nils-herrmann/clean_scirepeval huggingface dataset:
Learning based CBCT correction using alternating random forest based on auto context model
Venue: Medical physics
Year: 2019

Cone Beam CT (CBCT) imaging is increasing in demand for precise image guided radiotherapy because it provides a foundation for advanced image guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning based approach to improve CBCT's image quality for extended clinical applications.  An auto context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images.  The learning based CBCT correction algorithm was evaluated using the leave one out cross validation method applied on a cohort of 12 patients' brain data and 14 patients' pelvis data. The mean absolute error (MAE) peak signal to noise ratio (PSNR) normalized cross correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE 12.81 2.04 and 19.94 5.44 HU, mean PSNR 40.22 3.70 and 31.31 2.85 dB, mean NCC 0.98 0.02 and 0.95 0.01, and SNU 2.07 3.36% and 2.07 3.36% for brain and pelvis data.  Preliminary results demonstrated that the novel learning based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT guided adaptive radiotherapy.
Yersinia pestis genomes of the first and second plague pandemic recovered from ancient DNA
Year: 2020

Epidemic infectious diseases have shaped human history, but studies on the history of diseases were in the past limited to indirect evidence due to the elusiveness of the causative agents. This has fundamentally changed through the emergence of ancient pathogen genomics which allows us to reconstruct genomes of microorganisms from ancient DNA. This dissertation focuses on Yersinia pestis, responsible for at least two historical pandemics. The first paper presents eight Y. pestis genomes of the First Pandemic (541 750) covering at least the first century of this pandemic. The results suggest that the Justinianic Plague (541 544) already reached the British Isles, show that the causative lineage diversified early during the pandemic in multiple strains, and give indications for its persistence in Europe or close by. A deletion discovered in the youngest strains might hold clues for ecological adaptations. The second paper presents 34 genomes of the Second Pandemic (1346 18th c. recovered from ten sites dating to the 14th 17th c. A genome from Russia testifies the initial entry through East Europe, the low diversity during the Black Death (1346 1353) shows a rapid spread. The close relationship of all Second Pandemic strains suggests a local persistence and diversification. A deletion in one clade similar to the one detected in the first paper, coinciding with an accelerated substitution rate, could be interpreted as convergent evolution. The third paper challenges previous claims in a recently published paper about the origin of the Justinianic Plague through a reanalysis of the two presented genomes. The phylogenetic analysis of one sample suggests rather an identification as a strain potentially basal to the Black Death. The essay gives a short introduction on the history of plague research and a comprehensive overview of the recent discoveries of archaeogenetic studies, including insights into the evolution of the bacterium enabled by prehistoric plague genomes.
Automated 3D geometry segmentation of the healthy and diseased carotid artery in free hand, probe tracked ultrasound images
Venue: Medical physics
Year: 2019

Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient specific three dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high resolution, 3D, noninvasive imaging modality such as ultrasound (US) In this study, a high resolution two dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view.  A slow sweep (v 5 mm/s) was made over the subject's neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen plaque boundary, Otsu's automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient specific solid mechanics model of the complete arterial wall.  The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89% in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images  With this method, a subject specific biomechanical model can be constructed directly from a hand held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B mode images in both healthy and diseased arteries.
The development of student worksheet assisted by interactive multimedia of photoelectric effect to build science process skills
Year: 2017

This research aims to produce student worksheet based on interactive multimedia on photoelectric effect to grow science process skills that are attractive, easy, useful, and study to improve students learning out comes. The development design used in this research by Sugiono which implemented in 8 stages, they are potential and problem, data collecting, product design, validation design, revision design, product trial, product, and trial usage. Trial usage of the product was conducted at SMA Negeri 2 Bandar Lampung in October 2016 to November 2016 and the research subject was twelve grade classes. The sampling technique of product trial subjects done by purpose sampling, it took two same classes. One class used as an experimental class and the other class as a control class. Trial product design used Matching Only Pretest Post test Control Group Design method. Data collecting technique used questionnaire and test (pretest and post test) The data were analyzed by using a descriptive quantitative method. The conclusions of the research are: (1) Student worksheet to build scientific process skills on photoelectric effect should include predicting and hypothesizing activities, planning the experiment, doing the practicum, interpreting the observation, and communicating. (2) Student worksheet of development result has an attractiveness level with the average score is 3.27 or 81.74% with "interesting" category, ease level with the average score is 3.25 or 81.32 with "simple" and usefulness level with the average score is 3.21 or 80.13% of "useful" category. (3) Student worksheet of development result is effective to improve students learning outcomes in science process skills with N gain average is 0.63 with the medium.
Variable Gain Gradient Descent based Robust Reinforcement Learning for Optimal Tracking Control of Unknown Nonlinear System with Input Constraints
Venue: ArXiv
Year: 2019

