Towards the Quantification of Estrone and 17β-Estradiol Conjugates in Dairy Cattle Urine Sorptive Stir Bar Extraction and Gas Chromatography-Mass Spectometry

The global concern regarding the presence of compounds with estrogenic properties in the environment has grown significantly. Endocrine-disrupting compounds (EDCs) are environmental pollutants that disrupt the endocrine systems of both wildlife and humans. Estrogens, with their potent estrogenic effects, are particularly noteworthy among the various substances with endocrine-disrupting properties. They are of concern due to their prevalence in the environment, especially due to dairy waste contamination. These lipophilic compounds can accumulate in livestock fat tissues, potentially entering the human food chain. Sulfated forms of estrone and β- estriol, which exhibit a higher degree of saturation than their non-conjugated counterparts, have also been detected in the environment. The persistence of these saturated conjugated estrogens in the environment raises significant environmental and health concerns, as they possess estrogenic activity and can lead to greater human endocrine disruption than non-conjugated estrogens. While free estrogen forms in the environment have been extensively studied, their conjugated counterparts have received less attention. In this study, we present a chromatographic method for the quantification of estrone and β- estriol and their conjugated forms. Specifically, the gas chromatographic portion of the method involves converting all respective sulfated conjugates to estrone or β-estradiol through acid hydrolysis. We apply this method to quantify estrogen conjugates present in dairy cattle waste and the surrounding dairy farm environment, aiming to monitor their distribution and impact on the ecosystem. This analytical approach contributes to our understanding of the environmental fate of estrogens and their conjugated forms, shedding light on potential risks associated with their presence in the environment and food chain

A comprehensive first principles calculations on (Ba0.82K0.18)(Bi0.53Pb0.47)O3 single-cubic-perovskite superconductor

In this present study, the pseudopotential plane-wave (PP-PW) pathway in the scheme of density functional theory (DFT) is utilized to investigate the various physical properties on (Ba0.82K0.18)(Bi0.53Pb0.47)O3 (BKBPO) single perovskite superconductor. We have analyzed elastic constants and moduli at zero and elevated pressures (up to 25 GPa) as well. We also have investigated the anisotropic nature incorporating both the theoretical indices and graphical representations in 2D and 3D dimensions, which reveals a high level of anisotropy. The flatness of the energy bands near EF is a sign of Van-Hf singularity that might increase the electron pairing and origination of high-TC superconductivity. The computed band structure exhibits its metallic characteristics is confirmed by band overlapping. A band of DOS is formed for the strong hybridization of the constituent elements. The orbital electrons of O-2p contribute most dominantly at EF in contrast to all orbital electrons. The orbital electrons at the EF are higher from both the partial density of states and charge density mapping investigation. The coexistence of the electron and hole-like Fermi sheets exhibits the multi-band nature of BKBPO. On the other hand, Fermi surfaces with flat faces promote transport features and Fermi surface nesting as well. The calculated value of the electron-phonon coupling constant ({\lambda} = 1.46) is slightly lower than the isostructural superconductor, which indicates that the studied BKBPO can be treated as a strongly coupled superconductor similar to the reported isostructural perovskite superconductors. Furthermore, the thermodynamic properties have been evaluated and analyzed at elevated temperature and pressure by using harmonic Debye approximation (QHDA).Comment: 20 pages, 7 figures, 6 table

BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset

While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33,695 human annotated document samples from six domains - i) books and magazines, ii) public domain govt. documents, iii) liberation war documents, iv) newspapers, v) historical newspapers, and vi) property deeds, with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models

Using the Macroscopic Fundamental Diagram to Characterize the Traffic Flow in Urban Network

