Estimation Of Products Research Articles

Stability-indicating RP-HPLC method development and validation for the quantification of amlodipine besylate and valsartan tablets in solid oral dosage form.

The present study discusses the development of simple, rapid, specific, precision, accuracy, stability indicating the HPLC method for the analysis of amlodipine besylate and valsartan tablet dosage form. The chromatographic separation was achieved using phosphate buffer with 1% triethyl amine (pH3.0) as mobile phase-A and mixed Methanol and buffer in the ratio of (65:35)(v/v) as mobile phase-B. The detection of components was made at 237 nm for amlodipine besylate and valsartan. Analytical techniques should enrich sensitivity and specificity for the estimation of pharmaceutical drug products. Evaluated stress studies under different types of ICH conditions. The optimized HPLC method was validated as per the current ICH guidelines. The validated HPLC method was obtained highly specific with linearity ranging between 25 and 200 μgmL-1 of amlodipine besylate and 40-320 μgmL-1 of valsartan and both components correlation coefficient was > 0.999. The method showed high accuracy more than 97%. In stress studies, amlodipine besylate and valsartan were found to be sensitive to acid stress conditions and oxidation stress conditions. The method was found to be suitable for the quality control of amlodipine besylate and valsartan in the tablet as well as in stability-indicating studies. The method was applied to the analysis of stability samples.

Spatialization and Analysis of China’s GDP Based on NPP/VIIRS Data from 2013 to 2023

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development.

On the fractional heat semigroup and product estimates in Besov spaces and applications in theoretical analysis of the fractional Keller–Segel system

This paper is concerned with the fractional Keller–Segel system in temporal and spatial variables. We consider fractional dissipation for the physical variables including a fractional dissipation mechanism for the chemotactic diffusion, as well as a time fractional variation assumed in the Caputo sense. We analyze the fractional heat semigroup obtaining time decay and integral estimates of the Mittag–Leffler operators in critical Besov spaces, and prove a bilinear estimate derived from the nonlinearity of the Keller–Segel system, without using auxiliary norms. We use these results in order to prove the existence of global solutions in critical homogeneous Besov spaces employing only the norm of the natural persistence space, including the existence of self-similar solutions, which constitutes a persistence result in this framework. In addition, we prove a uniqueness result without assuming any smallness condition on the initial data.

Impact of adding high-concentration neutron poisons to reactor moderator system for guaranteed shutdown

To ensure a reliable shutdown of the proposed advanced heavy water reactor (AHWR) of Indian origin during emergency conditions, it is imperative to use an increased concentration of liquid neutron poison for injection into the moderator system due to its higher reactivity. The sudden injection of this liquid poison may cause a surge in radiolytic product (H2O2 and H2) yield, posing safety risks. If H2/D2 yield exceeds 4 % (v/v), a combustible mixture with oxygen can be formed and presence of oxidizing H2O2 can enhance corrosion. Hence, accurate estimation of H2O2 and H2 is imperative from the reactor safety point of view. We investigated gadolinium and boron (two liquid neutron poisons) at varying concentrations through high energy electron beam and gamma radiolysis. The radiolytic yields from both the liquid neutron poisons of equivalent concentrations at a constant dose and operating moderator conditions in electron beam and gamma radiolysis were compared. The molecular product yield was observed to be concentration and dose-rate-dependent for both the neutron poisons. Electron radiolysis exhibited a higher yield of H2O2, whereas gamma radiolysis resulted in a greater H2 yield. In electron radiolysis, the H2O2 yield decreased with increasing B concentration. These results give an overall estimation of radiolytic products for a particular dose rate, total dose and effect of head space volume over liquid irradiation zone from these two liquid poisons of a particular concentration. These insights will aid in selecting suitable poisons and managing their effects effectively to avoid hydrogen deflagrations after emergency shutdown.

Irish GDP since independence

AbstractThis paper constructs annual gross domestic product (GDP) estimates for Ireland (1924–47) to join the first complete official aggregates. The new series is deployed to revisit Ireland's economic performance in the post‐independence decades. Ireland's economy grew at just under 1.5 per cent per annum and average living standards improved by 40 per cent. The bulk of this was due to labour productivity improvements. Starting in 1924 captures the civil war recovery and paints a more positive picture of the 1920s, whilst the traditional narrative of a ‛mild’ Great Depression is upheld. The 1930s recovery was aided by strong contributions from services and industry, whilst the economy contracted by almost 7 per cent during the ‛Emergency’. Though supporting O'Rourke's view that Irish growth was not unique against European peers, the new data provide evidence of stronger convergence against UK regions. Industry contributed most to growth during the period, growing at 3.6 per cent per annum. The equivalent rate for services was 1.3 per cent, though it contributed substantially during recovery periods. Agricultural output hardly changed due to its post‐war contraction. This paper joins a growing number of studies that suggest that Ireland was poorer at independence than previously believed.

