In this study, three new demountable joints consisting of reinforced concrete columns and steel beams were proposed and their seismic performance was investigated using cyclic loading tests. The test results demonstrated that the three demountable RCS joints had good seismic performance. Using the finite element software ABAQUS, the influence of parameters such as beam flange thickness, bolt strength, and connector steel strength on the seismic performance of each joint was analyzed, and the influence of different parameters on the seismic behavior of the joint was determined. The results also showed that the three demountable RCS joints were sensitive to changes in the thickness of the steel beam flange, while the connector steel strength and bolt type had little impact on the joint's capacity. Additionally, shear capacity calculation formulas for the joint core area provided by different codes and researchers were compared with test and finite element results. The results showed that the calculation results of the ASCE guidance method, the Nishiyama method, and the CECS 347-2013 method were higher than the test values, while the calculation results of the Para method were lower because it did not consider the contribution of the cylindrical steel plate to the shear capacity of the joint core area.

The modern material design aims to achieve multifunctionality by integrating structures in a diverse range, resulting in simple materials with embedded functions. Biological materials and organisms are typical examples of this concept, where complex functionalities are achieved through a limited material base. This review highlights the inspiration from multifunctional organisms and materials in nature, emphasizing their structural and functional integration. In particular, we discuss complex hierarchical architectures ranging from the nanoscale to the macroscale, to achieve multifunctional properties such as extreme pressure, impact and wear resistance, shape memory and self-healing, adhesiveness and anti-fouling, sensing-actuating and sensing-camouflage, and extreme environmental survival. These strategies of integrating structure and function are critical for promoting innovations and breakthroughs in the modern engineering materials and applications.

In a global context, the story of the Jesuit compound in Shanghai, since its establishment by French Jesuits in 1847, reflected not only conflicts between rival powers in Europe but also the fight for their interests in the Eastern world. The Pope, who was stuck without legal status in the Vatican after 1861, was also seeking the chance to save the authority of the Church in the face of questions regarding the extent of his temporal power and the status of Rome in the context of Italian unification. As in the Reformation, a break-through in the east seemed to offer a solution for losses in Europe. However, the Jesuits to the East in the late 19th century were not only troops working and fighting on behalf of the Pope; their identities under the French Protectorate added complexity to an already complicated story involving not just the Church, but the course of world history.The female Catholic orders at the east bank of Zi-ka-wei compound became a unique window to approach the complexity.

Coilia nasus is a typical anadromous migratory fish in the lower reaches of the Yangtze River. Every year, C. nasus cluster offshore and swim upstream along the Yangtze River into the tributaries and lakes in the middle and lower reaches of the Yangtze River to breed. In this study, female C. nasus collected as the subjects from Chongming section in Shanghai, Taizhou section in Jiangsu and Anqing section in Anhui. The ovaries were used for examining in tissue sections and researching of gene expression including follicle stimulating hormone receptor (fshr), luteinizing hormone receptors (lr), kisspeptin-1 (kiss1) and forkhead box l2 (foxl2) related to the reproductive development, and the estrogen (estradiol, E2) and progesterone (17α,20β-dihydroxy-4-pregenen-3-one, 17α,20β-DHP) were also analyzed in serum levels. The results showed that, first, the growth period of oocytes was small in stage II of ovarian development, in which both E2, 17α,20β-DHP levels and gene expression were low. Then, in stage Ⅲ, the growth period of oocytes were become large, as well as the yolk granules and oil droplets began to appear. Simultaneously, E2 and expression of kiss1 and foxl2 were significantly elevated. Finally, stage Ⅳ was the period of large accumulation of nutrients in oocytes, and 17α,20β-DHP level and expression of fshr and lhr were significantly elevated. The results have enriched the theoretical study of ovarian development in the natural population of C. nasus, supplemented the biological basis of C. nasus reproduction, and scientifically supported the study of C. nasus population ecology and resource conservation.

The development of U.S. Army and NATO data link systems is introduced first, and then the development trend of future intelligent data link is summarized into integration, generalization, multifunctionality and high security. A unit-level combat system architecture based on the global combat cloud, which is capable of realizing the flexible scheduling of global combat resources and maximizing the overall combat effectiveness, is proposed. Intelligent data link is an important part of this solution, providing strong information support for future urban unit-level warfare.

Target recognition and fine-grained ship classification in remote sensing face the challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of region aware instantiation and attention mechanism. Implement adversarial learning through image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the do-main encoder and domain discriminator modules are responsible for extracting feature representations of foreground and background, and further aligning ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on 8 typical fine-grained classification algorithms, and the accuracy of fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

Signal-to-noise ratio (SNR) is an important index to evaluate radiation performance and image quality of optical imaging systems under low illumination background. Under the nighttime lighting condition, the illumination of remote sensing objects is low and varies greatly, usually ranging from several lux to tens of thousands of lux. Nighttime light remote sensing imaging requires high sensitivity and large dynamic range of detectors. Luojia 1-01 is the first professional nighttime light remote sensing satellite in the world. In this paper, we took the nighttime light remote sensing camera carried on the satellite as research object, proposed an in-orbit SNR test method based on time series images to overcome the problem of low spatial resolution. We first analyzed the process of luminous flux transmission between objects and satellite and established a radiative transfer model. By combining the parameters of large relative aperture optical system and high sensitivity CMOS device, we established SNR model and specially analyzed the effect of exposure time and quantization bits on SNR. Finally we used the proposed in-orbit test method to calculate SNR of lighting images acquired by satellite. And the measured result is in good agreement with the model predicted data. Under the condition of 10lx illumination, the SNR of typical objects can reach 27.02dB, which is much better than the requirement of 20dB for engineering application.

This article couples human metabolic factors and heating environmental factors, and uses a 57 node human thermal physiological model to study the heating effect of radiators. It provides a way to objectively calculate and directly quantify the effect of heating equipment on human thermal physiological parameters. As the inlet speed increases, the surface temperature of the human body first increases and then decreases. At a speed of 3m/s, it basically reaches a high value. Continuing to increase the wind speed, as the indoor convection intensity further increases, the surface temperature of the human body will decrease, leading to a decrease in thermal comfort. Compared with floor radiation heating under similar indoor temperature conditions, forced convection radiator heating has similar skin temperatures in various parts of the human body after the indoor design temperature exceeds 20 ℃, and the overall thermal comfort of the two technical solutions is equivalent. Research has shown that the turbulence wind speed of convective radiators should not be too small, with a wind speed range of 2.5m/s to 3m/s. The heating effect of using horizontal air outlet from the lower part of the radiator is better than that of vertical air outlet from the upper part. Convection type radiator heating is more suitable for buildings with rooms that are not too high due to the large indoor temperature gradient.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

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

Abstract: Toxicity of emerging organic pollutants to photosystems of aquatic plants are still not well clarified. This study aimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by SECM (scanning electrochemical microscopy). The protocol was also checked by confocal laser scanning microscopy (CLSM), the traditional Clark oxygen electrode method and chlorophyll fluorescence technique. The typical persistent organic pollutant 2,4-dichlorophenol (2,4-DCP) in water environment and the common aquatic Microsorium pteropus (M. pteropus) were chosen as the model organic pollutant and tested plant, respectively. It was found that the SECM method could well discriminated the responses of stoma micromorphology and spatial pattens of photosynthetic oxygen evolution on single stoma. The shape of stoma blurred with increasing 2,4-DCP concentration, which was in good agreement with the CLSM images. The dose-response curves and IC50 values obtained from the SECM data were verified by the data measured by the traditional Clark oxygen electrode method and chlorophyll fluorescence test. The IC50 value of single stoma oxygen emission of 24 h exposed plant leave, which was derived from the SECM current data (32535 μg L-1), was close to those calculated from the maximum photosynthetic efficiency (Fv/Fm) measured by chlorophyll fluorescence test (33963 μg L-1), and the Clark oxygen electrode method photosynthetic oxygen evolution rate (32375 μg L-1). The 72 h and 96 h 2,4-DCP exposure data further confirmed the reliability of the nanoscale stoma oxygen emission mapping methodology for ecotoxicological assessment. In this protocol, the procedures for how to collect effective electrochemical data and how to extract useful information from the single stoma oxygen emission pattern were well established. This study showed that SECM is a feasible and reliable ecotoxicological tool for evaluation of toxicity of organic pollutants to higher plants with unique nanoscale visualization advantage over the conventional methods.

