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Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R² values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R² values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R² values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R² values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves. Full article

This study investigated the nexus between food security, environmental sustainability, and sustainable growth from a global perspective of 63 economies spanning 2010–2021. Different econometric strategies including the Generalized Method of Moments (GMMs), the Fully Modified Ordinary Least Squares (FMOLSs), and the Dynamic Ordinary Least Squares (DOLSs) methods were employed to accomplish the investigation. The empirical outcomes indicate that the coefficients of food export, agricultural production, fertilizer consumption, FDI, population growth, and employment are positively and statistically associated with economic growth but have negative relationships with poverty and unemployment, except for population growth and unemployment, which revealed insignificant results. Conversely, the coefficient of food import revealed a positive association with poverty and unemployment but is negatively associated with economic growth. Additionally, the Environmental Kuznets Curve (EKC) hypothesis is also established in the considered countries. Nations, governments, and policymakers must prioritize environmentally friendly economic and green policies that can support sustainable agriculture. International policies to enhance food security collaboration because of nations’ diverse natural endowments to achieve all-level inclusive growth and development must be highly prioritized to reduce global inequality. Innovativeness and the sustainable use of land and processing of food must be encouraged to reduce emissions and other forms of pollution to support eco-fishing, aquaculture, and agriculture in order to ensure food security and achievement of the SDGs. Full article

The gardenia flower not only has extremely high ornamental value but also is an important source of natural food and spices, with a wide range of uses. To support the development of gardenia flower products, this study used headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) technology to compare and analyze the volatile organic compounds (VOCs) of fresh gardenia flower and those after using four different drying methods (vacuum freeze-drying (VFD), microwave drying (MD), hot-air drying (HAD), and vacuum drying (VD)). The results show that, in terms of shape, the VFD sample is almost identical to fresh gardenia flower, while the HAD, MD, and VD samples show significant changes in appearance with clear wrinkling; a total of 59 volatile organic compounds were detected in the gardenia flower, including 13 terpenes, 18 aldehydes, 4 esters, 8 ketones, 15 alcohols, and 1 sulfide. Principal component analysis (PCA), cluster analysis (CA), and partial least-squares regression analysis (PLS-DA) were performed on the obtained data, and the research found that different drying methods impact the VOCs of the gardenia flower. VFD or MD may be the most effective alternative to traditional sun-drying methods. Considering its drying efficiency and production cost, MD has the widest market prospects. Full article

Jujube (Ziziphus jujuba Mill.) makes up a traditional characteristic industry with ecological significance in the Lüliang Mountain of middle reaches of the Yellow River (LMMRYR). However, low economic efficiency has reduced local farm households’ willingness to continue jujube cultivation, threatening the sustainable maintenance and development of jujube forests and the ecological environment. In response, Lüliang City implemented a technological innovation program, that is, the Jujube Forest High Grafting and Optimization Program (JFHGOP), in 2018. Based on survey data from 302 local farm households, an empirical analysis using propensity score matching and ordinary least squares methods revealed that the program significantly enhanced the economic, ecological, and social benefits for participating farm households, improving their overall welfare. Robustness tests confirmed these findings, and a heterogeneity analysis showed varied impacts across different dimensions. The program improved welfare through government support and cooperatives’ assistance. To further promote green development and farm households’ welfare, recommendations include advancing forestry innovation technology, supporting small farm households with policy, capital, and technology, optimizing subsidy mechanisms, supporting new business entities, and promoting cooperation and benefit-sharing among stakeholders. Full article

