Spot Rate Research Articles

An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection

Disturbances to forests are getting worse with climate change and urbanization. Assessing the functionality of forest ecosystems is challenging because it requires not only a large amount of input data but also comprehensive estimation indicator methods. The object of the evaluation index of forest ecosystem restoration relies on the ecosystem function instead of the area. To develop the appropriate index with ecological implications, we built the hybrid assessment approach including ecosystem structure-function-habitat representatives. It was based on the Normalized Burn Ratio (NBR) spectral indicator and combined with the local forest management inventory (LFMI), Landsat, Light Detection and Ranging (LiDAR) data. The results of the visual interpretation of Google Earth’s historical imagery showed that the total accuracy of the hybrid approach was 0.94. The output of the hybrid model increased as the biodiversity index value increased. Furthermore, to solve the multi-source data availability problem, the random forest model (R2 = 0.78, RMSE = 0.14) with 0.77 total accuracy was built to generate an annual recovery index. A random forest model based on tree age is provided to simplify the hybrid approach while extending the results on time series. The recovery index obtained by the random forest model could facilitate monitoring the forest recovery rate of cold spots. The regional ecological recovery time could be predicted. These two results could provide a scientific basis for forest managers to make more effective forest restoration plans. From the perspective of space, it could ensure that the areas with slow recovery would be allocated enough restoration resources. From the perspective of time, the implementation period of the closed forest policy could also be estimated.

The effectiveness of a computer-aided system in improving the detection rate of gastric neoplasm and early gastric cancer: study protocol for a multi-centre, randomized controlled trial

BackgroundThis protocol is for a multi-centre randomised controlled trial to determine whether the computer-aided system ENDOANGEL-GC improves the detection rates of gastric neoplasms and early gastric cancer (EGC) in routine oesophagogastroduodenoscopy (EGD).MethodsStudy design: Prospective, single-blind, parallel-group, multi-centre randomised controlled trial.Settings: The computer-aided system ENDOANGEL-GC was used to monitor blind spots, detect gastric abnormalities, and identify gastric neoplasms during EGD.Participants: Adults who underwent screening, diagnosis, or surveillance EGD.Randomisation groups:1. Experiment group, EGD examinations with the assistance of the ENDOANGEL-GC;2. Control group, EGD examinations without the assistance of the ENDOANGEL-GC.Randomisation: Block randomisation, stratified by centre.Primary outcomes: Detection rates of gastric neoplasms and EGC.Secondary outcomes: Detection rate of premalignant gastric lesions, biopsy rate, observation time, and number of blind spots on EGD.Blinding: Outcomes are undertaken by blinded assessors.Sample size: Based on the previously published findings and our pilot study, the detection rate of gastric neoplasms in the control group is estimated to be 2.5%, and that of the experimental group is expected to be 4.0%. With a two-sided α level of 0.05 and power of 80%, allowing for a 10% drop-out rate, the sample size is calculated as 4858. The detection rate of EGC in the control group is estimated to be 20%, and that of the experiment group is expected to be 35%. With a two-sided α level of 0.05 and power of 80%, a total of 270 cases of gastric cancer are needed. Assuming the proportion of gastric cancer to be 1% in patients undergoing EGD and allowing for a 10% dropout rate, the sample size is calculated as 30,000. Considering the larger sample size calculated from the two primary endpoints, the required sample size is determined to be 30,000.DiscussionThe results of this trial will help determine the effectiveness of the ENDOANGEL-GC in clinical settings.Trial registrationChiCTR (Chinese Clinical Trial Registry), ChiCTR2100054449, registered 17 December 2021.

Beyond Co-integration: New Tools for Inference on Co-movements

Abstract Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate.

MP40-14 ARE EAU RISK GROUPS RELIABLE IN THE STRATIFICATION OF MEN WITH BIOCHEMICAL RECURRENCE AFTER RADICAL PROSTATECTOMY AND RESTAGED WITH PSMA-PET?

