bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification
Authors: Anbeek Petronella , Vincken Koen L. , Viergever Max A.
Published: January 2022

This paper proposes a new method for fully automated multiple sclerosis (MS) lesion segmentation in cranial magnetic resonance (MR) imaging. The algorithm uses the T1-weighted and the fluid attenuation inversion recovery scans. It is based the K-Nearest Neighbor (KNN) classification technique. The data has been acquired at the Children�s Hospital Boston (CHB) and the University of North Carolina (UNC). Manual segmentations, composed by a human expert of the CHB, were used for training of the KNN-classification. The method uses voxel location and signal intensity information for determination of the probability being a lesion per voxel, thus generating probabilistic segmentation images. By applying a threshold on the probabilistic images binary segmentations are derived. Automatic segmentations were performed on a set of testing images, and compared with manual segmentations from a CHB and a UNC expert rater. Furthermore, a combined segmentation was composed from segmentations from different algorithms, and used for evaluation. The proposed method shows good resemblance with the segmentations of the CHB rater. High specificity and lower specificity has been observed in comparison with the combined segmentations. Over- and undersegmentation can be easily corrected in this procedure by varying the threshold on the probabilistic segmentation image. The proposed method offers an automated and fully reproducible approach that accurate and applicable on standard clinical MR images.
Morphological Appearance Manifolds in Computational Anatomy: Groupwise Registration and Morphological Analysis
Authors: Lian Nai-xiang , Davatzikos Christos
Published: January 2022

The ﬁeld of computational anatomy has developed rigorous frameworks for analyzing anatomical shape, based on diffeomorphic transformations of a template. However, differences in algorithms used for template warping, in regularization parameters, and in the template itself, lead to different representations of the same anatomy. Variations of these parameters are considered as confounding factors. Recently, extensions of the conventional computational anatomy framework to account for such confounding variations has shown that learning the equivalence class derived from the multitude of representations can lead to improved and more stable morphological descriptors. Herein, we follow that approach, estimating the morphological appearance manifold obtained by varying parameters of the template warping procedure. Our approach parallels work in the computer vision ﬁeld, in which variations lighting, pose and other parameters leads to image appearance manifolds representing the exact same ﬁgure in different ways. The proposed framework is then used for groupwise registration and statistical analysis of biomedical images, by employing a minimum variance criterion to perform manifold-constrained optimization, i.e. to traverse each individual’s morphological appearance manifold until all individuals’ representations come as close to each other as possible. Eﬀectively, this process removes the aforementioned confounding eﬀects and potentially leads to morphological representations reﬂecting purely biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local approximations of the manifold via PCA.
Dose Painting with a Variable Collimator for the Small Animal Radiation Research Platform (SARRP)
Authors: Cho Nathan , Wong John , Kazanzides Peter
Published: January 2022

The goal of radiation treatment is to irradiate cancer cells (i.e., a target region) without destroying adjacent healthy tissue. Thus, it is advantageous to form the beam so that it best approximates the target, thereby reducing the amount of dose absorbed in critical regions outside the target area. While multi-leaf collimators are common in human clinical systems, small animal radiotherapy systems are typically limited to a set of fixed-size collimators. For these systems, dose painting can be used for conformal dose delivery, but is significantly slower than a multi-leaf collimator. As a compromise solution, a variable rectangular collimator has been developed for the Small Animal Radiation Research Platform (SARRP). This enables more efficient dose painting via the decomposition of a 2D target region into a minimum number of rectangles of variable size, which is the topic of this paper. The proposed method consists of several distinct steps and was implemented on the SARRP Treatment Planning System (TPS).
Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification
Authors: Van opbroek Annegreet , Van der Lijn Fedde , De Bruijne Marleen
Published: January 2022

We present a method for automated brain-tissue segmentation through voxelwise classification. Our algorithm uses manually labeled training images to train a support vector machine (SVM) classifier, which is then used for the segmentation of target images. The classification incorporates voxel intensities from a T1-weighted scan, an IR scan, and a FLAIR scan; features to encode the voxel position in the image; and Gaussian-scale-space features and Gaussian-derivative features at multiple scales to facilitate a smooth segmentation. An experiment on data from the MRBrainS13 brain-tissue-segmentation challenge showed that our algorithm produces reasonable segmentations in a reasonable amount of time.
Head and Neck Auto Segmentation Challenge based on Non-Local Generative Models
Authors: Orbes Arteaga Mauricio , Cardenas Peña David , Castellanos Dominguez Germán
Published: January 2022

A new patch based label fusion method based on generative approach is proposed for segmentation of mandible, brainstem, parotid and submandibular glands, optic nerves and the optic chiasm in head and neck CT images. The proposal constructs local classifiers from a dictionary of patches and weights their contribution using a generative probabilistic criterion. Also, a gaussian slide window is used to weight the multiples estimations of neighboring voxels. The proposed method was evaluated on a set of 15 CT images (10 off-site and 5 onsite) provided by the organizers of the Head and neck Auto-Segmentation challenge(MICCAI 2015), where the obtained results are comparable to many of the other methods used in the challenge.
On the Generation of Ground Truth Data for Depth Reconstruction
Authors: Staub Christoph , Haider Andreas , Grimm Markus , Knoll Alois
Published: January 2022

