nils-herrmann/clean_scirepeval huggingface dataset:
Fate of heavy metals during molten salts thermal treatment of municipal solid waste incineration fly ashes.
Venue: Waste management
Year: 2020

Thermal treatment could effectively realize the detoxification of heavy metals in municipal solid waste incineration (MSWI) fly ash through the approach of removal or stabilization process. To lower the operating temperature and suppress the evaporation of heavy metals, a molten salts (NaCl CaCl2) thermal treatment method was proposed for the detoxification of heavy metals from MSWI fly ash at a relatively mild condition (600/800 degC) The fate of heavy metals during the heating process and their stabilization properties in the remained ash slag after molten salts thermal treatment were investigated. The results showed that, compared with the traditional thermal treatment, heavy metals were more easily chlorinated by the means of molten salts thermal treatment. The well distributed chloride in molten salts facilitated the direct chlorination of PbO/CdO. Furthermore, Al2O3 in ash enhanced the indirect chlorination of CuO/PbO/CdO, except for ZnO. In contrast, SiO2 showed better performance in promoting the indirect chlorination of heavy metal oxides. Meanwhile, some Zn2+ was precipitated from molten salts as Si/Al Zn composite oxides through the interactions with ash containing Si/Al oxides. On the other hand, the dissolved heavy metals in molten salts showed a good thermal stability during the thermal treatment. The volatilization fractions of all detected heavy metals were less than 5% After the molten salts thermal treatment, heavy metals in the ash slag were well stabilized and the amount of heavy metals leached was significantly lower than that from the raw fly ash.
Security in networks of unmanned aerial vehicles for surveillance with an agent based approach inspired by the principles of blockchain
Venue: Ad Hoc Networks
Year: 2019

Unmanned aerial vehicles (UAVs) can support surveillance even in areas without network infrastructure. However, UAV networks raise security challenges because of its dynamic topology. This paper proposes a technique for maintaining security in UAV networks in the context of surveillance, by corroborating information about events from different sources. In this way, UAV networks can conform peer to peer information inspired by the principles of blockchain, and detect compromised UAVs based on trust policies. The proposed technique uses a secure asymmetric encryption with a pre shared list of official UAVs. Using this technique, the wrong information can be detected when an official UAV is physically hijacked. The novel agent based simulator ABS SecurityUAV is used to validate the proposed approach. In our experiments, around 90% of UAVs were able to corroborate information about a person walking in a controlled area, while none of the UAVs corroborated fake information coming from a hijacked UAV.
Application of Electrical Resistivity Method (ERM) in Groundwater Exploration
Year: 2018

The geophysical method which dominant by geophysicists become one of most popular method applied by engineers in civil engineering fields. Electrical Resistivity Method (ERM) is one of geophysical tool that offer very attractive technique for subsurface profile characterization in larger area. Applicable alternative technique in groundwater exploration such as ERM which complement with existing conventional method may produce comprehensive and convincing output thus effective in terms of cost, time, data coverage and sustainable. ERM has been applied by various application in groundwater exploration. Over the years, conventional method such as excavation and test boring are the tools used to obtain information of earth layer especially during site investigation. There are several problems regarding the application of conventional technique as it only provides information at actual drilling point only. This review paper was carried out to expose the application of ERM in groundwater exploration. Results from ERM could be additional information to respective expert for their problem solving such as the information on groundwater pollution, leachate, underground and source of water supply.
PROBABILITY AND VARIANCE BASED STOCHASTIC DESIGN OPTIMIZATION OF A RADIAL COMPRESSOR CONCERNING FLUID STRUCTURE INTERACTION
Year: 2013

Since the engineering of turbo machines began the improvement of specific physical behaviour, especially the efficiency, has been one of the key issues. However, improvement of the efficiency of a turbo engine, is hard to archive using a conventional deterministic optimization, since the geometry is not perfect and many other parameters vary in the real approach. In contrast, stochastic design optimization is a methodology that enables the solving of optimization problems which model the effects of uncertainty in manufacturing, design configuration and environment, in which robustness and reliability are explicit optimization goals. Therein, a coupling of stochastic and optimization problems implies high computational efforts, whereby the calculation of the stochastic constraints represents the main effort. In view of this fact, an industrially relevant algorithm should satisfy the conditions of precision, robustness and efficiency. In this paper an efficient approach is presented to assist reducing the number of design evaluations necessary, in particular the number of nonlinear fluid structure interaction analyses. In combination with a robust estimation of the safety level within the iteration and a final precise reliability analysis, the method presented is particularly suitable for solving reliability based structural design optimization problems with ever changing failure probabilities of the nominal designs. The applicability for real case applications is demonstrated through the example of a radial compressor, with a very high degree of complexity and a large number of design parameters and random variables.
Fixed point and Bregman iterative methods for matrix rank minimization
Venue: Math. Program.
Year: 2011

