With the growth of air traffic demand in busy airspace, there is an urgent need for airspace sectorization to increase air traffic throughput and ease the pressure on controllers. The purpose of this paper is to develop a method framework that can perform airspace sectorization automatically, reasonably, which can be used as an advisory tool for controllers as an automatic system, especially for eliminating irregular sector shapes generated by simulated annealing algorithm (SAA) based on region growth method. Two graph cutting method, dynamic Monte Carlo method by changing location of flexible vertices (MC-CLFV) and Monte Carlo method by radius changing (MC-RC) were developed to eliminating irregular sector shapes generated by SAA in post-processing. The experimental results show that the proposed method framework of AS can automatically and reasonably generate sector design schemes that meet the design criteria. Our methodology framework and software can provide assistant design and analysis tools for airspace planners to design airspace, improve the reliability and efficiency of airspace design, and reduce the burden of airspace planners. In addition, this lays the foundation for reconstructing airspace with more intelligent method.

Soil aggregates, as the basic component of soil, make great contributions to the stability of soil structure and soil carbon (C) sequestration. Recently, grassland is experiencing continuous grazing, which greatly affects soil aggregation and soil C storage. However, how soil aggregates and soil C in different grassland respond to grazing remain unclear. In this study, three national fenced grassland monitoring field stations which represented mountain meadow (MM), temperate steppe (TS), temperate steppe desert (TSD) were selected, soil samples at 0–10 cm depth of inside and outside the fence were collected to explore the effect of grazing and grassland type on composition, stability and nutrients of soil aggregates. The results showed that the bulk soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN) and available phosphorus (AP) varied greatly among the three grassland types, with the highest values in MM. Soil aggregates composition showed significantly response to both grassland type and grazing, especially the proportion of soil aggregates >2 mm, which significantly decreased 51.68% on average in grazing plots compared with fenced plots, whereas sharply decreased in MM and TSD. A significant decrease (on average 25.08%) of the mean weight diameter (MWD) of soil aggregates under grazing was detected across all grassland types. The effect of grazing on nutrients in macroaggregates (>0.25 mm) was greater than that in microaggregates (<0.25 mm). Aggregate-associated SOC concentration decreased under grazing in MM and TSD, however, SOC density showed different response to grazing across all grassland types, with minor effect on MM, decreasing SOC density in TS and increasing on TSD, respectively. The magnitude of grazing effect size on aggregate-associated SOC varied in different soil particle size, with greater response in aggregates > 2 mm and the biggest value in TDS. In addition, correlation analysis showed that MWD of soil aggregates had significant negative correlation with soil bulk density, but significant positive correlation with SOC, TN and TP concentration. Results of RDA showed that BD, soil nutrients were the main influence factors of the composition and stability of aggregates. Overall, grazing had significant influence on soil aggregation, stability and SOC, played a crucial role in grassland soil stability and the accumulation of SOC.

Based on the two-flavors Polyakov quark-meson model, we study the bubble dynamics of a first-order phase transition in chiral phase diagram at high density but low temperature in the framework of the homogeneous thermal nucleation theory. Under the mean-field approximation, after obtaining the effective potential with inclusion of the fermionic vacuum term, we build a geometric approach to probe two possibly existing minima, which can actually tunnel through a bounce interpolated between a metastable vacuum to an adjacent true vacuum. By separating our discussions into a weak first-order hadron quark phase transition and strong ones, the bubble profiles, the surface tension, the typical radius of the bounce and the saddle point action in the presence of a nucleation bubble as a function of temperature are numerically calculated in detail as the chemical potentials are fixed at $\mu=306 \mathrm{MeV}$ and $\mu=310 \mathrm{MeV}$. our results show that the surface tension remains a small value even when the density is very high and phase boundaries for hadron and quark matter should be resized properly according to the saddle point action evaluated on the bounce. It is also noticed that the inclusion of the deconfinement phase transition in term of the Polyakov loop does not change chiral phase transition dramatically for light quarks.

This study explores the potential of natural compounds, including Daidzin, Luteolin, and Silibinin, as inhibitors of human monoamine oxidase B (MAO-B) for the treatment of Parkinson's disease. Docking simulations were performed using Autodock Vina with Pyrx program to evaluate the binding affinities of these compounds with MAO-B. Results indicate that Daidzin, Luteolin, and Silibinin exhibit promising binding affinities with MAO-B, with Daidzin demonstrating the highest affinity. Toxicity parameters were further predicted using the PkCSM Server, confirming the low toxicity of these compounds. Overall, the findings suggest that Daidzin, Luteolin, and Silibinin have the potential to be effective and safe treatments for Parkinson's disease through their inhibition of MAO-B. Further experimental validation is warranted to support their therapeutic efficacy in clinical settings.

The multitarget therapeutic strategy, as opposed to the more traditional ‘one disease-one tar-get-one drug’, may hold promise in treating multifactorial neurodegenerative syndromes, such as Alzheimer’s disease (AD) and related dementias. Recently, combining a photopharmacology approach with the multitarget-directed ligand (MTDL) design strategy, we disclosed a novel donepezil-like compound, namely 2-(4-((diethylamino)methyl)benzylidene)-5-methoxy- 2,3-dihydro-1H-inden-1-one (1a), which in the E diastereomeric form (and about tenfold less in the UV-B photo-induced isomer Z) showed the best activity as dual inhibitor of the AD-related targets acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B). Herein, we investigated further photoisomerizable 2-benzylideneindan-1-one analogs 1b-h with the unconjugated tertiary amino moiety bearing alkyls of different bulkiness and lipophilicity. For each compound the thermal stable E diastereoisomer, along with the E/Z mixture as produced by UV-B light irradia-tion in the photostationary state (PSS, 75% Z), was investigated for the inhibition of human ChEs and MAOs. The pure E-diastereoisomer of the N-benzyl(ethyl)amino analog 1h achieved low nanomolar AChE and high nanomolar MAO-B inhibition potencies (IC50s 39 and 355 nM, re-spectively), whereas photoisomerization to the Z isomer (75% Z in the PSS mixture) resulted in a decrease (about 30%) of AChE inhibitory potency, and not in the MAO-B one. Molecular docking studies were performed to rationalize the different E/Z diastereoselectivity of 1h toward the two target enzymes.

