Miracle-dz/UniFAR-FTUs huggingface dataset:
Coherent Hypothesis Testing

ABSTRACT Multiple hypothesis testing, an important quantitative tool to report the results of scientific inquiries, frequently leads to contradictory conclusions. For instance, in an analysis of variance (ANOVA) setting, the same dataset can lead one to reject the equality of two means, say μ1 = μ2, but at the same time to not reject the hypothesis that μ1 = μ2 = 0. These two conclusions violate the coherence principle introduced by Gabriel in 1969, and lead to results that are difficult to communicate, and, many times, embarrassing for practitioners of statistical methods. Although this situation is common in the daily life of statisticians, it is usually not discussed in courses of statistics. In this work, we enrich the teaching and discussion of this important topic by investigating through a few examples whether several standard test procedures are coherent or not. We also discuss the relationship between coherent tests and measures of support. Finally, we show how a Bayesian decision-theoretical framework can be used to build coherent tests. These approaches to coherence enlighten when such property is appealing in multiple testing and provide means of obtaining it.
Optimal multi-block mesh generation for CFD

ABSTRACT An assessment of various automatic block topology generation techniques for creating structured meshes has been performed in the first part of the paper. The objective is to find out optimal blocking methods for generating meshes suitable for flow simulations. The comparison has been carried out using an adjoint-based error analysis of the meshes generated by these block topologies. Different objective functions and numerical schemes have been used for this assessment. It is found that, in general, the medial axis-based approaches provide optimal blocking and yields better accuracy in computing the functional of interest. This is because the medial axis-based methods produce meshes which have better flow alignment specially in case of internal flows. In the second part of the paper, the adjoint-based error indicator has been used to adapt the block topology in the regions of large error.
Identification of the nonlinear vibration system of power transformers

This paper focuses on the identification of the nonlinear vibration system of power transformers. A Hammerstein model is used to identify the system with electrical inputs and the vibration of the transformer tank as the output. The nonlinear property of the system is modelled using a Fourier neural network consisting of a nonlinear element and a linear dynamic block. The order and weights of the network are determined based on the Lipschitz criterion and the back-propagation algorithm. This system identification method is tested on several power transformers. Promising results for predicting the transformer vibration and extracting system parameters are presented and discussed.
Permainan Tradisional Pasaran dalam Meningkatkan Kemampuan Numerik Peserta didik

Faktor yang dapat mempengaruhi pembelajaran matematika yaitu kemampuan numerik yang dimana hal tersebut merupakan bagian dari operasi menghitung dan sangat dibutuhkan dalam menyelesaikan masalah pembelajaran matematika. Dengan menggunakan model roleplay berupa permainan tradisional pasaran selain meningkatkan minat belajar peserta didik, permainan tradisional pasaran dapat memudahkan peserta didik untuk belajar kemampuan numerik menggunakan problem yang terdapat dalam permainan tersebut. Dalam penelitian ini Peneliti menggunakan metode eksperimen dengan desain one group pretest-posttest. Penelitian ini dilaksanakan pada bulan Januari 2022 hingga bulan Juli 2022 di SD Negeri Gemiring Kidul 01 Jepara. Sampel pada penelitian ini yaitu seluruh peserta didik kelas V SDN Gemiring Kidul 01 Jepara yang berjumlah 30 peserta didik dengan teknik pengambilan sampel yang digunakan yaitu meggunakan teknik sampling jenuh. Berdasarkan hasil penelitian peserta didik mengalami peningkatan rata-rata sebesar 30,33 dengan hasil N-Gain 0,6450 dengan kriteria sedang yang diperoleh dapat disimpulkan bahwa penerapan model roleplaying dengan menggunakan permainan pasaran memberikan pengaruh terhadap peningkatan kemampuan numerik peserta didik.
Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network

Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing. Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral features, DL-based FE methods have shown great potentials for HSI processing. However, most of the DL-based FE methods are supervised, and the training of them suffers from the absence of labeled samples in HSIs severely. The training issue of supervised DL-based FE methods limits their application on HSI processing. To address this issue, in this paper, a novel modified generative adversarial network (GAN) is proposed to train a DL-based feature extractor without supervision. The designed GAN consists of two components, which are a generator and a discriminator. The generator can focus on the learning of real probability distributions of data sets and the discriminator can extract spatial–spectral features with superior invariance effectively. In order to learn upsampling and downsampling strategies adaptively during FE, the proposed generator and discriminator are designed based on a fully deconvolutional subnetwork and a fully convolutional subnetwork, respectively. Moreover, a novel min–max cost function is designed for training the proposed GAN in an end-to-end fashion without supervision, by utilizing the zero-sum game relationship between the generator and discriminator. Besides, the proposed modified GAN replaces the original Jensen–Shannon divergence with the Wasserstein distance, aiming to mitigate the unstability and difficulty of the training of GAN frameworks. Experimental results on three real data sets validate the effectiveness of the proposed method.
Environmental Degradation in the Nepalese Society: An Eco-Critical Approach

