Miracle-dz/UniFAR-FTUs huggingface dataset:
Pricing VIX Options with Realized Volatility

It is well known that realized measures of volatility, which are computed from high-frequency intraday data, provide accurate measurements of the latent volatility process. We investigate the role of realized volatility in pricing VIX options using the GARV model (Christoffersen et al., 2014), and the Realized GARCH model (Hansen et al., 2012). A closed-form pricing formula for the (affine) GARV model is developed. For the (non-affine) log-linear Realized GARCH model, we introduce a novel approximation approach to derive its analytical pricing formula. We show that the newly proposed approximation method is fast, with a high degree of accuracy. An extensive empirical application on VIX options from 2006 to 2020 shows that models with realized volatility significantly outperform conventional GARCH-type models based on daily returns only. Among these, the Realized GARCH model provides the best pricing performance due to its fewer constraints and a more flexible modeling structure. Our results hold both in-sample and out-of-sample.
A parallel‐plate waveguide antenna radiating through a perfectly conducting wedge

John L. Tsalamengas, School of Electrical and Computer Engineering, National Technical University of Athens, GR‐15773 Zografou, Athens, Greece. Email: itsal@central.ntua. gr Abstract Parallel‐plate waveguide antennas hosted in an electrically perfectly conducting wedge are studied. The analysis relies on an exact singular integral equation formulation. Asymptotic extraction techniques are employed to recast the kernel functions as the sum of two terms: one singular in closed form and another having the form of either a rapidly converging eigenfunctions expansion or a computationally efficient diffraction integral. The obtained integral equation is discretised in the framework of the Nystrom method using rapidly converging quadratures that take into account both the singularities of the kernels and the geometrical singularities at the edges. Numerical examples and case studies demonstrate the high accuracy and efficiency of the proposed algorithms. The analysis covers the canonical problem of a parallel‐plate waveguide antenna backed on a perfectly conducting wedge as a special case.
Ranking structural analysis software applications using AHP and Shannon’s entropy

ABSTRACT This paper aims to compare the ranking of ten structural analysis software applications in terms of six factors: Standardization, Reliability, Longevity, Usability, Price, and Functionality. The study surveyed structural design engineers from various countries around the world, collecting their opinions on the relative importance between the six factors. The respondents were also asked to score ten structural analysis programs for each of the six factors. The factor weights were derived using two methods: Analytic Hierarchy Process (AHP) and a hybrid method that combines AHP with Shannon’s entropy. The weighted average of the scores was then used to rank the preference of the programs. The results indicate that the factors of most concern for users are Reliability and Functionality, while Price was of the least concern. Significant differences in preferences were also between certain groups based on location and years of experience. The programs can be classified into three groups: one program that is highly favored, a set of programs that are “Above Average”, and a set of programs that are “Average”.
Team and Nation: Sports, Nationalism, and Attitudes Toward Refugees

How do major national events influence attitudes toward non-nationals? Recent research suggests that national sports team wins help foster national pride, weaken ethnic attachments, and build trust among conational out-group members. This paper asks a related question: By heightening nationalism, do these victories also affect attitudes toward foreign out-groups, specifically refugees? We examine this question using the 2019 Africa Cup football match between Kenya and Tanzania, which Kenya narrowly won, coupled with an online survey experiment conducted with a panel of 2,647 respondents recruited through Facebook. We find that winning increases national pride and preferences for resource allocation toward conationals, but it also leads to negative views of refugees’ contribution to the country’s diversity. However, we present experimental evidence that reframing national sports victories as a product of cooperation among diverse players and highlighting shared superordinate identities can offset these views and help foster positive attitudes toward refugees.
The effect of Helicobacter pylori eradication on C-reactive protein: results from a meta-analysis

Introduction Helicobacter pylori is a bacterium that causes chronic gastroduodenal infection and affects various systemic diseases. An increase in the blood level of C-reactive protein (CRP; a systemic inflammatory marker), at a low-grade chronic inflammation level, is observed in cases of infection. However, the effect of H. pylori eradication on CRP remains undetermined. Therefore, we aimed to evaluate the circulating CRP levels in eradicated patients through a meta-analysis. Material and methods The PubMed database was searched from its inception to June 2020. Studies that described the CRP levels following H. pylori eradication were collected. A random-effects meta-analysis was then performed using inverse variance with standardized mean difference. Results A total of 10 eligible studies (642 subjects in total) were available. The median age in the studies was 49.9 years. The CRP level was 6.0 (median) mg/l before H. pylori eradication and 5.8 (median) mg/l after eradication. From the results of the overall meta-analysis, there was found to be a significant reduction in the CRP levels with H. pylori eradication (standardized mean difference: –0.64; 95% confidence interval: –1.02 to –0.27). The result was not similarly confirmed in a subanalysis of the available randomized controlled trials. Conclusions Weak evidence exists regarding the effects of H. pylori eradication on CRP levels. Further research is called for.
Intellectual disability and mental illness among women sentenced to death in the U.S.: Constitutional and evidentiary dilemmas

