Background: Plasma levels of von Willebrand factor (VWF) are significantly elevated in patients with COVID-19. However, the dynamic changes and prognostic value of this biomarker in hospitalized patients with COVID-19 are not determined. Methods: 124 patients infected with SARS-CoV2 were prospectively recruited for the study. Serial blood samples were obtained at the time of admission (D1), 3-4 days following standard of care treatments (D2), and 1-2 days prior to discharge or death (D3). Plasma VWF antigen, ADAMTS13 antigen and activity, as well as the ratio of VWF/ADAMTS13 were determined, followed by statistical analyses. Results:While admission plasma levels of VWF in COVID-19 patients were significantly elevated, no statistical significance was detected among patients of different disease severity. Plasma ADAMTS13 activity, but not its antigen levels, were significantly lower in patients with severe or critical COVID-19 compared with other groups. Interestingly, the ratios of plasma VWF antigen to ADAMTS13 antigen were significantly higher in patients with severe or critical COVID-19 than in those with mild to moderate disease. More importantly, plasma levels of VWF and VWF/ADAMTS13 ratios were persistently elevated in patients with COVID-19 during hospitalization. Kaplan-Meier and Cox proportional hazard regression analysis revealed that an increased plasma VWF or ratio of VWF/ADAMTS13 at D2 and D3 associated with an increased mortality rate. Conclusions: Persistent endotheliopathy, marked by an elevated level of plasma VWF or an increased VWF/ADAMTS13 ratio, is present in all hospitalized patients following SARS-CoV2 infection and strongly associated with mortality.

Background: Endotheliopathy is common pathologic findings in patients with acute and long COVID-19. It may be associated with disease severity and predispose to long-term complications. Plasma levels of a proteoglycan syndecan-1 are found to be significantly elevated in patients with COVID-19, but its roles in assessing the disease severity and predicting long-term outcome are not fully understood. Methods: 124 consecutive hospitalized patients with SARS-CoV2 infection were prospectively enrolled and blood samples were collected on admission (T1), 3-4 days following treatment (T2), and 1-2 days prior to discharge or death (T3). Plasma levels of syndecan-1 were determined using an immunosorbent assay; various statistical analyses were performed to determine the association between plasma syndecan-1 levels and disease severity or the 60-day mortality rate. Results: Compared with those in the healthy controls, plasma levels of syndecan-1 in patients with critical COVID-19 were significantly higher (p<0.0001). However, there was no statistically significant difference among patients with different disease severity (p>0.05), resulting from large individual variability. Longitudinal analysis demonstrated that while the levels fluctuated during hospitalization in all patients, plasma syndecan-1 levels were persistently elevated from baseline in critical COVID19 patients. Cox proportional hazard regression analyses revealed that elevated plasma levels of syndecan-1 (>260 ng/mL at T1, >1018 ng/mL at T2, and >461 ng/mL at T3) were significantly associated with the 60-day mortality rate. Conclusions: Endotheliopathy, marked by glycocalyx degradation and elevated plasma syndecan-1, occurs in nearly all hospitalized patients with SARS-CoV2 infection; the elevated plasma syndecan-1 is associated with increased mortality in COVID-19 patients.

Water pollution, intensified by the release of hard-to-degrade pollutants, poses severe threats to ecosystems, human health, and economic development. The existing advanced oxidation processes often involve high operational costs and may result in secondary pollution, highlighting the necessity for innovative and more sustainable solutions. To address these challenges, our study introduces a cost-effective and eco-friendly corona discharge barrier plasma discharger for wastewater treatment. Through the central composite design/response surface methodology, a high decolorization rate of 98% of methylene blue (MB) within 10 minutes was achieved by optimizing parameters such as pH and voltage. Furthermore, the mechanisms of the generation of reactive oxygen species via this device was discussed in detail and the degradation pathways of MB were described. Moreover, this device is also very energy-efficient, with a low energy density and electrical energy per order of 0.15 watt/mL and 5.79 kWh/m³/order, respectively. In conclusion, the plasma discharger developed in this study provides a cost-effective and environmentally sustainable solution for dye wastewater treatment. This research contributes significantly to the advancement of sustainable dye wastewater management practices, offering an innovative method that meets both environmental and economic objectives.

Soil temperature reflects the impact of local factors，such as vegetation，soil and the atmosphere of a region. Therefore, it is important to understand the regional variation of soil temperature. However, lack of observations with adequate spatial and/or temporal coverage, it would be difficult to use the observation data to study the regional variation. Based on the observation data from Nanchang and Ganzhou stations and ERA-Interim/Land reanalysis data, this study analyzed the temporal-spatial distribution characteristics of soil temperature over Poyang Lake Basin. The results showed close correlations between observation data and reanalysis data at different depths. Reanalysis data could mainly reproduce the temporal-spatial distributions of soil temperature over the Poyang Lake Basin, but generally underestimate their magnitudes. Temporally, there is an obvious warming trend in the basin. Seasonally, the temperature raised fastest in spring and slowest in summer, except for the ST4, which rising fastest in spring and slowest in winter. In terms of depths, the temperature of ST1 rises fastest. For the other layers, the warming trend is almost similar. An abrupt change of annual soil temperature at all depths occurred in 1997, and annual soil temperatures at all depths were abnormally low in 1984. Spatially, annual soil temperature decreased with latitude, except for the summer ST1. Because of the high temperature and precipitation in summer, the ST1 are higher around the lake and the river. The climatic trend of soil temperature presents the general increase trend from south to north, opposite to the distribution of soil temperature. The findings provide a basis for understanding and assessing the variation of the soil temperature over the Poyang Lake Basin.

To elucidate the function of the cold resistance regulatory gene FmNAC1 from Fraxinus mandshurica, this study identified the role that the overexpression of the FmNAC1 gene plays in tobacco growth and cold stress regulation. The cloned FmNAC1 gene from F. mandshurica is 891 bp in length and encodes 296 amino acids. Our subcellular localization analysis confirmed that FmNAC1 is primarily located in the nucleus and functions as a transcription factor. FmNAC1 is responsive to cold and NaCl stress, as well as to the induction of IAA, GA, and ABA hormone signals. To further elucidate its function in cold resistance, four transgenic tobacco lines of FmNAC1 overexpression（FmNAC1-OE）were generated through tissue culture after the agrobacterium-mediated transformation of wild-type (WT) tobacco. These FmNAC1-OE plants exhibited accelerated growth after transplantation. When exposed to low-temperature conditions at -5°C for 24 hours, the rates of wilting and yellowing of the FmNAC1-OE plants were significantly lower than those of the WT tobacco plants. Additionally, the membrane integrity, osmotic regulation, and reactive oxygen species (ROS)-scavenging abilities of the FmNAC1-OE tobacco lines were better than those of the WT plants, indicating the potential of the FmNAC1 gene to improve plant cold resistance. The gene expression results further revealed that the FmNAC1 transcription factor exhibits regulatory interactions with growth-related genes such as IAA and AUX1; cold resistance-related genes such as ICE, DREB, and CBF1; and genes involved in the clearance of reactive oxygen species (ROS), such as CAT and SOD. All of this evidence shows that the FmNAC1 transcription factor from F. mandshurica plays a key role in contributing to the enhancement of growth, cold resistance, and ROS clearance in transgenic tobacco plants.

Background: As the opioid epidemic continues, understanding the geospatial, temporal and demand patterns is important for policymakers to assign resources and interdict individual, organization, and country-level bad actors. Methods: GIS geospatial-temporal analysis and extreme-gradient boosted random forests evaluate ICD-10 F11 opioid-related admissions and admission rates using geospatial analysis, demand analysis, and explanatory models, respectively. The period of analysis was January 2016 through September 2018. Results: The analysis shows existing high opioid admissions in Chicago and New Jersey with emerging areas in Atlanta, Salt Lake City, Phoenix, and Las Vegas. High rates of admission (claims per 10,000 population) exist in the Appalachian area and on the Northeastern seaboard. Explanatory models suggest that hospital overall workload and financial variables might be used for allocating opioid-related treatment funds effectively. Gradient-boosted random forest models accounted for 87.8% of the variability of claims on blinded 20% test data. Conclusions: Based on the GIS analysis, opioid admissions appear to have spread geographically, while higher frequency rates are still found in some regions. Interdiction efforts require demand-analysis such as that provided in this study to allocate scarce resources for supply-side and demand-side interdiction: prevention, treatment, and enforcement. Based on GIS analysis, the opioid epidemic is likely to spread or diffuse through the country, and interdiction efforts require demand-analysis such as that provided in this study to allocate scarce resources for supply-side and demand-side interdiction: prevention, treatment, and enforcement.

There are four subtypes of adenosine receptors (ARs), named A1, A2A, A2B and A3, all of which are G protein-coupled receptors. The A2BAR, coupled to both Gαi and Gαq G proteins, is one of the several G-protein-coupled receptors that are expressed in a significantly higher level in some cancer tissues in comparison to adjacent normal tissues. There is growing evidence that the A2BAR plays an important role in tumor cell proliferation, angiogenesis, metastasis, and immune suppression. Thus, A2BAR antagonists are potentially novel attractive anticancer agents. Several antagonists targeting at the A2BAR are currently in clinical trials for various types of cancers. In this review, we first describe the signaling, agonists, and antagonists of the A2BAR. We further discuss the role of the A2BAR in the progression of various types cancers, and the rationale of using A2BAR antagonists in cancer therapy

Waterbomb structures are origami-inspired deformable structural components used in new types of robots. They have a unique radially deployable ability that enables robots to better adapt to their environment. In this paper, we propose a series of new waterbomb structures with square, rectangle, and parallelogram base units. Through quasi-static axial and radial compression experiments and numerical simulations, we prove that the parallelogram waterbomb structure has a twist displacement mode along the axial direction. Compared with the square waterbomb structure, the proposed optimal design of the parallelogram waterbomb structure reduces the critical axial buckling load-to-weight ratio by 55.4% and increases the radial stiffness-to-weight ratio by 67.6%. The significant increase in the radial stiffness-to-weight ratio of the waterbomb structure and decrease in the critical axial buckling load-to-weight ratio make the proposed origami pattern attractive for practical robotics applications.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

As an important means of controlling the spread of cities, large-scale urban green belts have been widely used in the planning and construction of green space systems in various mega-size cities around the world. In previous studies, there has been more research on the overall isolation effect of green belts, while there has been less research on changes in their internal structure. There have been more perspectives on the land-use change (LUC) caused by urban expansion, and less systematic research on the dual driving factors of LUC. In this paper, we constructed a multi-dimensional index system to measure the "contraction-expansion" mechanism of land use in the overlapping vegetation areas of large green belts under the dual influence of urban expansion and urban greening, from the perspectives of quantity, space, and connectivity during different construction periods. Taking the boundary of two green belts in Beijing as an example, we hope to provide scientific support for the construction and ecological protection of Beijing's green belts.