The energy E takes the place of the gravitational mass m_G to produce gravity [1]. We wonder whether the inertial mass m_i in the inertial force is also replaced by E. The experiments to measure the inertial force and weight of a carrier are presented. Now we can test the principle of equivalence and study foundations of physics in condensed matter.

3,4-Dihydroquinolin-2(1H)-ones represent a class of valuable bioactive compounds with six-membered nitrogen-containing heterocyclic structures. The development of simple, mild and efficient synthetic methods has been widely concerned by synthetic chemists. In this review, we have summarized a series of different synthetic strategies for the synthesis of dihydroquinoline-2(1H)-ones via catalytic annulation of α,β-unsaturated N-arylamides in the past decade, including covering electrophilic cyclization, radical initiated cyclization and photochemical cyclization reactions. Besides, the substrate scope and mechanistic details are also discussed. This paper will provide a useful reference for the development of diverse synthesis methodologies of 3,4-dihydroquinolin-2(1H)-ones.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

As a critical transmitter, the compact 532 nm lasers operating on high repetition and narrow pulse widths have been used widely for airborne or space-borne laser active remote sensing. We developed a free space pumped TEM₀₀ mode sub-nanosecond 532 nm laser that occupied a volume of less than 125 mm × 50 mm × 40 mm (0.25 liters). The fundamental 1064 nm laser consists of a passively Q-switched composite crystal microchip laser and an off-axis, two-pass power amplifier. The pump sources were two single-emitter semiconductor laser diodes (LD) of 808 nm with a maximum continuous wave (CW) power of 10 W each. The average power of fundamental 1064 nm laser was 1.26 W with the laser operating at 16 kHz repetition rates, and 857ps pulse widths. Since the beam distortion would be severe in microchip lasers in terms of the increase in heat load, for obtaining a high beam quality of 532 nm, the beam distortion was compensated by adjusting the distribution of pumping beam in our experiment of fundamental amplification. Furthermore, better than 0.6 W average power, 770 ps, beam quality of M² ＜1.2, and 16 kHz pulse output at 532 nm was obtained by a Type I LiB₃O₅ (LBO) crystal in the critical phase matching (CPM) regime for second harmonic generation (SHG).

Nitrogen (N) is a major limiting factor for plant growth and vegetable production. Understanding the regulatory mechanisms of N uptake, transport, and assimilation is key to improving nitrogen use efficiency in plants. Ammonium transporters (AMTs) play an important role in plant N metabolism. In this study, we isolated an important AMT1 subfamily member (BcAMT1;5) with a highly conserved signatural AMT1 subfamily motif from flowering Chinese cabbage. Based on functional complementation in yeast mutant 31019b and overexpression of BcAMT1;5 in Arabidopsis, BcAMT1;5 is a functional ammonium transporter. Tissue expression analysis showed that BcAMT1;5 was mainly expressed in roots and showed multiple N regime transcript patterns to respond to varying nutritional conditions. This was up-regulated by N-deficiency and down-regulated by supplying NH4+. The glucuronidase (GUS) activities of BcAMT1;5pro::GUS showed a similar change in response to different N conditions. Overexpression of BcAMT1;5 accelerated the growth of transgenic seedlings, increased NH4+ net influxes, and enhanced the content and accumulation of NH4+ and NO3- at low N concentrations. Additionally, it increased the transcript levels of N assimilation-related genes in shoots. These results indicate that the transcriptional regulation of BcAMT1;5 in flowering Chinese cabbage may participate in N uptake and assimilation under various N conditions.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Echinococcus granulosus is a harmful cestode parasite which could cause Cystic Echinococcosis in humans, various livestock species and wild animals. Calmodulin (CaM), a Ca²⁺ sensor protein, is widely expressed in eukaryotes and mediates a variety of cellular signaling activities. In our study, the CaM in Echinococcus granulosus (rEgCaM) was successfully cloned and the molecular and biochemical characterizations of rEgCaM were identified. The results showed that rEgCaM was a highly conserved calcium-binding protein, consisting of 149 amino acids. Immunoblot analysis revealed that rEgCaM could be identified using E. granulosus infected sheep serum. The usage of rEgCaM as antigen was evaluated by indirect ELISA which exhibited a high sensitivity of 90.3% but low specificity (47.1%). rEgCaM was mainly located in the tegument tissues and parenchymal region of protoscoleces, the tegument and inner body of adult worm and predominantly expressed in the germinal layer. The mRNA expression of rEgCaM in PSCs were gradually decreased with the increase death of PSCs. In electrophoretic mobility tests and ANS assays, rEgCaM showed a typical calcium-binding protein characteristics. This is the first report on CaM from E. granulosus and rEgCaM is considered to be involved in some important biological function of E. granulosus as a calcium-binding protein.

For an enterprise, the most critical aspect of development is resource management, which has a significant impact on all aspects of the enterprise. Therefore, enterprises must pay attention to resource management allocation, which can better promote the sustainable development of the enterprise and obtain optimal benefits. In the production and development of modern enterprises, the acquisition of benefits also involves the allocation of resources in enterprise management. This paper proposed a benefit optimization scheduling algorithm based on a macroeconomic prediction model under auction mechanism and a grid resource scheduling algorithm driven by a benefit function to allocate resources reasonably. This article used macroeconomic forecasting models to fully understand the resource needs of various departments and the resources held by enterprises, and rationally allocate various resources in various departments. This can improve the work efficiency of various departments, thereby reducing the cost of the enterprise, and achieving optimal benefits for the enterprise. The experimental results in this paper showed that the cost of resource management allocation for the benefit optimization scheduling algorithm and the grid resource scheduling algorithm based on the benefit function driven under the auction mechanism was 105.6 yuan and 46.8 yuan respectively when the task volume was 125 under the multi user environment. The time allocated for resource management was 36.6s and 18.9s respectively. It can be seen that the efficiency function driven grid resource scheduling algorithm had a lower cost and time for resource management allocation, so the efficiency function driven grid resource scheduling algorithm can achieve enterprise efficiency optimization.

The molecular clusters ((HO)3Si-O-Si(OH)3, and (HO)3Al-O-Si(OH)3 ) representative of aluminosilicate mineral surface were employed to study the dissolution of aluminosilicate in acidic condition via density functional theory with the M06-2X+G(d,p) methodology. The surface termination sites (Si and Al) were both tetra-coordinated and the terminal oxygen was protonated in acidic condition. In the dissolution reaction, the calculated barrier height of the six-membered ring transition state complex containing two water molecules was predicted to be lower than that of four-membered ring transition state complex containing one water molecule. In addition, the calculated barrier heights for Al-terminated sites were predicted to be lower than those for the Si-terminated sites, suggesting that breaking the Al-O bond is easier than Si-O bond in the aluminosilicate mineral surface; the barrier heights of the surface termination sites with protonated terminal oxygen were lower than those without protonated terminal oxygen. With the fracture of Si-O and Al-O bonds, the Si and Al release from the aluminosilicate. These results confirm the experimental conclusions that the acidic condition facilitates the release of Si and Al from the aluminosilicate, and the concentration of Al leaching from aluminosilicate is higher than Si.

Metabolites present in the hemolymph of honey bees play a crucial role in modulating the metamorphic process within the species. However, the precise alterations in metabolite composition, along with the associated variances and regulatory pathways implicated during the larvae-to-pupae metamorphosis of honey bees, remain incompletely elucidated. In this investigation, we gathered hemolymph samples from honey bee larvae at three distinct physiological stages-feeding, prepupal, and pupae and subjected them to metabolite analysis utilizing the liquid chromatography-mass spectrometry (LC-MS) technique. Employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), we identified significant differential metabolites and conducted a metabolic pathway analysis on those differentially up-regulated during the prepupal and pupae stages. Notably, metabolites up-regulated in the hemolymph of prepupal stage larvae primarily governed glucose metabolism and fat digestion and absorption, while those in pupae stage larvae were involved in regulating chitin and lipopolysaccharide precursor formation, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. These findings bear significant implications for advancing our comprehension of the metamorphic processes in honey bees.

Omicron variants have higher infectivity, more immune escape but lower risk of severe clinical outcomes than the ancestral variants. However, people are still wondering whether SARS-CoV-2 may become lethal again or whether some highly-pathogenic strain may emerge in the future. Omicron is more selective proliferation in the upper respiratory tract (URT). The temperature of human URT is 33–34°C, and the virus strains other than Omicron cannot replicate effectively at this temperature. While Omicron adapted to the low temperature environment, so its distribution mainly depends on the expression levels of ACE2 in cells. Similar distribution switch from lungs to URT has been observed for the 1918 H1N1 influenza virus, who vanished after 1921 and has been turned to be the seasonal human influenza A viruses. However, some relatively high-pathogenic SARS-CoV-2 strains and influenza-virus strains may still emerge in the future. Dynamic changes in the viral virulence should be monitored constantly.