In recent times, a variety of Reinforcement Learning (RL) algorithms have been proposed for optimal tracking problem of continuous time nonlinear systems with input constraints. Most of these algorithms are based on the notion of uniform ultimate boundedness (UUB) stability, in which normally higher learning rates are avoided in order to restrict oscillations in state error to smaller values. However, this comes at the cost of higher convergence time of critic neural network weights. This paper addresses that problem by proposing a novel tuning law containing a variable gain gradient descent for critic neural network that can adjust the learning rate based on Hamilton Jacobi Bellman (HJB) error and instantaneous rate of variation of Lyapunov function along augmented system trajectories. By allowing high learning rate the proposed variable gain gradient descent tuning law could improve the convergence time of critic neural network weights. Simultaneously, it also results in tighter residual set, on which trajectories of augmented system converge to, leading to smaller oscillations in state error. A tighter bound for UUB stability of the proposed update mechanism is proved. In order to obviate the requirement of nominal dynamics, a neural network based identifier is chosen from existing literature that precedes the RL controller. Numerical studies are then presented to validate the effectiveness of the combined identifier and robust Reinforcement Learning control scheme in controlling a continuous time nonlinear system.
Membrane condenser as emerging technology for water recovery and gas pre treatment: current status and perspectives
Venue: BMC Chemical Engineering
Year: 2019

The recent roadmap of SPIRE initiative includes the development of "new separation, extraction and pre treatment technologies" as one of the "key actions" for boosting sustainability, enhancing the availability and quality of existing resources. Membrane condenser is an innovative technology that was recently investigated for the recovery of water vapor for waste gaseous streams, such as flue gas, biogas, cooling tower plumes, etc. Recently, it has been also proposed as pre treatment unit for the reduction and control of contaminants in waste gaseous streams (SOx and NOx, VOCs, H2S, NH3, siloxanes, halides, particulates, organic pollutants) This perspective article reports recent progresses in the applications of the membrane condenser in the treatment of various gaseous streams for water recovery and contaminant control. After an overview of the operating principle, the membranes used, and the main results achieved, the work also proposes the role of this technology as pre treatment stage to other separation technologies. The potentialities of the technology are also discussed aspiring to pave the way towards the development of an innovative technology where membrane condenser can cover a key role in redesigning the whole upgrading process.
A REVIEW OF HEAD POSE CLASSIFICATION WITH DIFFERENT METHODS
Year: 2021

Head pose classification is an area of interest for researchers as it is emerging as a popular field. A significant number of works have been done in this field. Detecting the head poses provides ease in various applications. It can enhance the process of face recognition and emotion analysis. This paper performed a survey and studied different approaches designed for Head Pose Estimation (HPE) It can be perceived from the study that neural networks are widely used networks to this end. Along with this, it has become a prominent technology to detect faces covered with masks due to Covid 19 disease. This survey showed the different HPE to understand all the latest approaches designed. IndexTerms Head Pose Estimation, Classification, Neural networks, Covid 19.
Something Something Game Something A Visual Approach to Definitions in Games
Year: 2017

Something Something Soup Something (SSSS) is a game designed by Stefano Gualeni, a person who most of you probably know from previous papers in this conference series. As many of you also know, he is usually the person talking about how game design can be a transformative practice, in many different senses, such as game design as critical reflexive practice (2016) game design as situated knowledge (2016) game design as self transformation (2015) game design as self liberation (2014) and as his most recent monograph discusses, game design (and virtual worlds at large) as tools for philosophy (2016)
Lipid nanostructures for antioxidant delivery: a comparative preformulation study
Venue: Beilstein journal of nanotechnology
Year: 2019