The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Although the latter can be cast as a semidefinite programming problem, such an approach is computationally expensive to solve when the matrices are large. In this paper, we propose fixed point and Bregman iterative algorithms for solving the nuclear norm minimization problem and prove convergence of the first of these algorithms. By using a homotopy approach together with an approximate singular value decomposition procedure, we get a very fast, robust and powerful algorithm, which we call FPCA (Fixed Point Continuation with Approximate SVD) that can solve very large matrix rank minimization problems (the code can be downloaded from http:/www.columbia.edu/~sm2756/FPCA.htm for non commercial use) Our numerical results on randomly generated and real matrix completion problems demonstrate that this algorithm is much faster and provides much better recoverability than semidefinite programming solvers such as SDPT3. For example, our algorithm can recover 1000 x 1000 matrices of rank 50 with a relative error of 10 5 in about 3 min by sampling only 20% of the elements. We know of no other method that achieves as good recoverability. Numerical experiments on online recommendation, DNA microarray data set and image inpainting problems demonstrate the effectiveness of our algorithms.
Cambios en las dinamicas familiares, y apoyo social percibido por cuidadores de ninos con leucemia
Year: 2016

En Colombia, el sistema de salud presenta diferentes problematicas por falencias en su estructura y por los sobrecostos que implica la atencion a millones de pacientes durante largos periodos de tiempo, por complicaciones en su estado de salud y por la exposicion a factores de riesgo socioculturales. Este es el caso de las enfermedades cronicas, las que, por sus caracteristicas propias, requieren de una atencion prolongada e integral, con costos muy elevados, tanto para el sistema de salud, como para los pacientes y sus familias. La poblacion del departamento del Quindio no es ajena a dicha problematica, ademas el aumento en las tasas de morbilidad proyectadas en las estadisticas nacionales, departamentales y locales son alarmantes; es un llamado a intervenir de manera oportuna, generando estrategias en pro de la promocion de la salud y prevencion de esta y otras enfermedades. El objetivo general de esta investigacion es: conocer los cambios en las dinamicas familiares y el apoyo social percibido, en cuidadoras de ninos con Leucemia. Para lograr el anterior objetivo general, se plantearon los siguientes objetivos especificos: Describir la experiencia de ser cuidadores de ninos con leucemia e Identificar las dinamicas familiares en el contexto de cuidar un nino con leucemia. El trabajo fue llevado a cabo por medio de un estudio cualitativo de corte descriptivo, realizado con cuatro cuidadoras, de ninos de genero masculino, (uno cada una) con edades comprendidas entre 8 y 10 anos, pertenecientes a la Fundacion Oncologos de Occidente la ciudad de Armenia, departamento del Quindio. El instrumento utilizado fue una entrevista a profundidad, que permitio describir la experiencia de ser cuidadoras de ninos con leucemia, identificar las dinamicas familiares en el contexto de cuidar un nino con leucemia y describir el apoyo social percibido por el cuidador ante el cuidado de un nino con leucemia. Los resultados senalaron la existencia de significativos cambios en la dinamica familiar, y como el apoyo social percibido influye en dicha dinamica por los multiples cuidados que requieren ninos y cuidadoras.
High Precision BLAS on FPGA enhanced Computers
Venue: ERSA
Year: 2007

The emergence of high density reconfigurable hardware devices gives scientists and engineers an option to accelerating their numerical computing applications on low cost but powerful "FPGA enhanced computers" In this paper, we introduced our efforts towards improving the computational performance of Basic Linear Algebra Subprograms (BLAS) by FPGA specific algorithms/methods. Our study focus on three BLAS subroutines: floating point summation, matrix vector multiplication, and matrix matrix multiplication. They represent all three levels of BLAS functionalities, and their sustained computational performances are either memory bandwidth bounded or computation bounded. By proposing the group alignment based floating point summation method and applying this technique to other subroutines, we significantly improved their sustained computational performance and reduced numerical errors with moderate FPGA resources consumed. Comparing with existing FPGA based implementations, our designs are efficient and compact with improved numerical accuracy and stability.
The Evolution of the European Public Good Assessment in the EU Endorsement Process of IFRS
Year: 2020

