Zein-based anti-browning cling wraps for fresh-cut apple slices

The usage of cling wraps is emerging as an easy and cost-effective approach to protect fresh-cut fruits and vegetables from dust, whilst improving visual appeal on retail counters. This study focused on developing an alternate, protein-based packaging material as a food grade cling wrap for food packaging applications. Zein-based cling wraps were produced, and their physical and mechanical characteristics were evaluated and compared with conventionally used chitosan biopolymer films and commercial synthetic polymer films. Antioxidant potential of the prepared films was studied, and the effectiveness of the developed films as anti-browning cling wraps was evaluated using studies conducted on fresh-cut apple slices at ambient conditions. Anti-browning effects were in par with polymeric counterparts; however, zein cling wraps could better prevent weight loss in apple slices. Zein-based films can be adopted as biodegradable food grade cling wraps as an alternative to chitosan and synthetic polymeric materials.     

A visual tool for computer supported learning: The robot motion planning example

We introduce an effective computer aided learning visual tool (CALVT) to teach graph-based applications. We present the robot motion planning problem as an example of such applications. The proposed tool can be used to simulate and/or further to implement practical systems in different areas of computer science such as graphics, computational geometry, robotics and networking. In the robot motion planning example, CALVT enables users to setup the working environment by creating obstacles and a robot of different shapes, specifying starting and goal positions, and setting other path or environment parameters from a user-friendly interface. The path planning system involves several phases. Each of these modules is complex and therefore we provide the possibility of visualizing graphically the output of each phase. Based on our experience, this tool has been an effective one in classroom teaching. It not only cuts down, significantly, on the instructor’s time and effort but also motivates senior/graduate students to pursue work in this specific area of research.     

Bio-derived fuels may ease the regeneration of diesel particulate traps

Particulate is the most problematic emission from diesel engines. To comply with environmental regulations, these engines are often equipped with particulate traps, which must be regenerated frequently for the sake of efficiency. The regeneration is commonly achieved by rising the temperature in the trap till the particulate self-ignites. However, this method implies energy losses and thermal shocks in the trap. Alternatively, catalysts and additives have been recently considered for reducing the ignition temperature of particulate, but these techniques suffer from poisoning and undesirable byproducts. The present experimental study shows that the ignition temperature of particulate from seed-derived oils (SO) and from blends of SO with diesel fuel oil (DO) can be lower than that of particulate from neat DO. If substantiated by more extensive studies, such finding could have noteworthy implications on the future of fuels and traps.     

Developing human factors/ergonomics as a design discipline

This paper deals with internal challenges that the human factors/ergonomics (HFE) research faces when wishing to strengthen its contribution to development of work systems. Three established characteristics of high-quality HFE, i.e., HFE takes a systems approach, HFE is design-driven, and HFE focuses on two closely related outcomes, performance and well-being, are taken as a starting point of a methodological discussion, in which conceptual innovations, e.g. adopting the technology-in-use perspective, are proposed to support development of HFE towards the high-quality aims. The feasibility of the proposed conceptual choices is demonstrated by introducing a naturalistic HFE analysis approach including four HFE functions. The gained experience of the use of this approach in a number of complex work domains allows the conclusion that becoming design-driven appears as that most difficult quality target for HFE to reach. Creating an own design discipline identity in a multi-voiced collaboration is the key internal challenge for human factors/ergonomics.     

Generalizing Multi-agent Graph Exploration Techniques

International Journal of Control, Automation and Systems - The system of multiple agents working in coordination for a given task has several advantages on faster completion, fault-tolerance, etc....     

Imagined community and death

This paper addresses the limits of net discourse in virtual communities. I will analyse incidents of death that shattered the 'rhetorical vision' of one particular virtual community, Xena: Warrior Palace. The observations will trace some characteristics of the community's vision of itself, how vulnerable that vision can be and how it can be maintained or revised after some unexpected occurrence shatters it. I argue that even though the rhetorical vision of virtual community can be imagined, it has to stay in touch with the 'real world' in order to have real influence on constructed online culture.     

Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer in VGG assumes that the training samples belong to exactly only one class, which is not valid in a real situation, such as in medical image diagnosis. To overcome this situation, SVM is employed instead of the softmax layer in VGG. Data augmentation is also employed as DLA usually requires a large number of samples. VGG model with different SVM kernels is built to classify the mammograms. Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%.