Significant Figures Research Articles

Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms.

The taste quality of rice is determined by protein and amylose percentages, with low levels indicating high-quality taste in Japan. However, accurate non-destructive screening remains a challenge for the industry. We explored the use of machine learning models and near-infrared spectra to classify rice taste quality. Three models were optimized using 796 brown rice samples from Hokkaido, Japan, produced between 2008 and 2016, and tested on 278 distinct samples from the same region produced between 2017 and 2019. Logistic regression and support vector machine models outperformed the partial least-squares discriminant analysis model, achieving high accuracy (94%), f1-score (90%), average precision (0.94), and low classification error (4%) and allowing accurate non-destructive classification of rice quality. These results not only improve rice quality, post-harvest technology, and producer output in Japan but also could enhance quality control processes and foster the production of high-quality products for other agricultural goods and food commodities worldwide.

High‐resolution inverse synthetic aperture radar imaging of satellites in space

AbstractIn view of the increasing number of space objects, comprehensive high‐quality space surveillance becomes ever more important. Radar is a powerful tool that, in addition to detection and tracking of objects, also enables spatially high‐resolution imaging independent of daylight and most weather conditions. Together with the technique of Inverse Synthetic Aperture Radar (ISAR), very high‐resolution and distance‐independent two‐dimensional images can be obtained. However, advanced high‐performance radar imaging of space objects is a complex and demanding task, touching many technological and signal processing issues. Therefore, besides theoretical work, the Microwaves and Radar Institute of German Aerospace Center (DLR) has developed and constructed an experimental radar system called IoSiS (Imaging of Satellites in Space) for basic research on new concepts for the acquisition of advanced high‐resolution radar image products of objects in a low earth orbit. Based on pulse radar technology, which enables precise calibration and error correction, IoSiS has imaged space objects with a spatial resolution in the centimetre range, being novel in public perception and accessible literature. The goal of this paper is therefore to communicate and illustrate comprehensively the technological steps for the construction and successful operation of advanced radar‐based space surveillance. Besides the basic description of the IoSiS system design this paper outlines primarily useful theory for ISAR imaging of objects in space, together with relevant imaging parameters and main formulae. All relevant processing steps, necessary for very high‐resolution imaging of satellites in practice, are introduced and verified by simulation results. Finally, a unique measurement result demonstrates the practicability of the introduced processing steps and error correction strategies.

A highly scalable CF recommendation system using ontology and SVD-based incremental approach

In recent years, the need of recommender systems has increased to enhance user engagement, provide personalized services, and increase revenue, especially in the online shopping industry where vast amounts of customer data are generated. Collaborative filtering (CF) is the most widely used and effective approach for generating appropriate recommendations. However, the current CF approach has limitations in addressing common recommendation problems such as data inaccuracy recommendations, sparsity, scalability, and significant errors in prediction. To overcome these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines the incremental singular value decomposition approach with an item-based ontological semantic filtering approach in two phases, online and offline. The ontology-based technique is leveraged to enhance the accuracy of predictions and recommendations. Evaluating our method on a real-world movie recommendation dataset using precision, F1 scores, and mean absolute error (MAE) demonstrates that our system generates accurate predictions while addressing sparsity and scalability issues in recommendation system. Additionally, our method has the advantage of reduced running time.

Between Medicine and Revolution: towards the biography of Elena Nikolaevna Oshanina

Biographical research has been an integral part of historical science since its inception. However, as a rule, the most significant historical figures have always fallen into the sphere of interests of scientists. In post-Soviet historiography, biographies of people who are difficult to classify as outstanding figures began to appear more and more often. Despite this, through the analysis of their biographies, it is possible to trace how the inheritance of cultural traditions formed in different historical periods took place, the ways of assimilation of social, gender and other ideas, their transfers and influence on people's behavior in specific historical circumstances. The purpose of the article is to restore the biography of Elena Nikolaevna Oshanina (1874–1943), a physician by profession and a Socialist Revolutionary by party affiliation, based on unpublished archival documents from the central, local and personal archives, to identify her place in the revolutionary and women's social movement of the early twentieth century. A particular example of the biography makes it possible to understand how the process of formation of diverse and often layered identities of women of the late XIX – early XX centuries took place. As a result, it can be concluded that Elena Nikolaevna Oshanina's self-realization was in the field between two identities – professional and revolutionary, which were layered on top of each other, but both were very important for the heroine of this biography.