Resection of malignant lesions in the oral and maxillofacial area causes functional and morphological defects. To recover from these defects, reconstruction surgery is needed such as a primary closure or a flap. There are advantages and disadvantages to the method of using the primary closure and the flap, and the choice of procedure is entirely up to a surgeon. The purpose of this study is to evaluate availability of primary closure in patients. For 10 years from January 2010 to May 2020, patients who recovered using flaps after removing malignant lesions from the oral cavity and those who closed the lesion using primary closure were investigated. The investigation was conducted by searching the database of the hospital. There were 85 patients who removed malignant lesions in the oral and maxillofacial area, and among them 16 patients closed the soft tissue defects using primary closure. Operation time, histologic types, stages, wound status, and period of hospitalization according to the patients' medical records were investigated retrospectively. Between the staging and the primary closure, statistically significant differences were observed between stage I and stage II patients in 13 patients using the primary closure. (P=0.046). Statistically significant differences were identified between operation time and primary closure, with an average of 4.02 hours (P=0.015). The average period of hospitality for patients who underwent primary closure was 13.4 days, compared to 26.7 days for those who underwent flap surgery. This difference was statistically significant (P=0.0003). This study evaluated the use of primary closure in patients who underwent resection of malignant lesions in the oral and maxillofacial area. Although the majority of patients underwent reconstruction with flaps, our results showed that primary closure can be a viable option for certain patients with early-stage lesions. Therefore, oral and maxillofacial surgeons should carefully consider various evaluation criteria and select appropriate reconstruction methods to optimize functional and aesthetic outcomes for each patient.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

A novel metal-doped carbon quantum dot, zinc-chlorine co-doped carbon quantum dots (Zn/Cl-CQDs), has been developed for the fluorescent probe detection of vitamin B2 and the analysis of the correlation properties of this carbon quantum dot and vitamin B2. Stability experiments demonstrate that Zn/Cl-CQDs possess good fluorescence properties under alkaline conditions. However, when vitamin B2 is added into Zn/Cl-CQDs, the fluorescence intensity decreases sharply, indicating that a fluorescence quenching phenomenon occurs between vitamin B2 and Zn/Cl-CQDs. Lastly, the use of Zn/Cl-CQDs in the detection of vitamin B2 tablets and vitamin B2-rich fruits resulted in recovery rates of 98.2% and 100.6%, respectively. Therefore, this method can be well applied to the detection and analysis of vitamin B2, and has great development prospects in the pharmaceutical industry and food monitoring fields.

Oil has become the object of global exploits and fierce competition among the major world powers as it is a key strategic non-renewable resource. Transnational petroleum investment is therefore an important mechanism available to countries and international corporations to control oil resources even though there are numerous inherent uncertainties and risks. A new risk assessment index system is proposed in this paper based on use of the Delphi expert scoring system and fuzzy comprehensive evaluation that aims to minimize the potential risks inherent to multinational petroleum investment. This approach encapsulates political, legal, socioeconomic, and infrastructural factors to develop a technical method that can be used for transnational petroleum investment risk assessment. An evaluation of oil investment risk within a case study area is also presented; results provide reference data that can be applied by national and international oil companies to mitigate risks of transnational oil investment.

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

The one-dimensional MoS₂/carbon nanofibers (1D MoS₂/CNFs) are synthesized by electrospinning using exfoliated MoS₂ nanosheets and polyacrylonitrile as raw materials. The exfoliated MoS₂ nanosheets with size of about 150 nm are encapsulated in carbon nanofibers, and the free-standing MoS₂/CNFs can be easily cut into flexible tablet and directly used as binder-free anode for lithium storage. The resultant 1D MoS₂/CNFs exhibit a very high reversible capacity of 700 mAh g^-1 at 100 mA g^-1 after 50 cycles, high rate capacity (450 mAh g^-1 at 1000 mA g^-1 after 200 cycles) and good cycle stability.

This study utilises NaOH to treat composite cementitious materials containing aeolian sand and zeolite powders, which were used to replace 50% of the cement in aeolian-sand concrete (ASC). The mechanical properties of treated ASC considerably improved, especially when the NaOH dosage was 4% by mass. After curing this sample (denoted as AAZC-4) for 28 d, its compressive strength improved by 17.2% and its split tensile was increased by 16.3%. Potassium feldspar and montmorillonite in zeolite powder and SiO2 in the sand were decomposed by OH− and combined with other elements to generate various silicate gels and A-type potassium zeolite crystals inside the concrete. Microscopic examination showed that the gels and crystals intertwined to fill the pores, decreasing (increasing) the percentage of large (small) pores, thus optimising the pore structure. This substantially improved the mechanical properties of ASC. Freeze–thaw salt-intrusion tests showed that the extent of mass loss, degree of damage and loss of compressive strength of AAZC-4 were similar to those of ordinary concrete but were reduced by 36.8%, 19% and 52.1%, respectively, compared with those of ASC. Therefore, AAZC-4 has sustainable working performance in the chloride ion permeable environment in cold and arid areas.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

In recent years, Chinese manufacturing enterprises compete to chase the wave of "digital revolution", digital empowerment has become an important strategic path of technological reform for many manufacturing enterprises. Based on the micro data of listed companies, this paper investigates the impact of digital transformation of Chinese listed companies on the quality of their export products. It is found that digital transformation can significantly improve the quality of enterprises' export products. After a series of robustness tests, this conclusion is still valid, and there are two ways to improve innovation performance and total factor productivity. The export product quality enhancement effect of enterprise digital transformation also has the heterogeneity of ownership, region and industry. In the further study, this paper also examines the impact of digital transformation on the internal salary gap of enterprises, and finds that digital transformation may increase the internal salary gap of enterprises, and form a "masking effect" on the quality of export products. To a certain extent, this paper deepens the understanding of the study of enterprise digital transformation on the quality of export products and its differences, and provides certain guidance for enterprises to implement the strategy of digital transformation.

This paper investigates the influence of different vegetation on the permeability of the shallow soil layers of slopes under rainfall infiltration. Firstly, four large slopes are filled in the outdoor natural environment, and the overburdens of the four slopes are Magnolia multiflora, Cynodon dactylon, Magnolia multiflora mixed with Cynodon dactylon, and no vegetation. Secondly, the four slopes are cultivated in the outdoor natural environment for one year. After the vegetation overburdens are matured, the field artificial rainfall test is carried out through the self-developed artificial rainfall device to monitor the water migration law inside the four slopes in real time. Finally, the unsaturated permeability coefficients of the shallow soil layers of slopes are calculated. The results show that the infiltration rate of rainwater in each overburden slope from fast to slow is Magnolia multiflora overburden slope, no vegetation slope, Cynodon dactylon overburden slope, Magnolia multiflora mixed with Cynodon dactylon overburden slope. In the early stage of rainfall, Magnolia multiflora increases the permeability coefficient of the shallow soil layer of the slope, thus weakening the anti-seepage ability of the slope, but the influence of Magnolia multiflora is not obvious in the later stage. Cynodon dactylon and Magnolia multiflora mixed with Cynodon dactylon can significantly reduce the permeability coefficient of the shallow soil layers of the slopes, thereby increasing the anti-seepage ability of the slopes, and the mixed planting of Magnolia multiflora and Cynodon dactylon can minimize the permeability coefficient of the shallow soil of the slope, resulting in the best anti-seepage effect.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

To estimate the degree of quantum entanglement, it is important to understand the statistical behavior of functions of spectrum of density matrices such as von Neumann entropy, quantum purity, and entanglement capacity. These entangle- ment metrics over different generic state ensembles have been studied intensively in the literature. As an alternative metric, in this work we study sum of square root spectrum of density matrices, which is relevant to negativity and fidelity in quantum information processing. In particular, we derive the exact mean and vari- ance of sum of square root spectrum over the Bures-Hall generic state ensemble extending known results obtained recently over the Hilbert-Schmidt ensemble.

The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

The two-scale method (TSM) is an effective approach to simulate electromagnetic (EM) scattering from rough sea surface, to interpret physical interaction of EM wave with rough sea surfaces. The sea surface roughness spectrum and the cutoff wavenumber are two key factors affecting the accuracy of TSM simulation. This paper takes the geophysical model functions (GMFs) of different band as reference, and quantitatively studies the two factors based on the numerical simulation of different polarization backscattering coefficients. The numerical simulation demonstrates that Apel spectrum is more suitable for the backscattering simulation in C/X/Ku-band, and the optimal cutoff wavenumbers are also derived for TSM simulation of EM scattering in C/X/Ku-band. Then an empirical model function of optimal cutoff wavenumbers with respect to frequency, incident angle and wind speed are obtained with regression method. Finally, the simulation results with the new model function of cutoff wavenumber are compared with the GMF and measured data in different bands and they are all consistent. Moreover, a physical explanation for the optimal cutoff wavenumber is also given.

This paper studies a model of dynamic compensation and capital structure with managerial traits. We show that Optimistic manager perceives equity as more undervalued than debt, while, confident manager perceives debt as more undervalued than equity. Managerial risk aversion mitigates manager’s bias. The risk aversion of the optimistic manager has a convex effect on the optimal coupon. There exists the level of risk aversion eliminating the bias on the leverage. The managerial optimist has an ambiguous effect on the owner’s bankruptcy level. The risk aversion has a convex effect on the owner’s bankruptcy level. The optimistic/confident manager underestimates the credit spread. The risk aversion has convex effect on the credit spread. In contrast to rational manager, the optimistic/confident manger has higher level of effort. The risk aversion has a negative effect on the effort.

Muscle is one of the most critical organs for mammals, which governs multiple movement and physiological functions. Circular RNA (circRNA) is a kind of novel endogenous RNA without 5'-Caps and 3'-poly(A) structures formed by pre-mRNA's back-splicing. RNA binding proteins (RBPs) control the production and degradation of circRNA, help nucleus-cytoplasm transport and locate circRNA, and regulate circRNA translation. Therefore, circRNAs and the chaperoned RBPs play critical roles in muscle growth, development, and disease progression. In this review, we systematically characterize the possible molecular mechanism of circRNA-protein interactions. Also, we summarize the latest researches on circRNA-protein interactions in muscle development and diseases. Besides, we provide several valid prediction methods and experimental verification approaches. Our review reveals the importance of circRNAs and their protein chaperones and provides a reference for further study in this field.