This paper is concerned with developing an efficient numerical algorithm for fast implementation of the sparse grid method for computing the $d$-dimensional integral of a given function. The new algorithm, called the MDI-SG ({\em multilevel dimension iteration sparse grid}) method, implements the sparse grid method based on a dimension iteration/reduction procedure, it does not need to store the integration points, nor does it compute the function values independently at each integration point, instead, it reuses the computation for function evaluations as much as possible by performing the function evaluations at all integration points in a cluster and iteratively along coordinate directions. It is shown numerically that the computational complexity (in terms of CPU time) of the proposed MDI-SG method is of polynomial order $O(Nd^3 )$ or better, compared to the exponential order $O(N(\log N)^{d-1})$ for the standard sparse grid method, where $N$ denotes the maximum number of integration points in each coordinate direction. As a result, the proposed MDI-SG method effectively circumvents the curse of dimensionality suffered by the standard sparse grid method for high-dimensional numerical integration.

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, the operating boundaries for both Depth of Discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

The gravitational charge should be the energy instead of mass. This modification will lead to some different results and the experiments to test are also presented. Especially, we propose a scheme to achieve the negative energy and gravitational repulsive force in the lab.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

Desert ecosystem occupies an important position in the composition of global biodiversity. The Tarim Basin locates in south Xinjiang of China and has the world 's second largest mobile desert, the Taklamakan Desert. As an endemic species in this region, Phrynocephalus forsythii has been demonstrated with potential high extinction risk to climate change. In order to understand the overall genetic status and provide accordant conservation strategies of the species, we investi-gated the genetic diversity and population structure of P. forsythii from 15 sites in the Tarim Basin using 21 highly polymorphic microsatellite markers. We found significant genetic structure across the study region. We also revealed generally low levels of gene flow between the 25 sites, sug-gesting individual dispersal and migration may be restricted within populations. In addition, geographical distance and ambient temperature might be important factors in explaining the observed genetic structure. Our results will provide scientific basis for the future protection of P. forsythii in this area, as well as an important reference for the conservation and management of biodiversity in desert ecosystems.

This literature review paper delves into the synergies between Climate-Smart Agriculture (CSA) and the effective management of food losses. With the escalating challenges posed by climate change and the pressing need to address food security concerns, the intersection of these two critical domains becomes paramount for sustainable agricultural practices. The review explores existing research on the implementation of climate-smart agricultural techniques and their impact on mitigating food losses throughout the agricultural value chain. The paper synthesizes findings related to climate-resilient crop varieties, precision farming, water-use efficiency, and sustainable soil management, highlighting their contributions to reducing post-harvest losses and enhancing overall food security. In addition to summarizing current knowledge, this paper identifies gaps in the literature and proposes avenues for future research. This review serves as a foundation for researchers, policymakers, and practitioners seeking to contribute to the advancement of climate-smart agriculture and the reduction of food losses, paving the way for a more sustainable and resilient global food system.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

Halide perovskite materials gain enormous attention for their semiconducting properties, higher power conversion efficiency and potential applications in a wide range field of studies along with their two key limitations: stability and toxicity. In spite of great progress made on the halide perovskites and many promising research developments, overcoming these limitations is not realized yet. Therefore, coordination engineering of new framework for obtaining alternative new halide perovskite materials and fundamental understanding of the coordination chemistry and electronic interactions forming the structure of these newly engineered halide perovskite materials might be one way in order to overcome the issues related to both stability and toxicity. In this review, the current development of halide perovskite families, both lead halide perovskites and lead free halide perovskites followed by coordination engineering of the new frameworks to engineer new halide perovskite materials will be reviewed comprehensively. Moreover, all the concerns of the fundamental ideas of coordination chemistry and electronic interactions that are keys in forming the halide perovskite structures are discussed in detail and form the main aim of this review. Interestingly, we also discuss recent potential energy applications beyond photovoltaic and this review has completed with an essential and open question: ‘what could happen in the future of halide perovskites?’ in order to excite commercial enterprises and research institutions again as well as to get motivating new predictions on the future continuity of this field.

Both in lung adenocarcinoma (LUAD) and severe acute respiratory syndromes (SARS) uncontrolled inflammation could be detected in lung tissue. Whether the similarity mechanism exists is still unknown. PDZ-binding motif (PBM) in SARS-CoV E protein has been demonstrated as virulence factor induce inflammation storm. Study function of PBM in LUAD is significant for mechanism exploration of carcinogenesis mediated by SARS-CoV. To identify gene expression fluctuation induced by PBM, a microarray sequencing data of lung tissue infected by wild type (SARS-CoV-E-wt) and recombinant virus (SARS-CoV-E-mutPBM) was analysis followed by functional enrichment analysis. To understand the role of screened specific genes in LUAD, overall survival and immune correlation were calculated. A total of 12 genes (MAPK1, PRKCA, FGFR4, KDR, PTPRD, BCL2L15, UBD, MAMDC2, LTBP4, PTPRB, LGI3 and ITGA8) might participate in initial and development stage of LUAD through expression variation and mutation. Meanwhile, a total of 12 genes (CARHSP1, EIF4E2, HMGA1, IL1R2, MAGOHB, PVR, ADCY9, ELF5, ESYT3, SCML4, SECL14L4 and THRA) could lead to poorer prognosis via dysregulation. In addition, MAMDC2 and ITGA8 down-regulated by PBM could also alter prognosis. Though the conservative PBM (-D-L-L-V-) could be found at the end of carboxyl terminal in multi E proteins of coronaviruses, the specific function of each one depend on the whole amino acid sequence simultaneously. In conclusion, PBM of SARS-CoV E protein could promote carcinogenesis of LUAD by dysregulating important gene expression profiles followed by influence immune response and overall prognosis. The results in present study also provided reference for the therapy of SARS-CoV-2 in LUAD patients.

Analyzing urban-rural spatial structures and changes are of great significance for under-standing the urban-rural relationship evolution. While previous studies mostly focused on the urban internal spatial structure evolution, less from the regional scale to explore the urban and rural evolution. Here, taking Dezhou city, a rapidly urbanization city in China as a case study, we employed the local contour tree method and nighttime light data to map urban and active rural regions’ range during 2012-2020, and further explored the respective development processes of them. This study found that different from rural regions, internal structures of urban regions are more complex, and there are often multiple hot spots inside them. The urban-rural regions’ area had increased significantly by 39.3% during 2012-2020 (p<0.05). The urban and rural region rankings of the identified counties are basically consistent with the urban and rural population rankings. Particularly, different from perspectives of earlier land use (i.e., built-up land or impervious surface), this study identified urban and active rural regions in view of the scope of active human activities. This study therefore found populations were greatly responsible for affecting changes of urban and active rural region structures. These findings could be helpful for understanding urban-rural dynamics.

Buckling defect will appear on the outer surface of the deformed ring during constrained ring rolling (CRR) of aluminum alloy thin wall conical ring with inner high ribs (AATWCRIHR), if the geometrical dimension of the ribs doesn't match the wall thickness. To avoid the buckling defect, a quantitative method for characterizing the degree of the buckling defect is proposed using the area of buckling profile. Then an orthogonal experimental scheme was designed taking the width of the middle rib, thickness of wall, and height of the middle rib as design variables and defining the area of buckling profile as optimization objective. Subsequently, a quadratic polynomial response surface model was established by combining the optimization algorithm with the finite element method (FEM), and the geometrical dimension of the middle ribs of the deformed AATWCRIHR is optimized. Also the optimal parameter combination to minimize the area of buckling profile is obtained and verified using FE simulation. Results show that the AATWCRIHR after optimization doesn’t generate the buckling defect during constrained ring rolling, and it is proved that the quantitative buckling defect representation method and the optimization design method based on the response surface model and the finite element simulation results are feasible for the constrained ring rolling process of the AATWCRIHR.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

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

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

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

Pneumonia remains a major cause of morbidity and mortality worldwide, especially during COVID-19 pandemic. With the significant global health burden that pneumonia poses, it is es-sential to improve therapeutic and management strategies. The increasing emergence of antibiotic resistant bacterial strains limits options for effective antibiotic use. New antibiotics for treatment of pneumonia may address deficits in current antimicrobial drugs, with an ability to cover both typical, atypical, and resistance pathogen. Several of these newer drugs also have structural characteristics that allow for a decreased propensity in development of bacterial resistance. Po-tential use of stem cell therapies in place of corticosteroid treatments may also offer an im-provement in patient outcomes. Human mesenchymal stem cell treatments have shown efficacy and safety in treating COVID-19 induced pneumonia. Combined treatment with both stem cells and antibiotics in pneumonia in a rabbit model has also shown significantly increased efficacy in comparison to antibiotic treatment alone, presenting yet another possible route for a novel strategy in treating pneumonia, though additional future studies are necessary before clinical implementation. While pneumonia remains a major disease of concern, having newer approved antibiotics as well as novel therapies such as stem cell treatments in the pipeline offers clinicians more options in effectively treating pneumonia.

