Miracle-dz/UniFAR-FTUs huggingface dataset:
On Minimaxity and Limit of Risks Ratio of James-Stein Estimator Under the Balanced Loss Function

The problem of estimating the mean of a multivariate normal distribution by diﬀerent types of shrinkage estimators is investigated. Under the balanced loss function, we establish the minimaxity of the James-Stein estimator. When the dimension of the parameters space and the sample size tend to inﬁnity, we study the asymptotic behavior of risks ratio of James-Stein estimator to the maximum likelihood estimator. The positive-part of James-Stein estimator is also treated.
Highly stable field emission from ZnO nanowire field emitters controlled by an amorphous indium–gallium–zinc-oxide thin film transistor

Lowering the driving voltage and improving the stability of nanowire field emitters are essential for them to be applied in devices. In this study the characteristics of zinc oxide (ZnO) nanowire field emitter arrays (FEAs) controlled by an amorphous indium–gallium–zinc-oxide thin film transistor (a-IGZO TFT) were studied. A low driving voltage along with stabilization of the field emission current were achieved. Modulation of field emission currents up to three orders of magnitude was achieved at a gate voltage of 0–32 V for a constant anode voltage. Additionally, a-IGZO TFT control can dramatically reduce the emission current fluctuation (i.e., from 46.11 to 1.79% at an emission current of ∼3.7 µA). Both the a-IGZO TFT and ZnO nanowire FEAs were prepared on glass substrates in our research, demonstrating the feasibility of realizing large area a-IGZO TFT-controlled ZnO nanowire FEAs.
Sociolingustics Approach: Impoliteness Strategy in Instagram Cyberbullying in @Lambe_Turah’s post of KPAI’s Case

Social media is used by most Indonesian people, it brings good and bad influence. One of the bad influences is cyberbullying. Cyberbullying that is found in Instagram is different from other social media. Instagram has become the source of hate campaign by the occurrence of gossip account. @Lambe_turah is one of the biggest gossip accounts in Indonesia. The posts in @Lambe_turah have triggered its followers to perform cyberbullying to the person in their post, in this research is about KPAI. The aim of this study is to investigate the comments which contain cyberbullying by using impoliteness strategy proposed by Culpeper (2005). This study uses qualitative approach. It is shown by the data collection method which is done by examining documents. The analysis of this study is done by interpreting the collected data. The result shows that negative impoliteness is the most common impoliteness strategy to be used in cyberbullying. The second impoliteness strategy that is used in cyberbullying is bald on record. From both of these strategies, it can be concluded that cyberbullying tend to attack the addressee directly by using various types of statements. Abstrak Media sosial digunakan oleh hampir seluruh masyarakat Indonesia, ini bisa membawa pengaruh baik dan buruk bagi penggunanya. Salah satu pengaruh buruknya adalah cyberbullying perundungan secara online). Cyberbullying yang ditemukan di Instagram berbeda dengan media sosial yang lain. Instagram menjadi sumber dari kampanye kebencian yang berasal dari akun gosip. @Lambe_turah adalah salah satu akun gosip terbesar di Indonesia. Unggahan di akun @Lambe_turah telah memicu para pengikutnya atau yang disebut followers untuk melakukan cyberbullying kepada orang yang ada pada unggahan terkait, dalam penelitian ini penulis menggunakan KPAI. Tujuan dari penelitian ini ada untuk menginvestigasi komen-komen yang mengandung cyberbullying dengan menggunakan strategi ketidaksopana yang digagas oleh Culpeper (2005). Penelitian ini menggunakan pendekatan kualitatif, ini terlihat dari metode pengumpulan data yang dilakukan dengan meneliti dokumen. Analisis dari penelitian ini dilakukan dengan menginterpretasi data-data yang sudah didapat. Hasil dari penelitian ini menunjukkan bahwa tipe ketidaksopanan negatif adalah strategi ketidaksopanan yang paling sering digunakan pada cyberbullying, dan yang kedua adalah secara langsung. Dari teratas kedua yang digunakan dalam cyberbullying, bisa disimpulkan bahwa pelaku penindasan di media sosial lebih banyak melakukan penyerangan langsung dengan menggunakan berbagai macam tipe umpatan. Kata kunci: cyberbullying, sosiolinguistik, strategi ketidaksopanan
Estimates of solutions to nonlinear degenerate elliptic equations

