Explore the vast range of titles available.

The United States and China are key nations in global agricultural and food trade. They share a complex bilateral agri-food trade network in which disruptions could have a global ripple effect. Yet, we do not understand the spatially resolved connections in the bilateral US–China agri-food trade. In this study, we estimate the bilateral agri-food trade between Chinese provinces and U.S. states and counties. First, we estimate bilateral imports and exports of agri-food commodities for provinces and states. Second, we model link-level connections between provinces and states/counties. To do this, we develop a novel algorithm that integrates a variety of national and international databases for the year 2017, including trade data from the US Census Bureau, the US Freight Analysis Framework database, and Multi-Regional Input-Output tables for China. We then adapt the food flow model for inter-county agri-food movements within the US to estimate bilateral trade through port counties. We estimate 2,954 and 162,922 link-level connections at the state-province and county-province resolution, respectively, and identify core nodes in the bilateral agri-food trade network. Our results provide a spatially detailed mapping of the US–China bilateral agri-food trade, which may enable future research and inform decision-makers.

We contribute to the debate on trade and the environment by investigating the relationship between countries’ international trade and their groundwater use. We go beyond the virtual water literature that calculates the virtual water content of trade to assess how water use relates to international trade. We develop a suite of econometric regressions to account for the impact of trade liberalizations in a framework that includes standard determinants of countries’ water supply and demand. We focus on the agricultural sector and on the contribution of regional trade agreements (RTAs) to capture the impact of trade liberalizations. We find that more openness to trade due to an increasing number of RTAs may actually reduce total groundwater abstractions, which is qualified by countries’ comparative advantage. We do not find a statistically significant impact of trade on groundwater depletion, which also depends on the recharge rate of aquifers. Our analysis calls for assessing water use in a global world and in a framework that accounts for all the drivers of water use on the demand and supply side, including those behind international trade.

Population growth is often intuitively linked with proportionally higher use of fresh water resources. However, this implies that water use per capita does not change with population growth. We not only find that birth rates of regions are negatively related with its water use per capita (i.e., higher birth rate is associated with lower water use), but also that birth rates scale with the latter with a negative power. We use population and water withdrawal data from 1950 to 2005 at irregular 5-year intervals; with virtual water content, virtual water trade and agricultural production data from 1960 to 2000 for the seven continents to investigate the scaling relationship and interpret it through the lens of metabolism theory. Our analysis reveals that the scaling exponent lies between −1/3 and −1/2. Deviations from the power relationship are observed for Europe and Africa, which are attributed to lower than expected and higher than expected birth rates, respectively. Europe’s deviation from the average scaling relationship may be due to the higher rate of return on human capital in industrialized countries. But why Africa deviates, while other developing and developed regions follow the power relationship more closely, remains a puzzle.

This paper presents the stability analysis of three real large rock slopes prone to failure with different mechanisms to investigate the applicability of the response surface methodology (RSM) for reliability-based rock slope problems. Initially, a detailed review of studies was performed to identify the gaps and common types of reliability-based rock slope problems. The applicability of RSMs based reliability methods was then investigated for three types of identified rock slope reliability problems via three case studies–type (1) Chenab rock slope prone to stress-controlled failures neglecting spatial variability, type (2) Deccan gold mine slope prone to stress-controlled failures considering spatial variability, and type (3) Rishikesh-Badrinath Highway slope prone to structurally controlled failures neglecting spatial variability. Analysis was performed by coupling MATLAB coded RSMs with advanced numerical tools and results were compared with those of direct Monte-Carlo Simulations (MCSs) based reliability method. Further, a detailed comparative study was carried out to evaluate the effect of the use of different response surfaces, i.e., polynomial based, Radial Basis Functions (RBFs) based, Support Vector Machine (SVM) based, kriging, Moving Least Square (MLS) and Gaussian Process Regression (GPR) on the accuracy and efficiency of RSMs based reliability analysis. Accuracy was evaluated using the Nash–Sutcliffe Efficiency (NSE) and Relative Difference in Reliability Index (RDRI), while computational efficiency was evaluated via the computational time required to evaluate the reliability with acceptable accuracy. RSMs based methods are observed to be highly accurate and efficient for the reliability analysis of large rock slopes. Further, the accuracy and efficiency are observed to be dependent upon the employed response surfaces and type of the problem. Suggestive guidelines are provided for selecting most suitable response surfaces for different problem types– a) for type 1 problem, Kriging and Least Square (LS)-SVM, b) for type 2 problem, some RBFs based and LS-SVM, and c) for type 3 problem, some RBFs based and LS-SVM are the most suitable RSMs.

