The impact of the digital economy (DE) on urban environmental quality (EQ) is a critical aspect of China's economic development. This study investigates the impact of DI on urban EQ using the data ...from prefecture-level cities spanning the period from 2011 to 2021 and updates some disparate conclusions of related studies. It is discovered that a non-linear correlation exists between DE and urban EQ. Currently, DE can effectively improve local city EQ. This conclusion remains valid even after robustness tests and endogeneity treatment. The impact of DE on improving EQ can be classified as the impact of technological innovation, industrial upgrading, resource allocation, infrastructure construction, environmental governance, and changes in public lifestyle. Heterogeneity analysis reveals that the influence of DE is particularly pronounced in cities located in central and eastern regions of China, those with higher levels of administrative management, resource-based urban areas, and those with more stringent environmental regulations.
In the face of the traditional fossil fuel energy crisis, solar energy stands out as a green, clean, and renewable energy source. Solar photovoltaic tracking technology is an effective solution to ...this problem. This article delves into the sustainable development of solar photovoltaic tracking technology, analyzing its current state, limiting factors, and future trends. The adjustment of solar panel orientation using solar tracking technology to maximize energy generation efficiency has been widely implemented in various fields, including solar power plants. Currently, limiting factors for this technology include energy generation efficiency, costs, and the complexity of various environmental conditions. In terms of sustainable development, this article emphasizes the importance of photovoltaic materials and manufacturing innovation, energy efficiency improvements, as well as the integration of smart and digital technologies. Future trends include higher precision, broader applications, and lower costs. Solar photovoltaic tracking technology will play a pivotal role in global energy production, fostering the realization of a clean and sustainable energy future.
The quality of Tibetan matsutake drops during cold chain transportation. To extend the shelf life and improve the market value, this study analyzed the matsutake logistics process, and optimized the ...dynamic monitoring and quality management systems for post-harvest matsutake with different preservation packaging in the cold chain. This system monitored the micro-environmental parameters of the cold chain in real time, and it identified the best preservation method by analyzing the quality change characteristics of the matsutake with different preservation packaging. It was concluded that the matsutake were best preserved under the conditions of modified atmosphere packaging. The data analysis on the collected data verified the performance of the system. Relevant personnel were invited to participate in the system performance analysis and offer optimization suggestions to improve the applicability of the established monitoring system. The optimized model could provide a more effective theoretical reference for the dynamic monitoring and quality management of the system.
Tunnelling-induced ground deformations inevitably affect the safety of adjacent infrastructures. Accurate prediction of tunnelling-induced deformations is of great importance to engineering ...construction, which has historically been dependent on numerical simulations or field measurements. Recently, some surrogate models originating from machine learning methods have been developed, showing satisfactory prediction performance with high computational efficiency. However, these purely data-driven models show weak robustness in the absence of sufficient training data. This study proposed a hybrid deep learning model integrating both data-driven and physics-based strategies to decrease calculation costs and eliminate the dependence on large numbers of training data. The underlying physical mechanism of ground deformation due to tunnel excavation is coupled into the deep learning framework to form a physics-informed neural network (PINN) model. The performance of the hybrid model is first assessed by comparing it with the classical Verruijt-Booker solution and a conventional purely data-driven model. The potential of the proposed PINN model for engineering applications is then illustrated. It is found that the proposed PINN model can reasonably reproduce ground deformation fields obtained numerically with only a small amount of training data. This paper provides a new paradigm for incorporating hybrid deep learning frameworks and field monitoring systems to predict ground deformation fields in real time.
