Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Design of Silicon Heterojunction Solar Cells for Field Operating Conditions

Solar modules are currently characterized at standard test conditions (STC), defined at 1000 W/m2 and 25 °C. However, solar modules in actual outdoor operating conditions typically operate at lower illumination and higher temperature than STC, which significantly affects their performance ratio (average harvesting efficiency over efficiency in STC). Silicon heterojunction (SHJ) technology displays both good temperature coefficient and good low-illumination performances, leading to outstanding performance ratios. We investigate here SHJ solar cells that use a-SiCx(n) layer as front doped layer with different carbon contents under different climates conditions. Adding carbon increases transparency but also resistive losses at room temperature (compared with carbon-free layers), leading to a significant decrease in efficiency at STC. We demonstrate that despite this difference at STC, the difference in energy harvesting efficiency is much smaller in all investigated climates. Furthermore, we show that a relative gain of 0.4%–0.8% in harvesting efficiency is possible by adding a certain content of carbon in the front (n) layer, compared with carbon-free cells optimized for STC.
CLUBS: An RGB-D dataset with cluttered box scenes containing household objects

With the progress of machine learning, the demand for realistic data with high-quality annotations has been thriving. In order to generalize well, considerable amounts of data are required, especially realistic ground-truth data, for tasks such as object detection and scene segmentation. Such data can be difficult, time-consuming, and expensive to collect. This article presents a dataset of household objects and box scenes commonly found in warehouse environments. The dataset was obtained using a robotic setup with four different cameras. It contains reconstructed objects and scenes, as well as raw RGB and depth images, camera poses, pixel-wise labels of objects directly in the RGB images, and 3D bounding boxes with poses in the world frame. Furthermore, raw calibration data are provided, together with the intrinsic and extrinsic parameters for all the sensors. By providing object labels as pixel-wise masks, 3D, and 2D object bounding boxes, this dataset is useful for both object recognition and instance segmentation. The realistic scenes provided will serve for learning-based algorithms applied to scenarios where boxes of objects are often found, such as in the logistics sector. Both the dataset and the tools for data processing are published and available online.
Bagging of Xgboost Classifiers with Random Under-sampling and Tomek Link for Noisy Label-imbalanced Data

Fitting label-imbalanced data with high level of noise is one of the major challenges in learning-based intelligent system design. In this paper, for the two-class problem, we propose a bagging-based algorithm with Xgboost classifier (Gradient Boosting Machine) and under-sampling approaches to overcome the challenge. To avoid model misspecification caused by imbalanced data, random sampling with replacement is employed to obtain several balanced training sets; and to mitigate the problem of misleading information produced by noise, Tomek Link method is introduced to eliminate the cross-class overlapped instances, which are the primal sources of noise. And to obtain robust individual learners, we utilize Xgboost, a novel Gradient Boosting Machine-based classifier with convenient parameter tuning interface, to fit each component of the bagging ensemble. The performance of the proposed method is tested with Mandarin radio records (MFCC features) with the task of keywords recognition, and experimental results show that the new method could outperform single Xgboost classifier, verified the rationality and effectiveness of the bagging scheme. The method proposed in the paper could offer a novel solution to the challenge of noisy imbalanced data classification, and the implementation of Xgboost in this area could also serve as an innovative work.
Detection and Defense of Cache Pollution Attacks Using Clustering in Named Data Networks

Named Data Network (NDN), as a promising information-centric networking architecture, is expected to support next-generation of large-scale content distribution with open in-network cachings. However, such open in-network caches are vulnerable against Cache Pollution Attacks (CPAs) with the goal of filling cache storage with non-popular contents. The detection and defense against such attacks are especially difficult because of CPA's similarities with normal fluctuations of content requests. In this work, we use a clustering technique to detect and defend against CPAs. By clustering the content interests, our scheme is able to distinguish whether they have followed the Zipf-like distribution or not for accurate detections. Once any attack is detected, an attack table will be updated to record the abnormal requests. While such requests are still forwarded, the corresponding content chunks are not cached. Extensive simulations in ndnSIM demonstrate that our scheme can resist CPA effectively with higher cache hit, higher detecting ratio, lower hop count, and lower algorithm complexity compared to other state-of-the-art schemes.
Vulnerabilidade à cárie dentária em indivíduos diagnosticados com doenças genéticas raras

