Miracle-dz/UniFAR-FTUs huggingface dataset:
Easily Optimizable Dual-Band Frequency-Selective Surface Design

Mutual interference between indoor adjacent wireless networks is becoming an important issue as it reduces the communication speed significantly. Isolating the wireless networks by using frequency-selective surfaces (FSSs) can be an efficient solution for such interference problems. As a solution proposal, a new periodic element geometry design, which stops incoming 2.4 and 5.8 GHz industrial, scientific, and medical radio (ISM) signals, is presented in this work. Mutual effect between each resonator (2.4 and 5.8 GHz) mostly leads to the optimization stage's being inadequate or time consuming at multiband FSS designs. Therefore, a new simple design technique is presented to be able to optimize each resonance frequency independent from the other. Besides, as an important feature, this technique also allows adjusting the frequency interval between the stopbands. Analysis of the proposed surfaces is executed with Ansoft HFSS v.15 software.
EFFECT OF SLAUGHTERING METHOD ON THE KEEPING QUALITY OF BROILER CHICKENS' MEAT

An experiment was conducted to study the effect of different slaughtering methods (Islamic traditional Islamic, hanging and electrical stunning). A total of 81male broiler chickens 42 day of age were selected according to average body weight and randomly divided into 3 groups. The birds were further divided into three replicates and were allotted the completely randomized design. The chickens within each group were weighed and were slaughtered by one of three slaughtering methods (traditional, hanging and stunning). The bled blood of each bird was collected and weighed. The carcass was weighed> Three parts from each carcass were chosen (breast, thigh and wing) to estimate the deterioration level at 3 different times after slaughtering (6 hrs, 48 hrs and 96 hrs). There was a significant difference (P ≤ 0.01) in bled blood weight with respect to the slaughtering method. Stunning method had the lowest amount (50 gm) while the hanging method had the highest amount (67 gm). There was no significant difference (P > 0.05) on the bacterial count due to the slaughtering method at 6 hours postmortem. Nevertheless, the breast had the highest and the thigh had the lowest bacterial count. There was a significant difference (P ≤ 0.01) on the bacterial count with respect to slaughtering method at 48 hours postmortem, where the electrical stunning method having the highest count (83 x 10 6 ) and the Islamic hanging method had the lowest count (55 x 10 6 ). At 96 hours, post mortem there was a significant difference (P ≤ 0.01) on the bacterial count with respect to slaughtering method on the same trend as at 48 hours. The Islamic hanging method seem to be the best one among the three methods, and therefore it is recommend to be used for slaughtering of the broiler chickens. Key Word: Slaughtering Method, Keeping Quality, Chickens' Meat
Red Blood Cells Consumption: An Optimization Method

Optimal red blood cells (RBCs)consumption management as an invaluable product can contribute noticeably to communities' healthcare. Therefore, the main purpose of this research is to help the medical community and the human community in providing fast and timely blood products at the lowest cost. Blood products' life are taken into account since ordering policies are depended on the time of inventory. For each hospital, a new decision is made for RBCs production for the whole day. The decision-making process is repeated the next day with respect to the inventory status. Markov decision-making process is used as a decision-making tool for blood inventory problem. We used MATLAB 2016b software to solve the Markov decision-making for RBCs with sequential approximation algorithm. Real-life data are used to investigate the model. Obtained results after 278256 iterations show that 13.19%, 45.13% and 41.68% of costs are related to (LIFO-FIFO), (LIFO-LIFO)and (FIFO-FIFO)policies, respectively. By analyzing the different types of costs and comparing the policies, it is believed that (LIFO-LIFO)policy is better than other two policies and cumulated costs in this policy is lower than other ones in long time.
A Modified Multi-objective Simulated Annealing Algorithm for Fixed-outline Floorplanning

