Co-integration and Causality was built to conduct studies on causality relation between carbon intensity and coal consumption leading to providing important basis for the transition to a low carbon economy. The EG two-step method was performed to study the relation between carbon intensity and coal consumption of China during 1990-2015 and the co-integration and Granger test was constructed to build up the co-integration and error correction models for analysis of the interaction between carbon intensity and coal consumption. The results showed that in long term there is a stable co-integration relation and a positive correlation between carbon intensity and coal consumption; whereas fluctuations exist in short term and there is a one-way Granger causality of carbon intensity with respect to coal consumption.

Ti17 alloy is mainly used to manufacture aero-engine discs due to its excellent properties such as high strength, toughness and hardenability. It is often subjected to creep-fatigue cyclic loading in service environments. Shakedown theory describes the state in which the accumulated plastic strain of the material stabilizes after several cycles of cyclic loading, without affecting its initial function and leading to failure. This theory includes three behaviors: elastic shakedown, plastic shakedown, and ratcheting. In this paper, the creep-fatigue tests (CF) were conducted on Ti17 alloy at 300℃ to study its shakedown behavior under creep-fatigue cyclic loading. Based on the plasticity-creep superposition model, a theory model that accurately describes the shakedown behavior of Ti17 alloy was constructed, and ABAQUS finite element software was used to validate the accuracy of the model. TEM analysis was performed to observe the micro-mechanisms of shakedown in Ti17 alloy. The results reveal that the Ti17 alloy specimens exhibit plastic shakedown behavior after three cycles of creep-fatigue loading. The established finite element model can effectively predict the plastic shakedown process of Ti17 alloy, with a relative error between the experimental and simulation results within 4%. TEM results reveal that anelastic recovery controlled by dislocation bending and back stress hardening caused by inhomogeneous deformation are the main mechanisms for the plastic shakedown behavior of Ti17 alloy.

Flower color is an important ornamental feature, often modulated by the contents of flavonoid. Chalcone synthase is the first key enzyme in the biosynthesis of flavonoid, but little is known about the role of R. delavayi CHS in flavonoid biosynthesis. In this paper, three CHS genes (RdCHS1-3) were successfully cloned from R. delavayi flowers. According to multiple sequence alignment and phylogenetic analysis, only RdCHS1 contained all the highly conserved and important residues, and classified into the cluster of bona fide CHSs. Thus RdCHS1 was then subjected for further functional analysis. Real-Time PCR analysis revealed that transcripts of RdCHS1 was the highest in leaves and lowest in root, did not match the anthocyanin accumulation patterns during flower development. Biochemical characterization displayed that RdCHS1 could catalyze p-coumaroyl-CoA and malonyl-CoA molecules to produce naringenin chalcone. Physiological function of RdCHS1 was checked in Arabidopsis mutant and tobacco, the results showed RdCHS1 transgenes could recover the color phenotypes of tt4 mutant and made tobacco flower color from pink to dark pink through modulating the expression of endogenous structural and regulatory genes in tobacco. All these results demonstrated that RdCHS1 is a bona fide CHS and contribute to flavonoid biosynthesis in R. delavayi.

In this work, a ZIF-67/MWCNTs/Nafion sensor platform was constructed based on the good adsorption capacity of ZIF-67, the electrical conductivity of multiwalled carbon nanotubes (MWCNTs) and the excellent chemical stability of Nafion for the detection of Cu2+ in water. Meanwhile, the modified materials were characterized by scanning electron microscopy (SEM), Transmission electron microscopy (TEM), BET specific surface area test, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FT-IR). Cyclic voltammetry (CV), electrochemical impedance (EIS), and square wave solvation voltammetry (SWSV) electrochemical methods were used to perform applied test studies on ZIF-67/MWCNTs/Nafion/GCE. The results show that ZIF-67/MWCNTs/Nafion/GCE has high sensitivity (57.5 μA/μM) and a low limit of detection (15.0 nM) for the electrochemical detection of Cu2+ ions in an electrochemical sensing system. It has high adsorption selectivity for Cu2+, and the recovery of Cu2+ in real water reached 98.6%-103%. The modified electrode has good repeatability, reproducibility, anti-interference, and stability, which makes this sensing platform can be practically applied to the detection of domestic water.

The ridge-furrow plastic mulching (RFPM) system and nitrogen application have been effective strategies for improving crop productivity in the Loess Plateau of China. However, it is not clear how the ridge-furrow ratio and nitrogen fertilizer type (NT) affect the utilization of water, nitrogen, heat and radiation resources for increased rain-fed wheat production. Two nitrogen fertilizer types [Traditional urea (TU) and controlled-release urea (CRU)] and four planting patterns [conventional flat planting (F) and the RFPM system of 20 cm ridge with 40 cm furrow (R2F4), 40 cm ridge with 40 cm furrow (R4F4), and 60 cm ridge with 40 cm furrow (R6F4)] were tested from September, 2018 to June, 2021 during winter wheat growing season. The results showed that the RFPM system can increase the soil thermal time (TTsoil) by 41.0–152.1°C d compared to F. The thermal effect of the RFPM system shortens the vegetative growth period and extends the reproductive growth period by 2–7 days, promoting the increase of the leaf area index (LAI) and final dry matter (DM) accumulation. These significantly increased the grain yield (GY) in the RFPM system by 51.6–115.2% and enhanced the thermal time use efficiency (TUE) by 48–99.5%, water productivity (WP) by 37.4–76.3%, radiation use efficiency (RUE) by 16.3–34.4%, and partial factor productivity of nitrogen (PFPN) by 51.6–115.2% compared to F. Although a high ridge-furrow ratio combined with CRU increased GY and resources use efficiency, it also exacerbated the soil water depletion, especially in the 40–140 cm soil layer. Overall, applying CRU in combination with the RFPM system of 40 cm ridge with 40 cm furrow maximized resources use efficiency and increased wheat production in the Loess Plateau of China.

(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of -0.99. The dependency relation followed with -0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and -0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity.

Abstract: An Al-Co-Y diffusion coating was prepared on a TiAl alloy by pack cementation process at a low temperature range of 650-720 ℃. The influence of different activators and co-deposition temperatures on the coating’s microstructure and formation process, and high temperature oxida-tion performance was studied. The results showed that NaF was a more suitable activator for preparing the Al-Co-Y diffusion coating on TiAl alloy compared to NH4Cl and AlCl3. A more compact coating structure can be obtained when deposition was performed at 680 ℃ using NaF as the activator. The coating was mainly composed of a TiAl3 outer layer, a TiAl2 inner layer, and a thin Al-rich TiAl inter-diffusion layer. The growth of the coating exhibited a positive linear correlation between coating thickness increase and the square root of deposition time. The high-temperature oxidation tests showed that a dense scale mainly composed of Al2O3 and a small amount of TiO2 formed on the coating after oxidation at 950 ℃ for 100 h. The scale possessed excellent high-temperature oxidation resistance, with a parabolic rate constant of the oxidation weight gain approximately 4.9×10-3 mg2/cm4 h, which is lower than that of the TiAl substrate by about three orders of magnitude, and lower than that of the pure Al diffusion coating more than one order of magnitude.

In response to the problem of no supporting equipment for the cultivation of Codonopsis in the hilly and mountainous areas of northwest China, a combined machine for transplanting outcrop of Codonopsis with micro ridge covered with film is designed. The key components of the prototype are analyzed and designed, and the structures and working parameters of the seedbed preparation device, seedling casting device, rotary tillage soil-covering device, film covering device, seedling head burial and film edge soil-covering device are determined. The transmission system scheme is established, and the working mechanism of the core components is analyzed. Field experiments show that when the target seedling spacing is 4.4cm and the machines moves forward at the speed of 0.1, 0.15, and 0.2m/s, the variation coefficient of planting spacing and the qualification rate of planting depth all meet the standard requirements. The qualified rate of planting posture and film side outcrop are greatly affected by the operating speed of the machine, and decrease with the increase of operating speed. When the operating speed reaches 0.1m/s, the average variation coefficient of planting spacing is 0.08%, the average qualified rate of planting depth, planting posture and film side outcrop is 95.83%, 94.17% and 93.33% respectively, which shows that the operating performance is better than that of the operating speed of 0.15m/s and 0.2m/s. This study provides new reference for the theoretical research and design of mechanized and automated transplanting machinery for Codonopsis seedlings.

