Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Beamforming NOMA for mmWave Communications

This paper studies hybrid beamforming (HB)-based non-orthogonal multiple access (NOMA) in multiuser millimeter wave (mmWave) communications. HB offers power-efficient and low-complexity precoding for downlink multiuser mmWave systems which increases multiplexing gain and spectral efficiency of the system. Applying NOMA to HB-based systems, called HB-NOMA, can scale the number of users while offering a high spectral efficiency. However, an imperfect correlation between the effective channels of users in each NOMA cluster seriously degrades the achievable rate of HB-NOMA. In this paper, first a sum-rate maximization problem is formulated for HB-NOMA, and an algorithm is proposed to solve it effectively. It is then shown that the relationship between the effective channels of the users in each NOMA cluster can be approximated by a correlation factor. Next, the effect of imperfect correlation is analyzed, and a lower bound on the achievable rate of the users is derived for both perfect and imperfect correlation. Finally, the rate gap resulting from an imperfect correlation is evaluated and a tight upper bound is derived for that. Simulation results show that low correlation degrades the achievable rate of users. The lower bounds are tight in the large dimensional regime and in single-path channels.
Modeling epistemic propositions

We discuss several recent approaches to the modelling of knowledge ope~ators in Philosophical Logicand Artificial Intelligence. Particular attention will be given to the following topics: . 1. The representation of several different modal operators within the same system (e.g., knowledge, belief, memory) 2. Epistemic logics based on nonclassical concepts of inference (e.g., partial and constructive inference) 3. Epistemic operators in the area of nonmonotonic knowledge representation systems. Our main aim is to point out so me connections between these areas, in particular . between (2) and (3).
Fabricating lightweight and ultrastrong mechanical metamaterials

In the search for materials that are both light and strong, classic material design—such as optimizing the chemistry and/or microstructure of bulk materials—has been systematically exploited over centuries, leaving limited room for further improvements.1 Although major advancements have been made with respect to mechanical strength and density, light materials generally remain weak and heavy materials strong; hence, the two properties have historically been considered to be connected. However, in recent years, the field of so-called ‘metamaterials’ (materials engineered to possess properties not usually found in nature) has made considerable advances in the development of materials that are both light and strong. Metamaterials usually consist of assemblies of multiple repeating elements, and their special properties are primarily determined by their topology rather than their composition. Initially, these materials were designed to display unique optical, electromagnetic, or acoustic characteristics. Recently, mechanical metamaterials have also emerged, with principally opposing mechanical properties, such as both high stiffness and high damping (mechanical energy dissipation) capability2 or a negative Poisson’s ratio (i.e., a material that expands laterally when stretched). 3 In addition, a class of lightweight mechanical metamaterials has been developed, inspired by natural hierarchical cellular materials and triggered by the recent evolution of high-resolution 3D printing technologies that enable the miniaturization of lattice structures. The properties of these lightweight metamaterials depend on the microscopic length scales of their patterns as well as their topologies.5–9 Because of their specifically designed architectures, these lattice materials reach remarkable strengths at low densities that might never be achieved using classic material Figure 1. Scanning electron microscopy images of a glassy carbon nanolattice. (a,b) A polymer microlattice fabricated by 3D printing. (c,d) Vacuum pyrolysis transforms the polymer to glassy carbon and isotropically shrinks the lattice by 80%, producing a nanolattice. Lattice distortion during pyrolysis is eliminated by including pedestals and coiled spring supports, distancing the lattice from the substrate. Scale bars: (a,c) 5 m, (b,d) 1 m. Reproduced with permission.4
State and mode feedback control for discrete-time Markovian jump linear systems with controllable MTPM

In this note, the state and mode feedback control problems for a class of discrete-time Markovian jump linear systems ( MJLSs ) with controllable mode transition probability matrix ( MTPM ) are investigated. In most achievements, controller design of MJLSs pays more attention to state / output feedback control for stability, while the system cost in practice is out of consideration. In this paper, we propose a control mechanism consisting of two parts: finite-path-dependent state feedback controller design with which uniform stability of MJLSs can be ensured, and mode feedback control which aims to decrease system cost. Differing from the traditional state / output feedback controller design, the main novelty is that the proposed control mechanism not only guarantees system stability, but also decreases system cost effectively by adjusting the occurrence probability of system modes. The effectiveness of the proposed mechanism is illustrated via numerical examples.
Operation State Recognition of Renewable Energy Unit Based on SSAE and Improved KNN Algorithm

Quickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state recognition method for renewable energy units based on sparse stacked auto-encoder (SSAE) feature extraction and improved k-nearest neighbor (KNN) algorithm. The characteristics of this method is that the electrical parameters of the unit port are collected directly without relying on the unit’s supervisory control and data acquisition (SCADA) system, whose acquisition speed is too slow to meet the recognition accuracy requirement, and that the unit operation states can be recognized quickly and accurately. Firstly, operation states of renewable energy unit are divided, and the framework for the unit’s state recognition is proposed. Moreover, improved strategies for state recognition of renewable energy unit are proposed. Finally, the power system analysis software package (PSASP) is used to obtain the electrical parameters of renewable energy units and the improved KNN algorithm is used to recognize operation states after extracting features based on SSAE. By comparing the method proposed with the traditional KNN algorithm, the effect of the proposed method for states recognition is shown to be the best, with an accuracy of 98.16% and computing time of 50ms. The results show the validity of the proposed method.
Predicting Students’ Performance Using Machine Learning Algorithms: A Review

Educational Data Mining is a discipline focused on developing ways for studying the unique and increasingly large-scale data generated by educational settings and applying those methods to better understand students and the environments in which they learn. Predicting student performance is one of the most critical concerns in educational data mining, which is gaining popularity. Student performance prediction attempts to forecast a student's grade before enrolling in a course or completing an exam. The goal of this paper is to present a systematic literature review on predicting student performance using machine learning techniques and how the prediction algorithm can be used to identify the most important attribute(s) in a student's data. The study showed that neural networks is the most used classifier for predicting students’ academic results and also provided the best results in terms of accuracy. Also, 87% of the algorithms used were supervised learning as compared to 13% for unsupervised learning and 59% of the studies employed various feature selection methods to improve the performance of the machine learning models.
What We Learned about the Feasibility of Gene Electrotransfer for Vaccination on a Model of COVID-19 Vaccine

DNA vaccination is one of the emerging approaches for a wide range of applications, including prophylactic vaccination against infectious diseases and therapeutic vaccination against cancer. The aim of this study was to evaluate the feasibility of our previously optimized protocols for gene electrotransfer (GET)-mediated delivery of plasmid DNA into skin and muscle tissues on a model of COVID-19 vaccine. Plasmids encoding the SARS-CoV-2 proteins spike (S) and nucleocapsid (N) were used as the antigen source, and a plasmid encoding interleukin 12 (IL-12) was used as an adjuvant. Vaccination was performed in the skin or muscle tissue of C57BL/6J mice on days 0 and 14 (boost). Two weeks after the boost, blood, spleen, and transfected tissues were collected to determine the expression of S, N, IL-12, serum interferon-γ, the induction of antigen-specific IgG antibodies, and cytotoxic T-cells. In accordance with prior in vitro experiments that indicated problems with proper expression of the S protein, vaccination with S did not induce S-specific antibodies, whereas significant induction of N-specific antibodies was detected after vaccination with N. Intramuscular vaccination outperformed skin vaccination and resulted in significant induction of humoral and cell-mediated immunity. Moreover, both boost and adjuvant were found to be redundant for the induction of an immune response. Overall, the study confirmed the feasibility of the GET for DNA vaccination and provided valuable insights into this approach.
CORRELATION OF THE DURATION OF DIABETES WITH ANTHROPOMETRIC INDICES AMONG DIABETIC PATIENTS IN A TERTIARY CARE HOSPITAL.

