Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Level Shifted PWM Technique for Unequal and Equal Power Sharing in Quasi Z-Source Cascaded Multilevel Inverter for PV Systems

Conventional phase-shifted pulsewidth modulation (PS-PWM) is a usual switching technique for Z-source/quasi-Z-Source (qZS)-based cascaded multilevel inverters (CMIs) (qZS-CMIs). The PS-PWM scheme causes higher switching losses and creates an electromagnetic interference (EMI) problem for higher number of cascaded modules. To address these issues, a novel, modified level-shifted PWM (LS-PWM) technique is proposed with the aim of obtaining equal power from cascaded modules under abnormal conditions. The direct use of the alternate phase-opposed disposed PWM (APOD-PWM) results in unequal power sharing between the qZSI modules, under all operating conditions. An effective carrier rotation is incorporated in the conventional APOD-PWM to make equal power sharing between the qZSI modules. The proposed scheme is an excellent solution for photovoltaic (PV) systems to address the problem of partial or complete shading, temperature variation, PV module failure, and dust accumulation on the PV panels. Furthermore, the relationship between PS-PWM and APOD-PWM is geometrically obtained, which indicates that the proposed modulation scheme gives higher voltage gain over LS-PWM and PS-PWM techniques. In addition, detailed switching loss analysis for the proposed PWM methods is added to validate low-switching losses, and thus high efficiency. The MATLAB Simulink simulations are presented to verify the proposed modulation. An experimental prototype is developed, and the experimental outcomes validate the improved performance of the multilevel qZSI with proposed modulation.
Instrument design of remedy test assisted by multiple representations using computer-based test model on redox materials

This study discusses the mastery of students’ learning outcomes who join the remedy program using the test instruments based on multiple representations arranged in Computer-based Test (CBT) model.CBT used in this study using the Beesmart application. The multiple representations studied include three levels of representation, namely macroscopic, microscopic, and symbolic on redox learning. Research subjects are 100 students of X grade of natural science class in MAN 2 Semarang (Islamic senior high school 2 Semarang). The test instrument was validated by 3 instrument experts with a mean score of 38.7 from a maximum score of 44, and 2 media experts obtained a mean score of 24.5 with a maximum score of 32 with a decent category tested. The level of completeness of student learning outcomes who join the remedy program is calculated using the percentage of mastery. The results showed that the level of ability of students’ learning outcomes that followed remedy was 83%.
Neumonía secundaria a aspiración silenciosa en pacientes con trastorno de deglución mayores de 30 años, en el Hospital Universitario San Ignacio

Objetivo: Identificar pacientes en riesgo de cursar con neumonía aspirativa secundaria a trastornos de deglución y comorbilidades asociadas. Difundir la utilidad del estudio funcional dinámico de deglución (FEES, por sus siglas en inglés) para la evaluación de estos pacientes. Diseño: Observacional descriptivo. Materiales y métodos: Revisión de historias clínicas de 2005 a 2008 del Hospital San Ignacio con diagnóstico de: neumonitis debida a aspiración de alimento y disfagia. Resultados: En nuestro hospital el 90,5% de los pacientes con posibles trastornos de deglución no son estudiados. Conclusiones e importancia clínica: Se debe implementar para pacientes con factores de riesgo el estudio funcional dinámico de la deglución si se sospecha aspiración silenciosa. Como veremos más adelante, el estudio funcional dinámico de la deglución representa una alternativa válida, ofrece ventajas con respecto a la cinedeglución y debe ser considerado como un complemento al estudio radiológico de los pacientes con trastornos de la deglución. Estudiar mejor estos pacientes permitirá desarrollar estrategias para disminuir episodios de aspiración y por consecuencia eventos de neumonía.
A Gestão de Pessoas com a Implementação de Treinamento dos Colaboradores e a Ferramenta Kaizen para Melhoria ao Atendimento dos Clientes no Ambiente Hospitalar

