The relationship between cognitive ability, the big five, task and contextual performance: a meta-analysis

For years, researchers and human resources specialists have been searching for predictors of performance as well as for relevant performance dimensions (Barrick & Mount, 1991; Borman & Motowidlo, 1993; Campbell, 1990; Viswesvaran et al., 1996). In 1993, Borman and Motowidlo provided a framework by which traditional predictors such as cognitive ability and the Big Five personality factors predicted two different facets of performance: 1) task performance and 2) contextual performance. A meta-analysis was conducted to assess the validity of this model as well as that of other modified models. The relationships between predictors such as cognitive ability and personality variables and the two outcome variables were assessed. It was determined that even though the two facets of performance may be conceptually different, empirically they overlapped substantially (p= .75). Finally, results show that there is some evidence for cognitive ability as a predictor of both task and contextual performance and conscientiousness as a predictor of both task and contextual performance. The possible mediation of predictor-- criterion relationships was also assessed. The relationship between cognitive ability and contextual performance vanished when task performance was controlled.

Improving Novice Analyst Performance in Modeling the Sequence Diagram in Systems Analysis: A Cognitive Complexity Approach

The Unified Modeling Language (UML) has quickly become the industry standard for object-oriented software development. It is being widely used in organizations and institutions around the world. However, UML is often found to be too complex for novice systems analysts. Although prior research has identified difficulties novice analysts encounter in learning UML, no viable solution has been proposed to address these difficulties. Sequence-diagram modeling, in particular, has largely been overlooked. The sequence diagram models the behavioral aspects of an object-oriented software system in terms of interactions among its building blocks, i.e. objects and classes. It is one of the most commonly-used UML diagrams in practice. However, there has been little research on sequence-diagram modeling. The current literature scarcely provides effective guidelines for developing a sequence diagram. Such guidelines will be greatly beneficial to novice analysts who, unlike experienced systems analysts, do not possess relevant prior experience to easily learn how to develop a sequence diagram. There is the need for an effective sequence-diagram modeling technique for novices. This dissertation reports a research study that identified novice difficulties in modeling a sequence diagram and proposed a technique called CHOP (CHunking, Ordering, Patterning), which was designed to reduce the cognitive load by addressing the cognitive complexity of sequence-diagram modeling. The CHOP technique was evaluated in a controlled experiment against a technique recommended in a well-known textbook, which was found to be representative of approaches provided in many textbooks as well as practitioner literatures. The results indicated that novice analysts were able to perform better using the CHOP technique. This outcome seems have been enabled by pattern-based heuristics provided by the technique. Meanwhile, novice analysts rated the CHOP technique more useful although not significantly easier to use than the control technique. The study established that the CHOP technique is an effective sequence-diagram modeling technique for novice analysts.

The Development of High Performance Liquid Chromatography Systems for the Analysis of Improvised Explosives

Existing instrumental techniques must be adaptable to the analysis of novel explosives if science is to keep up with the practices of terrorists and criminals. The focus of this work has been the development of analytical techniques for the analysis of two types of novel explosives: ascorbic acid-based propellants, and improvised mixtures of concentrated hydrogen peroxide/fuel. In recent years, the use of these explosives in improvised explosive devices (IEDs) has increased. It is therefore important to develop methods which permit the identification of the nature of the original explosive from post-blast residues. Ascorbic acid-based propellants are low explosives which employ an ascorbic acid fuel source with a nitrate/perchlorate oxidizer. A method which utilized ion chromatography with indirect photometric detection was optimized for the analysis of intact propellants. Post-burn and post-blast residues if these propellants were analyzed. It was determined that the ascorbic acid fuel and nitrate oxidizer could be detected in intact propellants, as well as in the post-burn and post-blast residues. Degradation products of the nitrate and perchlorate oxidizers were also detected. With a quadrupole time-of-flight mass spectrometer (QToFMS), exact mass measurements are possible. When an HPLC instrument is coupled to a QToFMS, the combination of retention time with accurate mass measurements, mass spectral fragmentation information, and isotopic abundance patterns allows for the unequivocal identification of a target analyte. An optimized HPLC-ESI-QToFMS method was applied to the analysis of ascorbic acid-based propellants. Exact mass measurements were collected for the fuel and oxidizer anions, and their degradation products. Ascorbic acid was detected in the intact samples and half of the propellants subjected to open burning; the intact fuel molecule was not detected in any of the post-blast residue. Two methods were optimized for the analysis of trace levels of hydrogen peroxide: HPLC with fluorescence detection (HPLC-FD), and HPLC with electrochemical detection (HPLC-ED). Both techniques were extremely selective for hydrogen peroxide. Both methods were applied to the analysis of post-blast debris from improvised mixtures of concentrated hydrogen peroxide/fuel; hydrogen peroxide was detected on variety of substrates. Hydrogen peroxide was detected in the post-blast residues of the improvised explosives TATP and HMTD.