The role of the Radiology Technologist in the assessment of DXA unit precision error and Least Significant Change/LSC

The role of the Radiology Technologist in the assessment of DXA unit precision error and Least Significant Change/LSC

Software Support for Arbitrary Precision Arithmetic in Programming Languages

Arbitrary precision arithmetic, also known as bignum arithmetic, is a computational technique that allows programmers to perform arithmetic operations on numbers with significantly higher precision and magnitude than what is typically supported by the built-in numerical data types in programming languages. This technique is especially useful when working with extremely large or extremely precise numbers, such as in cryptography or scientific computations. Arbitrary precision arithmetic has many applications in the areas of Cryptography, Numerical Computation, Statistical Analysis, and High Precision measurements. For example, the calculation of the modulus in the RSA algorithm involves numbers with 1024-bit numbers and higher. An arithmetic calculation involving Multiplication and exponentiation of such numbers using Modulo arithmetic cannot be easily carried out in the existing programming Languages unless special software is provided. We can calculate the mathematical constant Pi to many thousand decimal places using the support provided in Programming Languages. Many programming languages provide built-in support for libraries for arbitrary precision arithmetic. We discuss the support provided in C/C++, Java and Python Languages with examples. Besides Programming Languages, Toolkits like Matlab and Sagemath also are used for scientific computation and special software support provided in these toolkits can enable arbitrary precision arithmetic. Most Programming Languages have support for floating-point arithmetic. We also discuss how arbitrary precision floating point arithmetic. can be supported in C/C++, Java, and Python. In addition, we discuss support for arbitrary precision integer arithmetic in Ruby, Javascript and Matlab and support for arbitrary precision floating point arithmetic in the Perl Language. Finally, we provide an example of computing the constant Pi to many decimal places using the sagemath tool.

Calculation of the Least Significant Change Value of Bone Densitometry Using a Dual-Energy X-ray Absorptiometry System

Purpose: The precision error of a bone density meter reflects the equipment and reproducibility of results by an examiner. Precision error values can be expressed as coefficient of variation (CV), CV%, and root mean square-SD (RMS-SD). The International Society for Clinical Densitometry (ISCD) currently recommends using RMS-SD as the precision error value. When a 95% confidence interval is applied, the least significant change (LSC) value is calculated by multiplying the precision error value by 2.77. Exceeding the LSC value reflects a significant difference in measured bone density. Therefore, the LSC value of a bone density equipment is an essential factor for accurately determining a patient's bone density. Accordingly, we aimed to calculate the LSC value of a bone density meter (Lunar iDXA, GE) and compare it with the value recommended by the ISCD. We also assessed whether the value measured by the iDXA equipment was below the LSC value recommended by ISCD. Material and Methods: The bone densities of the lumbar spine and thighs of 30 participants were measured twice, and the LSC values were calculated using the precision calculation tool provided by the ISCD (http://www.iscd.org). To check the reproducibility of the measurement, patients were asked to completely dismount from the equipment after the first measurement; the patient was then repositioned before proceeding with the second measurement. Results: The LSC values derived using the CV% values recommended by the ISCD were 5.3% for the lumbar spine and 5.0% for the thigh. The LSC values measured using our bone density equipment were 2.47% for the lumbar spine and 1.61% for the thigh. The LSC value using RMS-SD was 0.031 g/cm2 for the lumbar spine and 0.017 g/cm2 for the thigh. Conclusion: that the findings confirm that the CV% value measured using our bone density meter and the LSC value using RMS-SD were maintained very stably. This can be helpful for obtaining accurate measurements during bone density follow-up examinations.