People management has undergone a major transformation process as the studies on this topic are deepened, and a divergence has been identified within a hospital environment, and its patient care. The objective of this study is to analyze the factors that influence the quality of patient care. The applicability of the model is tested through the analysis of implementation of this tool in a hospital patient care sector starting with the rescue of the concept of people management and continuous improvement in its various theoretical perspectives, followed by the presentation improving tool continues, the kaizen. It lists the main results expected from the implementation of the model of managing people and carried out an analysis of the implementation process of this issue addressed. The efficiency of people management model is checked from the perception presented customers regarding trained employees adequately in the hospital, so it is put into practice, which points to the success of the standard, while showing the holding of care achieved.
Deep Reinforcement Learning Approaches for Content Caching in Cache-Enabled D2D Networks

Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled device-to-device (D2D) communication technology is expected to relieve network pressure with the fact that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this article, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches [the echo state network (ESN) and the long short-term memory (LSTM) network] are employed to predict users’ mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay and energy consumption.
Comparison of tools for automated tests of the graphical user interface using the the Sikuli and AutoIT example

The aim of the article is to compare the tools for automating user interface tests on the example of Sikula and AutoIT. The conducted research has focused on the time of testing scripts, their complexity, ease of maintenance and reliability. For the purposes of the study, an application in C # was created and several representative automatic tests were written in each tool.
The development of viewing strategies in patients with macular disease

Background: This thesis describes the results of the first longitudinal study of visual behaviour in patients with newly developed macular disease (MD). The study is of a natural history, case series design. Methods: Twenty patients with age-related macular disease and five with juvenile forms of MD were recruited. All patients had developed scotomas in their second affected eye within the previous two weeks. Patients were assessed five times over the next twelve months. In addition to clinical tests, fixation behaviour was assessed using a scanning laser ophthalmoscope, eye movements were measured using an infra-red eyetracker and reading speed was recorded. Multivariate statistical techniques were applied to determine which factors limit reading speed and which variables lead to a change in reading speed. Results: All 25 patients developed a preferred retinal locus (PRL) within six months. Sixteen patients made an adaptation whereby they were unaware of using the PRL. By the end of the study, fifteen patients (60%) repeatedly made eye movements which displayed the characteristics of non-foveating saccades. Saccade efficiency reached normal levels in eight patients (32%). Over the course of the study, reading speed improved in four patients (16%), deteriorated in 7 patients (28%) and remained constant in the remaining 14 patients. Changes in reading speed were due to changes in fixation stability, non-awareness of using the PRL and developing a strategy of repeatedly using the same number of PRLs. The likelihood of a change in reading speed could not be predicted by disease type, visual acuity or scotoma size. Conclusions: It was not possible to predict which patients' reading speed will change from the measures used in this study. The conclusions of this thesis have implications for the counselling of patients with macular disease and the development of training programs for patients with this common, debilitating condition.
Design and Implementation of Adaptive Energy Shaping Control for DC–DC Converters With Constant Power Loads

The negative impedance caused by constant power load (CPL) may lead to the instability of overall power system. In this article, the regulation problem of output voltage of dc–dc converters subject to unknown CPLs is addressed. Our main contribution in this article is to design an adaptive energy shaping control (ESC) algorithm, which combines passivity theory with immersion and invariance technology, to stabilize dc–dc buck converter and boost converter with CPLs, respectively. Three good features of the proposed control method are that (1) the stability analysis and controller design are based on large signal model, (2) the proposed control scheme is implemented without needing the knowledge of power load, and (3) the estimation of domain of attraction is presented to guarantee strictly the asymptotic convergence of closed-loop system. The performance of the proposed controller is assessed via detailed simulation and experimental results.
The role of aesthetic reasoning in knowledge management: the case of elegant systems architecture design

ABSTRACT Nonaka and Takeuchi foundational work brought tacit knowledge to the attention of the Knowledge Management (KM) community. During the same years, research in cognitive science was offering new insights on how tacit knowledge operates by highlighting the role of visual perception and aesthetic appreciation. Despite these developments, the relationship between tacit knowledge and aesthetics has received scarce attention in KM literature. Drawing from studies in Neuro-Aesthetics, Gestalt psychology, Art critique and Design, we focus on the relationship between aesthetics and ambiguity resolution and adopt as empirical unit engineering systems representations. We show that more effective system representations can be achieved through the application of a set of aesthetic principles supporting the achievement of an optimal level of complexity in the representation (effective complexity). The empirical findings provide evidence that more aesthetically pleasant system representations built following this approach leads to the design of both more elegant and performant systems.
Use of semantic description of reference scene for visual navigation solutions