Acquiring data sets for stereo or multi-view reconstruction with known ground truth is costly and time consuming. Freely available data sets mostly focus on specific scene conditions and might not reflect the intended application scenario. We propose a GPU accelerated ray tracing framework that allows to generate realistic images with adjustable camera properties and projection geometry. Due to the nature of ray tracing, ground truth data can easily be obtained. The system also supports projectors as used e.g. for structured light systems. This facilitates to study interaction effects between different pattern designs, camera and projector properties, as well as the utilized reconstruction algorithm.
Unified framework for development, deployment and testing of image analysis algorithms
Authors: Joshi Alark , Scheinost Dustin , Okuda Hirohito , Murphy Isabella , Staib Lawrence , Papademetris Xenophon
Published: January 2022

Developing both graphical and command-line user interfaces for image analysis algorithms requires considerable effort. Generally developers provide limited to very rudimentary user interface controls to their users. These image analysis algorithms can only meet their potential if they can be used easily and frequently by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires that the software be stable and appropriately tested.

We present a novel framework that allows for rapid development of image analysis algorithms along with graphical user interface controls. Additionally, our framework allows for simplified nightly testing of the algorithms to ensure stability and cross platform interoperability. It allows for development of complex algorithms by creating a custom pipeline where the output of an algorithm can serve as an input for another algorithm. All of the functionality is encapsulation into the object requiring no separate source code for user interfaces, testing or deployment. This makes our framework ideal for developing novel, stable and easy-to-use algorithms for computer assisted interventions (CAI). The framework has been deployed at the Magnetic Resonance Research Center at Yale University and has been released for public use.
Multimodal Registration of White Matter Brain Data via Optimal Mass Transport
Authors: Rehman Tauseef , Haber Eldad , Pohl Kilian , Haker Steven , Halle Mike , Talos Florin , Wald Lawrence , Kikinis Ron , Tannenbaum Allen
Published: January 2022

The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets.
Semi-automatic Segmentation of 3D Liver Tumors from CT Scans Using Voxel Classification and Propagational Learning
Authors: Zhou Jiayin , Xiong Wei , Tian Qi , Qi Yingyi , Liu Jiang , Leow Wee Keng , Han Thazin , Venkatesh Sudhakar , Wang Shih-chang
Published: January 2022

A semi-automatic scheme was developed for the segmentation of 3D liver tumors from computed tomography (CT) images. First a support vector machine (SVM) classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumor-containing slices were processed. The method was tested using 3D CT images with 10 liver tumors and a set of quantitative measures were computed, resulted in an averaged overall performance score of 72.
Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing
Authors: Qi Yingyi , Xiong Wei , Leow Wee Keng , Tian Qi , Zhou Jiayin , Liu Jiang , Han Thazin , Venkatesh Sudhakar , Wang Shih-chang
Published: January 2022

Automatic segmentation of liver tumorous regions often fails due to high noise and large variance of tumors. In this work, a semi-automatic algorithm is proposed to segment liver tumors from computed tomography (CT) images. To cope with the variance of tumors, their intensity probability density functions (PDF) are modeled as a bag of Gaussians unlike the previous works where the tumor is modeled as a single Gaussian, and employ a three-dimensional seeded region growing (SRG) method. The bag of Gaussians are initialized at manually selected seeds and updated during growing process iteratively. There are two criteria to be fulﬁlled for growing: one is the Bayesian decision rule, and the other is a model matching measure. Once the growing is terminated, morphological operations are performed to reﬁne the result. This method, showing promising performance, has been evaluated using ten CT scans of livers with twenty tumors provided by the organizer of the 3D Liver Tumor Segmentation Challenge 2008.
Skull Base Surgery Navigation System Based on Updating Preoperative Images Using Positional Information of Surgical Tools
Authors: Hayashi Yuichiro , Fujii Masazumi , Kajita Yasukazu , Wakabayashi Toshihiko , Mori Kensaku
Published: January 2022

In this paper, we introduce a new concept of surgical navigation which processes information interactively between the real and virtual spaces, namely, updating preoperative images using the positional information of surgical tools. Although the organs are deformed by operative procedures during surgery, surgical navigation systems usually do not change the reference images that are taken prior to surgery. It is useful to generate deformed reference images during surgery while it progresses. We develop a skull base surgery navigation system that updates the preoperative images during surgery. To estimate the resected regions, our proposed system utilizes the positional information of the surgical tools that can be tracked by a surgical navigation system. Our proposed system reflects the bone removal on preoperative images by changing the voxel values of the preoperative images using the positional information of the tracked tools. The updated reference images are generated by visualizing the updated preoperative images using a volume rendering method. We evaluated the proposed system on a skull phantom created from CT images by a 3D printer. The experimental results showed that the proposed system updated the reference images in real time based on the surgical tasks including bone removal process. The accuracy of our proposed method was about 1 mm. It is very useful for surgeons to drill into such complex bone structure as the skull base.
Voxelizer plug-in for Blender
Authors: Grothausmann Roman , Gout Julien , Kühnel Mark
Published: January 2022