Precision and bias of carbon storage estimations in wetland and mangrove sediments.

Peaty sediments in coastal wetlands play an important role in the sequestration of atmospheric carbon dioxide and its belowground storage. Sediment cores are used to quantify organic matter (OM) density, estimated by multiplying the bulk density of a core segment by its OM fraction. This method can be imprecise, as repeated samples often differ widely. Recent studies have shown that sediment bulk density and OM fraction are not independent but tightly related by a function called the ideal-mixing model. Thus, the bulk density of the sediment can be directly estimated from its OM fraction. Statistical theory and simulations demonstrate that the high variance in the product estimation of OM density is the result of error propagation in the product of two functionally related variables with independent errors. Estimating OM density in wetland sediments using the ideal-mixing model is more precise than the traditionally used product estimate, especially in highly organic sediments.

Combining evolutionary computation with machine learning technique for improved short-term prediction of UT1-UTC and length-of-day

Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study.Graphical

Second Order Biases and Mean Squared Errors of Some Estimators Using Single Auxiliary Variable

The ratio, product, chain ratio, chain product, chain regression and ratio-cum product estimators have been considered by Chand (1975), Kiregyera (1980, 1984), Upadhyaya et. al. (1990, 1992), Srivastava et. al. (1990), Prasad et. al. (1992), Sahoo and Sahoo (1993), Singh (1993). Most of them discussed these estimators along with their first order bias and mean square error. In this paper, we have tried to find out the second order biases and mean square errors of these estimators based on simple random sampling. Finally, we have compared the performance of these estimators with some numerical illustration.

Approximations and inference for envelopment estimators of production frontiers

Nonparametric methods have been commonly used to assess the performance of both private and public organizations. Among them, the most popular ones are envelopment estimators such as Free Disposal Hull (FDH) or Data Envelopment Analysis (DEA), which estimate the attainable sets and their efficient boundaries by enveloping the cloud of observed units in the appropriate input-output space. However, these nonparametric envelopment techniques do not provide estimates of marginal products and other coefficients of economic interest. This paper presents a new approach that provides local estimates of all the desired partial derivatives and economic coefficients, which complement and complete the analysis based on nonparametric envelopment estimators. We improve nonparametric estimators by estimating nonparametrically smoothed efficient boundaries and providing derivatives and other coefficients without having to assume any parametric structure for the frontier and the inefficiency distribution. Our approach offers several advantages, such as a flexible nonparametric adjustment of the efficient frontier based on local linear models; a general multivariate efficiency model based on directional distances where one can choose the desired benchmark direction; the possibility of assessing the impact of external-environmental variables; a bootstrap-based statistical inference for deriving confidence intervals on the estimated coefficients for nonparametric and robust frontier approximations; the possibility of including factors aggregating inputs or outputs and recovering the estimated coefficients in the original units. To demonstrate the usefulness of the proposed approach, we provide an illustration in the field of education, where economic coefficients are important but the parametric assumptions have been questioned.

Enhancing Agricultural Practices Through IoT-Based Smart Farming Technologies: A Case Study on Banana Crop Yield Optimization

Aims: Smart farming and precision agriculture have emerged as transformative paradigms in modern agricultural practices, leveraging technological innovations to enhance productivity, sustainability, and efficiency in food production. This paper provides a comprehensive review of the concepts and technologies related to smart farming. The work proposed implements a sensor assisted IoT based model to enhance the overall yield of banana crop, by monitoring the current conditions existing at the site and predicting the alterations to achieve optimal conditions for conducive and healthy growth, further early identification and prediction of infection causing pathogens to have also been embedded in the model. Model designed has been tested on the farm field of banana crop located in the premises of Amity University Lucknow Campus equipped with moisture and humidity sensors (DHT11), NPK soil nutrient sensor, PIR motion sensor (HC-SR501), pH Sensor (pH -450), rain drop sensor along with camera installed drone for infection identification. Data from sensors and drone camera were monitored at regular intervals for future predictions about health and productivity estimate of the crop.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution (LTE)-Advanced and 5G New Radio (NR) mobile networks. However, these techniques introduce Passive Inter-Modulation (PIM) interference in Frequency-Division Duplexing (FDD) systems. In this paper, a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals, thereby achieving effective PIM Cancellation (PIMC) in multi-band scenarios. In the model, transmitted signals are independently processed to simulate Inter-Modulation Distortions (IMDs) and Cross-Modulation Distortions (CMDs). Furthermore, the Finite Impulse Response (FIR) filter, basis function generation, and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios. Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC, achieving up to 19 dB in PIMC performance. Compared to other models, this approach offers superior PIMC performance, exceeding them by more than 5 dB in high transmitting power scenarios. Additionally, its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Challenges in current inhalable tobacco toxicity assessment models: A narrative review.