Analysis and comparison of network performance with different network parameters

The purpose of this study was to analyze the network performance by observing the effect of varying network size and data link rate on one of the most commonly found network configurations. Computer networks have been growing explosively. Networking is used in every aspect of business, including advertising, production, shipping, planning, billing, and accounting. Communication takes place through networks that form the basis of transfer of information. The number and type of components may vary from network to network depending on several factors such as requirement and actual physical placement of the networks. There is no fixed size of the networks and they can be very small consisting of say five to six nodes or very large consisting of over two thousand nodes. The varying network sizes make it very important to study the network performance so as to be able to predict the functioning and the suitability of the network. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected significantly due to the increase in the size of the network and that there exists a correlation between the various parameters and the size of the network. These variations are not only dependent on the magnitude of the change in the actual physical area of the network but also on the data link rate used to connect the various components of the network.

Environmental analysis of polar herbicides in complex organic-rich matrices by high performance liquid chromatography atmospheric pressure ionization mass spectrometry (HPLC-API-MS)

A comprehensive forensic investigation of sensitive ecosystems in the Everglades Area is presented. Assessing the background levels of contamination in these ecosystems represents a vital resource to build up forensic evidence required to enforce future environmental crimes within the studied areas. This investigation presents the development and validation of a fractionation and isolation method for two families of herbicides commonly applied in the vicinity of the study area, including phenoxy acids like 2,4-D, MCPA, and silvex; as well as the most common triazine-based herbicides like atrazine, prometyne, simazine and related metabolites like DIA and DEA. Accelerated solvent extraction (ASE) and solid phase extraction (SPE) were used to isolate the analytes from abiotic matrices containing large amounts of organic material. Atmospheric-pressure ionization (API) with electrospray ionization in negative mode (ESP-), and Chemical Ionization in the positive mode (APCI+) were used to perform the characterization of the herbicides of interest.

An automated approach for performance deviation analysis of evolving software systems

A manutenção e evolução de sistemas de software tornou-se uma tarefa bastante crítica ao longo dos últimos anos devido à diversidade e alta demanda de funcionalidades, dispositivos e usuários. Entender e analisar como novas mudanças impactam os atributos de qualidade da arquitetura de tais sistemas é um pré-requisito essencial para evitar a deterioração de sua qualidade durante sua evolução. Esta tese propõe uma abordagem automatizada para a análise de variação do atributo de qualidade de desempenho em termos de tempo de execução (tempo de resposta). Ela é implementada por um framework que adota técnicas de análise dinâmica e mineração de repositório de software para fornecer uma forma automatizada de revelar fontes potenciais – commits e issues – de variação de desempenho em cenários durante a evolução de sistemas de software. A abordagem define quatro fases: (i) preparação – escolher os cenários e preparar os releases alvos; (ii) análise dinâmica – determinar o desempenho de cenários e métodos calculando seus tempos de execução; (iii) análise de variação – processar e comparar os resultados da análise dinâmica para releases diferentes; e (iv) mineração de repositório – identificar issues e commits associados com a variação de desempenho detectada. Estudos empíricos foram realizados para avaliar a abordagem de diferentes perspectivas. Um estudo exploratório analisou a viabilidade de se aplicar a abordagem em sistemas de diferentes domínios para identificar automaticamente elementos de código fonte com variação de desempenho e as mudanças que afetaram tais elementos durante uma evolução. Esse estudo analisou três sistemas: (i) SIGAA – um sistema web para gerência acadêmica; (ii) ArgoUML – uma ferramenta de modelagem UML; e (iii) Netty – um framework para aplicações de rede. Outro estudo realizou uma análise evolucionária ao aplicar a abordagem em múltiplos releases do Netty, e dos frameworks web Wicket e Jetty. Nesse estudo foram analisados 21 releases (sete de cada sistema), totalizando 57 cenários. Em resumo, foram encontrados 14 cenários com variação significante de desempenho para Netty, 13 para Wicket e 9 para Jetty. Adicionalmente, foi obtido feedback de oito desenvolvedores desses sistemas através de um formulário online. Finalmente, no último estudo, um modelo de regressão para desempenho foi desenvolvido visando indicar propriedades de commits que são mais prováveis a causar degradação de desempenho. No geral, 997 commits foram minerados, sendo 103 recuperados de elementos de código fonte degradados e 19 de otimizados, enquanto 875 não tiveram impacto no tempo de execução. O número de dias antes de disponibilizar o release e o dia da semana se mostraram como as variáveis mais relevantes dos commits que degradam desempenho no nosso modelo. A área de característica de operação do receptor (ROC – Receiver Operating Characteristic) do modelo de regressão é 60%, o que significa que usar o modelo para decidir se um commit causará degradação ou não é 10% melhor do que uma decisão aleatória.

