Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise reduction system by using CNN deep learning model

In this paper, we propose a new algorithm to reduce the acoustic noise of hearing aids. This algorithm improves the noise reduction performance by the deep learning algorithm using the neural network adaptive prediction filter instead of the existing adaptive filter. The speech is estimated from a single input speech signal containing noise using a 80-neuron, 16-filter convolutional neural network(CNN) filter and an error backpropagation algorithm. This is by using the quasi-periodic property of the voiced section in the speech signal, and it is possible to predict the speech more effectively by applying the repeated pitch. In order to verify the performance of the noise reduction system proposed in this research, a simulation program using Tensorflow and Keras libraries was coded and a simulation was done. As a result of the experiment, the proposed deep learning model improves the mean square error(MSE) of 28.5% compared to using the existing adaptive filter and 17.2% compared to using the FNN(full-connected neural network) filter.
Exploring the effect of community institutional forces on corporate philanthropy in professional sport

ABSTRACT Research question Although a growing body of literature has explored the strategic value and organizational drivers of corporate philanthropy (CP) by professional sport teams, we know less about the influence of geographic community which may constrain or enable the philanthropic behavior of teams. The purpose of this study is to explore how community institutional forces affect the level of philanthropic giving by professional sport teams in the U.S. across four major leagues. Research methods We hypothesized that team philanthropic giving will be greater when teams are in communities which value or promote philanthropic behaviors. To test this idea, we used longitudinal data of philanthropic giving of professional sport team foundations and factors in teams’ community institutional environments between 2005 and 2015 (n = 921). Results and Findings The results revealed that higher state income tax rates and a greater presence of nonprofit organizations in the community increased the level of team philanthropic giving, whereas the effect of community volunteer rates on team philanthropic giving was not found to be significant. Implications Our study advances sport management research by adding knowledge of how professional sport teams are anchored in the institutional and social structures of community. The findings provide practical implications to inform sport teams to be attuned to signals from the community to make the most significant social impact. Furthermore, policymakers might leverage different community institutional forces to exert pressure on teams to engage in appropriate levels of CP to achieve desired collaborative social objectives.
Blockchain can both enhance and undermine compliance but is not inherently at odds with EU privacy laws

Purpose To explain the authors’ position that the use of blockchain technology is not incompatible with European Union privacy laws and in particular the EU General Data Protection Regulation (GDPR). Design/methodology/approach Explains the basics of blockchain technology and the GDPR, several reasons why some scholars consider BC not to be compatible with the GDPR, and why the authors believe that the GDPR will be able to regulate the use of blockchain technology. Findings The current perception is that blockchain is not compatible with EU privacy laws. The authors disagree that this is the case and explain why none of the issues identified by legal scholars and stakeholders are likely to pose issues for blockchain technology. Their conclusion is that EU privacy laws are well able to regulate also this new technology. This does however not mean that blockchain will thus be suitable for all use and deployment cases. Originality/value Practical guidance and explanation of complex issues by lawyers with extensive experience and expertise in dealing with data protection, cybersecurity, privacy, intellectual property and related issues.
Sintesis Nanokomposit Karbon-TiO2 sebagai Anoda Baterai Lithium

Sintesis material anoda baterai lithium telah dilakukan dengan bahan baku dari karbon aktif dan titanium dioksida (TiO 2 ). Pembuatan karbon aktif dilakukan dengan mengaktivasi tempurung kemiri menggunakan larutan H 3 PO 4 2,5% sebagai aktivator dengan temperatur aktivasi 700 o C. Selanjutnya dibuat material anoda dengan menggunakan metode solid state reaction dengan variasi massa karbon aktif-TiO 2 yaitu 5% : 95%, 10% : 90%, 15% : 85% dan 20% : 80%. Material anoda dibuat dalam bentuk pellet dan dikarakterisasi menggunakan SEM, XRD, LCR Meter dan Cyclic Voltammetry. Hasil SEM menunjukkan bahwa material anoda yang terbuat dari karbon aktif dan TiO 2 tercampur hampir merata. Hasil XRD menunjukkan bahwa titanium dikosida (TiO 2 ) memiliki struktur kristal yang sama yaitu Tetragonal dan karbon pada variasi massa karbon-TiO 2 (20% : 80% dan 15% : 85%) memiliki struktur kristal Hexagonal sedangkan variasi massa karbon-TiO 2 (10% : 90% dan 5% : 95%) memiliki struktur kristal Rhombohedral . Konduktivitas listrik tertinggi didapatkan pada variasi massa karbon-TiO 2 (10%:90%) yaitu 1,11x10 -7 S/cm dan kapasitansi tertinggi didapatkan variasi massa karbon-TiO 2 (20%:80%). Kata kunci: baterai lithium, karbon aktif, TiO 2 , konduktivitas, kapasitansi
Static and dynamic performances of an offset bearing under a micropolar lubricant