Xanthine oxidase (XOD) is a key enzyme that promotes the oxidation of xanthine/hypoxanthine to form uric acid, and the accumulation of uric acid leads to hyperuricaemia. The prevalence of gout caused by hyperuricaemia is increasing year by year. TAOZHI (TZ) can be used for the treatment of rheumatic arthralgia due to qi stagnation and blood stasis and contains a large number of polyphenolic components. The aim of this study was to investigate the relationship between chromatograms and XOD inhibition of 21 batches of TZ total polyphenol extract samples. Chemometric methods such as grey correlation analysis, bivariate correlation analysis, and partial least squares regression were used to identify the active ingredient groups in the total polyphenol extracts of TZ, which were validated using molecular docking techniques. The total polyphenol content contained in the 21 batches did not differ significantly, and all batches showed inhibitory effects on XOD. Spectroeffect correlation analysis showed that the inhibitory effect of TZ on XOD activity was the result of the synergistic effect of multiple components, and the active component groups screened to inhibit XOD were F2 (4-O-Caffeoylquinic acid), F4, and F10 (naringenin). The molecular docking results showed that the binding energies of all nine dockings were lower than −7.5 kcal/mol, and the binding modes included hydrogen bonding, hydrophobic forces, salt bridges, and π-staking, and the small molecules might exert their pharmacological effects by binding to XOD through the residue sites of the amino acids, such as threonine, arginine, and leucine. This study provides some theoretical basis for the development and utilisation of TZ total polyphenols. Full article

Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods. Full article

This study examines the evolving consumer-brand dynamics within Saudi Arabia, particularly focusing on the integration of sustainability into consumer preferences and brand loyalty. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) and non-parametric statistical methods, this research is anchored in Consumer Culture Theory (CCT) and Self-Congruence Theory, exploring how brand personality and consumer self-congruence influence sustainable consumption behaviors and the adoption of circular economy practices among Saudi consumers. The findings reveal a significant correlation between brand loyalty and sustainable purchase decisions, underscoring the pivotal role of brand identity in fostering eco-conscious consumer choices. Additionally, the research highlights a nuanced landscape of brand loyalty, where attributes, such as social responsibility, though currently less influential, present opportunities for brands to align more closely with consumer values and national sustainability goals. The study also identifies demographic factors, such as age and income level, as significant influencers of sustainable purchasing decisions. This study provides insights into the generational shift towards environmental awareness and the implications for businesses and policymaking within the context of Saudi Arabia’s Vision 2030. Full article

In the field of statistics, uncertain regression analysis occupies an important position. It can thoroughly analyze data sets contained in complex uncertainties, aiming to quantify and reveal the intricate relationships between variables. It is worth noting that the traditional least squares method only takes into account the reduction in the deviations between predictions and observations, and fails to fully consider the inherent characteristics of the correlation uncertainty distributions under the uncertain regression framework. In light of this, this paper constructs a statistical invariant with symmetric uncertainty distribution based on the observations and the disturbance term. It also proposes the least squares estimation of unknown parameters and disturbance term in the uncertain regression model based on the least squares principle and, combined with the mathematical properties of the normal uncertainty distribution, gives a numerical algorithm for solving specific estimates. Finally, in order to verify the effectiveness of the least squares estimation method proposed in this paper, we also design two numerical examples and an empirical study of forecasting of electrical power output. Full article

Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model prediction and is still an unsolved problem. In this regard, some robust estimators of the biased parameter are proposed based on the least trimmed squares (LTS) technique and its extensions using a semidefinite programming approach. Based on a set of observations with a sample size of n, and the integer trimming parameter h ≤ n, the LTS estimator computes the hyperplane that minimizes the sum of the lowest h squared residuals. Even though the LTS estimator is statistically more effective than the widely used least median squares (LMS) estimate, it is less complicated computationally than LMS. It is shown that the proposed robust extended Liu estimators perform better than classical estimators. As part of our proposal, using Monte Carlo simulation schemes and a real data example, the performance of robust Liu estimators is compared with that of classical ones in restricted partially linear models. Full article

The photoelectric sensing unit in a large-space measurement system primarily determines the measurement accuracy of the system. Aiming to resolve the problem whereby existing sensing units have difficulty accurately measuring the hidden points and free-form surfaces in large components, in this study, we designed a multi-node fusion of a combined sensor. Firstly, a multi-node fusion hidden-point measurement model and a solution model are established, and the measurement results converge after the number of nodes is simulated to be nine. Secondly, an adaptive front-end photoelectric conditioning circuit, including signal amplification, filtering, and adjustable level is designed, and the accuracy of the circuit function is verified. Then, a nonlinear least-squares calibration method is proposed by combining the constraints of the multi-position vector cones, and the internal parameters of the probe, in relation to the various detection nodes, are calibrated. Finally, a distributed system and laser tracking system are introduced to establish a fusion experimental validation platform, and the results show that the standard deviation and accuracy of the three-axis measurement of the test point of the combined sensor in the measurement area of 7000 mm × 7000 mm × 3000 mm are better than 0.026 mm and 0.24 mm, respectively, and the accuracy of the length measurement is within 0.28 mm. Further, the measurement accuracy of the hidden point of the aircraft hood and the free-form surface is better than 0.26 mm, which can meet most of the industrial measurement needs and expand the application field of large-space measurement systems. Full article