Although legally the criteria to adopt IFRS in the EU have not changed since the adoption of the 2002 IAS Regulation, the way they are assessed evolved over time. The purpose of this paper is to analyse how the European public good (EPG) criterion has been interpreted since the beginning of the EU endorsement process and how it is interpreted today. After retracing the origin of the European public good criterion in the IAS Regulation we identify different periods in the EU endorsement process where the application of the EPG criterion changed. These changes are explained by a variety of factors, not least the financial crisis of 2008. The scope of what is considered to be part of the EPG has substantially expanded, a trend that is still ongoing.
Bi Axial Buckling of Laminated Composite Plates Including Cutout and Additional Mass
Venue: Materials
Year: 2019

In the presented paper, a study of bi axial buckling of the laminated composite plate with mass variation through the cutout and additional mass is carried out using the improved shear deformation theory (ISDT) The ISDT mathematical model employs a cubic variation of thickness co ordinates in the displacement field. A realistic parabolic distribution of transverse shear strains through the plate thickness is assumed and the use of shear correction factor is avoided. A Cdeg finite element formulation of the mathematical model is developed to analyze the buckling behavior of laminated composite plate with cutout and additional mass. As no results based on ISDT for the considered problem of bi axial buckling of the laminated composite plate with mass variation are available in the literature, the obtained results are validated with the data available for a laminated composite plate without cutout and additional mass. Novel results are obtained by varying geometry, boundary conditions and ply orientations.
Detecting hospital acquired infections: A document classification approach using support vector machines and gradient tree boosting
Venue: Health Informatics J.
Year: 2018

Hospital acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.
Layer Segmentation of Retinal OCT Images using Deep Convolutional Encoder Decoder Network
Year: 2019

In medical image analysis, segmentation is considered as a vital process since it partitions an image into coherent parts and extracts interesting objects from the image. In this paper, we consider automatic segmentations of OCT retinal images to find six layer boundaries using convolutional neural networks. Segmenting retinal images by layer boundaries is very important in diagnosing and predicting progress of eye diseases including diabetic retinopathy, glaucoma, and AMD (age related macular degeneration) We applied well known CNN architecture for general image segmentation, called Segnet, U net, and CNN S into this problem. We also proposed a shortest path based algorithm for finding the layer boundaries from the outputs of Segnet and U net. We analysed their performance on public OCT image data set. The experimental results show that the Segnet combined with the proposed shortest path based boundary finding algorithm outperforms other two networks.
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Year: 2015

Bai bao trinh bay ve viec van dung phuong phap day hoc theo du an trong day hoc nhung ung dung ki thuat cua vat li de day chuong "C o so cua nhiet dong luc hoc" chuong trinh Vat li lop 10. Cac ket qua thu duoc buoc dau cho thay, phuong phap day hoc tren da phat huy duoc tinh tich cuc, sang tao va tao hung thu cho hoc sinh trong qua trinh hoc tap. Phuong phap nay can duoc nghien cuu, van dung nhieu hon nua trong thuc tien day hoc o pho thong. Normal 0 false false false EN US X NONE X NONE Style Definitions table.MsoNormalTable {mso style name:"Table Normal" mso tstyle rowband size:0; mso tstyle colband size:0; mso style noshow:yes; mso style priority:99; mso style parent:" mso padding alt:0cm 5.4pt 0cm 5.4pt; mso para margin:0cm; mso para margin bottom:.0001pt; text align:justify; line height:130% mso pagination:widow orphan; font size:13.0pt; mso bidi font size:11.0pt; font family:"Times New Roman""serif"
Early Secondary Alveoloplasty in Cleft Lip and Palate
Venue: The Journal of craniofacial surgery
Year: 2008

The aims of this study were to present a personal surgical technique throughout the review of international literature concerning surgical techniques, objectives, and outcomes in early secondary alveoloplasty and to describe our personal surgical techniques in alveolar bone defect repair in cleft lip and palate. Throughout a literature analysis, it is now settled that early secondary alveoloplasty could reestablish the continuity of alveolar bone and prevent upper dental arch collapse after presurgical orthopedic upper maxilla expansion; it also might give a good bone support for teeth facing the cleft and allow the eruption of permanent elements with the bone graft and rebalance the symmetry of dental arch, improve facial aesthetic, guarantee an adequate amount of bone tissue for a further prosthetic reconstruction with implant, and finally close the eventual oronasal fistula. The surgical technique we are presenting permitted a total number of 35 early secondary alveoloplasty on which a long term follow up is still taking place. We can assess that early secondary alveoloplasty must be performed before permanent canine eruption. Iliac crest is the suggested donor site for bone grafting; orthopedic and orthodontic treatments must be performed in association with surgery, and if there is the dental element agenesia, an implantation treatment must be considered.
Implementation of the SmartSync Concept on the Burj Khalifa An Application of Structural Identification for Tall Buildings
Year: 2016