Floorplanning is an indispensable step in very large scale integration (VLSI) design flow. Fixed-outline floorplanning is a challenging problem because it requires additional constraint that the floorplanning must meet both width and height criteria. In this paper, a modified simulated annealing (MSA) algorithm is presented with a new cost function for this problem. In our algorithm, a two-step strategy is used to improve the efficiency of the algorithm and balance the main function and secondary function. In addition, a local search strategy is employed to make sure a feasible solution can be found. Experimental results indicate that the success rate of each benchmark is 100% in deferent aspect ratio
Displacement flows under elastic membranes. Part 1. Experiments and direct numerical simulations

The injection of fluid into the narrow liquid-filled gap between a rigid plate and an elastic membrane drives a displacement flow that is controlled by the competition between elastic and viscous forces. We study such flows using the canonical set-up of an elastic-walled Hele-Shaw cell whose upper boundary is formed by an elastic sheet. We investigate both single- and two-phase displacement flows in which the localised injection of fluid at a constant flow rate is accommodated by the inflation of the sheet and the outward propagation of an axisymmetric front beyond which the cell remains approximately undeformed. We perform a direct comparison between quantitative experiments and numerical simulations of two theoretical models. The models couple the Föppl–von Kármán equations, which describe the deformation of the thin elastic membrane, to the equations describing the flow, which we model by (i) the Navier–Stokes equations or (ii) lubrication theory. We identify the dominant physical effects that control the behaviour of the system and critically assess modelling assumptions that were made in previous studies. The insight gained from these studies is then used in Part 2 of this work, where we formulate an improved lubrication model and develop an asymptotic description of the key phenomena.
On Hybrid IR and AR Service Provisioning in Elastic Optical Networks

This paper investigates hybrid immediate reservation (IR) and advance reservation (AR) service provisioning in elastic optical networks, with the objective to minimize IR/AR service conflicts. We design algorithms to coordinate service provisioning of IR and AR requests. Specifically, both proactive and reactive IR provisioning schemes are considered to minimize IR service failures. Our AR scheduling algorithm can coordinate AR service provisioning with various IR traffic patterns, balance spectrum utilization in both time and spectral domains, and reduce IR/AR service conflicts. Simulation results verify that our proposed IR + AR schemes can significantly reduce IR service failures as well as routing and spectrum allocation reconfigurations in IR service provisioning. Moreover, the results indicate that the proposed IR + AR schemes can achieve more performance gain from AR flexibility when compared with two existing benchmarks.
Invariant Information Learning for Image Recognition

Neural network is difficult to understand the invariance of input data, which is one of the causes of weak neural network generalization. So the researchers usually carry out data augmentation method on the training set, which makes the neural network remember different deformation patterns. We propose an invariant information learning framework:original CNN+Spatial information Function Zone(SFZ). This framework uses correlation matrix method instead of data augmentation method to make the neural network have the ability to learn the invariance of input data. Finally, our experiment shows that CNN+SFZ can effectively help improve generalization ability without data augmentation. In the absence of data augmentation for the training set, the network with SFZ reduced the error rate by 9.01% over the original network.
Persecutory delusions and the determination of self-relevance: an fMRI investigation

Background. People with persecutory delusions regard ambiguous data in the social domain as self-relevant and selectively attend to threatening information. This study aimed to characterize these social cognitive biases in functional neuroanatomical terms. Method. Eight schizophrenic patients with active persecutory delusions and eight matched normal controls underwent functional magnetic resonance imaging while determining the self-relevance of ambiguous self-relevant or unambiguous other-relevant neutral and threatening statements. Results. In determining self-relevance, the deluded subjects showed a marked absence of rostral–ventral anterior cingulate activation together with increased posterior cingulate gyrus activation in comparison to the normal subjects. The influence of threat on self-relevance determination did not yield statistically significant differences between deluded and normal subjects. Conclusions. Abnormalities of cingulate gyrus activation while determining self-relevance suggest impaired self-reflection in the persecutory deluded state. This may contribute to persecutory belief formation and maintenance.
Research and Development of Virtual Try-On System Based on Mobile Platform

