Estimation Method Research Articles

Garlic yield monitoring using vegetation indices and texture features derived from UAV multispectral imagery

Remote sensing and machine learning are widely used to estimate crop yield. The use of these technologies for yield estimation of bulbous vegetables is challenging because the yield is underground and can't be directly monitored by remote sensing images. Among the bulbous vegetables, garlic (Allium sativum L.) is one of the most widely cultivated in the world. The aim of this study was to develop an accurate and transferable machine learning model to monitor and to estimate garlic yield using unmanned aerial vehicle (UAV) multispectral images. Data were collected over three growing seasons (2021, 2022, and 2023) at four different garlic phenological phases (202, 405, 407, and 409 of BBCH). The random forest (RF) algorithm was used to estimate the garlic yield by comparing two different training feature sets: the vegetation indices (VIs) and the VIs with the addition of the texture features extracted from the UAV images. The most important VIs were selected using the recursive feature elimination algorithm. Two estimation methods were compared: a direct bulb estimation and an indirect bulb estimation using the aboveground biomass as a proxy. To evaluate the transferability of the RF models, two cross-validation strategies were compared: a nested leave-one-fold-out cross-validation (LOFOCV) and a leave-one-year-out cross-validation (LOYOCV). The best performance was achieved by the direct bulb estimation using the LOFOCV strategy. Regarding the transferability of the RF models between years (i.e. LOYOCV), the indirect estimation method showed a higher transferability than the direct estimation method. Finally, the addition of texture features improved the accuracy of the RF models, but in general, their contribution was poor. This study demonstrated that the yield of bulbous vegetables can be accurately estimated by remote sensing, and that UAVs are a suitable tool to provide rapid and reliable support for garlic yield monitoring.

A deep learning-based method for estimating the main stem length of sweet potato seedlings

The length of the main stem of sweet potato seedlings is a crucial criterion for determining whether to harvest it. However, accurately estimating the main stem length is challenging due to two growth forms: leaf occlusion and self-curvature, which complicate automated harvesting and grading. This paper considers the morphological characteristics of sweet potato seedlings and proposes a main stem reconstruction and length estimation algorithm based on a two-stage classifier and feature extraction and search. Semantic segmentation is used to obtain masks of the stems. This is followed by image processing to segment clustered skeletons into singular lines, facilitating feature analysis of the main stem. Object detection is employed to identify regions of interest such as roots, growth joints, and stem-leaf intersections. Using the category and centroid information obtained from object detection, a prioritized search relationship is established among the line segments extracted through image processing. Subsequently, the main stems are classified and reconstructed based on constraints including direction, distance, and curvature. Finally, utilizing stereo depth information, the main stem is divided into four parts to estimate its length. Experimental results demonstrate that the algorithm achieves effective main stem reconstruction under four conditions: un-occluded, partially occluded, curved main stem, and low-density clustered multi-seedling states. The algorithm achieves a median length estimation error of 1.7 cm, with maximum errors of 3.2 cm and minimum errors of 0.3 cm. It provides an automated solution for length estimation during sweet potato seedling harvesting and grading.The length of sweet potato seedling main stems is critical for harvest decisions, but accurately measuring it is challenging due to leaf occlusion and self-curvature. This study proposes a method for stem reconstruction and length estimation using a two-stage classifier, feature extraction, and semantic segmentation to obtain stem masks. Image processing separates clustered skeletons into singular lines for detailed analysis. Object detection identifies roots, stem-leaf intersections, and establishes a search relationship among segments. Stems are reconstructed based on directional, distance, and curvature constraints, then divided into four parts for length estimation using stereo depth information. Experimental results demonstrate effectiveness under various conditions: un-occluded, partially occluded, curved stems, and low-density clusters. Median length estimation error is 1.7 cm, with maximum and minimum errors of 3.2 cm and 0.3 cm respectively. This automated method provides a reliable solution for sweet potato seedling harvest and grading.

Does Sovereign ESG Shape Corporate Cash Management in Emerging Markets?

Environmental, social, and governance aspects are becoming increasingly crucial factors for investors decisions. This paper examines the effect of sovereign environmental, social, and governance (SESG) practices on the cash-holding decisions of firms in the Gulf Cooperation Council (GCC) region. Utilizing firm-level data from 2010 to 2022, we find that improved SESG standards lead firms to reduce their cash holdings. The results are robust to sample selection, additional control variables, different estimation methods, and endogeneity concerns. Furthermore, we show a negative relationship between each SESG pillar (environmental, social, and governance) and corporate cash holdings. These findings offer valuable empirical implications for various stakeholders. Regulators can consider the potential impact of SESG standards on corporate financial health, potentially informing future regulations. Shareholders can leverage these findings to assess the potential financial benefits of strong SESG adherence by the companies in which they invest.