This study investigates optimal decisions in a green supply chain consisting of a manufacturer and a leading retailer considering the green marketing and fairness preferences of member firms. Four Stackelberg game decision models are constructed in which the manufacturer and the re-tailer engage in green marketing separately when the manufacturer has no and has fairness preferences. The impacts of fairness preferences and green marketing on the optimal decision in the green supply chain are comparatively analyzed. The study finds that member firms perform green marketing regardless of the presence or absence of fairness preferences and that such be-havior increases the wholesale price, retail price, and market demand of low-carbon products as well as the profits of member firms and the supply chain. A more interesting finding is that the profit growth of member firms and the supply chain due to the manufacturer’s green marketing is more pronounced than that due to the retailer’s green marketing. When the retailer and the manufacturer engage in conduct green marketing, the manufacturer's fairness preferences have different effects on the wholesale price, retail price, market demand, level of green marketing efforts, member enterprises and profits of supply chain. Therefore, firms should consider the impact of green marketing and fairness preferences to make pricing and performance decisions, so as to achieve efficient operation of the whole supply chain and avoid double marginal effects. Finally, the above conclusions are verified through numerical simulation, providing a reference for the decision-making of member firms in the green supply chain.

Little is known about the association between equol and bioavailable testosterone (BT) in adults. We examined the associations of urinary equol concentrations with serum total, bioavailable and free testosterone (FT), dehydroepiandrosterone sulfide (DHEAS), free androgen index (FAI) and sex hormone-binding globulin (SHBG) concentrations. This cross-sectional study included 1904 women aged 59.7 years. Urinary equol and serum sex hormone concentrations were measured. Overall, urinary equol tended to be inversely associated with bioactive forms of androgenic indices (BT, FT or FAI) but not with total testosterone (TT) or DHEAS. Urinary equol was also positively associated with SHBG. In multi-covariate-adjusted analyses stratified by menopausal status, graded and inverse associations between urinary equol and bioactive forms of androgenic indices (BT, FT and FAI) were observed in postmenopausal women (all p-trends <0.05), but not in premenopausal women. A significant positive association between urinary equol and SHBG was observed only in postmenopausal women. No significant associations were observed between urinary equol and TT or DHEAS in either group. A path analysis indicated that these associations of equol with androgens in postmenopausal women might be mediated by SHBG. Our findings indicated urinary equol exhibited graded and inverse associations with BT or FT but not TT in women.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

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

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

Concrete cracks and local damage can affect the bond performance between concrete and steel bars, thereby reducing the durability of reinforced concrete structures. Compared with general concrete crack repair methods, biomineralization repair not only has effective bonding capabilities but is also particularly environmentally friendly. In view of this, this study aimed to apply biomineralization technology to repair damaged fiber-reinforced lightweight aggregate concrete (LWAC). Two groups of LWAC specimens were prepared. The control group used lightweight aggregates (LWAs) without bacterial spores and nutrient sources, and the experimental group used LWAs containing bacterial spores and nutrient sources. These specimens were first subjected to compression tests and pull-out tests, respectively, and thus were damaged. After the damaged specimen healed itself in different ways for 28 days, secondary compression and pull-out tests were conducted. The self-healing method of the control group involved placing the specimens in an incubator. The experimental group was further divided into experimental group I and experimental group II. The self-healing method of experimental group I was the same as that of the control group. The self-healing method of experimental group II involved soaking the specimen in a mixed solution of urea and calcium acetate for two days, and then taking it out and placing it in an incubator for two days, with a cycle of four days. The test results showed that, compared with the control group specimens, the relative bond strength ratio of the experimental group II specimens increased by 17.9%. Moreover, the EDS and XRD analysis results confirmed that the precipitate formed at the crack was calcium carbonate, which improved the compressive strength and bond strength after self-healing. This indicates that the biomineralization maintenance method used in experimental group II is more effective.

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

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

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers since they provide visual cues of an individual's emotions for a broad range of potential applications such as lie detection and criminal detection. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Different from other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. In contrast to other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which incorporates Shifted Patch Tokenization and Locality Self-Attention into the backbone network Swin Transformer, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach also performs well in the MEGC 2021 spotting task with an overall F1-score of 0.1824 and 0.1357 on CAS(ME)^2 and SAMM Long Videos, respectively. The code is publicly available on GitHub (https://github.com/eddiehe99/pytorch-expression-spotting).

Hydrogen sulfide (H₂S) is a novel gasotransmitter, sucrose (SUC) is both cellular energy and signaling molecule. However, their signaling interaction in maize thermotolerance is poorly known. In this study, using maize seedlings as materials, metabolic and functional interaction of H₂S and SUC in maize thermotolerance was investigated. Data show that under heat stress survival rate and tissue viability were increased by exogenous SUC, while malondialdehyde content and electrolyte leakage were decreased by SUC, indicating SUC could upraise maize thermotolerance. Also, SUC-upraised thermotolerance was enhanced by H₂S, while weakened by inhibitor (propargylglycine) and scavenger (hypotaurine) of H₂S and SUC-transport inhibitor (N-ethylmaleimide). To figure out underlying mechanism of H₂S-SUC interaction-upraised thermotolerance, redox parameters in mesocotyls of maize seedlings were measured before and after heat stress. The data display that activity and gene expression of H₂S-metabolizing enzymes were up-regulated by SUC, whereas H₂S had no significant effect on activity and gene expression of SUC-metabolizing enzymes. Besides, activity and gene expression of catalase, glutathione reductase, ascorbate peroxidase, peroxidase, dehydroascorbate reductase, monodehydroascorbate reductase, and superoxide dismutase were reinforced by H₂S, SUC, and their combination under non-heat and heat conditions in varying degrees. Similarly, content of ascorbic acid, flavone, carotenoid, and polyphenol was increased by H₂S, SUC, and their combination, whereas production of superoxide radical and hydrogen peroxide level were impaired by these treatments to different extent. These results imply that the metabolic and functional interaction of H₂S and sucrose exists in the formation of maize thermotolerance through redox homeodynamics.

Background: With the trend of intelligent display, the interface design of in-vehicle HUD is an expanding research field; Methods: To solve the subjectivity and uncertainty in the optimization of HUD interface design schemes, this paper proposed a hybrid scheme evaluation and optimization method based on entropy weight and VIKOR. The entropy weight method was used to reduce the subjectivity the decision-maker's weighting and obtain the objective weight of each indicator; The VIKOR method was used to obtain the best ranking of alternative schemes, and then the optimal interface design scheme was selected; Results: The evaluation of in-vehicle HUD interface design schemes were taken as an example for verification and calculation. The results showed that this method considers the subjectivity and uncertainty of the decision-making process in the optimization of design scheme, which can effectively improve the objectivity and accuracy of the evaluation results, and provide a reference for designers to optimize interface design schemes.

Whether there is any inclination between structures and functions of antimicrobial peptides (AMPs) is a mystery yet to be unraveled. AMPs have various structures associated with many different antimicrobial functions, including antibacterial, anticancer, antifungal, antiparasitic and antiviral activities. However, none has yet reported any antimicrobial functional tendency within a specific category of protein/peptide structures nor any structural tendency of a specific antimicrobial function with respect to AMPs. Here we examine the relationships between structures categorized by three structural classification methods (CATH, SCOP and TM) and seven antimicrobial functions with respect to AMPs using an enrichment analysis. The results show that antifungal activities of AMPs were tightly related to two-layer sandwich structure of CATH, knottin fold of SCOP, and the first structural cluster of TM. The associations with knottin and TM cluster 1 even sustained through the AMPs with a low sequence identity. Besides, another significant mutual enrichment was observed between the third cluster of TM and anti-gram-positive-bacterial/anti-gram-negative-bacterial activities. The findings of the structure-function inclination further our understanding of AMPs and could help us design or discover new therapeutic potential AMPs.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

Heptanuclear {GdIII7} (complex 1) and tetradecanuclear {GdIII14} (complex 2) have been synthesized using a Rhodamine 6G ligand HL (Rhodamine 6G salicylaldehyde hydrazone) and characterized. Complex 1 has a rare disc-shaped structure, where the central Gd ion was connected to the peripheral six GdIII ions via CH3O-/μ3-OH- bridges. Complex 2 has an unexpected three-layer double sandwich structure with a rare μ6-O2- ion in the center of the cluster. Magnetic studies revealed that complex 1 exhibits a magnetic entropy change of 17.4 J kg−1 K−1 at 3 K and 5 T. On the other hand, complex 2 shows a higher magnetic entropy change of 22.3 J kg−1 K−1 at 2 K and 5 T.