Emerging tobacco products such as electronic nicotine delivery systems (ENDS) and heated tobacco products (HTPs) have a dynamic landscape and are becoming widely popular as they claim to offer a low-risk alternative to conventional smoking. Most pre-clinical laboratories currently exploit in vitro, ex vivo, and in vivo experimental models to assess toxicological outcomes as well as to develop risk-estimation models. While most laboratories have produced a wide range of cell culture and mouse model data utilizing current smoke/aerosol generators and standardized puffing profiles, much variation still exists between research studies, hindering the generation of usable data appropriate for the standardization of these tobacco products. In this review, we discuss current state-of-the-art in vitro and in vivo models and their challenges, as well as insights into risk estimation of novel products and recommendations for toxicological parameters for reporting, allowing comparability of the research studies between laboratories, resulting in usable data for regulation of these products before approval by regulatory authorities.

Improved Product-type Estimator for Estimating Population Mean using Harmonic Mean

In this paper, we have developed a new product-type estimator using harmonic mean of known auxiliary variable. The bias and mean square error of proposed newly product -type estimator has been derived up to first order of approximation. An empirical study has been carried out to show the performance of proposed estimator along with existing estimators. It is observed that the proposed product-type estimator is more efficient than the competing estimators. For theoretical support a general study is also carried out using normal and Weibull distribution.. KEYWORDS :Bias, Mean square error, Product estimator, Auxiliary variable, Normal distribution, Weibull distribution.

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.

Combining prior and post‐release data while accounting for dispersal to improve predictions for reintroduction populations

AbstractAttempts to reintroduce species to managed areas may be compromised by dispersal into the surrounding landscape. Therefore, decisions regarding the selection and ongoing management of reintroduction areas require predicting dispersal as well as the survival and reproduction rates of the species to be reintroduced. Dispersal can potentially be measured directly by tracking animals, but this is often impractical. However, dispersal can also be inferred from re‐sighting surveys done within reintroduction areas if such data are available from multiple areas with varying connectivity to the surrounding landscape, allowing apparent survival and recruitment to be modelled as a function of connectivity metrics. Here, we show how data from 10 previous reintroductions of a New Zealand passerine, the toutouwai (Petroica longipes), were used to predict population dynamics at a predator‐controlled reintroduction area with high connectivity, and predictions then updated using post‐release data. Bayesian hierarchical modelling of the previous data produced prior distributions for productivity, adult survival and apparent juvenile survival rates that accounted for random variation among areas as well as rat density and connectivity. The modelling of apparent juvenile survival as a function of connectivity allowed it to be partitioned into estimates of survival and fidelity. Bayesian updating based on post‐release data produced posterior distributions for parameters that were consistent with the priors but much more precise. The prior data also allowed the recruitment rate estimated in the new area to be partitioned into separate estimates for productivity, juvenile survival and juvenile fidelity. Consequently, it was possible to not only estimate population growth under current management, but also predict the consequences of reducing the scale or intensity of predator control, facilitating adaptive management. The updated model could then be used to predict population growth as a function of the connectivity and predator control regime at proposed reintroduction areas while accounting for random variation among areas.

Forecasting Dairy Sales Estimates: A Comparative Analysis of Logistic Regression and Random Forest Algorithm

This study examines the effectiveness of logistic regression and random forest algorithms in forecasting sales estimates for dairy products. Using a comprehensive data set that includes factors such as product features, pricing dynamics, promotional efforts and consumer demographics, the models are trained to accurately predict future sales figures. Logistic regression provides a transparent framework for estimating probabilities, while random forest uses ensemble learning to capture complex relationships between variables. Through careful evaluation and comparison, the research aims to identify the strengths and weaknesses of each algorithm in producing reliable sales forecasts for dairy products. The results show that while logistic regression offers interpretability and simplicity, random forest excels in handling non-linear relationships and achieving higher prediction accuracy. Insights gathered from this analysis can help dairy industry stakeholders make informed decisions, optimize resource allocation and improve sales forecasting strategies.

Modified correlated measurement errors model for estimation of population mean utilizing auxiliary information

The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators.

A new method of high-resolution NPP estimation and its implementation in caragana shrublands on the Loess Plateau, China

ABSTRACT Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management.

An Improved Product Estimator for Finite Population Mean in the Presence of Nonignorable Nonresponse

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Estimator of entropy production for partially accessible Markov networks based on the observation of blurred transitions.

A central task in stochastic thermodynamics is the estimation of entropy production for partially accessible Markov networks. We establish an effective transition-based description for such networks with transitions that are not distinguishable and therefore blurred for an external observer. We demonstrate that, in contrast to a description based on fully resolved transitions, this effective description is typically non-Markovian at any point in time. Starting from an information-theoretic bound, we derive an operationally accessible entropy estimator for this observation scenario. We illustrate the operational relevance and the quality of this entropy estimator with a numerical analysis of various representative examples.