During pandemics, such as COVID-19, supplies of vaccines can be insufficient for meeting all needs, particularly when vaccines first become available. Our study develops a dynamic methodology for vaccine allocation, segmented by region, age, and timeframe, using a time-sensitive, age-structured compartmental model. Based on the objective of minimizing a weighted sum of deaths and cases, we used the Sequential Least Squares Quadratic Programming method to search for a locally optimal COVID-19 vaccine allocation for the United States, for the period from 16 December 2020 to 30 June 2021, where regions corresponded to the 50 states in the United States (U.S.). We also compared our solution to actual allocations of vaccines. From our model, we estimate that approximately 1.8 million cases and 9 thousand deaths could have been averted in the U.S. with an improved allocation. When case reduction is prioritized over death reduction, we found that young people (17 and younger) should receive priority over old people due to their potential to expose others. However, if death reduction is prioritized over case reduction, we found that more vaccines should be allocated to older people, due to their propensity for severe disease. While we have applied our methodology to COVID-19, our approach generalizes to other human-transmissible diseases, with potential application to future epidemics. Full article

This article investigates a variable selection method in semi-functional partially linear regression (SFPLR) models for strong $\alpha$-mixing functional time series data. We construct penalized least squares estimators for unknown parameters and unknown link functions in our models. Under some regularity assumptions, we establish the asymptotic convergence rate and asymptotic distribution for the proposed estimators. Furthermore, we make a comparison of our variable selection method with the oracle method without variable selection in simulation studies and an electricity consumption data analysis. Simulation experiments and real data analysis results indicate that the variable selection method performs well at extracting the primary information and reducing dimensionality. Full article

This study examines the moderating role of procedural fairness between land titling programs and agricultural investment. We constructed a theoretical model that introduces perceived security of land tenure and procedural fairness into the traditional “property rights-investment incentives” analytical framework. Moreover, we empirically analyze the impact of land titling and its procedural fairness on agricultural investment using data from the “Thousands of People, Hundreds of Villages” survey held in 2018 among 9596 households in China. The empirical analyses were conducted by using the ordinary least squares (OLS), probit, and instrumental variable methods. Our analysis showed that land titling in China significantly promotes agricultural investment by farm households and that procedural fairness has a significant positive moderating role in the investment incentive effect of land titling as well as significantly improving the institutional credibility of land titling and enhancing farmers’ perceived land tenure security. Full article

This paper investigates algorithms for identifying the optimal policy for pricing American Options. The American Option pricing is reformulated as a Sequential Decision-Making problem with two binary actions (Exercise or Continue), transforming it into an optimal stopping time problem. Both the least square Monte Carlo simulation method (LSM) and Reinforcement Learning (RL)-based methods were utilized to find the optimal policy and, hence, the fair value of the American Put Option. Both Classical Geometric Brownian Motion (GBM) and calibrated Stochastic Volatility models served as the underlying uncertain assets. The novelty of this work lies in two aspects: (1) Applying LSM- and RL-based methods to determine option prices, with a specific focus on analyzing the dynamics of “Decisions” made by each method and comparing final decisions chosen by the LSM and RL methods. (2) Assess how the RL method updates “Decisions” at each batch, revealing the evolution of the decisions during the learning process to achieve optimal policy. Full article

This paper studies an artificial neural network (ANN) for multi-asset European options. Firstly, a simple three-layer ANN-3 is established with undetermined weights and bias. Secondly, the time–space discrete PDE of the multi-asset option is given and the corresponding discrete data are fed into the ANN-3. Then, using least squares error as the objective function, the weights and bias of ANN-3 are trained well. Numerical examples are carried out to confirm the stability, accuracy and efficiency. Experiments show the ANN’s relative error is about $0.8\%$. This method can be extended into multi-layer ANN-$q(q>3)$ and extended into American options. Full article