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality globally. Studies show that airway mucus hypersecretion strongly compromises lung function, leading to frequent hospitalization and mortality, highlighting an urgent need for effective COPD treatments. MUC5AC is known to contribute to severe muco-obstructive lung diseases, worsening COPD pathogenesis. Various pathways are implicated in the aberrant MUC5AC production and secretion MUC5AC. These include signaling pathways associated with mucus-secreting cell differentiation [ nuclear factor-κB (NF-кB)and IL-13-STAT6- SAM pointed domain containing E26 transformation-specific transcription factor (SPDEF), as well as epithelial sodium channel (ENaC) and cystic fibrosis transmembrane conductance regulator (CFTR)], and signaling pathways related to mucus transport and excretion-ciliary beat frequency (CBF). Various inhibitors of mucus hypersecretion are in clinical use but have had limited benefits against COPD. Thus, novel therapies targeting airway mucus hypersecretion should be developed for effective management of muco-obstructive lung disease. Here, we systematically review the mechanisms and pathogenesis of airway mucus hypersecretion, with emphasis on multi-target and multi-link intervention strategies for the elucidation of novel inhibitors of airway mucus hypersecretion.

Perinatal brain injury is a major public health problem burdened with high morbidity, mortality and severe neurological sequelae. Despite advances in neonatal life-saving technologies, neonatal encephalopathy lacks specific and effective treatment. Therefore, mechanisms of central nervous system changes associated with perinatal brain injury have been a hot topic of neonatology research. In recent years, researchers have shifted more focus from neurons themselves to perineuronal cells and intercellular interactions. As a member of the nervous system network, astrocytes are one of the dominant glial cells that regulate ion homeostasis, blood flow, and antioxidant function in the brain. Their changes in molecular mechanisms (e.g., alterations in RNA regulatory networks) during brain injury storms deserve further attention. Non-coding RNAs participate in and promote reactive pathophysiologic changes in astrocytes. The review summarizes and discusses the role of non-coding RNAs in astrocytes in recent years in studies of neonatal brain injury. It focuses on the most studied disease-related ncRNAs: miRNAs, lncRNAs and circRNAs, and hopes to provide possible directions for pathophysiological studies and therapeutic approaches of neonatal brain injury.

Autonomous vehicles (AVs) may not only reduce parking demand, but also free up on-street parking spaces. From the perspective of bus priority, converting on-street parking lots into dedicated bus lanes can both ensure bus priority and accommodate non-bus traffic, helping to reduce congestion and pollution. In order to study the impact of conversion of on-street parking lots to bus lanes on traffic flow, a microscopic simulation framework based on VISSIM is proposed in this paper. Data was collected using the floating car method and video recording method to quantify the factors affecting traffic flow due to the conversion of on-street parking. The VISSIM simulation model was then calibrated. According to the proportion of the on-street parking lots into a bus lane, the on-street parking lots conversion simulation model of different scenarios was established, and the influence of different on-street parking lots conversion ratio on traffic efficiency and pollution emission was quantitatively analyzed. Finally, the optimization method based on bus lane network connectivity is discussed. The results indicate that: When the on-street parking lots conversion ratio is approximately 100%, bus speed increases by 21.03%, bus delays decrease by 14.27%, and bus parking instances decrease by 11.31%. Total emissions are negatively correlated with vehicle speed. As the conversion rate of on-street parking increases, the emissions of road segments and the entire road network gradually decrease. At a conversion ratio of 100%, the average total emissions for road segments have the highest reduction rate, decreasing by 4.12%. At a conversion ratio of 50%, the average emission total for the road network shows the largest reduction, falling by 2.88%.

In this work, the Ni/Ti/Nb multilayer composite was successfully manufactured by accumulative pack-roll bonding. The microstructure evolution and mechanical properties of the composite during the accumulative roll bonding (ARB) process were investigated by scanning electron microscopy(SEM), energy dispersive spectrometer(EDS), transmission electron microscopy (TEM), micro-hardness and tensile tests. The results showed that after 5 passes of the ARB process, the deformations of layers were relatively uniform, and no large number of interlayer fractures occurred. The microstructures of Ni and Ti were both equiaxed grains with a grain size of 200 nm and 150 nm, respectively, and finer equiaxed grains of the Ni layer were observed at the interface. The laminar structure of Nb layer was observed. The tensile strength and micro-hardness increased significantly as the number of ARB increased. After 5 passes of the ARB process, the tensile strength of the composite reached 792.3 MPa, and the micro-hardness of Ni, Ti, and Nb were increased to 270.2, 307.4, and 243.4 HV, respectively.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

The hospital indoor environment plays a significant role in shaping the microbial exposures that humans encounter. Resistance to antibiotics is one of the mechanisms employed by bacteria to develop resilience against stress in indoor environments. The extensive usage of antibiotics has been a key factor in driving changes in the ecological function of antibiotic resistance genes (ARGs) and their acquisition by pathogenic bacteria. By integrating the 16S rRNA Illumina sequencing and high throughput-qPCR approaches with water and air dust samples across seven departments at Peking University Shenzhen Hospital, China, this study yielded intriguing findings regarding the department-specific variation, transmission, and correlation of bacteria, ARGs and MGEs within the hospital indoor environment. One notable observation was that the seasonal variation played a vital role in shaping the bacterial composition in the overall hospital indoor environment across seven studied departments. Nevertheless, a similar significant seasonal impact was only observed in the blood test room and emergency departments, rather than in the other five departments. Another intriguing finding was the correlation between ARGs and MGEs abundance. This correlation was evident in the overall hospital indoor environment, which was conversely not found in the emergency, pneumology and gynecology departments. Additionally, while there was no significant relationship that occurred between bacterial communities and ARGs/MGEs within the hospital, the gynecology department demonstrated a close link between bacterial communities and the presence of ARGs/MGEs. Notably, the gynecology department also stood out as an important source of bacteria and ARGs/MGEs for other departments. This study underscores the need for careful surveillance and monitoring of antibiotic resistance, particularly in Legionella spp. in man-made water systems. The findings also show the importance of monitoring and understanding the genetic elements such as Tp614 involved in gene transfer and recombination, as well as their impact on antimicrobial treatment efficacy.

Magnetic Nanoparticles (MNPs) is a promising technique to cure brain diseases. On the one hand, by serving as drug carriers, they can bypass the blood-brain barrier and deliver drug molecules to the brain parenchyma; on the other hand, their transport trajectory can be manipulated by applying an external magnetic field. However, due to the complex microstructure of brain tissues, e.g. the anisotropy of white matter (WM), how to achieve desired drug distribution patterns, e.g. uniform distribution, by tuning the drug delivery system is largely unknown. Here, in this study, by adopting a mathematical model capable of capturing the diffusion trajectories of MNPs in the microstructures, we systematically investigated the effects of key parameters in the MNPs delivery system on the equivalent diffusion coefficient of MNPs in the microenvironment of brain WM. The results show that uniform distribution of MNPs in anisotropic tissues can be achieved by adjusting the particle size and magnetic field. We have not only obtained a deeper understanding on how to optimise the MNPs delivery system, it can also be anticipated that an improved mathematical model could even help to achieve complex drug distribution patterns in the complicated brain environment by designing an appropriate combination of the key parameters.

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements’ polarization in the vertical plane of the breast while the second array has half elements’ polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in micro-wave imaging and sensing for biomedical applications.

In the face of the current energy and environment problems, the full use of biomass resources instead of fossil energy to produce a series of high-value chemicals has great application prospects. 5-hydroxymethylfurfural (HMF), which can be synthesized from lignocellulose as raw material, is an important biological platform molecule. Its preparation and catalytic oxidation of subsequent products have important research significance and practical value. In the actual production process, porous organic polymer (POPs) catalysts are highly suitable for biomass catalytic conversion due to its high efficiency, low cost, good designability, and environmentally friendly features. Here, we briefly described the application of various types of POPs (including COFs、PAFs、HCPs、CMPs) in the preparation and catalytic conversion of HMF from lignocellulosic biomass, and analyzed the influence of the structural properties of catalysts for the catalytic performance. Finally, we summarized some challenges that POPs catalysts will face in biomass catalytic conversion, and prospected the important research directions in future. This review would provide valuable references for the efficient conversion of biomass resources into high-value chemicals in practical applications.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

Abstract: Characterization the 3-D structure of clouds is needed for a more complete understanding of the Earth's radiative and latent heat fluxes. Here we develop and explore a “ray casting” algorithm applied to the Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra satellite, to reconstruct 3-D cloud volumes for observed clouds. The ray casting algorithm is first applied to geometrically simple synthetic clouds to show that, under the assumption of perfect, clear-conservative cloud masks, the reconstruction method yields overestimation whose magnitude depends on the cloud geometry and the resolution of the reconstruction grid relative to the image pixel resolution. The method is then applied to two select MISR scenes, fully accounting for MISR’s viewing geometry for reconstructions over the Earth’s ellipsoidal surface. The MISR Radiometric Camera-by-camera Cloud Masks at 1.1 km resolution and custom cloud masks at 275 m resolution independently derived from MISR RGB channels are used as input cloud masks. A wind correction method, termed “cloud spreading”, is devised and applied to the cloud masks to offset potential cloud movements over short time intervals (around 7 minutes at maximum) between the cameras. The MISR cloud top height product is used as a constraint to reduce the overestimation at the cloud top. The reconstruction results show that their uncertainty is significant when the wind correction is applied, and that they have more refined structures when the input cloud mask has a higher resolution. Recommendations for improving the presented cloud volume reconstructions as well as for future passive remote sensing satellite missions are discussed.

Surface acoustic wave (SAW) is effective for the manipulation of fluids and particles in microscale. The current approach of integrating interdigitated transducers (IDTs) for SAW generation into microfluidic channels involves complex and laborious microfabrication steps. These steps often require the full access to clean room facilities and hours to align the transducers to the precise location. This work presents an affordable and innovative method for fabricating SAW-based microfluidic devices without the need of clean room facilities and alignment. The IDTs and microfluidic channels are fabricated in the same process and thus precisely self-aligned in accordance with the device design. With the use of the developed fabrication approach, a few types of different SAW-based microfluidic devices have been fabricated and demonstrated for particle separation and active droplet generation.

This paper studies the pilot control strategy and workload of tilt-rotor aircraft dynamic conversion procedure between helicopter mode and fixed-wing mode. A nonlinear flight dynamics model of tilt-rotor aircraft with full flight modes is established. On this basis, a nonlinear optimal control model of dynamic conversion is constructed, considering factors such as conversion corridor limitations, pilot control, flight attitude, engine rated power, and wing stall effects. To assess pilot workload, an analytical method based on wavelet transform is proposed, which examines the mapping relationship between pilot control input's amplitude, constituent frequencies, and control tasks. By integrating the nonlinear optimal control model and the pilot workload evaluation method, the analysis of pilot control strategy and workload during the conversion procedure is conducted, leading to the identification of strategies to reduce pilot workload. The results indicate that incorporating the item of pilot workload in the performance index results in a notable reduction in the magnitude of collective stick inputs and longitudinal stick inputs. Moreover, it facilitates smoother adjustments in altitude and pitch attitude. Additionally, the conversion of the engine nacelle can be achieved at a lower and constant angular velocity. In summary, the conversion and reconversion procedures are estimated to have a low workload (Level 1 ~ Level 2), with relatively simple and easy manipulation for the pilot.