A mobile-based virtual try-on system is proposed to deal with the problems of high cost and conflicts between computational complexity and simulation effects. In this paper, several modules are included, such as automatic 3D face reconstruction based on a single image, auto-skinning and realtime local simulation of cloth. According to the experiments, the virtual try-on system introduced in this paper is able to achieve better fitting effects with lower constructing and computing costs, in which case good experience of mobile-based virtual try-on system is provided.
Comparison of Multivariable Techniques for Brand Classification of Turmeric Powders by Near-infrared (NIR) Spectroscopy

In this work, near-infrared (NIR) spectroscopy was applied for the rapid classification of 7 different brands of turmeric powder. Multivariate calibrations including nearest neighbor (kNN) and support vector machine (SVM) were studied and compared to achieve an optimal result. At first, to visualize the different clustering of the turmeric brands principal component analysis (PCA) was performed to visualize and principal components (PCs) were derived which were applied as inputs to the kNN and SVM models. Cross validation was used to extract number of PCs and optimization parameters. The reflectance spectra of 84 total samples were taken by NIR spectrometer for the analysis purpose out of which 70% was used for the training purpose and 30% for testing purpose. Figures of merit (FOM) were extracted from the confusion matrices of the kNN and SVM classifiers. Comparing the two investigated models, both kNN model and SVM model effectively classified all samples in test set according to their brands with accuracy ranging from 0.76-0.91% and 0.79-0.95% respectively.
End-to-End Automated Verification for OS Kernels

Formal verification has been recognized as an essential part in improving the correctness and soundness of OS kernels. The verification of OS kernels faces many challenges. For example, the formal proof always comes with difficulties and non-trivial cost. Researchers often have to verify the OS kernel in high level programming languages and ignore low level languages. In this paper, we propose a framework for verifying the whole OS kernel with a low proof burden. We claim it as an end-to-end automated verification framework for the purposes of: (1) verifying the OS kernel that consists of assembler and C languages by formally modeling assembler programs in C level; (2) reasoning the verification with no manual proofs on the basis of SMT. We successfully apply the framework to automatic verifying a commercial operating system µC/OS-II in different hardware architecture, including all the 74 system calls and the core written in mixed-language (i.e., assembler and C). Our framework helps to catch several overflows and type mismatch vulnerabilities of µC/OS-II kernel.
Perception and opinion of medical students about Pap smear test: a qualitative study.

OBJECTIVE the objective of this study is to explore the perceptions of medical students regarding the Pap smear test. METHODOLOGY Focus group discussion was held with twenty three medical students. The students were divided into three focus groups; two groups of female participants consisting of 9 and 8 students; respectively. The third group consisted of 6 male students. Questions regarding Pap smear testing included barriers that prevented women from taking the test, gender preference for the physician to conduct the cervical screening test, willingness to suggest Pap smear test to the family or friends. The data obtained were classified into various categories and analyzed manually. RESULTS Most of the study participants mentioned that the main barriers for women to not perform Pap smear test is lack of awareness 16 (70%), followed by shyness 12 (52%) and the cost of the test 12 (52%). Most agreed that the gender of the physician will affect the women decision to do Pap smear test. All mentioned that the advantage of a regular Pap smear test is to detect the abnormality at the early stage of cervical cancer. Some of the participants 9 (39%) mentioned that the disadvantages are expense, possible injury in the vagina due to the test procedures 8 (35%), associated infection 7(30.4%) and pain 7(30.4%) . The majority, 20 (87%) mentioned that the most effective prevention methods for cervical cancer are having sex only after getting married with the spouse only, HPV vaccination 15 (65%) and Pap smear 14 (61%). CONCLUSION The main barriers for women to not perform Pap smear test is lack of awareness, shyness and the cost of the test. Gender of the physician will affect the women decision to do Pap smear test.
Reliability of an Al2O3/SiO2MIM Capacitor for 180nm (3.3V) Technology