In this research paper, the theoretical performance of a circular offset bearing is evaluated. The objective of this paper is to discuss the effects of eccentricity ratios and micropolarity parameters on the static and dynamic characteristics of an offset bearing with respect to micropolar lubrication. The modified Reynolds' equation for a dynamic state is solved using the finite-element method and Galerkin technique. The performance of an offset bearing is computed for a chosen range of eccentricity ratios (0.4–0.6) and for a length–diameter ratio (2.4). Enhancement in the performance of a bearing system is achieved in terms of increasing load and decreasing whirling at the higher values of eccentricity and offset especially at higher micropolarity.
Recognizing poverty pattern in Central Java using Biclustering Analysis

Poverty is a complex and multidimensional problem and becoming a development priority. In analyzing the pattern of poverty in a region, one of the statistical procedures that is usually used is the cluster analysis. However, it does not consider the different levels of performance by region in different characteristics at a particular time. In this study, an alternative approach, namely Cheng and Church’s biclustering algorithm, was used to simultaneously identify the poverty pattern in Central Java by region and poverty dimension variables. Using this algorithm, we found two biclusters with different characteristics. The first bicluster represents the general condition of poverty in Central Java, but they are better in labor and housing quality indicators. While the second bicluster is poorer in some indicators of labor, education, and housing quality than Central Java.
Sequential Band Fusion for Hyperspectral Anomaly Detection

This article proposes a new approach to hyperspectral band processing for anomaly detection, to be called sequential band fusion (SBF), derived from the band sequential (BSQ) data acquisition format used by a hyperspectral imaging sensor which fuses one single band at a time with previously fused band subset sequentially. In order to realize SBF, four versions, SBF-BSQ, initial band driven SBF (IBD-SBF), band prioritization SBF (BP-SBF), and band selection SBF (BS-SBF), are developed. Furthermore, to validate the sequentially fused results by SBF identical to that produced by combining all joint bands together, its mathematical theory and derivations are also presented. Finally, the full utility of SBF in anomaly detection is demonstrated through extensive experiments.
Das metastasierte nichtkleinzellige Lungenkarzinom im Wandel – Einblick in neue therapeutische Strategien zur Behandlung des NSCLC

Zusammenfassung Das Lungenkarzinom ist weltweit die häufigste Todesursache unter den Krebserkrankungen. 1 In Deutschland erkrankten im Jahr 2016 etwa 21 500 Frauen und 36 000 Männer an bösartigen Tumoren der Lunge, 16 481 Frauen und 29 324 Männer verstarben an dieser Erkrankung. 2 In den letzten Jahren ist die Bedeutung molekularer Testung von Tumorgewebe bei Patienten mit metastasierten nichtkleinzelligen Lungenkarzinom (NSCLC) immer weiter in den Vordergrund gerückt und bestimmt nunmehr unser therapeutisches Handeln. Neue zielgerichtete Therapiestrategien wie die Tyrosinkinase-Inhibitoren und die Immuntherapie rücken immer weiter in den Fokus und haben die Diagnostik und das Management der Erkrankung über die Jahre revolutioniert. Durch den gezielten Einsatz dieser neuen Substanzen hat sich die Prognose des metastasierten NSCLC dramatisch gewandelt. Besonders Patienten mit aktivierenden Treibermutationen haben von den Entwicklungen der letzten Jahre enorm profitiert. Am Beispiel der EGFR-Mutation und der ALK- Fusion sehen wir Überlebensdaten, die noch vor wenigen Jahren utopisch erschienen.
Deep imitation reinforcement learning for self-driving by vision

