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During the additive manufacturing (AM) process, energy is transferred from the energy beam to the processed material. The high-energy input and uneven temperature distribution result in the high-temperature gradient, large thermal stress, and warping deformation. The scanning strategy, one of the representative AM processing parameters, plays an important role in the microstructures, mechanical properties, and residual stresses of 3D printed parts. It is necessary to review the current state of research about scanning strategy in additive manufacturing, and this paper seeks to address this need. This review mainly focuses on the scanning strategies in selective laser melting process. Various scanning strategies and their effects on mechanical properties, microstructures, and residual stresses of selective laser melted parts are summarized. Finally, some suggestions on the optimization of scanning strategy for better performance are provided based on the above analysis.

Based on microdata of A-share listed companies in Shanghai and Shenzhen from 2009 to 2022 as research samples, this paper takes the implementation of Free Trade Zones policy as the starting point and using the difference in differences method to analyze the impact of the establishment of Free Trade Zones on the internationalization level of enterprises. The empirical results indicate that (1) Free Trade Zones policysignificantly improved enterprises’ internationalization level, and the conclusion still holds after a series of endogeneity and robustness tests. (2) Mechanism analysis showedthat digital transformation, resource allocation efficiency, and green innovation are intermediary variables affecting enterprises’ internationalization levels in Free Trade Zones. The establishment of Free Trade Zones can promote digital transformation, resource allocation efficiency, and green innovation of enterprises, thereby enhancing their internationalization level. (3) Heterogeneity analysis found that establishing free trade experiments has a more significant effect on improving the internationalization level of enterprises in low city levels, large-sized companies, capital-intensive and technology-intensive companies, businesses with executives who have overseas experience, and high-tech companies (4) Further research has found that the establishment of Free Trade Zones also has a positive spatial spillover effect on enterprises in surrounding cities. The above conclusion enriched and expanded the research on the impact of the establishment of Free Trade Zones on the internationalization level of enterprises and has important practical significance for enhancing the competitiveness of enterprises.

Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–37·6) in those aged 80 years or older. These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. UK Medical Research Council.

Since the first discovery of the fatigue phenomenon in the late 1830s, efforts to fight against fatigue failure have continued. Here we report a fatigue resistance phenomenon in nano-TiB2-decorated AlSi10Mg enabled by additive manufacturing. This fatigue resistance mechanism benefits from the three-dimensional dual-phase cellular nanostructure, which acts as a strong volumetric nanocage to prevent localized damage accumulation, thus inhibiting fatigue crack initiation. The intrinsic fatigue strength limit of nano-TiB2-decorated AlSi10Mg was proven to be close to its tensile strength through the in situ fatigue tests of a defect-free microsample. To demonstrate the practical applicability of this mechanism, printed bulk nano-TiB2-decorated AlSi10Mg achieved fatigue resistance more than double those of other additive manufacturing Al alloys and surpassed those of high-strength wrought Al alloys. This strategy of additive-manufacturing-assisted nanostructure engineering can be extended to the development of other dual-phase fatigue-resistant metals.An ultrahigh fatigue-resistant AlSi10Mg alloy is achieved by additive manufacturing, with its three-dimensional dual-phase cellular nanostructure acting as a strong volumetric nanocage to inhibit fatigue damage accumulation.

The classification of tongue shapes is essential for objective tongue diagnoses. However, the accuracy of classification is influenced by numerous factors. First, considerable differences exist between individuals with the same tongue shape. Second, the lips interfere with tongue shape classification. Additionally, small datasets make it difficult to conduct network training. To address these issues, this study builds a two-level nested tongue segmentation and tongue image classification network named IF-RCNet based on feature fusion and mixed input methods. In IF-RCNet, RCA-UNet is used to segment the tongue body, and RCA-Net is used to classify the tongue shape. The feature fusion strategy can enhance the network’s ability to extract tongue features, and the mixed input can expand the data input of RCA-Net. The experimental results show that tongue shape classification based on IF-RCNet outperforms many other classification networks (VGG 16, ResNet 18, AlexNet, ViT and MobileNetv4). The method can accurately classify tongues despite the negative effects of differences between homogeneous tongue shapes and the misclassification of normal versus bulgy tongues due to lip interference. The method exhibited better performance on a small dataset of tongues, thereby enhancing the accuracy of tongue shape classification and providing a new approach for tongue shape classification.