We find estimates of the L norm of solutions to special nonlinear degenerate elliptic partial differential equations in terms of norms of the data. We also discuss a special isoperimetric inequality involved in the definition of the ellipticity of the above equations. RESUMEN Encontramos estimaciones de la norma L de las soluciones de ecuaciones diferenciales parciales elípticas degeneradas nolineales en términos de la norma de los datos. Además, discutimos una desigualdad isoperimétrica especial involucrada en la definición de la elipticidad de las ecuaciones anteriormente descritas. 60 Fabrizio Cuccu, Petar Popivanov and Giovanni Porru CUBO 11, 4 (2009)
Design and Development of Mask Detectors in Effort to Prevent the Spread of Hepatitis Post-Covid-19 Pandemic using Viola-Jones Algorithm

- In the current New Normal Era, people are starting to be free to do activities outside the house, with the condition of wearing a mask to prevent the coronavirus. After the number of cases of the Covid-19 pandemic began to decline, the World Health Organization (WHO) received a report on April 5, 2022, from the United Kingdom regarding cases of acute hepatitis that began to spread in children and whose aetiology was unknown. The spread of the disease can be through the air, so everyone must wear a mask outside the house. Through the Ministry of Health, the Indonesian government reported that as of June 16, 2022, at 16.00, there were 28 suspected cases of severe acute hepatitis with unknown or mysterious causes for Indonesian children. This number increased by three cases from the June 9, 2022 report. Of the 28 cases, there were 13 probable and 15 pending classifications in classifying hepatitis. This research aims to design a mask detector that assists in monitoring health protocols by detecting the use of masks in public areas or office buildings so that they can continue to carry out everyday activities during the pandemic. The design of this mask detection tool uses the Viola-Jones Algorithm, where this method has four primary keys, namely the Haar feature, Integral Image, adaboost, and cascade classifier. Based on the test results, this mask detector can detect mask objects at a distance of 1 to 2 meters, with a limit of the number of people detected up to 3. Future research is expected to be able to develop a mask detection device using raspberry pi hardware to replace the arduino uno to better suit system needs and also add a sound system.
Effect of frequency alteration regimes on the heating patterns in a solid-state-fed microwave cavity

ABSTRACT High-power solid-state sources are expected to be the core elements in efficient controlling characteristics of microwave thermal processing. In this paper, we analyze the effect made by frequency alteration within the 2.4–2.5 GHz ISM band on the patterns of dissipated power density in the processed material. A dual-source rectangular cavity loaded by a rectangular block characterized by different dielectric properties is considered. Two regimes of alteration involving equidistant and resonant frequencies are examined using an finite-difference time-domain model validated against theoretical data on eigen frequencies and corresponding eigen fields. The resulting heating patterns generated after a full cycle of alteration are qualitatively analyzed as superpositions of contributions by the distributions at different frequencies. It is shown that both regimes are capable of effectively mixing up the diverse heating patterns and thus produce more uniform distributions of dissipated power. Alteration over resonant frequencies appears to have the potential for leading to more energy-efficient processes.
Prediction of Thermal Conductivity and Viscosity of Ionic Liquid-Based Nanofluids Using Adaptive Neuro Fuzzy Inference System

