Science Progress最新文献

A non-suppurative inflammatory disease of the middle ear with middle ear effusion and conductive hearing loss is called otitis media with effusion. Poor eustachian tube function, immunological factors, viral factors, and other factors are primarily involved in its pathophysiology, which has not yet been completely understood. Several researcher scholars have recently studied platelet-activating factor and they found that platelet-activating factor is closely associated to the occurrence, development, and outcome of otitis media with effusion. Platelet-activating factor is a significant element contributing to the extension of otitis media with effusion. In order to give a reference for further investigation into the mechanism and clinical management of this illness, the research status of platelet-activating factor and otitis media with effusion during the past two decades is reviewed in this study, along with the mechanisms of otitis media with effusion leading to otitis media with effusion.

Dengue, a viral infection transmitted by mosquitoes, has become a substantial public health issue in Bangladesh. The high population density and subtropical-tropical climate of the nation create conducive environments for the transmission of the virus. The recent increase in dengue cases in Bangladesh prompts an inquiry into the potential for the virus to progress into an epidemic manifestation. Bangladesh is prone to dengue outbreaks due to a multitude of contributing factors. To commence, the virus is endemic to tropical and subtropical regions, and climate change is contributing to the expansion of its range. Additionally, the high population density in Bangladesh amplifies the vulnerability to dengue transmission. Intimate human proximity elevates the probability of contracting mosquito stings and transmitting viruses. The escalating incidence of dengue in Bangladesh is substantiated by the growing count of documented cases. The emergence of severe dengue is a contributing aspect that raises concerns about the potential worldwide consequences of the disease. It could potentially head from Bangladesh to neighboring nations via an infected individual. There exist apprehensions due to the substantial employment of Bangladeshi laborers overseas, compounded by the presence of foreign laborers within Bangladesh. The endeavor to control dengue in Bangladesh continues to face ongoing challenges. This review addresses the complexities of dengue transmission, assesses Bangladesh's readiness for managing epidemics, analyzes risk factors associated with dengue, and suggests preventive measures to mitigate the possibility of worldwide consequences of dengue originating within the nation.

Studying the impact of the digital economy on carbon emissions in the distribution industry is of great significance for realizing sustainable development goals and coping with climate change. This study finds that increasing the level of digital economy development can reduce the carbon emission intensity of the circulation industry through fixed-effects modeling. Moreover, the effect is different in different geographic regions, and the improvement of the digital economy development level in the east and central regions can significantly reduce the carbon emission intensity of the distribution industry. The digital economy can simultaneously reduce the carbon emission intensity of the circulation industry by reducing the degree of labor factor mismatch in the circulation industry and improving the regional green innovation capacity. Therefore, in order to promote the green development of the distribution industry, it is also necessary to make efforts to improve the construction of network infrastructure, accelerate the process of research and development and cultivation of green technology, and break down the barriers of cross-regional mobility of talents.

During the peak tourist season, large theme parks often experience a simultaneous influx of visitors, resulting in prolonged waiting times for popular attractions. This extended waiting significantly reduces tourists' satisfaction and may negatively impact their willingness to revisit the theme park. In Taiwan, schools at all levels often plan graduation trips to theme parks for their students. Students are divided into groups and must enter and exit the theme park at the same time. This article presents a new theme park problem with multitype facilities (TPP-MTF) for student groups. Based on the group's preference for theme park facilities, multitype reserved tickets with popular facilities are designed for groups, so groups do not need to wait for the reserved facilities. Since the waiting time for groups can be reduced, the theme park can also obtain ticket fees in advance and estimate the number of visitors to the theme park, so the theme park and the group can achieve a win-win situation. This article proposes a new decoding approach for a random permutation of integer sequence and embeds it into an immune-based algorithm, genetic algorithm, and particle swarm optimization algorithm to solve the TPP-MTF problem. A theme park in Taiwan was taken as an example and numerical results of the three algorithms were analyzed and compared to verify the effectiveness of the proposed algorithms.

High-quality development represents a fundamental shift in China's economy from scale and speed to quality and efficiency. In this transition, environmental, social, and governance (ESG) practices are not only closely integrated with China's development strategy but have also become a core standard for measuring development quality. Nevertheless, research on how ESG practices specifically promote high-quality development in Chinese enterprises remains limited. Based on the environmental Kuznets curve (EKC) theory framework, this study uses data from the Huazheng ESG Index from 2009 to 2022 and employs fixed effect models, mediation effect models, and heterogeneity analysis to examine the specific impact of China's ESG practices on corporate high-quality development from multiple dimensions. The results show that there is a "U-shaped" relationship between the ESG practices of Chinese listed companies and green total factor productivity (GTFP). Initially, improving ESG practices may temporarily suppress GTFP, but as ESG levels further improve, they exhibit a significant positive impact on GTFP. Corporations significantly boost productivity by alleviating financing constraints and promoting green technological innovation, although the direct impact on technological advancement remains relatively limited. In China's western regions and industries such as manufacturing, electricity, heat, gas, and water production and supply, wholesale and retail, and information transmission, software, and information technology services, the positive effects of ESG on GTFP are particularly evident. Based on these findings, it is recommended that governments and financial institutions collaborate to promote green credit policies and increase support for the research and development of green technologies. Additionally, environmental education and resource use optimization should be strengthened, especially in industries such as agriculture, forestry, animal husbandry, and fisheries, and the exchange of ESG technologies and experiences between industries should be encouraged.