Various theories have been proposed to describe vehicular traffic flow in cities on an aggregate level. This dissertation work shows that a number of MFDs exist in an urban network. The number of MFDs basically indicate the existence of different levels of service on different network routes. It also demonstrate that the modification of control strategy can optimize the signal timing plan for the links with high congestion and spillbacks. With the proposed control strategy, the location of points are shifted from lower MFDs to upper MFDs which means the congestion are reduced and the overall network traffic flow operation is improved. In this thesis, the emergency vehicle preemption (EVP) operation is also evaluated by using the MFDs. The concept of MFD can help to illustrate the effect on various types of roads due to EVP operation. The results show that the volume of links along the emergency route is increased and the volume of other links closed to the emergency route is decreased due to preemption. The researchers and practitioners can apply the proposed approach to identify the affected links and minimize the total network delay during EVP. Master of Scienc

An ensemble learning approach based on decision trees and probabilistic argumentation

This research discusses a decision support system that includes different machine learning approaches (e.g. ensemble learning, decision trees) and a symbolic reasoning approach (e.g. argumentation). The purpose of this study is to define an ensemble learning algorithm based on formal argumentation and decision trees. Using a decision tree algorithmas a base learning algorithm and an argumentation framework as a decision fusion technique of an ensemble architecture, the proposed system produces outcomes. The introduced algorithm is a hybrid ensemble learning approach based on a formal argumentation-based method. It is evaluated with sample data sets (e.g. an open-access data set and an extracted data set from ultrasound images) and it provides satisfactory outcomes. This study approaches the problem that is related to an ensemble learning algorithm and a formal argumentation approach. A probabilistic argumentation framework is implemented as a decision fusion in an ensemble learning approach. An open-access library is also developed for the user. The generic version of the library can be used in different purposes

An ensemble learning approach based on decision trees and probabilistic argumentation

This research discusses a decision support system that includes different machine learning approaches (e.g. ensemble learning, decision trees) and a symbolic reasoning approach (e.g. argumentation). The purpose of this study is to define an ensemble learning algorithm based on formal argumentation and decision trees. Using a decision tree algorithmas a base learning algorithm and an argumentation framework as a decision fusion technique of an ensemble architecture, the proposed system produces outcomes. The introduced algorithm is a hybrid ensemble learning approach based on a formal argumentation-based method. It is evaluated with sample data sets (e.g. an open-access data set and an extracted data set from ultrasound images) and it provides satisfactory outcomes. This study approaches the problem that is related to an ensemble learning algorithm and a formal argumentation approach. A probabilistic argumentation framework is implemented as a decision fusion in an ensemble learning approach. An open-access library is also developed for the user. The generic version of the library can be used in different purposes

An ensemble learning approach based on decision trees and probabilistic argumentation

This research discusses a decision support system that includes different machine learning approaches (e.g. ensemble learning, decision trees) and a symbolic reasoning approach (e.g. argumentation). The purpose of this study is to define an ensemble learning algorithm based on formal argumentation and decision trees. Using a decision tree algorithmas a base learning algorithm and an argumentation framework as a decision fusion technique of an ensemble architecture, the proposed system produces outcomes. The introduced algorithm is a hybrid ensemble learning approach based on a formal argumentation-based method. It is evaluated with sample data sets (e.g. an open-access data set and an extracted data set from ultrasound images) and it provides satisfactory outcomes. This study approaches the problem that is related to an ensemble learning algorithm and a formal argumentation approach. A probabilistic argumentation framework is implemented as a decision fusion in an ensemble learning approach. An open-access library is also developed for the user. The generic version of the library can be used in different purposes

Synergistic effect of Mg addition on the enhancement of the mechanical properties and evaluation of corrosion behaviors in 3.5 wt % NaCl of aluminum alloys