High in reliability, multi in function, strong in tracking and detecting, active phased array antennas have been widely applied in radar system. Heat dissipation is a major technological barrier preventing the realization of next-generation high-performance phased array antennas. As a result of the advancement of miniaturization and the in-tegration of microelectronics technology, the study and development of embedded di-rect cooling or heat dissipation has significantly enhanced the heat dissipation effect. In this paper, a novel swept-back fishnet embedded microchannel topology (SBFEMCT) is designed, and various microchannel models with different fishnet runner mesh den-sity ratios and different fishnet runner layers are established to characterize the chip Tmax, runner Pmax, and Tmax and analyze the thermal effect of SBFEMCT under these two operating conditions. The Pmax is reduced to 72.37% and 57.12% of the original at mesh density ratios of 0.5, 0.25, and 0.125, respectively. The maximum temperature reduction figures are average with little change in maximum velocity and a small increase in maximum pressure drop for the number of fishing mesh runner layers of 0-4. This paper provides a study of the latest embedded thermal dissipation from the dimension of a single chip to provide a certain degree of new ideas and ref-erences for solving the thermal technology bottleneck of next-generation high-performance phased array antennas.

This study was aimed to investigate the effect of xylan depolymerizing enzyme namely endo-xylanase (Xyn) combined with debranching enzymes namely arabinofuranosidase (Afd) and feruloyl esterase (FE) on digestion, growth performance and intestinal volatile fatty acid profile of piglets. The in vitro experiments were firstly conducted to examine the enzymological properties of Xyn, Afd and FE, the synergy among these enzymes, together with the effect of combination of these enzymes on digestion of piglet diet. The in vivo experiment was then implemented by allocating 270 35-d-old postweaning piglets into 3 treatment groups: control group, Xyn group and (Xyn+Afd+FE) group. Each group had 6 replicates (15 piglets/replicate). The results revealed a satisfying thermostability and pH stability of Xyn, Afd and FE. Combination of Xyn, Afd and FE had a superiority (P < 0.05) over Xyn alone and its combination with Afd or FE in promoting degradation of different bran fibers rich in arabinoxylan (Abx). Treatment with combination of Xyn, Afd and FE had advantages over Xyn alone to induce increasing trends (P < 0.10) of in vitro digestibility of dietary nutrients (dry matter, crude protein, crude ash and gross energy) and piglet growth performance (average daily gain, final body weight and feed efficiency), concurrent with a reduction (P < 0.05) of diarrhea rate and increases (P < 0.05) in cecal acetic acid, butyric acid and total volatile fatty acids concentrations as well as pH value of piglets. Collectively, combination of Xyn, Afd and FE was efficient in benefiting degradation of Abx in brans, as well as improving digestion, growth performance and intestinal volatile fatty acid profile of piglets.

The absolute absorption cross section of the ethyl peroxy radical, C2H5O2, in the Ã←X ̃ electronic transition with the peak wavelength at 7596 cm-1, has been determined by the method of dual wavelengths time resolved continuous wave cavity ring down spectroscopy. C2H5O2 radicals were generated from pulsed 351 nm photolysis of C2H6/Cl2 mixture in presence of O2 and detected on one of the CRDS paths. Two methods have been applied for the determination of the C2H5O2 absorption cross section: (i) based on Cl-atoms being converted alternatively to either C2H5O2 by adding C2H6 or to hydro peroxy radicals, HO2, by adding CH3OH to the mixture, whereby HO2 was reliably quantified on the second CRDS path in the 21 vibrational overtone at 6638.2 cm-1 (ii) based on the reaction of C2H5O2 with HO2, measured under either excess HO2 or under excess C2H5O2 concentration. Both methods lead to the same peak absorption cross section of C2H5O2,7596 cm-1 = (1.0±0.2) × 10-20 cm2. The rate constant for the cross reaction between of C2H5O2 and HO2 has been measured to be (6.5±1.6) × 10-12 cm3 molecule-1 s-1.

A typical vibrational spectrum in the ice phase has four separate bands: translation, libration, bending and stretching. Ice X, the final ice phase under high pressure, shows an exotic vibrational spectrum. Theoretically, an ideal crystal of ice X only has one peak at 998 cm-1 for Raman scattering and two peaks at 450 cm-1 and 1507 cm-1 for infrared absorption in this work. These three characteristic peaks are indicators of the phase transition between ice VII/VIII and ice X. Despite much experimental and theoretical work on ice X, only this study has clearly indicated these characteristic peaks in the region of the IR band. The phonon density of states shows quite different features than ice VIII, which could be verified by inelastic neutron scattering in the future. The dynamic processes of 15 vibrational normal modes are discussed and the typical hydrogen bonds are missing.

To investigate whether the ech42 gene in Clonostachys rosea can improve the biocontrol efficacy of Bacillus amyloliquefaciens and its molecular mechanism.Compared to the wild type, the B. amyloliquefaciens transformed with the ech42 gene exhibited a higher chitinase activity. The B. amyloliquefaciens-ech42 also showed a significantly higher biocontrol efficiency against B. cinerea when tomato plants were pre-treated with amyloliquefaciens-ech42. No significant difference of control efficiency was observed between the wild type and amyloliquefaciens-ech42 when tomato plants were first infected by B. cinerea. In addition, the activity of the defense-related enzyme polyphenol oxidase, but not superoxide dismutase was significantly higher in amyloliquefaciens-ech42 than in the wild type.The ech42 enhances the Bacillus amyloliquefaciens biocontrol efficiency by increasing the capacity of protection/prevention to plants, rather treating/killing the pathogens.

Glycoside Hydrolase 3 (GH3) is a phytohormone-responsive family of genes that has been found in many plant species. It is implicated in the biological activity of indolacetic (IAA) and jasmonic acids (JA), and also affects plant growth and developmental processes and some stresses. In this study, GH3 genes were identified in 48 plants, which belong to algae, moss, fern, gymnosperm and angiosperm. No GH3 representative gene has been found in algae, and our research identified 4 genes in mosses, 19 in ferns, 7 in gymnosperms, and numerous in Angiosperms. The results showed that GH3 genes mainly occur in seed plants. Phylogenetic analysis of all GH3 genes showed three separate clades. Group I was related to JA adenylation, group II was related to IAA adenylation, and group III was separated from group II but the function was not clear. The structure of GH3 protein indicated highly conserved sequence in the plant kingdom. The analysis of JA-adenylation related to gene expression of GH3 in potato (Solanum tuberosum) showed that StGH3.12 highly responded to Methyl Jasmonate (MeJA) treatment. Expression levels of StGH3.1, StGH3.11, and StGH3.12 were high in flower and StGH3.11 expression was also high in stolon. Our research revealed the evolution of the GH3 family, which is useful for studying the precise function about JA-adenylation GH3 genes in S. tuberosum under development and biotic stresses.

Contract farming can reduce transaction costs and improve agricultural productivity. With the establishment of stable and effective contractual relationships, not only have agricultural products been provided with a stable market, but the standardization, refinement, and branding transformation of agricultural products can also be realized, thus further promoting the progress of agricultural modernization. The willingness of farmers to renew their contracts is a key factor in maintaining long-term, stable cooperative relationships. This research has empirically verified the impact model of tie strength between companies and farmers in contractual agriculture on the willingness of farmers to renew their contracts and revealed the working mechanism of tie strength on sustainable participation of farmers in contract farming. This study has utilized the survey data from 286 agricultural households in Inner Mongolia, China, and the method of structural equation modeling（SEM）, with the following results:(1)Interaction and reciprocity have a significant positive influence on trust. (2)Trust has a significant positive impact on farmers' willingness to renew their contracts. Also, reciprocity and interaction have an indirect impact on contract renewal willingness through trust .(3)Perception of economic value can significantly increase the contract renewal willingness of farmers and plays a mediating role between trust and contract renewal willingness.Overall, on the basis of tie strength, this research has provided a new perspective for the investigation of the sustainable stability of contract farming and empirical evidence for the sustainable development of the contract farming supply chain.