Maximum likelihood estimators for colony-forming units.

Measuring the abundance of microbes in a sample is a common procedure with a long history, but best practices are not well-conserved across microbiological fields. Serial dilution methods are commonly used to dilute bacterial cultures to produce countable numbers of colonies, and from these counts, to infer bacterial concentrations measured in colony-forming units (CFUs). The most common methods to generate data for CFU point estimates involve plating bacteria on (or in) a solid growth medium and counting their resulting colonies or counting the number of tubes at a given dilution that have growth. Traditionally, these types of data have been analyzed separately using different analytic methods. Here, we build a direct correspondence between these approaches, which allows one to extend the use of the most probable number method from the liquid tubes experiments, for which it was developed, to the growth plates by viewing colony-sized patches of a plate as equivalent to individual tubes. We also discuss how to combine measurements taken at different dilutions, and we review several ways of analyzing colony counts, including the Poisson and truncated Poisson methods. We test all point estimate methods computationally using simulated data. For all methods, we discuss their relevant error bounds, assumptions, strengths, and weaknesses. We provide an online calculator for these estimators.Estimation of the number of microbes in a sample is an important problem with a long history. Yet common practices, such as combining results from different measurements, remain sub-optimal. We provide a comparison of methods for estimating abundance of microbes and detail a mapping between different methods, which allows to extend their range of applicability. This mapping enables higher precision estimates of colony-forming units (CFUs) using the same data already collected for traditional CFU estimation methods. Furthermore, we provide recommendations for how to combine measurements of colony counts taken across dilutions, correcting several misconceptions in the literature.

Learning real-world heterogeneous noise models with a benchmark dataset

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling algorithm. However, due to the inaccurate estimation method of noise-free images and limited shooting scenes, the current realistic datasets could not describe the diverse noise properties sufficiently. Moreover, popular parametric noise models are not sophisticated enough to characterize the real-world noise exactly. In this work, we first construct a more comprehensive dataset of the real world by capturing more indoor and outdoor scenes under different lighting conditions using diverse smartphones, then we propose a non-parametric noise estimation method capable of modeling the spatial heterogeneity of real-world noise patterns. Specifically, in order to characterize the spatial heterogeneity of real-world noise, we assume a non-i.i.d Gaussian distribution and propose a deep convolutional neural network (DCNN)-based approach for learning pixel-wise noise variance maps. To learn the pixel-wise variance map, we have constructed a variance estimation network mapping from the conditional signals (clean image, ISO, and camera model) to surrogate labels obtained from the nonlocal search of similar patches from the clean-noisy image pair. Finally, we conducted denoising and classification experiments using different kinds of simulated noisy images, compared to the Poisson-Gaussian and Noise Flow noise models, the proposed method achieves denoising performance improvements (PSNR) of 1.13 dB and 2.51 dB respectively on the proposed real-world test dataset, denoising and classification results on the real noisy data captured by mobile phones have verified that our approach is more accurate than current noise modeling methods.

Oblique view imaging analysis of a hypothetical earth observation satellite enabled with metamaterial-based image sensors

This study presents the image quality estimates of a hypothetical earth observation satellite (EOS) equipped with metaxel-based image sensors for the oblique viewing angle of the pitch manoeuvre scenario. Oblique-view image estimation methods are proposed and demonstrated for the optical and detector images. Owing to the small detector period (p) of the metaxel supercells ( p = 1.32 μ m ), the optical system image is optics limited. For oblique angle of θ p = 15 ∘ , 30 ∘ , 45 ∘ , 60 ∘ , image quality degradation owing to the aberration remained minor and average signal to noise ratio (SNR) for nadir view was SNR = 64 because of the local field enhancement of the metaxels. However, changes in the viewing angle reduced the image quality. Correlation coefficient between synthetic ground truth and oblique viewing cases was corr = 0.833, 0.818, 0.793 and 0.745 for the viewing angles of interest listed above.

