A Repertoire of Virtual-Reality, Occupational Therapy Exercises for Motor Rehabilitation Based on Action Observation

There is a growing interest in action observation treatment (AOT), i.e., a rehabilitative procedure combining action observation, motor imagery, and action execution to promote the recovery, maintenance, and acquisition of motor abilities. AOT studies employed basic upper limb gestures as stimuli, but—in principle—the AOT approach can be effectively extended to more complex actions like occupational gestures. Here, we present a repertoire of virtual-reality (VR) stimuli depicting occupational therapy exercises intended for AOT, potentially suitable for occupational safety and injury prevention. We animated a humanoid avatar by fitting the kinematics recorded by a healthy subject performing the exercises. All the stimuli are available via a custom-made graphical user interface, which allows the user to adjust several visualization parameters like the viewpoint, the number of repetitions, and the observed movement’s speed. Beyond providing clinicians with a set of VR stimuli promoting via AOT the recovery of goal-oriented, occupational gestures, such a repertoire could extend the use of AOT to the field of occupational safety and injury prevention.

Development of a tool to optimize economic and environmental feasibility of food waste chains

The Sustainable Development Goal 12.3 focuses on food and its inedible parts that exit the supply chain and thus are lost or wasted. This work addresses the food waste problem by presenting the development of a tool to design business models to reduce the production of food waste. This has been developed within the LIFE16 project iRexfo, coordinated by the University of Perugia. The tool aims at transferring the results obtained in a pilot region (Umbria, Italy) to 4 other regions in Europe. It has been coded in Python and has a graphical user interface (GUI) to insert inputs and display outputs. The GUI has been developed in FLASK and it is hosted in the website of PythonAnywhere. A case study on the application of the software is also presented, mainly based on data retrieved in the Umbria region, Italy. Together with economic analysis, also, environmental assessment is performed.

PENERAPAN k-MODES CLUSTERING DENGAN VALIDASI DUNN INDEX PADA PENGELOMPOKAN KARAKTERISTIK CALON TKI MENGGUNAKAN R-GUI

Prospective TKI workers who apply for passports at the Immigration Office Class I Non TPI Pati have countries destinations and choose different PPTKIS agencies. Therefore, the grouping of characteristics prospective TKI needed so that can be used as a reference for the government in an effort to improve the protection of TKI in destination countries and carry out stricter supervision of PPTKIS who manage TKI. The purpose of this research is to classify the characteristics of prospective TKI workers with the optimal number of clusters. The method used is k-Modes Clustering with values of k = 2, 3, 4, and 5. This method can agglomerate categorical data. The optimal number of clusters can be determined using the Dunn Index. For grouping data easily, then compiled a Graphical User Interface (GUI) based application with RStudio. Based on the analysis, the optimal number of clusters is two clusters with a Dunn Index value of 0,4. Cluster 1 consists of mostly male TKI workers (51,04%), aged ≥ 20 years old (91,93%), with the destination Malaysia country (47%), and choosing PPTKIS Surya Jaya Utama Abadi (37,51%), while cluster 2, mostly of male TKI workers (94,10%), aged ≥ 20 years old (82,31%), with the destination Korea Selatan country (77,95%), and choosing PPTKIS BNP2TKI (99,78%).

Best-Estimate for System Codes (BeSYC): A New Software to Perform Best-Estimate Plus Uncertainty Analyses with Thermal-Hydraulic and Safety System Codes for Both Fusion and Fission Scenarios

The development and the validation of old and new software in relevant DEMO reactor conditions have been exploited in the latest years within the EUROfusion Consortium. The aim was to use—if possible—the software already validated for fission reactors and to fill the gaps with new ad-hoc software. As contribution to this effort, the Karlsruhe Institute of Technology (KIT) developed and tested a novel software to apply the Best-Estimate Model Calibration and Prediction through Experimental Data Assimilation methodology to the system codes RELAP5-3D, MELCOR 1.8.6, and MELCOR 2.2. This software is called Best-estimate for SYstem Codes (BeSYC), and it is developed as a MATLAB App. The application is in charge of applying the mathematical framework of the methodology, writing and executing the code runs required by the methodology, and printing the obtained results. The main goal of BeSYC is to wrap up the methodology in a software suitable to be used by any user through a simple graphical user interface. Albeit developed in the fusion research context, BeSYC can be applied to any reactor/scenario type supported by the specific system code. The goals of BeSYC, the mathematical framework, the main characteristics, and the performed verification and validation activities are described in this paper.