ABSTRACT Nowadays, ionic liquid-based nanofluids are introduced as a new class of heat transfer fluids, which exhibit superior thermal properties compared to their base ionic liquids. Potential applications of these nanofluids make it necessary to know their thermophysical properties such as thermal conductivity and viscosity. Therefore, adaptive neuro fuzzy inference system (ANFIS) has been successfully developed to predict thermal conductivity and viscosity of ionic liquid-based nanofluids. The developed models have investigated the influence of temperature, nanoparticle concentration, and ionic liquid molecular weight on the thermophysical properties of nanofluids. After developing ANFIS structure, the capability and accuracy of the developed neuro fuzzy models have been evaluated by comparison of model predictions with experimental data extracted from the literature and calculation of statistical parameters such as coefficient of determination (R2) and average relative deviation (ARD). The ARD of ANFIS model in prediction of thermal conductivity of nanofluids is 0.72%, with a high R2 of 0.9959. The values of ARD and R2 for estimation of nanofluids viscosity are 5.1% and 0.9934, respectively, which indicates a satisfactory degree of accuracy for the proposed models.
DEVELOPMENT OF ANALYTICAL MODELS FOR PREDICTING THE FLEXURAL BEHAVIOUR OF ENGINEERED CEMENTITIOUS COMPOSITES – HIGH STRENGTH STEEL COMPOSITE BEAMS

This paper presents the results of three-dimensional (3D) finite element (FE) and analytical models on the prediction of the flexural behaviour of composite beams comprising high strength steel (HSS) I-section and Engineered Cementitious Composite (ECC) slab. In the FE approach, 108 composite beam models which cover a wide range of key parameters including HSS grade, ECC compressive strength, HSS section depth, ECC slab thickness, ECC slab width were generated and analysed. The flexural strength of these composite beam models was subsequently employed to validate the accuracies of some commonly used flexural strength prediction methods that are originally based on the rigid plastic analysis (RPA). It was found that these methods generally underpredicted the flexural strength of the ECC-HSS composite beams. Hence, a simple analytical model was proposed, and validation results of its accuracy showed that this simple analytical model produced more accurate predictions than the RPA methods. In order to allow designers to obtain the load-deflection curves of the beams, a full analytical model which is based on strain compatibility and force equilibrium was also developed. Validations against both FE model and test results showed that this full analytical model produced the most accurate flexural strength predictions.
A New Framework for Model Reduction of Complex Nonlinear Dynamical Systems

Considerable progress in computing technology in the past decades did not alleviate difficulty inherent in simulating complex dynamical systems. Reduced order models (ROMs) can be used to unburden these systems of redundant computations. While a variety of methods have been developed for reduced order modeling, they cannot be used for parametric study of nonlinear and complex systems, wherein we constantly change the parameters, input values, and energy levels. Parametric study is essential to determine the dynamics of complex systems. Only robust and persistent reduced order models, which remain stable with these changes, can be used for parametric study. In this dissertation, we develop a framework which measures the robustness and persistency of reduced order models. The framework quantifies the changes in the reduced models and singles out the most robust and persistent ones. The main advantage of this methodology is that it is applicable to the majority of databased model reduction methods. The approach begins with specifying a range of system’s initial states, parameters, and inputs for the parametric study. The data is collected from simulations of the system with the parameters chosen randomly within that range. The dominant structures of data are then identified using the multivariate analysis methods such as proper orthogonal decomposition (POD) and smooth orthogonal decomposition (SOD). The framework identifies the persistent and robust structures and combines them to obtain the models suitable for parametric study within the specified range. Our aim is to investigate the fidelity of the framework for persistent model order reduction of large and complex dynamical systems. The framework is validated using several numerical examples including a large linear system and two complex nonlinear systems with material and geometrical nonlinearities. While the method is used for identifying the robust subspaces obtained from both POD and SOD, the results show that SOD outperforms POD in terms of stability, accuracy, speed, and robustness. Also, showing that SOD-based ROMs are robust, we no longer need to simulate full-scale models for many parameters. We only need to do few simulations using the full-scale model to build ROMs. In addition, we extend the application of the proposed approach to model order reduction of nonlinear control systems. We use SOD to identify the dynamically relevant modal structures of the control system. The identified SOD subspaces are used to develop persistent ROMs. Performance of the resultant SOD-based ROM is compared with POD-based ROM by evaluating their robustness to the changes in system’s energy level. Results show that SOD-based ROMs are valid for a relatively wider range of the nonlinear control system’s energy when compared with POD-based models. In additions, the SOD-based ROMs show considerably faster computation time compared to the POD-based ROMs of the same order. For the considered dynamic system, SOD provides more effective reduction in dimension and complexity compared to POD.
Saca-AVF: A Quantitative Approach to Analyze the Architectural Vulnerability Factors of CNN Accelerators