Creep is a fundamental property naturally exists in some types of rock, which is significant for the long-term stability of roadways during the mining process. In this paper, the long-term strength of coal and rock were obtained via laboratory experiments, and a Cvisc elasto-viscoplastic model was adopted and introduced in FLAC3D, based on the 3101 transport roadway in Hongqinghe Coal Mine, to investigate the influence of creep on the stability of deep-high stress roadway. The test results show that the long-term strength of 3-1 coal and sandy mudstone, respectively, is 18.65 MPa, 39.95 MPa. The plastic zone, the deformation, and the damage of roadway surrounding rock behave an obvious increase after being excavated for 720d as the creep model was chosen. The plastic zone failure with models shear-p (1090.7 m3), shear-n (381.7 m3), tension-n (98.4 m3), and tension-p (30.8 m3), the damage value has an obvious increment of increased 21.2% (0.053), the deformation increases in the order is two-sidewalls (1978 mm), roof (907 mm), floor (101 mm). The creep of the roadway can be divided into three stages: the accelerating stage, the decaying stage, and the stable stage. The creep speed of each stage is greatly affected by the presence or absence of anchor spray support: the creep speed of the bare roadway roof, roadway side, and floor stability, respectively, is 1.01, 1.02, and 0.12 mm/d; After anchor spray support, the creep velocity, correspondingly, decreased to 0.69, 0.37, and 0.12 mm/d, and the amount of surrounding rock damage decreased from 0.302 to 0.243. This indicates that the anchor spray support can significantly reduce the creep effect of the roadway. The Cvisc creep model was verified reliable and can provide guidance for deep high-stress coal roadway support.

This paper applied the space-time generalized finite difference scheme for solving the nonlinear dispersive shallow water waves as the modified Camassa−Holm equation, modified Degasperis-Procesi equation, Fornberg-Whitham equation, and its modified form. The proposed meshless numerical scheme was composed of the space-time generalized finite difference method, the two-step Newton-Raphson method, and the time-marching method. The space-time approach can treat the temporal derivative as one of the spatial derivatives. This numerical technique enables all the partial derivatives in the governing equation can be discretized by a spatial discretization method, and the mixed derivative can efficiently deal with using the proposed meshless numerical scheme. The space-time generalized finite difference method is advanced from the Taylor series expansion and the moving-least square method. The numerical discretization process is only related to functional data and weighting coefficients on the central node and its nearby nodes. Thus, the matrix system composed of nonlinear algebraic equations will be a sparse matrix and can be efficiently solved by the two-step Newton-Raphson method. Furthermore, the time-marching method was utilized to proceed with the space-time domain along the time axis. In this paper, several numerical examples were tested to verify the capability of the proposed space-time generalized finite difference scheme.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

The objective of this study is to design an optimal model predictive control (MPC) strategy using manipulated variables to control the production of a sufficient amount of hydrogen through low temperature autothermal reforming of dimethyl ether (DME), a reforming reaction performed using PdO/ZnO/γ-Al2O3 catalysts coated on honeycomb cordierite ceramics. Experiments and simulation have verified that the optimal activity temperature of the catalyst is 400 °C, and the hydrogen volume fraction in syngas is over 43%. In the implementation of the hydrogen production system, the MPC controller can precisely determine the feed rates of DME, high-purity air, and water based on the space state equation of the reformer, to achieve the anti-disturbance of the reformer temperature. Thus, the reduction of hydrogen yield and sintering of the catalyst as a result of overheating are prevented. As the static and dynamic performance of hydrogen production exhibits excellent tracking of the setpoints, an autonomous, automated, and reliable continuous system was designed to meet the desired hydrogen demand situation. This study shows that an autonomous, automated, and reliable continuous hydrogen production reforming system can be designed to actively respond to the on-board hydrogen usage situation.

Based on the data of A-share listed companies in Shanghai and Shenzhen of China from 2011 to 2018 and the digital inclusive finance index of Peking University (2011-2018), this paper empirically tests the impact of digital finance development on corporate social responsibility in various provinces of China and its impact mechanism. The results show that: (1) the development of digital finance helps to promote the fulfillment of corporate social responsibility; (2) the influence mechanism of the development of digital finance to promote the fulfillment of corporate social responsibility lies in that it reduces the cost of debt financing and leads to the improvement of corporate social responsibility.(3) Further research shows that the positive relationship between digital finance and corporate social responsibility is more significant in private enterprises. At the same time, the impact is more significant in areas with poor market environment. The above research shows that the development of digital finance has a significant positive effect, which will improve the level of corporate social responsibility.

In this work, we discuss a newly proposed scalar-tensor theory in Minkowski spacetime. The basic frame in Minkowski spacetime is obtained. Consider a charged particle in the electromagnetic field, we make an attempt to connect this theory with conventional quantum theory. Two specific models are chosen to calculate values of the time delay.

Semisolid casting can provide excellent castings, but the nature of the pore-forming mechanisms has not been properly clarified. In the current communication, it was suggested that hydrogen precipitated during slurry making might have a decisive role in the formation of both gas and shrinkage porosity. Intensive stirring at the end of the slurry making process may act as a degassing step. Without the intense stirring, structures of primary slurry particles form around the hydrogen pores, strongly affecting pore formation and feeding during the intensification stage.

In the manufacturing industry, orders are typically scheduled and delivered through batches, and the probability of machine failure under high-load operation is high. On this basis, we focus on a single machine batch scheduling problem with a maintenance interval (SMBSP-MI). The studied problem is expressed by three-field representation as 1|B,MI|\sum{F_j+\mu}m, and the optimization objective is to minimize total flow time and delivery costs. Firstly, 1|B,MI|\sum{F_j+\mu}m is proved to be NP-hard by Turing reduction. Secondly, shortest processing time (SPT) order is shown the optimal scheduling of SMBSP-MI, and a dynamic programming algorithm based on SPT (DPA-SPT) with the time complexity of O(n^3T_1) is proposed. A small-scale example is designed to verify the feasibility of DPA-SPT. Finally, DPA-SPT is approximated to a fully-polynomial dynamic programming approximation algorithm based on SPT (FDPAA-SPT) by intervals partitioning technique. The proposed FDPAA-SPT runs in O(\frac{n^5}{\varepsilon^2})\ time with the approximation (1+\varepsilon).

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

A series of D–π–A diketopyrrolopyrrole (DPP)-based small molecules have been designed for organic light-emitting diodes (OLEDs) and organic solar cells (OSCs) applications. Appling the PBE0/6-31G(d,p) method, the ground state geometry and relevant electronic properties were investigated. The first excited singlet state geometry and the absorption and fluorescent spectra were simulated at the TD-PBE0/6-31G(d,p) level. The calculated results reveal that the photophysical properties are affected through the introduction of different end groups. Furthermore, the electronic transitions corresponding to absorption and emission exhibit intramolecular charge transfer feature. It was disclosed that the designed molecules act not only as luminescent for OLEDs, but also donor materials in OSCs. Moreover, they also can be used as potential electron transfer materials using for OLEDs and OSCs.

A new optical steganography scheme is proposed that transmits a stealth optical code-division multiple-access (OCDMA) signal through a public binary phase-shift keying (BPSK) channel. Polarization beam splitters and arrayed waveguide gratings are used to implement a spectral-polarization coding (SPC) system with an incoherent optical source. We employ a Walsh–Hadamard code as the signature code of the user who wants to transmit stealth information using the system. A free space optical link applied to this system maintains the polarization states of light during propagation. The secret data is extracted using correlation detection and balanced subtraction in the OCDMA decoder of the intended receiver, and the other signal from the public channel is reduced by the OCDMA decoder. At the demodulator of the public channel, BPSK demodulation eliminates the stealth signal so that the public channel is not affected by the stealth signal. The two signals cannot interfere with each other. The results of this study show that our proposed optical steganography system is highly secure. The stealth signal can be favorably hidden in the public channel when the average source power of the stealth signal, public noise, and public signal are −5, −3, and 0 dBm, respectively.