This study examined the cases of women on death in which evidence of the defendant’s intellectual disability or mental illness was presented at trial. Of the total population of 52 women on death row at the beginning of 2020 and seven recently-executed women, over 50% qualified for the study. According to expert testimony, most of the women in the sample had a below-average IQ score and/or a neurological deficit, and all but three suffered from a serious mental illness. Almost all had abusive and dysfunctional backgrounds. Recent Supreme Court rulings have banned the execution of defendants with intellectual disability and opened the door to a consideration of execution exemption for defendants with severe mental illness or brain abnormalities. However, considerable judicial equivocation over the meaning and measurement of mental impairments remains. Even as a mitigating factor, the mental health problems of the defendants in this study were given little weight.
Drought Stress Can Induce the Pathogenicity of Cryptostroma corticale, the Causal Agent of Sooty Bark Disease of Sycamore Maple

Reports of sooty bark disease of maples caused by the fungus Cryptostroma corticale have recently been emerging from across Europe. The aims of our study were to describe the first report of sooty bark disease in Slovenia, to determine the pathogenicity of C. corticale, to confirm the optimum temperature for the growth of the fungus, and to determine the mass loss of Acer pseudoplatanus wood inoculated by C. corticale. We confirmed the presence of C. corticale on A. pseudoplatanus via morphological and molecular analysis. The optimal growth of C. corticale was measured in vitro on potato dextrose agar and was determined to occur at 25 °C. Pathogenicity tests were performed on 30 saplings of A. pseudoplatanus under two treatments, humid and drought stress, and the fungus was pathogenic in both treatments. The mean length of bark lesions and wood discoloration of the drought-stressed saplings was significantly greater than that in the humid treatment. Re-isolations of C. corticale were successful from all inoculated saplings, and thus Koch’s postulates were confirmed. The mass loss of A. pseudoplatanus wood was determined by mini-block test in a period of 10 weeks and was observed as minimal. Based on the results, we conclude that C. corticale is a weak and opportunistic pathogen that most likely expresses itself intensively under hot and dry conditions.
The Endocannabinoid, Anandamide, Acts as a Novel Inhibitor of LPS-Induced Inflammasome Activation in Human Gastric Cancer AGS Cell Line: Involvement of CB1 and TRPV1 Receptors

Inflammasome activation is a pivotal step for the maturation of IL-1β, which is involved in the development and progression of gastric cancer (GC). Endocannabinoids, such as anandamide (AEA), are emerging as new anticancer therapeutic agents; however, their effects on inflammasome components and underlying mechanisms have not been well elucidated. This study was designed to investigate the effects of AEA on the expression of inflammasome components in lipopolysaccharide- (LPS-) stimulated AGS cells. Moreover, we explored the involvement of cannabinoid receptors (CRs), including CB1R and TRPV1R, in the observed effects of AEA. Our results showed that inflammation was induced by LPS (10 μg/ml) in AGS cells, and inflammasome components (NLRP3, MLRC4, ASC, IL-18, and IL-1β) were overexpressed. Exposure to AEA (10 μM, 24 h) before or after inflammation induction downregulated the expression of inflammasome components and attenuated inflammasome activation as demonstrated by cleavage of caspase 1 and matured IL-1β secretion, although AEA pretreatment showed more reducing effects on the inflammasome activation. In addition, blocking of CB1R and TRPV1R by application of AM-251 and AMG-9810 antagonists remarkably reversed the observed effects of AEA and revealed that NLRP3, NLRC4, and IL-1β genes were mainly regulated via CB1R, while TRPV1R could only regulate the expression of IL-1β and IL-18 genes. In conclusion, our results would indicate a novel anticancer effect of anandamide by attenuation of inflammasome activation and consequently reducing IL-1β production in human AGS cancer cell line via CB1R and TRPV1R.
Sophorolipid Amine Oxide Production by a Combination of Fermentation Scale-up and Chemical Modification

