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Numerous researchers have examined the environmental and regional characteristics, as well as the mother's dwelling air quality during pregnancy, that influence children's lung disease. The most common type of medicine is Western medicine, however Chinese medicine is more effective at treating lung conditions in youngsters. The common cold, pneumonia, bronchitis, asthma, chronic obstructive pulmonary disease (COPD), and lung cancer are among the respiratory ailments that traditional Chinese medicine (TCM) is frequently used to treat. TCM has qualities like resolving a variety of issues, focusing on several areas, and reducing harmful side effects. TCM has justified its anti-asthma, bronchiolitis, and pulmonary fibrosis (PF) effect in clinical practice but its underlying mechanism and specific role in mentioned disease are still unknown. According to some animal research, the traditional recipe, precise measurements, and organic substances extracted from TCM could considerably reduce changes in the structure of the airways and show anti-inflammatory properties. By examining these results and information, we will talk about the potential Patho mechanism that underlies asthmatic airway inflammation and remodeling as well as the special function of TCM in asthma treatment by controlling various signaling pathways. We provide a summary of the developments in research on TCM extracts for the treatment of asthma, bronchiolitis, PF, and lung cancer in this review paper. Additionally, we shall discuss a few cellular aspects.

The performance of superconducting quantum circuits for quantum computing has advanced tremendously in recent decades; however, a comprehensive understanding of relaxation mechanisms does not yet exist. In this work, we utilize a multimode approach to characterizing energy losses in superconducting quantum circuits, with the goals of predicting device performance and improving coherence through materials, process, and circuit design optimization. Using this approach, we measure significant reductions in surface and bulk dielectric losses by employing a tantalum-based materials platform and annealed sapphire substrates. With this knowledge we predict the relaxation times of aluminum- and tantalum-based transmon qubits, and find that they are consistent with experimental results. We additionally optimize device geometry to maximize coherence within a coaxial tunnel architecture, and realize on-chip quantum memories with single-photon Ramsey times of 2.0 − 2.7 ms, limited by their energy relaxation times of 1.0 − 1.4 ms. These results demonstrate an advancement towards a more modular and compact coaxial circuit architecture for bosonic qubits with reproducibly high coherence. Understanding loss mechanisms in superconducting circuits is crucial for improving qubit coherence. Here the authors use a multimode resonator to study loss mechanisms in thin-film superconducting circuits and demonstrate on-chip quantum memories with lifetimes exceeding 1ms, using Ta thin-films and high-temperature substrate annealing

Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set X of n elements, the problem asks for a subset S of k≪n elements with maximum diversity, as quantified by the dissimilarities among the elements in S. In this paper, we study diversity maximization with fairness constraints in streaming and sliding-window models. Specifically, we focus on the max–min diversity maximization problem, which selects a subset S that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set X is partitioned into m disjoint groups by a specific sensitive attribute, e.g., sex or race, ensuring fairness requires that the selected subset S contains ki elements from each group i∈[m]. Although diversity maximization has been extensively studied, existing algorithms for fair max–min diversity maximization are inefficient for data streams. To address the problem, we first design efficient approximation algorithms for this problem in the (insert-only) streaming model, where data arrive one element at a time, and a solution should be computed based on the elements observed in one pass. Furthermore, we propose approximation algorithms for this problem in the sliding-window model, where only the latest w elements in the stream are considered for computation to capture the recency of the data. Experimental results on real-world and synthetic datasets show that our algorithms provide solutions of comparable quality to the state-of-the-art offline algorithms while running several orders of magnitude faster in the streaming and sliding-window settings.

Sonar images are inherently affected by speckle noise, which degrades image quality and hinders image exploitation. Despeckling is an important pre-processing task that aims to remove such noise so as to improve the accuracy of analysis tasks on sonar images. In this paper, we propose a novel transformer-based generative adversarial network named SID-TGAN for sonar image despeckling. In the SID-TGAN framework, transformer and convolutional blocks are used to extract global and local features, which are further integrated into the generator and discriminator networks for feature fusion and enhancement. By leveraging adversarial training, SID-TGAN learns more comprehensive representations of sonar images and shows outstanding performance in speckle denoising. Meanwhile, SID-TGAN introduces a new adversarial loss function that combines image content, local texture style, and global similarity to reduce image distortion and information loss during training. Finally, we compare SID-TGAN with state-of-the-art despeckling methods on one image dataset with synthetic optical noise and four real sonar image datasets. The results show that it achieves significantly better despeckling performance than existing methods on all five datasets.