In a complex working environment, the fault signal of the gearbox is greatly affected by the outside world, and fault feature recognition is difficult, so the fault diagnosis accuracy is difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the Particle Swarm Optimization algorithm in Adaptive Weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that, compared with existing commonly used methods of fault diagnosis, the fault diagnosis model proposed in this paper can reduce the influence of noise in the original signal can better learn the deep-level features in the original signal and has higher diagnosis accuracy, faster diagnosis speed and good generalization in fault diagnosis.

Regional Climatic Comfort Index (CCI) deteriorated significantly due to the climate change and anthropogenic interference. Knowledge regarding the long-term temporal dynamics of CCI in typical regions should be strengthened. In this study, we analyze the temporal and spatial evolution of CCI from 1969 to 2018 in Guangdong Province, based on meteorological indicators, including heat, humidity, wind and cloth loading etc.. Additionally, the population exposure to climate unconformity was examined since 1990 with the help of population data. Our study found that: (1) the warming and humidifying of the summer climate served as the main driving force for the continuous deterioration of CCI, with the comfortable days decreased by 1.06d/10a and the extremely muggy days increased by 2.83d/10a; (2) spatially, the lowest climate comfortability concentrated in southwestern Guangdong with more than 50 uncomfortable days each year, while the climate comfortability in northeastern Guangdong tends to deteriorated whit higher rate, which can reach as high as 6d/10a; (3) in summer, the population exposure to uncomfortable climate highly centralized in the Pearl River Delta, Shantou, Jieyang, and the surrounding areas, and both area and population exposure showed increasing trends. Particularly, Shenzhen held the highest growth rate of population exposure with an increase rate of 2.94 million/10a; (4) although the discomfort distribution and deterioration rate vary across the province, the spatial heterogeneity of comfortability is diminishing in Guangdong Province. This study will provide scientific reference for regional urban planning, thermal environment improvement, local resident health risk analysis, and key strategy implementation, etc.

Facing the charging difficulties of free-floating shared electric vehicle and the high cost of mobile charging for single demand, this article proposes a collaborative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the minimization model of system charging total cost, seeking the optimal charging scheme and the number of mobile charging vehicles. The lower model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% compared with the pure fixed charging scheme and 14.9% compared with the pure mobile charging scheme

The potential effect of implementing environmental regulations on economic growth is a controversial issue for a long time. As portrayed by Porter hypothesis, environmental regulations may impact on economic growth by reconstructing the economic structure. A number of previous literature proved the connections between the above three parties. With the intention of exploring the nature of this mechanism, it is important to understand the internal inter-relation of the three parties, requiring data from a large economy experiencing the transition in economic structure. By constructing a panel containing data from 30 Chinese provinces over 10 years (2008-2019), this paper investigate the dynamic inter-relation of the three parties by introducing PVAR model with Granger Test. Results reveal that environmental regulations give a “U” shaped impact to economic growth. In return, economic growth promotes the development of economy with a weakening strength. In addition, results also support the hypothesis that economic structure is the intermediate of economic growth and environmental regulations. There is a rooftop for the effect of economic structure to environmental regulations. The rooftop may signify the best optimization of primary structure.

The Shimensi super-large tungsten polymetallic deposit is located in Late Jurassic- Early Cretaceous Porphyry-Skarn tungsten ore belt in the south Yangtze metallogenic belt. There are 3 types of mineralization: veinlet-disseminated type, thick quartze vein type and hydrothermal crypto-explosive breccia type. Based on geological studies, this paper presents new petrographic, microthermometric, laser Raman spectroscopic and hydrogen and oxygen isotope research of the fluid inclusions from the deposit. The results show that there are five different types of fluid inclusions: liquid-rich inclusions, vapor-rich inclusions, pure liquid inclusions, pure vapor inclusions, and fluid inclusions containing a solid crystal. The homogenization temperatures of the fluid inclusion range from 140℃-270℃, the salinities are 3%-5% NaCleq and the densities of ore-forming fluid range from 0.64 g/cm3 to 0.99 g/cm3. For the analyses of Laser Raman spectroscopic, the ore-forming fluids can be approximated by a Ca2+-Na+-SO42--Cl- fluid system with small amount of CO2, CH4 and N2. Otherwise, the datas of the pressure, pH and Eh show a fluid metallogenic environment of low pressure, weak acid and weak reduction. The values of the homogenization temperature in 3 types of orebodies suggest that the mineralization is characterized by the decrease of the temperature under the condition of the fluid immiscibility. The H-O isotope values are interpreted to indicate that the ore-forming fluids are mainly composed of magmatic water, and also the meteoric water was added with the process of the magma rising.

Background: There is considerable evidence to support the association between environmental tobacco smoke (ETS) exposure and children’s burden of disease. However, literature on the health outcomes of prenatal ETS exposure among Chinese children has not yet been comprehensively reviewed. Objective: This systematic scoping review examines the currently available evidence and identifies gaps for further research on the health consequences of prenatal ETS exposure among Chinese children. Methods: Following the JBI scoping review methodological framework, we conducted a computer-aid search of three electronic databases-- PubMed, EBSCOhost, and ProQuest to include studies from January 2011 to May 2023 that addressed the health outcomes of Chinese children whose mothers were exposed to ETS at any stage of the pregnancy. Further, a methodological quality assessment of the selected articles was conducted, using JBI Critical Appraisal Checklists. Results: A total of 30 articles were reviewed, including eleven high-quality studies and nineteen moderate-quality studies. Five main themes, including hypertension, foetal and children’s development, behavioural disorders, respiratory outcomes, and other health outcomes. Majority of studies showed positive link between prenatal ETS exposure and increase risk of preterm birth, and moderately, foetal growth restriction. Few studies explored other potential adverse outcome of ETS, including hypertension, respiratory morbidity, lung function and asthma in their children. Conclusion: The current available evidence on prenatal ETS exposure in Chinese children has unveiled a wide range of health outcomes, including preterm birth, foetal development, behavioural disorders and much more. However, Chinese studies in this area are still lacking and a gap still exists in relation to the strength of association between prenatal ETS exposure and some health risks. Efficient anti-smoking policies and smoking cessation programs should be developed to promote maternal and child health. Further research is also needed to provide better evidence in this field.

The nuclear reactor measurement system is a crucial component of the nuclear measurement system in a nuclear power plant. Its primary function is to measure the power distribution within the reactor core using detectors, calibrate, and provide data on core fuel consumption. Therefore, the study of the detectors' reliability is crucial for ensuring the safe operation of the nuclear power system. The article describes the operational mechanism of the gas ionization chamber detector utilized in the intermediate-range detector of the off-board nuclear measurement system. It also analyzes the movement of particles between the plates during the ionization process of the nuclear reaction taking place in the detector. The functional relationship between the current in the recombination region and the ionization region involved in the actual working condition, and the change with the increase of voltage between the plates, is deduced. A mathematical model is established for the number of pulses generated between the plates and the magnitude of the voltage between the plates. Subsequently, the change in the offset of the voltage corresponding to the inflection point of the ping curve and the change rule of the slope of the ping curve are investigated. Combined with the analysis of experimental data, the results show that the initial particle energy of Ar+, the primary sensitive particle on the detector surface, follows a normal distribution and is influenced by the energy loss associated with the initial velocity and the voltage between the plates. The trends of the inflection points and slopes of the ping curves can be utilized to evaluate the performance and operational status of the detector. Combined with the results of the experimental data analysis, the fit is good, and the inflection point currents in the normal state decrease by about 1.5% annually. The intelligent diagnostic system, based on the method studied in this paper, has been successfully implemented in the RPN digital retrofit and upgrade project at Qinshan No. 2 Plant.

In this work, we studies the principle of ground electrode current field through ground communication technology, signal design, and optimal working mode of excitation source, so as to adapt the working mode, working frequency, and transmission medium condition of signal transmission. Through waveform design, energy is concentrated in the main conduction direction, which is beneficial for signal filtering at the receiving end and achieving high reliability data transmission; On this basis, on-site environmental testing was conducted to verify the detection and recognition technology of weak signals. Signal design and detection recognition are a very important part of the grounding electrode current field through ground communication technology. The grounding electrode current field through ground communication technology is mainly applied to solve the problem of communication between rescue personnel and trapped personnel before and during tunnel engineering collapse and rescue.

This paper presents a novel approach for semantic segmentation of building roof in dense urban environment with Deep Convolution Neural Network (DCNN) using imagery acquired by a Chinese Very High Resolution (VHR) satellite mission, i.e. GaoFen-2 (GF-2). To provide an operational end-to-end work flow for accurate build roof mapping with feature extraction as well as image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform the VHR image analysis for labeling pixels. Since the diverse urban patterns and building styles in large areas, sample image data sets of building roof and non-building roof are collected over different metropolitan regions in China. We selected typical cities with dense urban environment in each metropolitan region as study areas for collecting training and test samples. High performance cluster with GPU-mounted workstations is employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with multiple NN layers is developed for building roof labeling. The validation of the building roof map shows that the overall accuracy(OA) and the mean Intersection Over Union( mIOU) of DCNN based segmentation are 94.67%, 0.85 respectively, while CRF-refined segmentation achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas across different urban and building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline for operational built-up area monitoring and timely update of building roof map over large areas.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

The e-commerce platform in the digital economy era has evolved into a data platform ecosystem built around data resources and data mining technology systems. The most typical applications of big data are also concentrated in the field of e-commerce. E-commerce companies should first grasp the interactive relationship among the three major factors of data, technology and innovation, e-commerce platform operation is a multidisciplinary research field. It is not easy for researchers to obtain a panoramic view of the knowledge structure in this field. Knowledge graph is a kind of graph that shows the development process and structure relationship of knowledge with the field of knowledge as the object. It is not only a visual knowledge mapping, but also a serialized knowledge pedigree, which provides researchers with a quantitative research method for the development trend of statistics and academic status. The purpose of this research is to help researchers understand the key knowledge, evolutionary trends and research frontiers of current research. This study uses Citespace bibliometric analysis to analyze the data of the Science Net database and finds that: 1) The development of the research field has gone through three stages, and some representative key scholars and key documents have been recognized; 2) the common knowledge mapping of literature The co-occurrence of citations and keywords shows research hotspots; 3) The results of burst detection and central node analysis reveal research frontiers and development trends. Today, the visualization of big data brings different challenges. The abstraction between the world and today's data visualization occurs when the data is captured. Every user sees his own visualization data generated by standardized calculations. At the same time, there are still many controversies in the theoretical model, structure and structural dimensions. This is the direction that future researchers need to further study.