Effectiveness and efficiency of urban water access in the Democratic Republic of Congo: A panel directional approach

In the 2030 Sustainable Development Goals (SDG) of the United Nations, countries are committed to achieving universal and equitable access to safe and affordable drinking water for all. The Democratic Republic of Congo is a paradox as it owns the second-largest basin in the world while more than half of the population has no access to basic drinking water. This fact is our starting point to conduct a performance evaluation exercise of the 11 provinces from 2008 to 2019. Our approach has five distinguished features: we take account of population trends; we use a tailored and complete database of urban centres; we define and decompose flexible indicators, and we use a non-parametric estimation method. Our results show that there is inefficiency and ineffectiveness in urban water access. Overall, larger efficiency–effectiveness differences are over time observed mostly due to a lack of technological change and a resource constraint. We also highlight the role of public policies.

Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore

Synthetic accessibility prediction is a task to estimate how easily a given molecule might be synthesizable in the laboratory, playing a crucial role in computer-aided molecular design. Although synthesis planning programs can determine synthesis routes, their slow processing times make them impractical for large-scale molecule screening. On the other hand, existing rapid synthesis accessibility estimation methods offer speed but typically lack integration with actual synthesis routes and building block information. In this work, we introduce BR-SAScore, an enhanced version of SAScore that integrates the available building block information (B) and reaction knowledge (R) from synthesis planning programs into the scoring process. In particular, we differentiate fragments inherent in building blocks and fragments to be derived from synthesis (reactions) when scoring synthetic accessibility. Compared to existing methods, our experimental findings demonstrate that BR-SAScore offers more accurate and precise identification of a molecule's synthetic accessibility by the synthesis planning program with a fast calculation time. Moreover, we illustrate how BR-SAScore provides chemically interpretable results, aligning with the capability of the synthesis planning program embedded with the same reaction knowledge and available building blocks.Scientific contributionWe introduce BR-SAScore, an extension of SAScore, to estimate the synthetic accessibility of molecules by leveraging known building-block and reactivity information. In our experiments, BR-SAScore shows superior prediction performance on predicting molecule synthetic accessibility compared to previous methods, including SAScore and deep-learning models, while requiring significantly less computation time. In addition, we show that BR-SAScore is able to precisely identify the chemical fragment contributing to the synthetic infeasibility, holding great potential for future molecule synthesizability optimization.

How host-country firms’ information transparency affects MNEs’ joint venture entry mode of OFDI: evidence from Chinese listed manufacturing firms

Purpose Information transparency is an important factor in enhancing trust and promoting interfirm cooperation. By combining transaction cost theory and institutional theory, this study aims to examines whether host-country firms’ information transparency prompt multinational enterprises’ (MNEs) to choose the joint venture entry mode for outward foreign direct investment (OFDI). Design/methodology/approach Using Heckman two-stage estimation method, this study examines Chinese listed manufacturing firms for the period 2014–2019. Findings The findings indicate that the higher the information transparency of host-country firms, the higher the possibility of MNEs choosing the joint venture entry mode for OFDI. This study further finds that the positive relationship between host country firms’ information transparency and the possibility of choosing the joint venture entry mode is enhanced by institutional distance, but weakened by MNEs’ host-country experience. Originality/value How to choose the appropriate entry mode of OFDI in the internationalization strategy is an important issue for MNEs to consider. As the postpandemic world is characterized by increased global risks, decoupling of economies, disruption of global value chains and the retreat of globalization (Contractor and Cantwell, 2022), how to further strengthen cooperation, reduce the cost and risk of MNEs and truly realize common construction and sharing is one of the hot issues in both practice and research.

Unbiased news recommendation model combining time and content

The influence of popularity bias on news recommendation systems is substantial, as it increased the exposure of popular news and their being pushes to irrelevant users frequently, leading to less-than-ideal performance of the system. Existing popularity debiasing methods aim to optimize the fairness of recommendations so that unpopular news have more chances to be recommended. However, they ignore the timeliness that characterizes news, and some outdated news are recommended to users due to their low popularity. Unfortunately, users are not interested in outdated news. In addition, existing debiasing methods use content-independent popularity computation, which cannot accurately capture the dynamic changes in news popularity, thus reducing the effectiveness of debiasing. In this research, we introduce an innovative method named Time and Content-aware Causal Model, referred to as TCCM, which mitigates the popularity bias using current causal inference techniques that work well. The difference is that, first, we design a novel causal graph to analyze user interaction considering that news is timeliness. Based on this causal graph, TCCM models the influences of three factors on user interaction, namely, the timeliness of news, the popularity of news, and the matching between the content of news and the interest of the user, in order to solve the problem of recommending outdated news to users after de-biasing. Second, we consider the influences of news content on popularity and propose a new popularity estimation method. In TCCM, we improve the debiasing effect by combining news content (entity and word popularity) to obtain more accurate news popularity. Extensive experiments conducted on widely recognized benchmark datasets show that TCCM surpasses several cutting-edge approaches.