MP40-14 ARE EAU RISK GROUPS RELIABLE IN THE STRATIFICATION OF MEN WITH BIOCHEMICAL RECURRENCE AFTER RADICAL PROSTATECTOMY AND RESTAGED WITH PSMA-PET?

Seasonality and forecasting analysis of the South-East Asian container freight market

PurposeWhile previous studies focused mainly on East Asia to Europe or United States trade routes, in recent years, trade among South-East Asian countries has increased notably. The price of transporting a container is not fixed and can fluctuate heavily over the course of a week. Besides, extant literature only identified seasonality patterns in the container freight market, but did not explore route-varying seasonality patterns. Hence, this study analyses container freight seasonality patterns of the six South-East Asian routes of the South-East Asian Freight Index (SEAFI) and the index itself and forecasts them.Design/methodology/approachData of the composite SEAFI and six routes are collected from the Shanghai Shipping Exchange (SSE) including 167 weekly observations from 2016 to 2019. The SEAFI and individual route data reflect spot rates from the Shanghai Port to South-East Asia base ports. The authors analyse seasonality patterns using polar plots. For forecasting, the study utilize two univariate models, autoregressive integrated moving average (ARIMA) and seasonal autoregressive neural network (SNNAR). For both models, the authors compare forecasting results of original level and log-transformed data.FindingsThis study finds that the seasonality patterns of the six South-East Asian container trade routes are identical in an overall but exhibits unique characteristics. ARIMA models perform better than SNNAR models for one-week ahead test-sample forecasting. The SNNAR models offer better performance for 4-week ahead forecasting for two selected routes only.Practical implicationsMajor industry players such as shipping lines, shippers, ship-owners and others should take into account the route-level seasonality patterns in their decision-making. Forecast analysts can consider using the original level data without log transformation in their analysis. The authors suggest using ARIMA models in one-step and four-step ahead forecasting for majority of the routes. The SNNAR models are recommended for multi-step forecasting for Shanghai to Vietnam and Shanghai to Thailand routes only.Originality/valueThis study analyses a new shipping index, that is, the SEAFI and its underlying six routes. The authors analyze the seasonality pattern of container freight rate data using polar plot and perform forecasting using ARIMA and SNNAR models. Moreover, the authors experiment forecasting performance of log-transformed and non-transformed series.

Numerical simulation of cathode-spot crater formation and deuterium desorption process on hydrogen titanium cathode with flux boundary conditions and different impregnation degree

In this paper, the crater formation process of single cathode spot on hydrogen titanium electrode is modeled. In this model, combined with the deuterium diffusion equation, the flux boundary condition was considered to calculate the cathode spot desorption rate with different impregnation degree, and the deformation process of the cathode spot crater was simulated by a hydrodynamic model. The simulation results show that the current, size, and depth of a single cathode spot crater tend to decrease with increasing impregnation degree, which is consistent with many related experimental results. The simulation result shows that the desorption of deuterium in a single cathode spot crater mainly occurs in the ignition stage, and the positions of desorption are successively distributed in the liquid metal flow area on the side wall of the crater and the high temperature area in the center of the crater. The desorption rate of deuterium drops rapidly after cathode spot quenching. At the same time, the increase of arc current and impregnation degree can improve the deuterium desorption rate of a single cathode spot.

Thymol Edible Coating Controls Postharvest Anthracnose by Regulating the Synthesis Pathway of Okra Lignin.