Production scale-up of high-purity diacetylated C18:1 sophorolipid lactone was demonstrated from lab to pilot scale with the Starmerella bombicola lactone esterase overexpression strain (oe sble) as production organism. The 150 L fermentation using oleic acid and yeast extract, characterized by a titer of 199 g/L and a volumetric productivity of 0.9 g/L·h, was most successful in obtaining a highly pure (>98%) and uniform (96% C18:1 SL lactone) sophorolipid product suitable for chemical derivatization. The fermentation product was subsequently modified to produce sophorolipid amine oxides, which cannot be produced enzymatically. First, the fermentation product was transformed into an intermediate sophorolipid aldehyde via methanolysis and protection of the sugar head through acetylation and ozonolysis. This aldehyde intermediate was then used for the synthesis of the sophorolipid amine oxides via reductive amination, oxidation, and deprotection of the sugar head. The total yield of this synthetic pathway a.
Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids

Accurate forecasting is a crucial task for energy management systems (EMSs) used in microgrids. Despite forecasting models destined to EMSs having been largely investigated, the analysis of criteria for the practical execution of this task, in the framework of an energy management algorithm, has not been properly investigated yet. On such a basis, this paper aims at exploring the effect of daily forecasting frequency on the performance of rolling-horizon EMSs devised to reduce demand uncertainty in microgrids by adhering to a reference planned profile. Specifically, the performance of a sample EMS, where the forecasting task is committed to a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), has been studied under different daily forecasting frequencies, revealing a representative trend relating the forecasting execution frequency in the EMS and the reduction of uncertainty in the electrical demand. On the basis of such a trend, it is possible to establish how often is convenient to repeat the forecasting task for obtaining increasing performance of the EMS. The obtained results have been generalized by extending the analysis to different test scenarios, whose results have been found coherent with the identified trend.
On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

PurposeCracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.Design/methodology/approachThis paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.FindingsIt was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.Originality/valueLiterature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.
Flexible and high-throughput structures of Camellia block cipher for security of the Internet of Things

The advancements in wireless communication have created exponential growth in the Internet of Things (IoT) systems. Security and privacy of the IoT systems are critical challenges in many data ‐ sensitive applications. Herein, high ‐ throughput and flexible hardware implementations of the Camellia block cipher for IoT applications are presented. In the proposed structures, sub ‐ blocks of the ciphers are implemented based on optimised circuits. The proposed structures for Camellia are designed and shared for implementing the encryption process and generating some intermediate key values in the two separate times. The most complex block in these ciphers is the substitution box (S ‐ box). The S ‐ boxes are implemented based on area ‐ optimised logic circuits. The Camellia S ‐ boxes consist of a field inversion over F 2 8 and two affine transformations over F 2 . The inversion operation is implemented over the composite field F ð 2 4 Þ 2 instead of an inversion over F 2 8 which is an important factor to reduce area consumption. A large number of gates, in the structure, have been implemented by 2 ‐ input NAND and 2 ‐ input NOR gates to reduce delay and area. Also, the flexible structure for Camellia that can do various configurations of this cipher to support variable key sizes 128, 192 and 256 bits was proposed. Implementation results of the proposed architectures in 180 nm CMOS technology for different key sizes are achieved. The results show improvements in terms of execution time, throughput and throughput/area compared to the other related works.
Alleviation of diabetic nephropathy by zinc oxide nanoparticles in streptozotocin‐induced type 1 diabetes in rats

Abstract This study examines the effect of nanoparticles with zinc oxides (ZnONPs) on diabetic nephropathy, which is the primary cause of mortality for diabetic patients with end‐stage renal disease. Diabetes in adult male rats was induced via intraperitoneal injection of streptozotocin. ZnONPs were intraperitoneally administered to diabetic rats daily for 7 weeks. Diabetes was associated with increases in blood glucose level, 24‐h urinary albumin excretion rate, glomerular basement membrane thickness, renal oxidative stress markers, and renal mRNA or protein expression of transforming growth factor‐β1, fibronectin, collagen‐IV, tumour necrosis factor‐α and vascular endothelial growth factor‐A. Moreover, the expression of nephrin and podocin, and the mRNA expression of matrix metalloproteinase‐9 were decreased in the diabetic group. These changes were not detected in the control group and were significantly prevented by ZnONP treatment. These results provide evidence that ZnONPs ameliorate the renal damage induced in a diabetic rat model of nephropathy through improving renal functionality; inhibiting renal fibrosis, oxidative stress, inflammation and abnormal angiogenesis; and delaying the development of podocyte injury. The present findings may help design the clinical application of ZnONPs for protection against the development of diabetic nephropathy.
Application of Fundamental Techniques for Physicochemical Characterizations to Understand Post-Formulation Performance of Pharmaceutical Nanocrystalline Materials

