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The preliminary aircraft design process comprises multiple disciplines. During performance analysis, parameters of the design mission have to be optimized. Mission performance optimization is often challenging, especially for complex mission profiles (e.g. for unmanned aerial vehicles [UAVs]) or hybrid-electric propulsion. Therefore, the purpose of this study is to find a methodology that supports aircraft performance analysis and that is applicable to complex profiles and to novel designs.

As its core element, the developed method uses a computationally efficient C++ software “Aircraft Performance Program” (APP), which performs a segment-based mission computation. APP performs a time integration of the equations of motion of a point mass in the vertical plane. APP is called via a command line interface from a flexible scripting language (Python). On top of APP’s internal radius of action optimization, state-of-the-art optimization packages (SciPy) are used.

The application of the method to a conventional climb schedule shows that the definition of the top of climb has a significant influence on the resulting optimum. Application of the method to a complex UAV mission optimization, which included maximizing the radius of action, was successful. Low computation time enables to perform large parametric studies. This greatly improves the interpretation of the results.

The scope of the paper is limited to the methodology that allows for advanced performance analysis at the conceptual and preliminary design stages with an emphasis on novel propulsion concepts. The methodology is developed using existing, validated methods, and therefore, this paper does not contain comprehensive validation. Other disciplines, such as cost analysis, life-cycle assessment or market analysis, are not considered.

With the proposed method, it is possible to obtain not only the desired optimum mission performance but also off-design performance of the investigated design. A thorough analysis of the mission performance provides insight into the design’s capabilities and shortcomings, ultimately aiding in obtaining a more efficient design.

Recent developments in the area of hybrid or hybrid-electric propulsion systems have shown the need for performance computation tools aiding the related design process. The presented method is especially valuable when novel design concepts with complex mission profiles are investigated.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.

This paper aims to propose a new solution for producing customized three-dimensional (3D)-printed flat-shaped splints, which are then thermoformed to fit the patient’s hand. The splint design process is automated and is available to clinicians through an online application.

Patient anthropometric data measured by clinicians are associated with variables of parametric 3D splint models. Once these variables are input by clinicians in the online app, customized stereo lithography (STL) files for both splint and half mold, in the case of the bi-material splint, are automatically generated and become available for download. Bi-materials splints are produced by a hybrid manufacturing process involving 3D printing and overmolding.

This approach eliminates the need for 3D CAD-proficient clinicians, allows fast generation of customized splints, generates two-dimensional (2D) drawings of splints for verifying shape and dimensions before 3D printing and generates the STL files. Automation reduces splint design time and cost, while manufacturing time is diminished by 3D printing the splint in a flat position.

The app could be used in clinical practice. It meets the demands of mass customization using 3D printing in a field where individualization is mandatory. The solution is scalable – it can be extended to other splint designs or to other limbs. 3D-printed tailored splints can offer improved wearing comfort and aesthetic appearance, while maintaining hand immobilization, allowing visually controlled follow-up for edema and rapidly observing the need for revision if necessary.

An online application was developed for uploading patient measurements and downloading 2D drawings and STL files of customized splints. Different models of splints can be designed and included in the database as alternative variants. A method for producing bi-materials flat splints combining soft and rigid polymers represents another novelty of the research.

Libraries throughout the world use OCLC’s EZproxy software to manage access to e-resources. When cleaned, processed, visualized and enhanced, these logs paint a valuable picture of a library’s impact on researcher’s lives. The purpose of this paper is to share techniques and procedures for enhancing and de-identifying EZproxy logs using Tableau, a data analytics and visualization software, and Tableau Prep, a tool used for cleaning, combining and shaping data for analysis.

In February 2018, The Ohio State University Libraries established an automated daily process to extract and clean EZproxy log files. The assessment librarian created a series of procedures in Tableau and Tableau Prep to union, parse and enhance these files by adding information such as user major, user status (faculty, graduate or undergraduate) and the title of the requested resource. She last stripped the data set of identifiers and applied best practices for maintaining confidentiality to visualize the data.

The data set is currently 1.5m rows and growing. The visualizations may be filtered by date, user status and user department/major where applicable. Safeguards are in place to limit data presentation when filters might reveal a user’s identity.

