Miracle-dz/UniFAR-FTUs huggingface dataset:
A Note on Hypothesis Testing with Random Sample Sizes and its Relationship to Bayes Factors

Frequentist and Bayesian hypothesis testing are often viewed as "two separate worlds" by practitioners. While theoretical relationships of course exist, our goal here is to demonstrate a practical example where one must be careful conducting frequentist hypothesis testing, and in that context illustrate a practical equivalence between Bayesian and frequentist testing. In particular, if the sample size is random (hardly unusual in prac- tical problems where the sample size may be "all available experimental units"), then choosing an α level in advance such as 0.05 and using it for every possible sample size is inadmissible. In other words, one can find a dif- ferent overall procedure which has the same overall type I error but greater power. Not coincidentally, this alternative procedure is based on Bayesian testing procedures.
Kernel based Digital Image Correlation

Digital image correlation (DIC) is an effective displacement field measurement method featuring non-contact and full-field, which has been successfully applied in lots of fields, especially in the field of experimental mechanics. Unfortunately, the traditional DIC technique (TDIC) depends on the output values of image sensor heavily that usually noised in practical imaging system. So, TDIC cannot deal with bias error introduced by the image noise effectively. In this paper, a kernel based DIC is proposed to help reduce the bias error by establishing and optimizing a weighted correlation function. Compared with the methods of low-pass filtering, KDIC preserves the high frequency information well but reduces the bias error caused by image noise effectively. To demonstrate, two kinds of kernel function are analyzed. One is Epanechnikov kernel, with which KDIC is equivalent to TDIC. The other is Gaussian kernel, which can be used to improve the anti-noise performance of TDIC and get more accurate subpixel displacements. Both simulation analyses and experimental results validate the effectiveness of this new method.
Control and Scaling Approach for the Emulation of Dynamic Subscale Torque Loads

Research and development of electrified aircraft propulsion powertrains are relying on the use of electromechanical systems to emulate turbomachinery/rotor loads. Replacing a physical turbomachinery/rotor with a model driving a subscale electromechanical system capable of emulating subscale torque loads and responses is a lower risk, lower cost alternative to using the full-scale turbomachinery/rotor for initial control system verification. This paper outlines a novel control and scaling approach for emulating dynamic subscale torque loads using electric machine (EM) hardware for electrified aircraft propulsion research and development purposes. The approach, known as the Sliding Mode Impedance Controller with Scaling (SMICS), drives a mechanically coupled, two-EM system to behave like a subscale hardware representation of a hybrid-electric turbomachinery shaft. One EM reflects the inertial dynamics and torque load of the subscale turbomachinery under steady-state and transient operation while the second EM represents a motor/generator connected to the shaft, which is intended to hybridize the turbomachinery. This closed loop control and scaling algorithm applies impedance and sliding mode control schemes, along with parameter scaling, to match subscale, desired dynamics in real-time and to allow this system to be driven by a full-scale hybrid-electric turbomachinery model and control. The paper elaborates on the concept of the closed loop control and scaling approach and explains the significance of using impedance and sliding mode control. It shows a derivation of the closed loop control and scaling algorithm, its implementation, and presents a comparison of theoretical and actual simulation results acquired during hardware-in-the-loop testing of a partial turboelectric propulsion concept aircraft at the NASA Electric Aircraft Testbed (NEAT). The results show that the intended dynamic responses of the hardware and the aircraft model are achieved in both the time and frequency domain. Full scale propulsion control systems can be tested using this hardware and software approach
Matching feature points of multi-temporal space-borne SAR based on SIFT

Registering multi-temporal SAR images automatically is the key preprocessing for monitoring,but it is difficult to register SAR images using traditional matching methods owing to the speckle noise and distinctive characteristic of SAR image.In this paper we introduce scale invariant feature transform(SIFT) algorithm to matching feature points in SAR image.The SIFT method is used to extract feature points in two temporal space-borne SAR images with different rotation angle and different resolution,the effective rate of successful matching reaches 70% and the results demonstrate that it is feasible to use SIFT feature points to register multi-temporal SAR image.
Phase Retrieval, New Results on an Old Problem.