The annual yield of
Spirulina
(
Arthrospira
) in China is approximately 10,000 tonnes, accounting for 60–70% of the total capacity in the world. As the largest
Spirulina
production base in China, ...Ordos
Spirulina
Industrial Park in Inner Mongolia plays an important role in the
Spirulina
industry. Yet, to date little is known about the dynamic changes of
Spirulina
under real production in this region. Therefore, the growth, quality, and nutrients consumption of
Spirulina
in diel cycle and semi-continuous culture mode under commercial production scale were investigated and analyzed to help optimization and management of production. Significant variations in biomass, photosynthetic activity, and biochemical components were observed during the diel cycle. Biomass increased during the daytime and decreased at night, losing 22% of what had been accumulated in the daytime. The change of carbohydrate content was consistent with that of biomass. The photosynthetic pigments declined obviously at midday due to the much higher light intensity and dissolved oxygen (DO), and always recovered at night. Under the semi-continuous culture mode, a biomass productivity of 11.2 g m
−2
day
−1
was obtained. The change of nitrogen and phosphorus concentration in the medium indicated that the nutrients supply was imbalanced, and the phosphorus was quite possibly excessive in this mass culture. It is recommended that the supply of phosphorus can be decreased to one-third of the original concentration without harmful effect on growth and quality of
Spirulina
. The present study may deepen our understanding of
Spirulina
production on a commercial scale and provide the guidance for cost-effective production of
Spirulina
in this region.
Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, ...especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.
Background
Acute necrotizing pancreatitis (NP), a severe form of acute pancreatitis (AP), has higher mortality and worse outcome than non-necrotizing pancreatitis (non-NP). Infected NP is a ...devastating subgroup of NP. To date neither NP nor infected NP has robust prediction strategies, which may delay early recognition and timely intervention. Recent studies revealed correlations between disturbed gut microbiota and AP severity. Some features of intestinal microbiota have the potential to become biomarkers for NP prediction.
Methods
We performed 16S rRNA sequencing to analyze gut microbiota features in 20 healthy controls (HC), and 58 AP patients on hospital admission. The AP patients were later classified into NP and non-NP groups based on subsequent diagnostic imaging features. Random forest regression model and ROC curve were applied for NP and infected NP prediction. PIRCUSt2 was used for bacterial functional pathway prediction analysis.
Results
We found that the three groups (HC, NP, and non-NP) had distinct microorganism composition. NP patients had reduced microbial diversity, higher abundance of
Enterobacteriales
, but lower abundance of
Clostridiales
and
Bacteroidales
compared with the non-NP group. Correlation analyses displayed that intestine bacterial taxonomic alterations were related to severity, ICU admission, and prognosis. By pathway prediction, species more abundant in NP patients had positive correlation with synthesis and degradation of ketone bodies, and benzoate degradation.
Enterococcus faecium
(ASV2) performed best in discriminating NP and non-NP patients.
Finegoldia magna
(ASV3) showed the maximal prediction capacity among all ASVs and had comparable accuracy with Balthazar CT to detect patients with infected NP.
Conclusions
Our study suggests that NP patients have distinct intestinal microbiota on admission compared to non-NP patients. Dysbiosis of intestinal microbiota might influence NP progression through ketone body or benzoate metabolism.
Enterococcus faecium
and
Finegoldia magna
are potential predictors for NP and infected NP. Our findings explore biomarkers which may inform clinical decision-making in AP and shed light on further studies on NP pathophysiology and management.
Psoriasis is a chronic and recurrent immune-related skin disease that often causes disfigurement and disability. Due to the visibility of lesions in patients and inadequate understanding of ...dermatology knowledge in the general public, patients with psoriasis often suffer from stigma in their daily lives, which has adverse effects on their mental health, quality of life, and therapeutic responses. This review summarized the frequently used questionnaires and scales to evaluate stigmatization in patients with psoriasis, and recent advances on this topic.
,
, and
have been commonly used. The relationship between sociodemographic characteristics, disease-related variables, psychiatric disorders, quality of life, and stigmatization in patients with psoriasis has been thoroughly investigated with these questionnaires. Managing the stigmatization in patients with psoriasis needs cooperation among policymakers, dermatologists, psychologists, psychiatrists, researchers, and patients. Further studies can concentrate more on these existing topics, as well as other topics, including predictors of perceived stigmatization, stigmatization from non-patient groups, influence of biologics on stigmatization, and methods of coping with stigmatization.
