Miracle-dz/UniFAR-FTUs huggingface dataset:
New CCII-based voltage-mode universal biquadratic filter

A new voltage-mode universal biquadratic filter configuration using two secondgeneration current conveyors (CCIIs) and four passive components is presented. The proposed configuration can realize the second-order low-pass, band-pass, high-pass, notch and all-pass transfers with unity gain. Both its active and passive sensitivities are small. PSPICE simulation results are given to confirm the theoretical analysis.
FALSE HEARING AND THE N 400 : THE EFFECTS OF LINGUISTIC CONTEXT ON LANGUAGE PERCEPTION

False hearing is a phenomenon where one mishears what has been said to them based on linguistic contextual cues used to make a prediction (Rogers et al., 2012). The incorrect hearing usually has similar phonemic properties to other likely words and syntactic relation to what was said prior. Our study used methods from audiology and electrophysiology to analyze how linguistic contextual predictions impact perception in hearing. We observed the N400 response (Kutas & Hillyard, 1980) to incongruent phonologic lures used as target words that were presented in noise at a + 3dB SNR. We used the phonologic lure (PL) condition to collect data in instances where false hearing occurred (FH+) to compare to non-false hearing trails (FH-) as well as trials that used congruent (CON) or incongruent baseline (IB) words. We found a larger N400 effect in the IB condition compared to CON, and an intermediate N400 to the PL condition. Although our findings were not statistically significant, we did observe a trend of a decrease in amplitude in FH+ trials when contrasted to FHtrials. This may indicate that in false hearing, the participants response is somewhere in between hearing the congruent and the incongruent word with a very small threshold for choosing the word with a strong semantic fit to the perceived cue word over the actual incongruent phonologically similar target word. This suggests that false hearing may have a perceptual rather than a post-perceptual locus.
GANSim-3D for conditional geomodelling: theory and field application

Geomodelling of subsurface reservoirs is important for water resources, hydrocarbon exploitation, and Carbon Capture and Storage (CCS). Traditional geostatistics-based approaches cannot abstract complex geological patterns and are thus not able to simulate very realistic earth models. We present a Generative Adversarial Networks (GANs)-based 3D reservoir simulation framework, GANSim-3D, which can capture geological patterns and relationships between various conditioning data and earth models and is thus able to directly simulate multiple 3D realistic and conditional earth models of arbitrary sizes from given conditioning data. In GANSim-3D, the generator, designed to only include 3D convolutional layers, takes various 3D conditioning data and 3D random latent cubes (composed of random numbers) as inputs and produces a 3D earth model. Two types of losses, the original GANs loss and condition-based loss, are designed to train the generator progressively from shallow to deep layers to learn the geological patterns and relationships from coarse to fine resolutions. Conditioning data can include 3D sparse well facies data, 3D low-resolution probability maps, and global features like facies proportion, channel width, etc. Once trained on a training dataset where each training sample is a 3D cube of a small fixed size, the generator can be used for geomodelling of 3D reservoirs of large arbitrary sizes by directly extending the sizes of all inputs and the output of the generator proportionally. To illustrate how GANSim-3D is used for field geomodelling and also to verify GANSim-3D, a field karst cave reservoir in Tahe area of China is used as an example. The 3D well facies data and 3D probability map of caves obtained from geophysical interpretation are used as conditioning data. First, we create a training dataset consisting of facies models of 64×64×64 cells with a process-mimicking simulation method to integrate field geological patterns. The training well facies data and the training probability map data are produced from the training facies models. Then, the 3D generator is successfully trained and evaluated in two synthetic cases with various metrics. Next, we apply the pretrained generator for conditional geomodelling of two field cave reservoirs of Tahe area. The first reservoir is 800m×800m×64m and is divided into 64×64×64 cells, while the second is 4200m×3200m×96m and is divided into 336×256×96 cells. We fix the input well facies data and cave probability maps and randomly change the input latent cubes to allow the generator to produce multiple diverse cave reservoir realizations, which prove to be consistent with the geological patterns of real Tahe cave reservoir as well as the input conditioning data. The noise in the input probability map is suppressed by the generator. Once trained, the geomodelling process is quite fast: each realization with 336×256×96 cells takes 0.988 seconds using 1 GPU (V100). This study shows that GANSim-3D is robust for fast 3D conditional geomodelling of field reservoirs of arbitrary sizes.
IDENTIFYING INFLUENTIAL SPREADERS IN COMPLEX NETWORKS BY WEIGHTED VOTE RANKING AND HYBRID METHODS