The SmartSync concept, which utilizes the local area network of a building for the transmission of data, provides a flexible, cost efficient system for the full scale monitoring of tall buildings. Data collected through this system has been used to validate underlying wind tunnel and finite element models used for the design of this building and provide deeper insights into their dynamic response and effect on the comfort of their occupants. This information is then used guide long term maintenance and management. The SmartSync concept was initially demonstrated on the Burj Khalifa and has been used for its long term monitoring since 2009. Test Structure and Measured Data Since the inception of the profession, structural engineers have been tasked with the design of projects of immense size, cost and ever increasing complexity for which full scale prototyping is not possible. This forces reliance on scaled physical models and computational models fraught with approximations and assumptions and requiring considerable conservatism. Moreover, as designers push the limits of conventional structures, there are legitimate questions surrounding the extensibility of established design and construction techniques. This is especially true for tall buildings, which are lighter, more flexible and less damped than conventional buildings, resulting in dynamically sensitive structures that have complex interactions with the surrounding urban environment and their occupants. In recent decades, full scale monitoring has provided the only source of feedback to the design of these complex and expensive structures. Regrettably, post commissioning monitoring programs have not been widely adopted for buildings outside of earthquake zones, primarily due to concerns over liability and reputational harm to these high value properties. This trend must be reversed in order to take advantage of the insights monitoring can provide and that begins with providing owners with low cost, low intrusion, flexible monitoring systems. Much as conventional design methods cannot be extended readily for tall buildings, traditional approaches to instrumentation fall short as well. These approaches historically relied on centralized systems networking sensors with shielded cables. The wired system addressed the problems of synchronization and loss of data in transmission, but running cables vertically over highly partitioned tall buildings is frankly infeasible. Therefore, significant research was invested into wireless arrays using multi hop wireless transmission of the data. While this system enables distributed sensing at a reasonable expense, wireless sensing has added complexities of ensuring all sensor data is synchronized and that there is no loss of data in the wireless transmission, which can be challenging on mechanical levels, generally the only levels where sensor installations are permitted in these buildings
On the Acceptability of English Translation of Public Signs
Venue: Theory and Practice in Language Studies
Year: 2019

The public sign, a "window" of a city or scenic spot, carries more information and plays the informative role in people's daily life. The translation of public signs not merely transfers the linguistic information of the signs, but also acts as a cross cultural communication activity. The study analyzes the types of public signs, investigates the common mistakes of English translation of public signs, and puts forward some suggestions for the public signs translation. The improvement of translators' competence and cross cultural awareness, the uniformity of the text, readers' response, and the cultural context should be taken into consideration. They can contribute to the greater acceptability of public signs translation.
Face Mask Detection Using Deep Learning
Venue: 2021 International Conference on Computer Communication and Informatics (ICCCI)
Year: 2021

In order to prevent the spread of CORONA virus, everyone must wear a mask during the pandemic. In these tough times of COVID 19 it is necessary to build a model that detects people with and without mask in real time as it works as a simple precautionary measure to prevent the spread of virus. If deployed correctly, this machine learning technique helps in simplifying the work of frontline warriors and saving their lives. A basic Convolutional Neural Network (CNN) model is built using TensorFlow, Keras, Scikit learn and OpenCV to make the algorithm as accurate as possible. Javascript API helps in accessing webcam for real time face mask detection. Since Google Colab runs on web browser it can't access local hardware like a camera without APIs. The proposed work contains three stages: (i) pre processing, (ii) Training a CNN and (iii) Real time classification. The first part is the Pre processing section, which can be divided into "Grayscale Conversion" of RGB image, "image resizing and normalization" to avoid false predictions. Then the proposed CNN, classifies faces with and without masks as the output layer of proposed CNN architecture contains two neurons with Softmax activation to classify the same. Categorical cross entropy is employed as loss function. The proposed model has Validation accuracy of 96% If anyone in the video stream is not wearing a protective mask a Red coloured rectangle is drawn around the face with a dialog entitled as NO MASK and a Green coloured rectangle is drawn around the face of a person wearing MASK.