Dr. K. Venkatesh 1 , Dr. C. Raghuram 2 and Dr. A. Revanth kumar 3 . 1. Post Graduate, Department of General Medicine, ASRAM Medical College, Eluru, West Godavari (D), Andhra Pradesh. 2. Assistant professor, Department of General Medicine, ASRAM Medical College, Eluru, West Godavari (D), Andhra Pradesh. 3. Post Graduate, Department of Community Medicine, ASRAM Medical College, Eluru, West Godavari (D), Andhra Pradesh(orcid:0000-0002-6980-4088). Manuscript Info Abstract . Manuscript History
Evaluación de microbiota de las costas de Sonora y Baja California contra cepas patógenas de Vibrio parahaemolyticus y Vibrio harveyi, agentes causales de la necrosis hepatopancreática en camarón blanco (Litopenaeus vannamei)

La acuicultura se enfrenta a grandes pérdidas económicas en cultivos de camarón blanco (Litopenaeus vannamei) debido a las enfermedades microbianas y virales. Una de estas enfermedades es la Necrosis Hepatopancreática Aguda (AHPND), la cual emergió en China en el año 2009, reportándose en México hasta el año 2013. Los agentes causales de AHPND en el camarón blanco son Vibrio parahaemolyticus y Vibrio harveyi, los cuales provocaron el 80% de las pérdidas en cultivos en los estados de Sonora, Sinaloa y Nayarit, México. Por este motivo, los objetivos de este estudio se enfocaron en identificar bacterias marinas como probióticos, capaces de inhibir a los agentes causales de AHPND. Las bacterias se aislaron de almejas, cangrejos, algas y agua marina de ecosistemas de Sonora y Baja California. Se analizó su capacidad hemolítica, degradación de celulosa y amilasa de las cepas seleccionadas, y su capacidad de inhibir a los patógenos. La identificación de las cepas probióticas se realizó con ensayos bioquímicas usando el sistema API y se confirmó mediante la amplificación del gen 16S rRNA. Se obtuvo que 10 aislados presentaron capacidad antagónica contra V. harveyi y se identificaron como: H3 Aeromonas hydrophila, mientras que los aislados J1, G10, H3 M, H2, G2, G2.1, 14 2.1, G3.2 y E 2.1 como Bacillus pumilus. En el caso de V. parahaemolyticus fueron: G2.1, H2, J1, G3.2 y E2.1. Para los ensayos de actividad hemolítica los aislados J1, G10 y E2.1 (B. pumilus) presentaron β hemolisis. Los aislados identificados como B. pumilus y A. hydrophila demostraron tener actividad antagónica contra V. paraheamolyticus y V. harveyi por lo que pueden ser catalogados como probióticos potenciales en base a los resultados obtenidos, los cuales contribuyen a conocer nuevas cepas benéficas para el biocontrol de estos patógenos y su aplicación permitirá evitar el uso de antibióticos en la acuicultura, reduciendo la incidencia de resistencia estos fármacos.
Reconfigurable and frequency-agile on-chip microwave photonic bandpass and bandstop filters using stimulated Brillouin scattering

In this paper, we present our recent results in the area of microwave photonics. Integrated microwave photonic bandpass and bandstop filters were realized using stimulated Brillouin scattering (SBS). Our recent breakthrough in the fabrication of chalcogenide waveguides has allowed us to achieve an on-chip SBS gain of >40 dB, enabling for the first time the tailoring of the SBS response well beyond the intrinsic linewidth (~30 MHz). An electrical comb generated by an arbitrary waveform generator was modulated onto an optical carrier to generate a broadened pump which via the SBS effect created a flat and rectangular bandpass filter response in the RF domain. Controlling the number of pump lines allowed bandwidth reconfigurability from 30 MHz to 440 MHz. The measured selectivity and the passband ripple were >20 dB and <1.9 dB, respectively and the center frequency of the filter was tuned up to 30 GHz. A bandstop filter response was realized by using a novel RF interferometry technique via accurate control of the amplitude and phase of the sidebands of the modulated probe. The bandwidth was reconfigurable from 75 MHz-300 MHz and the central frequency of the filter was tunable up to 30 GHz.
Exploring Explanations for Matrix Factorization Recommender Systems