As doencas raras sao todas as doencas que afetam ate 65 pessoas em cada 100.000 habitantes. Ha uma grande diversidade de sinais e sintomas presentes nos individuos afetados, que variam conforme a etiologia da doenca e tambem de pessoa para pessoa. Em muitas doencas raras os sinais e sintomas sao percebidos ao nascimento ou ainda na infância. Comumente, individuos com doencas raras apresentam alteracoes no sistema nervoso e/ou musculoesqueletico, havendo comprometimento cognitivo, disturbios neuropsicomotores e ma formacoes craniofaciais. Algumas manifestacoes orais, como ma oclusoes e anomalias dentarias, tambem sao comuns nessa parcela da populacao. O conceito de vulnerabilidade em saude baseia-se na compreensao das suscetibilidades dos individuos ao adoecimento, sendo a doenca considerada o resultado de um conjunto de fatores individuais, coletivos e contextuais. Este estudo objetivou analisar a chance de carie dentaria em individuos com doencas raras. Foi realizado um estudo transversal com uma amostra pareada de 140 individuos [70 com doencas raras - Mucopolissacaridoses (n=29) / Osteogenese Imperfeita (n=41) - e 70 sem doencas raras] e os pais/responsaveis. A amostra foi selecionada em dois hospitais referencia para atendimento de pacientes com doencas raras de Belo Horizonte, regiao sudeste do Brasil. Os individuos com doencas raras e sem doencas raras foram examinados quanto a respiracao bucal, ma oclusao, anomalia dentaria, higiene bucal e carie dentaria. Os pais/responsaveis responderam um questionario estruturado sobre o filho (caracteristicas individuais / comportamentais e historico medico / odontologico). Analises univariada, bivariada (teste X2) e multivariada (regressao logistica multipla) foram realizadas, sendo a carie dentaria a variavel dependente. Foi considerada uma significância estatistica de p <0,05. Esta pesquisa foi aprovada pelo Comite de Etica em Pesquisa em Seres Humanos da Universidade Federal de Minas Gerais. A faixa etaria dos individuos examinados foi de dois a 27 anos, com uma media de 10,3 anos (± 6,5) de idade. Independente de ter doenca rara ou nao, os individuos com higiene bucal inadequada apresentaram uma chance 4,70 vezes maior de pertencer ao grupo com carie dentaria (IC: 2,03-10,16, 95%). Os individuos com doenca rara tiveram 2,92 vezes mais chances de estar no grupo com carie dentaria (IC: 1,38-6,18, 95%). Concluiu-se que os individuos identificados com uma higiene bucal inadequada, bem como aqueles diagnosticados com alguma doenca rara apresentaram maior chance de pertencerem ao grupo com carie dentaria. Com base neste estudo, os individuos com doencas raras foram considerados vulneraveis a carie dentaria.
Time-resolved imaging of guided wave phenomena

In the past decade, increasing demand and rapid developments in classical and quantum sciences resulted in advanced novel multipixel single photon detector arrays engineered on a single electronic chip. Silicon single photon avalanche detector (Si-SPAD) is one of the mainstream solution for low level light detection in visible and near-infrared wavelength region due to the dependable amplification of light signal. This thesis mainly focusses on three key experiments to showcase the potential applications of a single photon detector (Megaframe 32) consists of 32×32 square array Si-SPADs with picosecond timing circuits. With ≈ 50 ps timing resolution, each SPAD can perform time-correlated single photon counting independently. First, the concept of multiplexed single-mode wavelength-to-time mapping (WTM) of multimode light was investigated. The spacetime imaging capability of the Megaframe was then demonstrated by imaging the spatial modes emerging from a few-mode fibre enabling WTM of spatial modes. Finally, timeresolved discrete imaging in laser inscribed photonic lattices was demonstrated. By placing a photonic lattice in a linear cavity and re-injecting the output mode profile back to the lattice, the propagation of light was measured in quasi-real time manner. The experimental demonstrations using Megaframe will find applications in Raman spectroscopy, soliton imaging, quantum optics, and discrete waveguide optics.
Thermomechanical Stress Distribution Analysis of Junction Box on Silicon Photovoltaic Modules Based on Finite Element Analysis