The fracture behaviour of artificial metamaterials often leads to catastrophic failures with limited resistance to crack propagation. In contrast, natural materials such as bones and ceramics possess microstructures that give rise to spatially controllable crack path and toughened material resistance to crack advances. This study presents an approach that is inspired by nature’s strengthening mechanisms to develop a systematic design method enabling damage-programmable metamaterials with engineerable microfibers in the cells that can spatially program the micro-scale crack behaviour. Machine learning is applied to provide an effective design engine that accelerate the generation of damage-programmable cells that offer advanced toughening functionality such as crack bowing, crack deflection, and shielding seen in natural materials; and are optimised for a given programming of crack path. This paper shows that such toughening features effectively enable crack-resisting mechanisms on the basis of the crack tip interactions, crack shielding, crack bridging and synergistic combinations of these mechanisms, increasing up to 1,235% absorbed fracture energy in comparison to conventional metamaterials. The proposed approach can have broad implications in the design of damage-tolerant materials, and lightweight engineering systems where significant fracture resistances or highly programmable damages for high performances are sought after. Due to the complex, stochastic nature, fracture is a key challenge of artificial materials causing catastrophic failures. The authors overcame this by designing metamaterials that program damage and translate crack-resisting mechanisms

To tackle the high resource consumption in occluded person re-identification, sparse attention mechanisms based on Vision Transformers (ViTs) have become popular. However, they often suffer from performance degradation with long sequences, omission of crucial information, and token representation convergence. To address these issues, we introduce AIRHF-Net: an Adaptive Interaction Representation Hierarchical Fusion Network, named AIRHF-Net, designed to enhance pedestrian identity recognition in occluded scenarios. Our approach begins with the development of an Adaptive Local-Window Interaction Encoder (AL-WIE), which aims to overcome the inherent subjective limitations of traditional sparse attention mechanisms. This innovative encoder merges window attention, adaptive local attention, and interaction attention, facilitating automatic localization and focusing on visible pedestrian regions within images. It effectively extracts contextual information from window-level features while minimizing the impact of occlusion noise. Additionally, recognizing that ViTs may lose spatial information in deeper structural layers, we implement a Local Hierarchical Encoder (LHE). This component segments the input sequence in the spatial dimension, integrating features from various spatial positions to construct hierarchical local representations that substantially enhance feature discriminability. To further augment the quality and breadth of datasets, we adopt an Occlusion Data Augmentation Strategy (ODAS), which bolsters the model’s capacity to extract critical information under occluded conditions. Extensive experiments demonstrate that our method achieves improved performance on the Occluded-DukeMTMC dataset, with a rank-1 accuracy of 69.6% and an mAP of 61.6%.

Nanotechnology holds immense promise in revolutionising healthcare, offering unprecedented opportunities in diagnostics, drug delivery, cancer therapy, and combating infectious diseases. This review explores the multifaceted landscape of nanotechnology in healthcare while addressing the critical aspects of safety and environmental risks associated with its widespread application. Beginning with an introduction to the integration of nanotechnology in healthcare, we first delved into its categorisation and various materials employed, setting the stage for a comprehensive understanding of its potential. We then proceeded to elucidate the diverse healthcare applications of nanotechnology, spanning medical diagnostics, tissue engineering, targeted drug delivery, gene delivery, cancer therapy, and the development of antimicrobial agents. The discussion extended to the current situation surrounding the clinical translation and commercialisation of these cutting-edge technologies, focusing on the nanotechnology-based healthcare products that have been approved globally to date. We also discussed the safety considerations of nanomaterials, both in terms of human health and environmental impact. We presented the in vivo health risks associated with nanomaterial exposure, in relation with transport mechanisms, oxidative stress, and physical interactions. Moreover, we highlighted the environmental risks, acknowledging the potential implications on ecosystems and biodiversity. Lastly, we strived to offer insights into the current regulatory landscape governing nanotechnology in healthcare across different regions globally. By synthesising these diverse perspectives, we underscore the imperative of balancing innovation with safety and environmental stewardship, while charting a path forward for the responsible integration of nanotechnology in healthcare. Graphical abstract

Lactate is an abundant oncometabolite in the tumor microenvironment (TME). Lactate driven by metabolic reprogramming leads to acidic microenvironment formation to promote the immune evasion of tumor cells and reduce the effectiveness of immunotherapy for patients with tumors. The expression of mitochondrial pyruvate carrier (MPC) is crucial for pyruvate metabolism, and its dysregulation can lead to the formation of an acidic microenvironment caused by excessive lactic acid. However, the impact of MPC on tumor metabolic processes and biological behavior, as well as how lactate impacts immunosuppression, remains unclear. Here, we found that MPC1 and MPC2, two subunits of MPC, were downregulated in patients with colorectal cancer (CRC). Co-overexpression of MPC1 and MPC2 decreased lactate levels and inhibited cell proliferation, migration and invasion in vitro and tumor growth in vivo in the setting of CRC. Knockdown of MPC1 or MPC2 increased lactate levels and promoted the proliferation, migration and invasion of CRC cells. Mechanistically, the accumulation of lactate promotes the elevation of histone lactylation levels, and MPC regulates the expression of CD33, a marker of dendritic cell (DC) maturation, via histone lactylation, decreasing CD8 + T cell functions. In addition, the overexpression of MPC increased the therapeutic effect of the anti-PD-1 antibody. Our findings reveal that MPC downregulation-mediated lactate production impacts DC maturation via histone lactylation-dependent transcriptional regulation to impair CD8 + T cell responses, suggesting that targeting MPC could enhance immunotherapy efficacy by modulating the TME.