Diffraction imaging Knowledge of phase crucial to build electron density map Initial success in certain cases by using very specific prior kno Prize for Hauptman and Karle (1985) Still important today: e.g. macromolecular crystallography fo [Candes et al., 2011] Sensors only record the magnitude of diffracted rays, and lose the phase. The phase is required to invert the 2D Fourier transform and reconstruct the sample density.
A MODEL OF FACTORS INFLUENCING ADULTS’ INTENTION TO USE E-WALLET DURING COVID-19 PANDEMIC IN MALAYSIA: A CONCEPTUAL FRAMEWORK

: Currently, an e-wallet is a revolutionary type of payment system in the development of financial technology (Fintech). Prior e-wallet research has concentrated on factors such as perceived risk and variables from the Technology Acceptance Model (TAM) that influence e-wallet usage. During the COVID-19 epidemic, however, the impact of government support and perceived risk on individual behaviour was not well studied yet. Hence, the purpose of this study is to look at the impacts of perceived risk, perceived usefulness, behavioural intention
A Survey on Threshold Signature Schemes

—Threshold signatures, an alternative of current signature systems, provide better security than its formers. Nevertheless, constructing a threshold signature scheme from a non-threshold one is a complex process that requires two main challenges to be solved. The ﬁrst challenge, namely the key generation part can be centralized or decentralized. The solution to the second challenge, distributed signing, is affected by the signature scheme being used. In this paper, these two main challenges will be analyzed by giving insights on the signature schemes ECDSA, BLS, and Schnorr.
Aspect – Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is an advanced NLP application that aims to identify aspect terms present in the given review and predict the sentiment associated with those aspect terms. ABSA is better than sentence-based sentiment classification because it considers the aspect terms present in the reviews to determine the sentiment rather than considering the individual sentence. Entrepreneurs could make use of ABSA to understand the customers' opinions about different aspects of their products or services. The task of Aspect-based sentiment analysis can be divided into two subtasks: Aspect Term Extraction (ATE) and Aspect Term Sentiment Classification (ATSC). In this paper, an SVM model is proposed for the task of ATE, and an Attention based LSTM model is proposed for the task of ATSC. The proposed models will be trained and tested on SemEval-2014 dataset.
A Note on Listwise Deletion versus Multiple Imputation

This letter compares the performance of multiple imputation and listwise deletion using a simulation approach. The focus is on data that are “missing not at random” (MNAR), in which case both multiple imputation and listwise deletion are known to be biased. In these simulations, multiple imputation yields results that are frequently more biased, less efficient, and with worse coverage than listwise deletion when data are MNAR. This is the case even with very strong correlations between fully observed variables and variables with missing values, such that the data are very nearly “missing at random. ” These results recommend caution when comparing the results from multiple imputation and listwise deletion, when the true data generating process is unknown.
Explaining the Environmental Kuznets Curve: Structural Change and International Agreements in Reducing Sulphur Emissions

: Environmental Kuznets curves have been estimated using a simple re- duced-form model that gives no information on the mechanisms underlying the estimated inverted U-shaped relationship between some pollutants and income. Various intuitive appealing explanations for the observed patterns have been offered, such as structural changes and environmental policy, but these have rarely been empirically in- vestigated. Expanding the reduced-form model with explanatory variables may introduce serious multicollinearity problems, a reason why decomposition analysis is a preferable alternative for investigating the origins of change in emissions. Applying decomposition analysis fails to ﬁnd evidence for structural changes as an important determinant of the impressive reductions in SO 2 emissions of developed economies during the 1980s. Environmental policy, fostered by international agreements, gives a better explanation why pollution curbs downward at high income levels. Besides the level of income, the present state of the environment seems an important, but often neglected, variable that explains the ambition of environmental policy.
Primary Literature in Undergraduate Science Courses: What are the Outcomes?

Primary literature—consisting of novel peer-reviewed articles and conference proceedings—has been associated with several positive outcomes for students within undergraduate science courses. Primary literature is the principal method of written scientific communication and emphasizes the development of scientific knowledge. It also provides a window into researchers’ methods and logic used to explain natural phenomena. However, despite evidence demonstrating the benefits of using primary literature in undergraduate science courses, no review of this literature exists. This article reviews several approaches to teaching with primary literature in undergraduate science courses and student outcomes associated with these approaches. Databases that feature research in science education, including Web of Science and ERIC, were searched for the keywords “primary literature” and “scholarly literature. ” Only those articles that featured descriptions of the use of primary literature in undergraduate science courses and outcomes were selected. After selecting all of the papers found through these searches that fit this description, the bibliographies of each of these papers were explored for further selection. In all, 19 articles were selected for review. Articles reviewed were published between 1997 and 2017, described instruction in general science, biology, neuroscience, chemistry, and biochemistry courses, and relied heavily on self-reported data. Use of primary literature ranged from a single assignment to a full semester of primary literature as the sole source of curriculum materials to a yearlong undergraduate research program. In this literature review the major outcomes that have been documented will be described, as will the methods used to help students learn through primary literature. Recommendations for future research and practice are also provided. The outcomes emerged through review of the introduction sections of several articles (Carter & Wiles, 2017; Gottesman & Hoskins, 2013; Hoskins et al., 2007; Hoskins et al., 2011; Kozeracki et al., 2006; Porter et al., 2010; Round & Campbell, 2013; Segura-Totten & Dalman, 2013; Stevens & Hoskins, 2014; Stover, 2016) and sorting based on the descriptions of the outcomes provided. Example phrases and descriptions that were categorized under each outcome include:
Journal of Environmental Biology