THE MOVEMENT OF SHEIKH AHMAD YASIN AND THE MISSION OF PALESTINIAN LIBERATION

This research aims to explore the activities of Sheikh Ahmad Yasin in the Islamic resistance movement (Hamas) in Palestine. The results reveal that Sheikh Ahmad Yasin, who founded Hamas in 1987, held a prominent position among the leaders of organization and was regarded as one of the significant figures of Palestinian martyrs. While serving as the leader of Hamas, Sheikh Ahmad Yasin played a pivotal role in the establishment of several institutions, including zakat collection agencies, peace institutions to resolve disputes among residents, Islamic schools, social institutions, and more. Hamas plays a role in Palestinian society for socialization, interaction, mobilization, and the liberation of Palestinian society from all conflicts and Israeli occupation. Sheikh Ahmad Yasin's movement has a profound and beneficial impact on Palestinian society, providing healthcare, education, and religious guidance to the population in the Gaza Strip, an area controlled by Palestine. It can be concluded that Sheikh Ahmad Yasin's movement and the mission of Palestinian liberation serve a crucial role in society for socialization, interaction, and realizing the struggle for independence through the Hamas movement.

Investigating the Epidemiological Trend and Estimating the Burden of COVID-19 in the Military Population of the Islamic Republic of Iran

Background: The Burden of Disease study provides a comprehensive picture of mortality and disability across countries, time periods, age groups, and genders. Objectives: This study was conducted to determine the epidemiological trend and estimate the burden of COVID-19 in the military population of Iran. Methods: This cross-sectional study used the methods by the World Health Organization for the national burden of disease studies to estimate the years of life lost due to premature death and burden of disability (YLD), years of life lost from mortality (YLL), years of life lost due to disability diseases, and the total burden of death and disability in terms of disability-adjusted life years (DALY). The information to calculate the desired indicators for COVID-19 in the military population of Iran, including the number of deaths, the number of hospitalizations, and the costs of hospitalization and treatment of patients, was obtained from the insurance organization of the armed forces. Excel software version 2017 is used to calculate 3 indicators. Results: The total DALY for COVID-19 in the Iranian military population during the three-year study period was calculated to be 162,429 years, which is a significant figure. The disease burden showed an increasing trend in the study years and reached its peak in 2021, which aligns with the overall disease situation in the country. In general, the burden of disease was higher in men than in women. Conclusions: The impact of COVID-19 on the military and its economic burden was substantial in the studied areas. Therefore, it is crucial to undertake careful planning in addressing these issues. Using economic burden studies and examining their dimensions provides policymakers with sufficient evidence to allocate financial resources to vital sectors and adopt appropriate policies in critical situations.

Nested Simulation for Conditional Value-at-Risk with Discrete Losses

Nested simulation has been an active area of research in recent years, with an important application in portfolio risk measurement. While majority of the literature has been focusing on the continuous case where portfolio loss is assumed to follow a continuous distribution, monetary losses of a portfolio in practice are usually measured in discrete units, oftentimes due to the practical consideration of meaningful decimal places for a given level of precision in risk measurement. In this paper, we study a nested simulation procedure for estimating conditional Value-at-Risk (CVaR), a popular risk measure, in the case where monetary losses of the portfolio take discrete values. Tailored to the discrete nature of portfolio losses, we propose a rounded estimator and show that when the portfolio loss follows a sub-Gaussian distribution or has a sufficiently high-order moment, the mean squared error (MSE) of the resulting CVaR estimator decays to zero at a rate close to [Formula: see text], much faster than the rate of the CVaR estimator in the continuous case which is [Formula: see text], where [Formula: see text] denotes the sampling budget required by the nested simulation procedure. Performance of the proposed estimator is demonstrated using numerical examples.

On evanescent waves and blowing-ups of the finite series technique in spherical wave expansion of shaped beams

In generalized Lorenz-Mie theory, the beam shape coefficients of Gauss-like beams obtained with finite series method blow up when the wave order is high. The blowing-ups caused by the loss of significant digits may be suppressed by increasing numerical precisions. Once the elimination of such blowing-ups is carried out, blowing-up phenomena still occur whose origin is still not clear to us. The work presented here attempts to provide an explanation on this issue. Based on the angular spectrum representation of the beam field, it is found that the blowing-up originates from the contribution of evanescent waves.

GF-1 WFV Surface Reflectance Quality Evaluation in Countries along “the Belt and Road”

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV.

Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros

Abstract In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing for excessive zero values based on the zero-inflated negative binomial distribution with score dynamics. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study, we find that split transactions cause between 92 % and 98 % of zero and close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than the incorrect treatment of zero values in continuous models.