Helicobacter pylori infection, a worldwide health issue, is typically treated with standard antibiotic therapies. However, these treatments often face resistance and non-compliance due to side ...effects. In this umbrella review, we aimed to comprehensively assess the impact of probiotics supplementation in different preparations on Helicobacter pylori standard treatment. We searched PubMed, Embase and Cochrane Central Register of Controlled Trials in the Cochrane Library from inception to June 1, 2023, to identify systematic reviews with meta-analyses that focused on eradication rates, total side effects and other outcomes of interest. The most comprehensive meta-analysis was selected for data extraction. AMSTAR 2 was used to assess quality of meta-analyses. Overall, 28 unique meta-analyses based on 534 RCTs were included. The results suggests that probiotics supplementation with pooled probiotic strains was significantly associated with improved eradication rates (RR 1.10, 95% CI 1.06-1.14) and reduced risk of total side effects (RR 0.54, 95% CI 0.42-0.70) compared with standard therapy alone. Single-strained or multi-strained preparation of probiotics supplementation showed similar results. Despite Bifidobacterium spp. showing the highest potential for eradication, the study quality was critically low for most meta-analyses, necessitating further high-quality research to explore the optimal probiotic strains or their combinations for Helicobacter pylori treatment.aq_start?>Kindly check and confirm the edit made in article title.
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•Improved framework included vigor, organization, resilience and ecosystem service.•Proposed a deep learning method to assess the ecological system health status.•The distribution ...pattern of ecosystem health level exhibited spatial heterogeneity.•Differentiated management approaches need to be adopted for different pastures.
Arid pastoral ecosystems are particularly vulnerable to degradation and desertification, exacerbated by factors such as overgrazing, land use changes, and climate change impacts. Therefore, research on ecosystem health is imperative for ecosystem management and restoration in arid pastoral areas. Although ecosystem health studies have been conducted in various regions, research on ecological health assessment in arid pastoral areas of Central Asia remains limited, especially within a mountain-basin system (MBS). Taking the Chinese pastoral region of Fuyun County in Altay Prefecture, Xinjiang, as our study area, we improved an evaluation framework in terms of Vigor-Organization-Resilience-Service (VORS) based on remote sensing and GIS technology, and innovatively employed a deep learning method to assess the ecological system health status and spatiotemporal patterns for the years 2000, 2010, and 2020.
Our results indicate the following: (1) The evaluation method based on deep learning can achieve a more comprehensive, efficient and objective evaluation of ecosystem health. (2) The ecosystem health index (EHI) ranged from 0.0759 to 0.5735 in 2000, 0.0798 to 0.5952 in 2010, and 0.0415 to 0.5657 in 2020. (3) The distribution pattern of EHI exhibited spatial heterogeneity, with the indexes decreasing from north to south. The highest proportion of high ecosystem health level (EHL) was found in summer pastures, followed by those in north of spring/autumn pastures and along the Irtysh and Ulungur rivers. On the other hand, the EHLs were relatively low in spring/autumn and winter pasture areas. The EHL exhibited minimal alteration between 2000 and 2010. From 2010 to 2020, areas experiencing an increase in EHL were primarily concentrated along the Ulungur River, with another part of the region focused on the interface between winter pastures and spring/autumn pastures. Based on the results, rotational grazing and grazing prohibition should be put into place for the winter pastures in the central and southern regions of the study area, as well as for the spring and autumn pastures. Meanwhile, it is recommended to plant premium, high-yield artificial forage grass alongside the Irtysh and Ulungur rivers. These findings will assist ecosystem managers in implementing effective measures to enhance ecosystem health in arid pastoral areas. Moreover, the presented evaluation framework and method enable a more objective, comprehensive, and efficient assessment of complex ecosystems, and they can be applied to ecosystem health assessments in similar regions.
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