With the prevalence of smartphones, the scope of mobile applications has been significantly broadened in the past few years including almost every aspect of our daily life. Among the applications, message dissemination services play a significant role. Traditionally, users send messages to the servers, which forward them according to friend lists. This client-server architecture is efficient with the support of infrastructures, however, it restricts the application scope and may hinder the wide deployment of location-based mobile applications. This paper proposes a novel communication model called passive broadcast for local message dissemination. In this new model, each node does not maintain a connection state and data delivery is initialized by a receiver via a ’scan’ operation. The representative carriers of passive broadcast include Bluetooth and WiFi-Direct, both of which define a mandatory ‘peer discovery’ function. Passive broadcast features negligible cost for establishing and maintaining direct links and is extremely suitable for short message dissemination in proximity. Furthermore, we study the operation management in the proposed communication model with complete protocols and in-depth analysis for optimization. We prototyped our solution on commercial phones and evaluated it with extensive experiments and simulation.

In the last decades, we have witnessed a spectacular information explosion over the Internet. Millions of users are consuming the Internet through various services, such as mobile applications, and online games. The service providers, at the back-end side, are supported by state-of-art infrastructures. Targeting on providing the services at scale, virtualization is one of the emerging technologies used in data centers and cloud environments to improve the quality of services. In this project, we aim to develop a dynamic resource management scheme based on virtual containers. It collects the runtime job progress from the running tasks and allocates the resources dynamically to improve the overall system performance.

Smartphone has become one of the most revolutionary devices in the history of computing. With various kinds of applications, the scope of smartphone has been significantly broadened in the past few years including almost every aspect in our daily life. However, due to the limited on-board resources such as CPU, storage, network bandwidth and battery power, smartphones and the mobile network serving them bring new challenges that have not been encountered in the traditional computing and networking environments. This dissertation focuses on the research areas of improving the network architecture and enhancing the current applications on smartphones. It mainly investigates the areas in the following two directions for three representative categories of mobile services. - In the first direction, the dissertation aims to develop new communication models for smartphone Ad-Hoc networks to achieve efficient communication in the proximity. It is motivated by the fact that smartphone Ad-Hoc networks can help improve the current location-based services and propel new applications. Moreover, the new communication models provide complementary alternatives to the traditional infrastructure-based wireless networks. - In the second direction, the dissertation focuses on improving the other two categories of services, cloud storage services and real-time video streaming services for mobile devices. In the field of cloud storage, we introduce a cloud-assisted approach to provide a set of advanced file operations, such as encryption, decryption and compression, on smartphones. Furthermore, by utilizing the on-board Near Field Communication(NFC) module, we develop an algorithm to securely share the files between mobile devices. For the services of real-time video streaming, we propose approaches that identify a user’s status by analyzing the accelerometer data and then, dynamically adjust the buffer mechanism to save network bandwidth on smartphones.

Water depth estimation holds paramount importance in various domains including navigation, environmental monitoring, and resource management. Traditional depth measurement methods such as bathymetry can often be prohibitively expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, with a specific focus on the Baidu Easy DL model, for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using the ACOLITE software, the research compares the performance of several machine learning algorithms, including the Stumpf Model, Log-Linear Model, and the Baidu Easy DL Model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0-11 meter depth range. This study showcases the substantial potential of machine learning in the realm of remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research not only opens avenues for further exploration of machine learning applications in remote sensing but also highlights the promising prospects of online model APIs in streamlining remote sensing data processing.

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.

We have recently reported that a series of 1,3,4-oxadiazol-2-ylbenzenesulfonamides acts as potent and specific monoamine oxidase B (MAO-B) inhibitors with some compounds possessing potencies in the nanomolar range. While sulfonamide compounds (e.g., zonisamide) have been reported to inhibit the MAO enzymes, nanomolar MAO inhibition potencies for this class are not frequently observed. The present study expanded on these findings by replacing the 1,3,4-oxazole moiety with a 1,3-thiazole heterocycle. A series of 23 primary sulfonamides were synthesized by reaction of an α-haloketone with a substituted carbothioamide. The results of the MAO inhibition studies showed that the 1,3-thiazolylbenzenesulfonamides were specific inhibitors of human MAO-B. The most potent MAO-B inhibitor exhibited an in vitro IC50 value of 0.103 µM (3j). The derivatives were, however, weaker inhibitors of human MAO-A with the most potent compound exhibiting an IC50 value of 11.9 µM (3a). Examination of the structure-activity relationships (SARs) showed that the most potent MAO-B inhibition was obtained with substitution of the sulfonamide on the meta position of the phenyl rather than the para position. This study concludes that 1,3-thiazolylbenzenesulfonamides represent a class of potent and specific MAO-B inhibitors, which may serve as leads for the development of new treatments for disease states such as Parkinson’s disease.

Many Western studies have indicated that older women are generally more vulnerable in terms of mobility compared to older men, particularly regarding driving. However, the situation may differ in the context of China. This study, based on activity diaries and semi-structured inter-views, focuses on the spatiotemporal behavior of older adults in Tianjin and explores how the constraints posed by activity companions (in terms of type, size, and composition) shape the mo-bilities of older men and women, including activity locations, travel distances, and transporta-tion modes. The key findings are as follows: First, older women are more engaged with their families due to a higher percentage and longer duration of activities spent with family members. Second, older men tend to have more concentrated travel distances near their homes compared to older women. Third, older women exhibit a broader range of activities in different locations and engage in longer-distance leisure travel with family members when compared to older men. In the context of Western literature, this study discusses older women's enhanced social interactions, their earlier retirement in China, and the impact of COVID-19 as factors that help explain these findings. This study contributes to a deeper understanding of accompanied mobilities among Chinese older adults using geographical theory and methods, emphasizing the importance of flexible work schedules for the workforce and the organization of community-based activities to promote the social interactions and mobilities of older adults.

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.

The 2019-nCoV has been identified as the cause of an outbreak of respiratory illness in Wuhan, Hubei Province, China beginning in December 2019. This epidemic had spread to 19 countries with 11,791 confirmed cases, including 213 deaths, as of January 31, 2020. The World Health Organization declared it as a Public Health Emergency of International Concern. This study analyzed and discussed 70 research articles published until January 31, 2020 for a better understanding of the epidemiology, causes, clinical diagnosis, prevention and control of this virus. Studies thus far have shown origination in connection to a seafood market in Wuhan, but specific animal association has not been confirmed. The reported symptoms include fever, cough, fatigue, pneumonia, headache, diarrhea, hemoptysis, and dyspnea. Preventive measures such as masks, hand hygiene practices, avoidance of public contact, case detection, contact tracing, and quarantines are effective for reducing the transmission. To date, no specific antiviral treatment is proven effective, hence, infected people primarily rely on symptomatic treatment and supportive care. Although these studies had relevance to control a public emergency, more research need to be conducted to provide valid and reliable ways to manage this kind of public health emergency in both short- and long- term.

Piezoelectric properties are of significant importance to medical ultrasound, actuators, sensor, and countless other device applications. The mechanism of piezoelectric properties can be deeply understood from structure evolutions. In this paper, we report a diagram of structure evolutions of the Pb(Zn1/3Nb2/3)0.91Ti0.09O3 (PZN-9PT) single crystal with excellent piezoelectric properties among the orthorhombic, tetragonal, and cubic phases with temperature increasing from room temperature to 220 C. Through fitting the temperature dependence XRD curves with Gauss and Lorenz functions, we obtained the evolutions of the content ratio of three kinds of phases (orthorhombic, tetragonal and cubic) and the lattice parameters of the PZN-9PT system with the changes of temperature. The XRD fitting results together with Raman and dielectric spectrums show that the phase transitions of PZN-9PT are a typical continuous evolution process, very similar to the phase transition of paraffin from solid to liquid. Additionally, resonance and anti-resonance spectrum show excellent piezoelectric properties of the crystals, which probably originate from the nano twin domains, as demonstrated by the TEM images. Of particular attention is that the thickness electromechanical coupling factor kt is up to 72%.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival and other prognostic endpoints in multiple cancer types, including Lower-Grade Gliomas (LGG), Breast Invasive Carcinoma (BRCA) and many others. We also showed significant association of MNAI with genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Futhermore, we found that patients with high MNAI benefit more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated on LGG and BRCA that the multimodal integration of MNAI and the AI-empowerd Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

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.

This study presented a novel and sustainable method for producing chitin nanocrystals (PA-ChNCs) using phosphoric acid hydrolysis. The produced PA-ChNCs were used as stabilizers for Pickering emulsions and their physicochemical properties as well as stabilization mechanisms were systematically investigated. The PA-ChNCs exhibited small particle sizes and high surface areas, enabling an effective adsorption at the oil/water interface, which prevented coalescence in Pickering emulsions. The particle size and surface charge PA-ChNCs were strongly pH-dependent, which were critical factors for stabilizing Pickering emulsions. The study showed that emulsions with higher O:W ratios (50%) exhibited larger droplets but greater viscosity, forming a stable gel-like matrix that was more stable than emulsions with other O:W ratios after storage for 30 days. Emulsions with higher PA-ChNCs concentrations (2.0 wt%) and higher pH values (pH 10) showed small droplets and high viscosity, leading to stable emulsions. This research contributes to the development of sustainable and effective emulsifiers for use in the food industry, making utilization of seafood waste in an eco-friendly manner possible. Overall, this study highlights the potential of using newly developed PA-ChNCs as stabilizers for Pickering emulsions in food products.

Background: Coronavirus disease (COVID-19) related pneumonia is leaded by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which infects host cells through receptors named Angiotensin-converting enzyme 2 (ACE2). Smoking is thought to be related to poor disease prognosis, as a large amount of evidence highlights the negative effects of smoking on lung health and its causal relationship with various respiratory diseases. Methods: We first evaluated the role of ACE2 expression level with tobacco effect. And correlation analyses were used to identify the related genes of ACE2 both in smoker trait and human normal multi-tissues. After intersections, hub genes were later used to further GSEA and co-regulated mechanisms were explored by GeneMANIA. We used UALCAN to perform survival curves of pan-cancers included 33 types of cancers. New clinical model of top co-occurrence cancer type was constructed and validated. Results: Effected by cigarette smoking, the expression level of ACE2 expressed statistically upwards in current smokers compared with never smokers, upwards in groups after acute smoke exposed compared with normal control. But no strong evidence detected in third-hand smoke, as poor amounts of samples, only 4. A little trend of expressing upwards became in groups after third-hand smoke compared with groups after clean air exposing. 4 genes included PIR, ADH7, AKR1C2 and AKR1C3 were identified as ACE2 related genes in smoker trait and human normal multi-tissues. Then, we made the survival curves of pan-cancers in 4 genes. Brain lower grade glioma was the co-occurrence type as both ACE2, PIR and AKR1C3 had the significantly prognostic situation. Later, we made the new clinical prediction model as the C-index was 0.827 and the Area Under Curves (AUCs) of 1-year survival, 2-year survival and 3-year survival were 0.921(95%CI, 0.882-0.961), 0.911(95%CI, 0.860-0.962) and 0.878(95%CI, 0.818-0.939). In inner validation, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.777(95%CI, 0.640-0.914), 0.916(95%CI, 0.842-0.990) and 0.888(95%CI, 0.768-1.000). In outer validation one, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.764(95%CI, 0.672-0.856), 0.848(95%CI, 0.783-0.913) and 0.748(95%CI, 0.656-0.841). And in outer validation two the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.740(95%CI, 0.654-0.826), 0.766(95%CI, 0.688-0.843) and 0.794(95%CI, 0.721-0.866). Conclusions: In our study, we compared the ACE2 expression level between smokers and non-smokers crowds and identified associated hub genes. Then we explored the further role of hub genes in tumor fields to identify the correlation and influence between the avoidable host factors such as smoking on COVID-19 contamination and tumor. ACE2 and AKR1C3 are potential prognostic genes for brain lower grade glioma, and we created the web dynamic nomogram and encapsulation app through validations.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

Genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only consid-ers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experi-mentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineer-ing targets. Here we present the first genome-scale enzyme-constrained model (eciCW773) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of pheno-types and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used eciCW773 to identify several gene modifica-tion targets for L-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

The loss or dysfunction of skeletal tissue often necessitates surgical intervention, particularly in cases involving trauma, tumors, or abnormal development leading to critical-sized defects. Despite autografts being considered the gold standard for bone grafts, their limitations and complications prompt the exploration of alternative approaches for bone repair and regeneration. Current advancements in bone tissue engineering have led to innovative strategies aiming to regenerate injured bone structures, providing viable alternatives to traditional autografts or allografts. Among these strategies, Hydrogel Microparticles (HMPs) have emerged as promising scaffolds due to their notable characteristics, including high porosity and mechanical tunability. HMPs play an important role in facilitating vasculature formation, mineral deposition, and overall bone tissue regeneration. HMPs, fabricated through various techniques, exhibit versatility in functions such as drug and cell delivery, structural scaffolding, and bioinks for 3D printing. In addition, these microgels can be injected for minimally invasive delivery and can display modular properties with different designs and configurations. This review exclusively focuses on the advancements in HMPs for bone regeneration, delving into synthesis and functionalization techniques while highlighting their diverse applications documented in the literature. Our aim is to shed light on the unique benefits and potential of HMPs in the intricate field of engineering bone tissue.

With the rapid development of urbanization and industrialization in China, environmental issues have become a knotty problem, especially issues related to air, water, and solid-waste pollution. These pollutants pose threats to the health of the population and to that of communities and have a vicious influence on the healthcare system. Additionally, pollution also exhibits spill-over effects, which means that pollution in the local region could affect the healthcare services in a neighbouring region. Therefore, it is necessary to explore the relationship between pollution and healthcare. A spatial autocorrelation analysis was conducted and spatial panel econometric models were constructed to explore the characteristics of pollution and healthcare services in China and the relationship between them using data on all 31 provinces over twelve consecutive years (2006-2017). The results showed that the utilization of healthcare services and environmental pollution were not randomly distributed; unsurprisingly, air pollution and solid-waste pollution were mainly found in parts of northern China, while water pollution was highest in southern and coastal China. In addition, environmental pollution exhibited spill-over effects on healthcare services. For example, a 1% increase in solid waste in one specific geographical unit was estimated to increase the inpatient visits per capita in adjacent counties by 0.559%. Specifically, pollution showed different degrees of influence on healthcare services, which means that the impact of environmental pollution on the number of outpatient visits is greater than on the number of inpatient visits. Our results provide the government with evidence for effectively formulating and promulgating policies, especially policies aimed at tackling spill-over effects among different regions.

The performance of the absolute atom gravimeters used on moving platforms, such as vehicles, ships and aircrafts, is strongly affected by the vibration noise. To suppress its influence, we summarize a vibration compensation method utilizing data measured by a classical accelerometer. The measurements with the accelerometer show that the vibration noise in the vehicle can be 2 order of magnitude greater than that in the lab during daytime, and can induce an interferometric phase fluctuation with a standard deviation of 16.70π. With the compensation method, our vehicle-mounted atom gravimeter can work normally in these harsh conditions. Comparing the Allan standard deviations before and after the vibration noise correction, we find a suppression factor of 22.74 can be achieved in static condition with an interrogation time of T = 20 ms, resulting a sensitivity of 1.35 mGal/Hz1/2, and a standard deviation of 0.5 mGal with an average time of 10 s. We also demonstrate the first test of an atom gravimeter in a moving vehicle, in which a suppression factor of 50.85 and a sensitivity of 60.88 mGal/Hz1/2 were realized with T = 5 ms.

In order to study the petrogenesis and tectonic setting of Permian A-type granites and their relationships with hydrothermal mineralization along the Hegenshan-Heihe suture zone (HHSZ) in northeastern China, we select the newly discovered Hongyan Cu-polymetallic deposit in the northeastern part of the HHSZ that develops three stages of mineralization associated with the Shanshenfu alkali-feldspar granite (SAFG). The zircon U-Pb dating and whole rock geochemistry suggest that the SAFG is a typical A-type granite formed in the Early Permian. The zircon Hf isotopes and trace elements suggest that the SAFG has high Ti-in-zircon temperature (721–990℃), high magmatic oxygen fugacity and largely positive εHf(t) (+6.0 to +9.9). Therefore, we propose that the SAFG was derived from the crustal assimilation and fractional crystallization of the charnockitized juvenile crust. The high oxygen fugacity favors the chalcophile elements (e.g., Cu, Au, Ag) of the source region enriched in the fluid phases after magmatic fractional crystallization, consequently facilitating subsequent hydrothermal mineralization, which is also consistent with the characteristics of ore-forming fluids that changed from the initial high temperature, high salinity, high fO2 and CO2-rich magmatic-hydrothermal fluids of stage I to CO2-poor, dilute, and cooling meteoric fluids of stage III. Combined with regional geological background, the Permian A2-type granites along the HHSZ can be formed in post-collisional slab break-off process. In subsequent exploration for hydrothermal deposits along the HHSZ, the Permian A-type granites with arc-related juvenile crustal source and high fO2 have great potential and need more attention.

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

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.

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.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

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.

Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.

Iron overnutrition has been implicated with higher risk of developing metabolic and cardiovascular diseases, including metabolic syndrome (MetS), while iron deficiency anemia exacerbates many underlying chronic conditions. Hemoglobin (Hb) concentration in the blood, which reflects a major functional iron (i.e., heme iron) in the body, may serve as a surrogate of iron nutrition status. We conducted sex-specific observational association studies where we carefully titrated the association between Hb deciles and MetS and its components among the Taiwanese Han Chinese (HC) from the Taiwan Biobank and Europeans of White ancestry from the UK Biobank, representing two large ethnicities. Our data show that at higher-than-normal levels of Hb, increasing deciles of Hb concentration were significantly associated with MetS across all sex subgroups in both ethnicities, with the highest deciles resulting in up to three times greater risks than the reference group [Taiwanese HC: OR=3.17 (95% CI, 2.75-3.67) for Hb >16.5 g/dL in men, OR=3.11 (2.78-3.47) for Hb >14.5 g/dL in women; European Whites: OR=1.89 (1.80-1.98) for Hb >16.24 g/dL in men, OR=2.35 (2.24-2.47) for Hb >14.68 g/dL in women]. The association between stronger risks and increasing Hb deciles was similarly observed with all metabolic components except diabetes. Here we found that both the highest Hb decile groups and contrarily the lowest ones, with respect to the reference, were associated with higher odds of diabetes in both ethnic groups [e.g., Taiwanese HC men: OR=1.64 (1.33-2.02) for Hb >16.5 g/dL, OR=1.71 (1.39-2.10) for Hb <13.5 g/dL; European Whites women: OR=1.39 (1.26-1.45) for Hb >14.68 g/dL, OR=1.81 (1.63-2.01) for Hb <12.39 g/dL]. These findings confirm that elevated Hb concentrations, a potential indicator of iron overnutrition, may play a role in the pathophysiology of MetS and metabolic components.

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.

Due to the long duration and high cost of traditional pumping tests, the response of well water level to seismic wave, earth tide, and barometric pressure provides a feasible method for determining continuous changes in aquifer hydraulic parameters. Aiming at the problem that the response phase shift of well BB water level to tidal tide M2 is greater than that of tide O1, this paper preferentially calculated the time series changes of S, Be, n and BKu based on the response mechanism of well water level to barometric pressure and earth tide and with the help of smooth moving method. Then by using the mixed flow model, the variation of transmissivity and leakage coefficient over time were simultaneously obtained, and the evolution process and characteristics of aquifer parameters near well BB caused by the Wenchuan earthquake were ultimately discussed. The calculation results are basically consistent with the previous pumping test, and have been verified and compared by using other scholars' methods. This solving process avoids the problems such as excessive dependence on initial values, multiple solutions, and unstable tide O1, which has a promoting effect on the study of the impact of seismic activity on aquifer systems.

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.

Traditional discrimination techniques for Euler deconvolution use only the color spectrums of structural indexes, without considering the spatial distribution characteristics and inherent relationships among the Euler solutions to separate adjacent causative sources. In the present study, a new approach was developed for discriminating uncorrelated Euler solutions from coherent solutions based on the focusing levels indicated by the probability density distributions generated using multivariate kernel density estimations (KDE). A novel multiple coverage technique was proposed by using a series of different sized moving windows over gridded gravity data, which formed tight clusters of Euler solutions for different sized causative sources. The results of the probability density distributions were obtained using a 3-D KDE method for the Euler solution subsets {x, y, z} of synthetical models, and real data from a survey conducted in British Columbia (Canada) which had successfully established more credible and meaningful geological models when compared with three other subsets.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

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%.

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.

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.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

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.

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.

Moral identity can promote people’s well-being, but existing research has paid little attention to the mechanism of the link between the two. The current study proposed that the eudaimonic identity function is a critical mechanism that links moral identity and well-being. Specifically, the quality of identity commitment mediates the link between subjective well-being and the two dimensions of moral identity, namely, internalization and symbolization. We examined these hypotheses in 419 participants, who completed the Self-importance of Moral Identity Questionnaire, Satisfaction with Life Scale, Scale of Positive and Negative Experience, and Questionnaire for Eudaimonic Well-being. Results from the obtained data confirmed our hypotheses: There is a significant correlation between moral identity and subjective well-being. Both the internalization and symbolization dimensions of moral identity promote subjective well-being through the mediating role of identity commitment quality. We discussed these findings with respect to implications and proposed research suggestions for future studies.

The fusion of global contextual information with local cropped block details is crucial for segmenting ultra-high resolution images. In this study, we introduce a novel fusion mechanism termed Global-Local Deep Fusion (GL-Deep Fusion) based on an enhanced transformer architecture, which efficiently integrates global contextual information and local details. Specifically, we propose the Global-Local Synthesis Networks (GLSNet), a dual-branch network where one branch processes the entire original image, while the other branch handles cropped local patches as input. The feature fusion of different branches in GLSNet is achieved through GL-Deep Fusion, significantly enhancing the accuracy of ultra-high resolution image segmentation. Particularly effective is GLSNet in identifying tiny overlapping items. To optimize GPU memory utilization, we meticulously design a dual-branch architecture that proficiently leverages the features it extracts, seamlessly integrating them into the enhanced transformer framework of GL-Deep Fusion. Extensive experiments conducted on challenging benchmarks, including DeepGlobe and Vaihingen datasets, demonstrate that GLSNet achieves a new state-of-the-art performance in terms of GPU memory utilization and segmentation accuracy tradeoff.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

The COVID-19 pandemic has created huge damage to society and brought panics around the world. Such panics can be ascribed to the seemingly deceptive features of the COVID-19: compared to other deadly viral outspreads, it has medium transmission and mortality rates. As a result, the severity of the causative coronavirus, SARS-CoV-2, was deeply underestimated by the society at the beginning of the COVID-19 outbreak. Based on this, in this review, we define the viruses with features similar to those of SARS-CoV-2 as the Panic Zone viruses. To contain those viruses, accurate and fast diagnosis followed by effective isolation and treatment of patients are pivotal at the early stage of virus breakouts. This is especially true when there is no cure or vaccine available for a transmissible disease, which is the case for current COVID-19 pandemic. As of January 2021, more than two hundred kits for the COVID-19 diagnosis on the market are surveyed in this review, while emerging sensing techniques for SARS-CoV-2 are also discussed. It is of critical importance to rationally use these kits for the efficient management and control of the Panic Zone viruses. Therefore, we discuss guidelines to select diagnostic kits at different outbreak stages of the Panic Zone viruses, SARS-CoV-2 in particular. While it is of utmost importance to use nucleic acid-based detection kits with low false negativity (high sensitivity) at the early stage of an outbreak, the low false positivity (high specificity) gains its importance at later stages of the outbreak. When a society is set to reopen from the lock-down stage of the COVID-19 pandemic, it becomes critical to have antibody based immunoassay kits with high specificity to identify people who can safely return to the society after their recovery of SARS-CoV-2 infections. Given that the emergence of mutant viruses at the beginning of 2021 has complicated current battle against the COVID-19, we also discussed approaches and guidelines to detect viral mutants in the middle of the second wave of the pandemic that started at the end of 2020. Finally, since a massive attack from a viral pandemic requires a massive defense from the whole society, we urge both government and private sectors to research and develop more affordable and reliable point-of-care testing (POCT) kits, which can be used massively by the general public (and therefore called as massive POCT) to contain Panic Zone viruses in future.

Transition between free-surface and pressurized flows is an crucial phenomenon in many hydraulic systems, including water distribution systems, urban drainage systems, etc. During the transition, the force exerted on the structures changes drastically, thus it is meaningful to simulate this process. However, severe numerical oscillations are widely observed behind filling-bores, causing unphysical pressure variations and even computation failure. In this paper, some oscillation-suppressing approaches are reviewed and evaluated on a benchmark model. Then a new oscillation-suppressing approach is proposed to admit numerical viscosity when the water surface is at proximity of conduct roof which has first order accuracy. This approach adds numerical viscosity when water surface is at the proximity of conduct roof. It can sufficiently suppress numerical oscillations under an acoustic wave speed of 1000m/s and is simple to apply. In comparison with two experiments, the simulation results of this method show good agreement and little numerical oscillations. The results in this paper can help readers to choose an appropriate oscillation-suppressing method to improve the robustness and accuracy of flow regime transition simulations.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

In this paper, we synthesized MC(BeA-co-MMA) copolymer microcapsules through suspension polymerization. The pendent n-behenyl group of BeA is highly crystalline, and it acts as the side-chain in the structure of BeA-co-MMA copolymer. The highly crystalline n-behenyl side-chain provides BeA-co-MMA copolymer thermal-energy-storage capacity. In order to investigate the correlation between thermal properties and crystal structure of BeA-co-MMA copolymer, the effects of monomer ratio, temperature changing and changing rate, as well as synthesis method were discussed. The monomer ratio influenced crystal transition behavior and thermal properties greatly. The DSC results proved that when the monomer ratio of BeA and MMA was 3:1, MC(BeA-co-MMA)3 showed the highest average phase change enthalpy ΔH (105.1 J·g–1). It indicated that the n-behenyl side-chain formed relatively perfect crystal region, which ensured a high energy storage capacity of copolymer. All the DSC and SAXS results proved that the amount of BeA had a strong effect on the thermal-energy-storage capacity of copolymer and the long spacing of crystals, but barely on the crystal lamella. It was found that MMA units worked like defects in the n-behenyl side-chain crystal structure of BeA-co-MMA copolymer. Therefore, a lower fraction of MMA, that is, a higher fraction of BeA contributed to a higher crystallinity of BeA-co-MMA copolymer for providing a better energy storage capacity and thermoregulation property. ST(BeA-co-MMA) copolymer sheets with the same ingredients as microcapsules were also prepared through light-induced polymerization aiming at clarifying the effect of synthesis method. The results proved that synthesis method mainly influenced the copolymer chemical component, but lightly on the crystal packing of n-behenyl side-chain.

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.

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.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

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.

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.

Aberration in microRNA (miRNA) expression or DNA methylation is a causal factor for polycystic ovarian syndrome (PCOS), a common endocrine disorder and leading cause of infertility. However, the epigenetic interactions between miRNA and DNA methylation remain unexplored in PCOS. In this study, we conducted an integrated analysis of RNA-seq, miRNA-seq and MBD-seq on ovarian granulosa cells of PCOS and control groups to reveal the epigenetic interactions involved in the pathogenesis of PCOS. Firstly, we identified 830 genes and 30 miRNAs that were expressed differently in PCOS, and seven miRNAs were found to negatively regulate targeted mRNA expression. Next, in total, 130 miRNAs were found to be significantly differently methylated in promoter regions, while 13 were found to be associated with miRNA expression. Furthermore, the promoter hypermethylation of miR-429, miR-141-3p, and miR-126-3p was proven to suppress miRNA expression and therefore upregulate their corresponding genes, including XIAP, BRD3, MAPK14 and SLC7A5. Our results demonstrate that DNA methylation regulates miRNA expression and therefore controls its corresponding gene expression. The reactivation of the transcription of epigenetically silenced genes may be one of the key elements in PCOS pathogenesis. Meanwhile, the epigenetic mechanisms underlying the regulation of miRNA expression can provide a potential therapeutic target for PCOS in the future.

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.

Post-training quantization is pivotal in deploying convolutional neural networks for mobile applications. Block-wise reconstruction with adaptive rounding, as employed in prior works like BrecQ and Qdrop, facilitates acceptable 4-bit quantization accuracy. However, adaptive rounding is time-intensive, and its constraint on weight optimization space curtails the potential for quantization performance. The optimality of block-wise reconstruction hinges on the quantization status of subsequent network blocks. In this investigation, we delve into the theoretical underpinnings of the limitations inherent in adaptive rounding and block-wise reconstruction. Our exploration leads to the development of a post-training quantization methodology, designated as AE-Qdrop. This algorithm operates in two distinct phases: block-wise reconstruction and global fine-tuning. The block-wise reconstruction phase introduces a progressive optimization strategy, superseding adaptive rounding, which not only augments quantization precision but also significantly improves quantization efficiency. To mitigate the risk of overfitting, we introduce a random weighted quantized activation mechanism. During the global fine-tuning phase, we account for interdependencies among quantized network blocks. The weight of each network block will be corrected with logit matching and feature matching. Extensive experiments validate that AE-Qdrop achieves high-precision and efficient quantization. For instance, in the case of 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop by achieving a 6.26% enhancement in quantization accuracy and quintupling the quantization efficiency.

Although the concept of additive manufacturing has been proposed for several decades, momentum of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization in SLM to obtain high relative density and low surface roughness simultaneously in the previous literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. The statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. It is noted that the effects of the laser power and scanning speed on the above objective quality show highly significant, while hatch space behaves an insignificant impact. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

In this paper, the research progress on dynamic behaviors of RC structural members is reviewed from experimental, theoretical and numerical perspectives. Firstly, the basic overview, measurement methods, main conclusions and current limitations of available dynamic loading tests are presented. Then the theoretical studies on the dynamic constitutive models of RC materials, the dynamic increase factor (DIF) model for concrete and reinforcing steel and the proposed modified models of dynamic behavior parameter at the structural member level are summarized. Finally, the available modelling approach and the method for incorporating the dynamic effect in numerical simulation of RC structures are reviewed. Moreover, a brief introduction is made to the dynamic hysteretic model established on the experimental data, which provides an alternative approach to consider the dynamic effect beside the commonly used DIF method. This paper provides valuable reference for experimental studies and numerical simulations on the dynamic behaviors of RC structures, and also puts forward some issues that need to be solved in the future research works..

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, resulting in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking worn fingerprints. The research findings indicated that traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.

Diffuse large B cell lymphoma (DLBCL), the most frequent type of non-Hodgkin lymphoma in adulthood, remains challenging clinical issues. Despite enhanced remission rates can be achieved, there are one-third of patients who will not respond to current treatment or will relapse with resistant disease, necessitating ongoing efforts on effective treatment strategies and agents. The vacuole H+-ATPase inhibitor bafilomycin A1 is broadly used to block late stage of autophagy flux at high concentration. In this study, we show that, to our surprise, bafilomycin A1 effectively inhibited and killed DLBCL cells at nanomolar concentrations (5nM). Bafilomycin A1 targeted cell cycle regulators cyclin D1 and cyclin E2 to induce cell cycle arrest in G0/G1 phase. Meanwhile, it induced caspase-dependent apoptosis with concomitant cleaved caspase-3 and Parp. Furthermore, we found that bafilomycin A1 inhibited autophagy flux at both early and late stages of the autophagy flux through activating ERK and mammalian target of rapamycin signaling, as well as by inhibiting the degradation of autolysosomes. We speculated that bafilomycin A1 as autophagy inhibitor might enhance the effect of DLBCL chemotherapeutic drug rituximab. Accordingly, our results provided evidence that the combination of bafilomycin A1 with rituximab enhanced the inhibition of DLBCL cells notably. Taken together, our data suggest that bafilomycin A1 may be a promising candidate drug in the therapy of diffuse large B cell lymphoma.

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.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

Background: Physical and mental health of corporate employees is equally important, especially in international salespeople in the in vitro diagnostic (IVD) medical device industry. The rapid growth of the IVD market is driven by the increasing prevalence of chronic and infectious diseases. This study aims to identify the association of socio-demographic, occupational, organizational, and psychosocial factors with mental health outcomes for depression, anxiety, and somatic symptoms in Chinese IVD international salespeople. Methods: The study was a cross-sectional survey of ISs in IVD companies officially registered in China. An online survey was designed to collect data through email contact with IVD companies and social media between August 2022 and March 2023. Measured factors included effort-reward imbalance (ERI), health-promoting leadership (HPL), health climate (HC), inner strength (IS), and perceived social support (PSS). Mental health outcomes assessed using the Core Symptom Index (CSI) were depression, anxiety, and somatic symptoms. Results: A total of 244 salespeople responded to the survey. CSI scores indicated that 18.4% (n = 45) and 10.2% (n = 25) of the respondents had symptoms of major depression and anxiety, respectively. ERI was positively correlated, while the IS and PSS were negatively correlated with major depression, anxiety, and somatic symptoms (p < .01). Health climate was negatively correlated with major depression (p < .05). Education background was associated with somatic symptoms (p < .05). ERI, IS, and gender were significant predictors of major depression, anxiety, and somatic symptoms (p < .05). Conclusion: The prevalence of depression and anxiety in China’s IVD international salespeople was considered low compared with prevalence in Chinese populations during COVID-19, but higher than those before the pandemic. Effort–reward imbalance, inner strength, and gender were significant factors in major depression, anxiety, and somatic symptoms among IVD international salespeople.

The heat generated during the startup process of dry gas seals, such as the friction heat of the asperities, viscous shear heat, and the expansion heat of the gas film, easily leads to seal failure and endangers the stable operation of turbomachinery. This study combines the statistically modified contact model and the average Reynolds equation by considering the adiabatic index and the microscale effects of dry gas seals to explore the heat generation characteristics of dry gas seals formed in series at multiple times. In particular, the change laws and influencing factors among the components, including the friction heat of asperities and the shear heat and expansion heat of the gas film and total heat, are investigated. A comparative analysis indicates that the slip flow effect increases the total heat during the startup process. The friction heat is much greater than expansion heat and shear heat. Despite constant acceleration, exponential acceleration, or Harrision acceleration, the heat changes are uneven during the startup process. In particular, the heat changes sharply in the early stage and gently in the later stage. The Harrision acceleration mode is the most conducive to sealing stability.

Based on multi-source remote sensing data, scenario analysis, and model simulation, the Pareto optimal solutions for water supply, water purification (N retention), and carbon storage and sequestration services under different scenarios were sought by adjusting its land use structure. The results showed that. In Scenario 1(S1), the water supply service needs to increase by 86.820 to 11.211 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 to 11,700 tons, and the carbon storage and sequestration service need to decrease by 2.070 to 2.487 billion tons. In Scenario 2(S2), the water supply service needs to increase by 8.243–10.666 billion cubic metres, the water purification (N retention) service needs to decrease by 11,300–1.10 million tons, and the carbon storage and sequestration service needs to decrease by 2.033 to 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 7.832–11.437 billion cubic metres, the water purification (N retention) service needs to decrease by 1.16–10,800 tons, and the carbon storage and sequestration service needs to decrease by 19.220 to 2.380 billion tons. After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. After optimising the S3 ecosystem service supply pattern, the water supply, water purification (N retention), and carbon storage and sequestration services could connect the western and eastern ecosystem service supply areas, balance the overall ecosystem service supply pattern of the study area and meet the demand for ecosystem services. The results can guide regional land planning and ecosystem service management optimisation.

The corrected mechanism model of battery voltammetric function is helpful to guide the development and application of battery. There are two scientific issues that need to be answered: First, how many mechanisms do batteries have; Second, how to establish the mechanism model separately under the overlapping of these mechanisms. Volt-ampere characteristics of both linear state and nonlinear state exist; the monotonic decreasing of volt-ampere characteristics indicates that the battery have only three kinds of mechanisms. Without changing the basic form of the function and under the principle of the mechanism function’s working region considered, we propose a mechanism function which satisfies the monotonic decreasing characteristic of the voltammetric curve of battery, via the derivative law of each mechanism function in the voltammetric function of battery. By using the voltammetric data, the obtained cell mechanism function can accurately predict the potential (current or voltage) when the independent variable of the cell is zero, and provide the theoretical basis for the internal working mechanism of the cell, which can guide the practice.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Then, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

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.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

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.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument's spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the spectral response function (SRF) is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which looks like a similar distribution to the flat-topped multi-Gaussian function, is used to fit the measured data in a spectral calibration. According to the results, the SRF’s shapes, due to the Earth port, resemble a Gaussian curve with a flatted top could be concluded, which contains an FWHM of 1.78–1.82 nm for the AAS. The results show that the correlation coefficients are about 0.99, which proves that the fitting function could better characterize the SRF of the instrument.

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.