Metal-Insulator-Metal (MIM) capacitors are widely used in More-than-Moore technologies, such as RF devices and image sensors. These technologies typically use SiN for the MIM capacitor dielectric, which has good reliability but a relatively low capacitance density. In this report we examine the reliability and performance tradeoffs for a MIM capacitors based on an Al2O3/ SiO2 dielectrics.
Trcollage: Efficient Image Collage Using Tree-Based Layer Reordering

This paper proposes an efficient image collage approach called TrCollage using tree-based layer reordering, which takes into account not only the efficiency of the collage processing but also the quality of the final collage effects. Besides, our tree-based TrCollage also meets the enhanced demands from the rapid development of mobile technology, which call for robust solutions for efficient picture collage without computation-intensive processing, e.g. graph-cut, saliency detection. The experimental results have shown the efficiency and effectiveness of our tree-based TrCollage with high-quality image collage effects applying layer reordering.
Respiratory Sinus Arrhythmia in 10 Year Olds - Normal and Intrauterine Growth Restricted

Frequency domain analysis of RR has been determined by three methods, autoregressive model (AR), Fast Fourier Transform (FFT) and Lomb periodogram for 10 min segments. The first two methods were done after resampling and the third method without resampling RR series of all 75 children. AR was used in this work, and RSA was identified at night time during sleep. The area of the RSA was calculated for every 10 min interval and compared to the overall area of the 10 min segment, then the average RSA of all segments was calculated, as well as the overall percentage of the RSA energy to the total area for the whole period of sleeping. This was done firstly for a sample of Normal and IUGR 10 year olds. Secondly for all the children under study, an independent t-test concluded that there is no significant difference between the IUGR and Normal (p=0.7467).
New algorithm for identifying network topology based on artificial neural networks

Correct insight and interpretation of the network topology are crucial task of any state estimator. In order to improve state estimators accuracy and speed, a new approach of determining network topology is presented. Presented method is based on measurement of the injected test signal reflection from the impedance discontinuities in the network. Correct interpretation of the measured reflected signal gives possibility to determine the network topology at the reflection site. This paper presents interpretation of the reflected signal by the developed artificial neural network (ANN). The ANN system has been used for identifying a network topology of modeled and simulated certain part of Croatian transmission system. The results are displayed as a receiver operating characteristic (ROC) curve form for the best and worst prediction case.
3-Dimensional Convolution Based Iterative Model for Efficient Motion Map Generation for Representing Video Discriminative Information

In this paper, we present a simple method to integrate the discriminative information of video for the action recognition tasks. We introduce the concept of motion map to represent the prefix of video sequences by optimizing the recognition accuracy of original video. 3-dimensional convolution (3Dconv) based model is used to generate the new motion map by integrating current motion map and future video frame. This model is capable of increasing the length of training video in iterative manner and allow us to generate the final motion map. Experimental evaluation results on widely used datasets i.e HMDB51 and UCF101 have revealed effectiveness and flexibility of proposed method over other baseline schemes.
On the Frequency Dependence of Bulk Trap Generation During AC Stress in Si and SiGe RMG P-FinFETs

The impact of channel Germanium content (Ge%) and gate stack Nitrogen content (N%), on bulk trap generation <tex>$(\Delta \text{V}_{\text{oT}})$</tex> time kinetics is studied by using DC and AC stress at different voltage <tex>$(\text{V}_{\text{GSTR}})$</tex> and temperature (T) in RMG HKMG Si and SiGe p-FinFETs. It is shown that <tex>$\Delta \text{V}_{\text{OT}}$</tex> magnitude is dependent on frequency <tex>$(f)$</tex>, which can make the resulting threshold voltage shift <tex>$(\Delta \text{V}_{\text{T}})$</tex> dependent on <tex>$f$</tex> for some stress conditions. The <tex>$f$</tex> dependence of <tex>$\Delta \text{V}_{\text{OT}}$</tex> and <tex>$\Delta \text{V}_{\text{T}}$</tex> becomes stronger at higher Ge% and weaker at higher N%. The <tex>$\Delta \text{V}_{\text{T}}$</tex> time kinetics is modeled for DC and AC stress under different stress conditions (V<inf>GSTR</inf>, T and <tex>$f)$</tex> and for different processes (Ge%, N%).
Congestion Correlation And Classification from Twitter and Waze Map Using Artificial Neural Network