The present study investigates the failure mechanisms of industrial fiber materials, using a custom designed fiber cutting performance test bench. The fracture morphologies of single PA6 fibers are examined by scanning electron microscopy. The analysis reveals that fiber cutting can be distinguished according to four distinct stages of fiber failure represented by shearing, cutting, brittle fracture, and tensile failure, which are the result of different mechanisms active during the processes of crack initiation, extension and fracture. The results of fractographic analysis are further verified by an analysis of the blade assembly speed with respect to time over the entire fracture failure process based on high-speed camera data. The results of fractographic analysis and blade assembly speed are fully consistent.

Gallium oxide (Ga2O3) is an emerging wide bandgap semiconductor promising a wide range of important applications. However, mass production of high-quality crystalline Ga2O3 still suffers from limitations associated with poor reproducibility and low efficiency. Low-temperature grown amorphous Ga2O3 demonstrates comparable performance with its crystalline counterparts. Lanthanide Er3+-doped Ga2O3 (Ga2O3: Er) possesses great potential for developing light-emitting devices, photodetectors and optical waveguides. The host circumstance can exert a crystal field around the lanthanide dopants and strongly influence their photoluminescence properties. Here we present a systematical study of the impact of amorphous-to-crystalline transition on the upconversion photoluminescence in Ga2O3: Er thin films. Through controlling the growth temperature of Ga2O3: Er films, the upconversion luminescence of as-grown thin films are strongly enhanced over 100 times. Moreover, the variation of photoluminescence reflects the amorphous-to-crystalline transformation of the Ga2O3: Er thin films. These results will aid further design of favorable optoelectronic devices integrated with lanthanide-doped Ga2O3 thin films.

In this paper, an ultracompact combined sensor for displacement and angle synchronous measurement is proposed based on self-imaging effect of optical microgratings. Using a two-grating structure, linear and angular displacement can be measured by detecting the change of phase and amplitude of the optical transmission respectively within one single structure in meantime. The optical transmitted properties of the two-grating structure are investigated in both theory and simulation. Simulated results indicate that, the optical transmission changes in a sinusoidal relationship to the input linear displacement. Meanwhile, the amplitude of the curve decreases with an input pitch angle, indicating the ability for synchronous measurement within one single compact structure. The synchronous measurement of the linear displacement and the angle is also demonstrated experimentally. The results show a resolution down to 4nm for linear displacement measurement, and a sensitivity of 0.26mV/arcsec within a range of ±1° for angle measurement. Benefitting from a simple common-path structure without using optical components including reflectors and polarizers, the sensor shows an ultrahigh compactness for multiple-degrees-of-freedom measuring, indicating the great potential for this sensor in the fields such as integrated mechanical positioning and semiconductor fabrication.

Nanostructured transition metal nitrides (TMNs) have been considered a promising substitute for precious metal catalysts toward ORR due to their multi-electron orbitals, metallic properties, and low cost. To design TMNs catalysts with high catalytic activity toward ORR, the intrinsic features of the influencing factor on the catalytic activity toward ORR of nanostructured TMNs need to be investigated. In the paper, titanium nitride (TiN), zirconium nitride (ZrN), and hafnium nitride (HfN) nanoparticles (NPs) are high-efficiency and one-step synthesized by the direct current arc plasma. TiN, ZrN, and HfN NPs with an oxidation layer are applied as the catalysts of hybrid sodium-air batteries (HSABs). The effect of composition and structure attributes of TMNs on ORR catalysis is defined as follows. (ⅰ) Composition effect. With the increase of the oxygen content, the catalytic ORR capability of TMNs decreases progressively due to the reduction of oxygen adsorption capacity; (ⅱ) Structure effect. The redistribution of the density of states (DOS) of ZrN indicates higher ORR activity than TiN and HfN. HSABs with ZrN exhibit an excellent cyclic stability up to 137 cycles (about 140 h), an outstanding rate performance, and a specific capacity of 2817 mAh·g−1 at 1.0 mA·cm−2.

Disposal and treatment of excess sludge is a huge problem in waste water treatment plants. Discharge plasma oxidation is an effective approach for sludge dewatering and digestion. In this study, the excess sludge disintegration by non-thermal discharge plasma coupled with thiosulfate (TSA) was investigated. The soluble chemical oxygen demand (SCOD) increased to 404.93 mg L-1 after 20 min of single discharge plasma treatment, and it further increased to 549.08 mg L-1 after adding 15 mmol L-1 of TSA; the water content of filter cake also further decreased in the presence of TSA. There existed an appropriate TSA dosage. In the discharge plasma coupled with TSA system, reactive oxygen species (·OH and ·O2-) were produced and played important roles in sludge disintegration. The addition of TSA promoted the production of ·OH. These reactive oxygen species destroyed the floc structures and promoted the transformation of organic substances, leading to reduction in average size of sludge flocs. The ratio of soluble extracellular polymer substances (S-EPS) was enhanced, while the ratio of tightly bound fraction was reduced after treatment. Thus, discharge plasma coupled with TSA promoted the lysis of microbial cells and released intracellular organic matter and bound water, finally improving sludge dewaterability.

in order to enhance the separation performance and to reduce the heat loss of the transmembrane for membrane distillation, the thermal efficiency and hydrophobicity of the membrane distillation should be simultaneously enhanced. In this work, a PVDF/PET hydrophobic/hydrophilic membrane is prepared by non-solvent phase inducing method. Nanosized silica aerogel (SiAG) with high porosity is added to the composite membranes. The modifying effects and operating conditions on permeate flux and thermal efficiency in direct contact membrane distillation (DCMD) are investigated. Furthermore, the latent heat of vaporization and the heat transfer across the membranes are compared for SiAG addition, which indicates that the composite PVDF@SiAG/PET membranes display a great potential for distillation-separation application with high heat efficiency.

The key enzyme, 1-hydroxy-2-methyl-2-(E)-butenyl-4-pyrophosphate reductase HDR, responsible for the final step in the MEP pathway, was successfully cloned from the petals of Narcissus tazetta var. chinensis, herein referred to as NtHDR (accession number OQ739816). NtHDR exhibits a full-length coding sequence spanning 1380 base pairs, encoding a total of 460 amino acids. Through phylogenetic tree analysis, it has been determined that NtHDR shared the closest evolutionary relationship with monocotyledons, specifically Asparagus officinalis and Zingiber officinale. Utilizing quantitative real-time PCR analysis, it was observed that NtHDR exhibited significant expression in both petals and corona, with expression levels varying throughout the flowering process of Narcissus. Specifically, in petals, NtHDR expression demonstrated a pattern of initial increase followed by subsequent decrease, while in corona, it consistently increased. Subsequent subcellular localization experiments indicated that NtHDR was localized within the chloroplasts. To investigate the functional impact of NtHDR, stable transformation was performed, where NtHDR was introduced into Nicotiana benthamiana. The resulting transgenic N. benthamiana flowers were analyzed using solid-phase microextraction coupled with gas chromatography–mass spectrometry (SPME-GC-MS) to characterize their volatile components. The analysis revealed the presence of the monoterpene compound linalool, as well as the phenylpropanoids benzyl alcohol and phenylethanol, within the floral fragrance components of the transgenic N.benthamiana plants. However, these compounds were absent in the floral fragrance components of wild-type tobacco plants, thus highlighting the impact of NtHDR transformation on the floral scent profile.