This investigation is a study of new lipid nanoparticles for cutaneous antioxidant delivery. Several molecules, such as a tocopherol and retinoic acid, have been shown to improve skin condition and even counteract the effects of exogenous stress factors such as smoking on skin aging. This work describes the design and development of lipid nanoparticles containing antioxidant agents (a tocopherol or retinoic acid) to protect human skin against pollutants. Namely, solid lipid nanoparticles and nanostructured lipid carriers were prepared using different lipids (tristearin, compritol, precirol or suppocire) in the presence or absence of caprylic/capric triglycerides. The formulations were characterized by particle size analysis, cryogenic transmission electron microscopy, small angle X ray diffraction, encapsulation efficiency, preliminary stability, in vitro cytotoxicity and protection against cigarette smoke. Nanostructured lipid carriers were found to reduce agglomerate formation and provided better dimensional stability, as compared to solid lipid nanoparticles, suggesting their suitability for antioxidant loading. Based on the preformulation study, tristearin based nanostructured lipid carriers loaded with a tocopherol were selected for ex vivo studies since they displayed superior physico chemical properties as compared to the other nanostructured lipid carriers compositions. Human skin explants were treated with a tocopherol loaded nanostructured lipid carriers and then exposed to cigarette smoke, and the protein levels of the stress induced enzyme heme oxygenase were analyzed in skin homogenates. Interestingly, it was found that pretreatment with the nanoformulation resulted in significantly reduced heme oxygenase upregulation as compared to control samples, suggesting a protective effect provided by the nanoparticles.
Peran Pembangkit Listrik Tenaga Biogas Dalam Mewujudkan Ketahanan Energi Wilayah (Studi di Kecamatan Dendang, Kabupaten Belitung Timur, Provinsi Kepulauan Bangka Belitung)
Year: 2016

This research had three main goals. Firstly, to knew the condition of the biogas power plant (BPP) in Dendang district. Secondly,to knew the obstacles faced in the operating of the BPP, Thirdly, to knew the optimalization strategy in maximizing the BPPThe existence of the BPP in Dendang District, East Belitung Regency had three main benefits. First, it could power up to 2.500 3.000 households. Second, reduced greenhouse gas emissions and waste. Third, created technology transfer. Based on those, an optimalization was needed to created regional energy resilience. The method used was qualitative description, through direct observation and interviews with stakeholders in November 2015 until May 2016. According to research results, there were three main obstacles faced by the BPP. First, the difficulty raw materials which were mostly imported. Second, the price differences because power purchase agreement The BPP PT. AANE was considered to be established before the new law was issued. Third, the lack of goverment support. Based on those three obstacles, the optimalization strategy that could be done were: First, easing import regulations to eased raw materials supply. Second, the government must regulated and fixed the price differences issue. Third, the central and local government must actively supported the building of BPP and the field of new and renewable energy. ABSTRAK Penelitian ini memiliki tiga tujuan. Pertama, untuk mengetahui kondisi pembangkit listrik tenaga biogas (PLTBg) di Kecamatan Dendang. Kedua, untuk mengetahui kendala yang dihadapi dalam operasional PLTBg. Ketiga, untuk mengetahui strategi optimalisasi dalam melakukan optimalisasi PLTBg.Keberadaan PLTBg di Kecamatan Dendang memiliki tiga manfaat. Pertama, dapat mengaliri listrik hingga 2500 3000 rumah tangga. Kedua, membantu mengurangi emisi gas rumah kaca dan limbah, Ketiga, alih teknologi. Berdasarkan ketiga keuntungan tersebut, maka optimalisasi PLTBg perlu dilakukan dalam rangka mewujudkan ketahanan energy wilayah. Metode yang digunakan adalah deskripsi kualitatif, melalui obervasi langsung ke Kecamatan Dendang dan wawancara pihak pihak terkait pada bulan November 2015 hingga Mei 2016.Berdasarkan hasil penelitian, terdapat tiga kendala yang dihadapi oleh PLTBg di Kecamatan Dendang. Pertama, sulitnya bahan baku karena sebagian besar impor, Kedua, adanya perbedaan harga karena power purchase agreement PLTBg PT AANE dihitung berdiri sebelum undang undang baru keluar. Ketiga, kurangnya dukungan pemerintah. Berdasarkan ketiga kendala tersebut, maka strategi optimaliasi yang dilakukan adalah; Pertama, memudahkan regulasi untuk kemudahan bahan baku. Kedua, pemerintah mengatur dan meluruskan regulasi terkait perbedaan harga. Ketiga, pemerintah pusat dan daerah harus aktif memberikan dukungan terhadap pembangunan PLTBg dengan memperbesar pelatihan, pendidikan serta pengembangan kemampuan untuk pembuatan kebijakan dan analisis di bidang energi baru dan terbarukan.
Global financial crisis and weak form efficiency of Islamic sectoral stock markets: An MF DFA analysis
Year: 2017