This work focuses on the bifurcation behavior before chaos phenomenon happens. Traditional numerical method is unable to solve the unstable limit cycle of nonlinear system. One algorithm is introduced to solve the unstable one, which is based one of the continuation method is called DEPAR approach. Combined with analytic and numerical method, the two stable and symmetrical equilibrium solutions exist through Fork bifurcation and the unstable and symmetrical limit cycles exist through Hopf bifurcation of Lorenz system. With the continuation algorithm, the bifurcation behavior and its phase diagram is solved. The results demonstrate the unstable periodical solution is around the equilibrium solution, besides the trajectory into the unstable area cannot escape but only converge to the equilibrium solution.

Both lung adenocarcinoma and SARS-CoV-2 infection could cause pulmonary inflammation. Angiotensin-converting enzyme 2, not only as the functional receptor of SARS-CoV-2 but also play key role in lung adenocarcinoma. To study the risk of SARS-CoV-2 infection in lung adenocarcinoma patients, mRNA and miRNA profiles were obtained from TCGA and GEO databases followed by bioinformatics analysis. A regulatory network which regards angiotensin-converting enzyme 2 as the center would be structured. In addition, via immunological analysis about key factors in lung adenocarcinoma patients, to explore the essential reasons for the susceptibility of SARS-CoV-2. Compared with normal tissue, angiotensin-converting enzyme 2 was increased in lung adenocarcinoma patients. Furthermore, a total of 7 differently expressed correlated mRNAs (ACE2, CXCL9, MMP12, IL6, AZU1, FCN3, HYAL1 and IRAK3) and 5 differently expressed correlated miRNAs (miR-125b-5p, miR-9-5p, miR-130b-5p, miR-381-3p and miR-421) were screened followed by enrichment analysis. Interestingly, toll-like receptor signaling pathway with the most frequent occurrence was enriched by mRNA (IL6) and miRNA (miR-125b-5p) sets simultaneously. Finally through comprehensive analysis, it was assumed that miR-125b-5p-ACE2-IL6 axis in the structured regulatory network could alter risk of SARS-CoV-2 infection in lung adenocarcinoma patients.

Aim: This study aimed to investigate the association between periampullary diverticulum (PAD) and difficult biliary cannulation, as well as to evaluate the impact of different types of PAD on the cannulation success rate and adverse events. Methods: A total of 636 patients who underwent endoscopic retrograde cholangiopancreatography (ERCP) during the study period were prospectively studied and divided into two groups based on the presence or absence of PAD. In group A, 126 patients had PAD compared with 510 patients in group B without PAD. The primary outcome measurements were ERCP procedures time, selective cannulation techniques, and cannulation difficulty in addition to cannulation success rate and ERCP-related adverse events. The difficult cannulation was analyzed using logistic regression considering age, co-morbidities, the presence of PAD types, and indications as independent factors. Results: The average cohort age was 65.30±16.67 years, and 52.7% were male. Significant higher rates of choledocholithiasis, cholangitis, and biliary pancreatitis were reported in the group of PAD (p<0.05). Successful selective cannulation was achieved in 97.6% in group A and 95.3% in group B (p>0.05). The cannulation time was significantly longer in the presence of PAD (5.1 min, vs. 4.09 min, p<0.05). There was no significant difference in the rate of overall adverse events and post ERCP pancreatic PEP. Conclusion: The presence of PAD did not affect the duration or success of the ERCP procedure. Furthermore, it was associated with longer cannulation time and increase in the cannulation difficulty, especially with PAD type 1.

Abstract: Female pelvic floor dysfunction (FPFD) is a series of conditions caused by the displacement or dysfunction of the patient's pelvic organs due to defects, injuries, and deterioration of the pelvic floor supportive structures, with stress urinary incontinence and pelvic organ prolapse being the most common. The mechanism of pelvic floor dysfunction is complex and not yet fully understood, and is often related to one or more functional abnormalities of muscles, connective tissues, fascia, nerves and blood vessels. Specialized clinicians are often unable to cover the comprehensive medical professional information of various specialties such as gynecology and obstetrics, urology, anus and intestines, imaging, etc., and the systematic and comprehensive diagnosis and treatment of the disease will be limited. The application of artificial intelligence concepts in the medical field not only improves the diagnostic efficiency of physicians, but also provides reference and basis for the development of comprehensive treatment programs. This article follows and draws on the diagnosis and treatment process of pelvic floor dysfunction recommended in various guidelines, incorporates physical therapy techniques into the conventional non-surgical treatment of pelvic floor dysfunction, provides guidance for the diagnosis and treatment of pelvic floor dysfunction, and briefly introduces the clinical application and value of pelvic floor intelligent diagnosis and treatment in pelvic floor dysfunction.

Safe administration of highly cytotoxic chemotherapeutic drugs is a challenging problem in cancer treatment due to the adverse side effects and collateral damage to non-tumorigenic cells. To mitigate these problems, new promising approaches, based on the paradigm of controlled targeted drug delivery (TDD), utilizing drug nanocarriers with biorecognition ability to selectively target neoplastic cells, are being considered in cancer therapy. Herein, we report on the design and testing of a nanoparticle-grid based biosensing platform to aid in the development of new targeted drug nanocarriers. The proposed sensor grid consists of superparamagnetic gold-coated core-shell Fe₂Ni@Au nanoparticles, further functionalized with folic acid targeting ligand, model thiolated chemotherapeutic drug doxorubicin (DOX), and a biocompatibility agent, 3,6,-dioxa-octanethiol (DOOT). The employed dual transduction based on electrochemical and enhanced Raman scattering detection have enabled efficient monitoring of the drug loading onto the nanocarriers, attached to the sensor surface, as well as the drug release under simulated intracellular conditions. The grid’s nanoparticles serve here as the model nanocarriers for new TDD systems under design and optimization. The superparamagnetic properties of the Fe₂Ni@Au NPs aid in nanoparticles’ handling and constructing a dense sensor grid with high plasmonic enhancement of the Raman signals due to the minimal interparticle distance.

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

Chickens have extensive phenotypic variation. The Wuhua yellow chicken (WHYC) is an important traditional yellow-feathered chicken in China, characterized by white tail feathers, white flight feathers, and strong disease resistance. However, the genomic basis of traits associated with WHYC is still poorly understood. In this study, whole genome resequencing was performed with an average coverage of 20.77-fold to investigate heritable variation and identify selection signals in WHYC. Reads were mapped onto the chicken reference genome (Galgal5) with a coverage of 85.95%. After quality control, 11,953,471 SNPs and 1,069,574 InDels were obtained. In addition, 41,408 structural variants and 33,278 copy number variants were found. A comparative genomic analysis of WHYC and other yellow-feathered chicken showed that selected regions were enriched in genes involved in transport and catabolism, immune system, infectious diseases, signal transduction, and signaling molecules and interaction. Several genes associated with disease resistance were identified, including IFNA, IFNB, CD86, IL18, IL11RA, VEGFC, and ATG10. Furthermore, PMEL and TYRP1 may contribute to the coloring of white feathers in WHYC. These findings improve our understanding of the genetic characteristics of WHYC and may contribute to future breed improvement.

Three monoruthenium complexes 1(PF6)23(PF6)2 bearing a N(CH3)-bridged ligand have been synthesized and characterized. These complexes have a general formula of [Ru(bpy)2(L)](PF6)2, where L is a 2,5-di(N-methyl-N-(pyrid-2-yl)amino)pyrazine (dapz) derivative with various substituents and bpy is 2,2′-bipyridine. The photophyscial and electrochemical properties of these compounds have been examined. The solid-state structure of complex 3(PF6)2 is studied by single crystal X-ray analysis. These complexes show two well-separated emission bands centered at 451 and 646 nm (max = 195 nm) for 1(PF6)2, 465 and 627 nm (max = 162 nm) for 2(PF6)2, and 455 and 608 nm (max = 153 nm) for 3(PF6)2 in dilute acetonitrile solution, respectively. The emission maxima of the higher-energy emission bands of these complexes are similar, while the lower-energy emission bands are dependent on the electronic nature of substituents. These complexes display two consecutive redox couples owing to the stepwise oxidation of the N(CH3)-bridged ligand and ruthenium component. In addition, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations are carried out to elucidate these experimental findings.

