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Cross-sectional ovalization of thin-walled circular steel tube caused by large plastic bending usually occurs at initial large bending stage in tube’s continuous rotary straightening process. When the bending loads have been removed, there must be residual ovalization kept on the tube’s cross-section during the whole straightening process, to induce the poor roundness and the reduction of the tube flexural stiffness. In order to clarify the fundamental ovalization characteristics, find out what factors influence the residual flattening (value of ovalization), the ovalization behavior is investigated by the simulation method. Seven groups of the finite element (FE) simulations for the thin-walled tube straightening process are carried out on the stainless steel specimens with different geometric parameters using different working rolls of different bending radii. The residual cross-sectional flattening is obtained periodically along the hoop direction by tracing the x - and y -coordinate values of the nodes on the cross-section during the whole simulation process. From the simulation results, it is observed that the shape of the tube cross-section is not a perfect standard ellipse, and the appearance of the maximum residual flattening is usually found in the direction perpendicular to the tube’s centroid axis. Then, the influences of tube’s outer diameter, thickness, and bending radius on residual flattening are found, i.e., the residual flattening increases with the increase of the outer diameter, but it decreases with the increases of the bending radius and the wall thickness. Meanwhile, the variation of the cross-sectional flattening with the number of bending is also analyzed.

Semi-supervised learning (SSL) leverages unlabeled data to support model training, thereby improving model accuracy. However, most existing SSL methods rely heavily on unlabeled data for model correction while often overlooking the potential error correction capabilities of labeled data. In this paper, we propose Correction FixMatch, which harnesses the error correction potential of labeled data to achieve higher accuracy on the test set. Correction FixMatch is based on the FixMatch model but introduces a novel error-correction mechanism utilizing labeled data. In particular, a training set and a correction set are separated from the labeled dataset. The model is initially trained using the training set, and after a predefined number of steps, the correction set is employed to refine the model through error correction. According to experimental results, the suggested approach performs noticeably better in terms of accuracy than the original FixMatch model under identical testing conditions. This method opens up new possibilities for improving semi-supervised learning models.

Vital sign detection is used across ubiquitous scenarios in medical and health settings, and contact and wearable sensors have been widely deployed. However, they are unsuitable for patients with burn wounds or infants with insufficient areas for attachment. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions and has privacy concerns. Here we report a photonic radar for non-contact vital sign detection to overcome these challenges. This photonic radar can achieve millimetre-level range resolution based on synthesized radar signals with a bandwidth of up to 30 GHz. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. Moreover, we demonstrate that the optical signals generated from the proposed system can enable vital sign detection based on light detection and ranging (LiDAR). This demonstration reveals the potential of a sensor-fusion architecture that can combine the complementary features of radar and LiDAR to achieve improved sensing accuracy and system resilience. The work provides a technical basis for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.Photonic radar is exploited for non-contact vital sign detection with a demonstration on a cane toad with a view to application in humans. Optical signals generated from the system are also explored for LiDAR-based vital sign detection, which may yield improved accuracy and system robustness.

Diabetic cardiomyopathy (DCM) is a severe complication of diabetes, in which ferroptosis is a key pathogenic mechanism. This study examines how alpha-linolenic acid (ALA), a plant-derived omega-3 polyunsaturated fatty acid, protects against damage from ferroptosis in DCM. Using an in vitro model of H9C2 cardiomyocytes treated with high glucose/palmitate, combined with a high-fat diet and mouse model of low-dose streptozotocin (STZ)-induced diabetes, this research demonstrates for the first time that ALA significantly alleviates cardiac dysfunction and prevents ferroptosis. Mechanistically, ALA inhibits STAT3 phosphorylation by activating the AMPK signaling pathway, thereby reducing NCOA4-mediated ferritinophagy and mitigating mitochondrial iron overload and reactive oxygen species accumulation. It also enhances the function of the SLC7A11/GSH/GPX4 axis, reducing lipid peroxidation (LPO)-induced ferroptosis. Collectively, these findings indicate that ALA protects against diabetic cardiomyopathy by coordinating the regulation of ferritinophagy and antioxidant defense through the AMPK-STAT3 pathway, offering a potential therapeutic strategy for disease management.

Activation of scramblases is one of the mechanisms that regulates the exposure of phosphatidylserine to the cell surface, a process that plays an important role in tumour immunosuppression. Here we show that chemotherapeutic agents induce overexpression of Xkr8, a scramblase activated during apoptosis, at the transcriptional level in cancer cells, both in vitro and in vivo. Based on this finding, we developed a nanocarrier for co-delivery of Xkr8 short interfering RNA and the FuOXP prodrug to tumours. Intravenous injection of our nanocarrier led to significant inhibition of tumour growth in colon and pancreatic cancer models along with increased antitumour immune response. Targeting Xkr8 in combination with chemotherapy may represent a novel strategy for the treatment of various types of cancers. Downregulation of specific proteins named scramblases might enhance tumour immunosuppression. In this paper the authors first show that the scramblase Xrk8 is overexpressed in tumour cells upon treatment with chemotherapeutics, and then develop a nanomedicine platform for co-delivery of a cancer prodrug and an siRNA directed against the Xrk8 gene, showing therapeutic effect and enhanced immune response in animal tumour models.