Liaoning Provincial Natural Science Foundation project, Grant/Award Number: 2019‐zd‐0578 Abstract Deep reinforcement learning has achieved some remarkable results in self‐driving. There is quite a lot of work to do in the area of autonomous driving with high real‐time requirements because of the inefficiency of reinforcement learning in exploring large continuous motion spaces. A deep imitation reinforcement learning (DIRL) framework is presented to learn control policies of self‐driving vehicles, which is based on a deep deterministic policy gradient algorithm (DDPG) by vision. The DIRL framework comprises two components, the perception module and the control module, using imitation learning (IL) and DDPG, respectively. The perception module employs the IL network as an encoder which processes an image into a low‐dimensional feature vector. This vector is then delivered to the control module which outputs control commands. Meanwhile, the actor network of the DDPG is initialized with the trained IL network to improve exploration efficiency. In addition, a reward function for reinforcement learning is defined to improve the stability of self‐driving vehicles, especially on curves. DIRL is verified by the open racing car simulator (TORCS), and the results show that the correct control strategy is learned successfully and has less training time.
UV-advanced Oxidation Treatment of Micropollutants in Secondary Wastewaters

Ultraviolet light+hydrogen peroxide (UV/H2O2) advanced oxidation is known to effectively oxidise micropollutants in wastewater. Its relatively high cost and energy requirements in comparison to other treatment options, however, have limited its implementation. This research evaluated the use of coagulation as a modification to the pre-tertiary component of the wastewater treatment process. The objective was to reduce the background scavenging capacity of the wastewater as a means of improving the oxidation efficiency of UV/H2O2 while reducing the cost and energy requirements. Effluent organic matter (EfOM) was identified as the primary wastewater component responsible for scavenging of the hydroxyl radicals required for oxidation in UV/H2O2 treatment. The organic constituents of EfOM that are the driving force for its reactivity with the hydroxyl radical were identified as the high molecular weight components (biopolymers) and tryptophan-protein like components. Since EfOM concentration and composition can vary from one water matrix to another, a comparison of secondary wastewaters from membrane bioreactor (MBR) and activated sludge (AS) treatment systems showed that the average scavenging capacity of the AS systems exceeded the average of the MBRs such that MBRs may be more amenable to UV/H2O2
Are individual stock returns predictable?

We show that the previously documented predictability of macroeconomic and technical variables for market returns is also evident in individual stock returns. Technical variables generate better predictability on firms with high limits to arbitrage (small, illiquid, volatile firms), while macroeconomic variables better predict firms with low limits to arbitrage. Technical predictors show a stronger predictive power for high limits to arbitrage firms across the business cycle, whereas macroeconomic variables capture more predictive information for firms with low limits to arbitrage during recessions. JEL Classification: C58, E32, G11, G12, G17
Risk Factors for Adverse One-year Prognosis in Patients With Low Left Ventricular Ejection Fraction After Myocardial Infarction and Chronic Cerebral Ischemia

