To date, little research has been performed regarding the planning and management of “small” projects – those projects typically differentiated from “large” projects due to having lower costs. In 2013, The Construction Industry Institute (CII) set out to develop a front end planning tool that will provide practitioners with a standardized process for planning small projects in the industrial sector. The research team determined that data should be sought from industry regarding small industrial projects to ensure applicability, effectiveness and validity of the new tool. The team developed and administered a survey to determine (1) the prevalence of small projects, (2) the planning processes currently in use for small projects, and (3) current metrics used by industry to differentiate between small and large projects. The survey data showed that small projects make up a majority of projects completed in the industrial sector, planning of these projects varies greatly across the industry, and the metrics posed in the survey were mostly not appropriate for use in differentiating between small and large projects. This study contributes to knowledge through adding to the limited research surrounding small projects, and suggesting future research regarding using measures of project complexity to differentiate between small and large projects.

For the past three decades, the Saudi construction industry (SCI) has exhibited poor performance. Many research efforts have tried to identify the problem and the potential causes but there have been few publications identifying ways to mitigate the problem and describing testing to validate the proposed solution. This paper examines the research and development (R&D) approach in the SCI. A literature research was performed identifying the impact that R&D has had on the SCI. A questionnaire was also created for surveying industry professionals and researchers. The results show evidence that the SCI practice and the academic research work exist in separate silos. This study recommends a change of mindset in both the public and private sector on their views on R&D since cooperation is required to create collaboration between the two sectors and improve the competitiveness of the country's economy.

The principles of a new project management model have been tested for the past 20 years. This project management model utilizes expertise instead of the traditional management, direction, and control (MDC). This new project management model is a leadership-based model instead of a management model. The practice of the new model requires a change in paradigm and project management structure. Some of the practices of this new paradigm include minimizing the flow of information and communications to and from the project manager [including meetings, emails and documents], eliminating technical communications, reducing client management, direction, and control of the vendor, and the hiring of vendors or personnel to do specific tasks. A vendors is hired only after they have clearly shown that they know what they are doing by showing past performance on similar projects, that they clearly understand how to create transparency to minimize risk that they do not control, and that they can clearly outline their project plan using a detailed milestone schedule including time, cost, and tasks all communicated in the language of metrics.

Load associated fatigue cracking is one of the major distress types occurring in flexible pavements. Flexural bending beam fatigue laboratory test has been used for several decades and is considered an integral part of the Superpave advanced characterization procedure. One of the most significant solutions to sustain the fatigue life for an asphaltic mixture is to add sustainable materials such as rubber or polymers to the asphalt mixture. A laboratory testing program was performed on three gap-graded mixtures: unmodified, Asphalt Rubber (AR) and polymer-modified. Strain controlled fatigue tests were conducted according to the AASHTO T321 procedure. The results from the beam fatigue tests indicated that the AR and polymer-modified gap graded mixtures would have much longer fatigue lives compared to the reference (unmodified) mixture. In addition, a mechanistic analysis using 3D-Move software coupled with a cost-effectiveness analysis study based on the fatigue performance on the three mixtures were performed. Overall, the analysis showed that the AR and polymer-modified asphalt mixtures exhibited significantly higher cost-effectiveness compared to unmodified HMA mixture. Although AR and polymer-modification increases the cost of the material, the analysis showed that they are more cost effective than the unmodified mixture.

As gesture interfaces become more main-stream, it is increasingly important to investigate the behavioral characteristics of these interactions – particularly in three-dimensional (3D) space. In this study, Fitts’ method was extended to such input technologies, and the applicability of Fitts’ law to gesture-based interactions was examined. The experiment included three gesture-based input devices that utilize different techniques to capture user movement, and compared them to conventional input technologies like touchscreen and mouse. Participants completed a target-acquisition test and were instructed to move a cursor from a home location to a spherical target as quickly and accurately as possible. Three distances and three target sizes were tested six times in a randomized order for all input devices. A total of 81 participants completed all tasks. Movement time, error rate, and throughput were calculated for each input technology. Results showed that the mean movement time was highly correlated with the target's index of difficulty for all devices, providing evidence that Fitts’ law can be extended and applied to gesture-based devices. Throughputs were found to be significantly lower for the gesture-based devices compared to mouse and touchscreen, and as the index of difficulty increased, the movement time increased significantly more for these gesture technologies. Error counts were statistically higher for all gesture-based input technologies compared to mouse. In addition, error counts for all inputs were highly correlated with target width, but little impact was shown by movement distance. Overall, the findings suggest that gesture-based devices can be characterized by Fitts’ law in a similar fashion to conventional 1D or 2D devices.

This paper describes a novel method for displaying data obtained by three-dimensional medical imaging, by which the position and orientation of a freely movable screen are optically tracked and used in real time to select the current slice from the data set for presentation. With this method, which we call a “freely moving in-situ medical image”, the screen and imaged data are registered to a common coordinate system in space external to the user, at adjustable scale, and are available for free exploration. The three-dimensional image data occupy empty space, as if an invisible patient is being sliced by the moving screen. A behavioral study using real computed tomography lung vessel data established the superiority of the in situ display over a control condition with the same free exploration, but displaying data on a fixed screen (ex situ), with respect to accuracy in the task of tracing along a vessel and reporting spatial relations between vessel structures. A “freely moving in-situ medical image” display appears from these measures to promote spatial navigation and understanding of medical data.

Essential or enduring understandings are often defined as the underlying core concepts or “big ideas” we’d like our students to remember when much of the course content has been forgotten. The central dogma of molecular biology and how cellular information is stored, used, and conveyed is one of the essential understandings students should retain after a course or unit in molecular biology or genetics. An additional enduring understanding is the relationships between DNA sequence, RNA sequence, mRNA production and processing, and the resulting polypeptide/protein product. A final big idea in molecular biology is the relationship between DNA mutation and polypeptide change. To engage students in these essential understandings in a Genetics course, I have developed a hands-on activity to simulate VDJ recombination. Students use a foldable type activity to splice out regions of a mock kappa light chain gene to generate a DNA sequence for transcription and translation. Students fold the activity several different times in multiple ways to “recombine” and generate several different DNA sequences. They then are asked to construct the corresponding mRNA and polypeptide sequence of each “recombined” DNA sequence and reflect on the products in a write-to-learn activity.

The elongases of very long chain fatty acid (ELOVL or ELO) are essential in the biosynthesis of fatty acids longer than C14. Here, two ELO full-length cDNAs (TmELO1, TmELO2) from the yellow mealworm (Tenebrio molitor L.) were isolated and the functions were characterized. The open reading frame (ORF) lengths of TmELO1 and TmELO2 were 1005 bp and 972 bp, respectively and the corresponding peptide sequences each contained several conserved motifs including the histidine-box motif HXXHH. Phylogenetic analysis demonstrated high similarity with the ELO of Tribolium castaneum and Drosophila melanogaster. Both TmELO genes were expressed at various levels in eggs, 1st and 2nd instar larvae, mature larvae, pupae, male and female adults. Injection of dsTmELO1 but not dsTmELO2 RNA into mature larvae significantly increased mortality although RNAi did not produce any obvious changes in the fatty acid composition in the survivors. Heterologous expression of TmELO genes in yeast revealed that TmELO1 and TmELO2 function to synthesize long chain and very long chain fatty acids.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.