Okra has received extensive attention due to its high nutritional value and remarkable functional characteristics, but postharvest diseases have severely limited its application. It is important to further explore the methods and potential methods to control the postharvest diseases of okra. In this study, Colletotrichum fioriniae is the major pathogen that causes okra anthracnose, which can be isolated from naturally decaying okra. The pathogenicity of C. fioriniae against okra was preliminarily verified, and the related biological characteristics were explored. At the same time, an observational study was conducted to investigate the in vitro antifungal effect of thymol edible coating (TKL) on C. fioriniae. After culturing at 28 °C for 5 days, it was found that TKL showed an obvious growth inhibition effect on C. fioriniae. The concentration for 50% of the maximal effect was 95.10 mg/L, and the minimum inhibitory concentration was 1000 mg/L. In addition, it was found that thymol edible coating with a thymol concentration of 100 mg/L (TKL100) may cause different degrees of damage to the cell membrane, cell wall, and metabolism of C. fioriniae, thereby inhibiting the growth of hyphae and causing hyphal rupture. Refer to the results of the in vitro bacteriostatic experiment. Furthermore, the okra was sprayed with TKL100. It was found that the TKL100 coating could significantly inhibit the infection of C. fioriniae to okra, reduce the rate of brown spots and fold on the okra surface, and inhibit mycelium growth. In addition, the contents of total phenols and flavonoids of okra treated with TKL100 were higher than those of the control group. Meanwhile, the activities of phenylalaninammo-nialyase, cinnamic acid-4-hydroxylase, and 4-coumarate-CoA ligase in the lignin synthesis pathway were generally increased, especially after 6 days in a 28 °C incubator. The lignin content of TKL-W was the highest, reaching 65.62 ± 0.68 mg/g, which was 2.24 times of that of CK-W. Therefore, TKL may promote the synthesis of total phenols and flavonoids in okra, then stimulate the activity of key enzymes in the lignin synthesis pathway, and finally regulate the synthesis of lignin in okra. Thus, TKL could have a certain controlling effect on okra anthracnose.

Weighted least squares estimation of the risk-free rate from derivative prices

This study proposes a method for estimating the interest spread over an OIS-implied spot rate used in market-consistent derivative pricing. Our method generalizes previous proposed ordinary least squares methods in the literature in two ways. First, it utilizes intraday data rather than data from a single time. Second, it is formulated as weighted least squares to counteract heteroscedasticity. Additionally, we present a general methodology to quantify the performance difference between methods when the true value is unknown. We find that our method outperforms previously proposed methods with statistical significance and that the primary improvement is the utilization of intraday data.

Translation of transfer pricing adjustments in South Africa: A seemingly insignificant detail

The South African rand is one of the most volatile currencies in the world — at times the most volatile. To this is added a further area of uncertainty, namely the tax implications relating to transfer pricing. Section 31 of the South African Income Tax Act does not have any specific foreign currency translation rules. The general rule in s 25D of the Income Tax Act is therefore applicable, which provides for the translation of foreign currency to rand using the spot rate. However, given the nature of transfer pricing transactions, it raises the question whether the spot rate is indeed appropriate. The purpose of the study was to investigate South Africa’s translation rules and to seek guidance from an international perspective. The research design was non-empirical, adopting an interpretative paradigm, together with a doctrinal research methodology. The conclusion of this study is that the translation rules concerning transfer pricing adjustments have seemingly been overlooked. The study recommends a legislative amendment of s 31 and proposes that transfer pricing adjustments are converted using the average rate of exchange for the year of assessment to which the adjustments relate, as such an amendment will lead to certainty, equity and convenience.

A Fourier cosine expansion method for pricing FX-TARN under Lévy processes

<abstract><p>In this paper, we extend the Fourier cosine expansion (COS) method to the pricing of {foreign exchange} target redemption note (FX-TARN), a popular exotic currency option. We take the FX spot rate and the cumulated positive cash flow as two state variables and factor the joint distribution by two marginals that can be approximated by Fourier cosine expansions. To recover the Fourier coefficients recursively, we approximate the two-dimensional integration by higher-order quadratures such as Gauss-Legendre or Clenshaw-Curtis quadrature for the integration over the spot rate. We derive the analytical formulas for the price under different knock-out types. We demonstrate that fast Fourier transform (FFT) can be employed to obtain the Fourier coefficients efficiently. We also evaluate the performance and accuracy of the method through a number of numerical experiments.</p></abstract>