New Discourse of the Shennong Symbol in Vietnam

Objective: Researching the contemporary discourse surrounding the iconic figure of Shennong (a long-standing symbol in Vietnamese culture) aims to shed light on how this age-old symbol is being redefined in the present era. This research endeavors to provide insights for scholars and decision-makers, enabling them to generate fresh narratives for old symbols, and infuse new life into traditional icons to better serve the demands of modern life. Methods: Studying the new discourse surrounding the Shennong symbol in Vietnam requires an interdisciplinary scientific approach, incorporating fields such as cultural studies, literature, Sino-Nom studies, and communication studies. In the process of addressing this issue, the author utilizes research methods such as textual analysis, field research, text analysis, comparative analysis, logic-historical analysis, harmonizing contemporary and historical perspectives, and analytical synthesis. These research methods are consistently applied by the author to provide a comprehensive and specific approach suitable for the current research task. Results: Ancient records also affirm the blood relations of Shennong with significant figures in Vietnamese mythology, such as Lạc Long Quan, Au Co, and their descendants, the Hung kings. However, cultural expressions related to the figure of Shennong over the past 30 years (since 1991) have shown that the meaning of the Shennong symbol is evolving to better align with the demands of the modern era. Consequently, the significance of the Shennong symbol associated with agriculture still leaves an imprint on cultural practices, extending to symbolize the spirit of labor and building a new life in the contemporary age. Moreover, the Shennong symbol now holds a new meaning: a symbol of national unity. Conclusions: In Vietnam, legends and records related to Shennong, along with the religious practices and worship of this deity, traditionally focused on the symbol's significance for agriculture, embodying the deep agrarian spirit of the Vietnamese people. In the modern era, the Shennong symbol in Vietnam has expanded its meaning to encompass the spirit of labor in building a new life in a different age. Simultaneously, it is gradually becoming a symbol of national unity. This reflects the Vietnamese people's aspiration to utilize the Shennong symbol for constructing a more prosperous future and for fostering a sense of connection and unity among the diverse Vietnamese communities in various regions.

Stability of the Lanczos algorithm on matrices with regular spectral distributions

We study the stability of the Lanczos algorithm run on problems whose eigenvector empirical spectral distribution is near to a reference measure with well-behaved orthogonal polynomials. We give a backwards stability result which can be upgraded to a forward stability result when the reference measure has a density supported on a single interval with square root behavior at the endpoints. Our analysis implies the Lanczos algorithm run on many large random matrix models is in fact forward stable, and hence nearly deterministic, even when computations are carried out in finite precision arithmetic. Since the Lanczos algorithm is not forward stable in general, this provides yet another example of the fact that random matrices are far from “any old matrix”, and care must be taken when using them to test numerical algorithms.

The Dairy Meltdown

The case highlights the bankruptcy case of Kwality Limited, a significant Indian dairy company. Haldiram Snacks submitted a final bid of ₹1.45 billion to the Committee of Creditors for Kwality Limited, which is currently undergoing bankruptcy proceedings LiveMint (2019). This bid, a slight increase from their earlier rejected bid of ₹1.42 billion, has raised concerns due to its inadequate coverage of the company’s substantial outstanding debt of ₹19 billion Despite extensions, Haldiram Snacks remains the sole bidder, presenting a resolution plan under the Indian Insolvency and Bankruptcy Code (2016). The bankruptcy process was initiated in 2018 by financial creditors KKR India Financial and Punjab National Bank. The bid’s insufficiency, covering less than 10% of the debt, prompts reflections on overlooked warning signs and preventive strategies for the future. The larger context of the Indian dairy industry is provided, highlighting significant production and consumption figures, the growing organized dairy sector and the demand for value-added products. Kwality’s growth and expanding farmer network are briefly touched upon. The case overviews Kwality Limited’s bankruptcy, Haldiram Snacks’ bid, the company’s history and performance, and the broader Indian dairy industry landscape. It underlines the bid’s shortfall in comparison to the outstanding debt and raises pertinent questions about the company’s downfall and preventive measures moving forward.