Currently, active and intelligent packaging have been developed to solve the spoilage problem for rich protein foods during storage, especially by adding anthocyanin extracts. In such film system, the antioxidant and antibacterial properties were dramatically increased by adding anthocyanins. The physicochemical properties were enhanced through interactions between active groups in anthocyanins and reactive groups in polymer chains. Additionally, active and intelligent film could monitor the spoilage of rich protein foods in response to pH change. Therefore, this film could monitor the sensory acceptance, and extending the shelf life of rich protein foods simultaneously. In this paper, the structure and functional properties of anthocyanins, the composite actions of anthocyanin extracts and biomass materials, and their reinforced properties of active and intelligent film were discussed. Also, the applications of this film in quality maintenance, shelf-life extension and quality monitoring for fresh meat, aquatic product and milk were summarized. The film that achieves high stability and continuous release of anthocyanins on demand will become an underlying trend in packaging applications for rich protein foods.

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

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

Food waste is a common issue arising from grinding of food by experimental animals, leading to excessive food scraps falling into cages. In the wild, animals grind food by gnawing vegetation and seeds, potentially damaging the ecological environment. However, limited ecology studies have focused on food grinding behavior since the last century, with even fewer on rodent food grinding, particularly recently. Although food grinding function is partially understood, biological purposes remain under-investigated and driving factors unclear. This review aims to explain potential causes of animal food grinding, identify influencing factors, and discuss contexts and limitations. Specifically, we emphasize recent progress on gut microbiota significance for food grinding. Moreover, we show abnormal food grinding is determined by degree of excess normal behavior, emphasizing food grinding is not meaningless. Findings from this review promote comprehensive research on the myriad factors, multifaceted roles, and intricate evolution underlying food grinding behavior, benefiting laboratory animal husbandry and ecological environment protection, and identifying potential physiological benefits yet undiscovered.

Terrestrial plant resources are becoming increasingly scarce; moreover, the development of science and technology has facilitated the exploration of marine ecosystems. We isolated and identified six novel (1–6) and six (7–12) known secondary metabolites of Alternaria sp MCCC 3A00467 by chromatographic and spectroscopic techniques and investigated their antitumor activities in human myeloma (U266), liver cancer (HepG2), and lung cancer (A549) cells by the MTT assay. Among these compounds, the new compounds 2, 3 and 4 exhibited excellent anticancer activities with IC₅₀ values of 21.98, 24.99 and 14.78 μg/mL, respectively. These compounds obtained from a marine source have the potential to be developed into novel anticancer drugs.

Our skin is constantly exposed to blue light (BL), which is abundant in sunlight and emitted by digital devices. Prolonged exposure to BL can lead to oxidative stress-induced damages and skin hyperpigmentation. In this study, we investigated the protective effects of tocotrienol-rich fraction (TRF) on BL-induced oxidative stress and hyperpigmentation in B16-F1 melanocytes. Alpha-tocopherol (αTP) was used as a comparator. Our results showed that TRF effectively suppressed the formation of reactive oxygen species, and preserved the mitochondrial membrane potential. Additionally, TRF exhibited anti-apoptotic properties by reducing the activation of p38 mitogen-activated protein kinase molecule and downregulating the expression of cleaved caspase-3. Moreover, TRF modulated tyrosinase activity, resulting in a lowered rate of melanogenesis and reduced melanin production. In contrast, αTP did not exhibit the significant protective effects against skin damages and pigmentation in BL-induced B16-F1 cells. Therefore, this study indicates that TRF may offer superior protective effects over αTP against the effects of BL on melanocytes. These findings demonstrate the potential of TRF as a protective natural active against BL-induced skin damages and hyperpigmentation via its antioxidative and anti-melanogenic properties.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

The fluorine-less noble-gas containing anions OBONgO- and OCNNgO-have been studied by correlated electronic structure calculation and density functional theory. The obtained energetics indicates that for Ng = Kr and Xe, these anions should be kinetically stable at low temperature. The molecular structures and electron density distribution suggests that these anions are stabilized by ion-induced dipole interactions with charges concentrated on the electronegative OBO and OCN groups. The current study shows that in additional to the fluoride ion, polyatomic groups with strong electronic affinities can also form stable noble-gas containing anions of the type X-...NgO.

Food insecurity has adverse consequences on women and child health in a developing country. This study aims to fill the existing research gap by examining the dynamic impacts of food insecurity on women and child health outcomes, this study adds fresh large scale panel data; and unlike the existing studies, this study estimates the short-run dynamics on food insecurity on women as child health of developing countries. We found that there was a positive association between health expenditure, women's fertility rate, women, and child health outcomes. There was a negative and statistically significant impact of food insecurity on women anemia in developing countries of Asia. Overall, the empirical analysis found that there was a strong strength to be a negative correlation between food insecurity and women and child health outcome, particularly in relation to women’s participation as a productive labor force. The study suggests that there is need to multidimensional approaches such as women and child health outcome, is needed to advance this type of research areas and should be followed broad-spectrum policy interventions to improve the women and child health status as part of sustainable development goals.

Composite fiber materials are superior materials due to their high strength and light weight. Composites reflect the properties of their constituents, which is proportional to the volume fraction of each phase. There are different fiber reinforcement types and each affects it’s flexural, tensile and compression strength. When selecting a composite for a specific application, the forces excreted on the composite must be known in order to determine the reinforcement type. Unidirectional fiber reinforcement will allow very strong load resistance but only in one direction where as a random orientated fiber reinforcement can resist less load but can maintain this quota in all directions. These materials are said to be anisotropic. Certain composite fibers, taking into consideration there weights, are physically stronger than conventional metals. This research deals with the analysis of three composite materials with different reinforcement types, volume fraction and phase content. It was found that material A (glass epoxy) was the strongest in the longitudinal direction with a flexural strength of 534 MPa in the longitudinal and 420 MPa in the transverse direction. The flexural stresses of material B (glass silicone) and material C (glass polyester) where both found in the 120 to 135 MPa range. Differences were due to their differences in matrix composition and reinforcement type.

In mesoscale eddies, chemical properties and biological composition are different from those in surrounding water, due to their unique physical processes. It is unclear how physical-biological coupling occurs in warm-core eddies, particularly since no study has examined the relationship between bacteria and viruses. The purpose of the present study was to examine the influence of anticyclonic warm eddy on the relationship between bacterial and viral abundances as well as viral activity (viral production) in different depths. In the core of warm eddy, bacterial abundance (0.48 to 2.82 ×105 cells mL−1) fluctuated less than that of the outside eddy (1.12 to 7.03×105 cells mL−1). In particular, there was four-folds higher of viral-bacterial abundance ratio (VBR) estimated within the eddy below DCM than the outside eddy. We suggest that prevailing physical forces with downwelling at the center of the anticyclonic warm eddy, viruses can also be transmitted into the deep ocean directly through adsorption on particulate organic matter during sinking. Overall, our findings provide valuable insights into the interaction between bacterial and viral communities and their ecological mechanism within the warm eddy.