Creation of a Software Model with a Graphical User Interface for simulating the processes of functioning of subsystems of information and cyber protection of Distributed Information Systems of Critical Infrastructures

This article is dedicated to the development of a software model with a Graphical User Interface (GUI) to simulate the process of ensuring information and cyber security of information systems (IS) of Critical Infrastructure objects (CI) based on the analytical model developed by the author of this article. The specified software model with a GUI makes it possible, using the controls located on the main panel, to set the input parameters of the simulated object and observe its output characteristics using appropriate visualization elements such as special windows for displaying calculated numerical values of the main characteristics of the systems under study.

Prototyping neural networks to evaluate the risk of adverse cardiovascular outcomes in the population

Aim. To develop a neural network basis for the design of artificial intelligence software to predict adverse cardiovascular outcomes in the population.Materials and Methods. Neural networks were designed using the database of 1,525 participants of PURE (Prospective Urban Rural Epidemiology Study), an international, multi-center, prospective study investigating disease risk factors in the urban and rural areas. As this study is still ongoing, we analysed only baseline data, therefore switching prognosis and diagnosis task. Because of its leading prevalence among other cardiovascular diseases, arterial hypertension was selected as an adverse outcome. Neural networks were designed employing STATISTICA Automated Neural Networks (SANN) software, manually selected, cross-validated, and transferred to the original graphical user interface software.Results. Input risk factors were gender, age, place of residence, concomitant diseases (i.e., coronary artery disease, chronic heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and asthma), active or passive smoking, regular use of medications, family history of arterial hypertension, coronary artery disease or stroke, heart rate, body mass index, fasting blood glucose and cholesterol, high- and low-density lipoprotein cholesterol, and serum creatinine levels. Our neural networks showed a moderate efficacy in the virtual diagnostics of arterial hypertension (84.5%, or 1,289 successfully predicted outcomes out of 1,525, area under the ROC curve = 0.88), with almost equal sensitivity (83.6%) and specificity (85.3%), and were successfully integrated into graphical user interface that is necessary for the development of the commercial prognostication software. Cross-validation of this neural network on bootstrapped samples of virtual patients demonstrated sensitivity of 82.7 – 84.7%, specificity of 84.5 – 87.3%, and area under the ROC curve of 0.88 – 0.89.Conclusion. The artificial intelligence prognostication software to predict adverse cardiovascular outcomes in the population can be developed by a combination of automated neural network generation and analysis followed by manual selection, cross-validation, and integration into graphical user interface.

Excess Noise Factor in Avalanche Photodiodes with Dead Space Effect

This project aims to produce a graphical user interface (GUI) for MATLAB programs written byJ.S.Marsland as part of his research into the excess noise factor in avalanche photodiodes (APDs). TheGUI will be produced using the GUIDE package supplied with the MATLAB software combined withthe MATLAB programs. The GUI will then be used to compare this research work with the researchwork of others e.g. the Monte Carlo calculations made by the research group at the FrenchAerospace Laboratory (ONERA). Comparison with other research work will require the digitization ofsome graphs published in academic journals.

QVR: Quranic Verses Recitation Recognition System using PocketSphinx

The recitation of Quran verses according to the actual tajweed is obligatory and it must be accurate and precise in pronunciation. Hence, it should always be reviewed by an expert on the recitation of the Quran. Through the latest technology, this recitation review can be implemented through an application system and it is most appropriate in this current Covid-19 pandemic situation where system application online is deemed to be developed. In this empirical study, a recognition system so-called the Quranic Verse Recitation Recognition (QVR) system using PocketSphinx to convert the Quranic verse from Arabic sound to Roman text and determine the accuracy of reciters, has been developed. The Graphical User Interface (GUI) of the system with a user-friendly environment was designed using Microsoft Visual Basic 6 in an Ubuntu platform. A verse of surah al-Ikhlas has been chosen in this study and the data were collected by recording 855 audios as training data recorded by professional reciters. Another 105 audios were collected as testing data, to test the accuracy of the system. The results indicate that the system obtained a 100% accuracy with a 0.00% of word error rate (WER) for both training and testing data of the said audios via Quran Roman text. The system with automatic speech recognition (ASR) engine system demonstrates that it has been successfully designed and developed, and is significant to be extended further. Added, it will be improved with the addition of other Quran surahs.