In this paper we address the problem of finding explanations for collaborative filtering algorithms that use matrix factorization meth-ods. We look for explanations that increase the transparency of the system. To do so, we propose two measures. First, we show a model that describes the contribution of each previous rating given by a user to the generated recommendation. Second, we measure the influence of changing each previous rating of a user on the outcome of the recommender system. We show that under the assumption that there are many more users in the system than there are items, we can efficiently generate each type of explanation by using linear approximations of the recommender system’s behavior for each user, and computing partial derivatives of predicted ratings with respect to each user’s provided ratings
Gambaran Tingkat Stres Mahasiswa Dalam Mengikuti Pembelajaran Daring Pada Mahasiswa Prodi Ners Tingkat III Di STIKes Santa Elisabeth Medan Tahun 2021

Pembelajaran daring merupakan pembelajaran yang menggunakan jaringan internet dengan aksesibilitas, konektivitas, fleksibilitas, dan kemampuan untuk memunculkan berbagai jenis interaksi pembelajaran. Pelaksanaan pembelajaran daring tidak terlepas dari beberapa masalah dan hambatan yang mengakibatkan mahasiswa megalami stres. Stres merupakan salah satu reaksi atau respon psikologis manusia saat dihadapkan pada hal-hal yang dirasa telah melampaui batas atau dianggap sulit untuk dihadapi. Tujuan penelitian ini untuk mengetahui gambaran tingkat stres mahasiswa dalam mengikuti pembelajaran daring pada mahasiswa prodi Ners tingkat III di STIKes Santa Elisabeth Medan. Penelitian ini menggunakan metode penelitian deskriptif. Teknik pengambilan sampel menggunakan Total Sampling dengan jumlah sampel 94 responden. Alat ukur yang digunakan berupa kuesioner yang dibagikan secara online melalui google form. Berdasarkan hasil penelitian bahwa dari 94 responden paling banyak mengalami tingkat stres sedang sebanyak 24 responden (25,5%) dan paling sedikit mengalami tingkat stres sangat berat sebanyak 10 responden (10,6%). Proses penerapan pembelajaran daring memiliki dampak terhadap tingkat stres mahasiswa. Diharapkan menjadi bahan evaluasi agar pembelajaran daring dapat dilaksanakan dengan baik oleh mahasiswa, dosen dan pihak instansi pendidikan.
Causality and state dependence in the finance-growth nexus: an empirical investigation

Whether financial development promotes economic growth or not has been a contentious research question over the past century. This thesis is an empirical contribution to two main issues in the area: causality and state dependence. Chapter 2 presents extensive Monte Carlo simulation results on the finite sample performance comparisons of heteroskedasticity-robust panel unit root tests . The long-run finance-growth causality is discussed in Chapters 2--3 by means of panel unit root and and panel cointegration tests. The causality in the short-to-medium run is explored in Chapter 4. Chapter 5 investigates state dependence in the finance-growth nexus using a functional coefficient modeling approach. Application of this approach proceeds further to Chapter 6, this time with a special emphasis on trade and financial openness as factors underlying the finance-growth link. Concluding remarks are provided in Chapter 7.
Internet Addiction and its Relationship with Attachment Styles Among Tunisian Medical Students

Internet addiction is a growing addictive behavior and a major public health problem worldwide. Several psychological factors can contribute to the problematic use of the Internet. This study aimed to determine the prevalence of Internet Addiction (IA) in a sample of university students and to examine the relationship between IA, self-esteem, and attachment styles. The present study was a cross-sectional study, involving 135 students from the Faculty of Medicine of Monastir. The participants completed a questionnaire, which contained the socio-demographic data, the reasons for Internet use, the Young Cyberaddiction scale to seek IA, the Relationships-style-questionnaire-RSQ to assess attachment style, and the Rosenberg Self-Esteem Scale (RSES). The mean age of the students was 21.5 ± 1.9 years old. They were 112 (83%) females. The prevalence of Internet addiction was 23.7%. The average connection time was 3.5 ± 1.8 hours/day. The most frequent online activities were chat (online discussion, forums .) in 94.8% of cases followed by download activities (78.5%), scientific research (75.6%), online games (23.7%), and online shopping (13.3%). IA was associated with school failure, alcohol use, online gaming, and low self-esteem. The majority of the participants (84.4%) in this study reported an insecure attachment style. Logistic regression analysis showed a strong association between IA and fearful attachment style. IA was frequent among students. Fearful attachment style was found to be a risk factor for IA. This study highlights the impact of relationships between child and their caregivers on the development of addiction.
Analysis of the delivery plane in the golf swing using principal components