This paper estimates the weak form efficiency of Islamic stock markets using 10 sectoral stock indices (basic materials, consumer services, consumer goods, energy, financials, health care, industrials, technology, telecommunication, and utilities) The results based on the multifractal detrended fluctuation analysis (MF DFA) approach show time varying efficiency for the sectoral stock markets. Moreover, we find that they tend to show high efficiency in the long term but moderate efficiency in the short term, and that these markets become less efficient after the onset of the global financial crisis. These results have several significant implications in terms of asset allocation for investors dealing with Islamic markets.
Towards a Privacy Aware Quantified Self Data Management Framework
Venue: SACMAT
Year: 2018

Massive amounts of data are being collected, stored, and analyzed for various business and marketing purposes. While such data analysis is critical for many applications, it could also violate the privacy of individuals. This paper describes the issues involved in designing a privacy aware data management framework for collecting, storing, and analyzing the data. We also discuss behavioral aspects of data sharing as well as aspects of a formal framework based on rewriting rules that encompasses the privacy aware data management framework.
A comprehensive review of hydroxyapatite based coatings adhesion on metallic biomaterials
Year: 2018

Metallic biomaterials have been employed in replacing and reconstructing the structural parts of the human physical structure due to their high mechanical properties, superior biocompatibility, and high corrosion resistance. The most common metallic biomaterials that have been used in implants include magnesium, stainless steel, cobalt based alloy, titanium, and titanium based alloy. Hydroxyapatite (Ca10(PO4)6(OH)2) is one of the ceramic biomaterials considered as ideal materials for coating on metallic biomaterials as it possesses almost the closest similarity in chemical composition and excellent biocompatibility with natural bone tissue. Recently, the HAp based coating has increasingly drawn attention to improve the adhesion quality in metallic biomaterials. This study comprehensively reviews the current progress in the adhesion qualities of HAp based coatings on metallic biomaterials specifically for the biomedical application. It has been observed that a surface that meets the minimum unique characteristics will enhance the bonding force between the coating and metallic biomaterial as the substrate. Critical factors of coating/substrate materials, coating techniques, and coating thickness that determine the adhesion quality are thoroughly identified and discussed. The surface structure and microstructure of HAp based coating are also reviewed to confirm the findings.
Robust Lidar Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion.
Venue: IEEE transactions on neural networks and learning systems
Year: 2020

This article proposes a robust and precise localization scheme for unmanned ground vehicle (UGV) in global positioning system (GPS) denied and GPS challenged environments via multisensor fusion approach. The localization scheme is proposed to be under an available point cloud map. First, initialization in localization module is designed to calculate the initial position of UGV in map using the Gaussian projection approach and obtain the frame transformation between the 3 D lidar and the inertial measurement unit (IMU) Second, the best alignment between each scan frame and the available submap is obtained, and the pose of vehicle relative to the origin of map is calculated. Third, the precise pose of UGV is well predicted by integrating the data from 3 D lidar and IMU. Fourth, in order to visually verify the proposed localization scheme, the motion of vehicle is visualized by designing the visualization module. Note that the preprocessing module aims to process the raw scan data. The availability of our proposed localization scheme is verified by conducting experiments in our campus.