Multi-surveyor detection-mark-redetection as a powerful tool for butterfly population monitoring in the pre-imaginal stage

1. For many elusive insect species, which are difficult to cover by standard monitoring schemes, innovative monitoring methods are needed to gain robust data on population trends. We suggest a monitoring of overwintering larvae for the endangered nymphalid butterfly Limenitis reducta. 2. We tested one removal and three detection-mark-redetection (DMR) approaches in a field study in the “Alb-Donau” region, Germany. We replaced movement of the study organisms by random movement of multiple different surveyors, and we examined the model assumption of equal detectability using simulations. 3. Our results indicate that multi-surveyor removal/DMR techniques are suitable for estimating abundance of overwintering L. reducta larvae. Detection probabilities varied with surveyor experience and the uncertainty of population estimates increased with a decrease in personnel expenditure. Estimated larval densities on a spruce clear-cut ranged between one and three individuals per 100 m². 4. We suggest a detection-mark-redetection (DMR) approach with three trained surveyors for the monitoring of L. reducta populations in the pre-imaginal stage. Besides L. reducta, the proposed method is likely to be suitable for other insect taxa with specific immobile life-stages and some sessile organisms, e.g. corals, elusive plants, or fungi.

Behavior of Stock Return ; Evidence from Indonesia and Malaysia Shariah Stock Market

This study aims to determine stock return behaviour in Indonesia and Malaysia Shariah stock market. Indonesia and Malaysia are selected based on the countries level of development and geographical factor, since both countries are emerging market with a rapid growth of Shariah stock market not only in term of listed companies but also in term of number of investor. Based on geographical proximity, both countries close to each other and have a strong bilateral relationship which makes their stock market return behaviour influence by many factors. This studies relies on two major time series investigation techniques, namely Economteric Modeling of returns; The Autoregressive model, Assumption of Linearity, Volatility Modeling of GARCH and its extension. The result showed that stock return behavior happening in Indonesia and Malaysia Shariah Stock Market.

Return Autocorrelations and Volatilities of Kuala Lumpur Shariah Compliance that Coincide with Big News in Malaysia

This paper investigates the long term return behyavior of Kuala Lumpur Shariah Compliance. This studies relies on two major time series investigation techniques, namely Econometric Modeling of returns; The Autoregressive model, Assumption of Linearity, Volatility Modeling of GARCH and its extension. The statistical process from linearity and volatility modeling, stock return predictability and Shari’ah compliance integration by using GARCH model specification showed that in term of return behaviour particularly volatility of Shari’ah compliances in Malaysia are vulnerable towards events and news that happened in Malaysia.

CWL: A conditional weighted likelihood method to account for the delayed joint toxicity–efficacy outcomes for phase I/II clinical trials

The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity–efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient’s actual follow-up time. The CWL method makes no parametric model assumption on either the dose–response curve or the toxicity–efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics.

Behaviour of Stock Returns During COVID-19 Pandemic: Evidence from Six Selected Stock Market in the World

This paper investigates the short term return behavior of six selected stock market around the world during the COVID-19 Pandemic. USA, Indonesia, India, South Korea, Saudi Arabia, and Singapore are selected based on the size of their stock market and the countries have taken a considerable amount of decision and policy to mitigate the risk of before, ongoing, and aftermath COVID-19 Pandemic. This study relies on two major time series investigation techniques, namely Econometric Modeling of returns; The Autoregressive model, Assumption of Linearity, Volatility Modeling, namely the GARCH and WBAVR Test. The results suggest that the stock return behavior in six selected countries occurs in different forms. Our findings suggest that the policymakers must understand how to shift their policy to mitigate the risk of COVID-19 in the financial sector, since we observe a strong correlation between the public health crisis and stock market performances.

Dissimilarity of parameterized maps generated in geostatistics and the assumption of an automatic software model in precision agriculture

Geostatistics is the main technique used to efficiently determine spatial variability. The objective of this study was to evaluate the applicability of the principles of geostatistics in the use of semivariograms elaborated through parametric monitoring and the assumption automatically made by software in the map preparation of soil chemical attributes. Available phosphorus (P), potassium (K+), calcium (Ca2+), magnesium (Mg2+), base saturation (V%), sulfur (SO42-), and pH were compared from the soil chemical attributes of 60 samples of a Typical Oxisol collected at a 0-20 cm depth and a distance of 300 m between the points. The maps were compared using error matrices and evaluated by the Global Accuracy (GA), Kappa (K), and Tau (T) indexes. The parameterized semivariograms and the automatic software model assumption did not present a high coincidence for the available P and Mg2+, making it necessary to adjust the semivariogram variables in the spatial analysis as a function of the outliers, sum of squares of residuals, coefficient of determination, and cross-validation to better represent the variability of the data and thus avoid distortions of the sample point range that would affect the adequate representativeness of the attributes, which contrasts with the automatic model generated by the software.