Traffic congestion has become a big problem in cities around the world, especially in big cities. This causes information about traffic conditions very important to be known by the riders. Such information can be obtained quickly and easily through social media, but not yet known. Previous research has largely focused on classifying congestion data and traffic speed velocity analysis, while the correlation between congestion information from social media and actual traffic flow velocity has not been studied. In this study, we combine data from social media and traffic data collected for 1 week and focus on some major roads in Yogyakarta, Indonesia to investigate the correlation between congestion information in cyberspace through social media and actual traffic speed in Waze applications. The results in this study indicate that the highest precision value of all experiments is 84.01%, while the lowest precision value of all experiments is 0.37%.
A Novel Intelligent Thyroid Nodule Diagnosis System over Ultrasound Images Based on Deep Learning

At present, thyroid cancer has become a serious global public health problem, and ultrasound is the most important imaging method to assess thyroid nodules. But ultrasound diagnostic results of thyroid disease are susceptible to doctors' experiences, levels, status and other factors. So it needs intelligent diagnostic system to assist the doctors to make more objective qualitative and quantitative analyses, to reduce the impact of subjective experience on the diagnostic results. In this paper, a deep learning algorithm for thyroid nodule risk assessment based on ultrasound images is proposed, and an intelligent diagnostic system of thyroid ultrasound image based on this algorithm is constructed. As an aided diagnostic tool, the system is easy to use and can significantly improve the accuracy for determination of thyroid cancer. To verify the effectiveness of the system, we collaborate with the Peking Union Medical College Hospital to test this system.
Sentiment Analysis of Law Enforcement Performance Using Support Vector Machine and K-Nearest Neighbor

Sentiment analysis or opinion mining is a method to group opinions or reviews into positive or negative. It is important sources for decision making and can be extracted, identified as well as evaluated from online sentiments reviews. This research discussed sentiment analysis in law enforcement on a law case in Indonesia. The analysis uses Support Vector Machine and K-Nearest Neighbor (KNN) for data classification integrated with Particle Swam Optimization (PSO) to increase their performance. The experiment results show that PSO increase the performance of both algorithmof the paper is PSO method make value SVM with PSO where value C = 1.0 and Epsilon = 1.0 accuracy 100% while for algorithm KNN with PSO 93.08%. This result show SVM algorithm more accurate than KNN algorithm by using PSO optimization. The performance of law enforcers in the trial case get more positive responses from the people of Indonesia in accordance with their comments or tweets in social media.
Molecular dynamics simulations at constant pressure and/or temperature

In the molecular dynamics simulation method for fluids, the equations of motion for a collection of particles in a fixed volume are solved numerically. The energy, volume, and number of particles are constant for a particular simulation, and it is assumed that time averages of properties of the simulated fluid are equal to microcanonical ensemble averages of the same properties. In some situations, it is desirable to perform simulations of a fluid for particular values of temperature and/or pressure or under conditions in which the energy and volume of the fluid can fluctuate. This paper proposes and discusses three methods for performing molecular dynamics simulations under conditions of constant temperature and/or pressure, rather than constant energy and volume. For these three methods, it is shown that time averages of properties of the simulated fluid are equal to averages over the isoenthalpic–isobaric, canonical, and isothermal–isobaric ensembles. Each method is a way of describing the dynamics of a certain number of particles in a volume element of a fluid while taking into account the influence of surrounding particles in changing the energy and/or density of the simulated volume element. The influence of the surroundings is taken into account without introducing unwanted surface effects. Examples of situations where these methods may be useful are discussed.