Bio-based packaging materials and efficient drug delivery systems are gaining attention, carboxymethyl cellulose (CMC) is one of the most soluble cellulose derivatives, is a good candidate material due to its biocompatibility, biodegradability, and reproducibility. However, CMC-based films are lack of sufficient mechanical properties, limiting its widespread application. In this paper, the molecular interactions between CMC and additives with different molecular structures have been studied with rheological method. The additives are chitosan and cellulose derivatives including O-carboxymethylated chitosan (O-CMCh), N-2-hydroxypropyl-3-trimethylammonium-O-carboxymethyl chitosan (HTCMCh), hydroxypropyltrimethyl ammonium chloride chitosan (HACC), cellulose nanofibers (CNC), and cellulose nanofibers (CNF). The influencing factors, such as molecular structure, concentration of additives and temperature on the rheological properties were studied, which results revealed the information intermolecular interactions. Based on the rheological properties, several systems were selected to prepare films, which show very good transparency, wettability, mechanical properties, biodegradability, and cytotoxicity. These good characteristics of the selected films also proved the good biocompatibility of CMC and chitosan and cellulose derivatives. This work will provide an idea to prepare CMC-based food packaging and effective drug delivery materials with specific properties.

Liquid crystal elastomers (LCEs) are shape morphing materials whose large and reversible shape transformation results from the coupling between the mobile anisotropic properties of liquid crystal (LC) units and the rubber elastic of polymer networks. Their shape changing behaviors under the stimuli are largely directed by LC orientations, and therefore various strategies have been developed to spatially modulate the LC alignments. But most of the methods suffer from complex fabrication technologies or intrinsic limitations in applicability. To address this issue, programmable complex shape changes in some types of LCEs, such as polysiloxane side-chain LCEs or thiol-acrylate main-chain LCEs, etc, have been achieved by using a mechanical alignment programming process coupled with two-step crosslinking. Here, we report a polysiloxane main-chain LCE with programmable 2D and 3D shape changing performances achieved by mechanically programming the polydomain LCE between the two crosslinking steps. The resulting LCEs exhibited reversible thermal induced shape transformation between the initial and programmed shapes due to the two-way memory between the first network structure and the second network structure. Our work would be potential for expanding the applications of LCE materials in actuators, soft robotics and smart structures where arbitrary and easily programmed shape morphings are needed.

UV induced photoluminescence of organosilica films with ethylene and benzene bridging groups in their matrix and terminal methyl groups on the pore wall surface is studied to reveal optically active defects and understand their origin and nature. Careful selection of the film’s precursors and conditions of deposition and curing, analysis of chemical and structural properties led to the conclusion that luminescence sources are not associated with the presence of oxygen-deficient centers, as in the case in pure SiO2. It is shown that the sources of luminescence are the carbon-containing components that are part of the low-k-matrix, as well as the carbon residues formed upon removal of the template and UV induced destruction of organosilica samples. A good correlation between the energy of the photoluminescence peaks and the chemical composition is observed. This correlation is confirmed by the results obtained by the Density Functional theory. The photoluminescence intensity increases with porosity and internal surface area. The spectra become more complicated after annealing at 400 °C, although Fourier transform infrared spectroscopy does not show these changes. The appearance of additional bands is associated with compaction of low-k matrix and segregation of template residues on the surface of the pore wall.

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.

Amino acid efflux and influx transport systems play vital roles in industrial microorganisms’ cell growth and metabolism. However, although biochemically characterized, most amino acid transporters remain unknown at the molecular level in Bacillus licheniformis. This study focuses on the molecular and functional characterizations of three transporters, YdgF, YvbW, and YveA, mainly when catalyzing the cross-membrane flux of L-Aspartate. When growing in the minimal medium with L-Asp as the only carbon and nitrogen source, the growth of strains lacking proteins YdgF, YvbW, and YveA was significantly inhibited compared with wild-type strains, while supplementing the expression of the corresponding proteins in the single-gene knockout strains can alleviate the inhibition to some extent. Upon overexpression, the recombinant proteins mediate the accumulation of L-aspartate to varying degrees. Compared with wild-type strains, the single knockout strains of the three protein genes exhibited reduced absorption of L-aspartate. In addition, this paper focuses on the effects of these three proteins on the absorption of β-alanine, L-glutamate, D-serine, D-alanine, and glycine.

At present, the drilling rig for deep seabed shallow strata is widely used in the exploration of ocean cobalt-rich crust resources and other fields, which not only can obtain solid core samples at a specific station, but the operation process is relatively safe. This paper mainly presents the structure and mechanism of mobile drilling rig in acquiring the seafloor cores (up to 1.5 m long). Based on the function of the AMESim software, the hydraulic propulsion system model of mobile drilling rig is established, which is the basis and core part of the mobile drilling rig. Moreover, the control methods of closed-loop and PID are respectively used to control the hydraulic propulsion system for simulation analysis. Through the comparison of simulation results, it is found that the PID control method is more convincing in verifying the design rationality of hydraulic propulsion system. In the simulation of the PID-controlled hydraulic propulsion system, the co-simulation technology of AMESim and Matlab/Simulink not only establishes the hydraulic model and control model, but also determines the relevant simulation parameters, which is helpful to improve the system simulation efficiency. In its verification deployment in the South China Sea, the mobile drilling rig has been operated for many times at different depths, and some cores have been successfully obtained. Furthermore, the mobile drilling rig has been used during the 55th Voyage of China Oceanic Scientific Expedition supported by China Ocean Mineral Resources R&D Association. Several sites were explored and a large number of cobalt-rich crust cores were obtained. The powerful theory and sea trails are provided to support for the further research on survey of the abyssal resource.

Remdesivir (RDV) has garnered much hope for its moderate anti-COVID-19 effects, but its limited amelioration of survival in hospitalized patient causes a huge controversy over the applicability of RDV to COVID-19 treatment. Developing strategies to improve its antivirus efficacy is urgently required. As anticipated, RDV exhibits similar behavior with other nucleotide analogs to disrupt the metabolism of natural endogenous ribonucleotides (RNs) and deoxyribonucleotides (dRNs). Alterations in endogenous RNs and dRNs play a critical role in virus replication as well as other key cellular functions. Thus elucidation of the disturbances of RDV on RNs and dRNs could help to understand its exact mechanism of action. Here, the metabolic profiling determined by liquid chromatography–mass spectrometry method showed a general increase in the abundance of nucleotides and a more than 2-fold increase for specific nucleotides. However, the variation of pyrimidine ribonucleotides was relative slight or even contrary, resulting in obvious imbalance between purine and pyrimidine ribonucleotides, which implied the obstacle of RDV to pyrimidine synthesis and could further block the transcription and replication of viral RNA. Additionally, the extreme disequilibrium between cytidine triphosphate (CTP) and cytidine monophosphate might result from the inhibition of CTP synthase and provide a metabolic target for the treatment of COVID-19 infection. Since nucleotides metabolism pathways are vulnerable to nucleotide analogues and are liable to be the regulation targets, it is promising to enhance the efficacy of RDV through co-administration with CTP synthase inhibitors or de novo pyrimidine synthesis inhibitors to exacerbate the imbalance of nucleotide pools.

This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

The present work is focused on the numerical solution of the complete energy equation used in fluid film lubrication. The work was motivated by the fact the complete energy equation has no analytic solution that could be used for validations. Its accuracy and computation time are related to the employed numerical method and to the grid resolution. The natural discretization method (NDM) applied on different grids is systematically compared with the spectral method (the Lobatto Point Colocation Method or LPCM) with different polynomial degrees. A one dimensional inclined slider is used for the numerical tests and the energy equation is artificially decoupled from Reynolds. This approach enables to focus all the attention on the numerical solution of the energy equation. The results show that the LPCM is one or two orders of magnitudes more efficient than the NDM in terms of computation time. The energy equation is then coupled with Reynolds equation in a thermo-hydrodynamic analysis of the same 1D slider; the numerical results confirm again the efficiency of the LPCM. A thermo-hydrodynamic analysis of a two-lobe journal bearing is then presented as a practical application.