Based on a rich data set of recoveries donated by a debt collection business, recovery rates for non-performing loans taken from a single European country are modelled using linear regression, linear regression with Lasso, beta regression and inflated beta regression. We also propose a two-stage model: beta mixture model combined with a logistic regression model. The proposed model allows us to model the multimodal distribution we find for these recovery rates. All models are built using loan characteristics, default data and collections data prior to purchase by the debt collection business. The intended use of the models is to estimate future recovery rates for improved risk assessment, capital requirement calculations and bad debt management. They are compared using a range of quantitative performance measures under K-fold cross validation. Among all the models, we find that the proposed two-stage beta mixture model performs best.

With the rapid development of network science, Turing pattern on complex networks has attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator-prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing pattern is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing pattern. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing pattern is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing pattern, which is not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing pattern and spatiotemporal pattern. We believe that these findings contribute to a better understanding of self-organization on complex networks.

The Connor–Davidson Resilience Scale (CD-RISC) and the Brief Resilience Scale (BRS) are two widely used scales to measure resilience. Although both scales seek to assess an individual’s ability to recover from and adapt to disruptions or stressful events, they may capture different aspects of resilience. While the CD-RISC focuses on resources that can help individuals recover from and adapt to disruptions or stressful events, the BRS directly measures one’s ability to bounce back or be resilient. The aim of this study is to empirically examine the differences between the CD-RISC and the BRS. Two samples (n = 202, 246) consisting of undergraduate students from Taiwan were used. The results of confirmatory factor analysis show that the CD-RISC and the BRS are highly correlated but still distinct. The results of regression analyses show that the CD-RISC and the BRS have unique predictive effects on depression and life satisfaction. The research findings suggest that the CD-RISC and the BRS capture different aspects of resilience. For future research on resilience, researchers should pay attention to the differences between these scales and choose the one that most closely fits their research purpose.

Tulou dwellings in Southeast China have captivated global interest due to their distinctive appearance, sustainable construction materials and technologies, and their defensive and collective housing functions. Despite several recognized as World Cultural Heritage sites, the vast majority of tulou buildings are undergoing irreversible decline and destruction, necessitating a comprehensive and systematic study. Taking 83 Tulou buildings in Raoping County, Chaozhou City, Guangdong Province as the research object, this study reconstructs the historical scenes and systematically reveals the emergence, popularity, and consolidation process of tulou dwellings as integrated defensive and residential buildings for ordinary people by conducting a comprehensive analysis of historical documents and local chronicles. Based on an extensive field investigation, the study systematically analyzes the geospatial distribution and the spatial characteristics of Raoping tulou and its residential unit. The results demonstrate the adaptability and flexibility of tulou dwellings, showcasing their developmental process and revealing the inclusive nature of these traditional residences, as well as the initiative of those who reside within them. The research findings contribute to a more dynamic, comprehensive, and authentic understanding of tulou and Chinese traditional residences, providing valuable references for the preservation and sustainable development of tulou architectural heritage.

In this paper, by evaluating extreme value of the qth-power current, a torque sharing function (TSF) family for reducing the torque ripples in the switched reluctance motor (SRM) is proposed. The optimization criteria of the TSF has two secondary objectives, including the maximization of the torque-speed range and the minimization of copper loss. The evaluation indices in terms of the peak phase current, the rms phase current, and the torque ripple factor are compared between the proposed TSF family and four conventional TSFs including linear, sinusoidal, exponential, and cubic TSFs. An optimization objective function that combines the maximum absolute value of the rate-of-change of the flux linkage (MAV-RCFL) and the qth-power of current is proposed and a weighting factor is used to balance the influence of the two optimization objectives. An optimal TSF can be easily obtained by solving the optimization problem from the TSF family. The proposed TSF is validated by using simulations and experiments with a three-phase 6/4 SRM with 7.5 kW, 3000 rpm, and 270 V DC-link voltage. The dynamic simulation model is implemented in Matlab/Simulink. The results demonstrate the validity and superiority of the proposed control method; the optimal TSF provides better torque-speed performance, and a better reduction in copper loss and torque ripples at high speed compared to the conventional TSFs obviously.

This study mainly experimentally investigates and explores the effects of local low-frequency vibrations on the starting-up and heat transfer characteristics of the pulsating heat pipe. A micro motors with the vibration frequency of 200 Hz were imposed on the external surface of evaporation, condensation and adiabatic section of the pulsating heat pipe, respectively, and the starting-up temperature and the average temperatures along the evaporation section as well as the thermal performances of the vibrating heat pipe were experimentally scrutinized under the local vibrations of different positions. The following important conclusions can be achieved by the experimental study: 1) The effect of vibrations at the evaporation section and at the adiabatic section on the starting-up time of pulsating heat pipe is more significant than that at the condensation section. 2) The vibrations at different positions can reduce the starting-up temperature of the pulsating heat pipe. The effect of the vibrations at the evaporation section is the best as the heating power is lower, and the effect of the vibration at the adiabatic section is the best as the heating power is higher. 3) The vibrations at the evaporation section and at the adiabatic section can reduce the thermal resistance of the pulsating heat pipe. However, the vibrations at the condensation section have little effect on the thermal resistance of the pulsating heat pipe. 4) The vibrations at the evaporation section and at the adiabatic section can effectively reduce the temperature of evaporation section of the pulsating heat pipe, but the vibrations at the condensation section have no effect on the temperature of evaporation section of the pulsating heat pipe.

Hemodynamics is the eternal theme of the circulatory system. Abnormal hemodynamics and cardiac and pulmonary development intertwine to form the most important features of children with congenital heart diseases (CHDs), thus determining these children’s long-term quality of life. However, due to the lack of neonatal rodent models of CHDs, it is unclear how CHD-associated hemodynamics shape postnatal cardiac and pulmonary development. Here, we review the varieties of hemodynamic abnormalities that exist in children with CHDs, the recently developed neonatal rodent models of CHDs, and the inspirations these models have brought us in the areas of cardiomyocyte proliferation and maturation, and in alveolar development. Furthermore, current limitations, future directions, and clinical decision-making based on these inspirations are highlighted. Understanding how CHD-associated hemodynamic scenarios shape postnatal heart and lung development may provide a novel path to improving the long-term quality of life of children with CHDs, transplantation of stem cell–derived cardiomyocytes, and cardiac regeneration.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

The development of hybrid unmanned aerial underwater vehicles (HAUVs) compatible with the advantages of the aerial vehicles and the underwater vehicles is of great significance. This paper presents the first study on a new HAUV layout using four rotors to realize the medium crossing motion of a transverse slender body similar to the fuselage of a missile or a submarine, that is the hybrid aerial underwater quadrotor (HAUQ). Then a robust control strategy is proposed for the take-off HAUQ on the water in the presence of unknown disturbances and complex model dynamic uncertainties. As a semi-submersible HAUQ rises straightly from the water, the inside of the slender fuselage placed horizontally is filled with water. The center of the mass, the moment of inertia, and the arm of force of the HAUQ will change rapidly in the takeoff phase from the water since the rapid non-uniform change of mass caused by the passive fast drainage. It is difficult to establish a accurate mathematical model of the complex dynamic changes caused by the multi-media dynamics, the fast changing buoyancy, and the added mass crossing air–water surface. Therefore, an uncertain kinematic and dynamic model is established through the passive fast nonuniform change and the complex dynamics are considered as the unknown terms, and the external disturbances of gust and other factors are assumed as the bounded disturbance input. A robust design approach is introduced to deal with the fast time-varying mass disturbance based on the input-to-state stability (ISS) theorem. The complex dynamics are estimated using the basis function and the unknown weight parameters, and the adaptive laws are adopted for the on-line estimation of the unknown weight parameters. Consider the residual disturbance of the uncertain nonlinear system as a total disturbance term, a disturbance observer is introduced for disturbance observation. The numerical simulation shows the feasibility and robustness of the proposed algorithm.

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.

With the increasing prominence of renewable energy sources providing flexibility to the power grid, there is a growing recognition of the need to expand virtual Energy Storage Systems (ESS) to replace traditional ESS, which faces challenges in terms of supply and scalability. Grid-connected microgrids can supply power stably and are being built, so they can be used as virtual ESS to provide flexibility to the power system. This study proposes an approach to determine the optimal Power Conversion System (PCS) and battery capacity for a Virtual Energy Storage System(VEES) based on a grid-connected microgrid incorporating Photovoltaic (PV) and ESS. In this paper, it is assumed that microgrids can provide flexibility to the grid by participating in the energy trading market, there are corresponding profits and penalties for order. Numerical examples are performed to demonstrate the effectiveness of the proposed method.

Major emergencies can have a devastating impact on society, becoming a catalyst for social disorder and unrest and a trigger for social or political change. In today's world of frequent emergencies, the emergency management capacity of local governments is key to promoting safe operations and maintaining stability in cities and towns. Aiming to explore the direction and focus of local government emergency management capacity building, this paper establishes a local government emergency capacity evaluation system based on the emergency management process and combines Gray-Dematel, a gray decision laboratory analysis method, and AISM, an adversarial explanatory agency model, to derive the importance and intrinsic relationship among emergency capacity indicators. The algorithm describes the importance degree of each indicator in the system and also helps better understand the intrinsic relationship among the indicators. The outcomes of this research can be used to outline directional suggestions for the development of local government emergency response capacity and provide a new theoretical basis for more effective construction of local government emergency response capacity.

In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images. The retina model imitates the neurophysiological principles and dynamics of various optical neurons. Our proposed neural network model reduces the computational overhead compared to traditional signal-processing models while achieving results similar to complex deep learning models from a subjective perceptual perspective. By directly simulating retinal neuron functionalities with neural networks, we not only avoid manual parameter optimization but also lay the groundwork for constructing artificial versions of specific neurobiological organizations.

The influence of polysialic acid (PSA) and sialic acid (SA) on the gut microbial community composition and metabolites in healthy human was investigated using a bionic gastrointestinal reactor. The results indicated that PSA and SA significantly changed the gut microbiota and metabolites in different degrees. PSA can increase the relative abundances of Faecalibacterium and Allisonella, whereas SA can increase those of Bifidobacterium and Megamonas. Both can significantly increase the content of short-chain fatty acids. The results of metabolome analysis showed that PSA can upregulate ergosterol peroxide and gallic acid and downregulate the harmful metabolite N-acetylputrescine. SA can upregulate 4-pyridoxic acid and lipoic acid. PSA and SA affect gut microbiota and metabolites in different ways and have positive effects on human health. These results will provide reference for the further development of PSA- and SA-related functional foods and health products.