Aim: Methodology: Results: Interpretation: The aim of the present study was to optimise the nutrient requirements for Terminalia tomentosa (Asan) plant to increase the quality and quantity of leaves. This study used the Mitscherlich-Bray equation to formulate NPK requirements for the Asan plant. Independent experimental setups of N (5 levels), P (5 levels), and K (5 levels) designed in RCBD with three replications were carried out concurrently. Calculations were made to determine the theoretical maximum yield, the NPK constants c and c1, the NPK fertiliser recommendations for the Asan plant at various soil fertility levels, and the ideal fertiliser rates. Fertiliser recommendations were developed for a typical range of soil test values, but they still required additional field verification tests. The theoretical maximum yields determined by the Mitscherlich-Bray equation were 599.79g, 587.49 g, and 562.34 g per 50 leaves of the Asan plant for NPK, respectively. The study demonstrated that it is critical to consider the inherent soil fertility when developing fertiliser recommendations for Asan that will increase both leaf yield and overall productivity. It showed that soil K was less efficiently utilised by the Asan plant compared to the other soil nutrients. The c1/c ratio of the P experiment was lowest compared to the c1/c ratio values of N and K experiments, indicating that the Asan plant was more responsive to P fertilisation than other macronutrient fertilisation. The fertiliser recommendation chart serves the farmers to manage the Asan plants and is more beneficial for harvesting quality cocoons without degrading the ecosystem.
Displacement and Cable Tension Estimation for Submerged Floating Tunnel Using Acceleration and Strain Measurements

: A submerged floating tunnel (SFT) can be an attractive alternative to conventional bridges or immersed tunnels. Efforts to build SFTs are underway around the world, and the monitoring of tunnel displacement and mooring cable tension force would be critical to assess the structural integrity of the SFT. In this study, tunnel displacement and mooring cable tension force are simultaneously estimated by fusing accelerationand strain measurements. First, displacement is estimated from multi-point strain measurements using simplified mode shapes and mode scaling factors without requiring true mode shapes of the SFT. Here the mode scaling factors are automatically estimated using initial strain and acceleration measurements. The estimated displacement from the strain measurements is then fused with single-point acceleration measurement using a finite impulse response(FIR)filter to obtain a final displacement with higher accuracy. In addition, the tension force of a mooring cable is estimated from the displacement at the connection between the tunnel and the mooring cable. The feasibility of the proposed technique was examined through a series of numerical simulations and laboratory tests on an 8-m-long aluminum SFT mock-up structure.
Mergers and the Markups of Rival Firms: Evidence from European Markets

This paper analyzes the effects of mergers on the markups of non-merging rival ﬁrms in nar-rowly deﬁned markets. We exploit expert market deﬁnitions from the European Commission’s merger decisions to identify relevant competitors. Production functions are estimated to recover markups as a measure of market power. Our results indicate that rivals signiﬁcantly increase their markups after mergers relative to a matched control group. Consistent with increases in market power, the effects are particularly pronounced in markets with few players, high initial markups and concentration. We also provide evidence that merger rivals reduce their employment, sales and investment, while their proﬁts increase.
Circulating Tumor DNA in the Management of Early-Stage Breast Cancer

Liquid biopsies refer to the isolation and analysis of tumor-derived biological material from body fluids, most commonly blood, in order to provide clinically valuable information for the management of cancer patients. Their non-invasive nature allows to overcome the limitations of tissue biopsy and complement the latter in guiding therapeutic decision-making. In the past years, several studies have demonstrated that circulating tumor DNA (ctDNA) detection can be used in the clinical setting to improve patient prognosis and monitor therapy response, especially in metastatic cancers. With the advent of significant technological advances in assay development, ctDNA can now be accurately and reliably identified in early-stage cancers despite its low levels in the bloodstream. In this review, we discuss the most important studies that highlight the potential clinical utility of ctDNA in early-stage breast cancer focusing on early diagnosis, detection of minimal residual disease and prediction of metastatic relapse. We also offer a concise description of the most sensitive techniques that are deemed appropriate for ctDNA detection in early-stage cancer and we examine their advantages and disadvantages, as they have been employed in various studies. Finally, we discuss future perspectives on how ctDNA could be better integrated into the everyday oncology practice.
A Soft Robot with Three Dimensional Shape Sensing and Contact Recognition Multi-Modal Sensing via Tunable Soft Optical Sensors

