Object:The purpose of the current study is to determine the sensitivity and specificity of an eye tracking method as a classifier for identifying concussion.Methods:Brain injured and control subjects prospectively underwent both eye tracking and Sport Concussion Assessment Tool 3. The results of eye tracking biomarker based classifier models were then validated against a dataset of individuals not used in building a model. The area under the curve (AUC) of receiver operating characteristics was examined.Results:An optimal classifier based on best subset had an AUC of 0.878, and a cross-validated AUC of 0.852 in CT- subjects and an AUC of 0.831 in a validation dataset. The optimal misclassification rate in an external dataset (n = 254) was 13%.Conclusion:If one defines concussion based on history, examination, radiographic and Sport Concussion Assessment Tool 3 criteria, it is possible to generate an eye tracking based biomarker that enables detection of concussion with reasonably high sensitivity and specificity.

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Job rotation schedules are generally conducted without considering hand–arm vibration (HAV) exposure especially in manufacturing firms, such as heavy industry, which confront ergonomic risks easily. This does not create any occupational health and safety (OHS) issues in the short term; however, certain occupational diseases such as the white finger disease are inevitable in the long‐term. This paper investigates how the risks of developing HAV‐related occupational diseases can be minimized by producing optimal job rotation schedules using ergonomic mathematical models. In the proposed models, both ergonomic and traditional aspects of manufacturing environments are considered. Moreover, the skill level and workers' day off preferences are also considered for total system efficiency. The mixed‐integer programming approach is used to formulate the models. The applicability of the models is tested using real‐world data. It is seen that the total assignment cost of the models increases when the ergonomic aims are included in the models. Thus, there is a conflicting relationship between economic and ergonomic aims. The main findings of the models show that the employees' HAV exposure level can be kept under control with one simple constraint. By employing these models, employees and employers can be protected in terms of not only OHS but also economic issues.

Endüstri 4.0 ile hızlı bir dijitalleşme süreci emek yoğun işletmeler le birlikte tüm işletmeler için kaçınılmaz olmuştur. Öte yandan geleneksel iş etüdü teknikleri verimlilik ölçüm ve izlemede oldukça kritik bir role sahiptir. Bu çalışmada iş etüdü tekniklerinin dijitalleşme sürecindeki rolü ve değişimine yönelik bir araştırma ve değerlendirme yapılması amaçlanmıştır. Yöntem: İlgili değerlendirmeyi yapmak için ele alınan tekniklere ilişkin kapsamlı literatür araştırması ve gerçek dünya örneklerinin incelenmesi şeklinde bir yol izlenmiştir. Bulgular: Dijitalleşen iş süreçleri karşısında iş etüdü tekniklerinin gösterdiği tepki incelendiğinde, geleneksel tekniklerinin bu dönüşüm sürecine büyük oranda adapte olduğu söylenebilir. Endüstri devriminde işletmelerin dijitalleşmesinde kritik rol oynayan yapay zekâ tekniklerinin iş ölçümü tekniklerindeki dijitalleşmeyi de tetiklediği görülmektedir. Dahası, geleneksel iş etüdü tekniklerinin, işletmelerin Endüstri 4.0'a geçiş süreçlerinde ortaya çıkan ihtiyaçlarına yapay zekâ ve Endüstri 4.0 ile birlikte gelen diğer dijital teknikler ile bütünleşerek uyum sağladığı görülmektedir. Özgünlük: Üretim ortamlarındaki dijital dönüşüm süreçlerinin geleneksel iş etüdü teknikleri üzerindeki etkisinin incelenmesi, değişimin analiz edilmesi, bununla birlikte ilgili tekniklere yönelik bir gelecek projeksiyonunun sunulması bu çalışmayı özgün kılmaktadır.

The rapid spread of the COVID-19 pandemic has affected not only the health industry but also the education sector. E-learning systems have recently become a compulsory part of all education institutions, including schools, colleges, and universities worldwide because of the COVID-19 pandemic crisis. The objectives of the current study were twofold: (1) to conduct an analytical approach for ranking of distance education platforms based on human–computer interaction criteria and (2) to identify the most appropriate distance learning platform for teaching and learning activities by using multi-criteria decision-making approaches. Selection criteria were grouped into human–computer interaction-related criteria, such as ease of use, possibility of causing mental workload, user-friendly interface design, presentation method, and interactivity. In the selection procedure, a spherical fuzzy extension of Analytical Hierarchy Process was utilized to identify the weights of selection criteria and to rank distance education platforms. The results revealed that the most important criterion was the possibility of causing mental workload while the most preferable e-learning system was identified as “A3”.

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