The continental shale layers are more complex in matrix pore-microfracture interactions and connectivity characteristics under the influence of tuff devitrification, which limits the explicit understanding of the coupling relationship between wettability and pore structure, as well as hydrocarbon flow behavior. Therefore, the influence of tuffaceous components on the reservoir formation mechanism and oil-rich effect of the continental shale formation system is a critical issue to be solved. The Upper Triassic Yanchang Formation tuffaceous shale in Ordos Basin and the Middle Permian Jingjingzigou Formation tuffaceous shale in the southern margin of Junggar Basin is taken as the research objects, using whole-rock mineral composition analysis, FE-SEM observation and digital image processing and extraction, as well as wettability angle determination and spontaneous imbibition experiments to illustrate the influence of the difference of devitrification extent on reservoir pore connectivity and interfacial wettability in two different environments of freshwater and saline lacustrine basins, and summarize the reservoir formation differential pattern under the influence of devitrification. The results show that the saline lacustrine is significantly modified by devitrification. The pore plane porosity of the devitrified tuffaceous shales in the saline lacustrine is higher than that of the tuffaceous shales in the freshwater; the oleophilic of the saline lacustrine tuffaceous shales is better than that of the tuffaceous shales in the freshwater, moreover, the pore connectivity of the saline lacustrine tuffaceous shales is better than that of the tuffaceous shales in the freshwater. Wettability and connectivity control the storage performance of shale oil in the tuffaceous shales formation system. The more oleophilic the reservoir wettability is, the better the pore connectivity is, and the better the reservoir of tuffaceous shale formation system is. According to the different extents of devitrification, two different reservoir formation models of freshwater and saline lacustrine are established, which are of great theoretical and practical significance to enrich the theory of shale oil enrichment and reservoir formation in the continental shale formation system and to guide the exploration of continental shale oil benefits.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

Silicon anodes have been considered one of the most promising anode candidates for the next generation of high-energy density lithium-ion batteries due to the high theoretical specific capacity (4200 mAh g-1) of Si. However, high lithiation capacity endows silicon anodes with severe volume expansion effects during the charge/discharge cycling. The repeated volume expansions not only lead to the pulverization of silicon particles and the separation of electrode materials from the current collector, but also brings rupture/formation of solid electrolyte interface (SEI) and also continuous electrolyte consumption, which seriously hinders the commercial application of silicon anodes. Structural design and optimization is the key to improving the electrochemical performances of silicon anodes, which has attracted wide attention and research in recent years. This paper mainly summarizes and compares the latest research progress for the structural design and optimization of silicon anodes.

This study aimed to present a systematic meta-analysis examining the association between Helicobacter pylori (H. pylori) infection, hemoglobin A1c levels, and the development of type 1 diabetes mellitus. The initial search identified 451 articles related to the association between H. pylori infection and T1DM. Among them, 14 articles had 2,971 participants meeting the inclusion criteria for advanced meta-analysis. A significant association was observed between H. pylori infection and T1DM, with an odds ratio (OR) of 1.87 (95% confidence interval [CI]: 1.27–2.78, p = 0.002). A subgroup analysis on H. pylori status showed that a longer duration of diabetes diagnosis and higher hemoglobin A1c levels were significantly associated with H. pylori infection (p < 0.001 for both). However, there was no significant association between H. pylori infection and the diagnostic age of diabetes (p = 0.306). These findings contribute to the understanding of the association between H. pylori infection and T1DM, thereby highlighting the potential role of H. pylori in influencing the duration and glycemic control of diabetes.

There is an increasing trend that Unmanned Combat Air Vehicles (UCAVs) are employed to complete different combat missions in modern wars. This paper investigates a UCAV routing problem, which simultaneously considers the decisions for the configuration of weapons carried by the UCAV subject to its capacity and the allocation of weapons to the targets subject to their destroying requirements. An integer linear programming model is developed to formulate the problem. An adaptive large neighborhood search (ALNS) heuristic is proposed to solve the problem, in which seven neighborhood structures are designed and employed. Randomly generated instances covering the small, medium and large sizes are used to test the proposed ALNS algorithm. CPLEX is also utilized to solve the small-size instances, whose results are compared with that obtained by the ALNS algorithm. And the extensive experimental results confirm the effectiveness and superiority of the proposed ALNS algorithm.

Amylin, also known as islet amyloid polypeptide (IAPP), is a metabolic homeostasis-related hormone that is produced and released by the β cells of the pancreas, the same cells that produce insulin. Insulin-degrading enzyme (IDE) is a protease enzyme that plays an essential role in the breakdown and degradation of various peptides, including insulin and amylin. Direct binding & interaction between amylin and IDE is inextricably linked to the degradation of amylin, and research and development effort in this area is crucial to understand the potential therapeutic implications of disrupting the IDE-amylin interaction in the context of conditions where metabolic homeostasis needs to be regulated, such as diabetes and obesity. Here, this article incorporates currently available experimental complex structure of amylin and IDE, and delves deep into the interstructural biophysics underlying the binding interface of the two interacting partners. With a set of comprehensive structural biophysical analysis, this article identified an intriguing region of high electrostatic potential indicative of strong binding sites between the first N-terminal lysine (Lys1, K1) residue of amylin and Glu341 (E341) of IDE. This unique electrostatic hotspot presented herein paves the way for the rational design of drug-like small molecules that can selectively disrupt this interaction, offering a targeted therapeutic strategy for improved metabolic homeostasis, particularly for patients with diabetes and obesity.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Hyperuricemia (HU), characterized by elevated uric acid (UA) levels in the blood, represents a global health concern associated with various conditions, including cardiovascular diseases, gout, hypertension, metabolic syndrome, renal dysfunction, and neurodegenerative diseases. This review offers a contemporary analysis of HU, encompassing its causes, associated health risks, diagnosis, treatment, and future outlook. Recent studies have underscored the multifaceted origin of HU, implicating genetic predisposition, dietary patterns, lifestyle choices, and environmental influences. Genetic variations affecting enzymes and transporters involved in purine metabolism and UA excretion have been identified, laying the groundwork for personalized treatment strategies. Advances in diagnostic imaging and omics technologies provide enhanced precision in the detection and evaluation of risks. While pharmacological interventions remain central to managing HU, persistent challenges such as treatment resistance prompt exploration of novel drug targets and lifestyle modifications. Chinese herbal medicines offer a potentially alternative approach with fewer side effects. Emerging research on the impact of gut microbiota on UA metabolism opens new therapeutic avenues. Despite progress, challenges such as optimizing treatment duration and understanding long-term effects persist. Collaborative efforts are crucial to address these challenges and advance our comprehension of HU. The integration of precision medicine and holistic patient care approaches holds promise for improving outcomes and enhancing quality of life for individuals with HU.

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

Imidacloprid, a neonicotinoid, has proven as efficacious against hemipterans including the brown planthopper, Nilaparvata lugens (Stål). Frequent use, however, has resulted in high levels of resistance to imidacloprid among brown planthopper populations. Endosymbionts of insects are important for host insect growth and development and seem to play a role in resistance to imidacloprid. In this study, polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) was used to analyze the bacterial and yeast-like-symbiont communities of imidacloprid-resistant and imidacloprid-susceptible brown planthopper populations. The Shannon-Weaver diversity index and the evenness index indicated that there were no differences in the composition of the symbiotic communities of resistant and susceptible with a similarity coefficient of 0.53 for bacterial symbionts and 0.56 for yeast-like symbionts for the two planthopper populations. Sequence comparison analysis showed that the numbers of bacterial species in the imidacloprid-resistant versus the imidacloprid-susceptible populations were not significantly different. The bacterial species in the susceptible population were members of the Enterobacteriaceae and Moraxellaceae, while these in the resistant population were members of the Enterobacteriaceae, Oxalobacteriaceae, Rhodobacteriaceae, and Sphingomonadaceae. The imidacloprid-susceptible population had more yeast-like symbiont species than the resistant population.

Market uncertainty has a significant impact on market performance. Previous studies put much efforts in investigations on market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides quantity of transactions and market efficiency. In this paper, we put forward the concept of transaction entropy to measure the market uncertainty and see how it changes with price. The definition of transaction entropy is the ratio of total information entropy of all traders to quantity of transactions, reflecting the level of uncertainty in making transactions. And the transaction entropy is the lowest at equilibrium, it will be decreasing in a shortage market, and increasing in a surplus market. Additionally, we make a comparison of total entropy of the centralized market with that of the decentralized market, revealing that the price filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance.

Chronic hepatitis B infection remains a significant worldwide health burden, placing persons at risk for hepatocellular cancer and hepatic fibrosis. Chronic hepatitis B virus (CHB) infection is characterized by elevated levels of immunosuppressive regulatory T cells (Tregs), which can inhibit the function of effector T cells and lead to an insufficient immune clearance response against HBV in the host. Theoretically, suppression of Treg functionality and percentage could increase anti-HBV reactivity in CHB-infected patients, although this has not yet been explored. We attempted to enhance our previously established anti-CHB protocol utilizing the GM-CSF+IFN-α+rHBVvac regimen (GMI-HBVac) by incorporating Mafosfamide (MAF), which has been utilized in anticancer therapy in the past. Intravenous administration of MAF to rAAV8-1.3HBV-infected mice resulted in a dose-dependent reduction of Tregs in the blood, rebounding to pretreatment levels 10 days later. To determine the efficacy of anti-CHB, 2ug/ml MAF was combined with the GMI-HBVac in an HBV-infected animal model as an anti-Treg treatment. When rAAV8-1.3HBV-infected mice were immunized with MAF+GMI-HBVac, peripheral blood Tregs decreased significantly, leading to dendritic cell activation, HBV-specific T cell proliferation, and upregulation of IFN-gamma producing CD8+T cells. In addition, MAF+GMI-HBVac vaccination stimulates T cell infiltration in HBV-infected liver. These conditions may contribute to an enhanced immune response and the clearance of HBV-associated antigens, including serum HBsAg, serum HBcAg, and HBcAg+ hepatocytes. Overall, this is the first indication that MAF was utilized as an adjuvant to deplete Tregs and paired with GMI-HBVac as a unique therapeutic vaccine regimen against established CHB mice to produce a functional cure, as seen by the remarkable clearance of HBsAg.

