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Exports of goods can now be facilitated across international borders through RCEP. China’s cross-border e-commerce exports to RCEP countries are examined in a panel study. This study examines how economy scale, geographical distance, openness to trade, and Internet technology level impact Chinese e-commerce exports across borders. Consequently, Chinese cross-border e-commerce exporters are advised to develop differentiated export strategies so as to improve export efforts, increase online marketing and promotion in low- and middle-income countries, and provide high-quality products and services in high- income countries.

Given the highly temperature-sensitive nature of the polymorphic phase boundaries, attaining excellent piezoelectric coefficient with superior temperature stability in lead-free piezoceramics via direct compositional design remains a formidable challenge. We demonstrate the synergistic improvement of piezoelectric coefficient and thermal stability in lead-free piezoceramics via atomic-scale local ferroelectric structure design. Via modulation of the local Landau energy barrier at doping sites, we effectively mitigate fluctuations in piezoelectric d 33 . Our approach achieves an impressive d 33 of ~430 pC/N with a minimal temperature fluctuation range (△d 33  ~ 7%) across the room temperature to 100 °C in potassium sodium niobate ceramics. Further optimization through annealing extends this temperature up to 150 °C (△d 33  ~ 8%) while maintaining a high d 33 of ~380 pC/N, rivaling the performance of classic temperature stable lead zirconate titanate. This work establishes a framework for addressing the dilemma between high piezoelectric coefficient and inadequate temperature stability in lead-free piezoceramics. The authors reveal that the incorporation of doping elements with varying electronic structures and ionic radii alters the atomic-scale configuration, thereby affecting the local energy barrier associated with polarization rotation.

Next-generation advanced high/pulsed power capacitors rely heavily on dielectric ceramics with high energy storage performance. Although high entropy relaxor ferroelectric exhibited enormous potential in functional materials, the chemical short-range order, which is a common phenomenon in high entropy alloys to modulate performances, have been paid less attention here. We design a chemical short-range order strategy to modulate polarization response under external electric field and achieve substantial enhancements of energy storage properties, i.e. an ultrahigh energy density of ~16.4 J/cm 3 with markedly improved efficiency ~90% at an electric field of 85 kV/mm in Nb doped (Bi 0.2 Na 0.2 K 0.2 La 0.2 Sr 0.2 ) TiO 3 system. Atomic-scale scanning transmission electron microscopy observations show that Nb exhibits a chemical short-range order structure, with Nb enriched regions displaying ultrasmall polar nanoregions and more flexible polarization configurations, which is conducive to achieving high maximum polarization and low residual polarization. Moreover, refined grain size of ~0.25μm, suppressed oxygen vacancies and enhanced bandwidth contribute to a high breakdown field strength. These collective factors result in exceptionally high energy storage density and efficiency. This short-range order strategy is expected to enhance the functional performances in other high entropy relaxor ferroelectrics. The authors propose a strategy for designing chemical short-range ordering in high-entropy ferroelectric ceramics, where elements with chemical short-range order exhibit lower local ferroelectric potential barriers.