A New Set of Local Indices Applied to a Water Network through Demand and Pressure Driven Analysis (DDA and PDA)

In the analysis of drinking Water Distribution Networks (WDNs), performance indices are widely used tools for obtaining synthetic information about the WDN operating regime (pressures and flows). This paper presents applications of a series of local surplus indices that act in a new mathematical framework. This framework allows reworking many well-known performance and energetic indices and simultaneously allowing analysis of specific aspects of the WDN. The analyses are carried out using different resolutive hydraulic approaches: the Demand-Driven Analysis (DDA) and the Pressure-Driven Analysis (PDA), typical of software such as EPANET and WaterNetGen. The authors analyse the hypotheses necessary for the application of these models, and how these influence the results of both the hydraulic modeling and the resilience indices assessment. In particular, two resilience indices are reformulated through the new local surplus indices and all of them are then simulated in different conditions for a water network known in literature as the Kang and Lansey WDN. The solving model assumption effects are deepen, reporting graphical and numerical results for different consumption scenarios and the different hydraulic approaches used.

The origin of X-ray emission in the gamma-ray emitting narrow-line Seyfert 1 1H 0323+342

ABSTRACT We present the results of X-ray spectral and timing analyses of the closest gamma-ray emitting narrow-line Seyfert 1 (γ-NLS1) galaxy, 1H 0323+342. We use observations from a recent, simultaneous XMM–Newton/NuSTAR campaign. As in radio-quiet NLS1s, the spectrum reveals a soft excess at low energies (≲2 keV) and reflection features such as a broad iron K emission line. We also find evidence of a hard excess at energies above ∼35 keV that is likely a consequence of jet emission. Our analysis shows that relativistic reflection is statistically required, and using a combination of models that includes the reflection model relxill for the broad-band spectrum, we find an inclination of $i=63^{+7}_{-5}$ degrees, which is in tension with much lower values inferred by superluminal motion in radio observations. We also find a flat (q = 2.2 ± 0.3) emissivity profile, implying that there is more reflected flux than usual being emitted from the outer regions of the disc, which in turn suggests a deviation from the thin disc model assumption. We discuss possible reasons for this, such as reflection off of a thick accretion disc geometry.

Bayesian trans-dimensional full waveform inversion: synthetic and field data application

SUMMARY Seismic full waveform inversion (FWI) is a state-of-the-art technique for estimating subsurface physical models from recorded seismic waveform, but its application requires care because of high non-linearity and non-uniqueness. The final outcome of global convergence from conventional FWI using local gradient information relies on an informative starting model. Bayesian inference using Markov chain Monte Carlo (MCMC) sampling is able to remove such dependence, by a direct extensive search of the model space. We use a Bayesian trans-dimensional MCMC seismic FWI method with a parsimonious dipping layer parametrization, to invert for subsurface velocity models from pre-stack seismic shot gathers that contain mainly reflections. For the synthetic study, we use a simple four-layer model and a modified Marmousi model. A recently collected multichannel off-shore seismic reflection data set, from the Lord Howe Rise (LHR) in the east of Australia, is used for the field data test. The trans-dimensional FWI method is able to provide model ensembles for describing posterior distribution, when the dipping-layer model assumption satisfies the observed data. The model assumption requires narrow models, thus only near-offset data to be used. We use model stitching with lateral and depth constraints to create larger 2-D models from many adjacent overlapping submodel inversions. The inverted 2-D velocity model from the Bayesian inference can then be used as a starting model for the gradient-based FWI, from which we are able to obtain high-resolution subsurface velocity models, as demonstrated using the synthetic data. However, lacking far-offset data limits the constraints for the low-wavenumber part of the velocity model, making the inversion highly non-unique. We found it challenging to apply the dipping-layer based Bayesian FWI to the field data. The approximations in the source wavelet and forward modelling physics increase the multimodality of the posterior distribution; the sampled velocity models clearly show the trade-off between interface depth and velocity. Numerical examples using the synthetic and field data indicate that trans-dimensional FWI has the potential for inverting earth models from reflection waveform. However, a sparse model parametrization and far offset constraints are required, especially for field application.