Nanocrystalline materials (NCM, i.e., crystalline nanoparticles) have become an important class of materials with great potential for applications ranging from drug delivery and electronics to optics. Drug nanocrystals (NC) and nano co-crystals (NCC) are examples of NCM with fascinating physicochemical properties and have attracted significant attention in drug delivery. NCM are categorized by advantageous properties, such as high drug-loading efficiency, good long-term physical stability, steady and predictable drug release, and long systemic circulation time. These properties make them excellent formulations for the efficient delivery of a variety of active pharmaceutical ingredients (API). In this review, we summarize the recent advances in drug NCM-based therapy options. Currently, there are three main methods to synthesize drug NCM, including top-down, bottom-up, and combination methods. The fundamental characterization methods of drug NCM are elaborated. Furthermore, the applications of these characterizations and their implications on the post-formulation performance of NCM are introduced.
Exploring Viscosity Space in an Eddy‐Permitting Global Ocean Model: Is Viscosity a Useful Control for Numerical Mixing?

A generic shortcoming of constant‐depth (or “z‐coordinate”) ocean models such as MOM5 and Nucleus for European Models of the Ocean (NEMO) is a tendency for the advection scheme to produce unphysical numerical diapycnal mixing, which may exceed the explicitly parameterized mixing based on observed physical processes. Megann (2018, https://doi.org/10.1016/j.ocemod.2017.11.001) estimated the effective diapycnal diffusivity in the Global Ocean Version 5.0 (GO5.0) 0.25° global implementation of the NEMO model and showed that this was up to 10 times the explicit diffusivity used in the model's mixing scheme and argued that this was at least partly caused by large transient vertical velocities on length scales comparable to the horizontal grid scale. The current UK global NEMO configuration GO6, as used in the Global Coupled Model version 3.1 (GC3.1) and UK Earth System Model (UKESM1), is integrated in forced mode at 0.25° resolution with a range of viscosity parameterizations. In the present study, the effective diffusivity is evaluated for each integration and compared with the explicit value from the model mixing scheme, as well as with that in the control (using the default viscosity). It is shown that there is a strong correspondence between lower viscosity and enhanced numerical mixing and that larger viscosities lead to a marked reduction in the unrealistic internal temperature drift seen in the control configuration, without incurring excessive damping of the large‐scale circulation, mixed layer depths, or sea ice cover. The results presented here will inform the choices made in global ocean configurations used in climate and Earth System models following the sixth Coupled Model Intercomparison Project (CMIP6).
Fusion Predictive Control Based on Uncertain Algorithm for PMSM of Brake-by-Wire System

Brake-by-wire (BBW) system is an important safe component of intelligent vehicles. The fast and stable current control of permanent magnet synchronous motor (PMSM) is a key factor that ensures the dynamic response performance of BBW. Hence, a fusion predictive control (FPC) method based on uncertain algorithm is proposed, which solves the parameter sensitivity problem in traditional deadbeat current control of PMSM. It contains a fusion predictive controller, a flux observer, and an uncertain controller. The FPC integrates forward linear prediction (FLP) and deadbeat prediction (DP) to improve the current response speed. Considering the model parameter mismatch of the controller, the uncertain controller can adjust the reliability of FLP and DP in the FPC in real time through the uncertainty measurement. On the basis of the whole system, a flux observer is designed, which can dynamically adjust the parameters of flux to satisfy the current loop. Both simulation and experimental results show that this method reduces the sensitivity of system parameters and improves the dynamic performance of the current loop (up to 12.56%).
Teaching chemistry in context: what we know from teachers’ lesson plans

ABSTRACT This study took ‘context’ as a focal point in the context-based approach and its investigation was anchored on an analytical framework, which is composed of three dimensions of context: origins, intentions, and occasions. Content analysis was employed as the research methodology with 878 contexts identified from 96 copies of awarded chemistry lesson plans being the analysis targets. The occurrence of context adopted in these chemistry lesson plans was analysed along the three dimensions, each of which comprises three sub-dimensions. For each dimension, content analysis focused on the proportion of three sub-dimensions, different proportions between junior and senior high schools, and the changing trends of the three sub-dimensions during the period from 2001 to 2020. As found in this study, discrepancies existed in the proportions of the sub-dimensions of origins, intentions, and occasions of context that were adopted by chemistry teachers, and some significant differences lay in chemistry teaching between junior and senior high schools. The findings are discussed against the social background in Macao, where this study took place. This study has provided several implications for the research and practice of the context-based approach in chemistry education and beyond.