Most machine learning and deep learning algorithms can only use low-dimensional data as input, but the data that must be processed in practical applications is diverse and irregular. There are two main problems with big dynamic data. (1) The size of the embedding table grows linearly with the vocabulary size, resulting in massive memory consumption. (2) For different newly added vocabularies. To solve these two problems, this paper proposes a novel embedding algorithm that can learn attribute associations with entities based on deep and hash algorithms. Taking movie data as an example, the encoding method and the specific flow of the algorithm are presented in detail, and the effect of dynamic reuse of data models is realized. Compared with the four existing embedding algorithms which can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has obvious optimization of time and space complexity.

Although transplantation procedures have been developed for patients with end-stagec hepatic insufficiency or other diseases, allograft rejection still threatens patient health and lifespan. Over the last few decades, the emergence of immunosuppressive agents, such as calcineurin inhibitors (CNIs) and mammalian target of rapamycin (mTOR) inhibitors, have strikingly increased graft survival. Unfortunately, immunosuppressive agent-related neurotoxicity is commonly occurred in clinical situations, with the majority of neurotoxicity cases caused by CNIs. The possible mechanisms whereby CNIs cause neurotoxicity include: increasing the permeability or injury of the blood-brain barrier, alterations of mitochondrial function, and alterations in electrophysiological state. Other immunosuppressants can also induce neuropsychiatric complications. For example, mTOR inhibitors induce seizures; mycophenolate mofetil induces depression and headache; methotrexate affects the central nervous system; mouse monoclonal immunoglobulin G2 antibody against cluster of differentiation 3 also induces headache; and patients using corticosteroids usually experience cognitive alteration. Therapeutic drug monitoring, individual therapy based on pharmacogenetics, and early recognition of symptoms have greatly reduced neurotoxic events. Once neurotoxicity occurs, a reduction in the drug dosage, switching to other immunosuppressants, using drugs to treat the neuropsychiatric manifestation, or blood purification therapy have proven to be effective against neurotoxicity. In this review, we summarize the recent topics on the mechanisms of neurotoxicity of immunosuppressive drugs. In addition, some information about neuroprotective effects of several immunosuppressants are also discussed.

We investigated the response of leaf photosynthetic capacity to climate warming and its variation among provenances of Larix gmelinii (Dahurian larch). Seedlings of 11 L. gmelinii provenances were transplanted into two common gardens with different climate conditions (control and warming climate). We measured the leaf photosynthetic capacity and explored its influencing factors. The warming treatment significantly increased the maximum net photosynthetic rate (Pmax-a), photosynthetic nitrogen use efficiency (PNUE), maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), triose phosphate utilization rate (TPU), mesophyll conductance (gm), leaf nitrogen content (Narea), and chlorophyll content (Chlm). The Pmax-a was significantly positively associated with Vcmax, Jmax, TPU, gm, and Narea, and the slope of the correlations between Pmax-a and Vcmax, Jmax, and TPU was steeper in the warming treatment. The responses of Pmax-a, PNUE, Vcmax, Jmax, TPU, Narea, and Chlm to warming differed among provenances. The effects of warming on Pmax-a, Vcmax, Jmax, and TPU increased and then decreased as the aridity index of the original site increased. Overall, the warming treatment improved the photosynthetic capacity of L. gmelinii, but the extent of the improvement varied among provenances. These findings provide insights into the mechanisms underlying the responses of L. gmelinii to climate warming.

Fruit cracking or splitting, a severe physiological disease that highly affects appearance and quality, compromise the commodity value of fruit and also causes huge economical losses to the producers of several fleshy fruit crops. The growth-promoting phytohormone gibberellic acid (GA) and growth-inhibiting phytohormone abscisic acid (ABA) antagonistically regulate numerous developmental processes throughout the plant life cycle. Homeostasis of endogenous GA and ABA play a significant role in the normal growth of fruits, and its imbalance may lead to the occurrence of cracking or splitting during the process of fruit growth, development, ripening and postharvest storage. The pathways of GA and ABA metabolism and signaling have been studied widely, and the major components were well characterized, which including the genes encoding major biosynthesis and catabolism enzymes and the key signaling components. Nevertheless, our knowledge of the mechanisms of GA and ABA governing fruit cracking is not comprehensive enough. In this review, we not only focus on the scenarios of effects of endogenous GA and ABA contents in the fruit and exogenous plant growth regulators GA and ABA treatments, but also endeavor to provide some genetic cues on the function of GA and ABA responsible for fruit cracking modulation. Information from this review aims to strengthen the understanding of potential molecular bases underlying GA and ABA in fruit cracking control and provide guidance in breeding of fruit cultivars with cracking-resistant ideotypes, and also make great theoretical significance in guiding the establishment of integrated prevention and control measures to fruit cracking or splitting.

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological re-search on jujube. This review began by summarizing advancements in jujube genomics. Subse-quently, we provided a comprehensive overview of the integrated application of genomics, tran-scriptomics, and metabolomics to explore pivotal genes governing jujube domestication traits, quality attributes (including sugar synthesis, terpenoids, and flavonoids), and responses to abiotic stress, and discussed the transcriptional regulatory mechanisms underlying these traits. Further-more, challenges in multi-omics research on jujube biological traits were outlined, and we pro-posed the integration of resources such as pan-genomics and sRNAome to unearth key molecules and regulatory networks influencing diverse biological traits. Incorporating these molecules into practical breeding strategies, including gene editing, transgenic approaches, and progressive breeding, holds the potential for achieving molecular-design breeding and efficient genetic en-hancement of jujube.

Broomrape (Orobanche cumana), root parasitic weed, negatively affects sunflower, thereby causing severe yield losses and required controls on O. cumana infestation. Brassinosteroids (BRs) plays key roles in plant growth and provides resilience to weed infection. This designed study evaluated the mechanisms by which BRs ameliorates O. cumana infection in sunflower (Helianthus annuus). The pretreatment of sunflower seeds with various concentrations of BRs (1, 10, and 100 nM) were applied with and without O. cumana infestation. Under O. cumana infection, a significant reduction in plant height (38.61%), fresh/dry biomass, photosynthesis and regulation in plant defense (POX, GST), BRs signaling (BAK1, BSK1 to BSK4) and synthesis (BRI1, BR6OX2) associated genes. Moreover, O. cumana elevated the levels of MDA, OH‒, H2O2 and O2•‒ in leaves/roots by 77%/112%, 62%/104%, 57%/98% and 55%/89.4% and caused ultrastructural damages in leaves/roots, suggesting the severe oxidative or cellular damages in sunflower. In response, plants activate activities of SOD and POD enzymes as well as reduced or oxidized glutathione, but unable to stimulate APX and CAT activities. Addition of BRs, especially at 10 nM notably lowered the production of oxidative stress, activated the enzymatic and non-enzymatic antioxidants. The downregulation in the reported genes by BRs attributed to the increased resilience of sunflower (via susceptible reaction). In nutshell, the pretreatment of BRs enhanced the sunflower resistance to O. cumana infection by improving the planta growth and biomass, photosynthesis, activating the antioxidant defense system, lowering the oxidative stress, cellular damages and modulating the expression of BR synthesis and signaling genes.

The recent advancements in communication technology have facilitated the widespread deployment of electronic communication equipment globally, resulting in the pervasive presence of electromagnetic pollution. Consequently, there is an urgent necessity to develop a thin, lightweight, efficient, and durable electromagnetic interference (EMI) shielding material, capable of withstanding severe environmental conditions, and possessing a scalable production method. In this paper, we proposed an innovative and scalable method for preparing EMI shielding films with a tunable sandwich-structure. The film possesses a nylon mesh (NM) backbone, with AgNWs serving as the shielding coating and aramid nanofibers (ANFs) acting as the cladding layer. The prepared film was thin and flexible, with a thickness of only 0.13 mm. AgNWs could easily form a conductive network structure with excellent performance, and the film had a high EMI shielding efficiency, up to 50.6 dB with 1.0mg/cm2. Due to the outstanding thermal resistance, acid and alkali resistance properties of aramid fibers, the NAAANF film demonstrates remarkable robustness in the face of complex usage environments. Such a thin, efficient and environmentally resistant EMI shielding film provided new ideas for the broad EMI shielding market.