The electrical failure associated with concentration of heat and thermomechanical stress inside junction boxes is one of the main failure modes of photovoltaic (PV) modules. Three types of potting junction boxes with partial 72 Si cells PV panel are modeled under a constant current steady-state condition. The temperature distribution results are verified through thermal dissipation tests. It is found that when one diode is in the <sc>on</sc>-state, the split kind of junction box shows the highest temperature distribution on the surface, which is nearly <inline-formula><tex-math notation="LaTeX">$15\;^\circ {\rm{C}}$</tex-math></inline-formula> higher than the other two types under <inline-formula><tex-math notation="LaTeX">$25\;^\circ {\rm{C}}$</tex-math></inline-formula> with 11 A current applied. When three diodes are working, the other two types of junction box with three diodes inside will be intensely heated. The internal temperature distribution shows that bigger size and more copper coverage can help improve the thermal dissipation. Thermomechanical stress analysis based on the steady-state conditions is then carried out. The results show stress concentration on the side wall of the type I junction box while the other two types having conspicuous impact to the backsheet.
A Sliding Mode Control with Nonlinear Fractional Order PID Sliding Surface for the Speed Operation of Surface-Mounted PMSM Drives Based on an Extended State Observer

A novel sliding mode controller (SMC) with nonlinear fractional order PID sliding surface based on a novel extended state observer for the speed operation of a surface-mounted permanent magnet synchronous motor (SPMSM) is proposed in this paper. First, a new smooth and derivable nonlinear function with improved continuity and derivative is designed to replace the traditional nonderivable nonlinear function of the nonlinear state error feedback control law. Then, a nonlinear fractional order PID sliding mode controller is proposed on the basis of the fractional order PID sliding surface with the combination of the novel nonlinear state error feedback control law to improve dynamic performance, static performance, and robustness of the system. Furthermore, a novel extended state observer is designed based on the new nonlinear function to achieve dynamic feedback compensation for external disturbances. Stability of the system is proved based on the Lyapunov stability theorem. The corresponding comparative simulation results demonstrate that the proposed composite control algorithm displays good stability, dynamic properties, and strong robustness against external disturbances.
PREVALENCE OF POOR SLEEP QUALITY AMONGWOMEN IN GENERAL POPULATIONIN MILITARY AREA , JEDDAH,2018.

Aim: This study aimed to determine sleep quality status among women adult age ≥18living at National Guard residential city in Jeddah,2018. Method: this was a cross sectional study where a total of 252women adults were studied. Participants were recruited using multistage sampling. Face to face interview was used to collect data on sociodemographic and Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality. Results: showed that the prevalence of poor sleep quality was 61%. The average sleep latency was 31 minutes (SD ± 27.9), 116 (46.1%) had reported prolonged sleep-onset latency (>30minutes), only 93 (36.9%)
Left and right generalized Drazin invertible operators on Banach spaces and applications

In this paper, left and right generalized Drazin invertible operators on Banach spaces are defined and characterized by means of the generalized Kato decomposition. Then, new binary relations associated with these operators are presented and studied. In addition, a new characterization of the generalized Drazin pre-order and a sufficient condition for that to be a partial order are given by using a matrix operator technique. Mathematics subject classification (2010): 15A09, 06A06, 47A10.
Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization

We investigate the learning rate of multiple kernel leaning (MKL) with elastic-net regularization, which consists of an l1-regularizer for inducing the sparsity and an l2-regularizer for controlling the smoothness. We focus on a sparse setting where the total number of kernels is large but the number of non-zero components of the ground truth is relatively small, and prove that elastic-net MKL achieves the minimax learning rate on the l2-mixed-norm ball. Our bound is sharper than the convergence rates ever shown, and has a property that the smoother the truth is, the faster the convergence rate is.
Empiryczna estymacja chropowatości powierzchni oraz składowych siły szlifowania i sygnału emisji akustycznej w procesie szlifowania powierzchni płaskich

W artykule przedstawiono empiryczne modele wybranych parametrów wyjściowych procesu szlifowania powierzchni płaskich stali NC10 z zastosowaniem ściernicy z ziarnami elektrokorundu szlachetnego 99A. Dwuparametryczne modele regresyjne opracowano dla: chropowatości powierzchni, składowych siły szlifowania oraz sygnału emisji akustycznej. Modele te pozwalają na wybór optymalnych parametrów procesu szlifowania ze względu na wartość składowych siły oraz emisję fal naprężeń występujących w strefie obróbki, jak i uzyskiwaną jakość powierzchni przedmiotu. Funkcje mogą być zastosowane do weryfikacji modeli symulacyjnych lub w celu uniknięcia ewentualnych defektów termicznych powierzchni przedmiotu obrobionego i uzyskania zakładanej dokładności i powtarzalności obróbki.
A SURVEY OF HIGH RESOLUTION IMAGE CLASSIFICATION METHODS AND TECHNIQUES

Classification is turning into a growing valuable technique for giving huge zone of complete land cover data that utilize the images which has high resolution. High resolution images have the attributes of copious geometric and complete data and that data are useful to exhaustive classification. The proposed algorithm integrates three cues in CRF model by demonstrating the probabilistic potentials are spectral, spatial contextual and spatial location. The unary potentials displayed the spectral cues can gives essential data for separating the different land cover classes. The spatial contextual data modeled by the pair wise potentials that setting up the adjacent connections between pixels to support spatial smoothing. The higher order potentials unequivocally encode the spatial location cues. The non local scope of the spatial location associations into the objective pixel and its closest preparing tests is considered by the higher order potentials. This information can gives valuable data to the classes that are effectively mistaken for other land-cover types in the spectral appearance. We have proposed an image super resolution that employs image processing techniques to reproduce the image which has high resolution from a set of low resolution observations of the same scene.
What Is the Real Clausius Statement of the Second Law of Thermodynamics?

In this paper, we first analyze the difference between the second law of thermodynamics and the laws in other disciplines. There are some phenomena in other disciplines similar to the Clausius Statement of the second law, but none of them has been accepted as the statement of a certain law. Clausius’ mechanical theory of heat, published in the nineteenth century, is then introduced and discussed in detail, from which it is found that Clausius himself regarded “Theorem of the equivalence of the transformation of heat to work, and the transformation of heat at a higher temperature to a lower temperature”, rather than “Heat can never pass from a colder to a warmer body without some other change”, as the statement of the second law of thermodynamics. The latter is only laid down as the fundamental principle for deriving the theorem of the equivalence of transformations. Finally, based on the theorem of the equivalence of transformations and the average temperature method, a general quantitative relation among the heat, the work, and the temperatures is obtained for arbitrary cycles, which is thus recommended as an alternative mathematic expression of the second law. Hence, the theorem of the equivalence of transformations is the real Clausius Statement of the second law of thermodynamics.
Comparative Study of Role Based Access Control in Cloud Databases and NoSQL Databases

Role Based Access Control (RBAC) is a mechanism to restrict access to unauthorized users for security of computer systems. Many organizations have adopted non-relational databases which are generally referred to as NoSQL databases. Security issues are the main concerns, as sensitive data is stored in both Cloud Databases and NoSQL Databases, especially access control to those data by unauthorized users. The working conditions and characteristics of RBAC of different Cloud Databases and NoSQL Databases vary. The main objective of this paper is to give clear-cut idea about RBAC in different Cloud Databases and NoSQL Databases. As a consequence, we have compared the RBACs of the most popular Cloud Databases Oracle 12c, Microsoft SQL Azure and NoSQL Databases MongoDB, Cassandra. We have presented the extensive comparative study of Role Based Access Control Model in Cloud Databases and NoSQL Databases under the specific parameters.
Atomic-scale mechanisms for magnetostriction in 
CoFe2O4
 and 
La0.5Sr0.5CoO3
 oxides determined by differential x-ray absorption spectroscopy

For financial support, we thank the Spanish Ministerio de Ciencia, Innovacion y Universidades, and AEI Project No. RTI2018-098537-C22, cofunded by FEDER from the EU, and Project E12-17R from the Diputacion General de Aragon.
Change of MTF for Sampling Interval in Digital Detector