Using the techniques of computer-aided drug design, the docking of Survivin and known active small molecules was simulated and then the key amino acid residue fragment of the target protein was analyzed. It led to the discovery of active groups capable of binding to the critical sites. Through the use of the natural product, Oleanolic Acid, as a lead compound, the introduction of the active groups onto the A-ring, and the modification of the carboxyl group at the C-28 position using esterification or amidation, twenty new Oleanolic acid derivatives had been designed and synthesized.A549 and SGC-7901 cells were used to screen the antitumor activity in vitro through the standard MTT method. The compounds, II₃, III₅ and Ⅳ_4, exhibited more potent cytotoxicity than positive drugs.

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called Sparsity based Locality-Sensitive Discriminative Dictionary Learning(SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

Bipolar neutrosophic set is an important extension of bipolar fuzzy set. This set is a hybridization of bipolar fuzzy set and neutrosophic set. Every element of a bipolar neutrosophic set consists of three independent positive membership functions and three independent negative membership functions. In this paper, we develop cross entropy measures of bipolar neutrosophic sets and prove its properties. We also define cross entropy measures of interval bipolar neutrosophic sets and prove its properties. Thereafter, we develop two novel multi-attribute decision making methods based on the proposed cross entropy measures. In the decision making framework, we calculate the weighted cross entropy measures between each alternative and the ideal alternative to rank the alternatives and choose the best one. We solve two illustrative examples of multi-attribute decision making problems and compare the obtained result with the results of other existing methods to show the applicability and effectiveness of the developed method. In the end, the main conclusion and future scope of research are summarized.

The purpose of this study is to ascertain whether implementation difficulty can be used in slot allocation model as a new mechanism for slightly weakening grandfather right. According to which, a linear integer programming model is designed to compare and analyze displacement, implementation difficulty and priority with different weights. Test results show that the implementation difficulty can be significantly reduced without causing excessive displacement and disruption of existing priorities, by weight setting while declared capacity is cleared. In addition to this, whether the movements are listed in order of descending priority or not have great impact on displacement and implementation difficulty within slot allocation model. Capacity is surely a key factor affecting displacement and implementation difficulties. This study contributes to propose a new mechanism for slightly weakening grandfather right, which can help decision makers to upgrade slot allocation policies.

The production of instant green tea requires hot-water extraction, which is time consuming and contributes to losses in aromatic compounds. In this study, an ultrasonic-assisted technology was used to improve the extraction efficiency of green tea, thereby shortening extraction time from 45 to 15 min. In pure water, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 0.23 mg/mL and total tea polyphenol dissolution decreased by 3.2 mg/mL. In 76.2% ethanol, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 1.57mg/mL and total tea polyphenol dissolution decreased by 1.5 mg/mL. Additionally, we used microwave-assisted technology to further improve the extraction efficiency of green tea, which shortened the extraction time to 2 min. However, the extraction rate remained unchanged. In pure water, the dissolution of caffeine and theanine did not change significantly, but the dissolution of total catechins increased by 0.41 mg/mL and the dissolution of tea polyphenols decreased by 2.9 mg/mL. Ultrasonic treatment increased the proportion of 3-hydroxybutan-2-one, (5S)-5-(hydroxymethyl)oxolan-2-one and 2-phenylethanol, which were the main aroma compounds in tea. Microwave treatment changed the aroma compounds in tea, causing losses in aroma compounds with low boiling point and maintaining (5S)-5-(hydroxymethyl)oxolan-2-one. The taste and aroma of instant green tea improved based on sensory evaluation results.

Tellurium exhibits exceptional intrinsic electronic properties. However, investigations into the modulation of tellurium's electronic properties through physical modification are notably scarce. Here, we present a comprehensive study focused on the evolution of the electronic properties of tellurium crystal flakes under plasma irradiation treatment by employing conductive atomic force microscopy and Raman spectroscopy. The plasma-treated tellurium experienced a process of defect generation through lattice broken. Prior to the degradation of electronic transport performance due to plasma irradiation treatment, a remarkable observation emerged: in the low-energy region of hydrogen plasma-treated tellurium, a notable enhancement in conductivity was unexpectedly detected. The mechanism underlying this enhancement in electronic transport performance was thoroughly elucidated by comparing it with the electronic structure induced by argon plasma irradiation. This study not only fundamentally uncovers the effects of plasma irradiation on tellurium crystal flakes, but also unearths an unprecedented trend of enhanced electronic transport performance at low irradiation energies when utilizing hydrogen plasma. This abnormal trend bears significant implications for guiding the prospective application of tellurium-based 2D materials in the realm of electronic devices.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide, and despite advances in treatment, survival rates are still low; therefore, the development of novel drugs is imperative. Acetylcorynoline (ACN) is derived from Corydalis ambigua. The effect of ACN on colon cancer is still unknown. Therefore, we investigated its potential effects. Our data showed that ACN inhibited cell viability and proliferation. Moreover, ACN induced apoptosis and cell cycle arrest by inhibiting cell growth. In the present study, we hypothesized that ACN regulates c-Myc through CNOT2 or MID1IP1. ACN reduced the protein expression of oncogenic genes, decreased c-Myc half-life, and rapidly inhibited the serum stimulation response. Moreover, knockdown of CNOT2 and MID1IP1 with ACN increased apoptosis and further reduced the expression of oncogenes. In addition, ACN exhibited a synergistic effect with low-dose of 5-fluorouracil (5-FU) and doxorubicin (Dox). Collectively, our data demonstrates that ACN inhibited c-Myc expression through CNOT2 and MID1IP1 and induced apoptosis. These findings indicate the potential of ACN as a therapeutic agent against colon cancer.

China combines green energy and industrial policy in its power market reform with various policy initiatives, including price support scheme for electricity from renewable sources and subsidies in the push for broader use of greener energy. This study focuses on the impacts of power market reform on the stock price volatility of listed power companies: 1) we use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural break points in stock prices; 2) we analyze the characteristics of stock price volatility based on the GARCH model; 3) we report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. The result suggests three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies. We find that industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run. However, our study also indicates the recent policies related to renewable energy do not have a very significant impact on the power stock market.

The welding and construction processes of H-type thick-plate-bridge steel involve complex multi-pass welding processes, which is difficulty to ensure its welding performance. Accordingly, it is crucial to explore the inherent correlation among the welding processes parameters and welding quality, and applied it into the welding robots, eliminating the instability from manual welding. In order to improve the welding quality, the GMAW (Gas Metal Arc Welding) welding process parameters are simulated using the Q345qD bridge steel flat joint model. Four welds with X-shaped grooves are designed to optimize the parameters of welding current, welding voltage, and welding speed. The optimal welding process parameters are investigated through thermal-elastic-plastic simulation analysis and experimental verification. The results indicate that when the welding current is set to 230A, the welding voltage to 32V, and the welding speed to 0.003m/s, the maximum deformation of the welded plate is 0.52mm, with a maximum welding residual stress of 345MPa. Both the simulation results of multi-pass welding and experimental tests meet the welding requirements, as they show no excessive stress or strain. These parameters can be applied into building large steel frame bridges with welding robots, improving the quality of welded joints.

Though Cyber Physical Systems (CPS) become very popular in last the decade, dependability of CPS is still a critical issue and related survey is rare. We try to spell out the jigsaw of technologies and figure out the technical trends of dependable self-managing CPS. This survey first recalls the motivation and the similar concepts. By analyzing four generic architectures, we summarize the common characteristics and related assurance technologies, and propose a more generic environment-in-loop processing flow of CPS and a formal interaction flow between physical space and cyber space. Further, the similarity between correctness and dependability is formally analyzed and the new five research questions of dependable self-managing CPS are presented. Then we review the critical technologies and related correctness verification & validation (V&V) methods, the architectures for dependable self-managing CPS. Further, the detail dependability management and V&V technologies are surveyed, which covers the areas of running-time fault management methods and whole life cycle V&V technologies, maintenance and available tool sets. For holistic CPS development, Modeling techniques and MDE (model driven engineering) based V&V methods are analyzed in detail. Then we complete the jigsaw of technologies and figure out the missing part. Further, we propose the technical challenges and the further direction. To our best knowledge, this is the first comprehensive survey on dependable self-managing CPS development and evaluation.

To instruct interior lighting design of aviation cockpit and ensure the work efficiency and comfort of pilots, and reduce safety accidents caused by light environment and visual fatigue during flight, it is important to establish a multi-dimensional ergonomic evaluation mechanism of cockpit light environment. In this paper, by improving Jdms' mental load theory, we establish a thorough experimental scheme based on physiological workload, mental workload, work performance and physiological work performance to evaluate the cockpit ergonomic under different light environment multi-dimensionally. Firstly, 12 evaluation indexes including job accuracy, job speed, blinking, heart rate variability, average fixation time, saccadic speed, mental demand, physical demand, operational performance, time demand, degree of confusion and degree of effort are obtained through the experiment, and then the weight of each evaluation index is calculated by entropy method to construct the ergonomic evaluation model. The evaluation results of physiological workload, mental load, work performance and physiological work performance were combined to obtain the final ergonomic evaluation results. According to our experimental results, it was found that light environment for pilot to get the highest ergonomic efficiency is 1500 lx light illumination and 5000K color temperature. The results provide a reference for the ergonomic experiment of aircraft cockpit and the design of aviation cockpit lighting system.

Container crane has been widely used for port operation. However, the structure of port container crane is large, which always lead to crane damage under the vibration and strong wind. Therefore, a method for vibration reduction of container crane structure based on particle damping technology is proposed. In this investigation, the scale 1:80 crane model is built, the equivalent mechanical model is also established to preliminary verify the effect of vibration suppression. Furthermore, the dependence of vibration suppression effect on the key parameters of the damper, i.e., filling material, filling rate, particle size, and installation position of dampers are analyzed through experiments. The results indicate that the vibration peak of the crane structure displayed in Simulink oscilloscope is weakened and lags behind, the installation of the damper brings the effect of vibration suppression for the crane, the vibration suppression effect reaches 25%; the crane mode obtains the best vibration suppression effect under the condition that the material filled in the damper has a large density and elastic modulus, sufficient collision space and high collision frequency, and the dampers are installed far away from the crane center of gravity. The optimum parameters of the damper obtained from experiments, 12 mm lead beads, filling rate of 60%, installed in the distal ends within the strength range of the forearm of the crane. It is thus concluded that the particle damping technology has provided an effective way in wind and vibration reduction for container crane.