This document describes a plug-in for Blender (www.blender.org) that allows to rasterize 3D mesh objects into 3D voxel data, i. e. it allows to voxelize Blender meshes. In 2D, this process can be compared to rasterization of vector graphics into pixel graphics. The voxelization is done by VTK (www.vtk.org) functions. A simple GUI allows to choose the type of voxelization and to specify necessary parameters. Depending on the type chosen, only the surface of the mesh objects is voxelized or the enclosed volume, i. e.“filled”. Beside the size of the output, one can specify if the result should be “anti-aliased” or not. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the script described in this paper.
On the Importance of Location and Features for the Patch-Based Segmentation of Parotid Glands
Authors: Wachinger Christian , Brennan Matthew , Sharp Greg , Golland Polina
Published: January 2022

The segmentation of parotid glands in CT scans of patients with head and neck cancer is an essential part of treatment planning. We introduce a new method for the automatic segmentation of parotid glands that extends existing patch-based approaches in three ways: (1) we promote the use of image features in combination with patch intensity values to increase discrimination; (2) we work with larger search windows than established methods by using an approximate nearest neighbor search; and (3) we demonstrate that location information is a crucial discriminator and add it explicitly to the description. In our experiments, we compare a large number of features and introduce a new multi-scale descriptor. The best performance is achieved with entropy image features in combination with patches and location information.
Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests
Authors: Keraudren Kevin , Oktay Ozan , Shi Wenzhe , Hajnal Joseph V. , Rueckert Daniel
Published: January 2022

In this paper, we present the use of a generic image segmentation method, namely a succession of Random Forest classifiers in an autocontext framework, for the MICCAI 2014 Challenge on Endocardial 3D Ultrasound Segmentation (CETUS). The proposed method segments each frame independently in 90 sec, without requiring temporal information such as end-diastolic or end-systolic time points nor any registration. For better segmentation accuracy, non-local means denoising can be applied to the images at the cost of an increased run-time. The mean Dice score on the testing dataset was 84.4% without denoising and 86.4% with denoising. The originality of our approach lies in the introduction of two classes, the myocardium and the mitral valve, in addition to the left ventricle and the background classes, in order to gain contextual information for the segmentation task.
Model-based left ventricle segmentation in 3D ultrasound using phase image
Authors: Wang Chunliang , Wang Chun , Smedby Orjan
Published: January 2022

In this paper, we propose a semi-automatic method for left ventricle segmentation. The proposed method utilizes a multi-scale quadrature filter method to enhance the 3D volume, followed by a model-based level set method to segment the endocardial surface of the left ventricle. The phase map from the quadrature filters is also used to weight the influence of contour points when updating the statistical model.
Suomalaiset korkeakoulut kestävää kehitystä edistämässä
Authors: Puurula Juuso , Konst Taru , Friman Mervi , Koivunen Tuija
Published: January 2022

Yksi YK:n Agenda 2030:n koulutukseen liittyvä tavoite on varmistaa vuoteen 2030 mennessä, että kaikki oppijat saavat kestävän kehityksen edistämiseen tarvittavat tiedot ja taidot. Suomalaiset ammattikorkeakoulut ovat marraskuussa 2020 hyväksyneet yhteisen kestävän kehityksen ja vastuullisuuden ohjelman, jonka lähtökohtana ovat YK:n kestävän kehityksen tavoitteet. Samaan aikaan myös yliopistot Suomessa julkaisivat yhteiset kestävän kehityksen ja vastuullisuuden teesinsä. Ohjelmat ja teesit tähtäävät YK:n tavoitteiden täyttämiseen, mutta korkeakoulusektorit ovat toimissaan eri vaiheissa. Tässä artikkelissa analysoimme laadullisen aineiston pohjalta, miten neljä korkeakoulua kuvaavat kestävän kehityksen linjauksiaan sekä kertovat niiden toteutuksista. Tutkimusaineiston muodostavat korkeakoulujen strategiat, niiden kestävän kehityksen ja vastuullisuuden ohjelmat ja teesit, sekä korkeakoulujen kestävää kehitystä käsittelevät verkkosivut. Artikkelissa tuomme esiin erityisesti korkeakoulujen tavoitteita, suunnitelmia ja toimenpiteitä muutoksessa kohti kestävää kehitystä. Tavoitteena on lisätä keskustelua korkeakoulutuksen roolista kestävän kehityksen edistämisessä ja tuoda esiin yhteisesti hyödynnettäviä tapoja edistää sitä. Tutkimustuloksista on todettavissa, että tapa, jolla korkeakoulut esittävät suhteensa kestävään kehitykseen ja sen edistämiseen, on osa sitä kuvaa, jonka korkeakoulut tuottavat itsestään vastuullisina kestävän kehityksen toimijoina. Miten hyvin korkeakoulujen toiminta käytännössä vastaa näitä kuvauksia - sitä ei voida tämän artikkelin aineiston perusteella kattavasti arvioida. Kestävässä korkeakoulussa kestävä kehitys kulkee juonteena läpi koko organisaation. Käytännössä se tarkoittaa holistista kehitysohjelmaa, jossa kestävyys on linkitetty kaikkeen organisaation toimintaan ja sitä myös arvioidaan säännöllisesti. Vielä on liian aikaista arvioida, kuinka korkeakoulut täyttävät sekä omat että kansainväliset kestävän kehityksen tavoitteensa.
University teachers’ digital stories of sustainable development: A method of learning to teach
Authors: Hakanurmi Satu , Murtonen Mari , Palonen Tuire
Published: January 2022