Aluminum alloys are highly preferred for their superior properties, including high corrosion resistance and lightweight in the automotive industry. To better understand how magnesium addition affects aluminum's corrosion and strengthening properties, three different percentages of magnesium-added aluminum alloys, as well as pure aluminum, were melted at a temperature of 800 ± 10 °C in a furnace and cast using the sand molding process. Subsequently, weight loss was used to conduct corrosion testing along with mechanical tests such as tensile, flexural, hardness, and impact tests. In-depth research revealed that the addition of magnesium at 3 wt %, 5 wt %, and 7 wt % strengthened the aluminum alloy. The addition of magnesium resulted in the formation of Al3Mg2, which restricted the movement of dislocation, induced grain refinement, and increased the strength of the alloy. However, it was observed that the addition of magnesium caused a decrease in the alloy's toughness and ductility, resulting in decreased impact energy and % elongation by 29.19 % and 34.87 % respectively by the addition of 5 wt% Mg compared to pure aluminum. Nevertheless, the optical microstructure and SEM image revealed refined grains and the formation of Al3Mg2, providing valuable insight into magnesium's strengthening behavior in aluminum. The study found that adding 7 wt % Mg to the aluminum alloy did not significantly improve its strength and hardness compared to adding 5 wt % Mg. This was because the 7 wt % Mg addition caused the grain size to increase, making it less effective at resisting dislocation movements. The grain coarsening of the 7 wt % Mg added alloy was also revealed in the optical microscope and the SEM images. The EDS analysis confirmed the presence of Al and Mg within the globular-shaped intermetallic particles, indicating the formation of the Al3Mg2 intermetallic phases. However, the highly reactive nature of magnesium results in a higher corrosion rate in terms of weight loss and corrosion current density, which causes the formation of pits and metal dissolution, leading to significant metal loss beneath the original surface when immersed in 3.5 wt % NaCl medium for a period of fifteen and thirty days. Localized corrosion was indicated by the SEM images, which showed concave and convex structures formed by the corrosion products on the alloys. The breakdown of the Al2O3 protective layer, which is the cause of the pits and cracks in the corrosion products, may be brought on by internal stress or the dehydration of hydroxides, which is known as Mg-induced stress corrosion cracking. However, more pits and cracks are found in the SEM image for the 7 wt % Mg addition as it was corroded more compared to the other alloys. The map analysis of the corroded alloy confirmed the corrosion behaviors of the Mg-added alloy by the presence of oxygen all over the surface. Because of the alloy's Al3Mg2 intermetallic compound's refinement and lower corrosion rate, 5 wt % of Mg was found to be the optimal amount for the addition of aluminum to increase strength and hardness without compromising the alloy's toughness and ductility

Forecasting of Maize Production in Bangladesh Using Time Series Data

Maize has been gaining importance as one of the major grain crops in Bangladesh in recent years. Due to its multiple uses, i.e., food, feed and other industrial uses, maize production and its possible trend have created great interest among the policy planners. This study aims to forecast future production of maize in Bangladesh using both Box-Jenkins autoregressive integrated moving average (ARIMA) and mixed model approach (dynamic regression model) using secondary yearly data, for the growing seasons 1970-71 to 2019-20, published by the Bangladesh Bureau of Statistics (BBS). Our analyses suggest that ARIMA (0, 2, 1) is the best model for forecasting maize production all over Bangladesh. However, when the area of maize is considered the mixed model with ARIMA (1, 0, 0) performs better than the univariate ARIMA (0, 2, 1) model. The length of the 95% confidence interval of the forecast values of the mixed model is smaller than that of the ARIMA model indicating its better predictive performance. These forecast values will be useful for planning resources and making appropriate decisions regarding imports and exports by the government before harvesting

Forecasting of Maize Production in Bangladesh Using Time Series Data

Maize has been gaining importance as one of the major grain crops in Bangladesh in recent years. Due to its multiple uses, i.e., food, feed and other industrial uses, maize production and its possible trend have created great interest among the policy planners. This study aims to forecast future production of maize in Bangladesh using both Box-Jenkins autoregressive integrated moving average (ARIMA) and mixed model approach (dynamic regression model) using secondary yearly data, for the growing seasons 1970-71 to 2019-20, published by the Bangladesh Bureau of Statistics (BBS). Our analyses suggest that ARIMA (0, 2, 1) is the best model for forecasting maize production all over Bangladesh. However, when the area of maize is considered the mixed model with ARIMA (1, 0, 0) performs better than the univariate ARIMA (0, 2, 1) model. The length of the 95% confidence interval of the forecast values of the mixed model is smaller than that of the ARIMA model indicating its better predictive performance. These forecast values will be useful for planning resources and making appropriate decisions regarding imports and exports by the government before harvesting