HighlightsThe structure–property relationship of PdSe2 is discussed, i.e., layer number vs. tunable bandgap, pentagonal structure vs. anisotropy-based polarized light detection.The synthesis approaches of PdSe2 are thoroughly compared, including bottom-up methods such as chemical vapor transport for bulk crystals, chemical vapor deposition for thin films and single-crystal domains, selenization of Pd films. Besides, top-down strategies are discussed, covering the mechanical exfoliation of bulk crystals, plasma thinning, and vacuum annealing as well as phase transition.The emerging devices of PdSe2 and its van der Waals heterostructures have been delivered such as metal/semiconductor contact, Schottky junction transistors, field-effect transistors, photodetectors, p–n junction-based rectifiers, polarized light detector, and infrared image sensors.Future opportunities of PdSe2-based van der Waals heterostructures are given including logic gate-based digital circuits, RF-integrated circuits, Internet of Things, and theoretical calculation as well as big data for materials science.The rapid development of two-dimensional (2D) transition-metal dichalcogenides has been possible owing to their special structures and remarkable properties. In particular, palladium diselenide (PdSe2) with a novel pentagonal structure and unique physical characteristics have recently attracted extensive research interest. Consequently, tremendous research progress has been achieved regarding the physics, chemistry, and electronics of PdSe2. Accordingly, in this review, we recapitulate and summarize the most recent research on PdSe2, including its structure, properties, synthesis, and applications. First, a mechanical exfoliation method to obtain PdSe2 nanosheets is introduced, and large-area synthesis strategies are explained with respect to chemical vapor deposition and metal selenization. Next, the electronic and optoelectronic properties of PdSe2 and related heterostructures, such as field-effect transistors, photodetectors, sensors, and thermoelectric devices, are discussed. Subsequently, the integration of systems into infrared image sensors on the basis of PdSe2 van der Waals heterostructures is explored. Finally, future opportunities are highlighted to serve as a general guide for physicists, chemists, materials scientists, and engineers. Therefore, this comprehensive review may shed light on the research conducted by the 2D material community.

The evolution of the contact scheme has driven the technology revolution of crystalline silicon (c‐Si) solar cells. The state‐of‐the‐art high‐efficiency c‐Si solar cells such as silicon heterojunction (SHJ) and tunnel oxide passivated contact (TOPCon) solar cells are featured with passivating contacts based on doped Si thin films, which induce parasitic optical absorption loss and require capital‐intensive deposition processes involving flammable and toxic gasses. A promising solution to tackle this problem is to employ dopant‐free passivating contact, involving the use of transparent and cost‐effective wide band gap materials. In this review, we first introduce the dopant‐free passivating contact, from carrier transport mechanisms, material classification to evaluation methods. Then we focus on the advances in different strategies to improve cell performance, including material property optimization, structural and interfacial engineering, as well as various post‐treatments. At the end, the challenge and perspective of dopant‐free passivating contact c‐Si solar cells are discussed. This article provides an overview of the mechanism and materials of dopant‐free passivating contacts for crystalline silicon solar cells, and focuses on the recent advances in contact configuration and interface engineering for efficiency and stability enhancement.

Background Hand-foot-and-mouth disease (HFMD) is considered to be self-limited, however, severe HFMD is a deadly threat for children worldwide, therefore, it is essential to define the clinical and epidemiologic characteristics of children with severe HFMD and identify the risk factors of death. Methods Between 2013 and 2018, children who diagnosed with severe HFMD from Chongqing, China were enrolled in this population-based study. A total of 459 severe HFMD children cases were identified during the study period, including 415 survivors and 44 fatal cases. Demographic, geographical, epidemiological and clinical data of the cases were acquired and analyzed. Results Risk factors of the death because of severe HFMD children included female, aged 1 ~ 3 years, enterovirus 71 infection, falling ill in winter, more than one children in home, being taken care of by grandparents, the caregivers’ education not more than 9 years, having fever more than 3 days, consciousness disorders, general weakness, vomiting, general weakness, abnormal pupillary light reflex, repeated cough, tachypnea, moist rales, white frothy sputum, pink frothy sputum, and cyanosis on lips or the whole body, tachycardia, arrhythmia, cold limbs, pale complexion, weakened pulse. (all p  < 0.05). Spatial-temporal analysis detected high-value clusters, the most likely cluster located at rural countries in the northern parts of Chongqing, from January, 2015 to July, 2017. ( p  < 0.01). Besides, some urban districts were also found high incidence of severe HFMD cases according to the incidence maps. Conclusions The detection of clinical risk factors and the temporal, spatial and socio-demographic distribution epidemiological characteristics of severe HFMD contribute to the timely diagnosis and intervention, the results of this study can be the reference of further clinical and public health practice.