Tableau used in concert with Tableau Prep allows an assessment librarian to clean and combine data from various sources. Once procedures for cleaning and combining data sources are established, the data driving visualizations can be set to refresh on a set schedule. This expedites the ability of librarians to derive actionable insights from EZproxy data and to share the library’s positive impact on researcher’s lives.

The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment.

A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier.

The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives.

The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

The purpose of this paper is to present a methodology for the evaluation of transport aircraft fuselages constructed in a semi-monocoque design.

A fuselage barrel was computed statically and dynamically using finite element methods. Static analysis was conducted using a global/local approach in which the section loads of the global model were used as load introduction in the local model. Subsequently, a crash analysis was performed, and the results from both disciplines were evaluated by either an optimization or parameter variation algorithm.

The presented process chain has been developed for use in preliminary design stages to assess aircraft configurations with regard to statics and dynamics. Parameter variation and optimization were conducted, proving functionality of the methodology.

In this early stage of methodology development only one exemplary static load case is considered and the fuselage design is limited to a constant section.

The presented process chain shows an approach to couple different disciplines to reduce the analysis time in aircraft preliminary design phase.

This methodology couples static design and crashworthiness aspects at an early design stage to avoid time- and cost-intensive redesign in subsequent detailed design stages. The process chain introduced in this paper uses a parameterized approach, making this methodology applicable for each fuselage in semi-monocoque design.

The media is an important actor in public procurement, but research on its role is limited. This paper aims to investigate how the media has engaged with public procurement, using UK newspapers as a case example.

The method consisted of searching Nexis database for news articles on public procurement; automatic extraction of article attributes such as length, section, authorship; and manually coding each article for its theme and industry context. This produced quantitative indicators about the extent and focus of press coverage on public procurement.

Press coverage of public procurement increased between 1985 and 2018. The focus of coverage has been on governance failure and socio-economic policy. Governance failure, which includes corruption, cronyism and supplier malpractice, is associated with construction, outsourcing and professional services sectors. Socio-economic policy, which includes supporting small suppliers and favouring domestic industry, is associated with manufacturing, defence and agriculture.

The analysis included UK media only. While the trends observed on the extent and focus of public procurement news coverage likely reflect the situation in other countries, international comparative research is still required.

Government officials should be more proactive in countering the “negativity bias” in news coverage of public procurement by showcasing projects where value-for-money has been achieved, services have been successfully delivered and social value has been realised.

The media accentuates the negatives of public procurement and omits positive developments. The end-result is a selective and, at times, self-serving media narrative that is likely to engender cynicism towards public procurement.

To the best of the authors’ knowledge, this is the first study on media coverage of public procurement. It highlights that while there are similarities between media and academic treatment of public procurement, particularly in relation to its socio-economic side, the media emphasises governance failings and negative developments to a greater extent.

The purpose of this paper is to present a novel non-contact method of using head movement to control software without the need for wearable devices.

A webcam and software are used to track head position. When the head is moved through a virtual target, a keystroke is simulated. The system was assessed by participants with impaired mobility using Sensory Software’s Grid 2 software as a test platform.

The target user group could effectively use this system to interact with switchable software.

Physical head switches could be replaced with virtual devices, reducing fatigue and dissatisfaction.

Using a webcam to control software using head gestures where the participant does not have to wear any specialised technology or a marker. This system is shown to be of benefit to motor impaired participants for operating switchable software.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Python codes are developed for the versatile structural analysis on a 3 spar multi-cell box beam by means of idealization approach.

Shear flow distribution, stiffener loads, location of shear center and location of geometric center are computed via numpy module. Data visualization is performed by using Matplotlib module.

Python scripts are developed for the structural analysis of multi-cell box beams in lieu of long hand solutions. In-house developed python codes are made available to be used with finite element analysis for verification purposes.

The use of python scripts for the structural analysis provides prompt visualization, especially once dimensional variations are concerned in the frame of aircraft structural design. The developed python scripts would serve as a practical tool that is widely applicable to various multi-cell wing boxes for stiffness purposes. This would be further extended to the structural integrity problems to cover the effect of gaps and/or cut-outs in shear flow distribution in box-beams.