BackgroundRecent studies have reported the correlation between left ventricular dysfunction and asymptomatic carotid artery stenosis So, we aimed to determine the predictors of poor long-term survival in patients with left ventricular systolic dysfunction after MI and chronic cerebral ischemia. Methods182 patients with left ventricular failure and chronic cerebral ischemia presented with Q-wave MI were recruited in a observational study. Of them, 149 (81.9%) were men and 33 (18.1%) were women. Their median age was 60.4 (53; 69) years. All patients underwent echocardiography, coronary angiography, carotid color duplex scanning, and were consulted by an interventional neurologist at the acute phase of MI. One year after MI, the hard endpoints were collected. Univariate and multivariate regression analyzes were performed.ResultsCerebral arteriopathy was confirmed in all patients from the study group. The thickness of the intima-media was over 1.0 mm. The mean carotid intima-media thickness was 1.8±06 mm. The majority of patients had mild to moderate encephalopathy. Carotid atherosclerotic plaques were found in 37.4% of patients. The degree of stenosis did not exceed 50% in all cases. One-year after MI, 77 (46.1%) patients reached hard endpoints. The multivariate regression model showed that previous stroke (RR 7.33 [95% CI 1.97-27.32], p = 0.003) was the most unfavorable predictor of mortality, whereas the most unfavorable predictors of endpoints were prior stroke (RR = 1.92 [95% CI 1.09-3.38], p = 0.025) and the presence of carotid atherosclerotic plaque (RR = 2.12 [95% CI 1.34-3.37], p = 0.001). ConclusionThe presence of carotid atherosclerotic plaques and previous stroke affect the one-year prognosis in patients with myocardial infarction, complicated by heart failure and chronic cerebral ischemia.
He$^+$ and Xe$^+$ velocities near the presheath-sheath boundary in a He/Xe discharge

The oopd1 particle-in-cell Monte Carlo collision (PIC-MCC) code is used to simulate He/Xe discharge in order to determine the velocities of helium and xenon ions at the presheath-sheath boundary in two ion species plasmas. Nine cases were simulated, seven of which are He/Xe mixtures with varying helium partial pressure and then two cases, one for pure helium and one pure xenon, while the discharge pressure is kept at 0.7 mTorr. As the partial pressure of helium is increased the velocities at the presheathsheath boundary also increase for both ion species. In all cases the xenon ions reach the presheath-sheath boundary at approximately their own sound speed and the helium ions reach the boundary at a velocity which is much lower than their sound speed.
Using Principal Component Analysis to Assess Water Quality from the Landing Stages in Coastal Region

Principal component analysis (PCA) was used to comprehensively evaluate the relationship between physicochemical and bacteriological parameters of water on banks of Wouri. At each sampling station, the physicochemical analysis focused on Water temperature, pH, dissolved oxygen, dissolved CO2, electrical conductivity, suspended solids, nitrates, ammonium ions, orthophosphates, color, salinity, and turbidity. The bacteriological analysis consisted of the isolation of heterotrophic aerobe bacteria (HAB), faecal bacteria, and some pathogenic bacteria. PCA showed that two factorial axes F1 (49.48%) and F2 (25.18%) explained 74.67% of the total inertia. pH, Suspended Solids, electrical conductivity and color are significantly and positively correlated with each other and also significantly and positively correlated with the F1 axis. HAB, faecal bacteria, Shigellae, dissolved CO2 and salinity are significantly and positively correlated with each other and, are also significantly and negatively correlated with the F1 axis and with the previous group of variables. The F1 axis discriminates in the negative coordinates of the Youpwe 1 and 2 stations, characterized by a high content of orthophosphates, ammonium ions, and low oxygenation. There is also a high concentration of bacteria such as HAB, faecal coliforms and Salmonella. The calculation of FC/FS ratio reflects exclusively contamination of animal origin in all the sampled stations.
Statistical arbitrage on the JSE based on partial co-integration

ABSTRACT Early forms of statistical arbitrage exploited the mean reversion of a model error extracted from pairs of instruments with a tendency to move together. Pairs trading was extended by Engle and Granger and by Johansen to include several co-integrated instruments. Partial co-integration was proposed by Clegg and Krauss to allow for model errors that contain both random walk and mean-reverting components. In this paper we implement a modified version of partial co-integration using a Kalman filter approach that allows the behaviour of the mean-reverting error component to be optimised. Co-integrated sets of shares are compiled over the period from January 1990 to November 2020 based on membership of sectors on the Johannesburg Stock Exchange. We demonstrate that optimal selection of the Kalman filter gain enables the improvement of risk-adjusted returns generated by the partial co-integration strategy. We optimise the parameters that define the partial co-integration trading strategy and find that it significantly outperforms market returns and a strategy based on normal co-integration. We observe higher returns during bear cycles compared with bull cycles, making statistical arbitrage based on partial co-integration an attractive option to combine with trading strategies that perform well during bull markets.
Etude des supraconducteurs à haute température critique par diffusion des neutrons