Instrumental activities of daily living (IADL) training is critical in clinical practice of occupational therapy (OT) for stroke patients. In the conventional approaches, the therapists simulate various occupational tasks in their healthcare settings or reach their nearby real circumstances for the training. As an alternative technology, immersive virtual reality (IVR) has been used widely in stroke rehabilitation for years; however, limited research has compared occupational performances between virtual and real environments. Our team has established a novel IVR shopping training system for stroke rehabilitation. We recruited 10 stroke patients from our department to test the system. All patients attempted to implement shopping tasks in the virtual and real environments respectively. The Wilcoxon test showed no significant differences between virtual and real environments in memory capacity and time-consuming for implementing tasks, and memory capacities in virtual and real environments presented excellent correlations with the IADL assessment. This study suggests that IVR shopping tasks can be used as an alternative approach in rehabilitation when there is limited resource for implementing shopping training in a real environment, while the psychometric property, clinical effect and the effect transferred to the real environment induced by virtual training require further study.

Numerous studies have shown that quinolones residues in the foods exceeding the MRL is harmful to the human health. There are some limitations in the existing methods for the residues, so we developed a new limit test method (TLC-SERS) to rapidly determine following residues: enrofloxacin(A), ciprofloxacin (B), ofloxacin(C), fleroxacin(D), sparfloxacin(E), enoxacin(F), gatifloxacin(G) and nadifloxacin(H). The residues can be preliminarily separated by TLC. Labeling the tested compounds’ position on the thin layer plate by their relative Rf under 254nm ultraviolet, and an appropriate amount of nanometer silver solution should be added to the position. The silver on the plate should be irradiated with 532nm laser to obtain SERS of the compounds. The results showed a significant differences of the 8 quinolones’ SERS; the LOD of the H, A, D, E, C, G, F and B were orderly 9.0, 12.6, 8.0, 19.0, 8.0, 8.4, 9.0 and 12.6 ng/mL, and the RSD≤4.9% for SERS of each quinolones.The limit test results of the 20 samples was consistent with that determined by the UPLC–MS/MS. The above results indicate that the TLC-SERS is specific, sensitive, stable, and accurate, providing a new reference for the rapid limit test of harmful residues in foods.

Over the last several decades, China has continuously introduced Duroc boars and used them as breeding boars. Although this crossbreeding method has increased pork production, it has affected pork quality. Nowadays, one of the primary goals of industrial breeding and production systems is to enhance the quality of meat. This research adds to our understanding of the molecular mechanisms that control the quality of pork and may be used as a guide for future efforts to enhance meat quality. We investigated the genetic mechanisms of cross-breeding for meat quality improvement by combining transcriptome and metabolome analysis, using Chinese native Jiaxing black (JXB) pigs and crossbred Duroc × Duroc × Berkshire × JXB (DDBJ) pigs. In the longissimus Dorsi muscle, the content of inosinic acid, polyunsaturated fatty acid, and amino acids was considerably higher in JXB pigs in contrast with that of DDBJ pigs, whereas DDBJ pigs have remarkably greater levels of polyunsaturated fatty acids than JXB pigs. Differentially expressed genes (DEGs) and differential metabolites were identified using transcriptomic and metabolomic KEGG enrichment analyses. Differential metabolites mainly include amino acids, fatty acids, phospholipids. In addition, we found several DEGs that may explain differences in meat quality between the two pig types, including genes associated with lipid metabolism (e.g., DGKA, LIPG, and LPINI), fatty acid metabolism (e.g., ELOVL5, ELOVL4, and ACAT2), and amino acid metabolism (e.g., SLC7A2, SLC7A4). Combined with the DEGS-enriched signaling pathways, the regulatory mechanisms related to amino acids, fatty acids, and phospholipids were mapped. The abundant metabolic pathways and DEGs may provide insight into the specific molecular mechanism that regulates meat quality. Optimizing the composition of fatty acids, phospholipids, amino acids, and other compounds in pork is conducive to improving meat quality. Overall, these findings will give useful information and further groundwork for enhancing the meat quality that may be achieved via hybrid breeding.

Large terrestrial carnivores play a crucial role in top-down control within terrestrial ecosystems, maintaining ecosystem stability and biodiversity. However, intense interspecific competition often arises among sympatric large carnivores, leading to population reductions or even extinctions. Spatial partitioning through divergent habitat selection helps mitigate such competition. In Xinlong County, Sichuan Province, we used 293 infrared cameras for monitoring from September to May 2016 and March to October 2022. By employing the Generalized Linear Model (GLM) and the Maximum Entropy Model (MaxEnt), we developed an ensemble model predicting the suitable habitat distribution of leopards (Panthera pardus) and wolves (Canis lupus). We analyzed the main environmental factors influencing habitat selection and the fragmentation of suitable habitats. We found that suitable habitat distribution differed significantly between them. Both species preferred areas with gentle slopes close to settlements. While leopards' habitat selection primarily depended on the distance from settlements, the slope was predominant for wolves. Suitable habitats displayed aggregation, yet wolves exhibited higher fragmentation and more complex patch shapes, indicating greater susceptibility to human activities. These results suggest that sympatric large carnivores, such as leopards and wolves, can reduce spatial competition intensity and promote spatial partitioning by selecting divergent suitable habitats, thereby facilitating species coexistence.

Nowadays, the world's refractory gold ore production accounts for about 30% of the world's total gold, such gold concentrate leaching rate is low, seriously limiting the efficient use of gold resources. The alkaline preoxidation process can improve the leaching rate of this kind of gold deposit, and has a good development and application prospect. So, it is of great significance to study the oxidation and dissolution behavior of pyrite in alka-line environment. In this paper, the oxidation process of gold-bearing pyrite in alkaline electrolyte was studied by electrochemical techniques, and the oxidation products of pyrite electrode in alkaline solution were characterized by XPS，SEM and other analytical methods. The results show that the optimum pH for pyrite electrochemical oxidation dissolution is about 12, and oxidation potential of pyrite should be above 0.8 V. In that proces of alkaline oxidation dissolution of pyrite, part of S element enters the electrolyte in the form of Sx2-, S2O32-, SO32-and SO42-, and a small amount of S element is adsorbed on the surface of the electrode in the form of S0 and becomes a part of the passive layer. Fe element is adsorbed on the surface of electrode in the form of Fe(OH)2, Fe2O3 and Fe2(SO4)3, which becomes the main component of passivation layer. This study provides a theoretical basis and reference data for the chemical preoxidation treatment of sulfide ores.

Abstract: OTUD6A is a deubiquitinase that plays crucial roles in various human diseases. However, the precise regulatory mechanism of OTUD6A remains unclear. In this study, we found that OTUD6A significantly inhibited the production of type I interferon. Consistently, peritoneal macrophages and bone marrow-derived macrophages from Otud6a^-/- mice produced more type I interferon after virus infection compared to cells from WT mice. Otud6a^-/- mice also exhibited increased resistance to lethal HSV-1 and VSV infections, as well as LPS attack due to decreased inflammatory responses. Mechanistically, mass spectrometry results revealed that UBC13 was an OTUD6A-interacting protein, and the interaction significantly enhanced after HSV-1 stimulation. Taken together, our findings suggest that OTUD6A plays a crucial role in the innate immune response and may serve as a potential therapeutic target for infectious disease.

Low-sodium salt substitute (LSSS) has the potential in regulating sodium and potassium intake and has been available in China market for years. However, its real-world use and impact to sodium\potassium intake is unclear. Baseline data of 4000 adult participants who participated in three similar-designed randomized controlled trails were pooled together for this study. Self-reported awareness and use of LSSS were collected using the uniform questionnaire, and the 24-h urinary sodium and potassium excretion was used to estimate their dietary intake. Mixed-effects models were developed to assess the relationship between LSSS and 24-h urinary sodium and potassium excretion. 32.0% of the participants reported awareness of LSSS and 11.7% reported its current use. After adjusting location, sex, age, and education, compared with the group of participants unaware of LSSS, participants who were aware but not using LSSS and those who were using LSSS had a lower 24-h urinary sodium excretion by –356.1(95% CI: –503.9, –205.9) mg/d and –490.6(95% CI: –679.2, –293.7), respectively (p&lt;0.001). No significant difference was found for 24-h urinary potassium excretion and sodium-to-potassium ratio among the three groups (p&gt;0.05). In conclusion, although associated with lower sodium intake, the awareness and real-world use of LSSS are still very low in China.