The paper aims to explore the direct or indirect link between the patriarchal Nepalese society and the degradation of Nepal’s forestry and environment. The males of Nepal have been dominating and disrespecting women as the inferior sex for centuries, and thereby treating natural resourses as feminine, weak to be manipulated and consumed objectively for their benefits. The research tool taken to carry out the research is eco-feminism. To notice the relationship between gender and natural resources of Nepal, the theoretical concerns of such eco-feminist critics as Greta Gaard and Vandana Shiva are chiefly focused on. The Nepalese society, like most of the South-Asian societies, is a male-dominated society. It exploits both women and nature regarding them to be inferior. The chief finding is that the natural resources of Nepal such as air, water, green fields, climate, land, forestry, minerals, environment, and the species of Nepal have been getting threatened because of the Nepalese patriarchal society. The Nepali people exploit nature as carelessly as they have been exploiting women. It is assumed that the research scholars interested in exploring Nepalese society from the perspective of eco-feminism can take the article as a reference.
A Design of Broadband and Low-Loss Multilayer Antireflection Coating in THz Region

An approach to the design and the realization of a broadband multilayer anti-reflection (AR) coating with high transmission is proposed in this study. A binominal multi-section transformer is employed to efficiently determine the thickness and the refractive index of each matching layer, while those layers can be further realized by doping different fractions of subwavelength-size silicon powders (for relatively-high-index layers) or air pores (for relatively-low-index layers) into the low-loss HDPE polymer host. Based on this scheme, we design a ten-layer AR coating for widely used silicon wafer. The designed AR coatings are double-sided integrated with a 375-μm-thick silicon wafer, which can enhance the overall THz transmission to higher than 95.00% from 0.250 THz to 0.919 THz (114.46% fractional bandwidth) for either TE-polarized or TM -polarized THz beam incident from an arbitrary angle below 50◦.
An Error-Resilient RISC-V Microprocessor With a Fully Integrated DC–DC Voltage Regulator for Near-Threshold Operation in 28-nm CMOS

This article presents an energy-efficient microprocessor design that fully integrates an error-resilient RISC-V core and an embedded dc–dc switched-capacitor voltage regulator (SCVR). The proposed design achieves high energy efficiency, high computation performance, and a small system footprint through several innovations. First, in situ error detection and correction (EDAC) flip-flops (FFs) and an error-resilient static random access memory (SRAM) interfacing technique enable error resilience on the microprocessor without any post-silicon calibration requirement. Next, a fully integrated SCVR featuring a multi-rate successive approximation (MRSA) algorithm and a dynamic conduction loss minimization technique is proposed to achieve high conversion efficiency, high-power density, and fast load regulation. A prototype chip that fully integrates the techniques described above was fabricated in the 28-nm standard CMOS technology with an active area of 0.42 mm2. The measurement results show that the proposed in situ EDAC effectively minimizes the timing margin without any post-silicon calibration to achieve a high-processor performance of 43 MHz with an energy-delay-product (EDP) of 0.57 $\text {pJ}\cdot \mu \text{s}$ , showing the state-of-the-art performance and energy efficiency in the standard CMOS technology.
Examining Awareness of Social Services and the Relationship Between Social Participation and Social Service Use Among Older Adults

Abstract Early in the COVID-19 pandemic, social services integral to the health and well-being of older adults were shuttered to curtail the spread of the virus. In this context, social participation and awareness of social services may be especially important factors in shaping older adults’ social service use. This study described older adults’ social participation, awareness of social services, and social service use. This study also examined awareness as a mediator in the relationship between social participation and service use among Korean older adults during the COVID-19 pandemic. This study used publicly available de-identified cross-sectional secondary data drawn from the 2020 Seoul Welfare Survey. Hayes PROCESS macro was utilized along with bivariate and multivariate analyses. This study found moderate awareness as well as low social participation and social service use. Social participation and service awareness contributed to variation in older adults’ service use. Awareness did not mediate the relationship between social participation and social service use. The findings offer a foundation for understanding the connection between the social participation of older adults and their social service use during a crisis. Based on the study findings, future research should examine the contribution of virtual social participation and its link to service use.
Self-Regulated Learning Enhancing Scientific Literacy for Higher Educations in 21st Century Education: A Systematic Literature Review