In order to help teachers teach sustainability more effectively, we need more knowledge about both their understanding of suitable pedagogical methods and their own positioning in regard to sustainable development. This qualitative research focuses on how teachers see themselves as educators of sustainability and how they experience creative methods such as digital storytelling in supporting their learning. Interviews were carried out with nine university teachers during a staff training course on sustainable development and how it is best taught and learnt. Findings indicate that teachers’ positioning in regard to the teaching of sustainable development varied according to their discipline and their understanding of its four dimensions, namely ecological, social, economic and cultural. Digital storytelling thus proved to be a promising method for supporting holistic learning and teachers’ self-positioning in regard to education concerning this complex domain. However, some restrictions exist in relation to resource-taking and teachers’ need for additional support.
150 ‘A Safe Learning Environment’: Simulation-Induced Stress Literature Review
Authors: Thompson Hazel , Brown Craig
Published: January 2022

Simulation-based education (SBE) is often celebrated as a safe learning environment, but this usually refers to the risk posed to patients, in this literature review the psychological safety for participants and the elements of SBE that generate or reduce stress are sought. Stress and learning have a complex relationship in adult learning; however, negative stress may inhibit memory formation and so the sustainable effect of SBE learning may be jeopardized by participants experiencing unnecessary stress during SBE. It is therefore important to identify the nature and trigger for stress in SBE to optimize this resource.

Using the online database PubMed and the search terms (stress and anxiety) AND (Simulation) AND ((clinical education, medical education)) without limits on publication type or date, 20 articles were returned. A non-systematic review was undertaken. Articles that were designed to deliberately introduce stress into SMEs to gauge the effect on performance were excluded. Included studies analysed the type, characteristics and potential triggers of stress evoked through participation in SBE. 17 studies were retained.

No studies in the UK were returned, SBE participants were from undergraduate and post-graduate settings and there was a mixture of professional groups included with three studies looking at team-based SMEs. Study design and method varied with an observational study being the most common method. Only one looked at qualitative data from focus groups of SME participants. Nearly all studies recorded a physical marker of stress – heart rate, cortisol level or visible signs of stress such as shaking hands. Two studies looked at techniques to actively reduce stress within the SBE activity; a mindfulness exercise before a task-based simulation and an introduction of a period of relaxation prior to debriefing. Faculty awareness of participant stress was measured objectively in only one study. SME design and equipment stressors were directly considered in two studies.

There are limited dedicated studies addressing SBE-induced stress and how this can be modified; furthermore, a lack of research into faculty impact on stress hinders the opportunity to change. This was not a systematic literature review and so the findings are limited, but can help inform practitioners: (1) Repeated exposure and familiarity with SME reduce stress. (2) Designate roles that participants would be expected to undertake in real clinical scenarios. (3) Minimize distracting factors in the environment unless directly contributing to learning outcome. (4) Introducing a purposeful period of calm before debriefing may improve retention of learning outcomes.
Analisis Pro-Poor Growth Kabupaten Pekalongan : Profil dan Dekomposisi Kemiskinan 2015-2019
Authors: Cahyo Nugroho Yoyok , Toni Taruno Hendrawan
Published: January 2022

Penelitian ini bertujuan mengkaji (analisis) pertumbuhan pro-poor di Kabupaten Pekalongan periode 2015-2019. Secara lebih spesifik, studi ini dilakukan untuk mengetahui profil kemiskinan, pengaruh pertumbuhan dan distribusi terhadap perubahan kemiskinan, dan derajat pertumbuhan pro-poor di Kabupaten Pekalongan selama periode 2015-2019. Penelitian ini menerapkan data Survei Sosial Ekonomi Nasional (Susenas) di Kabupaten Pekalongan tahun 2015-2019. Dimensi pendapatan dalam penelitian ini menggunakan data konsumsi (pengeluaran) rumah tangga sebagai pendekatan pendapatan. Dengan menggunakan analisis dekomposisi kemiskinan Shapley dan Poverty Equivalent Growth Rate (PEGR), studi ini menyarankan beberapa temuan. Pertama, selama periode 2015-2019, angka kemiskinan di Kabupaten Pekalongan mengalami penurunan yang juga diikuti dengan penurunan kesenjangan kemiskinan dan indeks keparahan kemiskinan. Kedua, sebagian besar penduduk miskin didominasi oleh kelompok usia non produktif, yaitu anak usia sekolah dan lansia. Ketiga, penurunan angka kemiskinan tidak dapat maksimal karena penurunan angka kemiskinan akibat pengaruh pertumbuhan terhambat oleh efek ketimpangan antar penduduk. Keempat, pertumbuhan selama periode 2015-2019 masih menetes ke bawah. Artinya, manfaat pertumbuhan ekonomi yang dirasakan oleh penduduk miskin secara proporsional lebih kecil dibandingkan dengan manfaat bagi yang tidak miskin
Precision Agriculture GIS Technologies for Mississippi, 1st. Edition
Authors: Fox Amelia A.A.
Published: January 2022