An automated approach for performance deviation analysis of evolving software systems

A manutenção e evolução de sistemas de software tornou-se uma tarefa bastante crítica ao longo dos últimos anos devido à diversidade e alta demanda de funcionalidades, dispositivos e usuários. Entender e analisar como novas mudanças impactam os atributos de qualidade da arquitetura de tais sistemas é um pré-requisito essencial para evitar a deterioração de sua qualidade durante sua evolução. Esta tese propõe uma abordagem automatizada para a análise de variação do atributo de qualidade de desempenho em termos de tempo de execução (tempo de resposta). Ela é implementada por um framework que adota técnicas de análise dinâmica e mineração de repositório de software para fornecer uma forma automatizada de revelar fontes potenciais – commits e issues – de variação de desempenho em cenários durante a evolução de sistemas de software. A abordagem define quatro fases: (i) preparação – escolher os cenários e preparar os releases alvos; (ii) análise dinâmica – determinar o desempenho de cenários e métodos calculando seus tempos de execução; (iii) análise de variação – processar e comparar os resultados da análise dinâmica para releases diferentes; e (iv) mineração de repositório – identificar issues e commits associados com a variação de desempenho detectada. Estudos empíricos foram realizados para avaliar a abordagem de diferentes perspectivas. Um estudo exploratório analisou a viabilidade de se aplicar a abordagem em sistemas de diferentes domínios para identificar automaticamente elementos de código fonte com variação de desempenho e as mudanças que afetaram tais elementos durante uma evolução. Esse estudo analisou três sistemas: (i) SIGAA – um sistema web para gerência acadêmica; (ii) ArgoUML – uma ferramenta de modelagem UML; e (iii) Netty – um framework para aplicações de rede. Outro estudo realizou uma análise evolucionária ao aplicar a abordagem em múltiplos releases do Netty, e dos frameworks web Wicket e Jetty. Nesse estudo foram analisados 21 releases (sete de cada sistema), totalizando 57 cenários. Em resumo, foram encontrados 14 cenários com variação significante de desempenho para Netty, 13 para Wicket e 9 para Jetty. Adicionalmente, foi obtido feedback de oito desenvolvedores desses sistemas através de um formulário online. Finalmente, no último estudo, um modelo de regressão para desempenho foi desenvolvido visando indicar propriedades de commits que são mais prováveis a causar degradação de desempenho. No geral, 997 commits foram minerados, sendo 103 recuperados de elementos de código fonte degradados e 19 de otimizados, enquanto 875 não tiveram impacto no tempo de execução. O número de dias antes de disponibilizar o release e o dia da semana se mostraram como as variáveis mais relevantes dos commits que degradam desempenho no nosso modelo. A área de característica de operação do receptor (ROC – Receiver Operating Characteristic) do modelo de regressão é 60%, o que significa que usar o modelo para decidir se um commit causará degradação ou não é 10% melhor do que uma decisão aleatória.

DC fault performance and cost analysis of DC grids for connecting multiple offshore wind farms

ACKNOWLEDGMENT We are thankful to RTE for financial support of this project.

IMPROVING THE PERFORMANCE OF DATABASE-CENTRIC APPLICATIONS THROUGH PROGRAM ANALYSIS

Modern software applications are becoming more dependent on database management systems (DBMSs). DBMSs are usually used as black boxes by software developers. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use nowadays. Using ORM, objects in Object-Oriented languages are mapped to records in the database, and object manipulations are automatically translated to SQL queries. As a result of such conceptual abstraction, developers do not need deep knowledge of databases; however, all too often this abstraction leads to inefficient and incorrect database access code. Thus, this thesis proposes a series of approaches to improve the performance of database-centric software applications that are implemented using ORM. Our approaches focus on troubleshooting and detecting inefficient (i.e., performance problems) database accesses in the source code, and we rank the detected problems based on their severity. We first conduct an empirical study on the maintenance of ORM code in both open source and industrial applications. We find that ORM performance-related configurations are rarely tuned in practice, and there is a need for tools that can help improve/tune the performance of ORM-based applications. Thus, we propose approaches along two dimensions to help developers improve the performance of ORM-based applications: 1) helping developers write more performant ORM code; and 2) helping developers configure ORM configurations. To provide tooling support to developers, we first propose static analysis approaches to detect performance anti-patterns in the source code. We automatically rank the detected anti-pattern instances according to their performance impacts. Our study finds that by resolving the detected anti-patterns, the application performance can be improved by 34% on average. We then discuss our experience and lessons learned when integrating our anti-pattern detection tool into industrial practice. We hope our experience can help improve the industrial adoption of future research tools. However, as static analysis approaches are prone to false positives and lack runtime information, we also propose dynamic analysis approaches to further help developers improve the performance of their database access code. We propose automated approaches to detect redundant data access anti-patterns in the database access code, and our study finds that resolving such redundant data access anti-patterns can improve application performance by an average of 17%. Finally, we propose an automated approach to tune performance-related ORM configurations using both static and dynamic analysis. Our study shows that our approach can help improve application throughput by 27--138%. Through our case studies on real-world applications, we show that all of our proposed approaches can provide valuable support to developers and help improve application performance significantly.