This study explores the importance of self-regulated learning to overcome the low level of scientific literacy in 21st-century education. This study aims to: (1) identify problems in scientific literacy, (2) explore the relationship between self-regulated learning to scientific literacy (3) explain the impact of self-regulated learning on scientific literacy. The research method uses qualitative research with a systematic literature review that analyses the literature on self-regulated learning and scientific literacy. The analysis uses Preferred Reporting Items for Systematic Review (PRISMA). The results of the study show that: (1) the problems in scientific literacy include: the relevance of the material in school to everyday life, a critical assessment of the credibility of research sources, scientific attitudes and scientific knowledge, and aspects of content, process, context and diversity of literacy (2) explores the relationship self-regulated learning in scientific literacy with self-efficacy, motivation, active learning, and engagement (3) this study finds that self-regulated learning has a positive impact on scientific literacy. This study illustrates the important role of self-regulated learning in overcoming the limitations of scientific literacy in universities.
A polytree‐based adaptive polygonal finite element method for topology optimization

We propose a highly effective approach using a novel adaptive methodology to perform topology optimization with polygonal meshes, called polytree meshes. Polytree is a hierarchical data structure based on the principle of recursive spatial decomposition of each polygonal element with n nodes into (n + 1) arbitrary new polygonal elements; enabling more efficient utilization of unstructured meshes and arbitrary design domains in topology optimization. In order to treat hanging nodes after each optimization loop, we define the Wachspress coordinate on a reference element and then utilize an affine map to obtain shape functions and their gradients on arbitrary polygons with n vertices and m hanging nodes, called side‐nodes, as the polygonal element with (n + m) vertices. The polytree meshes do not only improve the boundary description quality of the optimal result but also reduce the computational cost of optimization process in comparison with the use of uniformly fine meshes. Several numerical examples are investigated to show the high effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.
Planning for Automated Testing of Implicit Constraints in Behavior Trees

Behavior Trees (BTs) are a formalism increasingly used to control the execution of robotic systems. The strength of BTs resides in their compact, hierarchical and transparent representation. However, when used in practical applications transparency is often hindered by the introduction of implicit run-time relations between nodes, e.g., because of data dependencies or hardware-related ordering constraints. Manually verifying the correctness of a BT with respect to these hidden relations is a tedious and error-prone task. This paper presents a modular planning-based approach for automatically testing BTs offline at design time, to identify possible executions that may violate given data and ordering constraints and to exhibit traces of these executions to help debugging. Our approach supports both basic and advanced BT node types, e.g., supporting parallel behaviors, and can be extended with other node types as needed. We evaluate our approach on BTs used in a commercially deployed robotics system and on a large set of randomly generated trees showing that our approach scales to realistic sizes of more than 3000 nodes.
Expanding the Reach of Monoclonal Antibodies: A Review of Synthetic Nucleic Acid Delivery in Immunotherapy

Harnessing the immune system to combat disease has revolutionized medical treatment. Monoclonal antibodies (mAbs), in particular, have emerged as important immunotherapeutic agents with clinical relevance in treating a wide range of diseases, including allergies, autoimmune diseases, neurodegenerative disorders, cancer, and infectious diseases. These mAbs are developed from naturally occurring antibodies and target specific epitopes of single molecules, minimizing off-target effects. Antibodies can also be designed to target particular pathogens or modulate immune function by activating or suppressing certain pathways. Despite their benefit for patients, the production and administration of monoclonal antibody therapeutics are laborious, costly, and time-consuming. Administration often requires inpatient stays and repeated dosing to maintain therapeutic levels, limiting their use in underserved populations and developing countries. Researchers are developing alternate methods to deliver monoclonal antibodies, including synthetic nucleic acid-based delivery, to overcome these limitations. These methods allow for in vivo production of monoclonal antibodies, which would significantly reduce costs and simplify administration logistics. This review explores new methods for monoclonal antibody delivery, including synthetic nucleic acids, and their potential to increase the accessibility and utility of life-saving treatments for several diseases.
Free vibration analysis of FG‐GPL and FG‐CNT hybrid laminated nano composite truncated conical shells using systematic differential quadrature method

