Search Results (10,994)

To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm², and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making. Full article

High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region. Full article

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios. Full article

Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods. Full article

This study utilizes data from Chinese A-share listed companies from 2014 to 2022 to theoretically analyze and empirically test the governance effect of ESG assurance on corporate greenwashing behavior, as well as the role played by the legal environment and management shareholding in this context. The impacts of ownership and the governance mechanism of ESG assurance on corporate greenwashing behavior are also explored. This study employs text mining, OLS, PSM, IV-LIML, treatment effect models, feasible generalized least squares, placebo tests, bootstrap methods, etc., to conduct empirical analysis and conclude the following results: ESG assurance has a significant inhibitory effect on corporate greenwashing behavior, playing a crucial role in resource allocation, particularly in non-state-owned enterprises. The legal environment has a certain substitution effect on ESG assurance in inhibiting corporate greenwashing behavior, meaning that when the legal environment is weak, ESG assurance is more effective in curbing such behavior. Management shareholding also has a certain substitution effect on ESG assurance in inhibiting corporate greenwashing behavior, indicating that when management shareholding is low, ESG assurance is better at curbing such behavior. Further research reveals that corporate ESG performance plays a mediating role between ESG assurance and corporate greenwashing governance. This article provides policy references and empirical evidence for strengthening ESG assurance and enhancing corporate ESG performance and greenwashing governance to promote high-quality corporate development. Full article

Here, we present a general framework for computing the infrared anharmonic vibrational spectra of polyatomic molecules using Born–Oppenheimer molecular dynamics (BOMD) with PyRAMD software. To account for nuclear quantum effects, we suggest using a simplified Wigner sampling (SWS) approach simultaneously coupled with Andersen and Berendsen thermostats. We propose a new criterion for selecting the parameter of the SWS based on the molecules’ harmonic vibrational frequencies and usage of the large-time-step blue shift correction, allowing for a decrease in computational expenses. For the Fourier transform of the dipole moment autocorrelation function, we propose using the regularized least-squares analysis, which allows us to obtain higher-frequency resolution than with the direct application of fast Fourier transform. Finally, we suggest the usage of the pre-parameterized scaling factors for the IR spectra from BOMD, also providing the scaling factors for the spectra at the BLYP-D3(BJ)/6-31G, PBE-D3(BJ)/6-31G, and PBEh-3c levels of theory. Full article

Investigating different pier shapes and debris Finteractions in scour patterns is vital for understanding the risks to bridge stability. This study investigates the impact of different shapes of pier and debris interactions on scour patterns using numerical simulations with flow-3D and controlled laboratory experiments. The model setup is rigorously calibrated against a physical flume experiment, incorporating a steady-state flow as the initial condition for sediment transport simulations. The Fractional Area/Volume Obstacle Representation (FAVOR) technique and the renormalized group (RNG) turbulence model enhance the simulation’s precision. The numerical results indicate that pier geometry is a critical factor influencing the scour depth. Among the tested shapes, square piers exhibit the most severe scour, with depths reaching 5.8 cm, while lenticular piers show the least scour, with a maximum depth of 2.5 cm. The study also highlights the role of horseshoe, wake, and shear layer vortices in determining scour locations, with varying impacts across different pier shapes. The Q-criterion study identified debris-induced vortex generation and intensification. The debris amount, thickness, and pier diameter (T/Y) significantly affect the scouring patterns. When dealing with high wedge (HW) debris, square piers have the largest scour depth at T/Y = 0.25, while lenticular piers exhibit a lower scour. When debris is present, the scour depth rises at T/Y = 0.5. Depending on the form of the debris, a significant fluctuation of up to 5 cm was reported. There are difficulties in precisely estimating the scour depth under complicated circumstances because of the disparity between numerical simulations and actual data, which varies from 6% for square piers with a debris relative thickness T/Y = 0.25 to 32% for cylindrical piers with T/Y = 0.5. The study demonstrates that while flow-3D simulations align reasonably well with the experimental data under a low debris impact, discrepancies increase with more complex debris interactions and higher submersion depths, particularly for cylindrical piers. The novelty of this work lies in its comprehensive approach to evaluating the effects of different pier shapes and debris interactions on scour patterns, offering new insights into the effectiveness of flow-3D simulations in predicting the scour patterns under varying conditions. Full article

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes. Full article

Purpose: This study aims to investigate the impact of big data disclosure on audit quality in the Omani context. Design/methodology/approach: This study used data extracted from annual reports for a sample from financial companies listed on the Muscat Stock Exchange over the period from 2014 to 2020. We applied a content analysis approach to measure the level of big data disclosure in these firms. This study used ordinary least squares and panel data regression analysis to investigate the relationship between big data disclosure and audit quality. Moreover, we moderated the relationship between big data disclosure and audit quality with family members who are serving on the board of directors and with royal membership. Findings: The findings of the study indicated that big data disclosure played a vital role in enhancing the audit quality of the financial firms in the Omani context. In addition, family memberships positively moderated the association between big data disclosure and audit quality in these firms. However, royal members negatively moderated such relationship. Research limitations/implications: We included only financial institutions in the sample. Practical implications: The study offers practical implications for investors, managers, and policymakers. It will raise awareness on the importance of implementing regulations necessary for disclosing such information in annual reports, thereby enhancing the audit quality of firms and increasing the reliability and validity of financial reports. Originality/value: The study is considered the first, to the best of our knowledge, to examine the impact of big data disclosure on the audit quality in the Omani context. It contributes to the existing knowledge of digital transformation in the Omani financial firms. Full article