Although the swing plane has been a popular area of golf biomechanics research, the movement of the club relative to the swing plane has yet to be shown experimentally to have a relationship with performance. This study used principal component and subsequent multiple regression analysis to investigate the relationship between the movement of the club relative to the delivery plane and clubhead characteristics at ball impact. The principal components reflected deviations from an individual swing plane, and lower values of these components were associated with less variability in the clubface impact location. In the event that a golf coach wants to improve the precision of ball striking, the results from this study suggest that both simplicity of the route and alignment of the club to the final trajectory before impact could be advantageous. However, this does not suggest that the technique should be based on a ‘model’ swing plane.
Structural Rules in Natural Deduction with Alternatives

Natural deduction with alternatives extends Gentzen–Prawitz-style natural deduction with a single structural addition: negatively signed assumptions, called alternatives. It is a mildly bilateralist, single-conclusion natural deduction proof system in which the connective rules are unmodified from the usual Prawitz introduction and elimination rules—the extension is purely structural. This framework is general: it can be used for (1) classical logic, (2) relevant logic without distribution, (3) affine logic, and (4) linear logic, keeping the connective rules fixed, and varying purely structural rules.The key result of this paper is that the two principles that introduce kinds of irrelevance to natural deduction proofs: (a) the rule of explosion (from a contradiction, anything follows); and (b) the structural rule of vacuous discharge; are shown to be two sides of a single coin, in the same way that they correspond to the structural rule of weakening in the sequent calculus. The paper also includes a discussion of assumption classes, and how they can play a role in treating additive connectives in substructural natural deduction.
A Practical Approach to Determine Limiter Values of Inverter Control to Maximize Renewable Energy Penetration

The increase in renewable-energy-based generations, such as photovoltaic and wind turbines, inevitably leads to an increase in the number and capacity of inverters connected to the power system. This also increases the voltage on the inverter-connected and adjacent buses. By absorbing reactive power appropriately, it can suppress excessive voltage and increase the potential capacity of real power. Currently, most inverters connected to the power grid are set up in a way that does not involve adjusting the voltage at the point of connection. This means that the inverter controller's limiter settings have not significantly impacted the system's stability after a fault. This article examines the impact of reactive power absorption on grid-connected inverters’ stability and limiter values’ effects on stability. Additionally, to set the limiter values for a convenient inverter controller, the causes of instability are explained in a phasor diagram, and a method for setting the limiter values using this information is explained. The stability impact analysis and limiter value setting are carried out through accurate EMT model-based simulations. The infinite bus with the equivalent impedance is used for the stability analysis and limiter values setting, and the determined values are verified on the real power system. The simulation is conducted using the power system computer-aided design and electromagnetic transient including dc.
Development and Implementation of LED Street Lights with Bright and Extinguishable Controls and Power Converters

This study developed and implemented a driving power supply for light-emitting diode (LED) array streetlamps. The power stage was a quasi-resonant (QR)-flyback converter, its input power was the alternating-current power, and the LED array streetlamp was driven by the direct-current output power. The developed QR-flyback converter was operated in discontinuous conduction mode, and the pulse-width modulation (PWM) control chip was used to switch and conduct at the resonant valley of the drain-source voltage on the metal-oxide-semiconductor field-effect transistor (MOSFET) switch to reduce the switching loss. Moreover, the PWM control chip had a disable function, which was connected with a bright and extinguishable control circuit, and the high/low voltage level signal output by the Arduino development board can be used to control the output power of the QR-flyback converter, achieving bright and extinguishable controls for the LED array streetlamp.