Identifying influential users in complex networks has become the need of the hour in digital era and it has emerged as an important and interesting research topic. There are many methods like centrality measure, page rank, k-shell etc., to rank influential users, but each has its own shortfalls. In this paper we propose weighted vote ranking method and a hybrid ranking method as an extension of weighted vote ranking Method. The spreading ability of a node in a network mainly depend upon three key factors such as the support (vote) that it receives from neighbor nodes, the relationship strength (weight) and the nodes position in the network (coreness). So, to identify potential super spreaders we propose weighted vote ranking (WVR) method with all these three metrics such as vote, weight and coreness with a dynamic weight parameter control. Also, to improvise further we extended this proposed method by combining with weighted mixed degree decomposition (WMDD) method which has ability to consider removed and remaining nodes in decomposition technique. This hybrid method is obtained by combining these WVR and WMDD methods in 70:30 ratio which gives better results, compared to other ratio combinations. To study the spreading dynamics, Susceptible Infected Recovered (SIR) epidemic model is used. The average Kendall Tau of proposed weighted vote ranking and hybrid methods across various networks taken for experiment is 0.86 and 0.83 respectively whereas the highest average Tau from other methods is 0.79 (1 is maximum Tau value). Also, when we compared the average infection scale with other methods that is taken for experiment, we get 5.1%-14.5% higher infection scale for weighted vote ranking method and 8.8%-18.2% higher infection scale for hybrid method. Similar various other experiments like varying number of seed nodes and infection probability also show that proposed methods perform better when compared to recent other methods.
EDITORIAL : Corporate board structure , strategy and performance in uncertain times

Corporate managers make choices that seek to improve the performance of their organisation. These decisions involve interpreting and framing the environment, developing and implementing programmes and services, and creating processes and structures to monitor and control resources for optimal impact (Brown & Iverson, 2004). Improved performance results for organisations that systematically addressed these challenges (Miles, Snow, Mathews, Miles, & Coleman, 1997). Board performs a critical function to monitor environmental trends that might affect organisational performance. A misinterpretation of the environment could result in errant policies and programmes. Consequently, boards must have mechanisms in place to ensure understanding of critical environmental trends (Brown & Iverson, 2004). The spread of environmental awareness is guided by the strategic purpose of the organisation, and consequently, the structures in place should reflect those purposes. The strategy adopted by the corporate board will have a considerable impact on their performance. The literature also suggests that the composition of the board will be contingent upon the characteristics of the firm‟s external environment, the demands of its strategy and the salient contextual factors and the past financial performance of the company (Pfeffer & Salancik, 1978). Although research in corporate governance argues that the board of directors‟ composition and leadership structure can influence a variety of organisational outcomes (Dalton, Daily, Ellstrand, & Johnson, 1998). The academic literature is yet to provide specific guidance on the superiority of specific board composition and leadership structure. These issues are addressed in the collection of high-quality papers in this issue of Corporate Board: Role, Duties and Composition.
AUTOMATED ANOMALY DETECTION OF LASER-BASED ADDITIVE MANUFACTURING USING MELT POOL SPARSE REPRESENTATION AND UNSUPERVISED LEARNING

Advanced thermal imaging is increasingly invested in direct energy deposition (DED) additive manufacturing (AM) to cope with information visibility of melt pool and tackle process inconsistency. However, there are key challenges regarding the feasibility of current image-guided monitoring methodologies in the DED process. First, high-resolution thermal images consist of millions of pixels captured by hundreds of frames lead to the curse of dimensionality in analysis. Second, the presence of various exogenous noise, ill-structured data, and significant cluster imbalance limit the capability of the current methodologies to perform real-time monitoring. The objective of this research is to advance the frontier of melt pool monitoring in DED process by designing an automated and unsupervised anomaly detection on high-dimensional thermal image data. Specifically, we develop a variational autoencoder to generate a low-dimensional representation of each input thermal image data. A Gaussian mixture model and K-Mean clustering are integrated with the generative model to split latent space into homogenous regions and detect anomalies. Experimental results show that the proposed methodology is highly effective in detecting defective melt pools with accuracy up to 94.52% and a false alarm rate of less than 2.1%.
Implementation Group Task Assignment's Method to Increase Social Interaction Students on Online Learning System