Identification of Resistance to Pathogenesis Related Protein GmPR1L in Tobacco Botrytis cinerea Infection

Soybean (<i>Glycine max</i> (Linn.) Merr.) annual leguminous crop is cultivated all over the world. The occurrence of diseases has a great impact on the yield and quality of soybean. In this study, based on the RNA-seq of soybean variety M18, a complete CDS (Coding sequence) <i>GmPR1L</i> of the pathogenesis-related protein 1 family was obtained, which has the ability to resist fungal diseases. The overexpression vector and interference expression vector were transferred into tobacco NC89, and the resistance of transgenic tobacco (<i>Nicotiana tabacum</i> L.) to <i>Botrytis cinerea</i> infection was identified. The results show that: Compared with the control, the activities of related defense enzymes SOD (Superoxide dismutase), POD (Peroxidase), PAL (L-phenylalanine ammonia-lyase) and PPO (Polyphenol oxidase) in the over-expressed transgenic tobacco OEA1 and OEA2 increased to different degrees, and increased significantly at different infection time points. The activities of defense enzymes in the interfering strains IEA1 and IEA2 were significantly lower than those in the control strains. The results of resistance level identification showed that the disease spot rate of OEA1 was significantly lower than that of the control line, and the disease spot rate of OEA2 was significantly lower than that of the control line. The plaque rate of the interfering expression line IEA1-IEA2 was significantly higher than that of the control line. It is preliminarily believed that the process related protein <i>GmPR1L</i> can improve the resistance of tobacco to <i>B. cinerea</i>.

Laser surface treatment to enhance the adhesive bonding between steel and CFRP: Effect of laser spot overlapping and pulse fluence

Adhesive bonding is widely applied in joining between steel and carbon fiber reinforced plastics (CFRP), while the surface characteristics of steel have a significant effect on the joint strength. To improve the adhesive bonding between DP590 steel and CFRP, surface modifications of steel by laser surface treatment with different overlap rate of laser spot and pulse fluence were applied and investigated. The evolution of surface morphology, contact angle, surface energy, and surface chemical composition of DP590 steel after laser treatment with different parameters were also studied. The results showed that surface roughness, surface energy and polar component of surface energy after treatments were closely related to the interfacial bonding strength between steel and adhesives. Specifically, higher pulse fluence can increase the surface roughness of the steel, which reduced the contact angle and improve the surface energy, thus not only leading to a better mechanical interlocking effect, but also improving the surface wettability of the adhesive to the steel surface. When there was 50 % overlapping of the laser spots, the surface wettability was further improved. In addition, the strengths of steel-CFRP joints obtained with both epoxy adhesive E-120HP and polyurethane adhesive PU6700 were significantly enhanced by laser surface treatment. Especially for the joints bonded with E-120HP, which is a high-strength and poor-toughness adhesive, the shear strength can be increased by 299 %, from 4.10 ± 0.17 MPa to 16.35 ± 0.89 MPa after laser treatment. More importantly, the adhesive failure along steel-adhesive interface was entirely avoided. This study provides both technical and theoretical guidance of laser surface treatment for fabricating high-performance metal-polymer hybrid structures by adhesive bonding.