The precise control of output power by grid-connected converters relies on the correct identification and tracking of a grid voltage’s phase at the converter terminal. During severe grid faults, large disturbances cause the converter’s operating point to move away from the stable equilibrium point during normal operation. This leads to oscillations of both the active and reactive power fed into the grid. Using large-signal modelling, this study investigated the converter’s dynamic processes during and after such fault situations. The investigation considered the influence of the converter’s phase-locked loop (PLL), responsible for phase tracking, as well as that of the DC link on the converter-grid system, which has a major influence on the active power exchange with the grid. On this basis, this study also focused on the reactive current reference’s influence during and after fault clearing. Furthermore, an easily implementable strategy for reactive current injection, leading to minimum power oscillations, was presented. The results and the optimized strategies were validated via controller hardware-in-the-loop tests.

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people’s everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China’s carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper.

Grid-forming converters are attracting attention for their significant advantages in terms of stability in a weak grid and simulated inertia. However, while they offer great flexibility due to the use of power semiconductors, they are also affected by their low current-carrying capacity. This means that during a fault, instead of the usual voltage control, a current limiting control is active, which changes the dynamic performance of the converter and influences transient stability. This manuscript focuses on the dynamic performance of grid-forming converters during the restart phase at the post-fault period, and proposes an initial phase threshold to prevent the converter from going into current saturation. Based on this, the manuscript proposes several restart strategies during the post-fault period, by using some fast resynchronization methods in order to meet the requirements of the converter’s stable operation and fast active power restoration. Finally, the above findings and the proposed strategies are validated by a joint control hardware-in-the-loop system.

Genome-wide association studies (GWAS) are widely used to detect associations between genetic variants and phenotypes. However, few studies have thoroughly analyzed genes, the fundamental and most crucial functional units. Here, we develop an innovative strategy to translate genomic variants into gene-level functional haplotypes (FHs), effectively reducing the interference from complex genome structure and linkage disequilibrium (LD) present in the conventional genetic mapping framework. Using refined mixed linear models, gene-level FH is regressed with 20 cotton agronomic traits across 245 sets of phenotypic values in 3,724 accessions, directly identifying 532 quantitative trait genes (QTGs) with significant breeding potential. The biological function of a superior fiber quality QTG encoding ferulic acid 5-hydroxylase 1 is experimentally validated. Thereafter, we systematically analyze the genetic basis of cotton domestication and improvement at the gene level. This report provides genomic insight into the genetic dissection and efficient mapping of functional genes in plants. Significant efforts are needed to leap from the identification of trait-associated genetic variants to the revealing of the causal gene. Here, the authors translate genomic variants into gene-level functional haplotypes for GWAS and identify a gene encoding ferulic acid 5-hydroxylase 1 is responsible for superior cotton fiber quality.

The multifaceted chemo-immune resistance is the principal barrier to achieving cure in cancer patients. Identifying a target that is critically involved in chemo-immune-resistance represents an attractive strategy to improve cancer treatment. iRhom1 plays a role in cancer cell proliferation and its expression is negatively correlated with immune cell infiltration. Here we show that iRhom1 decreases chemotherapy sensitivity by regulating the MAPK14-HSP27 axis. In addition, iRhom1 inhibits the cytotoxic T-cell response by reducing the stability of ERAP1 protein and the ERAP1-mediated antigen processing and presentation. To facilitate the therapeutic translation of these findings, we develop a biodegradable nanocarrier that is effective in codelivery of iRhom pre-siRNA (pre-siiRhom) and chemotherapeutic drugs. This nanocarrier is effective in tumor targeting and penetration through both enhanced permeability and retention effect and CD44-mediated transcytosis in tumor endothelial cells as well as tumor cells. Inhibition of iRhom1 further facilitates tumor targeting and uptake through inhibition of CD44 cleavage. Co-delivery of pre-siiRhom and a chemotherapy agent leads to enhanced antitumor efficacy and activated tumor immune microenvironment in multiple cancer models in female mice. Targeting iRhom1 together with chemotherapy could represent a strategy to overcome chemo-immune resistance in cancer treatment. A pro-tumorigenic role of iRhom1 has been described in several cancer types. Here the authors show that iRhom1 regulates sensitivity to chemotherapy and immune response, as well they report that CD44 targeting nanoparticle-mediated co-delivery of iRhom1 pre-siRNA promotes anti-tumor immune responses in preclinical cancer models.