This article is devoted to the description generation of reference scene applying to visual navigation problem. The authors investigate the semantic descriptions that are more resistant to changing conditions of observation, thus being an alternative to existing approaches that use raster, feature, or object descriptions. There are no publications, considering similar approach of utilization for visual navigation problem solution. In the article the original description structure is proposed. The experiments involving descriptions of various scenes under various conditions have been conducted to assess the workability of the proposed approach. It is suggested that semantic descriptions are more resistant to partial occlusion of scene objects or changes in their visual features than other description types and can be used to solve visual navigation problems.
Deep Neural Network for Resource Management in NOMA Networks

Resource management plays a crucial role in improving sum rate of non-orthogonal multiple access (NOMA) networks. However, the traditional resource management methods have considerable complexity, creating a huge challenge in computational efficiency. To handle this challenge, a resource management method is proposed based on a deep neural network (DNN). The key advantage of the method is that the DNN can perform in almost real-time resource allocation because it requires a very simple operation. In this paper, the resource management problem of the NOMA system adopting imperfect successive interference cancellation (SIC) technology at the receivers is studied, including the power allocation stage and the user scheduling stage. For power allocation stage, the generic fully-connected DNN is trained to approximate the power allocation of interior point method (IPM), which not only greatly improves the computational efficiency but also increases the sum rate of the system. Based on the allocated power, the user scheduling algorithm is performed to further increase the system sum rate. Finally, simulation results verify some performances of the proposed algorithms.
Application and Research of Computer Big Data Based on Structure in Internet Learning

In recent years, the rapid development of the Internet and information industry has promoted the related technological development and innovation, and brought mankind into a new era of big data. Big data provides a great convenience service for people. It can not only realize the sharing of resources, but also enable people to obtain a large amount of information. The study and application of the Internet is becoming more and more important today. Computer big data, a technology closely related to the development of the Internet, plays an important role in Internet learning. The continuous popularization of the Internet and the continuous improvement of its speed have narrowed the distance between people and made various information knowledge more concentrated and rich. Big data can promote significant improvements in Internet learning efficiency with its own capabilities. Applying big data technology in Internet learning can help learners to acquire knowledge more quickly and conveniently, and can also continuously enrich the application of big data.
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining-based strategies is adopted to emphasize the misclassified samples, achieving promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited; or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issue. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors.
ANALISIS PEMANFAATAN SEARCH ENGINE DALAM MENINGKATKAN SELF-REGULATED LEARNING MAHASISWA TEKNOLOGI PENDIDIKAN

Kegiatan belajar di bangku perkuliahan mengharuskan mahasiswa untuk belajar mandiri ( Self-regulated learning ). Dalam belajar dan mengerjakan tugas kuliah, segala informasi dapat dicari dengan bantuan mesin pencarian ( search engine ). Mahasiswa perlu mengatur sendiri cara belajar mereka di luas dan bebasnya informasi internet. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk 1) mendeskripsikan persepsi mahasiswa Teknologi Pendidikan mengenai pemanfaatan s earch engine dalam kegiatan belajar, 2) mendeskripsikan kemampuan self-regulated learning mahasiswa Teknologi Pendidikan 3) mendeskripsikan kegiatan pemanfaatan search engine dalam meningkatkan self-regulated learning Mahasiswa Teknologi Pendidikan. Penelitian ini menggunakan metode penelitian kualitatif deskriptif. Data penelitian berupa catatan lapangan, hasil wawancara, hasil observasi, dan dokumentasi. Data berupa hasil wawancara dengan delapan mahasiswa dan seorang dosen sebagai narasumber. Hasil penelitian ini menemukan bahwa mahasiswa memanfaatkan internet dalam berbagai kebutuhan sehari-hari, terutama dalam belajar dan mengerjakan tugas kuliah. Beberapa mahasiswa menyadari bahwa tidak semua informasi yang ditemukan memiliki kualitas yang baik dan memuat informasi yang tepat. Mahasiswa masih melakukan pencarian dengan pola yang sederhana. Mahasiswa seringkali mencari ( searching ) satu informasi tanpa memadukan dengan informasi dari sumber lain yang didukung oleh fakta. Mahasiswa dapat melatih kemampuan belajar mandiri dengan meningkatkan motivasi belajar, kemampuan memadukan informasi dari beberapa sumber belajar, serta mengevaluasi dan merefleksi belajar diri mereka.
Review on secured data capabilities of cryptography, steganography, and watermarking domain