Convolutional neural network (CNN) accelerators are widely used in artificial intelligence applications, such as image recognitions, due to their superior computing performance. However, as manufacturing technology scales down, the shrinking of chip size and the increasing of integration density make CNN accelerators vulnerable to soft errors, which are key factors affecting the reliability of integrated circuits. For emerging CNN applications where reliability is critical (such as self-driving cars), visible faults in applications’ outputs caused by soft errors may lead to catastrophic consequences. Therefore, it is important to consider reliability into CNN accelerators’ architecture design. Recent modeling and fault injection approaches analyze the reliability features of CNN models, but none evaluate the soft error reliability of CNN accelerators from architecture level. The Architectural Vulnerability Factor (AVF) indicates the probability that a soft error results in faulty computation outcomes. The Architecturally Correct Execution (ACE) method analyzes AVF of structures by identifying ACE bits and calculating their residency time. Based on the traditional ACE-based AVF analysis model, we propose a quantitative approach saca-AVF to analyze the AVF of systolic array based CNN accelerators that perform homogeneous matrix arithmetic operations instead of various kinds of instructions. We explore the reliability features of CNN accelerators under different design choices at different levels leveraging saca-AVF. We also observe several reliability characteristics of CNN accelerator architecture. We believe our proposed saca-AVF and the observations we made are able to guide designers to discover the reliability hotspots of CNN accelerators and build highly reliable CNN accelerators.
Learning to Recommend via Inverse Optimal Matching

We consider recommendation in the context of optimal matching, i.e., we need to pair or match a user with an item in an optimal way. The framework is particularly relevant when the supply of an individual item is limited and it can only satisfy a small number of users even though it may be preferred by many. We leverage the methodology of optimal transport of discrete distributions and formulate an inverse optimal transport problem in order to learn the cost which gives rise to the observed matching. It leads to a non-convex optimization problem which is solved by alternating optimization. A key novel aspect of our formulation is the incorporation of marginal relaxation via regularized Wasserstein distance, significantly improving the robustness of the method in the face of observed empirical matchings. Our model has wide applicability including labor market, online dating, college application recommendation. We back up our claims with experiments on both synthetic data and real world datasets.
A family of Lyapunov-based control schemes for maximum power point tracking in buck converters

This paper presents a novel family of Lyapunov-based controllers for the maximum power point tracking problem in the buck converter case. The solar power generation system here considered is composed by a stand-alone photovoltaic panel connected to a DC/DC buck converter. Lyapunov function candidates depending on the output are considered to develop conditions which, in some cases, can be expressed as linear matrix inequalities; these conditions guarantee that the output goes asymptotically to zero, thus implying that the MPPT is achieved. Simulation and real-time results are presented, which validate the eﬀectiveness of the proposals.
Every Performance Is a Stage: Musical Stage Theory as a Novel Account for the Ontology of Musical Works

This paper defends Musical Stage Theory as a novel account of the ontology of musical works. Its main claim is that a musical work is a performance. The significance of this argument is twofold. First, it demonstrates the availability of an alternative, and ontologically tenable, view to well-established positions in the current debate on musical metaphysics. Second, it shows how the revisionary approach of Musical Stage Theory actually provides a better account of the ontological status of musical works.
IceCube/DeepCore Results and PINGU

As the low-energy infill array of the IceCube Neutrino Observatory at the South Pole, DeepCore allows performing high statistics studies of atmospheric neutrinos with energies down to around $5\,\mathrm{GeV}$. This low energy threshold enables DeepCore to resolve characteristic distortions of the atmospheric neutrinos' energy and zenith angle distributions. Based on a set of neutrinos collected over three years of operation, the atmospheric oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ are measured with a precision competitive with long-baseline experiments. With a further reduction of the in-ice energy threshold and improved reconstruction resolutions, the "IceCube Upgrade" will focus on dedicated oscillation measurements that are unique to the flux of atmospheric neutrinos. At the same time, the upgrade will serve as a stepping stone toward the physics goals of PINGU---an envisaged, even more densely instrumented array dedicated to determining the neutrino mass ordering.
Numerical investigation of the aerodynamic performance for a Wells-type turbine in a wave energy converter