To realize the energy recovery of wheat straw, the pyrolysis behavior of wheat straw was studied at three heating rates (10, 20, and 30 K/min) based on thermogravimetric analysis (TG–DTG). Kinetics and thermodynamics were analyzed using Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) model-free methods, and reaction mechanism was determined using Coats–Redfern (CR) model-fitting method. The results show that there are three weightlessness stages in the pyrolysis process, of which the second stage was the main weightlessness stage and two distinct peaks of weightlessness were observed in this stage. With increasing heating rate, the main pyrolytic weightlessness peaks of DTG curve shifts to higher temperature. The pyrolysis activation energy calculated by FWO and KAS methods are 165.17–440.02 kJ/mol and 163.72–452.07 kJ/mol, and the pre-exponential factor vary in the range of 2.58×1012–7.45×1036 s-1 and 1.91×1012–8.66×1037 s-1, respectively. The thermodynamic parameters indicates that wheat straw has the favorable conditions for product formation and containes potential energy to be utilized for bioenergy production, its pyrolysis reaction was non-spontaneous and the energy output is stable. CR method analysis shows that the A1/3 random nucleation model is the most suitable mechanism to characterize the pyrolysis process, and random nucleation may be in charge of the main pyrolysis stage. This study can provide a theoretical basis for the thermochemical conversion and utilization of wheat straw.

In this study, a new type of temperature sensor device was developed. The circular electrode of the thermal sensitive sensor was modified with TMT and O₂ plasma to enhance the conductivity by forming a thin SnOxCy layer on the electrode surface. The Nano-Au particles were subjected to O₂ plasma pretreatment to form peroxide groups on the surface. The thermally sensitive sensor was made by mixing the above-treated Nano-Au particles with N-isopropylacrylamide (NIPAAm) to form solution and then UV-induced grafting polymerization of the NIPAAm-containing solution onto the electrode substrate. The composite hydrogels on the electrode introduce thermo-sensitive polymeric surface films for temperature sensing. Using ambient environment resistance test to measure the resistance, the LCST (lower critical solution temperature) of Nano-Au (MUA) mixed with NIPAAm hydrogel was found to be 32 °C. At ambient temperatures higher than LCST, the electrode resistance decreases linearly.

Previous studies had shown that mRNA, miRNA and lncRNA were associated with cardiovascular diseases. The study was aimed to explore the differential expressions of mRNA, lncRNA and miRNA between coronary artery disease (CAD) and healthy control, and their interaction in CAD. We investigated the differential expression of ceRNA between CAD and healthy control through data collected from Gene Expression Omnibus (GEO) microarrays. Furthermore, we investigated the biological function of these differential expressions of ceRNAs by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Protein-protein interaction (PPI) network was created to identify the hub genes. Biosystems and literature search were performed for signaling pathways and their function of the included differential expression ceRNAs. A total of 456 miRNA expression profiles, 16,325 mRNA expression profiles, and 2,869 lncRNA expression profiles were obtained. Eleven Go and KEGG pathways (count ≥9), top 15 of PPI network node connectivity rank, and top 15 of ceRNA network node degree centrality rank were achieved at the statistical significance level (P<0.05). We further identified that several differential expressions of ceRNAs and their signaling pathways were associated with CAD through biosystems and literature search. Based on eleven Go and KEGG pathways, top 15 of PPI network node connectivity rank, and top 15 of ceRNA network node degree centrality rank in CAD population, our findings would contribute to further exploration for the molecular mechanism of CAD.

Permafrost covers about 23% ~ 25% of the land in the northern hemisphere, mainly in Russia, Canada, Alaska, Northeast and Qinghai Tibet Plateau (QTP) in China. Many engineering especially the transportation has been in the permafrost regions above. Under the action of highways construction and global warming, permafrost under infrastructures is degraded and causes serious damage. Two phases closed thermosyphon (TPCT) is a widely accepted green countermeasure against the problem in permafrost regions. Although it has been applied to prevent permafrost degradation, their application presents significant challenges on account of the stronger endothermic action of asphalt pavement. This paper focused on the thermosyphon technology and application in the permafrost. Moreover, the article highlighted the excellent working performance of the TPCT that improving the stability of the infrastructures and prevent it degrading due its excellent efficiency of heat transfer. The industrial applications of the TPCT were also summarized, along with their limitations. Finally, the results reported in this paper can provide a valuable guidance for the design of the TPCT and construction of the permafrost regions in the future.

Pyracantha angustifolia has been used in traditional medicine to treat a range of diseases of the stomach and improve digestion, blood circulation, diarrhea, dysentery, and hemostasis. This study evaluated bioactivity-guided fractionation as a means to identify therapeutic phytochemicals from P. angustifolia that can attenuate melanogenesis and oxidation. Seven compounds with inhibitory effects on melanin production and tyrosinase (TYR) activity, as well as ABTS and DPPH radical scavenging activities, and have not been reported as whitening materials, were isolated from the n-butanol fraction from P. angustifolia leaves (PAL). Among the seven compounds, p-hydroxybenzoic acid beta-d-glucosylester (HG), and cimidahurinine (CD) had strong inhibitory effects of melanin production, TYR activity, and ABTS and DPPH radical scavenging activities. Western blot analysis showed that HG and CD suppressed tyrosinase-related protein (TYRP)-1 and TYRP-2 expression. These results suggest that P. angustifolia containing active compounds, such as HG and CH, is a potent therapeutic candidate for the development of hypopigmenting agents.

Nereis as a mudflat organism has a variety of uses, in the history of China as a Chinese herbal record, because of its body contains rich amino acids, fatty acids and other nutrients, it can be used for therapeutic treatment and treatment can also be used as health care products. acids, fatty acids and other nutrients, it can be used for therapeutic treatment and treatment can also be used as health care products. In modern times, more and more researchers have studied the protease extract of Nereis as a mudflat organism. In modern times, more and more researchers have studied the protease extracted from the body of nereis and found that there are a variety of functional proteins in nereis, including superoxide dismutase, superoxide dismutase, superoxide dismutase and superoxide dismutase. More and more researchers have studied the proteases extracted from the body of nereis and found that there are a variety of functional proteins in nereis, including superoxide dismutase, adenylate cyclase, glycosidase, and plasminase. In this paper, the functional proteases in the extracts of nereis were summarized, and the functions and characteristics of proteases in nereis were discussed from the perspective of molecular structure.

COVID-19 greatly challenges human beings in the health sector and leaves behind a large amount of medical waste that poses many potential threats to the environment. In this paper, we compiled relevant data released by official agencies and the media, and conducted data supplementation based on previous studies to calculate the net value of medical waste production in Hubei Province during COVID-19 with the help of a neural network model. Then, we reviewed the data related to the environmental impact of medical waste per unit and designed four scenarios to estimate the environmental impact of new medical waste generated during the epidemic. The results showed that at a medical waste generation rate of\ 0.\ 5\ kg/(\ bed\ \cdot\ d) COVID-19 resulted in a net increase in medical waste volume of about 3366.99 tons in Hubei Province. In the four scenario assumptions, if the medical waste brought by COVID-19 is completely incinerated, it will have a large impact on the air quality. If it is disposed by distillation sterilization, it will produce a large amount of wastewater and waste residue. Based on the results of the study, three policy recommendations are proposed in this paper: strict control of medical wastewater discharge, reduction and transformation of the emitted acidic gases, and attention to the emission of metallic nickel in exhaust gas and chloride in soil. These policy recommendations provide a scientific basis for controlling medical waste pollution.

Background and Objectives: Although researchers suggested that non-pharmaceutical intervention (NPI) and vaccination together can provide the best protection from the COVID-19 [1], people’s willingness to adopt personal NPIs seems to decrease through the COVID-19 waves. This study seeks for an explanation for this observation based on the framework of risk perception. Materials and Methods: Two cross-sectional surveys (N = 407 in Survey 1 and N = 505 in Survey 2) were administered before and in the first outbreak of COVID-19 in Taiwan. The survey items were designed to measure people’s recognized severity of COVID-19, worry of COVID-19, intention to adopt personal NPIs, and attitude toward COVID-19 vaccines. We established a structural model according to the framework of risk perception, which was applied to fit the data of the two surveys. Results: The multigroup SEM (Structural Equation Modeling) results showed that worry (i.e., emotional component of risk perception) fully mediates the influence of recognized severity of COVID-19 (cognitive component of risk perception) on the intention to adopt NPIs in each survey. Before the outbreak (i.e., Survey 1), people’s attitude toward COVID-19 vaccines has nothing to do with their worry of COVID-19. However, when people have experienced the real outbreak of COVID-d19 (i.e., Survey 2), their attitude toward COVID-19 vaccines negatively predicts their worry of COVID-19, forming an indirectly negative effect on their intention to adopt personal NPIs, suggesting the occurrence of the Peltzman effect. That is, receiving vaccination makes people feel safer, that the other way around relaxes their alert for COVID-19, thus, declining the intention to adopt personal NPIs.

It has been proposed that tumourigenicity was an intrinsic feature of embryonic/germ cell developmental axis as well as embryonic/germ cell-related genes play a crucial role in tumourigenicity. Our previous studies indicated that primordial germ cell (PGC)-like potential could be reactivated in tumourigenesis. In this study, 4T1, 168FARN and 67NR cells which originated from the same mouse breast cancer were studied and the results indicated that the acquisition of embryonic/germ cell-like state is essential for tumourigenicity. We further demonstrated that somatic to PGC-like transformation (SPLT) was activated in 4T1 cells and that inhibition of PGC-like cell formation by depleting pluripotency and/or PGC specification-related genes markedly repressed SPLT and the tumourigenicity. Collectively, our findings reveal that tumourigenicity is linked to the acquisition of PGC-like state through SPLT in 4T1 cells, provide new insight into deeper understanding the biological nature of tumours and novel therapeutical strategies for cancer targeting.

Natural killer cells are associated with the pathogenesis of ulcerative colitis (UC), but their precise contributions remain unclear. The present study sought to investigate the diagnostic value of activated NK-associated genes (ANAGs) in UC. Bulk RNA-seq and scRNA-seq datasets were obtained from the Gene Expression Omnibus (GEO) and Single Cell Portal (SCP) databases. In the bulk RNA-seq, 92 differentially expressed genes (DEGs) were screened out by the “Batch correction” and “Robust rank aggregation” (RRA) methods. The immune infiltration landscape was estimated by single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT, which revealed a higher abundance of activated NK cells in noninflamed UC tissues. 54 DEGs correlated with activated NK cells were identified as ANAGs. Protein-protein interaction (PPI) analysis and least absolute shrinkage and selection operator (LASSO) regression were utilized to screen out 4 key ANAGs (SELP, TIMP1, MMP7, and ABCG2) and establish an activated NK-associated gene score (ANAG score). The ANAG score demonstrated excellent diagnostic value and was validated in three external datasets. The expression of the 4 key ANAGs was validated in UC patients and healthy controls (HC) samples. Through scRNA-seq data analysis, higher expression levels of SELP, TIMP1, MMP7, and ABCG2 were observed in post-capillary venules, inflammatory fibroblasts, enterocytes, and immature enterocytes. The cell scores based on the ANAGs showed enrichment in endothelial cells and fibroblasts. In conclusion, we established and validated an ANAG score with the ability to precisely diagnose UC. The 4 key ANAGs have the potential to serve as therapeutic targets in UC.