Reconfigurable hardware implementation of K-nearest neighbor algorithm on FPGA

Nowadays, Machine Learning is commonly integrated into most daily life applications in various fields. The K Nearest Neighbor (KNN), which is a robust Machine Learning algorithm, is traditionally used in classification tasks for its simplicity and training-less nature. Hardware accelerators such as FPGAs and ASICs are greatly needed to meet the increased requirements of performance for these applications. It is well known that ASICs are non-programmable and only fabricated once with high expenses, this makes the fabrication of a complete chip for a specific classification problem inefficient. As a better alternative to this challenge, in this work, a reconfigurable hardware architecture of the KNN algorithm is proposed where the employed dataset, the algorithm parameters, and the distance metric used to evaluate the nearest neighbors are all updatable after fabrication, in the ASIC case, or after programming, in the FPGA case. The architecture is also made flexible to accommodate different memory requirements and allow variable arithmetic type and precision selection. Both parameters can be adjusted before fabrication to account only for the expected memory requirement and the fixed point precision required or floating point arithmetic if needed. The proposed architecture is realized on the Genesys 2 board based on Xilinx’s Kintex-7 FPGA. The results obtained from the experiment are consistent with those obtained from the simulation and software analysis. The proposed realization reaches a frequency of up to around 110 MHz and a power consumption of less than 0.4 watts

Osteometric Dimension of Lower End of Femoral Bone among Bangladeshi Population

Introduction: The osteometric dimensions of the lower end of the femur have significant clinical implications, particularly in the context of total knee arthroplasty (TKA). Accurate measurements are crucial for the design of femoral components in TKA, which can influence surgical outcomes. This study aims to determine the measurements of different variables of the lower end of the femur, including femoral condylar width, intercondylar width and depth, and the depth of the medial and lateral condyles, in a Bangladeshi population. Methods: This cross-sectional study was conducted on 50 dried femurs collected from three medical colleges in Dhaka, Bangladesh. Measurements were obtained using digital sliding calipers and were rounded to two decimal places. Data analysis was performed using SPSS. Result: Statistical analysis revealed that 56% of the femurs were right-sided and 44% were left-sided. The mean femoral bicondylar width was 69.78 ± 5.38 mm, falling mostly in the "Above Average" range (30%). The mean intercondylar width was 23.77 ± 1.55 mm, with 50% falling in the "Medium" range. Other variables like femur medial condylar width had a mean of 31.05 ± 1.74 mm. Conclusion: Observationally, the study successfully delineates the osteometric dimensions of the lower end of the femur in a Bangladeshi cohort. The statistical results not only fulfill the study's initial aim but also provide a nuanced understanding of the osteometric variations. These findings are instrumental for clinicians in the design and customization of femoral components in TKA, thereby potentially enhancing surgical outcomes and patient satisfaction. The data also lay the groundwork for future research in this area.

Transforming sentiment analysis in the financial domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an addition evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35% enhanced performance in sentiment classification and a 36% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT’s potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.

A portable reflected-sunlight spectrometer for CO2 and CH4

Abstract. Mapping the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4) above source regions such as urban areas can deliver insights into the distribution and dynamics of local emission patterns. Here, we present the prototype development and an initial performance evaluation of a portable spectrometer that allows for measuring CO2 and CH4 concentrations integrated along a long (>10 km) horizontal path component through the atmospheric boundary layer above a target region. To this end, the spectrometer is positioned at an elevated site from which it points downward at reflection targets in the region, collecting the reflected sunlight at shallow viewing angles. The path-integrated CO2 and CH4 concentrations are inferred from the absorption fingerprint in the shortwave–infrared (SWIR) spectral range. While mimicking the concept of the stationary California Laboratory for Atmospheric Remote Sensing – Fourier Transform Spectrometer (CLARS-FTS) in Los Angeles, our portable setup requires minimal infrastructure and is straightforward to duplicate and to operate in various locations. For performance evaluation, we deployed the instrument, termed EM27/SCA, side by side with the CLARS-FTS at the Mt. Wilson Observatory (1670 m a.s.l.) above Los Angeles for a 1-month period in April/May 2022. We determined the relative precision of the retrieved slant column densities (SCDs) for urban reflection targets to be 0.36 %–0.55 % for O2, CO2 and CH4, where O2 is relevant for light path estimation. For the partial vertical column (VCD) below instrument level, which is the quantity carrying emission information, the propagated precision errors amount to 0.75 %–2 % for the three gases depending on the distance to the reflection target and solar zenith angle. The comparison to simultaneous CLARS-FTS measurements shows good consistency, but the observed diurnal patterns highlight the need to take light scattering into account to enable detection of emission patterns.