Precision agriculture is meant to improve on-farm efficiency in hopes of ultimately increasing profitability while also protecting the environment. However, this difficult process almost always includes the proper management and interpretation of data. Therefore, it is imperative that those individuals involved in making such decisions are educated on these processes. In a data-driven world, this textbook is a great resource for those wanting to learn how to utilize their data in hopes of making better informed on-farm decisions.
PEMODELAN DIRECT FIELD ORIENTED CONTROL (D FOC PADA PENGATURAN KECEPATAN MOTOR INDUKSI TIGA PHASA
Authors: Fauzi Rizana , Jumiyatun Jumiyatun , Masarrang Maryantho
Published: January 2022

The 3 phase induction motor in its use is increasingly growing, so research on the speed regulation of the 3 phase induction motor is also increasingly being studied. This is because the use of 3 phase induction motors in industry and especially hybrid vehicles is increasingly being developed. But there are some disadvantages of induction motors, one of which is the characteristics of non-linear parameters, especially rotor resistance which has varying values for different operating conditions, so it cannot maintain its speed constantly if there is a change in load. This of course can affect the performance of an induction motor. To get a constant speed and better system performance on load changes a controller / controller is needed. This study aims to model the parameters used in Direct Field Oriented Control (DFOC) using Matlab SIMULINK. With the controlled dq parameter, the induction motor will be more stable, because the dq parameter determines the stability of the changes in torque and flux in the induction motor. In the results of implementation, it can be seen that the use of DFOC can be used as an approach in the speed regulation of the induction motor. and with the use of PI controls it can help the output response get better with a shorter response time to reach the reference value.
EVALUASI SISTEM DISTRIBUSI DESA KAMARORA KECAMATAN NOKILALAKI KABUPATEN SIGI
Authors: Tatundang Franky , Arwan Mohammad , Sarjan Muhammad , Pirade Yulius S.
Published: January 2022

The development of technology from year to year is greatly improved, many supporting needs must be prepared specifically for electrical energy needs. The side ofthe distribution of electricity and its utilization must meet the requirements so that in its use can be more optimal and does not cause harm and accidents for humans and loss of property. Kamarora is one of the villages that also uses and utilizes electricity resources in Nokilalaki district. The village has 1 unit of 100 kVA capacity portal type transformer.

In this study used measurement methods, customer data collection, and checking directly on the connection in each phase to find out the percentage of transformer loading performance and minimize unbalance of the load transformer.

The results of research and analysis, the distribution transformer in Kamarora village have the highest loading conditions with peak load times (PLT) line 1 and 2 at 19:00 pm with a loading percentage value of 60.47 %, the value of imposing a transformer means that it is still underloaded. And then, the unbalance of the load transformer is high enough, the total power used in line 1is phase R (3600 VA), S (9400 VA), T (17400 VA). Line 2 westward is phase R (16150 VA), S (18900 VA), T (21950 VA) and line 2 eastward is phase R (17050 VA), S (18850 VA), T (3850 VA). Planning the load distribution for each line 1 and 2, namely in the line of 1is  phase R (10200 VA), S (9400 VA), T (10800 VA).Line 2 westward is phase R (19250 VA), S (18900 VA), T (18850 VA) and line 2 eastward is phase R (23841 VA), S (23844 VA), T (24188 VA).
Smart Optimization of Proactive Control of Petroleum Reservoir
Authors: Kuk Edyta , Kuk Michał , Janiga Damian , Wojnarowski Paweł , Stopa Jerzy
Published: January 2022

