carriers and controlled independently. The circuit model of the multiband system is provided to analyze the transmission gain of the communication channel, as well as the power delivery performance. The crosstalk interference between two carriers is discussed. In addition, the signal-to-noise ratios of the channels are also estimated, which gives a guideline for the design of mod/demod circuits. Finally, a 500-W WPT prototype has been built to demonstrate the effectiveness of the proposed WPT system.

INVISIBLE LABOR FOR DATA: INSTITUTIONS, INFRASTRUCTURE, AND VIRTUAL SPACE

Americans are accustomed to a wide range of data collection in their lives: census, polls, surveys, user registrations, and disclosure forms. When logging onto the Internet, users’ actions are being tracked everywhere: clicking, typing, tapping, swiping, searching, and placing orders. All of this data is stored to create data-driven profiles of each user. Social network sites, furthermore, set the voluntarily sharing of personal data as the default mode of engagement. But people’s time and energy devoted to creating this massive amount of data, on paper and online, are taken for granted. Few people would consider their time and energy spent on data production as labor. Even if some people do acknowledge their labor for data, they believe it is accessory to the activities at hand. In the face of pervasive data collection and the rising time spent on screens, why do people keep ignoring their labor for data? How has labor for data been become invisible, as something that is disregarded by many users? What does invisible labor for data imply for everyday cultural practices in the United States? Invisible Labor for Data addresses these questions. I argue that three intertwined forces contribute to framing data production as being void of labor: data production institutions throughout history, the Internet’s technological infrastructure (especially with the implementation of algorithms), and the multiplication of virtual spaces. There is a common tendency in the framework of human interactions with computers to deprive data and bodies of their materiality. My Introduction and Chapter 1 offer theoretical interventions by reinstating embodied materiality and redefining labor for data as an ongoing process. The middle Chapters present case studies explaining how labor for data is pushed to the margin of the narratives about data production. I focus on a nationwide debate in the 1960s on whether the U.S. should build a databank, contemporary Big Data practices in the data broker and the Internet industries, and the group of people who are hired to produce data for other people’s avatars in the virtual games. I conclude with a discussion on how the new development of crowdsourcing projects may usher in the new chapter in exploiting invisible and discounted labor for data.

Institutional Persistence and Change in England's Common Law: 1700-1865

Institutions are widely regarded as important, even ultimate drivers of economic growth and performance. A recent mainstream of institutional economics has concentrated on the effect of persisting, often imprecisely measured institutions and on cataclysmic events as agents of noteworthy institutional change. As a consequence, institutional change without large-scale shocks has received little attention. In this dissertation I apply a complementary, quantitative-descriptive approach that relies on measures of actually enforced institutions to study institutional persistence and change over a long time period that is undisturbed by the typically studied cataclysmic events. By placing institutional change into the center of attention one can recognize different speeds of institutional innovation and the continuous coexistence of institutional persistence and change. Specifically, I combine text mining procedures, network analysis techniques and statistical approaches to study persistence and change in England’s common law over the Industrial Revolution (1700-1865). Based on the doctrine of precedent - a peculiarity of common law systems - I construct and analyze the apparently first citation network that reflects lawmaking in England. Most strikingly, I find large-scale change in the making of English common law around the turn of the 19th century - a period free from the typically studied cataclysmic events. Within a few decades a legal innovation process with low depreciation rates (1 to 2 percent) and strong past-persistence transitioned to a present-focused innovation process with significantly higher depreciation rates (4 to 6 percent) and weak past-persistence. Comparison with U.S. Supreme Court data reveals a similar U.S. transition towards the end of the 19th century. The English and U.S. transitions appear to have unfolded in a very specific manner: a new body of law arose during the transitions and developed in a self-referential manner while the existing body of law lost influence, but remained prominent. Additional findings suggest that Parliament doubled its influence on the making of case law within the first decades after the Glorious Revolution and that England’s legal rules manifested a high degree of long-term persistence. The latter allows for the possibility that the often-noted persistence of institutional outcomes derives from the actual persistence of institutions.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Association for Learning Technology Annual Survey 2016 Data and Report

This report presents findings from the third Association for Learning Technology (ALT) Annual Survey. As with previous years, the survey was advertised predominately to ALT Members but at the same time promoted publically, and responses were collected between December and January. In total 245 responses have been analysed, 88% (n. 216) of these being submitted by ALT Members. The ALT Annual Survey contains a common core of questions asked in all annual surveys. This year the survey was supplemented with additional questions specifically aimed at informing the new ALT Strategy 2017–2020.