Modeling, Analysis and Performance Comparison of Two Direct Sampling DCSK Receivers under Frequency Nonselective Fading Channels

Two direct sampling correlator-type receivers for differential chaos shift keying (DCSK) communication systems under frequency non-selective fading channels are proposed. These receivers operate based on the same hardware platform with different architectures. In the first scheme, namely sum-delay-sum (SDS) receiver, the sum of all samples in a chip period is correlated with its delayed version. The correlation value obtained in each bit period is then compared with a fixed threshold to decide the binary value of recovered bit at the output. On the other hand, the second scheme, namely delay-sum-sum (DSS) receiver, calculates the correlation value of all samples with its delayed version in a chip period. The sum of correlation values in each bit period is then compared with the threshold to recover the data. The conventional DCSK transmitter, frequency non-selective Rayleigh fading channel, and two proposed receivers are mathematically modelled in discrete-time domain. The authors evaluated the bit error rate performance of the receivers by means of both theoretical analysis and numerical simulation. The performance comparison shows that the two proposed receivers can perform well under the studied channel, where the performances get better when the number of paths increases and the DSS receiver outperforms the SDS one.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Does video feedback analysis improve CPR performance in phase 5 medical students?

Background The use of simulation in medical education is increasing, with students taught and assessed using simulated patients and manikins. Medical students at Queen’s University of Belfast are taught advanced life support cardiopulmonary resuscitation as part of the undergraduate curriculum. Teaching and feedback in these skills have been developed in Queen’s University with high-fidelity manikins. This study aimed to evaluate the effectiveness of video compared to verbal feedback in assessment of student cardiopulmonary resuscitation performance Methods Final year students participated in this study using a high-fidelity manikin, in the Clinical Skills Centre, Queen’s University Belfast. Cohort A received verbal feedback only on their performance and cohort B received video feedback only. Video analysis using ‘StudioCode’ software was distributed to students. Each group returned for a second scenario and evaluation 4 weeks later. An assessment tool was created for performance assessment, which included individual skill and global score evaluation. Results One hundred thirty eight final year medical students completed the study. 62 % were female and the mean age was 23.9 years. Students having video feedback had significantly greater improvement in overall scores compared to those receiving verbal feedback (p = 0.006, 95 % CI: 2.8–15.8). Individual skills, including ventilation quality and global score were significantly better with video feedback (p = 0.002 and p < 0.001, respectively) when compared with cohort A. There was a positive change in overall score for cohort B from session one to session two (p < 0.001, 95 % CI: 6.3–15.8) indicating video feedback significantly benefited skill retention. In addition, using video feedback showed a significant improvement in the global score (p < 0.001, 95 % CI: 3.3–7.2) and drug administration timing (p = 0.004, 95 % CI: 0.7–3.8) of cohort B participants, from session one to session two. Conclusions There is increased use of simulation in medicine but a paucity of published data comparing feedback methods in cardiopulmonary resuscitation training. Our study shows the use of video feedback when teaching cardiopulmonary resuscitation is more effective than verbal feedback, and enhances skill retention. This is one of the first studies to demonstrate the benefit of video feedback in cardiopulmonary resuscitation teaching.

An Empirical Analysis of Performance Impacts Resulting From Conversion to Franchise Operations

Franchising is an important form of organizational control. Possible benefits of franchising include its ability to reduce agency costs that increase with costly monitoring, and provide incentives for the use of local information by onsite managers. However, these benefits may come at a cost, as franchisees may reduce quality by choosing to free ride. While many studies have investigated the reasons for franchising, few studies have documented the impacts of franchising on unit level operating performance. Using time-series data from a number of lodging properties that were converted to franchisee control from company control, this study documents the performance impacts of franchising. The analysis reveals that conversion results in a modest decline in financial performance and an immediate sharp decline in quality.

An Empirical Analysis of Continuing Improvements Following the Implementation of a Performance-Based Compensation Plan

Performance improvements subsequent to the implementation of a pay-for-performance plan can result because more productive employees self-select into the firm (selection effect) and/or because employees allocate effort to become more effective (effort effect). We analyze individual performance data for 3,776 sales employees of a retail firm to evaluate these alternative sources of continuing performance improvement. The incentive plan helps the firm attract and retain more productive sales employees, and motivates these employees to further improve their productivity. In contrast, the less productive sales employees’ performance declines before they leave the firm.