Artificial Intelligence plays an increasingly important role in many industrial applications as it has great potential for solving complex engineering problems. One of such applications is the optimization of petroleum reservoirs production. It is crucial to produce hydrocarbons efficiently as their geological resources are limited. From an economic point of view, optimization of hydrocarbon well control is an important factor as it affects the whole market. The solution proposed in this paper is based on state-of-the-art artificial intelligence methods, optimal control, and decision tree theory. The proposed idea is to apply a novel temporal clustering algorithm utilizing an autoencoder for temporal dimensionality reduction and a temporal clustering layer for cluster assignment, to cluster wells into groups depending on the production situation that occurs in the vicinity of the well, which allows reacting proactively. Then the optimal control of wells belonging to specific groups is determined using an auto-adaptive decision tree whose parameters are optimized using a novel sequential model-based algorithm configuration method. Optimization of petroleum reservoirs production translates directly into several economic benefits: reduction in operation costs, increase in the production effectiveness and increase in overall income without any extra expenditure as only control is changed.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Public Relations Principles for the Digital Communication in a Local Crisis
Authors: Zora Rezo Antonela , Kolić Stanić Matilda , Togonal Marijana
Published: January 2022

In dealing with the crisis, all institutions must react and urgently solve the problem, but a special challenge is to communicate with key publics, especially those publics that have suffered damage., What is imperative for public relations is fast and two-way communication. That is why digital platforms are indispensable in crisis communication because they enable almost simultaneous reaction and enable institutions to communicate directly with the affected public, without the mediation of information media. This paper aims to analyse the crisis communication of the City of Slavonski Brod through its official website and Facebook page during water pollution in March and April 2018. The analysis was performed by methods of qualitative content analysis and a case study analysis. During the environmental incident, the City of Slavonski Brod did not communicate following the PR principles of crisis communication nor took advantage of digital communication.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Novel Approach to Choosing Principal Components Number in Logistic Regression
Authors: Vrigazova Borislava
Published: January 2022

The confirmed approach to choosing the number of principal components for prediction models includes exploring the contribution of each principal component to the total variance of the target variable. A combination of possible important principal components can be chosen to explain a big part of the variance in the target. Sometimes several combinations of principal components should be explored to achieve the highest accuracy in classification. This research proposes a novel automatic way of deciding how many principal components should be retained to improve classification accuracy. We do that by combining principal components with the ANOVA selection. To improve the accuracy resulting from our automatic approach, we use the bootstrap procedure for model selection. We call this procedure the Bootstrapped-ANOVA PCA selection. Our results suggest that this procedure can automate the principal components selection and improve the accuracy of classification models, in our example, the logistic regression.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Digital Maturity of Higher Education Institutions in Bosnia and Herzegovina: Teachers’ Assessment
Authors: Mabić Mirela , Garbin Praničević Daniela
Published: January 2022

This paper presents the research results on digital maturity in higher education institutions in Bosnia and Herzegovina (BiH). Empirical research was conducted among employees of eight public higher education institutions in spring 2020. Digital maturity was examined through seven dimensions. The results show that higher education institutions in BiH started the digitalization process more than five years ago. They have been continuously working on the digitalization of all business processes and activities. According to employees, on a scale from 1 to 5, digitalization of their higher education institutions is somewhere in the middle (between 3 and 4). The institutions have room for improvement in all dimensions.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How Retailers Support Consumers during the Pandemic: Kaufland Case Study
Authors: Moraru Mădălina
Published: January 2022

Brands’ perspectives on consumption and consumers have strongly changed during the pandemic because, lately, they have focused on medical care and innovation. The context of advertising campaigns requires a different approach with specific communication characteristics: safety, medical care, protection, family assistance. The present study focuses on the advertising discourse delivered by retailers on the market to answer consumers’ needs. There are two types of perspectives to be considered here: encouraging consumption to survive and looking for solutions and psychological support to get over de-socialisation and fears. Retailers turn from simple suppliers into innovative volunteers who concern themselves with how to diminish fears and insecurity. As a research method, I conducted a multimodal discourse analysis focused on the online advertisements running on Facebook and Instagram to unfold communication strategies, linguistic tools, brand values, and techniques of adaptation to the pandemic. The period considered relevant for this study was during the Romanian lockdown, between March 16 and May 16, 2020, plus another month afterward to observe the consequences of brands’ attitudes towards consumers.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Raspberry Pi Based Intelligent Robot that Recognizes and Places Puzzle Objects
Authors: Kutlu Yakup , Alanoglu Zulfu , Gokcen Ahmet , Yeniad Mustafa
Published: January 2022

In this study, computer vision application was realized by using Raspberry Pi, USB camera and 3D robotic arm system, which are designed with 3D modeling programs and output from 3D printer. The system automatically determines the position information of the detected objects from the camera and calculates the directions and angles of the robot joints using the position information. Because of calculations, the robot Arm moves to the target. The robot arm is 6-axis industrial type. The Raspberry PI-based system uses python programming language and OpenCV library for image processing.
The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds
Authors: Altan Gokhan , Kutlu Yakup , Pekmezci Adnan Ozhan , Nural Serkan
Published: January 2022

Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the lung sounds from 12 different auscultation regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequencytime analysis.
Hubungan Pengungkapan Keberlanjutan Dan Nilai Perusahaan : Kasus Perusahaan Sub Sektor Perbankan Di Indonesia
Authors: Rahman Muhammad Abdul Izzatur , Domas Zico Karya Saputra , Firmansyah Amrie
Published: January 2022

perusahaan perbankan. Data penelitian ini menggunakan perusahaan sub sektor perbankan yang terdaftar di BEI berupa laporan keuangan, laporan keberlanjutan dan harga saham tahun 2018 dan 2019 yang diperoleh dari www.idx.co.id, www.finance.yahoo.com, dan situs resmi perusahaan. Berdasarkan purposive sampling, sampel penelitian ini berjumlah 60 observasi. Pengujian hipotesis dilakukan dengan menggunakan analisis regresi data panel. Penelitian ini menyimpulkan bahwa penelitian menunjukan bahwa peran pengungkapan keberlanjutan berpengaruh positif terhadap nilai perusahaan, sedangkan arus kas operasi tidak berpengaruh terhadap nilai perusahaan. Penelitian ini mengindikasikan bahwa Otoritas Jasa Keuangan tetap perlu meningkatkan kebijakan dan pengawasan penerapan keberlanjutan yang dilakukan oleh perusahaan sub sektor perbankan di Indonesia.
Big data in het veiligheidsdomein: onderzoek naar big-datatoepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie
Authors: Schuilenburg Marc , Soudijn Melvin
Published: January 2022

Big data in the security domain: research on the use of big data by the Dutch police and the positive effects of these applications on the police organization

In recent years, big data technology has revolutionised many domains, including policing. There is a lack of research, however, exploring which applications are used by the police, and the potential benefits of big data analytics for policing. Instead, literature about big data and policing predominantly focuses on predictive policing and its associated risks. The present paper provides new insights into the police’s current use of big data and algorithmic applications. We provide an up-to-date overview of the various applications of big data by the National Police in the Netherlands. We distinguish three areas: uniformed police work, criminal investigation, and intelligence. We then discuss two positive effects of big data and algorithmic applications for the police organization: accelerated learning and the formation of a single police organization.
Analysis of A Coendemic Model of COVID-19 and Dengue Disease
Authors: Fahlena Hilda , Oktaviana Widya , Farida Farida , Sudirman Sudirman , Nuraini Nuning , Soewono Edy
Published: January 2022

The coronavirus disease 2019 (COVID-19) pandemic continues to spread aggressively worldwide, infecting more than 170 million people with confirmed cases, including more than 3 million deaths. This pandemic is increasingly exacerbating the burden on tropical and subtropical regions of the world due to the pre-existing dengue fever, which has become endemic for a longer period in the same region. Co-circulation dengue and COVID-19 cases have been found and confirmed in several countries. In this paper, a deterministic model for the coendemic of COVID-19 and dengue is proposed. The basic reproduction ratio is obtained, which is related to the four equilibria, disease-free, endemic-COVID-19, endemic-dengue, and coendemic equilibria. Stability analysis is done for the first three equilibria. Furthermore, a condition for coexistence equilibrium is obtained, which gives a condition for bifurcation analysis. Numerical simulations were carried out to obtain a stable limit-cycle resulting from two Hopf bifurcation points with dengue transmission rate and COVID-19 transmission rate as the bifurcation parameter, representing a stable periodic coexistence of dengue and COVID-19 transmission. We identify the period of limit cycle decreases after reaching the maximum value.
Random Vibration Model in Linear and Non Linear Structure, Application in Engineering Structure
Authors: Dolu Anwar , Nasution Amrinsyah
Published: January 2022

Response of linear or complex nonlinear structures takes form in a characteristic functions and in the deterministic or stochastic external loads. Non linear model with non linear structure stiffness is a type of Duffing equation. Stochastic external loads system is referred to a random signal white noise with a constant power spectral density (So), while non linear system identification of deterministic system's is based on time history, phase plane and Poincare map. Methods of Galerkin and Runge-Kutta are used to solve the partial non linear governing diferential equations. Mean value , Standard deviation and Probability Density Function (PDF) is stated as statistical responses due to stochastic response of random variables. The analysis of random vibration in the solution of non linear stochastic differential equation is solved
Design and Analysis Effect of Gantry Angle Photon Beam 4 MV on Dose Distributions using Monte Carlo Method EGSnrc Code System
Authors: Yuliani Uum , Ramdani Ridwan , Haryanto Freddy , Perkasa Yudha Satya , Sanjaya Mada
Published: January 2022

Varian linac modeling has been carried out to obtain Percentage Depth Dose (PDD) and profiles using variations gantry angle 0o, 15o, 30o , 45o in the vertical axis of the surface, field size 10x10 cm2, photon beam 4 MV and Monte Carlo simulations. Percentage Depth Dose and profile illustrates dose distributions in a phantom water measuring 40x40x40 cm3, changes gantry is one of the factors that determine the distribution of the dose to the patient research shows changes in Dmax in the Percentage Depth Dose is affected by changes in the angle gantry resulted in the addition of the area build up so it can be used for therapy in the region and produce skin sparing effects that can be used to protect the skin from exposure to radiation. The graph result is profiles obtained show lack simetrisan in areas positive quadrant has a distribution of fewer doses than the quadrant of negative as well as the slope of the surface so that it can be used for some cases treatments that require a depth and a certain slope, dose calculations are more accurate and can minimize side effects.
An Application of Inversion Technique to 1-Dimensional Gravity Data in Bayesian Framework using Monte Carlo, Metropolis, and Simulated Annealing Algorithm
Authors: Putra Adam Sukma , Srigutomo Wahyu , Tiandho Yuant , Tarigan Herlin , Yanti Yanti
Published: January 2022