Basil, renowned for its aromatic properties, exhibits commendable drought tolerance and holds significant value both as an edible and medicinal plant. Recognizing the scarcity of studies addressing basil’s response to drought stress, we undertook a comprehensive analysis of the volatile metabolome and transcriptome of sweet basil across four distinct levels of drought stress. Our metabolic analysis meticulously identified 830 metabolites, with 215 differentially accumulated metabolites discerned. Notably, the primary constituents of sweet basil volatiles exhibited minimal alterations, while the differentially accumulated metabolites were predominantly lipids and terpenes. The transcriptome analysis unveiled 27,181 differentially expressed DEGs, with a predominant focus on metabolic pathways and biosynthesis of secondary metabolites. Integrating insights from both transcriptome and volatile metabolomic analyses highlighted the pivotal roles of Alpha-linolenic acid, flavonoid, and lignin metabolism in fortifying the resistance of sweet basil against drought stress. This holistic approach not only enriches our understanding of the molecular intricacies underpinning basil’s drought resistance but also furnishes valuable insights for the molecular breeding of basil varieties endowed with enhanced drought tolerance.

Gyroscopes are fascinating instruments with a history of about 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition for a sustainable future. Here, the systems are digitized in 2D, 2.5D and 3D and are made available for a world-wide community using OpenAccess platforms. The technologies being used are Computed Tomography, Computer Vision, Endoscopy and Photogrammetry. The workflows for combining voxel representations and colored point clouds are described, to create Digital Twins of the tangible assets. Advantages and disadvantages are discussed und work for near future is outlined in this new and challenging field of Tech Heritage digitization.

Background: The present retrospective study was designed to evaluate the relative diagnostic utility of breast-specific gamma imaging (BSGI) and breast magnetic resonance imaging (MRI) as means of evaluating female breast cancer patients in China. Methods: A total of 229 malignant breast cancer patients underwent ultrasound, mammography, BSGI, and MRI between January 2015 and December 2018 for initial tumor staging. Of these patients, 73 were subsequently treated via definitive breast surgery following neoadjuvant chemotherapy (NAC), of whom 17 exhibited a complete pathologic response (pCR) to NAC. Results: BSGI and MRI were associated with respective 76.8% and 69.6% sensitivity values as a means of detecting residual tumors following NAC, while both of these approaches exhibited comparable specificity in this diagnostic context (58.8% vs 70.6%, P=0.473). Conclusion: These results demonstrate that BSGI is a useful auxiliary approach to evaluating pCR to NAC treatment.

Airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units(IMUs), data-processing computer and Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing, such as array SAR, multinode imaging loads and so on. ADPOS can provide multinode high accuracy spatio-temporal reference information to realize multinode motion compensation with the various state estimation methods such as Central Difference Kalman Filtering (CDKF), modifed CDKF. However, these methods focus on noise optimization based on known nonlinear models and its linear minimum variance estimation criterion, which cannot gain better estimation performance for high-dimensional nonlinear systems. For this reason, the variational optimization process based on conjugate gradient algorithm is proposed for the estimation of filtering update step, which is more efficient than linear minimum variance estimation criterion. Therefore, aiming at ADPOS with high-dimensional and nonlinearity, Central Difference Variational Filtering (CDVF) using variational optimization based on conjugate gradient algorithm for mean correction in the filtering update stage is devoted to advancing the estimation accuracy of multinode motion parameters. A real ADPOS flight test has been conducted, the experimental results show that the accuracy of the slave motion parameters has a distinct progress than the up to date CDKF, and the compensation performance is distinct.

The integration of renewable energy and hybrid energy storage system (HESS) into electrified railways to build electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the energy management effectiveness for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow among the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study.

Nature-based solutions (NbS) rapidly develop globally to address societal challenges and provide human well-being and biodiversity. Watershed restoration plays an essential role in enhancing the ecological and socio-economic benefits of the region. The design and implementation of watershed restoration projects play a crucial role in their effectiveness, and NbS has been applied as a concept in ecosystem-related projects. This paper provides an idea to evaluate the design or implementation of watershed restoration projects based on the eight criteria proposed by the IUCN Global Standard for Nature-Based Solutions and to adjust the projects based on the evaluation results. The aim is to standardize the implementation process of watershed restoration projects to achieve more significant benefits and practically apply the concept of NbS in watershed restoration. implementation scheme of the Shan-Shui Initiative in the Jiulong River Watershed and concludes that the method is feasible in evaluating and improving the design and implementation scheme of ecological restoration projects in the actual basin. According to the assessment results, the degree of matching based on NbS for the implementation scheme of the Jiulong River Watershed Shan-Shui Initiative is 73%, which meets the criteria of NbS but needs to be improved in terms of monitoring and assessment, synergistic management, and benefit trade-offs.

Quantile regression model is widely used in variable relationship research of general size data, due to strong robustness and more comprehensive description of the response variables' characteristics. With the increase of data size and data dimension, there have been some studies on high-dimensional quantile regression under the classical statistical framework, including higher-efficient frequency perspective, which is however at cost of randomness quantification, or lower-efficient Bayesian method based on MCMC sampling. To overcome these problems, we propose the high-dimensional quantile regression with Spike-and-Slab Lasso penalty based on variational Bayesian (VBSSLQR), which can not only improve the computational efficiency but also measure the randomness via variational distributions. The simulation studies and real data analysis illustrate that the proposed VBSSLQR method is superior to or equivalent to other quantile and non-quantile regression methods (including Bayesian and non-Bayesian methods), and its efficiency is higher than any other method.

Based on the resource-based theory, the current study examines the relationship between competitive strategies and strategic alliance performance. Furthermore, big data predictive analytics is treated as a boundary condition between competitive strategies and strategic alliance performance. Big data of predictive analytics in operations and industrial management has been a focal point in the current era. There has been little attention has about big data predictive analytics influences on competitive strategies and strategic alliance performance, especially in developing countries like Pakistan. A survey instrument was used to record the responses from 331 employees of the telecom sectors companies working in Pakistan. Study findings show that big competitive strategies have a positive and significant relationship with strategic alliances performance. It was also found that big data predictive analytics plays the role of moderator between competitive strategies and strategic alliance performance. The study add a new perspective and contribution to the literature on big data predictive analytics, strategic alliance performance, and competitive strategies in Pakistan's telecom sector companies. Further, the study results explain that big data analytics is just like the companies' lifeblood in the current era. The efficient and effective use of big data analytics, companies can boost their standards in a competitive environment.

Infrared spectroscopy is an effective method for the determination of compositions and concentrations of liquids, with advantages of fast response, no-sampling, flexible in use and is able for on-line monitoring. However, for trace substances in drinking water, such as nitrates and heavy metals, infrared spectroscopy is not sensitive enough for the quantitative and qualitative measurement. In this study, we improved the sensitivity of infrared spectroscopy for nitrite determination by developing an ion-exchange-enhanced diffuse reflectance spectroscopy, which consist of an accessory based on ion-exchange resin for enrichment and a FTIR spectrometer for measurement. Using this method, the limit of detection for nitrate is 1.7 mg/L, which is enough for drinking water sensing. We also verified the quantitative measurement ability of the method. Furthermore, the limit of detection and quantitative measuring range could be adjusted by changing the mass of resin and adsorption time. This study demonstrated the method can be used to detect trace nitrites in drinking water, can be applied in the field, and is sensitive, rapid, and inexpensive with a wide dynamic range.

Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than common single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Some structural variants such as insertions-deletions, copy number variations, and minisatellites that are not very well tagged by SNPs cannot be fully explored by GWAS. Therefore, it is possible that some of these loci may have large effects on autoimmune disease risk. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

Cropland area per capita and pressure index on cropland is the important parameters measuring the social vulnerability and sustainability in the perspective of food security in a certain region in China during the historical periods. This study reconstructs the spatial distribution change of cropland area per labor/household and pressure index on cropland during the 17th-20th century by the methods of historical documents, regression analysis, pressure index model and GIS. Then it analyzed the impacting process of climate change and sustainability of cropland use during the different periods. It draws conclusions: (i) the spatial difference of labor/household density was obvious which had the same pattern as cropland distribution during the same periods, which is higher density in three agricultural areas. (ii) Cropland area per capita was relatively higher during the 17th-18th century, which were above 0.4 ha/person in majority counties and distributed homogenously. Till the 19th century and the beginning of 20th century, cropland area per capita in large amount of regions decreased below 0.2 ha/person embodying the increase of social vulnerability and unsustainability at that time. (iii) Pressure index on cropland also showed the similar spatial pattern as cropland area per capita which presented lower threshold than nowadays. During the 17th-18th century there was no pressure on cropland. While, in the 19th century and at the beginning of 20th century, two high value centers of pressure index on cropland appeared in Middle Shandong and the Jiaodong region, pressure on sustainable cropland use increased obviously and a food crisis is probably created. (iv) Higher sustainable extent of cropland use corresponds to cold period, and lower sustainable extent of cropland use corresponds to warm period in Shandong over the past 300 years. The turning point of 1680s from dry to wet attributes to the decrease of sustainable extent of cropland use in Shandong not very distinctively. More and more pressure on sustainability of cropland use finally since the beginning of 20th century would intensify the social conflict and increase the probability of social revolts.

Despite the strong anticancer activity of SN38 (7-ethyl-10-hydroxy-camptothecin), some defects prevent its clinical application, such as severe side effects and loss of anticancer activity due to lack of selectivity to cancer cells and hydrolysis of ring E, respectively. To address the issue, herein a multifunctional SN38 derivative (compound 9) containing biotin (tumor-targeting group) and valproic acid (histone deacetylase inhibitor, HDACi) was synthesized via click chemistry and evaluated using MTT assay. The in vitro cytotoxicity study showed that compound 9 exhibited superior cytotoxicity as irinotecan against human cervical cancer HeLa cells, albeit inferior to SN38. More significantly, compound 9 significantly reduced toxicity in mouse embryonic fibroblast NIH3T3 cells, indicating that compound 9 had the capacity to enhance tumor targeting due to its cell selectivity. Further studies demonstrated that compared with irinotecan, compound 9 induced similar apoptosis of cancer cells. Consequently, compound 9 can not only improve its tumor-targeting ability mediated by biotin but also exert the potent anticancer activity through the synergism of SN38 and valproic acid, indicating that the design concept is an effective strategy for structural modification of SN38.