Assessments of medical image was improved in accordance with development of medical imaging systems. One of them is edge method to determining MTF(Modulation Transfer Function) based on the Fujita method. Fujita was reduced sampling interval used slightly angulated slit to the direction of pixel array and composited finely sampled LSF to determine presampling MTF. In this study, we investigate the effect of sampling interval on the MTF under a digital imaging system by changing wire angle. The wire method was equivalent to the slit method except signal appearance. A Simens’s MAMMOMAT Inspiration with 0.085 × 0.085 mm pixel size made by amorphous selenium was used and 96% accuracy on MTF in twice sampling interval compared with Fujita was obtained. However, three times of sampling interval showed 93% accuracy on 50% of MTF and 85% accuracy on 10% of MTF. Keyword : X-ray imaging system, Image assessment, Modulation Transfer Function, Presampling, Sampling interval
Supervised Machine Learning with Control Variates for American Option Pricing

Abstract In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian process regression (Kriging) approach for American options pricing and compare its performance in estimating the continuation value with the Longstaff and Schwartz algorithm. Secondly, we explore the control variates technique in combination with Kriging to further improve the estimation of the continuation value. This method allows to reduce dramatically the standard errors and to improve the stability of the Kriging approach. For illustrative purposes, we use American put options on a stock whose dynamics is given by Heston model, and use European options on the same stock as control variates.
Wskaźniki rynku kapitałowego : zastosowanie w wycenach przedsiębiorstw oraz w strategiach inwestycyjnych

Ksiązka pt. "Wskaźniki rynku kapitalowego - zastosowanie w wycenach przedsiebiorstw oraz w strategiach inwestycyjnych" jest pierwszą zwartą publikacją na polskim rynku wydawniczym, ktora kompleksowo przedstawia tego rodzaju zagadnienia. W sposob szczegolowy ukazano w niej:• rodzaje, zasady budowy i metody kalkulacji wskaźnikow rynku kapitalowego,• analize przyczynową podstawowych mnoznikow,• sposoby kalkulacji miernikow rozwodnionych,• istote i warianty mnoznikowej metody wyceny przedsiebiorstw,• ograniczenia i czesto popelniane bledy w procesie wyceny przedsiebiorstw metodą mnoznikową,• zasady tworzenia strategii inwestycyjnych przy wykorzystaniu wskaźnikow rynku kapitalowego,• wyniki dotychczasowych badan nad skutecznością strategii inwestycyjnych bazujących na miernikach rynku kapitalowego. Ma ona charakter praktycznego przewodnika, ktory zostal napisany przystepnym jezykiem i zarazem wzbogacony o szereg przykladow. Skierowana jest do szerokiego grona odbiorcow, w tym: analitykow finansowych, osob rozpoczynających przygode z rynkiem finansowym oraz studentow kierunkow ekonomicznych. Moze pelnic role podrecznika podstawowego lub uzupelniającego do takich przedmiotow, jak: analiza finansowa, wycena przedsiebiorstw, finanse przedsiebiorstw.
Classificação de campos vetoriais homogêneos que comutam e C^2

Um campo de vetores homogeneo X em Cn e dado por X =Xnj=1Pj@@xj onde cada Pj , 1 -< j -, n, e um polinomio homogeneo. Tal campo vetorialX define uma folheacao holomorfa por curvas FX em Cn, onde as folhasde FX sao as componentes conexas das orbitas de X. Diremos que dois campos de vetores X e Y comutam se o colchete de Lie [X; Y ] = XY - Y X e identicamente zero.
A Switchable $S$ -/$C$ -Band Antenna Array With Dual Polarization and Modularity

A switchable $S$ -/ $C$ -band antenna array is presented with dual polarization and modularity. A $2\times 2\,\,S$ -band antenna array can be reconfigured into a $4\times 4\,\,C$ -band antenna array within the same physical aperture. A narrow ground strip inside the slot-ring antenna is used to reduce the mutual coupling between antenna elements and suppress the null in the radiation patterns in the $C$ -band array. In addition, the vertical coaxial-to-microstrip transition allows for feeding the antenna array from behind the ground plane, making it modular. Dual polarization capability is achieved in each band. This antenna array shows 64.3% and 66.7% fractional bandwidth with 8.4 and 14.3 dBi gain in the $S$ - and $C$ -band operating states, respectively. The isolation between different antenna ports, radiation patterns at different frequency points, and input third-order intermodulation product of the antenna is measured. A triangular lattice is implemented for the $S$ -band array to minimize the physical spacing between the antenna elements, and therefore enable beam steering to large angles.