Due to the increment of using Internet to transfer the critical and secret data, many studies interested in secured data and investigated many ways to secure the transferred information. This paper presents a review study on the field that used in a secured data domain. The main objective of this study is explore the capabilities of secured data that used widely by researchers. Furthermore, the benefits and the drawbacks for each of secured data domain are also studied. This paper concludes that cryptography techniques could be utilized with steganography and watermarking in secured data domain to enhance the security mechanisms.
Offline Signature Recognition and Verification Based on Artifical Neural Network

In this paper, a problem for Offline Signature Recognition and Verification is presented. A system is designed based on two neural networks classifier and three powerful features (global, texture and grid features). Our designed system consist of three stages: the first is pre-processing stage, second is feature extraction stage and the last is neural network (classifiers) stage which consists of two classifiers, the first classifier consists of three Back Propagation Neural Network and the second classifier consists of two Radial Basis Function Neural Network. The final output is taken from the second classifier which decides to whom the signature belongs and if it is genuine or forged. The system is found to be effective with a recognition rate of (%95.955) if two back propagation of the first classifier recognize the signature and (%99.31) if all three back propagation recognize the signature
Analysis of Power Electronic Converters for Electric Vehicle Applications

this work presents a performance analysis of various power electronic converters with RL load to reduce the total harmonic distortion. The power converters inspected are: ZETA converter, single-ended primary inductance converter, often written SEPIC converter and Z SOURCE converter. The objective is to analyze which power electronic converter exhibits less total harmonic distortion (THD) and more efficiency in order to select the suitable converter for electric vehicle propulsion system. Three above mentioned converters are designed, modeled, and simulated. The zeta converter is advantageous over other converters inspected and have reduced total harmonic distortion, higher output power and hence improved efficiency. The simulations are done with MATLAB/SIMULINK and the results are presented.
Septic Osteoarthritis Due to Streptococcus dysgalactiae Subspecies equisimilis in a Three-Year-Old Girl

The current paper presents a three-year-old toddler with an acute and severe osteoarticular infection of her left hip and proximal thigh. In the first 24 hours she underwent an arthrotomy and during the intravenous phase of antibiotic treatment multiple teeth were removed because of an extensive periodontal disease. Streptococcus dysgalactiae subsp. equisimilis (SDSE) was identified in her blood culture samples. The paper discusses the clinical spectrum of the disease caused by this pathogen and briefly reviews the literature. To the authors' best knowledge, this is the first case of septic osteomyelitis and arthritis caused by SDSE in a child.
New Efficient Transmission Technique for HetNets With Massive MIMO Wireless Backhaul

In order to cope with the rapid increase in power consumption of heterogeneous cellular networks, this article proposes a new efficient transmission technique for heterogeneous networks with massive MIMO wireless backhaul with the objective of minimizing power consumption cost. We assume that transmissions on the backhaul link and the access link occur simultaneously on the same frequency band (in-band) thanks to MIMO spatial multiplexing. On the other hand, we consider that uplink and downlink transmissions are separated in time. In order to prevent multi-user and inter-tier interference, block diagonalization beamforming is considered at the macro base station (MBS) and the signal from the small base station (SBS) to the MBS is transmitted orthogonally to the channel of the SBS's users. The problem of minimizing the transmit power of base stations under users’ minimum-rate constraints is formulated. We first derive analytically the optimal time splitting parameter and the allocated transmit power considering that the inter-SBS interference is generated by fixed power. Next, we solve the power allocation problem when the generated inter-SBS interference is no longer considered fixed by proposing an efficient iterative power allocation algorithm. A heuristic user scheduling algorithm is devised in order to deal with the feasibility problem. Finally, simulations validate our analysis and show that the proposed transmission technique outperforms the conventional reverse time division duplex with bandwidth splitting (out-band) in terms of total power consumption.