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In order to effectively manage the pollution from ships in ports, it is very necessary to understand the quantity and characteristics of the emissions from port pollution sources, so that more scientific management measures can be provided.This paper mainly analyzes the three pollution emission sources of Yangshan Port, namely port machinery, transport vehicles and ships, and uses the emission factor method to estimate and establish the air pollution emission inventory of Yangshan Port in 2021.The results show:1) The emissions of pollutants SO2, PM, NOx, CO, and VOCs are 5011t, 1252t, 27,560t, 1670t, and 2150t, respectively;2) Ships are the largest source of pollution, with container ships providing the largest pollution contribution among the different ship types, accounting for 38%;3) The main engine and auxiliary engines, as the main source of pollution from ships, accounted for 40% and 42% of the total amount of pollutants, respectively; the slow-speed state accounted for the highest proportion of pollution discharged in the four operating states, at 41%.

Transporting water and oil microdroplets is important for applications ranging from water harvesting to biomedical analysis but remains a great challenge. This is due to the amplified contact angle hysteresis and insufficient driving force in the micrometer scale, especially for low-surface energy oil droplets. Coalescence of neighboring droplets, which releases vast additional surface energy, was often required, but its relatively uncontrollable nature brings uncertainties to the droplet motion, and the methodology is not applicable to single droplets. Here we introduce a strategy based on slippery surface with immobilized lubricant menisci to directionally transport microdroplets. By simply mounting hydrogel dots on slippery surface, the raised menisci remotely pump microdroplets via capillary force with high efficiency, regardless of droplet size or surface energy. By proof-of-concept experiments, we demonstrate that our method allows for highly efficient water droplet collection and highly sensitive biomedical analyte detection.

Advancing the development of point‐of‐care testing (POCT) sensors that utilize interstitial fluid (ISF) presents considerable obstacles in terms of rapid sampling and analysis. Herein, an innovative strategy is introduced that involves the use of a 3D‐printed, hollow microneedle array patch (MAP), in tandem with a vacuum tube (VT) connected through a hose, to improve ISF extraction efficiency and facilitate expedited analysis. The employment of negative pressure by the VT allows the MAP device to effectively gather ≈18 µL of ISF from the dermis of a live rabbit ear within a concise period of 5 min. This methodology enables the immediate and minimally invasive measurement of glucose levels within the body, employing personal healthcare meters for quantification. The fusion of the VT and MAP technologies provides for their effortless integration into a comprehensive and mobile system for ISF analysis, accomplished by preloading the hose with custom sensing papers designed to detect specific analytes. Moreover, the design and functionality of this integrated VT‐MAP system are intuitively user‐friendly, eliminating the requirement for specialized medical expertise. This feature enhances its potential to make a significant impact on the field of decentralized personal healthcare. A 3D‐printed hollow MAP complemented with a hose‐linked vacuum tube (VT) is developed for efficient and rapid ISF collection and analysis. The visual representation showcases the MAP's gentle skin penetration, enhanced ISF extraction, and sensing functions. ISF analysis versatility with custom sensing papers and prioritize user‐friendly healthcare advancement, eliminating specialized training is emphasized.

Ultrastrong and deep-strong coupling are two coupling regimes rich in intriguing physical phenomena. Recently, hybrid magnonic systems have emerged as promising candidates for exploring these regimes, owing to their unique advantages in quantum engineering. However, because of the relatively weak coupling between magnons and other quasiparticles, ultrastrong coupling is predominantly realized at cryogenic temperatures, while deep-strong coupling remains to be explored. In our work, we achieve both theoretical and experimental realization of room-temperature ultrastrong magnon-magnon coupling in synthetic antiferromagnets with intrinsic asymmetry of magnetic anisotropy. Unlike most ultrastrong coupling systems, where the counter-rotating coupling strength g 2 is strictly equal to the co-rotating coupling strength g 1 , our systems allow for highly tunable g 1 and g 2 . This high degree of freedom also enables the realization of normalized g 1 or g 2 larger than 0.5. Particularly, our experimental findings reveal that the maximum observed g 1 is nearly identical to the bare frequency, with g 1 / ω 0  = 0.963, indicating a close realization of deep-strong coupling within our hybrid magnonic systems. Our results highlight synthetic antiferromagnets as platforms for exploring unconventional ultrastrong and even deep-strong coupling regimes, facilitating the further exploration of quantum phenomena. Deep-strong coupling in hybrid magnonic systems is yet to be explored. Here, the authors unveil unconventional coupling properties in synthetic antiferromagnets. The systems’ high degree of freedom enables a near-realization of deep-strong coupling.

Background The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States. Methods The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health. Results Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time. Conclusions The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities. Trial registration This article does not report the results of a health care intervention on human participants.

Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in two states of USA, Texas and New York state, for the period of March 18, 2020 to August 16, 2020. We emphasize that different models make different assumptions which are basically difficult to validate. Yet invoking different models allows us to examine the data from different angles, thus, helping reveal the underlying trajectory of the development of COVID-19 in Canada. The examinations of the data dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada.

The management of wound exudate is of vital importance for wound healing. Exudate accumulation around wound prolongs inflammation and hinders healing. Although traditional dressings can absorb wound exudate, they are unable to drain exudate in time, often resulting in a poor feature with wound healing. In recent years, the appearance of asymmetric wettability dressings has shown great potential in exudate management. Here, we summarize the latest progress of 3 kinds of asymmetric wettability wound dressings in exudate management, including Janus structure, sandwich structure, and gradient structure. The most common Janus structural dressing among asymmetric wettability dressings is highlighted from 2 aspects: single-layer modified Janus structure and double-layer Janus structure. The challenges faced by asymmetric wettability wound dressings are discussed, and the developing trends of smart wound dressings in this field are prospected.

Recently, the altermagnetic materials with spin splitting effect (SSE), have drawn significant attention due to their potential to the flexible control of the spin polarization by the Néel vector. Here, the direct and inverse altermagnetic SSE (ASSE) in the (101)‐oriented RuO2 film with the tilted Néel vector are reported. First, the spin torque along the x‐, y‐, and z‐axis is detected from the spin torque‐induced ferromagnetic resonance (ST‐FMR), and the z‐spin torque emerges when the electric current is along the [010] direction, showing the anisotropic spin splitting of RuO2. Further, the current‐induced modulation of damping is used to quantify the damping‐like torque efficiency (ξDL) in RuO2/Py, and an anisotropic ξDL is obtained and maximized for the current along the [010] direction, which increases with the reduction of the temperature, indicating the present of ASSE. Next, by way of spin pumping measurement, the inverse altermagnetic spin splitting effect (IASSE) is studied, which also shows a crystal direction‐dependent anisotropic behavior and temperature‐dependent behavior. This work gives a comprehensive study of the direct and inverse ASSE effects in the altermagnetic RuO2, inspiring future altermagnetic materials and devices with flexible control of spin polarization. The direct and inverse altermagnetic spin splitting effects (ASSE) in the (101)‐oriented RuO2 film with the tilted Néel vector, as well as the crystal direction‐dependent anisotropic behavior, confirm the presence of ASSE in the (101)‐RuO2. Furthermore, the observation of temperature‐dependent characteristics of ASSE and spin pumping signals confirm a dominated spin splitting in RuO2 at lower temperatures.

Objective: Pulmonary rehabilitation (PR) has been considered to be an effective treatment method for various respiratory diseases. However, the effects of exercise-based PR on patients with severe/very severe chronic obstructive pulmonary disease (COPD) are unclear. This review aimed to investigate the effects of exercise-based PR on patients with severe/very severe COPD. Methods: PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov databases were searched from inception to December 23, 2022, without language restrictions. Randomized controlled trials (RCTs) investigating the effects of exercise-based PR on patients with severe/very severe COPD were included. Study selection, data extraction, and risk of bias assessment were conducted independently. RevMan software (version 5.3) was used for meta-analysis. The quality of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation system. Results: Six studies (263 patients) were identified. Compared with the control group, the 6-min walking distance [MD = 52.91, 95% CI (3.80, 102.03)], the St. George’s Respiratory Questionnaire total scores [MD = −7.70, 95% CI (−14.32, −1.08)] and the Borg scale scores [MD = −0.68, 95% CI (−1.28, −0.08)] in the experimental group improved, respectively. The St. George’s Respiratory Questionnaire and Borg scale scores were rated as ‘moderate quality’ and ‘low quality’, respectively, and the 6-min walking distance was rated as ‘very low quality’. Conclusions: Exercise-based PR may improve the exercise capacity, quality of life and dyspnea of patients with severe/very severe COPD, which can be regarded as an adjuvant treatment. High quality and large sample RCTs are needed. Registration: This systematic review and meta-analysis protocol was prospectively registered with PROSPERO (No. CRD42022294085).

While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the public are available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from February 24, 2020 to October 14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the RoBERTa, Vader and NRC approaches, we evaluate sentiment intensity scores and visualize the results over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, “masks”, “vaccine”, and “lockdown”, are computed for comparison. We explore possible causal relationships among the variables concerning tweet activities and sentiment scores of COVID-19 related tweets by integrating the echo state network method with convergent cross-mapping. Our analyses show that public sentiments about COVID-19 vary from time to time and from place to place, and are different with respect to anti-epidemic measures of “masks”, “vaccines”, and “lockdown”. Evidence of the causal relationship is revealed for the examined variables, assuming the suggested model is feasible.