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

Insomnia is a prevalent sleep disorder that causes serious harm to individuals and society. There is growing evidence that environmental factors may be associated with sleep disorders, but few studies have explored the relationship between insomnia and urban functional structure from a spatial perspective. This study collected multi-source big data (e.g., insomnia posts on Weibo, locations of urban facilities on Baidu) and explored the effects of different urban spatial element configurations on residents’ insomnia. The ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the global and local effects of urban functional categories on residents’ insomnia. The results showed that the quantity of catering service facilities accounted for the largest proportion, and the consumer function was the most consistent with the distribution of insomnia. There is a domain relationship between the incidence of insomnia and urban functional zones. It has the strongest correlation with employment mixed functional zones and the weakest with residential mixed functional zones. These findings could serve as references for the functional structure and layout of urban space for improving the sleep health of residents and benefit for urban health. Full article

The digital economy is a prevailing trend in global development, yet traditional firms still face challenges in digital transformation. Under institutional pressure, firms might imitate the digital strategy of their peers to mitigate these issues; there is still a lack of empirical research to support this. Therefore, this study, drawing on new institutional theory, focuses on investigating whether and how the institutional environment influences companies in embracing digital transformation in the digital economy era. We employ generalized least squares (GLS) regression models on a sample of 2862 non-IT listed firms in China from 2012 to 2020. In addition, we conduct a series of robustness checks. The results show that firms’ mimetic isomorphism of digital transformation is related to the institutional environment. Specifically, both industrial digitalization and regional digitalization promote digital mimetic isomorphism independently; their interaction is positively related to the digital mimetic isomorphism of successful firms but negatively related to similar firms. The results provide empirical evidence for non-IT firms to converge upwards in digital transformation and achieve high-quality development. Full article

Digital Entrepreneurial Ecosystems (DEEs) are transforming the economic landscape through their integration of digital technologies, offering new opportunities for innovation and growth. This study explores the impact of DEEs on sustainable development, focusing specifically on Latin America. As DEEs continue to evolve, understanding their influence on economic, environmental, and social sustainability becomes crucial, particularly in a region characterized by significant developmental challenges. Utilizing a data panel from two different periods of analysis, from 2013 to 2017 and from 2018 to 2022, within the adapted DEE framework provided by the Global Entrepreneurship Development Institute (GEDI), we employ Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and fuzzy-set Qualitative Comparative Analysis (fsQCA 3.0) to analyze DEE components across 14 Latin American countries. These countries may not have the full spectrum of digital capabilities, yet they are still able to harness the digital elements they do possess effectively. This suggests that even partial digitalization, when strategically utilized, can lead to substantial gains in sustainable development. Additionally, Networking, Digital Protection, and Digital Tech Transfer are DEE components that present a higher magnitude in social, environmental, and economic development in Latin American countries. This study not only contributes to a deeper understanding of a DEE’s role in fostering sustainable development, but it also offers actionable insights for policymakers and entrepreneurs to leverage DEEs for broader societal benefits. The implications of the findings present perspectives under the existing literature, and the conclusion shows recommendations for future research and strategy development. Full article

Inter-basin water transfer projects, such as the Yellow River to Qingdao Water Diversion Project (YQWD), are essential for addressing water scarcity, but impact local aquatic ecosystems. This study investigates the seasonal characteristics of eukaryotic microbial communities in the Jihongtan Reservoir, the main water-receiving body of YQWD, over a one-year period using 18S rDNA amplicon sequencing. The results showed that the eukaryotic microbial diversity did not exhibit significant seasonal variation (p > 0.05), but there was a notable variance in the community structure (p < 0.05). Arthropoda and Paracyclopina, representing the most dominant phylum and the most dominant genus, respectively, both exhibited the lowest abundance during the winter. The Chlorophyta, as the second-dominant phylum, demonstrates its higher abundance in the spring and winter. The Mantel test and PLS-PM (Partial Least Squares Path Modeling) revealed that water temperature (WT), dissolved oxygen (DO), and pH influenced the seasonal dynamic of eukaryotic microbial communities significantly, of which WT was the primary driving factor. In addition to environmental factors, water diversion is likely to be an important influencing factor. The results of the co-occurrence network and robustness suggested that the spring network is the most complex and exhibits the highest stability. Moreover, keystone taxa within networks have been identified, revealing that these key groups encompass both abundant and rare species, with specificity to different seasons. These insights are vital for understanding the seasonal variation of microbial communities in the Jihongtan Reservoir during ongoing water diversions. Full article