(1) Background: Larval diet composition significantly influences the developmental, physiological, and reproductive traits of Ae. albopictus and Ae. aegypti, major arbovirus vectors. Optimizing larval nutrition is essential for mass-rearing programs supporting the sterile insect technique and incompatible insect technique. This study evaluated the effects of three larval diets on key fitness traits, including pupation rate, male flight ability, adult longevity, female fecundity, pupal size, and wing length, which are critical for the success of SIT and IIT programs. (2) Methods: Ae. albopictus (GT strain) and Ae. aegypti (AEG strain) were reared on three diets with varying protein sources: diet 1 (≈1.23 dollars/kg; porcine liver/shrimp/yeast = 6:3:1), the IAEA-recommended diet; diet 2 (≈1.78 dollars/kg; bovine liver/shrimp/yeast = 6:3:1), a modified IAEA diet; and diet 3 (≈0.55 dollars/kg; tortoise food), a low-cost laboratory formulation. Life history traits were assessed using standardized protocols, and data were analyzed with ANOVA and Tukey’s post hoc test. (3) Results: Diet 3 consistently improved pupation rates, adult longevity, and male flight ability compared with diet 2. Mosquitoes reared on diets 1 and 3 exhibited significantly larger pupae and longer wings, while diet 2 performed sub-optimally. Adult eclosion rates (~100%) remained high across all diets. Male flight ability varied by species, with Ae. albopictus performing best on diet 1 and Ae. aegypti on diet 3. Female fecundity was diet-dependent, with diet 1 favoring Ae. albopictus and diet 3 benefitting Ae. aegypti. Longevity was highest in mosquitoes reared on diet 3, with a median survival of 19.5 days for GT males and 37.5 days for GT females. (4) Conclusions: Diet 3 emerged as the most cost-effective option, enhancing key fitness traits essential for SIT and IIT. Future studies should refine nutrient formulations and validate findings under field conditions to optimize mass-rearing efficiency in vector control.

Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

The formation of Danxia landscapes is too slow to be observed in our life time and the paleoclimates in which Danxia landscapes developed are significantly different from the present. Thus, this study adopted experimental approaches to examine the lithological and paleoclimatic control on the formation of various landscape morphologies in the Mt. Danxiashan, South China. A total of 122 rock samples were collected from a range of Danxia landscape morphologies such as white spots, small and large through caves, honeycomb caves, horizontal grooves, natural bridges, stone pillars, and squama stones. Analyses of the collected samples were conducted in different kinds of experiments, including uniaxial mechanical strength testing; rock resistance against sulfuric acid erosion, freezing, and thawing; X-ray fluorescence spectrometry（XRF） analysis; inductively coupled plasma mass spectrometry（ICP-MS） analysis; and identification analysis under a polarizing microscope. The results indicated that the formation of the various Danxia landscape morphologies could result from one or more of the following processes： differential erosion due to lithological difference, chemical dissolution and recrystallization, freezing and thawing actions, acid corrosion, weathering, lateral erosion of river flows, and tectonic uplifts. Water erosion in humid monsoon climate and the alternations of intensive freezing and thawing actions in the Quaternary glacial stages and the interglacial stages could have had great influences on the formation of Danxia landscape topographies.

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems. Our understanding of human disease can be improved by integrating the abundance of high throughput biomedical data. Here, the authors use deep learning methods successfully used on images to integrate various types of omics data to improve patient classification and identify disease biomarkers.

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

With a decade of effort, significant progress has been achieved in the synthesis, processing, and applications of MXenes. Metal ions play many crucial roles, such as in MXene delamination, structure regulation, surface modification, MXene composite construction, and even some unique applications. The different roles of metal ions are attributed to their many interactions with MXenes and the unique nature of MXenes, including their layered structure, surface chemistry, and the existence of multi‐valent transition metals. Interactions with metal ions are crucial for the energy storage of MXene electrodes, especially in metal ion batteries and supercapacitors with neutral electrolytes. This review aims to provide a good understanding of the interactions between metal ions and MXenes, including the classification and fundamental chemistry of their interactions, in order to achieve their more effective utilization and rational design. It also provides new perspectives on MXene evolution and exfoliation, which may suggest optimized synthesis strategies. In this respect, the different effects of metal ions on MXene synthesis and processing are clarified, and the corresponding mechanisms are elaborated. Research progress on the roles metal ions have in MXene applications is also introduced. Interactions with metal ions play crucial roles in the development of MXenes. This review discusses in details the fundamental chemistry of these interactions, including intercalation, crosslinking and etching, and presents how these interactions affect MXene synthesis, processing and applications, which yields the approaches for the rational designs, optimized synthesis and potential applications of MXene‐based materials.