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....     

An International Collaborative Study on Trypsin Inhibitor Assay for Legumes,Cereals, and Related Products

For determining trypsin inhibitor activity (TIA) in soy products, the American Oil Chemists' Society (AOCS) Method Ba 12-75 has been used. It measures differences in absorbance at 410 nm of bovine trypsin activity toward a synthetic substrate (Nα-benzoyl-DL-arginine-p-nitroanilide) in the absence and presence of an inhibitor. Recently, a significantly improved method was developed (JAOCS, 2019, 96:635–645), featuring 5 mL of total assay volume, enzyme-last sequence, and single inhibitor level in duplicate. It is proposed as the AOCS Method Ba 12a-2020. As a part of the AOCS method approval process, a collaborative study involving 12 international laboratories was conducted to evaluate the performance of the proposed method. The study involved measuring TIA in 10 selected test samples plus a blind duplicate. They included soybeans, pulses, cereals, and their processed products (flours, concentrates, and isolates). After rigorous statistical treatment of the data, only three outliers were removed from the data of two samples. Repeatability relative standard deviations (RSDr) for the 11 samples ranged from 0.99% to 5.52%. Reproducibility RSD (RSD_R) ranged from 7.07% to 22.92%, with seven samples having RSD_R around 10% or less. The remaining four samples had very low TIA, and their RSD_R values ranged from 13.34% to 22.92%. The study has demonstrated reliable performance of the proposed AOCS method. Several collaborators carried out additional experiments addressing some aspects of the method, leading to further refinements. The proposed method is undergoing evaluation by the AOCS Uniform Methods Committee for adoption as an Official Method for measuring TIA in various legume and grain products.     

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%.     

Self-reported use of ICT (Information and communication technology) uptake in 2002 and discomfort amongst Finns aged 45-66

In recent years the use of new technical equipment and the Internet by middle-aged people has increased. This paper presents middle-aged people’s (45 years old or older) usage of new technical equipment and analyses how their (2704 persons) symptoms are associated with the equipment. Over 70% of Finland’s middle-aged population use mobile phones daily and less than 30% use desktop computers at leisure. For example, over 80% of middle-aged people had sometimes or often experienced pain, numbness or aches in the neck and about 70% had aches in the hip and lower back. The use of new technical equipment among the group of people who are outside working life was smaller than the people’s usage in general. In the future, when new technical equipment is developed, it is important to take into account, that people (outside working life) do not use, e.g., the Internet as much as people in general. The working environment will be much more ergonomic also at home and the Internet services will be easy to use for almost everyone, even those who have little experience or knowledge of computers and the Internet.     

3D computer vision based on machine learning with deep neural networks: A review

Recent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain's visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we hope to also provide inspiration for future advances in automated visual processing.     

Solving large multiple-depot multiple-vehicle-type bus scheduling problems in practice

We consider the multiple-depot multiple-vehicle-type scheduling problem (MDVSP) which arises in public transport bus companies and aims to assign buses to cover a given set of timetabled trips with consideration of practical requirements, such as multiple depots and vehicle types as well as depot capacities. An optimal schedule is characterized by minimal fleet size and minimal operational costs including costs for empty movements and waiting time. It is well-known that the MDVSP is NP-hard.Although progress has recently been made in solving large practical MDVSP to optimality with time-space network models, current optimization technology sets limits to the model size that can be solved. In order to approach very large practical instances we propose a two-phase method which produces close to optimal solutions. This modeling approach enables us to solve real-world problem instances with thousands of scheduled trips by direct application of standard optimization software. Furthermore, we introduce the concept of depot groups for the case that a bus may return in the evening into another depot than where it started in the morning.