The online lecture system, also known as e-Learning, is a lecture process using information and communication technology, in this case the internet. The online lecture system is an alternative for learning during the COVID-19 pandemic, where the COVID-19 pandemic has changed the order of human life, especially in activities. Many activities are now carried out online, one of which is lecture activities. This research was carried out based on phenomena that occurred in the world of education which showed that students in online lectures had low social interaction skills between students. Therefore, the problem in the research can be formulated as follows; "How to increase social interaction between students in online lectures? " The purpose of this research is to use the method of giving small group assignments to increase social interaction between students in online lectures. This study uses data collection techniques through the process of observation and interviews. Based on the discussion of the research results, it can be stated that the findings in this study, namely the application of the method of giving small group assignments effectively builds social interaction between students in online lectures. The method of giving small group assignments is carried out by independent work then the results of group assignments are presented in online lectures and give each other feedback on the topics that have been worked on, the discussions that occurred during lectures made a significant increase in social interaction between students during online lectures.
Access to the talar dome through the posteromedial approach: an anatomical study

Objective: To determine the posteromedial area of the talus that can be reached without malleolar osteotomy through the posteromedial approach. Methods: Fifteen human cadaveric ankles were dissected in standard fashion to expose the posteromedial talar dome. Seven of the approaches (46.7%) were done on the left ankle. We did not observe any significant difference between sides (chi-square test, p=0.715). Results: On average, 2.09 cm2 (range, 1.72-2.69) of the posteromedial talus dome or 15.27% (range, 12-20) of the total talus dome can be accessed without osteotomy. Conclusion: Osteochondral lesions within the area accessible through the posteromedial approach (mean 2 cm2), as seen on magnetic resonance imaging, may be treated without a medial malleolus osteotomy. Level of Evidence IV; Descriptive Study.
Environmental governance and social learning: towards more reflexive interactions

Facing the increasing complexity of social and environmental issues, environmental governance configurations are required to adopt increasingly collaborative strategies and reflexive learning oriented practices. The goal of this paper is to present a theoretical discussion about environmental governance and the contribution of the social learning approach to expand the reflexivity potential of multi-stakeholder interaction spaces in governance processes. We argue that social learning practices, understood as interactive and reflective, can broaden the critical responsiveness of participating in environmental governance systems. Social learningoriented processes are based on the promotion of dialogic spaces in which participants can learn from the diverse views present in the group and critically reflect on the assumptions that underlie decision making, thus creating possibilities for the emergence of innovative practices and new social configurations. We conclude that social learning processes driven by the stakeholders interactions in environmental governance spaces tend to broaden the ability of participants to critically evaluate the governance structure itself, in order to foster arrangements that can effectively promote practices for sustainability.
The Influence Of Corporate Governance , Jurnal Manajemen Keuangan Publik Capital Structure , And Company Size Vol . 5 , No . 2 , ( 2021 ) On The Company ' s Financial Performance Hal 98-108

This study aims to examine the effect of the implementation of corporate governance, capital structure, and firm size on the financial performance of banking companies. The implementation of good corporate governance is an obligation that must be carried out by companies which already have guidelines from the Financial Services Authority and other institutions. In fact, not all companies have applied good governance even though it can improve the performance of the company so it becomes interesting to study the impact of good governance implementation in Indonesia. This study uses panel data regression analysis with research samples from banking companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2019. The results of the study as overall show that corporate governance, capital structure and firm size have a positive effect on the company's financial performance. Managerial ownership as corporate governance proxy has a significant positive impact on financial performance partially.
Frontiers of Manufacturing and Design Science IV

This collection of 949 peer-reviewed papers on the Frontiers of Manufacturing and Design Science is intended to promote the development of Manufacturing, Design and Materials Science, the strengthening of international academic cooperation and communications and the exchange of research ideas. This book provides readers with a broad overview of the latest advances in the field of Manufacturing and Design Science.
Formulation of line-to-line voltage total harmonic distortion of two-level inverter with low switching frequency

