Oligopoly in grain production and consumption: an empirical study on soybean international trade in China

What has been neglected in much of the existing studies of the influence of seasonal and regional characteristics of agriculture on the market power and national security. This paper constructs a multivariate equations model to investigate the monopoly power of seasonal suppliers and national security in China’s soybean market. The results show no relationship between market share and monopoly power; and that CR3 and HHI show China’s soybean import market has been the highest oligopoly type, but the model suggest that exporters (the U.S., Brazil and Argentina) have very weak monopoly power and China has no monopsony power; and that the performance of some exporters’ soybeans is affected by others, while others are relatively independent in market. This is due to the non-substitutability of the product, the non-substitutability of the buyer and the seller, etc., which causes the mutual dependence of the seller and buyer, and their market power cancel each other out. The seasonality and regionality of soybean production is the root. Considering national security, it is necessary to take the seasonal and regional characteristics of exporters into account to disperse trade risks and oppose monopolisation of international food production and trad

Low-intensity pulsed ultrasound of different intensities differently affects myocardial ischemia/reperfusion injury by modulating cardiac oxidative stress and inflammatory reaction

IntroductionThe prevalence of ischemic heart disease has reached pandemic levels worldwide. Early revascularization is currently the most effective therapy for ischemic heart diseases but paradoxically induces myocardial ischemia/reperfusion (MI/R) injury. Cardiac inflammatory reaction and oxidative stress are primarily involved in the pathology of MI/R injury. Low-intensity pulsed ultrasound (LIPUS) has been demonstrated to reduce cell injury by protecting against inflammatory reaction and oxidative stress in many diseases, including cardiovascular diseases, but rarely on MI/R injury.MethodsThis study was designed to clarify whether LIPUS alleviates MI/R injury by alleviating inflammatory reaction and oxidative stress. Simultaneously, we have also tried to confirm which intensity of the LIPUS might be more suitable to ameliorate the MI/R injury, as well as to clarify the signaling mechanisms. MI/R and simulated ischemia/reperfusion (SI/R) were respectively induced in Sprague Dawley rats and human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs). LIPUS treatment, biochemical measurements, cell death assay, estimation of cardiac oxidative stress and inflammatory reaction, and protein detections by western blotting were performed according to the protocol.ResultsIn our study, both in vivo and in vitro, LIPUS of 0.1 W/cm2 (LIPUS0.1) and 0.5 W/cm2 (LIPUS0.5) make no significant difference in the cardiomyocytes under normoxic condition. Under the hypoxic condition, MI/R injury, inflammatory reaction, and oxidative stress were partially ameliorated by LIPUS0.5 but were significantly aggravated by LIPUS of 2.5 W/cm2 (LIPUS2.5) both in vivo and in vitro. The activation of the apoptosis signal-regulating kinase 1 (ASK1)/c-Jun N-terminal kinase (JNK) pathway in cardiomyocytes with MI/R injury was partly rectified LIPUS0.5 both in vivo and in vitro.ConclusionOur study firstly demonstrated that LIPUS of different intensities differently affects MI/R injury by regulating cardiac inflammatory reaction and oxidative stress. Modulations on the ASK1/JNK pathway are the signaling mechanism by which LIPUS0.5 exerts cardioprotective effects. LIPUS0.5 is promising for clinical translation in protecting against MI/R injury. This will be great welfare for patients suffering from MI/R injury

Object-Based Window Strategy in Thermal Sharpening

The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the “aggregation-then-disaggregation” strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001–2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R2) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18–2.60 K in RMSE and 0.00–0.15 in R2)

A Novel High Q Lamé-Mode Bulk Resonator with Low Bias Voltage

This work reports a novel silicon on insulator (SOI)-based high quality factor (Q factor) Lamé-mode bulk resonator which can be driven into vibration by a bias voltage as low as 3 V. A SOI-based fabrication process was developed to produce the resonators with 70 nm air gaps, which have a high resonance frequency of 51.3 MHz and high Q factors over 8000 in air and over 30,000 in vacuum. The high Q values, nano-scale air gaps, and large electrode area greatly improve the capacitive transduction efficiency, which decreases the bias voltage for the high-stiffness bulk mode resonators with high Q. The resonator showed the nonlinear behavior. The proposed resonator can be applied to construct a wireless communication system with low power consumption and integrated circuit (IC) integration

Local climate zone mapping using remote sensing: a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime light data

The local climate zone (LCZ) scheme has been widely utilized in regional climate modeling, urban planning, and thermal comfort investigations. However, existing LCZ classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments. In this study, we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations, i.e. daytime Ziyuan-3 stereo imageries (2.1 m) and Luojia-1 nighttime light (NTL) data (130 m). Firstly, we performed land cover classification using multiple machine learning methods (i.e. random forest (RF) and XGBoost algorithms) and various features (i.e. spectral, textural, multi-view features, 3D urban structure parameters (USPs), and NTL). In addition, we developed a set of new cumulative elevation indexes to improve building roughness assessments. The indexes can estimate building roughness directly from fused point clouds generated by both along- and across-track modes. Finally, based on the land cover and building roughness results, we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping. The results for Beijing, China, show that our method yielded satisfactory accuracy for LCZ mapping, with an overall accuracy (OA) of 90.46%. The overall accuracy of land cover classification using 3D USPs generated from both along- and across-track modes increased by 4.66%, compared to that of using the single along-track mode. Additionally, the OA value of LCZ mapping using 2D and 3D USPs (88.18%) achieved a better result than using only 2D USPs (83.83%). The use of NTL data increased the classification accuracy of LCZs E (bare rock or paved) and F (bare soil or sand) by 6.54% and 3.94%, respectively. The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livability in urban areas. Ultimately, this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates