The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI=R532nm / R641nm). Rust sources and products in steel girders can be identified by the unique spectral signatures and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the corrosion rust indicators. The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the absorption at 8000 cm-1 and 5850 cm-1.

Abiotic stress is responsible for significant reduction in crop plant productivity worldwide. Ultraviolet (UV) radiation is a natural component of sunlight and a permanent environmental stimulus. This study investigated the distinct responses of young wheat and einkorn plants to excessive UV-B radiation (180 min at λmax 312 nm) following foliar pretreatment with 1 µM synthetic cytokinin 4PU-30. Results demonstrated that UV radiation significantly amplified hydrogen peroxide levels in both wheat and einkorn, with einkorn exhibiting more pronounced increase compared to wheat. This elevation indicated the induction of oxidative stress by UV radiation in the two genotypes. Intensified antioxidant enzyme activities and increased accumulation of typical stress markers and non-enzyme protectants were evidenced. Transcriptional activity of genes encoding key antioxidant enzymes was also investigated aiming to elucidate the genetic regulation of the antioxidant systems in both wheat and einkorn seedlings. Our results suggested a role for POX1 and POX7 genes in the UV-B tolerance of the two wheat species as well as a cytokinin stimulated UV-B stress response in einkorn mediated through upregulation of the tau subfamily gene GSTU6. Based on these findings it could be concluded that 4PU-30 had the potential of alleviating oxidative stress by attenuating the symptoms of superfluous UV-B illumination in the two examined plant species.

As critical biocontrol agents, Trichoderma and Metarhizium have demonstrated vast potential in disease prevention and pest control respectively. Current applications, however, are predominantly confined to singular use or simplistic combinations of the two fungi. This study innovatively utilizes Metarhizium and Trichoderma as experimental subjects, employing non-targeted metabolomics to compare the differences in metabolites between co-culture and separate fermentation. The metabolite distinctions in co-culture are meticulously analyzed using sample correlation, least squares discriminant analysis, cluster analysis, and KEGG enrichment analysis. Metabolome sequencing analysis is then applied to compare the co-culture fermentation filtrate with Trichoderma atroviride and Metarhizium anisopliae. Differential metabolite analysis found that co-culture had significantly up-regulated or down-regulated metabolites compared to singular cultures.However,significant metabolite discrepancies are discovered in singular cultures, notably the up-regulated metabolites such as osthole and spirocyclic amine, which have documented antibacterial functions that may prove effective against Fusarium graminearum. Ultimately, the findings from this research contribute to the exploration of synergistic applications of biocontrol fungi and serve as a valuable reference for future development of bio-pesticides for disease and pest control.

Factorization reduces computational complexity and is therefore an important tool in statistical machine learning of high dimensional systems. Conventional molecular modeling, including molecular dynamics and Monte Carlo simulations of molecular systems, is a large research field based on approximate factorization of molecular interactions. Recently, the local distribution theory was proposed to factorize global joint distribution of a given molecular system into trainable local distributions. Belief propagation algorithms are a family of exact factorization algorithms for trees and are extended to approximate loopy belief propagation algorithms for graphs with loops. Despite the fact that factorization of probability distribution is their common foundation, computational research in molecular systems and machine learning studies utilizing belief propagation algorithms have been carried out independently with respective track of algorithm development. The connection and differences among these factorization algorithms are briefly presented in this perspective, with the hope to intrigue further development in factorization algorithms for physical modeling of complex molecular systems.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a two-sample bidirectional Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7,738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method. We further applied Cochran’s Q statistical analysis, MR-Egger intercept, and MR-PRESSO for heterogeneity and pleiotropy assessment, alongside a Bonferroni correction ((P

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Not occasional phamracokinetic variability and suboptimal exposure have been reported for micafungin in the intensive-care-unit (ICU) patients. This study aims to evaluate the quality of population pharmacokinetic models and provide rational using recommendation of this drug in clinical practice. Monte Carlo simulations were implemented to compare pharmakinetic parameters and probability of target attainment (PTA) towards various Candida species. Body weight, liver function, and SOFA score were the most frequent covariates in the final 16 included studies. micafungin clearance for ICU adults (SOFA≥10 or <10) was 39%–52% and 43%–57% higher, respectively, than non-ICU adults. For Candida infections with MICs <0.016 mg/L, the ICU group had lower proportions achieving PTA > 90% compared to the non-ICU group (2/6 (33%) vs. 4/7 (57%)), and for non-ICU and ICU adults, micafungin dosages of 100 and 150 mg were recommended, respectively. While for C. tropicalis and C. glabrata, 200 and 250 mg were recommended respectively. However, for C. krusei and C. parapsilosis, it failed to reach assumed PTA at MICs of 0.125-0.25 mg/L and 0.125-2 mg/L, respectively. Adjusting micafungin dosage in ICU patients may be necessary. Both ICU and non-ICU adults should modify their dosage regimens based on the Candida species and their respective MICs.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Monoclonal antibodies (mAbs) have become an increasingly important therapeutic modality in recent years, with applications in oncology, hematology, and immunology. This review discusses the production of therapeutic mAbs, including the various types of mAbs (murine, chimeric, humanized, and fully human), their key applications, and the techniques used for their production, including hybridoma technology, single B cell antibody technology, and phage display technology. The review then proposes advanced techniques to overcome them. These include microfluidic sorting, alternative expression systems (including plants, bacteria, and yeast, and cell-free production systems), and the development of complex antibody formats like bi-specific monoclonal antibodies, antibody fragments, antibody-drug conjugates, and nanobody development. These advanced techniques promise to enhance the efficiency, cost-effectiveness, and scalability of mAb production, paving the way for more effective therapeutic applications. This review highlights the theoretical significance of integrating innovative technologies to overcome existing challenges in mAb production, ultimately advancing the field of precision medicine.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

The cyber physical systems of smart cities are facing increasingly severe attack situations, and traditional separate protection methods are difficult to effectively respond to. It is urgent to coordinate public security and network security protection. However, the integration of the two faces many challenges and is a very promising research field. The aim of this study is to investigate technical approaches for the synergy between public safety and cyber security. This paper proposes a smart city safety protection model inspired by the human immune mechanism. It was found that through a three line defense architecture similar to the human immune mechanism, and with the help of certain algorithms and functional middleware modules, public security and network security protection components can be dynamically combined to achieve collaboration. This work has verified through experiments a promising path to effectively resist complicated attack threats intertwined with public safety and cyber security factors.

The synergetic features of a three-component photoinitiating systems (A/B/C) based on the measured data and proposed mechanism of Liu et al (Polymers, 2020, 12(6), 1394) are analyzed. The co-initiators/additives B and C have dual functions of : (i) regeneration of photoinitiator A and (ii) generation of extra radicals. The synergic effects lead to higher conversion of free radical polymerization (FRP) and cationic polymerization (CP). The key factors and rate constants influencing the conversion efficacy are explored by analytic formulas. Enhancing strategies for various photopolymerization systems are summarized including one component (or monomer) and one-wavelength, two-component and one, two and three-wavelength, and three-component, one-wavelength system. The synergetic effects for higher monomer conversion can be achieved by co-initiators for extra radicals and multiple wavelengths for reduced oxygen-inhibition. Co-initiator B, however, might also reduces the CP radical produced by initiator C. The conversion rate of FRP is proportional to the square root of the light intensity (I), absorption (b) and the initiator initial concentratios (A0, B0, C0), whereas CP is proportional to the linear power of a parameter defined as P=bIA0C0, a stronger dependence than FRP. The CP conversion is governed by a steady-state value proportional to P, and a rate fucntion defining the depletion rate of [A][C], or the increasing rate of the conversion profile. The measured results of Liu et al (Polymers, 2020, 12(6), 1394) are well analyzed and matching the predicted features of our modeling. The specific systems analyzed are: benzophenone derivatives (A) ethyl 4-(dimethylamino)benzoate (B), and (4-tert-butylphenyl)iodonium hexafluorophosphate (C) under a UV (365 nm) LED irradiation; and monomers of trimethylolpropane triacrylate (TMPTA, for FRP) and (3,4- epoxycyclohexane)methyl 3,4-epoxycyclohexylcarboxylate (EPOX, for CP).

To characterize the fecal microbiota of lactating Bactrian camels, this study employed a random selection of camels, assigning them to three groups: the first parity group, the third parity group, and the fifth parity group. The fecal microbial community of lactating camels was assessed using 16S rRNA amplicon sequencing, and the resulting library was sequenced on an Illumina NovaSeq platform. The first parity group was characterized by Verrucomicrobiaceae as a distinctive bi-omarker, whereas Clostridiaceae exhibited a similar role to the third parity group. In terms of functional analysis, the relative abundance of the Steroid biosynthesis pathway was highest in the first parity group, whereas the fifth parity group demonstrated a higher relative abundance of the Tuberculosis pathway compared to the other groups. The first parity group exhibited the greatest presence of seven types of enzymes, whereas the fifth parity group displayed the highest levels of two specific enzymes. Additionally, a positive association was observed between the relative abundance of the Steroid biosynthesis pathway and Verrucomicrobiaceae. Furthermore, the relative abundance of the Tuberculosis pathway displayed positive correlations with 14 enzymes. In conclusion, this study revealed that different parity was associated with distinct fecal microbial ecologies in lactating Bactrian camels.

The feasibility of a hurricane initialization framework based on the GSI-4DEnVar data assimilation system for the HWRF model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-3DEnVar data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the NCEP GFS ensemble Kalman filtering data assimilation system. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, MSLP, and MSW forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

Most astrophysicists today believe that the present universe was produced in a big bang. This article explores the big cycle of matter and energy in the universe, with the big bang as an important part of the cycle.The authors approve of the idea that the universe is gravitationally closed. That is, its average density is higher than the critical value. All the matter and radiation cannot fly out the closed universe.The 3k background radiation is a vast heat ocean in the closed universe, stable and eternal. It is not the afterglow of the thermal radiation of the decoupled fire ball, 380,000 years after the big bang, as many people believed so far. The big bang and the following big expansion drove the matter of more than 2×10¹² galaxies to the remote space. Their potential energies become higher and higher, while their huge kinetic energies that they derived from the big bang become smaller and smaller until drop down to zero. The galaxies will then return back one after another, passing the central region of the universe with tremendously great kinetic energies. They of course cannot all return back directly to their start point of the big bang and restore to the state of the original cosmic core. They will rush to pass the central region and fly to the other sides of the universe, and shuttle around the central part ceaselessly. From the big bang to the following expansion, to the numerous galaxies, to the more than 2×10²³ stars, and to the stars releasing huge nuclear energy due to their internal nuclear fusion each for about 10¹⁰ years, all these enormous processes are irreversible, resulting in enormous increase in entropy. These processes produce large amount of light and other radiations and pour them into the extremely vast heat ocean. These light and other radiations will also be shuttling ceaselessly in the vast heat ocean until, by their interaction with the rare dusts in the galaxies, they mingle into the 3K thermal radiation of the heat ocean. In all the galaxies, there are various black holes. Every black hole, since its birth, ceaselessly extracts the matter and radiation that approach it, and gets larger. In the above mentioned shuttling of all the galaxies, numerous stars and black holes in these galaxies have many chances to approach even meet each other. A black hole can annex other celestial bodies that it meets, including annex other black holes. All these processes carry on and on ceaselessly, resulting in the matter of these celestial bodies to concentrate again and again. The number of all the celestial bodies including the black holes reduces ceaselessly, and the mass of the survived black holes become larger, and their shuttling amplitudes become shorter. The larger black holes and the shorter amplitudes result in the rate of concentration of mass to be faster. Eventually, all the matter in the universe will be concentrated to form a tremendously large central black hole. The central black hole has an astonishingly large Schwarzschild sphere, absorbing the thermal radiation from the vast heat ocean very rapidly and ceaselessly until some threshold value of its internal energy is exceeded, and a new big bang breaks out. The black holes provide a way in the Universe to re-concentrate matter and energy again. Black holes are Maxwell’s demons in the Nature.

Studies on the domestication, genetic differentiation, and crop evolution of foxtail millet are re-viewed. Several genetic studies were carried out to elucidate the genetic relationships among foxtail millet accessions originating mainly from Eurasia, based on intraspecific hybrid pollen semi-sterility, isozymes, DNA markers, and single-nucleotide polymorphisms. Most studies suggest that China is the center of diversity of foxtail millet, and landraces were categorized into geographical groups. Furthermore, the evolution of genes was reviewed (i.e., the Waxy gene conferring amylose content in the endosperm, the Si7PPO gene controlling polyphenol oxidase, the HD1 and SiPRR37 genes controlling heading time, the Sh1 and SvLes1 genes involved in grain shattering, and the C gene controlling leaf sheath pigmentation), and the variation and distribu-tion of these genes suggested complex patterns of evolution under human and/or natural selec-tion.

Over the past few decades, phototherapy, encompassing the utilization of both natural and artificial light, has emerged as a dependable and non-invasive strategy for addressing a diverse range of illnesses, diseases, and infections. This therapeutic approach, primarily known for its efficacy in treating skin infections such as herpes and acne lesions, involves the synergistic use of specific light wavelengths and photosensitizers like methylene blue. Photodynamic therapy, as it is termed, relies on the generation of antimicrobial reactive oxygen species (ROS) through the interaction between light and externally applied photosensitizers. Recent research, however, has highlighted the intrinsic antimicrobial properties of light itself, marking a paradigm shift in focus from exogenous agents to the inherent photosensitivity of molecules found naturally within pathogens. Chemical analyses have identified specific organic molecular structures and systems, including porphyrins and conjugated C=C bonds, as pivotal components in molecular photosensitivity. Given the prevalence of these systems in organic life forms, there is an urgent need to investigate the potential impact of phototherapy on individual molecules expressed within pathogens and discern their contributions to the antimicrobial effects of light. This review delves into the recently unveiled key molecular targets of phototherapy, offering insights into their potential downstream implications and therapeutic applications. By shedding light on these fundamental molecular mechanisms, we aim to advance our understanding of phototherapy's broader therapeutic potential and contribute to the development of innovative treatments for a wide array of microbial infections and diseases.

Background: To evaluate derived neutrophil to lymphocyte ratio (dNLR) in predicting the prognosis of patients with locally advanced oral squamous cell carcinoma (LAOSCC), and the survival benefits from docetaxel, cisplatin, and 5-FU (TPF) induction chemotherapy (IC); Methods: Patients from a phase III trial involving TPF IC in stage III/IVA OSCC patients (NCT01542931) were enrolled. Receiver operating characteristic curves were built and area under the curve was computed to determine dNLR cutoff points. Kaplan-Meier estimates of survival and Cox proportional hazards models were used for longitudinal analysis; (3) Results: 224 patients were identified (median age, 55.4 yr; range, 26 to 75 yr; median follow-up, 90 months; range, 3.2 to 93 months); Cutoff points for dNLR was 1.555. Multivariate analysis showed dNLR was an independent negative predictive factor for survival (overall survival (OS): hazard ratio (HR) = 1.154, 95% confidence interval (CI): 1.018-1.309, P = 0.025, disease-free survival (DFS): HR = 1.123, 95% CI: 0.260-1.000, P = 0.050, local recurrence-free survival (LRFS): HR = 1.134, 95% CI: 1.002-1.283, P = 0.047, distant metastasis-free survival (DMFS): HR = 1.146, 95% CI: 1.010-1.300, P = 0.035). Low dNLR combining cTNM stage III disease predicts benefit from TPF IC for the patients [OS (χ² = 4.674, P = 0.031), DFS (χ² = 7.134, P = 0.008), LRFS (χ² = 5.937, P = 0.015) and DMFS (χ² = 4.832, P = 0.028)]; (4) Conclusions: dNLR is an independent negative predictive factor in LAOSCC patients. Patients of cTNM stage III and low dNLR can benefit from TPF IC.

Due to the demands of personal travels and entertainments, light airplanes and small business aircrafts are developing rapidly. Light airplane structure is simple; however, it lacks crashworthiness design, especially the considerations on the impact of energy absorption. Therefore, in an event of accident, significant damage to passengers will be usually incurred. Airplanes made of composite materials structurally have high specific strength and good aerodynamic configuration. These materials have become the primary choice for new airplane development. This study mainly explores the topology optimization analysis of the light aircraft’s cockpit made of carbon fiber reinforced composites. This paper compares the compression amounts in the original models of composite material and aluminum alloy fuselages with the models after optimization during the crash-landing, in order to investigate the safety of fuselages made of different materials after structural optimization under the dynamic crashing. This study found that the energy absorbed by the aluminum alloy fuselage during crash-landing is still higher than that by the carbon fiber reinforced composites fuselage. On the other hand, the aluminum alloy fuselage after topology optimization could have an energy absorption capability enhanced by 40%, as compared to the that of the original model of aluminum alloy fuselage.

As screen sizes are becoming larger and larger, which are beyond human’s physical limitation for direct touching interaction, remote controls are inevitable. However, as techniques applied so far, inertial gyroscopes are susceptible to position inaccuracies, and gesture recognition is limited by cameras’ focus depth and viewing angle. Grating antenna light trapping technology will be an ideal candidate providing the ghost point problem for multipoint inputs can be solved. Therefore, we propose a differential amplitude modulation scheme for grating antenna based multi beam optical touch, which can recognize different incidence points. Where, the amplitude of the incident beams was coded with different pulse width at first. Then, after been trapped by the grating antenna and been transferred into currents by the lined detector arrays, the incident points of the individual beams were recognized and differentiated. The scheme was successfully verified on an 18-inch screen, where two-point optical touch with position accuracy error under 3mm and response time less than 7ms under modulation frequency of 10kHz on both incident beams are obtained. This work paves a practical way for remote multi-point touch, which can make digital mouse in consistent to the users pointing direction obeying natural three-point one ling aiming rule instantaneously.

Right ventricular remodeling and its function are closely related to the symptom severity and survival of hypoxia pulmonary hypertension, but molecular mechanisms of cardiomyocyte hypertrophy and myocardial injury remain unclear. It evaluated the cardiac function (by right cardiac catheterization to measure right ventricular systolic pressure and mean pulmonary artery pressure), myocardial tissue morphology（Haematoxylin and Eosin）, myocardial hypertrophy (Wheat Germ Agglutinin Staining), then RNA sequencing was applied to explore the mechanism, which results were verified by western blot in final. Hypoxia developed pulmonary hypertension, right ventricular diastolic and systolic functions were enhanced in rats, such as the mean pulmonary artery pressure, systolic pressure of pulmonary artery, the max pressure of right ventricular, the mean right ventricular pressure, and the heart rate was significantly higher than that in control. We found that Hif-1 signaling pathway and p38-MAPK signaling pathway have been activated by hypoxia, and cardiomyocyte apoptosis also aggravated in the right ventricular, which might be one cause of cardiomyocyte hypertrophy and myocardial injury, and targeted intervention might be one potential prevention for cardiomyocyte hypertrophy and myocardial injury induced by hypoxia.

Kidney disease is a significant health problem worldwide, affecting an estimated 10% of the global population. Kidney disease encompasses a diverse group of disorders that vary in their underlying pathophysiology, clinical presentation, and outcomes. These disorders include acute kidney injury (AKI), chronic kidney disease (CKD), glomerulonephritis, nephrotic syndrome, polycystic kidney disease, diabetic kidney disease, and many others. Despite their distinct etiologies, these disorders share a common feature of immune system dysregulation and metabolic disturbances. The immune system and metabolic pathways are intimately connected and interact to modulate the pathogenesis of kidney diseases. The dysregulation of immune responses in kidney diseases includes a complex interplay between various immune cell types, including resident and infiltrating immune cells, cytokines, chemokines, and complement factors. These immune factors can trigger and perpetuate kidney inflammation, causing renal tissue injury and progressive fibrosis. In addition, metabolic pathways play critical roles in the pathogenesis of kidney diseases, including glucose and lipid metabolism, oxidative stress, mitochondrial dysfunction, and altered nutrient sensing. Dysregulation of these metabolic pathways contributes to the progression of kidney disease by inducing renal tubular injury, apoptosis, and fibrosis. Recent studies have provided insights into the intricate interplay between immune and metabolic pathways in kidney diseases, revealing novel therapeutic targets for the prevention and treatment of kidney diseases. Potential therapeutic strategies include modulating immune responses through targeting key immune factors or inhibiting pro-inflammatory signaling pathways, improving mitochondrial function, and targeting nutrient-sensing pathways such as mTOR, AMPK, and SIRT1. This review highlights the importance of the interplay between immune and metabolic pathways in kidney diseases and the potential therapeutic implications of targeting these pathways.

As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.

[Introduction]: In the contemporary international financial environment, marked by intricate complexities and increasing uncertainties, the impact of exchange rate fluctuations on national economies has become increasingly significant. In this context, this study uses the Chinese economy as a case study to delve deeply into the interaction effects between macroprudential and monetary policies, analyzing how these policies jointly address economic instability. The aim is to offer new perspectives and strategies for stabilizing the global economic and financial landscape. [Methods]: This paper constructs an open-economy Dynamic Stochastic General Equilibrium (DSGE) model specifically tailored for China, incorporating Bayesian estimation and numerical simulation techniques. The study meticulously examines how macroprudential and monetary policies can be coordinated to achieve economic stability in response to exchange rate shocks. [Results]: The research finds that (1) targeted coordination of macroprudential and monetary policies towards asset prices and price levels can maintain stability under exchange rate shocks; (2) the effective implementation of such coordinated policies not only mitigates the fluctuations in asset prices due to exchange rate shocks but also enhances the level of societal welfare; (3) in terms of choosing an exchange rate regime, a managed floating rate system can prevent policy overlaps and conflicts that might occur in a floating rate system during the coordination of macroprudential and monetary policies, thus offering comparative advantages. [Discussion]: The findings underscore the importance of policy integration in a dynamic and complex global economic environment. They provide policy recommendations for China and other emerging market countries on how to effectively maintain economic stability through the synergistic interplay of macroprudential and monetary policies amid fluctuations in exchange rates and asset prices.

In Tropical Monsoon climates, precipitation exceeds evapotranspiration, leading to increased surface runoff, especially in floodplain regions. The Bhagirathi-Hooghly River in India is the focus of the study, which analyses hydrology, sediment yield, and water balance in this floodplain area. This investigation employs the Soil and Water Assessment Tool (SWAT) model within the ArcGIS environment, using remote sensing data. The study area is divided into 4 sub-basins and 40 Hydrological Response Units (HRUs), with a focus on Land Use/Land Cover (LULC) parameters, soil attributes, and slope characteristics. Calibration (1998-2010) and validation (2011-2018) of river discharge and sediment yield are performed using the SUFI-2 algorithm within SWAT-CUP. The calibration process shows a strong correlation between observed and simulated data points, indicating the model's accuracy. The study reveals a pattern of early-year evapotranspiration followed by increasing rainfall during the monsoon season, leading to heightened surface runoff and sediment yield. Excessive rainfall, therefore, emerges as a pivotal driver of flood incidences within the floodplain. The insights derived from the SWAT model's floodplain data have the potential to inform flood control and sedimentation mitigation strategies. The assimilation of this data can empower local stakeholders and contribute to sustainable livelihoods and improved living conditions, offering guidance for future governmental policies.

Fish feeding behavior analysis plays a critical role in aquaculture, marine fisheries, and ecological monitoring. At present, the accuracy of current fish feeding behavior analysis is easily affected by the environment, and the hypoxia state in water and high temperature have a huge impact on behavior. In addition, the poor stability, and the speed is affected by the calculation amount of the model, so it cannot be applied to aquaculture ponds. In order to solve the above problems, we propose an improved RepVGG-based method for fish feeding behavior analysis to identify nine behavioral states of fish populations, including hypoxia, hot, and normal states of none, medium, and strong feeding. Firstly, our method adds the identity and residual branches to the block of the VGG network, focusing on the acceleration operation to ensure accuracy. At the same time, the Efficient Channel Attention (ECA) module is added to reduce dimensionality to balance speed and accuracy. To evaluate the effectiveness of the method, it is validated on a constructed fish feeding behavior dataset and compared with a Convolutional Neural Network (CNN) including AlexNet, VGG16, ResNet34, MobileNet V3, and RepVGG. The experimental results show that after 15 epochs of training, the average precision of proposed method reached 97%. Compared with the basic classification algorithm, our method can increase by at least 3% while the inference speed exceeds 85 Frames Per Second (FPS). This study can be integrated into the aquaculture vision system to guide users to plan feeding strategies, and provide new ideas and methods for water quality monitoring.

Keywords: underground engineering; numerical simulation; excavation length effect, major principal stress; displacement; damage initiation; CPU time

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

Weak signal spectral detection is a serious problem in many applications, especially the target possesses certain frequency band and under low signal-to-noise ratio (SNR). A kind of novel technique based on pre-whitening scale-transformation symmetric bistable parallel stochastic resonance (SR) is proposed to solve the problem. Firstly, the pre-whitening can ensure the even distribution of the receiving signal to fit the requirement of SR processing. Secondly, the scale- transformation can help to utilize the weak signal improvement property especially under low frequency band more effectively. Thirdly, the symmetric bistable parallel SR can try to reduce the variance of the clutter and additive noise fluently. Fourthly, by subtracting the steady state re-sponse of the selected symmetric bistable SR system from the parallel SR output, the weak signal spectral detection objective can be realized successfully. Simulation results based on the real sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach.

In a vacuum tube two identical and parallel Ag-O-Cs emitters A and B (work function 0.8eV) ceaselessly emit thermal electrons at room temperature. The thermal electrons are controlled by a static uniform magnetic field so that the number of electrons migrate from A to B exceeds the one from B to A (or vice versa). The net migration of thermal electrons from A to B quickly results in a charge distribution of A charged positively and B negatively, and a potential difference between A and B emerges, enabling a continuous output current and a stable power to an external load (e.g., a resistor). Thus, the tube cools down (slightly). The (slightly) cooled tube extracts heat from ambient air, and all the heat is converted into electric energy without other effect. We believe the experiment is in contradiction to the Kelvin statement of the second law.

The year 2021 marks the 70th anniversary of establishing the Tibet Autonomous Region (TAR) within the People’s Republic of China. As with China as a whole, Tibet has experienced a tumultuous history over these past seventy years. Especially since the 1980s, the region has witnessed significant economic development, poverty alleviation, and increased social indicators such as life expectancy and healthcare. At the same time, substantial disparity within Tibet, as well as between Tibet and the rest of China, persists. This article tracks 16 social, economic, and ecological indicators for the past several decades, as well as levels of economic assistance provided to Tibet by other Chinese provinces and the Chinese central government in an attempt to narrow the development gap between Tibet and the rest of the country. The results show that since 1951, despite progress in many aspects, there remains a substantial gap between Tibet’s indicators and the average level of China, with the biggest gap being the illiteracy rate. Uneven development between regions within Tibet, especially between urban and rural areas, also persists.

Periacetabular osteotomy (PAO) is an effective approach for surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip and limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 which is a state-of-the-art (SOTA) optical see-through head-mounted display (OST-HMD). For AR guidance, we propose an optical marker-based virtual-physical registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS, such that virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns the bone saw with osteotomy planes planned in preoperative images, and then provides surgeons with not only robot assistance but also AR guidance. Comprehensive experiments were conducted to evaluate both virtual-physical registration accuracy and osteotomy accuracy of the developed navigation system. The proposed virtual-physical registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The developed system achieved an average distance deviation of 0.82 ± 0.17 mm and an average angular deviation of 3.77 ± 0.85 . Experimental results demonstrated high virtual-physical registration accuracy. The sub-millimeter distance deviation demonstrated the osteotomy accuracy of the proposed system.

This study investigates the influence of expectations and trust on farmers' decisions regarding contractual choices in land transfer. The dataset used for analysis consists of 1101 households in Guizhou Province. The research focuses on two aspects: the selection between written and oral contracts, and the decision to continue the contractual relationship. The findings reveal that farmers' choices are significantly affected by their expectations and levels of trust.Specifically, when farmers' expectations regarding the land transfer process are not met, they tend to prefer written contracts over oral ones. This preference can be attributed to the greater clarity and risk reduction offered by written contracts. On the other hand, higher levels of trust increase the likelihood of farmers continuing the land transfer contract, as trust fosters a sense of confidence and security in the ongoing relationship.Interestingly, trust also plays a moderating role. Farmers who have aligned expectations and high levels of trust are more inclined to opt for oral contracts. This preference for oral contracts can be attributed to their flexibility and emphasis on interpersonal relationships, allowing for better adaptation to the changing dynamics of land transfer.These findings contribute to a deeper understanding of the factors influencing farmers' decision-making processes regarding contractual choices in land transfer. They provide valuable insights for policymakers involved in land transfer, enabling them to optimize policies and facilitate farmers' decision-making processes.

Accurately estimating the pose of spacecraft is indispensable for space applications. However, such targets are generally non-cooperative, i.e., no markers are mounted on it and no parts for operation. Therefore, the detection and measurement of a non-cooperative target is very challenging. Stereovision sensors are important solutions in the near field. In this paper, a rectangular natural feature recognition and pose measurement method for non-cooperative spacecraft is proposed. Solar panels or the bodies of spacecraft are selected as detection objects and their image features are captured by stereo vision. These rectangle features are then reconstructed in 3D Cartesian space through parallelogram fitting on the image planes of two cameras. The vertexes are used to calculate the center position of the rectangle and the direction of the fixed coordinate system to solve the pose and attitude of the non-cooperative target. An experimental system is built to validate the effectiveness of the algorithm. The experimental results show that the average position measurement error of the algorithm is about 10mm and the average attitude measurement error is less than 1°. The results show that the proposed method has high accuracy and efficiency.

The 2019 novel coronavirus (2019-nCoV) causes novel coronavirus pneumonia (NCP). Given that approved drug repurposing becomes a common strategy to quickly find antiviral treatments, a collection of FDA-approved drugs can be powerful resources for new anti-NCP indication discoveries. In addition to synthetic compounds, Chinese Patent Drugs (CPD), also play a key role in the treatment of virus related infections diseases in China. Here we compiled major components from 38 CPDs that are commonly used in the respiratory diseases and docked them against two drug targets, ACE2 receptor and viral main protease. According to our docking screening, 10 antiviral components, including hesperidin, saikosaponin A, rutin, corosolic acid, verbascoside, baicalin, glycyrrhizin, mulberroside A, cynaroside, and bilirubin, can directly bind to both host cell target ACE2 receptor and viral target main protease. In combination of the docking results, the natural abundance of the substances, and botanical knowledge, we proposed that artemisinin, rutin, glycyrrhizin, cholic acid, hyodeoxycholic acid, puerarin, oleanic acid, andrographolide, matrine, codeine, morphine, chlorogenic acid, and baicalin (or Yinhuang Injection containing chlorogenic acid and baicalin) might be of value for clinical trials during a 2019-nCov outbreak.

Cost-efficient catalyst for hydrogen evolution reaction (HER) and methanol oxidization reaction (MOR) is of great importance for industrial application. The alloying of platinum with cheap transition metal offers a great opportunity to reduce cost and enhance the electro-catalytic performance. Herein, we report a simple and green route to synthesize porous Pt-Cu catalysts, where Pt-Cu solid particles were first produced by using cuprous chloride (CuCl) nanoparticles as the template, followed by a dealloying treatment to obtain final porous product. The porous alloy nanoparticles show higher catalytic activity and superior stability for HER and methanol oxidation reaction, which is beneficial from the porous structure and synergetic effect between Pt and Cu.

Fresh water is valuable nonrenewable resource and plays an important role of maintaining economic and social development. Condisering its large population and consumption potential, water resources deficit will certainly impede basic industries sustainable development of China in the near future. Application of sewage irrigation, to some extent, was regarded as an alternative way to solve the problem of agricultural irrigation water shortage in some areas (such as North China). However, accompanied with extensive implementation of sewage irrigation, some problems on sewage irrigation in agriculture are gradually obvious, especially serious pollution and destruction for farmland. In this paper, the effects of sewage irrigation on soil physical (soil bulk density, soil resistance to penetration and field capacity), chemical (pH, soil organic matter, nitrogen, phosphrous, patassium, heavy metal and organic pollutants) and biological characteristics (soil microorganism and enzyme activities) of farmland in China were systematically reviewed on the base of the current utilization status of China’s farmland sewage irrigation and some feasible suggestions were put forward to the development prospect for the future. This review will be beneficial for promoting healthy development of sewage irrigation and providing theoretical support for reclamation and high efficiency of effluents in China.

At present paper, we aim to study the long-time behavior of a stochastic semi-linear degenerate parabolic equation on bounded or unbounded domain and driven by a nonlinear noise and defined. Since the theory of pathwise random dynamical systems can not be applied directly to the equation with nonlinear noise, first, we establish the existence of weak pullback mean random attractors for the equation by applying the theory of mean-square random dynamical systems; then, we prove the existence of (pathwise) pullback random attractors for the Wong-Zakai approximate system of the equation. In addition, we establish the upper semicontinuity of pullback random attractors for the Wong-Zakai approximate system of the equation under consideration driven by a linear multiplicative noise.

The main aim of sustainable development is to ensure an intelligible and long-lasting balance between the economy, society, and the environment. Sustainable tourism could only be successful if the inter-relationships between all three dimensions are accepted. In the limited number of research analyses, the focus of the research is on competition between tourist countries and destinations. This study has used Game theory to analyze the competition applies time-series data in selected neighboring countries measure of a VAR-based spillover index, developed by [1] to investigate the time-varying relationship between tourism and Gross Domestic Product. Each country analyzed Vector Error Correction (VEC) and Granger analysis to explore the causal short and long-term tourism and use a sample that spans from 1997 to 2019. From the main results of Cholesky, the total spillover index is 59.0% between Russia and Mongolia which suggests a moderate interdependence among the four variables. Findings indicate that neither China nor Mongolia have a short-run influence on tourism development. China's inbound tourism is affected in the long run by Mongolia's inbound tourism but not vice versa can be explained by the fact that the number of tourists visiting Mongolia would include China in their travels.

A double Roman dominating function on a graph G is a function f : V(G) → {0, 1, 2, 3} 2 with the condition that every vertex u for which f(u) = 0 is adjacent to at least one vertex v for 3 which f(v) = 3 or two vertices v1 and v2 for which f(v1) = f(v2) = 2, and every vertex u for which 4 f(u) = 1 is adjacent to at least one vertex v for which f(v) ≥ 2. The weight of a double Roman dominating function f is the value w(f) = ∑u∈V(G) 5 f(u). The minimum weight over all double 6 Roman dominating functions on a graph G is called the double Roman domination number γdR(G) 7 of G. In this paper we determine the exact value of the double Roman domination number of the 8 generalized Petersen graphs P(n, 2) by using a discharging approach.

A spinning gyroscope precesses about the vertical due to a torque acting upon the wheel. The torque is generated by the shift of moment of force by gravity and it points to the vertical instead of the tangential direction of precession. This intuition offers an alternative and straightforward view of precession dynamics in comparison with the literature. It also presumes a dynamic balance of momentum between circular motions of the wheel spin and precession. Accordingly, the gyroscopic dynamics is then applied to the study of galactic motion of the solar system in space and the Galactic mass is calculated with the inclusion of gyroscopic effect of the solar planets. Results indicate that the gyroscopic effect of Mercury orbiting around the Sun can increase the calculated Galactic mass by 23% in comparison with the result obtained by the classic approach.

Our previous retrospective observational study demonstrated the safety of laparoscopically assisted subtotal colectomy with ileorectal anastomosis and preservation of the superior rectal artery (SRA), without instances of leakage, in patients with slow-transit constipation (STC). Thus, we extended the enrollment period and enlarged the sample size to detect the differences in the post-operative complications, and surgical and functional outcomes between patients who underwent laparoscopically assisted subtotal colectomy with and without SRA preservation. We conducted a retrospective single-center analysis of patients with STC who underwent laparoscopically assisted subtotal colectomy between 2016 and 2020. The diagnosis of STC was based on the colonic transit and anal functional tests, and barium enema to exclude secondary causes. Patients were divided into group A that underwent surgery with SRA preservation, and group B that underwent ligation of the SRA during surgery. Outcome assessments for both groups included the incidence of anastomotic breakdown, intraoperative complications, length of hospital stay, estimated blood loss, time to first flatus, and complications. Propensity-score matching allocated 34 patients to groups A and B each. Postoperative bowel function, including time to first flatus, stool, and oral intake, recovered better in group A than that in group B. Anastomotic leakage, a significant postoperative complication, was less frequent in patients with SRA preservation. Preservation of the SRA in patients undergoing laparoscopically assisted subtotal colectomy with ileorectal anastomosis for STC is associated with favorable postoperative bowel function recovery and lower anastomotic leakage rates.

Mesoporous silica nanoparticles (MSNs) can be used as carrier materials for the controlled release of pesticides while reducing their negative environmental impact. In this study, we screened an active ingredient, p-cymene (PC), with an excellent inhibitory effect on rice bacterial blight. Subsequently, the PC was successfully loaded onto MSNs via physisorption(PC@MSNs). PC@MSNs, characterized by a regular spherical shape, smooth surface, and an MSN average size of 262.9 nm, achieved an 8.6 % drug loading capacity. The release kinetics of the PC from the PC@MSNs demonstrated a sustained release (288 h) pattern influenced by drug diffusion. The efficacy of the PC@MSNs against Xanthomonas oryzae pv. Oryzae paralleled those of PC. Acute toxicity assays re-vealed that the PC@MSNs were less toxic to aquatic life (LC50=257.867 mg/L) and that the formu-lation showed no adverse effects on rice seedling growth. In summary, these results suggest that PC@MSNs can broaden PC's application scope in managing rice diseases.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

Three new compounds (1, 11 and 12), together with 32 known ones, were isolated from the root bark of Morus alba L. using various chromatographic methods. The structures of the undescribed compounds were elucidated based on 1D, 2D NMR and HRESIMS data analysis, while the known ones were identified by comparison of their spectroscopic data with those reported in the literature. All the isolates were evaluated for their cytotoxic activities against human gastric cancer HGC27 cells by CCK-8 assay. Among them, compounds 5, 8, 10 and 30 exhibited cytotoxic activities on HGC27 cells with IC50 values of 10.24 ± 0.89 μM, 28.94 ± 0.72 μM, 6.08 ± 0.34 μM and 33.76 ± 2.64 μM, respectively. Furthermore, compound 10 was confirmed to reduce proliferation ability, increase apoptosis rate and inhibit cell migration pathway by annexin V/PI double staining experiment, transwell experiment and western blot analysis.

A self-consistent charge density-functional tight-binding method combined with molecular dynamics simulations is employed to reveal the effect of polymorph on the thermal decomposition stability of 1,1-Diamino-2,2-Dinitroethylene (FOX-7). Two types of heating, constant temperature heating and temperature-programmed heating, are adopted. Potential evolution indicates that γ-FOX-7 possesses the lowest thermal stability, as it is closer to the decomposition state. Crystal form has an important influence on the thermal decomposition of FOX-7, resulting in different decomposition rates and initial reactions. In general, β- and γ-FOX-7 always decompose more completely than α-FOX-7. This work emphases the importance of polymorph dependent initial decay of an energetic polymorphic compound once heated in a volume constrained condition.

Software fault prediction is the very consequent research topic for software quality assurance. Data driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of dataset. Many software datasets suffers from the problem of class imbalance. In this regard, under-sampling is a popular data pre-processing method in dealing with class imbalance problem, Easy Ensemble (EE) present a robust approach to achieve a high classification rate and address the biasness towards majority class samples. However, imbalance class is not the only issue that harms performance of classifiers. Some noisy examples and irrelevant features may additionally reduce the rate of predictive accuracy of the classifier. In this paper, we proposed two-stage data pre-processing which incorporates feature selection and a new Rough set Easy Ensemble scheme. In feature selection stage, we eliminate the irrelevant features by feature ranking algorithm. In the second stage of a new Rough set Easy Ensemble by incorporating Rough K nearest neighbor rule filter (RK) afore executing Easy Ensemble (EE), named RKEE for short. RK can remove noisy examples from both minority and majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse dataset, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach. Such as, the impact of Rough set theory on noise-filter, the relationship between model performance and imbalance ratio etc. comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve (AUC).

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF tracking loops. Firstly, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Secondly, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other receivers in complex dynamic environments.

The purpose of this work was to achieve non-destructive detection of the internal defects of in-shell walnuts using X-ray radiography technology based on improved Faster R-CNN network model. First, the FPN structure was added to the feature extraction layer to extract richer image information. Then ROI Align was used to instead of ROI Pooling for eliminating the localization bias problem caused by quantization operation. Finally, the Softer-NMS module was introduced to the final regression layer with the predicted bounding box for improving the localization accuracy of the candidate boxes. The results indicated that the internal defects in intact walnuts with shells could be identified effectively using the proposed improved network model in this study. Specifically, the discrimination accuracy of the in-shell sound, shriveled and empty-shell walnuts were 96.14%, 91.72% and 94.80% respectively, and the highest overall accuracy can reach 94.22 %. Contrasted with original Faster R-CNN network model, the mAP and F1-value of the improved Faster R-CNN model increased by 5.86% and 5.65%, respectively. Consequently, the proposed method can be applied for the in-shell walnuts with shriveled and empty-shell defects.

The integration of metal nanoparticles and solid carriers can achieve ideal stability, high load and good conductivity. In this work, copper nanoparticles (Cu NPs) were sequentially deposited on a cobalt metal-organic framework (Co-MOF) by bonding with exposed imino groups, followed by a reduction reaction to prepare a new Cu@Co-MOF composite. Cu@Co-MOF acts as a non-enzymatic electrochemical sensor to detect glucose (Glu) in an alkaline medium. The composite working electrode of Cu@Co-MOF/GCE (GCE = glassy carbon electrode) improves the electrocatalytic activity for Glu oxidation. Cu@Co-MOF/GCE shows excellent electrocatalytic performances in Glu concentration ranging 0.005~1.8 mmol∙L⁻¹ (mM): the sensitivities are 282.89 μA∙mM⁻¹∙cm⁻² in 0.005-0.4 mM Glu and 113.15 μA∙mM⁻¹∙cm⁻² in 0.4-1.8 mM Glu respectively with low detection limit of 1.6 μM (S/N = 3) and high selectivity and stability.

The recent outbreak of coronavirus disease 2019 caused by the new coronavirus, SARS-CoV-2, has become an international emergency. Since there is no effective therapy for the treatment of this disease, drugs or vaccine that can prevent or cure the SARS-CoV-2 infection are urgently needed. The viral 3-chymotrypsin-like cysteine protease (3CLpro), which plays a key role in the replication of coronavirus, is a potential drug target for the development of anti-SARS-CoV-2 drugs. With the crystal structure of 3CLpro, we performed virtual screening from a small chemical library of a Traditional Chinese Medicine recipe- FuFang Zhenzhu Tiaozhi (FTZ). Five compounds with the best scores were screened and could be considered as potential hit compounds to be investigated further with bioassays for their anti-virus effects.

Translation is spatiotemporally regulated and ER-associated mRNAs are generally in efficient translation. It is unclear whether the ER-associated mRNAs are deadenylated or degraded on the ER surface in situ or in the cytosol. Here, we showed that ER possessed active deadenylases, particularly the poly(A)-specific ribonuclease (PARN), in common cell lines and mouse tissues. Consistently, purified recombinant PARN exhibited a strong ability to insert into the Langmuir monolayer and liposome. ER-anchored PARN was found to be able to reshape the poly(A) length profile of the ER-associated RNAs by suppressing long poly(A) tails without significantly influencing the cytosolic RNAs. The shortening of long poly(A) tails did not affect global translation efficiency, suggesting that the non-specific action of PARN towards long poly(A) tails was beyond the scope of translation regulation on the ER surface. Transcriptome sequencing analysis indicated that the ER-anchored PARN trigged the degradation of a small subset of ER-enriched transcripts. The ER-anchored PARN modulated the translation of its targets by redistributing ribosomes to heavy polysomes, suggesting that PARN may play a role in dynamic ribosome reallocation. During DNA damage response, MK2 phosphorylated PARN-Ser557 to modulate PARN translocation from the ER to cytosol. By promoting the decay of ER-associated MDM2 transcripts with low ribosome occupancy, the ER-anchored PARN modulated DNA damage response and thereby cell viability. These findings revealed that a highly regulated communication between mRNA degradation rate and translation efficiency is present on the ER surface in situ and that PARN may contribute to this communication by modulating the dynamic ribosome reallocation between transcripts with low and high ribosome occupancies.

To study the influence of Latin dance and Tai Chi on knee joint proprioception and balance. Method: All experiments were performed in the Beijing Normal University School of Physical Education and Sport Biomechanics Laboratory. An isokinetic test system (Biodex system 4, BS4) and a balance testing system (Biodex Balance System, BBS)from the United States Biodex medical system were used to test related indexes. Results: In the LOS test, the overall score of Latin group is much better than sedentary group. In the ASL test, Latin group have a better balance ability in anterior and posterior direction and overall score than sedentary group(p＜0.05); TAI CHI group have a better balance ability than sedentary group in anterior and posterior direction(p＜0.05)and right and left direction (p＜0.01). When the knee joint was at 15, 30, and 45degree positions, the Tai Chi exercise group was much better than sedentary group (P < 0.05), and the 45 degree position in the Tai Chi group was much better than that in the Latin dance group (P < 0.01).When the knee joint was at the 30 degree position, the quadriceps force sense was significantly better in the Tai Chi group than in the Latin dance and sedentary groups. In a test of the four angles, the hamstring force sense in the Tai Chi exercise group was obviously better than that in the sedentary group (P < 0.05); at the 15 degree position, the Tai Chi group was significantly better than the Latin group (P < 0.05); and at 30 degree position, the hamstring force sense of the Latin group was obviously better than that of the sedentary group (P < 0.05).

The fluorinated organic compounds have superior physicochemical properties than general organic compounds due to the strong C-F single bond, and widely used in medicine, biology, pesticides, and materials science. In order to gain a deeper understanding of the physicochemical properties of fluorinated organic compounds, fluorinated aromatic compounds have been investigated by various spectroscopic techniques. 2-Fluorobenzonitrile and 3-fluorobenzonitrile are important fine chemical intermediates, and their excited state S1 and cationic ground state D0 vibrational features remain unknown. In this paper, we used two-color resonance two photon ionization (2-color REMPI) and mass analyzed threshold ionization (MATI) spectroscopy to study S1 and D0 states vibrational features of 2-fluorobenzonitrile and 3-fluorobenzonitrile. The precise excitation energy (band origin) and adiabatic ionization energy were determined to be 36028 2 cm-1 and 78650 5 cm-1 for 2-fluorobenzonitrile; and 35989 2 cm-1 and 78873 5 cm-1 for 3-fluorobenzonitrile, respectively. The density functional theory (DFT) at the levels of RB3LYP/aug-cc-pvtz, TD-B3LYP/aug-cc-pvtz, and UB3LYP/aug-cc-pvtz were used to calculate the stable structures and vibrational frequencies for the ground states S0, excited state S1, and cationic ground state D0, respectively. Franck-Condon spectral simulations for transitions of S1 S0 and D0 S1 were performed based on above DFT calculations. The theoretical and experimental results are in good agreement. The observed vibrational features in S1 and D0 states are assigned according to the simulated spectra and the comparison with structurally similar molecules. Several experimental findings and molecular features were discussed in detail.

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed by attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.

The composite post insulator composed of a large diameter mandrel and flange pressed by one-time integral pultrusion has high strength and good toughness, widely used in the existing flexible converter valve. For constructing the finite element model of the crimped post insulator, most of the flange surface and the mandrel surface are directly connected, making the simulation model's stiffness deviate from the actual model and the model distortion. Based on this, this study proposes a new finite element simplified model of crimped composite post insulator based on Multi-Point Constraints (MPC), which accurately simulates the crimping area of man-drel and flange in engineering practice. It verifies the model's reliability through static tests and characteristic vibration tests. The results show that the difference between the finite element simulation and test results is within 5 %. The finite element modeling method based on MPC local constraints is very reliable in applying crimped composite post insulators. Based on the proposed model, the influence of flange height and thickness on the mechanical properties of composite post-insulators is further explored. The results show that the influence of flange height on mechanical properties can be ignored when the height of the insulator stiff-ener ( non-press common area ) is constant. Within the allowable thickness range of the flange crimping process, the increase in flange thickness will significantly reduce the maximum bend-ing stress of the insulator and increase its safety margin. It provides a good design idea for the design of crimping composite insulators.

The prevalence of antiviral drugs (AVTs) has seen a substantial increase in response to the COVID-19 pandemic, leading to heightened concentrations of these pharmaceuticals in wastewater systems. The hydrophilic nature of AVTs has been identified as a significant factor contributing to the low degradation efficiency observed in wastewater treatment plants. This characteristic often necessitates the implementation of additional treatment steps to achieve complete degradation of AVTs. Semiconductor-based photocatalysis has garnered considerable attention due to its promising potential in achieving efficient degradation rates and subsequent mineralization of pollutants, leveraging the inexhaustible energy of sunlight. However, in recent years, there have been few comprehensive reports that have thoroughly summarized and analyzed the application of photocatalysis for the removal of AVTs. This review commences by summarizing the types and occurrence of AVTs. Furthermore, it places a significant emphasis on delivering a comprehensive summary and analysis of the characteristics pertaining to the photocatalytic elimination of AVTs. Ultimately, the review sheds light on the identified research gaps and key concerns, offering invaluable insights to steer future investigations in this field.

Aim: Sca-1+CD31− cells were shown to be endothelial stem/progenitor cells, found in many mammalian tissues, including heart, were able to differentiate into cardiomyocytes in vitro and in vivo. Our previous work indicated that heart derived Sca-1+CD31− cells increased Nr1d1 mRNA level and decreased cells plasticity with aging. However, little is known about how NR1D1 affects Sca-1+CD31− cells plasticity. Methods: Lentiviral vector was used to stably overexpress Nr1d1 in young Sca-1+CD31− cells and to knockdown Nr1d1 in aged Sca-1+CD31− cells. Cell differentiation, proliferation, apoptosis, and cell cycle were evaluated. Results and Conclusions: The overexpression of Nr1d1 in young Sca-1+CD31− cells inhibited cell proliferation and promoted apoptosis. Knockdown of Nr1d1 in aged Sca-1+CD31− cells promoted cell proliferation and inhib-ited apoptosis. Using mouse cardiac myocytes cell line, confirmed the effect of Nr1d1 and indi-cated that Nr1d1 induce Serpina3 expression via Nr1d1 interaction with Nr4a3. Nr1d1 may there-fore be identified as a potent anti-aging receptor and be a therapeutic target for aging relative diseases.

The testing requirements of the active phased array antennas are very different from those of traditional passive antennas, due to its beam steering capability. Usually, each beam profile of the active phased array needs a separate radiation pattern test, which makes the overall testing time extremely long. Thus the traditional antenna test method can no longer meet the efficiency and cost requirements of new active phased array antennas test. In this paper, a fast test method tailored for phased array antennas is proposed that offers significantly reduced testing time at the expense of slight sacrifice of the accuracy. Using the simulated element pattern in array and ideal port excitation, the beam profile in any direction can be predicted by testing only a certain beam profile. Through theoretical derivation and experiments, the effectiveness of the method is verified, and the testing efficiency of the phased array antenna is demonstrated to be improved by ten times or even more.

In recent 40 years, the quantity and spatial patterns of farmland in Western Jilin have changed dramatically, which had great impact on soybean production potential. This study used one of the most advanced crop production potential models, the Global Agro-cological Zones model, to calculate the soybean production potential in Western Jilin based on meteorological, terrain, soil and land use data, and analyzed impact of farmland change on soybean production potential during 1975-2013. The main conclusions were the following. First, the total soybean production potential in Western Jilin in 2013 was 89.22 thousand tons. The production potential of eastern area was higher than the other areas of Western Jilin. Second, farmland change led to a growth of 33.03 thousand tons in soybean production potential between 1975 and 2000, and a decrease of 10.30 thousand tons between 2000 and 2013. Third, taking account of two situations of farmland change, the conversion between dryland and other categories, and the change of irrigation percentage led to the total soybean production potential in Western Jilin increased by 23.13 and only 2.87 thousand tons respectively between 1975 and 2000, and increased by 1.13 and 2.81 thousand tons respectively between 2000 and 2013. In general, the increase of soybean potential production was mainly due to grassland and woodland reclamation. The results of this study would be a good reference for protecting safe baseline of farmland, managing land resources, and ensuring continuity and stability of soybean supply and food security.

As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method’s effectiveness in practice.

Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Though many CNN based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, we propose a new network structure R-PReNet for single image deraining with good background structure maintaining. This framework fully utilizes the cyclic recursive structure of PReNet. Moreover, we introduce residual channel prior (RCP) and feature fusion modules for better deraining performance by focusing on background feature information. Compared with the previous methods, our method has significantly improvement effect on the rainstorm image with the artifacts removing and good visual detail restoring.

Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little research that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green index and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

Vanadium titano-magnetite is a significant resource in China, and in this study, we characterize its isothermal reduction mechanisms in the mixture of H₂, CO, and N₂ where the variables considered here include reduction time, reduction temperature, gas composition, and pellet size. The kinetics of the reduction process are mainly studied, which follows a shrinking core model. The results indicate that the reduction degree of oxidized VTM pellets increases with the increase of reduction time, reduction temperature but decreases with the increase of pellet size. Moreover, we found that an increase of H₂/(H₂+CO) ratio induces an increase of the reduction degree. Then the transformation of main Ti-bearing mineral phases is discussed, and the most probable reaction mechanism is revealed. In the whole reduction process, the kinetic results confirm the existence of an early stage and a latter stage, which are controlled by interface chemical reaction and diffusion, respectively. Furthermore, the results show that the diffusion-control step can be observably shortened with the decrease of pellet size because a thinner product layer is formed during the reduction process. Our study thus provides a valuable technical basis on the VTM industrial application.

Shale gas content is the key parameter for shale gas potential evaluation and favorable area prediction. Therefore, it is very important to determine shale gas content accurately. Generally, we use the USBM method for coal reservoirs to calculate gas content of shale reservoirs. However, shale reservoirs are different from coal reservoirs in depth, pressure, core collection, etc. This method would inevitably cause problems. In order to make the USBM method more suitable for shale reservoir, an improved USBM method is put forward on the basis of systematic analysis of core pressure history and temperature history during shale gas desorption. The improved USBM method modifies the calculation method of the lost time, and determines the temperature balance time of water heating. In addition, we give the calculation method of adsorption gas content and free gas content, especially the new method of calculating the oil dissolved gas content and water dissolved gas content which are easily neglected. We used the direct method (USBM and the improved USBM) and the indirect method (adsorption gas, free gas and dissolved gas) to calculate the shale gas content of 16 shale samples of the Triassic Yanchang Formation in the Southeastern Ordos Basin, China. The results of the improved USBM method show that the total shale gas content is high, with an average of 3.97 m³/t, and the lost shale gas content is the largest proportion with an average of 62%. The total shale gas content calculated by the improved USBM method is greater than that of the USBM method. The results of the indirect method show that the total shale gas content is large, with an average of 4.11 m³/t, and the adsorption shale gas content is the largest proportion with an average of 71%. The oil dissolved shale gas content which should be taken attention accounts for about 7.8%. The relative error between the improved USBM method and indirect method is much smaller than that between USBM method and indirect method, which verifies the accuracy of the improved USBM method.

Inflammatory reaction after ischemia-reperfusion contributes significantly to prognosis, and microglia activation is the main resource of inflammation in nervous system. STAT5 is proving to be a highly effective anti-inflammatory therapy with great potential, and inhibition of STAT5 has demonstrated significant anti-inflammation and therapeutic effects, but rarely focus on mechanism of neuroinflammation and brain injury from ischemia-reperfusion. It is the first time to found that the anti-inflammation of dauricine is mainly through STAT5-NF-κB pathway, might act as a STAT5 inhibitor. Dauricine suppressed the inflammation cytokines Eotaxin, KC, TNF-α, IL-1α, IL-1β, IL-6, IL-12β, IL-17α, and also inhibited the microglia activation. STAT5b mutant at Tyr-699 reversed the protective effect of dauricine on oxygen-glucose deprivation-reperfusion injury of neurons, and reactivated the suppression of dauricine on P-NF-κB of microglia. These results suggest that dauricine might suppress the neuroinflammation and protect the neuron from the injury of post-ischemia-reperfusion via mediating the microglia activation through STAT5-NF-κB pathway, and ss a potential therapeutic target for neuroinflammation, STAT5 needs to be raised concern in ischemic stroke.

Novel 1,2,3-triazole compounds 3–4 were synthesised via the regioselective addition and cyclization reaction of 1,1-enediamines (EDAMs) 1 with p-methylbenzenesulfonyl azide (TsN₃) 2 in 1,4-dioxane at refluxing for 5 hours. As a result, 1,2,3-triazoles 3–4 can be easily and efficiently obtained via catalyst-free click chemistry. The reactions have some advantages such as metal-free catalyst, excellent yield, inexpensive raw materials, and convenient final treatment.

Thunderstorm electrification has been studied for hundreds of years. Several mechanisms have been proposed to elucidate the electrification, including convective charging, inductive precipitation charging, and ice crystal-graupel collision charging. Field observations and model studies have demonstrated the vital roles that graupel and ice crystals play in the electrification, but the mechanism of the collision charging is still unclear. The fundamental essence of relative growth rate theory used for explaining the tripole charge structure in thunderclouds also needs a further exploration. We analyze the processes of ice crystal-graupel collision charging from charge migration inside hydrometeors to charge separation between two hydrometeors. The driving effects of temperature gradient and chemical potential gradient in charge migration are clarified, as well as the applicability of the relative growth rate theory, thermoelectric effect and surface tension gradient in different humidities. Based on the understanding from these electrification mechanisms, we propose that the essence of charge separation is driven by non-thermal equilibrium, and future studies on thunderstorm electrification should focus on the dynamical non-thermal equilibrium of cloud particles.

Zika virus (ZIKV) co-circulates with several closely related flaviviruses which exhibit similar clinical manifestations thus, clinicians rely on molecular and serological techniques for diagnosis. Cross-reactivity of patient specimens to flaviviruses is a significant impediment to serological diagnosis in areas where multiple flaviviruses co-circulate. Furthermore, patient exposure history to any of these viruses could complicate serological response patterns which could result in over and/or underdiagnosis of ZIKV infection. Three strains of ZIKV, dengue serotypes 1-4, West Nile virus, Japanese Encephalitis virus, and Yellow Fever virus were evaluated for neutralizing properties against 3 monoclonal antibodies, 4 ZIKV-naïve patients with flavivirus exposure history, 5 patients with verified ZIKV exposure and unknown flavivirus exposure history, and 5 flavivirus-naive patients with ZIKV-only exposure. Patients naïve for ZIKV exposure effectively neutralized multiple strains of ZIKV. Overall, the prototype ZIKV isolate MR-766 did not behave like the other ZIKV isolated used in this study. MR-766 was neutralized more completely by polyclonal patient serum than recent ZIKV isolates. MR-766 was neutralized better than dengue virus in ZIKV-naïve patients with prior dengue exposure. MR-766 was neutralized significantly less than recent ZIKV isolates when treated with monoclonal antibodies. The data herein show that without RT-PCR, serological diagnosis may not be possible in areas where multiple flaviviruses are endemic.

Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

Digital Microfluidic Biochips (DMFBs), used in various kinds of fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than previous ways. However, their reliability is often compromised by their inability to adapt to all kinds of errors. All errors in biochips can be categorized into two types: known errors and unknown errors. Known errors are detectable before the start of the routing process through sensors or cameras. Unknown errors, in contrast, become apparent only during the routing process and remain undetected by sensors or cameras, which is the biggest issue to unexpectedly stop the routing process and diminishes the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm designed to manage not only known errors but also unknown errors. Our experiments demonstrate that our algorithm outperforms previous ones in terms of the success rate of the routing in the scenario including both known errors and unknown errors. Additionally, our algorithm contributes to detecting unknown errors during the routing process and identifying the most efficient routing path with high probability.

Information-theoretically provable unique true random numbers, which cannot be correlated or controlled by an attacker, can be generated based on quantum measurement of vacuum state and universal-hashing randomness extraction. Quantum entropy in the measurements decides the quality and security of the random number generator (RNG). At the same time, it directly determines the extraction ratio of true randomness from the raw data, in other words, it obviously affects quantum random bits generating rate. In this work, we commit to enhancing quantum entropy content in the vacuum noise based quantum RNG. We have taken into account main factors in this proposal to establish the theoretical model of quantum entropy content, including the effects of classical noise, the optimum dynamical analog-digital convertor (ADC) range, the local gain and the electronic gain of the homodyne system. We demonstrate that by amplifying the vacuum quantum noise, abundant quantum entropy is extractable in the step of post-processing even classical noise excursion, which may be deliberately induced by an eavesdropper, is large. Based on the discussion and the fact that the bandwidth of quantum vacuum noise is infinite, we propose large dynamical range and moderate TIA gain to pursue higher local oscillator (LO) amplification of vacuum quadrature and broader detection bandwidth in homodyne system. High true randomness extraction ratio together with high sampling rate is attainable. Experimentally, an extraction ratio of true randomness of 85.3% is achieved by finite enhancement of the laser power of the LO when classical noise excursions of the raw data is obvious.

Synthesis of ZSM-5 zeolite from OSDA-free systems has been a very interesting alternative method because it can avoid the usage of organic templates and consequent calcination step. However, in opened reports, organic substance such as alcohol is more or less involved. In the present work, a calcined commercial ZSM-5 zeolite was used as a seed, sodium aluminate as an aluminum source, silica sol as a silicon source, which ensures that there is no any organics (template) in the synthesis system, and polycrystalline ZSM-5 aggregates consisting of rod-like nanocrystals are successfully prepared in a completely OSDA-free system. The effects of seed (chemical component, dosage) and crystallization conditions on the synthesis of ZSM-5 were systematically investigated. The results show that highly crystallinity ZSM-5 aggregate consisting of primary nano-sized crystals with a size of less than 100 nm can be yielded from a gel precursor with 5.6 wt.% seed after hydrothermal treatment for 48 h. The results also shows that the chemical composition of the seeds had little effect on the topological structure and pore structure of the synthesized samples, but the seed with a low Si/Al ratio is more conducive to the rapid crystallization of zeolite and to the improvement of the acid, especially the strong acid centers of the catalyst.

Background: Atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. Methods: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. Conclusions: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

The base excitation of mechanical equipment can result in pipeline system vibration which seriously affects the safe operation of the equipment. Particle damping technology is based on the particle collision dissipation principle to realize the structural vibration energy dissipation. This paper introduces the particle damping method into the vibration control of pipeline system. A particle damper has been designed for the structural characteristics of the pipeline. The influence of the material and structure of the pipeline particle damper, the external excitation and other parameters on the energy dissipation characteristics of particle damping are revealed. The energy dissipation mechanism of the pipeline particle collision damper under various parameter is analyzed. A pipeline vibration reduction test system with particle damping has been established. The effects of different excitation conditions, damper materials and number of unit structures of particle damper on the vibration damping effect of pipeline systems are investigated comparatively. The influence of damper parameters on the vibration suppression of pipeline system is obtained. The research in this paper can provide technical references for vibration suppression of pipeline systems.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

Multi-sensor data fusion of E-tongue and E-nose can provide a more comprehensive and more accurate analysis results. However, it also brings some redundant information, it is a hot issue to reduce the feature dimension for pattern recognition. In this paper, the taste-olfactory data fusion based on E-tongue and E-nose combined with Support Vector Machine (SVM) was used to classify five different beers. First, the taste and olfactory feature information were obtained based on E-tongue and E-nose. Second, the original feature data of single system were fused, then Principal Component Analysis (PCA) was applied to extract principal components, Genetic Algorithm-Partial Least Squares (GA-PLS) was used to select the characteristic variables, 20 subsets were generated with those variables based on the best Variable Importance of Projection (VIP) score. Finally, the classification models based on SVM were established, also c and g of SVM were calculated by Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the classification results of all subsets were obtained. The results showed that the classification accuracy using data fusion was much higher over single E-tongue and single E-nose, and the variable selection method by VIP had the best classification performance in #12 subset coupled with GA-SVM.

To address the trajectory tracking issue for quadrotors working under the influence of external disturbances, a control scheme is designed for position and attitude trajectory tracking. Firstly, the quadrotor dynamic model is derived for control design. Secondly, adaptive integral backstepping control (AIBS) is employed in the position loop in which adaptive estimation is used to estimate the upper bounds of disturbances. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) is proposed to follow desired Euler angles. The intro-duction of fast nonsingular terminal sliding mode and integral element is to realize fast convergence and accurate tracking. Dead zone technique is applied to minimize estimation errors. Besides, a saturation function is utilized to eliminate chattering phenomenon. Finally, simulation experiments are conducted under the Simulink environment. The proposed control is verified by comparing it with traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS).

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF- Inception to further improve model performance. Extensive experiments on three benchmarks, including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of 0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have been released on GitHub

As an active device, air source heat pumps (ASHP) are widely used for heating and cooling in buildings. However, in cold regions, their efficiency decreases, and frost formation becomes a significant issue during winter. This paper proposes a new scheme that couples air source heat pumps with passive heating. By using software simulations and mathematical models, the new scheme is compared to traditional ASHP systems to analyze its heating performance in rural homes during winter in China’s cold regions. According to the simulation and calculation analysis, on the coldest day of winter, the coupling scheme can provide approximately 99.41kWh of heat to the interior, surpassing the 86.67kWh required to maintain an indoor temperature of 20℃. The system’s power consumption is 36.96 kWh, which is 66.88% lower than that of traditional heat pump heating. The study shows that a coupled system of air source heat pumps and passive heating has better heating performance in cold regions. By incorporating phase change materials and heat storage media to address the shortage of solar energy at night, the system can meet the heating needs throughout the entire day.

In the paper, the authors find closed forms for derangement numbers in terms of the Hessenberg determinants, discover a recurrence relation of derangement numbers, present a formula for any higher order derivative of the exponential generating function of derangement numbers, and compute some related Hessenberg and tridiagonal determinants.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

Lanthanide-doped upconversion nanoparticles (UCNPs) are promising bioimaging nanoprobes due to their excellent photostability. As one of the most commonly-used lanthanide activators, Tm3+ ions have perfect ladder-type electron configuration and can be directly excited by bio-friendly near-infrared-II (NIR-II) wavelengths. Here, the emission characteristics of Tm3+-doped nanoparticles under laser excitations of different near-infrared-II wavelengths were systematically investigated. The 1064 nm, 1150 nm and 1208 nm lasers are proposed to be three excitation strategies with different response spectra of Tm3+ ions. Particularly we found that 1150 nm laser excitation enables intense three-photon 475 nm emission, which is nearly 100 times stronger than that excited by 1064 nm excitation. We further optimized the luminescence brightness after investigating the luminescence quenching mechanism of bare NaYF4:Tm (1.75%) core. After growing inert shell, ten-fold increase of emission intensity was achieved. Combining the advantages of NIR-II wavelength and the higher-order nonlinear excitation, a promising facile excitation strategy was developed for the application of thulium-doped upconversion nanoparticles in single nanoparticle imaging and cancer cell microscopic imaging.

Dietary intake of omega-3 polyunsaturated fatty acids (eicosapentaenoic acid, EPA) exerts antiarrhythmic effects, although the mechanisms are poorly understood. Here we investigated the possible beneficial actions of EPA on saturated fatty acid-induced changes of the L-type Ca2+ channel in cardiomyocytes. Cardiomyocytes were cultured with oleic acid/palmitic acid mixture (OAPA) in the presence or absence of EPA. Beating rate reduction of cardiomyocytes caused by OAPA were reversed by EPA. EPA also retrieved a reduction of Cav1.2-L-type Ca2+ current, mRNA and protein caused by OAPA. Immunocytochemical analysis revealed a distinct downregulation of the Cav1.2 channel caused by OAPA with a concomitant decrease in the phosphorylated component of a transcription factor adenosine-3’, 5’-cyclic monophosphate (cAMP) response element binding protein (CREB) in the nucleus, which were rescued by EPA. A free fatty acid receptor 4 (FFAR4) agonist TUG-891 reversed expression of Cav1.2- and CREB-mRNA caused by OAPA, whereas an FFAR4 antagonist AH-7614 abolished the effects of EPA. Excessive reactive oxygen species (ROS) accumulation caused by OAPA decreased Cav1.2- and CREB-mRNA expressions, which were reversed by an ROS scavenger. Our data suggest that EPA rescues cellular remodeling caused by OAPA lipotoxicity and oxidative stresses via both free fatty acid receptor 4-dependent and -independent pathways.

In recent years, frequent occurrences of low temperature disasters due to global climate change have significantly impacted the normal growth of crops. Research has indicated that inorganic selenium (which is more toxic) can not only enhance the effects of low temperature stress but also increase selenium content on plants; however, there is a scarcity of studies investigating the impacts of organic selenium and nano-selenium on crops. In order to investigate whether organic selenium and nano-selenium can enhance low-temperature tolerance and increase selenium content in pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), different concentrations (0, 5, 10, 20 mg L-1) of exogenous selenium were applied in this experiment to assess their effects on plant growth, nutritional quality and antioxidant properties. RNA-Seq technology was used to sequence the transcriptome of the leaves. Based on the transcriptome data, a Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a network that associates with physiological traits related to stress resistance. Two highly correlated gene co-expression modules were identified, and within them, nine hub genes associated with endocytosis, antioxidant stress, absorption, transport, and metabolism of selenium were discovered. The results indicated that the beneficial effects on yield and total selenium content under low temperature were attributed to (1) protection of photosynthetic pigments for enhancing photosynthetic capacity by the up-regulation of LHca2, LHcb1, LHca1, LHcb4 in KEGG pathway: photosynthesis-antenna proteins; (2) activation of antioxidant system for efficient ROS homeostasis such as SOD, POD and CAT by the genes such as Superoxide dismutase, Monodehydroascorbate and (3) selenium absorption by endocytosis, seleninum transportation by ABC transporter gene family and selenium metabolism related genes such as Cysteine synthase, Glutaredoxin. These findings provide a foundation for further investigation into the molecular mechanisms underlying the effects of organic selenium and nano-selenium on cyanochloride production at low temperatures.

With the advancement of next generation sequencing in past several years, a rising number of non-coding RNAs have been found as new actors to regulate gene expression. Non-coding RNAs not only play important roles in carcinogenesis, but also affect the clinical treatment strategies. They have been proved to be deeply correlated with chemoresistance in several cancers. Ovarian cancer is the leading cause of death in gynecological malignancy, with low 5-year survival rate. Most patients are identified when they have late-stage disease. This review mainly makes a compilation of the most relevant research literature in this field with the purpose of shedding light on the relation between ncRNAs and chemoresistance in ovarian cancer.

Copper mine tailings are the residual products after the purification of precious copper from copper ores, and their storage can create numerous environmental problems. Many researchers have used copper mine tailings for preparation of geopolymer. This paper studies the enhancement of the cementitious activity of copper mine tailings in geopolymer system. First, copper mine tailings are activated through a mechanical grinding activation. Afterward, the mechanically activated copper mine tailings are further processed through thermal activation and alkaline roasting activating. The cementitious activity index of copper mine tailings is characterized through the degree of concentration of alkali leaching silicon and aluminum. It was observed that the Si and Al alkali leaching concentration of mechanical activated tailings was increased by 26.03% and 93.33%, respectively. The concentration of Si and Al was increased by 54.19% and 119.92%, respectively. For alkaline roasting activating, roasting time, temperature and (C/N ratio) were evaluated through the orthogonal test, and the best condition was activation for 120 min at 600℃ with C/N ratio is 5:1. In this study, the SEM, XRD and IR analysis show that mechanically activation, thermal activation and alkaline roasting activating can improve the cementitious activity index of copper mine tailings.

The largest gene families in plants were found to be Glutathione transferase (GSTs), which played significant roles in regulating plant growth, development, and stress response. Within the GSTs gene family, members were found to play a crucial role in the low-temperature response process of plants. A comprehensive study identified a total of 70 BraGSTs genes. Cluster analysis results demonstrated that the BraGSTs in Brassica rapa(B.rapa) could be categorized into eight sub-families and were unevenly distributed across ten chromosomes. The 39 BraGSTs genes were found to be organized into 15 tandem gene clusters, with the promoters containing multiple cis-elements associated with low-temperature response. Cold stress was observed to stimulate the expression of 15 genes, with the BraGSTF2 gene exhibiting the highest level of expression, sug-gesting its significant involvement in winter B.rapa's response to low-temperature stress. Sub-cellular localization analysis of the BraGSTF2 protein indicated its potential expression in both the cell membrane and nucleus. The analysis of stress resistance in BraGSTF2 transgenic Arabidopsis thaliana lines emon-strated that the over-expression of this gene resulted in significantly elevated levels of SOD, POD activity, and SP content compared to the wild type following exposure to low temperatures. These levels reached their peak after 24 hours of treatment. Conversely, the MDA content was lower in the transgenic plants compared to the wild type. Additionally, the survival rate of BraGSTF2 transgenic Arabidopsis thaliana was higher than that of the wild type, suggesting that the BraG-STF2 gene may play a crucial role in enhancing the cold stress tolerance of winter B. rapa. This study lay a foundation for further study on the role of BraGSTs gene in the molecular regulation of cold resistance in winter B.rapa.

: Forty-nine children who started wearing cartilage conduction hearing aids (CC-HA) before elementary school graduation (including 17 cases of bilateral hearing loss and 32 cases of unilateral hearing loss) were followed up and examined. The wearing and utilization status of CC-HA, as well as the progress to date, were evaluated. In addition, 33 participants who purchased CC-Has were interviewed to assess the wearing effect. Eleven of the 17 children with bilateral hearing loss and 25 of the 32 children with unilateral hearing loss have continued using CC-HA. In terms of wearing effect, a good wearing effect was reported, even by those with one-sided hearing loss. In cases where it is difficult to wear CC-HAs stably with pasting or ear tips, it is possible to fix them stably using commercially available hair bands and eyeglass vines. In two cases, CC-HAs were worn from the age of 0. With ingenuity and appropriate educational and medical support, it is possible to wear CC-HA from infancy.

The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the actual one. This situation calls for novel ways to increase spectral efficiency. Therefore in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front-ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, $i.e.$, the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum-bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

The explosive emergence of wireless technologies and standards, covering licensed and unlicensed spectrum bands has triggered the appearance of a huge amount of wireless technologies, with many of them coexisting in the same band. Unfortunately, the wireless spectrum is a scarce resource, and the available frequency bands will not scale with the foreseen demand for new capacity. Certain parts of the spectrum, in particular the license-free ISM bands, are overcrowded, while other parts, mostly licensed bands, may be significantly underutilized. As such, there is a need to introduce more advanced techniques to access and share the wireless medium, either to improve the coordination within a given band, or to explore the possibilities of intelligently using unused spectrum in underutilized (e.g., licensed) bands. Therefore, in this paper, we present an open source SDR-based framework that can be employed to devise disruptive techniques to optimize the sub-optimal use of radio spectrum that exists today. Additionally, we describe three use cases where the proposed framework can be employed along with intelligent algorithms to achieve improved spectrum utilization.

Real-time strategy (RTS) game has proposed many challenges for AI research for its large state spaces, enormous branch factors, limited decision time and dynamic adversarial environment. To tackle above problems, the method called Adversarial Hierarchical Task Network planning (AHTN) has been proposed and achieves favorable performance. However, the HTN description it used cannot express complex relationships among tasks and impacts of environment on tasks. Moreover, the AHTN cannot handle task failures during plan execution. In this paper, we propose a modified AHTN planning algorithm named AHTNR. The algorithm introduces three elements essential task, phase and exit condition to extend the HTN description. To deal with possible task failures, the AHTNR first uses the extended HTN description to identify failed tasks. And then a novel task repair strategy is proposed based on historical information to maintain the validity of previous plan. Finally, empirical results are presented for the μRTS game, comparing AHTNR to the state-of-the-art search algorithms for RTS games.

Abstract: The chemical mechanical polishing (CMP) has been widely used for surface modification of critical materials and components and provided global planarization of topography with a low post-planarization slope high quality and efficiency. The colloidal silica slurries play an important role in the finishing processes of optical components. Monodisperse mesoporous silica nanospheres were synthesized with different surfactant by Stöber method. Their architectural features and texture parameters were characterized by XRD, FTIR, N2 adsorption–desorption isotherms, SEM, XPS, and TGA techniques. The spherical SiO2 products presented with the controllable 50-150 nm particle size and distribution. The AFM micrographs reveal that flat and smooth surfaces without distinct scratches residual particles are achieved by using the as-obtained composite particles as abrasives[The particle size of SiO2 slurry plays a critical role in the chemical-mechanical polishing process.

Mineral prospectivity mapping (MPM), aiming to outline and prioritize mineral exploration targets, has been spurred by data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from the scarcity of labeled data, over-fitting of models and uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL) combining data augmentation and transfer learning, which enables generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with a large synthetic training dataset of source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessment. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicate that the SMOTE-ed balanced dataset boosts the classification accuracy in the training process. The FSL models yield an arch-shaped prediction point pattern favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieve a high predictive performance (test AUC=0.9172) and the lowest quantitative over-fitting value, compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, are extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% known tungsten deposits, achieving superior predictive performance. This study highlights the capability of FSL framework for controlling over-fitting and generating high-confidence exploration targets with low uncertainty.

Potato (Solanumtuberosum L.) is the world's largest non-cereal crop, occupying a position critical for global food security, the fourth largest crop food in China. Due to its location and weather conditions, Yunnan province is the fourth largest potato producer in China with twice yearly production system. However, compared to high number of consumers, potato yield is still low to satisfy the population need. In this study, we have analyzed impacts of three cultivation factors: (1) three cultivation models (T1, T2, and T3), (2) planting density (T1, T2, and T3), (3) soil covering (T1, T2, T3, T4) on plant growth, and yield production. The soil moisture content, temperature, roots growth, stems development, leaf area index, and number-weight of tubers per plant and per plot (g) have significantly increased with commercial yield average of 3tones/mu. This suggests that the combination of these three factors would be a great potato extension model in winter climate zones similar to Yunnan Province. This will contributes in satisfaction of the world population potato consumption need.

Rare study on quantitative relationship between energetic impact of debris flows on the intensity and duration of growth disturbances of tree rings was carried out, partly due to lack of feasible approaches and detailed field evidence. In this study, we firstly determine the age of a recent debris flow derived from historic landslide deposits at Qingyang mountain (QYM) on the northeastern Tibet plateau by dendrogeomorphic technique. We acquired the quantitative data of annual widths of tree rings in history and confirmed the influence of debris flow rather than other factors (e.g. climatic event and inset outbreaking) in disturbing the growth of tree rings in a specific year. Using the approach, we determined the age of the debris flow at QYM occurred in 1982, which was speculated to be triggered by high monthly precipitation of July in 1982. Subsequently, based on the boundaries of historic debris flow identified on remote sensing images before and after 1982 and depth-integrated continuum model, we reconstructed the process of 1982-debris flow and obtained the kinematic energy of debris flow impacting on the sampled trees. Based on the study, we observed that two growth disturbance patterns of tree rings influenced by the reconstructed 1982-debris flow were revealed including growth suppression and asymmetric growth. A raw logarithm relationship between duration (i.e. lasting time for the disturbed tree rings to recover the initial width) and intensity of growth disturbances (i.e. growth suppression ratio of disturbed tree rings) was obtained. We concluded that there is a negative exponential relationship between simulated kinematic energy of debris flow impacting on the disturbed trees and time to recover the initial width of corresponding tree rings.

The global usage of veterinary antibiotics is significant. These antibiotics can be released into the aquatic environment and exert toxic effects on non-target organisms. To explore the physiological effects of tetracycline antibiotics on aquatic life, the growth characteristics of and toxin release from the cyanobacterium Microcystis aeruginosa (M. aeruginosa) were studied. Results showed that the toxicity order of the three target antibiotics was TC (tetracycline) > CTC (chlortetracycline hydrochloride) > OTC (oxytetracycline) in terms of inhibition occurrence time and the EC₁₀ and EC₂₅ values. Further, the target antibiotics regulated the production of MC-LR (microcystin-LR) to different degrees. CTC destroyed the M. aeruginosa cells and resulted in a decreased MC-LR release but stimulated the ability to synthesise MC-LR. OTC had a relatively weaker toxicity compared with CTC, while TC was the most toxic among the three antibiotics. Therefore, TC is friendly to the aquatic environment because it simultaneously reduced the intracellular and extracellular MC-LR content. These results aid our understanding of the effects of tetracycline antibiotics on Microcystis aeruginosa, which is important for environmental evaluation and protection. These results are also helpful for guiding the application of veterinary antibiotics in agricultural settings.