Soft optical sensing strategies are rapidly developing for soft robotic systems as a means to increase the controllability of soft compliant robots. In this paper, we present a roughness tuning strategy for the fabrication of soft optical sensors to achieve the dual functionality of shape sensing combined with contact recognition within a single multi-modal sensor. The molds used to fabricate the soft sensors are roughened via laser micromachining to achieve asymmetrical sensor responses when bent in opposite directions. We demonstrate the integration of these sensors into a fully soft robotic platform consisting of a multi-directional bending module with integrated 3D shape sensing and a gripper with tip position monitoring along with contact force recognition. We show the accuracy of our sensing strategy in validation experiments and a pick-and-place task is performed to demonstrate the robot's functionality.
Innovation of Competence Development of State Civil Apparatus Through Knowledge Management Model

Competency development of the State Civil Apparatus (ASN) has become an obligation for government agencies in Indonesia. This relates to the management of human resources carried out by the agency as an organization in order to find, use, maintain, and develop human beings. Innovation is carried out whose implementation is through changes in development patterns, changes in competency development governance, development of teaching tools such as learning management systems and knowledge management systems. Knowledge management system is a model that is currently being developed to accommodate the process of human resource management/ASN through training to maintain and develop the ability and willingness to share knowledge. The purpose of this study is to explain the various applications of knowledgemanagement systems that were developed as innovations for developing ASN competencies. This study uses a qualitative descriptive research methodology, with the method of extracting data through a desk study based on the knowledge management system model document developed by government agencies. Based on the results of this study, it was found that the knowledge management system is the choice for government agencies to develop the competence of their employees. This system is well managed through good governance and in accordance with the needs of employee competency development. Knowledge management developed by government agencies can be an option for employees to develop competencies independently through colleagues. In conclusion, KMS in several government agencies that are examples in this study still need to be developed andmaintained according to their respective © The Author(s) 2022 D. Mutiarin et al. (Eds.): ICOSI-HESS 2022, ASSEHR 710, pp. 489–505, 2022. https://doi.org/10.2991/978-2-494069-65-7_41 490 H. Saksono et al. maturity levels. In order for this KMS to be sustainable, the development strategy must be in line with the parent organization’s performance targets, because KMS is one of the instruments for developing the competence of the entire organization as a learning organization.
Drilling and Casing Pipes Corrosion Investigation in Water Based Drilling Mud of Iraqi Oil Fields Environment

The integrity of drilling and casing pipes under high-pressure and temperature conditions is a vital issue for oil or gas wells through the lifecycle of a well. Corrosion is a major challenge in the oil and gas industry in particular under high pressure and high-temperature conditions, it adds extra costs for maintenance of the drilling and production facilities, safety, and environmental damages. In this study, the effective corrosion parameters of AISI 1020 and AISI 1080 carbon steel in water-based mud under flowing conditions were examined. A new apparatus was designed and manufactured to allow the investigation of corrosion in AISI 1020 and AISI 1080 carbon steel. The Experiments were conducted in a reservoir-like environment over a wide range of parameters including temperature, pressure and chemistry of the drilling mud under dynamic conditions. The results showed that the increase in temperature, pressure and rotational speed significantly increases the corrosion rate for both types of pipes. The findings also depicted that AISI 1020 carbon steel has higher corrosion rates compared to AISI 1080 carbon steel. In addition, the developed apparatus can be used to simulate other complicated conditions in an environment similar to field experience and to improve the quality of the experimental data.
Binary Matrices in Qualitative Research of Complex Management Objects

- The purpose of manuscript is to substantiate the effectiveness, clarify the content and determine the features of matrix approach to research of complex management objects. The matrix approach based on descriptive and facet methods of research of management objects, described mainly by qualitative characteristics, and involves the joint use of actual classification attributes and dichotomies, as the result of which binary matrices create, the sectors of which contain variants of studied management objects. The manuscript describes the features of using binary matrices in research of complex management objects, develops it basic classification, substantiates the choice of methods for determining actual qualitative attributes and dichotomies of management objects, reflects the methodological aspects of matrix approach to digitalization of management objects, to structuring these objects and substantiating the content of definitions of it terms, to the study of options for modeling and transformation of these objects, as well as the principles of matrix approach to research of complex management objects clarify.