Un peu plus de 20 ans apres la decouverte des supraconducteurs a haute temperature critique (SHTC), la physique de ces composes reste une enigme. Nous nous sommes interesses a deux des nombreuses questions soulevees par ces composes : le spectre d'excitations magnetiques dans la phase supraconductrice du compose Bi2Sr2CaCu2O8+x (Bi-2212) et la nature de la phase de pseudogap. Les techniques de diffusion elastique et inelastique de neutrons sont des outils de choix pour cette etude. Bien que Bi-2212 soit le compose de reference pour les techniques de spectroscopie de charge (STM, ARPES), celui-ci a ete peu etudie par diffusion inelastique de neutrons. Notre etude a montre l'existence de deux modes de spin S=1 avec une dispersion compatible avec la dispersion mesuree dans YBa2Cu3O6+x. Cette etude a mis en evidence le lien entre la position en energie des excitations magnetiques et la distribution spatiale de gap observee par STM. Nous nous sommes aussi interesses a l'etude de la phase normale (dite phase de « pseudogap ») des SHTC. Celle-ci presente de fortes deviations a la theorie du liquide de Fermi. Motives par differentes approches theoriques, nous avons reporte l'existence d'un ordre magnetique non trivial dans la phase de pseudogap du compose YBa2Cu3O6+x. L'etude sur le compose La2-xSrxCuO4 semble egalement indiquer une reponse magnetique, cette reponse semble neanmoins differente de celle du compose YBa2Cu3O6+x.
A multistage successive approximation method for Riccati differential equations

Riccati differential equations have played important roles in the theory and practice of control systems engineering. Our goal in this paper is to propose a new multistage successive approximation method for solving Riccati differential equations. The multistage successive approximation method is derived from an existing piecewise variational iteration method for solving Riccati differential equations. The multistage successive approximation method is simpler in terms of computing implementation in comparison with the existing piecewise variational iteration method. Computational tests show that the order of accuracy of the multistage successive approximation method can be made higher by simply taking more number of successive iterations in the multistage evolution. Furthermore, taking small size of each subinterval and taking large number of iterations in the multistage evolution lead that our proposed method produces small error and becomes high order accurate.
Using Panel Data Analysis to Uncover Drivers of E-Participation Progress: A Global Insight and Regional Perspectives

This paper examines and uncovers the key drivers of e-participation progress or growth over the years, globally and regionally. The authors used fixed-effects regression model on a panel data of variables gathered by reputable world organizations for an 8-year period – one of the largest examined to date. They tested a research model including GDP per capita, ICT infrastructure, secondary education enrolment, technological knowledge creation and outputs, and six governance indicators: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. At the global level, the results indicate that e-participation progress is positively influenced by voice and accountability, GDP per capita, and ICT infrastructure. Analyses based upon six geographical regions of the world and countries' income-level classifications (i.e., low, low-middle, high-middle, high) show that determinants of e-participation progress vary by geographical and income-level contexts.
Dances with despots: tourists and the afterlife of statues

Abstract In times of liquid modernity, when human lifespan often exceeds that of grand political structures, monumental statues continue to be built and celebrated as symbols of enduring ideological triumphs. In their apparent permanence, these statues often outlive the political systems they were designed to glorify, creating a dilemma of how to exhibit their ambiguous or disgraced presence. In this article, we argue that the heritagization of political figures and pasts is central to the reframing of such narratives and that tourists have a key, if sometimes unwitting, role to play in the shaping of the emerging political imaginaries. Focusing on statue parks in Central and Eastern Europe showcasing communist-era sculptures, we examine strategies of exhibition and tourist responses to the multivalent presence of the monuments of past regimes. We identify four approaches of destruction, delegitimization, decontextualization, and depoliticization, each tied to a particular political moment and rhetorical goal. Examining these shifting modes of preservation, presentation and interpretation, we query the tourists’ role as participants in the processes of stabilization and peace-building, proposing that in times of global re-evaluation of the symbolism of past monuments, these sites can serve to guide much needed analysis and reflection.