Training deep learning models collaboratively on decentralized edge devices has attracted significant attention recently. The two most prominent schemes for this problem are Federated Learning (FL) and Split Learning (SL). Although there have been several surveys and experimental evaluations for FL in the literature, SL paradigms have not yet been systematically reviewed and evaluated. Due to the diversity of SL paradigms in terms of label sharing, model aggregation, cut layer selection, etc., the lack of a systematic survey makes it difficult to fairly and conveniently compare the performance of different SL paradigms. To address the above issue, in this paper, we first provide a comprehensive review for existing SL paradigms. Then, we implement several typical SL paradigms and perform extensive experiments to compare their performance in different scenarios on four widely used datasets. The experimental results provide extensive engineering advice and research insights for SL paradigms. We hope that our work can facilitate future research on SL by establishing a fair and accessible benchmark for SL performance evaluation.

The opening of arctic routes provides a new option for international navigation ships. The correlation between ship movement characteristics and ice conditions should be known, which will help ships adapt to the polar waters. Based on the voyage data and sea ice manual observation data of the ‘XUE LONG’ ship’s six voyages in polar waters, a correlation analysis model of ice conditions and ship movement characteristics was established in this work. First, the ship movement characteristics in polar waters were analyzed, such as the distribution characteristics of ship speeds, courses, and variation characteristics by using the descriptive statistical analysis method and data visualization analysis method. Then, by using multivariate correlation analysis and univariate controlled correlation analysis methods, the correlation between movement characteristics and ice conditions, such as ice concentration and thickness, and the correlation between different ice conditions themselves, were quantitatively analyzed. The result shows that the correlation analysis model of ice conditions and ship movement characteristics is reliable and effective and can obtain quantitative correlation analysis results. On the one hand, sea ice thickness has almost no significant correlation with ship movement characteristics, excluding the influence of sea ice concentration. On the other hand, excluding the influence of sea ice thickness, sea ice concentration is still significantly correlated with the absolute value of speed, speed variation, and course variation. The conclusions of this work have important reference significance for polar scientific investigations, commercial ships’ voyages in icy waters, and ships’ designs for icy waters.

To investigate the fear of hypoglycaemia in patients with type 2 diabetes mellitus (T2DM), to identify factors related to this fear, and thus to provide evidence for clinical assessment. A total of 385 patients with T2DM who were admitted to the departments of endocrinology in five tertiary grade-A hospitals in Chongqing, China were included in this study. A questionnaire for general information and a Chinese version of Hypoglycemia Fear Survey (HFS) were used to collect the data. The average total score on the HFS was 71.67 ± 17.06 (HFS-W was 38.15 ± 10.57; HFS-B was 33.52 ± 9.54).The three items with the highest average score for HFS-W were not recognising low blood glucose (BG), not having food available, experiencing a hypoglycaemic episode alone, and for HFS-B were eating large amount of snacks, measuring BG six or more times per day, and keeping BG > 150 mmol/L. Regressions showed that number of hospitalisations for T2DM, receiving health education on diabetes, age and hypoglycaemia history because of T2DM were associated with fear of hypoglycaemia (all p  < 0.05). Fear of hypoglycaemia in hospitalised patients with T2DM was strongly associated with diabetes health education, hospitalisation for diabetes, age, and hypoglycaemia history. Medical professionals should attach importance to the specific psychological interventions, health education on diabetes and the early prevention of hypoglycaemia or diabetic complications for patients with T2DM to reduce the fear of hypoglycaemia and improve their health status.