In the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation. The existing literature primarily focuses on analyzing individual features in fake news, overlooking multimodal feature fusion recognition. Compared to single-modal approaches, multimodal fusion allows for a more comprehensive and enriched capture of information from different data modalities (such as text and images), thereby improving the performance and effectiveness of the model. This study proposes a model using multimodal fusion to identify fake news, aiming to curb misinformation. The framework integrates textual and visual information, using early fusion, joint fusion and late fusion strategies to combine them. The proposed framework processes textual and visual information through data cleaning and feature extraction before classification. Fake news classification is accomplished through a model, achieving accuracy of 85% and 90% in the Gossipcop and Fakeddit datasets, with F1-scores of 90% and 88%, showcasing its performance. The study presents outcomes across different training periods, demonstrating the effectiveness of multimodal fusion in combining text and image recognition for combating fake news. This research contributes significantly to addressing the critical issue of misinformation, emphasizing a comprehensive approach for detection accuracy enhancement.

Background: Benzodiazepines (BZDs) are commonly prescribed as adjunctive drugs for patients with cardiovascular diseases (CVDs), particularly those who experience anxiety or insomnia. However, the relationship between the use of BZDs and incident risk of sudden cardiac arrest (SCA) has not been well investigated. In this study, we aimed to examine the association between the use of BZDs and the incident risk of SCA among patients with CVD.

Method: In this retrospective cohort study, a total of 74,715 eligible patients with new-onset CVD as a primary cause of hospitalization between July 2016 and August 2022 were included from the health information platform in Shenzhen, China. Among them, 61,761 BZD non-initiators were identified and matched to 12,954 BZD initiators by propensity score at a maximum ratio of 5:1. Propensity score-matched Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs).

Results: Over a 12-month follow-up period, 29 (2.24 per 1000 person-years) and 137 (2.22 per 1000 person-years) SCA cases occurred among propensity score-matched BZD initiators and non-initiators, respectively. Patients who initiated BZD treatment were associated with a 101% increased risk of SCA incidence compared with patients without BZD treatment (adjusted HR: 2.01, 95% CI: 1.42, 2.83). Furthermore, compared with the non-use (0 defined daily dose, DDD), the adjusted HR was 1.43 (95% CI: 1.32, 1.56) for the BZD consumption of ≤1 DDD and 2.58 (95% CI: 2.37, 2.81) for the BZD consumption of >1 DDD (P for trend < 0.001) within a 12-month follow-up period.

Conclusion: This study provides evidence that BZD initiation may be associated with an increased incident risk of SCA in patients with CVD. Our finding highlights the importance of cautious prescribing BZDs in the health management of patients with CVD.

Due to the advantages of high stiffness, high precision, high load capacity and large workspace, hybrid robots are applicable to drilling and milling of complicated components with large sizes, for instance car panels. However, the difficulty in establishing an exact dynamic model and external disturbances affect the high accuracy control directly, which will decrease the machining accuracy and thereby affect the machining quality and efficiency of the system. Sliding mode control is an effective approach for high-order nonlinear dynamic systems since that it is very insensitive to disturbances and parameter variations. However, chattering may exist in traditional sliding mode control with fixed parameters, which results from a constant approaching speed. Besides, the approaching speed will affect the chattering strength directly. To solve these problems, a modified sliding mode controller with self-adaptive parameters is proposed to enhance the trajectory-tracking performance of a 5-degree-of-freedom hybrid robot. Firstly, the kinematic model of the robot is established. Then adopting the principle of virtual work, a rigid dynamic model of the robot is built. Based on the built dynamic model, a modified sliding mode control method is developed, of which the approaching speed is dependent on the system state. Finally, the sliding mode controller with self-adaptive parameters is created for a hybrid robot. The proposed sliding mode controller can achieve a rapid approaching speed and suppress chattering simultaneously. Simulation results demonstrate that the proposed modified sliding mode controller can achieve a comparatively accurate and smooth trajectory, which owns good robustness to external disturbances.

Due to the discrete and non-homogeneous of similar materials and the inability to realize large-size and original scale modeling, it is difficult to restore the structure and stress state of underground coal and rock mass in similar simulation tests. To solve this problem, a lightweight and suitable for large-scale modeling similar material, rock-like porous material has been developed. The quasi-static uniaxial compression experiment was carried out by using the large tonnage multi-module electronic control test system. And the influencing factors of controllable mechanical behavior of rock-like porous materials were studied. The results showed that, under uniaxial compression conditions, the material stress-strain curve exhibits three phases: elastic stage, failure stage, and platform stage. The uniaxial compressive strength, elasticity modulus, stress drop, and softening modulus of rock-like porous materials basically increase with the increase of density. The stress after peak strength changes from a slow decrease to a "stepped" or even "cliff like" downward trend. Polypropylene fibers have the effect of enhancing the uniaxial compressive strength, elasticity modulus, stress drop, softening modulus, shear deformation, and residual strength stability of rock-like porous materials. The rock-like porous material has a critical loading velocity, and it increases with density. At the critical loading velocity, the material shows obvious shear failure, and the shear inclination angle is the largest, and so is the uniaxial compressive strength. Through the experimental research, the influence laws of density, polypropylene fiber, and loading velocity on the failure mode, mechanical parameters, and mechanical behavior of the material are clarified, and the quantitative relationship between density and each mechanical parameter is obtained. The research is helpful to realize the accurate control of mechanical behavior of rock-like porous materials and further inverts the deformation and failure mechanism of underground coal and rock structures through indoor similar simulation tests.

As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.