Sexual dimorphism of pelvic scarring: A new method of adult biological sex estimation.

Estimating biological sex is a crucial aspect of forensic anthropology, and is pivotal in forensic investigations. Presently, the most frequently adopted osteological sex estimation methods focus on the anterior pelvis, which is easily susceptible to postmortem damage, revealing a need for additional accurate methods. This study introduces a novel method for estimating adult sex through metric pelvic scar analysis, using a known skeletal sample (169 females; 51 males). Relationships between sex and scar dimensions were subjected to Kendall's tau-B testing, and the strongest associated measurements were further analyzed using binary logistic regression to determine their predictive capacity. The final estimation method was tested on an additional known-sex sample of 43 males and 43 females from the Spitalfields skeletal collection. All associations between biological sex and scar measurements were significant, with the preauricular sulcus and newly defined inferior interosseous cavity presenting the strongest relationships (τb 0.223-0.504). Individual regression models using the approximate volume of each feature predicted sex with over 80% accuracy, but when combined in a single regression model, the accuracy increased to an impressive 97.1%. When then applied to the validation sample, the final estimation model achieved an accuracy of 90.7%. These results highlight the high estimation accuracy achieved by simultaneously utilizing the approximate volume of the sulcus and the inferior cavity. This is not only highly accurate but also utilizes the sturdier posterior pelvis, making it a promising tool for forensic investigations and the wider field of osteology.

Bayesian Estimation of the Semiparametric Spatial Lag Model

This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sampler, and Gibbs sampling techniques to sample all the parameters. The paper conducts numerical simulations to validate the proposed Bayesian estimation theory using a numerical example. The simulation results demonstrate satisfactory estimation performance of the parameter part and the fitting performance of the nonparametric function under different spatial weight matrix settings. Furthermore, the paper applies the constructed model and its estimation method to an empirical study on the relationship between economic growth and carbon emissions in China, illustrating the practical application value of the theoretical results.

Improving the Estimation Accuracy of Soil Organic Matter Content Based on the Spectral Reflectance from Soils with Different Grain Sizes

Accurate and rapid estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture. Compared with traditional chemical methods, the hyperspectral estimation is superior in rapidly estimating SOM content. Soil grain size affects soil spectral reflectance, thereby affecting the accuracy of hyperspectral estimation. However, the appropriate soil grain size for the hyperspectral analysis is nearly unknown. This study propose a best hyperspectral estimation method for determining SOM content of farmland soil in the Ibinur Lake Irrigation Area (ILIA) of the northwest arid zones of China. The original spectral reflectance of the 20-mesh (0.85 mm) and 60-mesh (0.25 mm) sieved soil were obtained, and the feature wavebands were selected using five types of spectral transformations. Then, hyperspectral estimation models were constructed based on the partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) models. Results show that the SOM content had relatively higher correlation coefficient with spectral reflectance of the 0.85 mm sieved soil than that of the 0.25 mm sieved soil. The transformation of original spectral reflectance of soil effectively enhanced the spectral characteristics related to SOM content. Soil grain size obviously affected spectral reflectance and the accuracy of hyperspectral estimation models. The overall stability and estimation accuracy of RF model was significantly higher compared with the PLSR, SVM, and XGBoost. Finally, the RF model combined with the root mean first-order differentiation (RMSFD) of spectral reflectance of the 0.85 mm sieved soil (R2 = 0.82, RMSE = 2.37, RPD = 2.27) was identified as the best method for estimating SOM content of farmland soil in the ILIA.

Software cost estimation tool: A App based application, estimate the cost of software project

This paper presents the design and implementation of a software cost estimation tool integrated into a mobile application developed using Flutter. The tool incorporates various techniques for software cost estimation, including expert judgment, function point analysis, 3D point analysis, and the COCOMO model. The purpose of the program is to give software engineers and project managers a practical and effective tool for calculating the time and money needed for software development projects. The paper provides a thorough explanation of each estimation technique’s implementation, along with a discussion of the app’s main features and functionalities. Because of the app’s intuitive and user-friendly design, users can quickly enter project data and get precise cost estimates. The tool’s efficacy is assessed using case studies and contrasts with other software cost estimation methods currently in use. The outcomes show that the app can produce trustworthy and precise cost estimates, which makes it an important resource for software development projects.

Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy

Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy

Scalable summary statistics-based heritability estimation method with individual genotype level accuracy.

SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale datasets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. While the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared to other summary statistic methods and on par with methods that rely on individual-level data.

Evaluating NOx stack plume emissions using a high-resolution atmospheric chemistry model and satellite-derived NO2 columns

Abstract. This paper presents large-eddy simulations with atmospheric chemistry of four large point sources world-wide, focusing on the evaluation of NOx (NO + NO2) emissions with the TROPOspheric Monitoring Instrument (TROPOMI). We implemented a condensed chemistry scheme to investigate how the emitted NOx (95 % as NO) is converted to NO2 in the plume. To use NOx as a proxy for CO2 emission, information about its atmospheric lifetime and the fraction of NOx present as NO2 is required. We find that the chemical evolution of the plumes depends strongly on the amount of NOx that is emitted, as well as on wind speed and direction. For large NOx emissions, the chemistry is pushed in a high-NOx chemical regime over a length of almost 100 km downwind of the stack location. Other plumes with lower NOx emissions show a fast transition to an intermediate-NOx chemical regime, with short NOx lifetimes. Simulated NO2 columns mostly agree within 20 % with TROPOMI, signalling that the emissions used in the model were approximately correct. However, variability in the simulations is large, making a one-to-one comparison difficult. We find that temporal wind speed variations should be accounted for in emission estimation methods. Moreover, results indicate that common assumptions about the NO2 lifetime (≈ 4 h) and NOx:NO2 ratios (≈ 1.3) in simplified methods that estimate emissions from NO2 satellite data need revision.

From Estimating to Explaining and Eliminating Ethnic Disproportionality in Stop and Search

AbstractThis article revisits aspects of the current debate that surrounds the annual stop and search statistics, which has become polarised and narrowly focussed on measurement. It aims to broaden the focus of the debate and encourage more comprehensive understandings of the multiple causes of disproportionality in support of more effective policy intervention. The evidence for disproportionality is presented and an argument made for the experience of stop and search, and its effects, to be considered alongside relative stop and search rates. Traditional and alternative methods of estimating disproportionality are discussed, concluding that census‐based measures of the resident population remain the best denominator at the national level. The article closes by drawing out the explanations for disproportionality that are implied by the different estimation methods and proposing a social‐ecological model that captures micro‐ and macro‐level influences on racialised patterns of policing.

Causal relationships between COVID-19 and venous thromboembolism: A mendelian randomization analysis.

Objective: Observational studies show the correlation between COVID-19 and venous thromboembolism (VTE) risk. However, the causal effects remain uncertain. We aimed to explore the potential causal association between COVID-19 and VTE using Mendelian randomization (MR) design. Methods: Two-sample MR was used to evaluate the potential causality between COVID-19 and VTE by selecting single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) from genome-wide association studies (GWAS). The weighted median, MR-Egger, simple mode, and weighted mode were employed as supplementary methods for MR estimations, with the inverse-variance weighted (IVW) method serving as the principal analysis. In addition, we took sensitivity analyses, including Cochran's test, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analysis to ensure that we obtained stable and reliable results. Results: Our study selected 26 COVID-19 severity, 31 COVID-19 hospitalization, and 13 COVID-19 susceptibility SNPs as instrumental variables. The IVW analysis results revealed that there was no causal relationship between COVID-19 severity, hospitalization, or susceptibility and VTE, with odds ratios of 0.974 (95%CI: 0.936-1.013, p = 0.19), 0.976 (95%CI: 0.918-1.039, p = 0.447), and 0.908 (95%CI: 0.775-1.065, p = 0.235), respectively. The IVW approach yielded consistent results with MR-Egger, Weighted Median simple mode, and weighted mode. MR-PRESSO and sensitivity analysis further confirmed the stability and consistency of the MR results. Conclusions: This study did not find evidence to support a causal relationship between COVID-19 and VTE at the genetic level. Further investigation is warranted to determine if the significant association reported in previous observational studies between the two is due to confounding factors.

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.