In the present study, the free vibration of functionally graded graphene platelet‐reinforced (FG‐GPLs) and functionally graded carbon nanotube‐reinforced (FG‐CNTs) hybrid laminated nanocomposite truncated conical shells and panels are analyzed. Multi‐layers truncated conical shell and panel of pure FG‐CNTs, pure FG‐GPLs and hybrid CNTs‐GPLs reinforcement were evaluated. In light of its high accuracy in the calculation of thin and thick shells, a third‐order shear deformation theory is adopted. The governing equations and boundary conditions is derived using Hamilton's principle and is solved numerically using the systematic differential quadrature method (DQM) which uses Kronecker delta function. The effective mechanical properties of the CNT‐reinforced nanocomposite layers are estimated using the rule of mixtures, whereas those of the GPL‐reinforced nanocomposite layers is calculated using the Halpin‐Tsai micromechanical model. Convergence and accuracy evaluation of the presented study are confirmed and a number of parameters, including the CNTS volume fraction, GPLs mass fraction, distribution patterns (i.e., Uniform distribution (UD), Functionally graded O‐distribution (FG‐O), Functionally graded X‐distribution (FG‐X), Functionally graded V‐distribution (FG‐V) and FG‐A), different boundary conditions and the vertex angle of the cone, are investigated. The results obtained in this article for the combination of two different materials, GPLs and CNTs, showed that in controlling the natural frequency of the system, without changing the percentage of fiber and only by changing the arrangement, very wonderful results can be achieved.
A Structured Light RGB-D Camera System for Accurate Depth Measurement

The ability to reliably measure the depth of the object surface is very important in a range of high-value industries. With the development of 3D vision techniques, RGB-D cameras have been widely used to perform the 6D pose estimation of target objects for a robotic manipulator. Many applications require accurate shape measurements of the objects for 3D template matching. In this work, we develop an RGB-D camera based on the structured light technique with gray-code coding. The intrinsic and extrinsic parameters of the camera system are determined by a calibration process. 3D reconstruction of the object surface is based on the ray triangulation principle. We construct an RGB-D sensing system with an industrial camera and a digital light projector. In the experiments, real-world objects are used to test the feasibility of the proposed technique. The evaluation carried out using planar objects has demonstrated the accuracy of our RGB-D depth measurement system.
Mining Tutors’ Interesting Areas to Develop Researched Papers Using A Proposed Educational Data Mining System

Educational Data Mining (EDM) is the process of converting raw data from educational systems to useful information that can be used by educational software developers, students, teachers, parents, and other educational researchers. One of the difficulties in the educational institutes that face the tutor is how to write a paper. This work aims to help the tutor to write a researched paper on specific subject by finding another tutor who is also inter ested in the same subject. This is done by exploring the tutor database by using the proposed educational data mining system, the tutor database is arranged in multidimensional form will include: tutor ’ s teaching subjects, tutor ’ s interesting areas, tutor ’ s published researches, tutor ’ s Msc. and Ph.D research subjects. The proposed system implements SMC and Cosine similarity measures with new proposed representation of tutor ’ s database. A clustering K-Means techniques and associated rule generation is implemented by using WEKA data mining tool. The results obtained from that work are very useful for tutor and they give a rich analysis for developing researched papers for different tutors.
Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence

With the advancement of low-cost digital cameras and their widespread, especially in smartphones, these cameras are not designed for photogrammetry applications. The main aim of this study is to model and estimate the interior orientation parameters (IOPs) for images captured by volunteer-run cameras using artificial intelligence. These cameras were unavailable to perform traditional calibration processes or use images from unknown sources for image measuring. Estimating IOPs using random values within the range determined by testing the selected sample's consistency. Optimization was performed by Utilizing the Simulated Annealing algorithm based on stereo calibration to obtain the best possible values of these parameters that produce the minimum RMS-reprojection error attained. By The variance between the parameters from the pre-calibration process and estimated by an artificial intelligence system, The coefficient of determination in the IOPs (focal length R2 = 0.717 to 0.812, principal point (X, Y) R2 = (0.674 to 0.869, 0.504 to 0.613), Both radial and tangential coefficients, R2 was close to zero). Therefore, the estimations of radial distortions k1, k2, and tangential distortions p1 and p2 are invalid. A reasonably strong relationship between principal distance and principal point with low lens distortion parameters due to the significant relative differences between the distortion parameters, sufficient strength of the relationship between calculating parameters, and estimating according to accuracy tolerance in photogrammetry applications.
PVL-Cartographer: Panoramic Vision-Aided LiDAR Cartographer-Based SLAM for Maverick Mobile Mapping System

The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive environmental data. We propose the “PVL-Cartographer” SLAM (Simultaneous Localization And Mapping) approach for MMS to address these limitations. This proposed system incorporates multiple sensors to yield dependable and precise mapping and localization. It consists of two subsystems: early fusion and intermediate fusion. In early fusion, range maps are created from LiDAR points within a panoramic image space, simplifying the integration of visual features. The SLAM system accommodates both visual features with and without augmented ranges. In intermediate fusion, camera and LiDAR nodes are merged using a pose graph, with constraints between nodes derived from IMU (Inertial Measurement Unit) data. Comprehensive testing in challenging outdoor settings demonstrates that the proposed SLAM system can generate trustworthy outcomes even in feature-scarce environments. Ultimately, our suggested PVL-Cartographer system effectively and accurately addresses the MMS localization and mapping challenge.