Interaction Between Spot and Future Currency Market of India: Empirical Evidence

The study empirically analyses the interdependence and price discovery mechanism between the spot and futures (S&amp;F) prices of the Indian Foreign Exchange Market. Daily closing prices of S&amp;F currency pairs were collected from February 2010 to March 2021. Before investigating causality, the descriptive statistical test and unit root test (augmented Dickey–Fuller) are used to test the stationarity of data. An error correction model examines the long- and short-run relationship between S&amp;F market currency pairs. The currency returned series stationarity at I(1) identified the presence of heteroscedasticity and found the absence of the Autoregressive Conditional Heteroskedasticity effect. To avoid the possible ignorance of the long-run relationship between S&amp;F and to confirm the trustworthiness of regression at levels, the cointegration technique was employed under Johansen’s technique. The existence of cointegration at levels provides power to use Vector Error Correction Models, considering the level and difference in the estimation process. There is a long-run equilibrium relationship between spot rates and future rates, with a bidirectional causal association among currency S&amp;F prices of all the currency pairs. Additionally, the futures market tends to regulate any new data quicker than the spot market. It suggests that spot price is led by future price, thus contributing substantially to the price-discovery process. The Generalized Autoregressive Conditional Heteroskedasticity models establish persistence in volatility, and the bad news gives rise to more volatility as compared to good news.

Densest subgraph-based methods for protein-protein interaction hot spot prediction

BackgroundHot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots.ResultIn this study, we develop three graph theory-based methods to predict hot spots from only a single residue interaction network. We detect the important residues by finding subgraphs with high densities, i.e., high average degrees. Generally, a high degree implies a high binding possibility between protein chains, and thus a subgraph with high density usually relates to binding sites that have a high rate of hot spots. By evaluating the results on 67 complexes from the SKEMPI database, our methods clearly outperform existing graph theory-based methods on recall and F-score. In particular, our main method, Min-SDS, has an average recall of over 0.665 and an f2-score of over 0.364, while the recall and f2-score of the existing methods are less than 0.400 and 0.224, respectively.ConclusionThe Min-SDS method performs best among all tested methods on the hot spot prediction problem, and all three of our methods provide useful approaches for analyzing bionetworks. In addition, the densest subgraph-based methods predict hot spots with only one residue interaction network, which is constructed from spatial atomic coordinate data to mitigate the shortage of data from wet-lab experiments.

A hedging policy for seaborne forward freight markets based on probabilistic forecasts

Rate volatilities in seaborne freight markets lead charterers and ship owners to use financial agreements such as Forward Freight Agreements (FFA) for fixing freight rates in advance. The use of FFAs requires effective hedging policies since fixing freight rates in advance might cause both benefits and opportunity costs depending on future rate changes. We propose a data-driven hedging policy prescribing purchasing decisions for FFAs based on comparisons of FFA rates with future spot rate forecasts. The proposed approach is based on probabilistic forecasts instead of point forecasts because the most accurate forecasts in terms of predictive errors do not necessarily lead to the best decisions. We adjust spot rate forecasts by selecting percentiles that result in minimum prescriptive errors (i.e., cost) in cross-validation. Experiments on synthetic data show that the probabilistic forecast-based hedging policy outperforms the point forecast-based policies and benchmark policies, including data-driven policies from the literature. Experiments on Baltic Exchange data from 15 dry bulk and tanker routes confirm the performance of the proposed policy. Compared to two different point forecast-based policies defined in this study, the proposed approach achieves on average 3.31% and 3.02% total procurement cost reduction per route in 15 routes for four years testing period. It results in from 0.67% to 4.79% cost reductions against the benchmark policies.

Impact of currency futures on spot rate volatility in Indian foreign exchange market

The work aimed going to ascertain the efficacy of the currency futures on cash-market or Spot exchange rates volatility with respect of USD/INR, EURO/INR, GBP/INR &amp; JPY/INR. The daily closing of have been taken from RBI website. Also, the time series data is checked for stationarity using ADF test. Existence of ARCH effect examined in spot rate residual of the return series data and checked with Autoregressive conditional heteroskedasticity L.M. test by using GARCH (1,1) model. Findings of GARCH model shows that large volatility is persistent pre-introduction of currency futures and finding shows presence of time-varying conditional volatility of returns of currencies spot prices at pre-post and total period of currency futures introduction in currency exchange market. The variances in volatility of the exchange rate returns of pre and post introduction of currency futures time periods being analyzed by using statistical tools. Findings shows to be importantly varied and volatility has been came down in the post futures era also series were found more stable in terms of volatility.