Ocean waves constitute an extensive energy resource, whose extraction has been the subject of intense research activity in the last three decades. Among the different variants of Wave Energy Converters, the principle of the Oscillating Water Column (OWC) is one of the most promising ones. An OWC comprises two key elements: a collector chamber, which transfers the wave oscillations’ energy to the air within the chamber by back and forth displacement, and a power take off system, which converts the pneumatic power into electricity or some other usable form. The Wells turbine is a self-rectifying air turbine, a suitable solution for energy extraction from reciprocating air flow in an OWC. In the present work, the steady state, inviscid flow in the Wells turbine is investigated by numerical simulations. The relatively novel Virtual Multiple Reference Frame (VMRF) technique is used to account for the rotary motion of the turbine, and the overall performance is compared with results in the literature.
Pengembangan Aplikasi E-UKM Berbasis Android Untuk Mendukung Era Digitalisasi Badan Eksekutif Mahasiswa Universitas Bina Insani

The problems that often occur in SMEs (Small and Medium Enterprises) at Bina Insani University are the lack of socialization regarding the types of intracurricular activities available in the Student Executive Body, such as taekwondo unit, computer club, language club, paraswara, badminton, and spirituality. As a result, interested potential members have to contact the SME administrators via WhatsApp if they intend to join the SME. This is ineffective and inefficient because potential members may face obstacles when contacting the administrators. The e-SME application for students serves as a registration platform, provides updates on activity information, and handles administrative processes for requesting participation in SME activities. The methodology used in this research is prototype development, consisting of requirement gathering, prototype construction, prototype evaluation, system coding, system testing, system evaluation, and system utilization. The e-Student Activity Unit application facilitates potential SME members in registering as members and accessing information and articles about the activities within the SME. The aim is for the developed application to become a solution to the problems faced by the Student Executive Body of Bina Insani University regarding the registration process and other information related to SME activities. The desired outcome of this research is the creation of a product in the form of an android-based e-SME application prototype. Additionally, it aims to have the research published in a nationally accredited journal indexed in SINTA (Indonesian Science and Technology Index) and to establish Intellectual Property Rights over the application prototype.
Geomagnetically Induced Currents Caused by Interplanetary Shocks With Different Impact Angles and Speeds

The occurrence of geomagnetically induced currents (GICs) poses serious threats to modern technological infrastructure. Large GICs result from sharp variations of the geomagnetic field (dB/dt) caused by changes of large‐scale magnetospheric and ionospheric currents. Intense dB/dt perturbations are known to occur often in high‐latitude regions as a result of storm time substorms. Magnetospheric compressions usually caused by interplanetary shocks increase the magnetopause current leading to dB/dt perturbations more evident in midlatitude to low‐latitude regions, while they increase the equatorial electrojet current leading to dB/dt perturbations in dayside equatorial regions. We investigate the effects of shock impact angles and speeds on the subsequent dB/dt perturbations with a database of 547 shocks observed at the L1 point. By adopting the threshold of dB/dt = 100 nT/min, identified as a risk factor to power systems, we find that dB/dt generally surpasses this threshold when following impacts of high‐speed and nearly frontal shocks in dayside high‐latitude locations. The same trend occurs at lower latitudes and for all nightside events but with fewer high‐risk events. Particularly, we found nine events in equatorial locations with dB/dt > 100 nT/min. All events were caused by high‐speed and nearly frontal shock impacts and were observed by stations located around noon local time. These high‐risk perturbations were caused by sudden strong and symmetric magnetospheric compressions, more effectively intensifying the equatorial electrojet current, leading to sharp dB/dt perturbations. We suggest that these results may provide insights for GIC forecasting aiming at preventing degradation of power systems due to GICs.