The objective of this prospective observational study was to assess the clinical significance of neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR) and lymphocyte–monocyte ratio (LMR) as potential biomarkers to identify post-PNL SIRS or sepsis. Demographic data and laboratory data including hemoglobin (Hb), total leucocyte count (TLC), serum creatinine, urine microscopy and culture were collected. The NLR, LMR and PLR were calculated by the mathematical division of their absolute values derived from routine complete blood counts from peripheral blood samples. Stone factors were assessed by non-contrast computerized tomography of kidneys, ureter and bladder (NCCT KUB) and included stone burden (Volume = L × W × D × π × 0.167), location and Hounsfield value and laterality. Intraoperative factors assessed were puncture site, tract size, tract number, operative time, the need for blood transfusion and stone clearance. Of 517 patients evaluated, 56 (10.8%) developed SIRS and 8 (1.5%) developed sepsis. Patients developing SIRS had significantly higher TLC (10.4 ± 3.5 vs 8.6 ± 2.6, OR 1.19, 95% CI 1.09–1.3, p = 0.000002), higher NLR (3.6 ± 2.4 vs 2.5 ± 1.04, OR 1.3, 95% CI = 1.09–1.5, p = 0.0000001), higher PLR (129.3 ± 53.8 vs 115.4 ± 68.9, OR 1.005, 95% CI 1.001–1.008, p = 0.005) and lower LMR (2.5 ± 1.7 vs 3.2 ± 1.8, OR 1.18, 95% CI 1.04–1.34, p = 0.006). Staghorn stones (12.8 vs 3.24%, OR 4.361, 95% CI 1.605–11.846, p = 0.008) and long operative times (59.6 ± 14.01 vs 55.2 ± 16.02, OR 1.01, 95% CI 1.00–1.03, p = 0.05) had significant association with postoperative SIRS. In conclusion, NLR, PLR and LMR can be useful independent, easily accessible and cost-effective predictors for early identification of post-PNL SIRS/sepsis.

Characterization of properties governing the stability of rock slopes is essential for their design and analysis. Importance ranking of these properties can be obtained by the sensitivity indices that quantifies the extent to which different properties influence the stability of the slopes. This helps the designers to divert major laboratory/in situ investigation resources to evaluate highly ranked properties. Another usage is to perform the probabilistic stability analysis of slopes efficiently by treating low-ranked properties deterministically in the estimation of probability of failure (Pf). In this paper, the importance ranking of rock properties affecting the Pf of rock slopes prone to different failure mechanisms is performed using local/global sensitivity approaches (L/GSAs) and the accuracy of these approaches was assessed quantitatively. Four slope case studies indicating different structurally and stress-controlled failures were considered, and sensitivity analyses were performed using six different L/GSAs. Accuracy of the approaches was assessed by comparing the importance ranking of properties based on sensitivity approaches to that of the normalized errors, i.e., εi invoked in the Pf by neglecting the uncertainties in these properties. Results indicated the superior accuracy of GSAs as compared to LSAs. Importance ranking was dependent upon the considered slope (failure mechanisms), with some slopes showing higher sensitivities to external parameters and others to inherent rock properties. An important guideline based on the analysis is suggested to consider the properties as deterministic/random variables in the probabilistic analysis. For the slopes with the minimum interaction effects in their sensitivity (planar, wedge, and stress controlled), uncertainties in multiple properties can be neglected based on the allowable error in the Pf. Further, a dependence of εi and corresponding importance ranking was observed on the selected value of property of interest (assumed as deterministic) across its domain in the analysis.

In the current agricultural landscape, achieving sustainable and efficient nutrient management is crucial for addressing global food security and environmental challenges. Conventional fertilizers often suffer from low nutrient use efficiency and environmental concerns, highlighting the need for innovative alternatives. This study explores the potential of green-synthesized and commercially available iron (Fe) and zinc (Zn) nanoparticles (NPs) as sustainable nutrient sources to enhance seed germination, plant growth, productivity, and nutritional quality in pigeonpea (Cajanus cajan). Through laboratory and field experiments, green-synthesized NPs demonstrated superior stability and effectiveness compared to commercial variants. Optimized seed priming with nanoiron and nanozinc significantly improved germination, seed vigor, and early seedling growth. Field trials combining seed priming and foliar application demonstrated a 77.41% increase in seed yield (1728 kg ha −1 ), a 77.35% higher stalk yield (4285 kg ha −1 ), and 52.20% increase in husk yield (828 kg ha −1 ) compared to control, additionally, these treatments enhanced SPAD values by 27.82% (53.43) and NDVI values by 54.38% (0.88) relative to control. These findings highlight the potential of green-synthesized NPs as a sustainable alternative to synthetic fertilizers, offering a practical solution for enhancing crop productivity and nutritional security.

Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.

This study aimed to evaluate the effectiveness of biogenic iron nanoparticles as a novel priming solution to enhance seed germination and vigor in pigeonpea ( Cajanus cajan L.). Iron nanoparticles synthesized through green methods have received increasing scientific attention for their agricultural applications, particularly in enhancing seed germination and plant growth. In this study, eco-friendly, plant-based extracts were used to synthesize iron nanoparticles, explored as a nutritional supplement to boost pigeonpea seed germination. The green synthesis involved reducing iron salts using plant extracts, serving as both reducing and stabilizing agents. Characterization using UV-vis spectrophotometry, dynamic light scattering (DLS), and scanning electron microscopy (SEM) confirmed the nanoparticles’ nano-scale dimensions, stability, and crystalline structure. Seed priming with green-synthesized nanoparticles at an optimal concentration of 50 ppm significantly improved germination (97.76%), root and shoot lengths, and seedling dry weight, while reducing abnormal seedlings and dead seeds. These effects were attributed to enhanced bioavailability, improved enzymatic activity, and efficient nutrient delivery during germination. The results highlight the potential of green-synthesized iron nanoparticles as a effective seed priming agent in pigeonpea cultivation.

ABSTRACT Posterior urethral valve (PUV) is a common cause of obstructive uropathy in children, leading to renal failure and frequently associated with vesicoureteral reflux (VUR), which can rapidly progress to end-stage renal disease (ESRD). We describe a rare presentation of high-grade VUR opacifying the renal pyramids in a 5-month-old child with sepsis and renal failure.