A procedure for computing the spot production rate in transitional boundary layers

The present work describes a method for the computation of the nucleation rate of turbulent spots in transitional boundary layers from particle image velocimetry (PIV) measurements. Different detection functions for turbulent events recognition were first tested and validated using data from direct numerical simulation, and this latter describes a flat-plate boundary layer under zero pressure gradient. The comparison with a previously defined function adopted in the literature, which is based on the local spanwise wall-shear stress, clearly highlights the possibility of accurately predicting the statistical evolution of transition even when the near-wall velocity field is not directly available from the measurements. The present procedure was systematically applied to PIV data collected in a wall-parallel measuring plane located inside a flat plate boundary layer evolving under variable Reynolds number, adverse pressure gradient (APG) and free-stream turbulence. The results presented in this work show that the present method allows capturing the statistical response of the transition process to the modification of the inlet flow conditions. The location of the maximum spot nucleation is shown to move upstream when increasing all the main flow parameters. Additionally, the transition region becomes shorter for higher Re and APG, whereas the turbulence level variation gives the opposite trend. The effects of the main flow parameters on the coefficients defining the analytic distribution of the nucleation rate and their link to the momentum thickness Reynolds number at the point of transition are discussed in the paper.Graphical abstract

Vision-Based Continuous Sign Language Spotting Using Gaussian Hidden Markov Model

The presence of movement epenthesis ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</i> ) between two consecutive signs in a continuous sign language makes sign language recognition (SLR) a challenging task. In this letter, we propose a vision-based continuous SL spotting system, which separates the meaningful signs from the sign sequences by removing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</i> components from H.264/AVC compressed videos. The work is based on a two-state hidden Markov model (HMM) with Gaussian emission probability. The HMM is trained by the feature set extracted from the entire sign sequence video and finally, the hidden state-sequence is decoded using the Viterbi algorithm. From the decoded state-sequence, the sign spotting is done. The feature set comprises features extracted from the compressed domain as well as uncompressed domain analysis of the sign video. The video database is composed of American SL videos collected from Boston University database. From the experimental results, it is seen that the proposed system can spot the sign and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</i> frames with a spotting rate of about 83%.

Dynamic dependence and risk spillovers between RMB onshore spot and offshore NDF markets

ABSTRACT This paper investigates the dependence structure between the renminbi (RMB) onshore spot and offshore NDF markets using a GARCH-dynamic copula model. We document that the central parity reform in August 2015 marked a structural change in the dependence structure between the onshore spot and offshore NDF markets. Since the reform, the conditional correlation and tail dependence between the two markets have significantly increased and exhibited apparent time-varying patterns. We show that the difference between the central parity and spot rates and the degree of market segmentation between CNY and NDF markets are two crucial factors driving the time-varying CNY-NDF dependence. Furthermore, we find symmetric downside and upside risk spillovers between the two markets transmitted in both directions. The magnitude of spillovers has significantly increased since the reform. Our findings are particularly relevant for policy-making and portfolio risk management.

Exchange Rate and its Forecasting: Market-Based Forecasting and Forecasting with the Use of Currency Betas (βS)

This paper is using the market-based and the currency beta (β) theories of exchange rate forecasting. It is testing empirically these theories by using data, spot and forward rates, from seven different countries with respect the U.S., as our domestic country. The countries are: U.S. with Euro-zone, Mexico, Canada, U.K., Switzerland, Japan, and Australia. The results show that both methods, the market-based forecasting and the currency betas are giving very good forecasting for these seven exchange rates by minimizing the standard error of the regression (SER) and the root mean squared error (RMSE). Of course, uncertainty exits always in the forecasting of any economic variables, due to unanticipated public policies (monetary, fiscal, and trade) and other “innovations” in our financial markets and new philosophies in our way of living.