This paper studies the optimization method of pump hydraulic performance based on the response surface method. A parametric model of impeller and diffuser is established. Three-dimensional optimization is carried out on the basis of the initial model obtained by one-dimensional design method. We select the pump hydraulic efficiency and the head as objective function and constraint function. Response surface models are constructed to analyze the relationship between the objectives and the design variables, and the global optimization of hydraulic performance is realized. According to the internal flow characteristics of pump, this paper proposes the strategy of two steps optimization, which aims at meridional plane and blade shape, respectively, to solve the problem of large numbers of design parameters and computational cost. The optimization results show that the hydraulic efficiency of pump increased by 3.7%, and the head is nearly the same.

An eight-sided prism sample, obtained from a hot-rolled AZ31 magnesium alloy sheet, was compressed at room temperature along the transverse direction to investigate the influence of local strain on twinning behavior using electron backscatter diffraction (EBSD) measurements. The octagonal surface of the sample was divided into distinct regions based on hardness distribution and metallographic observations. The EBSD maps of different areas show significant variations, suggesting that these areas experienced different local strain conditions. The EBSD measurements provided detailed data from three distinct regions, with the strain state known in two regions and uncertain in the third. The combined analysis of the Schmid factor (SF) and the strain compatibility factor (m') was employed to study twin variant selection. The comparison of twinning behavior in different regions showed that the strain compatibility factor offered a more comprehensive explanation for the observed twinning pattern in the region with local shear strain.

The fatigue life of concrete is affected by many interwoven factors whose effect is nonlinear. Be-cause of its unique self-learning ability and strong generalization capability, the Bayesian regu-larized backpropagation neural network (BR-BPNN) is proposed to predict concrete behavior in tensile fatigue. The optimal model was determined through various combinations of network parameters. The average relative impact value (ARIV) was constructed to evaluate the correla-tion between fatigue life and its influencing parameters (maximum stress level Smax, stress ratio R, static strength f, failure probability P). ARIV results were also compared with other factor as-sessment methods (weight equation and multiple linear regression analyses). Using BR-BPNN, S-N curves were then obtained for the combinations of R=0.1, 0.2, 0.5; f=5, 6, 7MPa; P=5%, 50%, 95%. The tensile fatigue results under different testing conditions were finally compared for compatibility. It was concluded that Smax has the most significant negative effect on fatigue life; the degree of influence of R, P, and f, which positively correlate with fatigue life, decreases suc-cessively. ARIV is confirmed as a feasible way to analyze the importance of parameters and could be recommended for future applications. The tensile fatigue performance of plain concrete under different stress states (flexural tension, axial tension, splitting tension) does not differ sig-nificantly. Besides utilizing the valuable fatigue test data scattered in the literature, insights gained from this work could provide a reference for subsequent fatigue test program design and fatigue evaluation.

Since the launch of Protein Data Bank (PDB) in 1971, Cartesian coordinate system (CCS) has been the default approach to specify atomic positions in biomolecular experimental structures with X, Y and Z. In 2020, a local spherical coordinate system (LSCS) approach was proposed as an alternative to CCS, i.e., ρ, θ and φ. Recently, the continued application of deep learning technology in protein structure prediction (PSP) saw a leap forward in the accuracy of PSP, as evidenced by AlphaFold of Google’s DeepMind. However, there still is room for the improvement of the performances of PSP algorithms to date. Given that geometrically, CCS and LSCS are like the two sides of a coin, this short article puts forward a hypothesis that the time is now ripe to end the half-a-century burial of ρ, θ and φ in PDB, and use them as LSCS features for the design of novel PSP algorithms in future.

Different soil nutrients affect plant metabolites accumulation characteristics. The main soil nutrients and their correlation with Pepino metabolites were investigated in this study to evaluate differences between greenhouses on the Loess Plateau in northwest China. A total of 269 Pepino metabolites in the fruits were identified using a UPLC-QTOF-MS approach from plants grown in three major Pepino growing regions. Their differential distribution characteristics were analyzed. 99 metabolites differed among the Pepino fruits in the three regions. The main classes of the differentially accumulated metabolites were ranked as Amino acids and derivatives, Nucleotides and derivatives, Organic acids, Alkaloids, Vitamins, Saccharides and Alcohols, Phenolic acids, Lipids, and others. Environmental factor analysis indicated that soil nutrients were the primary differentiating factor. Five soil nutrient indicators: TN（total nitrogen）, TP（total phosphorus）, AP（available phosphorus）, AK（available potassium）, and OM（organic matter）, exhibited significant differences in three growing sites. Metabolite and soil nutrient association analysis using redundancy analysis (RDA) and Mantel test indicated that TNand OM contributed to the accumulation of amino acids and derivatives, nucleotides and derivatives, and alkaloids while inhibiting organic acids, vitamins coagulation biosynthesis. Moreover, AP and TP were associated with the highest accumulation of saccharides and, alcohols, phenolic acids. Consequently, differences in soil nutrients were reflected in Pepino metabolites variability. This study clarified the metabolite variability and the relationship between Pepino and soil nutrients in the main planting areas of northwest China. It provides a theoretical basis for the subsequent development of Pepino's nutritional value and cultivation management.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

Multiple sclerosis is an autoinflammatory condition that results in damage of myelinated neurons in affected patients. While disease-modifying treatments have been successful in slowing the progression of relapsing remittent disease, most patients still progress to secondary progressive disease that is largely unresponsive to disease-modifying treatments. Similarly, there is currently no effective treatment for patients with primary progressive MS. Innate and adaptive immune cells in the CNS play a critical role in initiating an autoimmune attack and in maintaining the chronic inflammation that drives disease progression. In this review, we will focus on recent insights into the role of T cells with regulatory function in suppressing the progression of MS, and, more importantly, in promoting the remyelination and repair of MS lesions in the CNS. We will discuss the exciting opportunity to genetically reprogram regulatory T cells to achieve immune suppression and enhance repair locally at sites of tissue damage, while retaining a fully competent immune system outside the CNS. In the future, reprogramed regulatory T cells with defined specificity and function may provide life medicines that can persist in patients and achieve lasting disease suppression after one cycle of treatment.

We present a new penalized method for estimation in sparse logistic regression models with group structure. Group sparsity suggests us to consider the Group Lasso penalty. Being different from penalized log-likelihood estimation, our method can be viewed as a penalized weighted score function method. Under some mild conditions, we provide non-asymptotic oracle inequalities promoting group sparsity of predictors. A modified block coordinate descent algorithm based on a weighted score function is also employed. The net advantage of our algorithm over the existing Group Lasso-type procedures is that the tuning parameter can be pre-specified. The simulations show that this algorithm is considerably faster and more stable than competing methods. Finally, we illustrate our methodology with two real data sets.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

Because Newton's gravity and Einstein's general theory of relativity are macroscopic gravitational theories, therefore, this paper attempts to establish a set of quantum gravity theory of the microscopic expression of Newton's and Einstein's theory of gravity to make up for the shortcomings of the existing macro-gravity theory at the micro level, and further develop the macroscopic gravity theory into the microscopic field. Based on the cognition of the field theory model, from the general assumption, space-time is further regarded as an ideal fluid, from the perspective of the distribution of ideal fluid density, this paper derives the Newton's universal gravitational equation and Einstein's general relativity equation. On the micro level, the gravitational field is further interpreted as a gradient field of space-time density; the Newtonian potential is further interpreted as the density of an object at the micro level; gravity is further interpreted as the potential pressure(space-time pressure) exhibited by the gradient of the density of the object at the micro level; flection space-time is further interpreted as the embodiment of the gradient distribution of the ideal fluid (space-time) density at the micro level. Based on a modified gravitational model, it explains the dynamics of the stars' rotation and revolution.