THD is the most common criterion to determine the harmonic content of each waveform. Conventional methods, which are usually used, result in approximate answers due to their inability to take all harmonic orders into account. This paper focuses on the three-phase two-level inverter and proposes two methods, founded on the basic definition of THD, to calculate its line voltage THD, precisely. The first method considers stepped essence of line voltage and the second uses its Fourier coefficients. By proposed methods, all harmonic orders of two-level inverter with any switching angles, named n -degree of freedom, are considered. Another feature of the introduced methods is deriving analytical formulas for line voltage THD. This helps some switching strategies like THD Minimisation (THDM) to get rid of heuristic methods, which find those switching angles that globally minimise THD. Superiority of the proposed methods for one and two degrees of freedom is verified by both numeric and graphic results, simulated in MATLAB. The comparison table is only used for higher degrees of freedom, because of impossibility of plotting n -dimensional space. Finally, experimental results for two sets of switching angles are compared with the results of proposed methods to evaluate the accuracy of introduced methods.
LOCATION SELECTION FOR A TEXTILE MANUFACTURING FACILITY USING NEURAL NETWORK

The location selection of a textile manufacturing facility is a crucial decision that could reconfiguration the economic map of any city. This selection of textile industrial zones is based on a set of criteria such as the proximity of the market, the suppliers and the logistical costs involved due to the chosen location and this in an ecosystem that must offer the most favorable conditions. Even though it is a very important decision, there are very few studies on the location selection of the textile manufacturing facility within the Moroccan context. A Neural Network Model (NNM) was developed to rank suitable areas for a textile manufacturing facility in Morocco. The data used in this study are as follow; Surface Area, Location and general infrastructure, Internal equipment, Urban planning prescriptions, Price, Occupancy, Population, Taxation, Means of communications. Then, according to the case study presented in this paper, which is based on the developed NNM, the area of SETTAT was determined as the most suitable area for the establishment of a textile manufacturing facility in Morocco. This study aims to make two contributions to the literature. Firstly, to fill the research gap in one of the most attractive countries for the establishment of an industrial facility, and more specifically a textile facility. Then, a robust decision and prediction model is used to select the most appropriate location
Towards Analyzing Computational Costs of Spark for SARS-CoV-2 Sequences Comparisons on a Commercial Cloud

In this paper, we developed a Spark application, named Diff Sequences Spark, which compares 540 SARS-CoV-2 sequences from South America in Amazon EC2 Cloud, generating as output the positions where the differences occur. We analyzed the performance of the proposed application on selected memory and storage optimized virtual machines (VMs) at on-demand and spot markets. The execution times and financial costs of the memory optimized VMs outperformed the storage optimized ones. Regarding the markets, Diff Sequences Spark reduced the average execution times and monetary costs when using spot VMs compared to their respective on-demand VMs, even in scenarios with several spot revocations, benefiting from the low overhead fault tolerance Spark framework.
Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive

An intelligent sliding-mode speed controller for achieving favourable decoupling control and high-precision speed tracking performance of permanent-magnet synchronous motor (PMSM) drives is proposed. The intelligent controller consists of a sliding-mode controller (SMC) in the feedback loop in addition to an online trained wavelet-neural-network controller (WNNC) connected in parallel with the SMC to construct a robust wavelet-neural-network sliding-mode controller (RWNNSMC). The RWNNSMC controller combines the merits of the SMC with robust characteristics and the WNNC which combines the capability of artificial neural networks for online learning ability and the capability of wavelet decomposition for identification ability. The theoretical analyses of both SMC and WNNC speed controllers are developed. The WNN bound observer is utilised to predict the uncertain system dynamics to relax the requirement of uncertainty bound in the design of SMC. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding-mode speed controller. An experimental system is established to verify the effectiveness of the proposed control system. All control algorithms are implemented in a TMS320C31 digital signal processor-based control computer. The simulated and experimental results confirm that the proposed RWNNSMC grants robust performance and precise response regardless of load disturbances and PMSM parameter uncertainties.
Soft‐lithography defined coaxial‐like transmission line with 2.5 dimensional features

A novel coaxial‐like transmission line is presented in this article that offers many of the advantages of single‐mode waveguides but is implemented using conventional lithographic methods. On the basis of full‐wave simulations, a prototype “2.5‐D MicroCoax” was fabricated using soft‐lithography techniques with polydimethylsiloxane serving as the substrate material. In addition to width and gap dimensional control, the structure offers an additional degree of freedom by changing the center conductor's height, thus controlling the characteristic impedance and phase velocity—all without having to compromise performance. Simulation and test results confirm the design's single‐mode, low‐loss operation up to the de‐embedded frequency range of 12.0 GHz. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2827–2830, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23798