Association for Learning Technology Annual Survey 2017 Data and Report

This is the fourth Association for Learning Technology (ALT) Annual Survey. As with previous years, the survey was advertised predominately to ALT Members but at the same time promoted publically, and responses were collected between December 2017 and January 2018. The ALT Annual Survey contains a common core of questions asked in all annual surveys. This year the survey was supplemented with additional questions specifically aimed at gaining feedback for Certified Member of ALT (CMALT) framework and to identify other priorities 2018.

Association for Learning Technology Annual Survey 2018 Data and Report

This is the fifth Association for Learning Technology (ALT) Annual Survey. As with previous years, the survey was advertised predominately to ALT Members but at the same time promoted publically. The ALT Annual Survey contains a common core of questions asked in all annual surveys.

Association for Learning Technology (ALT) Annual Survey 2019 Data and ALT Survey Report 2020

This is the sixth Association for Learning Technology (ALT) Annual Survey. As with previous years, the survey was advertised predominately to ALT Members but at the same time promoted publically. The ALT Annual Survey contains a common core of questions asked in all annual surveys.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

Integration and visualization of systems biology data in context of the genome

Background: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. Results: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data. A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. Conclusions: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

The exponentiated generalized gamma distribution with application to lifetime data

A four-parameter extension of the generalized gamma distribution capable of modelling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone and non-monotone failure rate functions, which are quite common in lifetime data analysis and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the exponentiated Weibull, exponentiated generalized half-normal, exponentiated gamma and generalized Rayleigh, among others. We derive two infinite sum representations for its moments. We calculate the density of the order statistics and two expansions for their moments. The method of maximum likelihood is used for estimating the model parameters and the observed information matrix is obtained. Finally, a real data set from the medical area is analysed.

Inheritance of growth habit detected by genetic linkage analysis using microsatellites in the common bean (Phaseolus vulgaris L.)

The genetic linkage map for the common bean (Phaseolus vulgaris L.) is a valuable tool for breeding programs. Breeders provide new cultivars that meet the requirements of farmers and consumers, such as seed color, seed size, maturity, and growth habit. A genetic study was conducted to examine the genetics behind certain qualitative traits. Growth habit is usually described as a recessive trait inherited by a single gene, and there is no consensus about the position of the locus. The aim of this study was to develop a new genetic linkage map using genic and genomic microsatellite markers and three morphological traits: growth habit, flower color, and pod tip shape. A mapping population consisting of 380 recombinant F10 lines was generated from IAC-UNA x CAL143. A total of 871 microsatellites were screened for polymorphisms among the parents, and a linkage map was obtained with 198 mapped microsatellites. The total map length was 1865.9 cM, and the average distance between markers was 9.4 cM. Flower color and pod tip shape were mapped and segregated at Mendelian ratios, as expected. The segregation ratio and linkage data analyses indicated that the determinacy growth habit was inherited as two independent and dominant genes, and a genetic model is proposed for this trait.

Myeloproliferative disorder and leukaemia in mice induced by different classes of constitutive mutants of the human IL-3/IL-5/GM-CSF receptor common beta subunit

Several constitutively active mutant forms of the common β subunit of the human IL-3, IL-5 and GM-CSF receptors (hβc), which enable it to signal in the absence of ligand, have recently been described. Two of these, V449E and I374N, are amino acid substitutions in the transmembrane and extracellular regions of hβc, respectively. A third, FIΔ, contains a 37 amino acid duplication in the extracellular domain. We have shown previously that when expressed in primary murine haemopoietic cells, the extracellular mutants confer factor-independence on cells of the neutrophil and monocyte lineages only, whereas V449E does so on all cell types of the myeloid and erythroid compartments. To study the in vivo effects and leukaemic potential of these mutants, we have expressed all three in mice by bone marrow reconstitution using retrovirally infected donor cells. Expression of the extracellular mutants leads to an early onset, chronic myeloproliferative disorder marked by elevations in the neutrophil, monocyte, erythrocyte and platelet lineages. In contrast, expression of V449E leads to an acute leukaemia-like syndrome of anaemia, thrombocytopaenia and blast cell expansion. These data support the possibility that activating mutations in hβc are involved in haemopoietic disorders in man.