The purpose of this paper is to present a simulation to the inversion methods applied to geophysical exploration. Anapplication of Monte-Carlo, Metropolis, and Simulated Annealing techniques to 1-Dimensional gravity inversion inBayesian framework has been studied. Differences between these methods are observed in both single parameterinversion and simultaneous multi parameter inversion. After selecting the best inversion strategy from the three methods,a further investigation was investigated. Multi parameter inversion for two anomalies is simultaneously carried out andresults are observed. The synthetical data of GRAV2DC free source were used instead of observed data.
Three-dimensional (X-Y-Z) Core Design of Long-Life Pressurized Water Reactor Using (Th-U)O2 Fuels with The Addition of Gd2O3 and Pa-231 as Burnable Poisons
Authors: Hariyanto Duwi , Permana Sidik
Published: January 2022

Pressurized water reactors (PWRs) are one of the most dominant types of nuclear power plants that have been operated commercially to produce electricity in the world. The purpose of this study was to perceive a three-dimensional (X-Y-Z) core design of long-life PWR using Thorium-Uranium dioxide ((Th-U)O2) fuels with the addition of Gadolinium (Gd2O3) and Protactinium-231 (Pa-231) as the burnable poisons. A combination of Thorium and enriched Uranium fuels have a higher conversion ratio than other fuels, therefore can guarantee the reactor to operate longer. The burnable poison isotopes could be used to reduce excess reactivity due to the very high thermal neutron absorption cross-section. For core geometry analysis, a three-dimensional (X-Y-Z) geometry and a fuel volume fraction of 40% were applied. The computer code of SRAC 2006 from the Japan Atomic Energy Agency (JAEA) and the JENDL 4.0 as a nuclear data library were used for calculation. In this study, different fractions of Uranium dioxide, Uranium-235, Gadolinium, and Protactinium-231 in fuel were carried out. The result of this study was a three-dimensional core design of 800 MWt PWR using 60% Uranium dioxide fuel with enriched Uranium-235 of 12%-11% and the addition of 0,025% Gd2O3 and 1,0% Pa-231 which could operate for ten years without refueling. This research is expected to be a reference for long-life PWR design using the Thorium and Uranium fuel cycles.
A Modelling of Tunnelling Current through a Trapezoidal Potential Barrier by Using Exponential Wavefunction Approach
Authors: - Fitriyadi
Published: January 2022

A tunnelling current through a trapezoidal barrier potential has been modelled. The transmittance is determined using the exponential wavefunction approach method. Furthermore, the transmittance is used to calculate the tunnelling current density by applying the Gauss-Laguerre quadrature method. The simulation results show the increasing bias voltage causes the raising tunnelling current, and an increase of temperature is proportional to the tunnelling current.
Modulus Elastisitas Campuran Beraspal, Standard Load Group, dan Angka Ekivalen pada MDP
Authors: Tjan Aloysius
Published: January 2022

Akurasi prediksi modulus campuran beraspal (yang tergantung pada banyak hal antara lain temperatur atau WMAPT), standar load group, angka ekivalen (ESA4, ESA5, SAR5, SAR7, SAR12) penting dalam proses desain perkerasan lentur. Ke tiga hal tersebut dibahas dalam makalah ini.Modulus campuran beraspal yang diprediksi MDP terlalu besar daripada nilai modulus berdasarkan AI. Koefisien yang disarankan MDP untuk memprediksi modulus pada temperatur yang berbeda dari 41oC tidak dapat dikonfirmasikan sebagai koefisien yang benar, karena koefisien (kalaupun ada) besarnya tidak konstan untuk semua kasus.Penentuan standard load group (sumbu tunggal roda tunggal, tandem, dan tripel) seharusnya mempunyai dampak kerusakan yang sama seperti sumbu standar. Namun berdasarkan hasil analisis ini tidak dapat mengkonfirmasi bahwa penentuan standar load group tersebut merupakan hal yang benar. Dampak kerusakan standar load group berkisar antara 0.21 sd 11.48 kerusakan sumbu standar "“ padahal seharusnya (jika benar) adalah 1.0.Penggunaan ESA4, ESA5 pada MDP berbeda dari penggunaannya pada Austroads. Selain itu ada SAR7 dan SAR12 yang perlu digunakan dalam proses desain "“ yang tidak dikenal dalam MDP.Berdasarkan hasil analisis ini, maka ketiga hal tersebut dalam MDP perlu ditelaah lebih lanjut. Akumulasi kekeliruan dari setiap hal tersebut dapat menyebabkan kesalahan yang fatal dalam desain.