Multipactor is a common discharge phenomenon occurred in space microwave systems. The surface microstructure has been verified to be effective to mitigate multipactor. In this work, we design a square coaxial low-pass filter (SCLPF) with dielectric sheets loaded to check the multipactor dependence on the structure parameters of the loaded dielectric sheets, and further model groove structures on the sensitive area surface to lower the risk of multipactor. Simulation results indicate that the SCLPF loaded with alumina and PTFE exhibits good operational characteristics, and the multipactor threshold is significantly improved after introducing the surface grooves. Then we investigate the effects of three typical groove parameters, groove depth, groove number, and aspect ratio, on the multipactor threshold of the SCLPF device. The results show that the multipactor threshold raise at first as the groove number and groove depth increase, and then the threshold reaches a saturation status. For a deeper analysis of multipactor, we discuss how the grooves shelter the secondary electrons, and further mitigate the electron avalanche. Furthermore, the mechanisms of threshold saturation under the effect of groove parameters are analyzed in detail, and a contour map for the multipactor threshold of PTFE-loaded SCLPFs is given, which makes significant sense for predicting the multipactor threshold of the devices with its sensitive surface being grooved. The regularity of modulating the multipactor threshold by the groove structures obtained in this study is of engineering significance for suppressing multipactor in microwave devices in practical applications.

Trueperella pyogenes (T. pyogenes) can cause a variety of infections in animals and lead to economic loss in animal husbandry. TatD DNases are considered to be potential virulence factors in Plasmodium falciparum and Streptococcus pneumoniae. However, the function of TatD DNases in T. pyogenes is still unclear. Therefore, the aim of this study was to illustrate the function of TatD DNases of T. pyogenes (TpTatDs) and investigate whether luteolin is able to inhibit DNase function. The findings of our study are anticipated to be crucial to treating infections caused by T. pyogenes. Bioinformatic analysis has been used for the prediction of crucial functional residues of TpTatDs. The function of TpTatDs was investigated in the presence of divalent cations by hydrolyzing DNA with recombinant TatD proteins. Luteolin is a candidate nuclease inhibitor evaluated in our study. The interactions between luteolin and TpTatDs were tested using molecular docking analysis and surface plasmon resonance (SPR) assays. The inhibitory effect of luteolin on TpTatDs was analyzed by agarose gel electrophoresis. Two genes in the genome of T. pyogenes are suspected to encode TatD DNases. According to the length of their nucleotide sequences, they were named tatD960 and tatD825. Both of the TpTatDs, which are magnesium-dependent, were able to hydrolyze linear DNA and plasmids. In this study, we found through molecular docking analysis and SPR assays that luteolin can stably bind with TpTatDs. The gel assay revealed that luteolin can inhibit the DNase activity of TpTatDs. Our results indicated that TatD DNases from T. pyogenes are Mg2+-dependent DNases and exhibit DNA endonuclease activity. Moreover, luteolin reduced their DNA hydrolysis ability by decreasing the binding between TpTatDs and DNA.

The enterohemorrhagic Escherichia coli (EHEC) O157: H7 EspF is known to be a multifunctional effector that triggers several damage processes in the host cells. However, in the process of EHEC O157: H7 infection, the interactions between EspF, its N- or C-terminus, and host proteins are still unclear. In this work, we use co-immunoprecipitation combined with mass spectrometry (CoIP-MS) to screen the interactions between EspF/EspF-N/EspF-C terminus and host proteins. A total of 311 host proteins are analyzed. The N-terminus of EspF is found to interact with 192 proteins, whereas 205 proteins interact with the C-terminus. These proteins are mainly involved in RNA splicing, endoplasmic reticulum stress, and a variety of metabolic signaling pathways. Surprisingly, MS results reveal that EspF can also phosphorylate H2AX, suggesting that EspF may directly mediate DNA damage. Here, by western blot and immunofluorescence (IF), we verified that EspF can cause phosphorylation of H2AX and mediates cell multi-nuclearation and cell hypertrophy. Furthermore, we verify here for the first time that SMC1 interacts with EspF -C-terminus, and provide evidence that EspF increases p-SMC1 levels. p-SMC1 is known to influence S-phase cell cycle arrest and usually increases during periods of DNA damage. Our work revealed a novel interaction between EspF and the host protein SMC1 and lays a foundation for further research on EspF-mediated host DNA damage, apoptosis, and even colorectal carcinogenesis.

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

Despite extensive use of intravitreal anti-vascular endothelial growth factor (anti-VEGF) biologics for over a decade, neovascular age-related macular degeneration (nAMD) or choroidal neovascularization (CNV) continues to be a major cause of irreversible vision loss in developed countries. Many nAMD patients demonstrate persistent disease activity or experience declined responses over time despite anti-VEGF treatment. The underlying mechanisms of anti-VEGF resistance are poorly understood, and no effective treatment strategies are available to date. Here we review evidence from animal models and clinical studies that supports the roles of neovascular remodeling and arteriolar CNV formation in anti-VEGF resistance. Cholesterol dysregulation, inflammation, and ensuing macrophage activation are critically involved in arteriolar CNV formation and anti-VEGF resistance. Combination therapy by neutralizing VEGF and enhancing cholesterol removal from macrophages is a promising strategy to combat anti-VEGF resistance in CNV.

Numerous researchers acknowledge that occupational pension protects employees. However, the total occupational pension is shared by employees and employers in China, which is a cost. This study aimed to explore the occupational pension on corporate social responsibility (CSR) and organizational resilience. We drew upon insights from cost-stickiness and resource-based theories to develop a model that illuminates how occupational pension affected firms’ stance toward CSR in the context of the COVID-19 pandemic and how this, in turn, has result organizational resilience. This study categorized CSR into strategic and responsive activities based on cost stickiness. We selected a total of 34,145 observations of Chinese listed companies over the period 2010-2023 as a sample to investigate whether occupational pension changes CSR strategies. The results showed that the cost pressure to contribute to occupational pension drove firms to reduce their responsive CSR and boost their strategic CSR. Moreover, strategic CSR improved organizational resilience, but responsive CSR did not. This relationship between occupational pension and CSR was significantly and negatively moderated by the minimum wage and population aging. Whereas, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities. This study has implications for decision-making regarding high-quality firm development.

Crystalline silicon nanomembrane (Si NM) is promising in wearable and implantable bioelectronics due to its electrical and mechanical performances. However, the current explorations are not enough to firmly bridge ultrathin encapsulation strategies with high performance crystalline Si NM based transistors. In this work, we utilized Al2O3/alucone as the ultrathin encapsulation strategies aim to thoroughly investigate the effect of gate dielectric surface area on the electrical performance of crystalline Si NM based metal-oxide-semiconductor capacitors (MOSCAPs). The comparative analysis was implemented on the three types of diameters (Ø) MOSCAPs with and without Al2O3/alucone encapsulation under planar and concave/convex bending conditions. Benefited from the Al2O3/alucone ultrathin encapsulations, the stability of gate leakage current density, and interfacial characteristics were maintained and improved under mechanical bending deformations for MOSCAPs with predefined Ø of 160, 240, and 320 μm compared to the bare MOSCAPs. Combined with the Ø-related mechanical analysis on the maximum strain in the critical layers and the practical measurements, the encapsulated MOSCAPs with Ø 160 μm showed the most stable electro-mechanical performances. The variations for encapsulated MOSCAPs with three types of Ø emerged in the oxide layer and the interface at dielectric/Si NM that were carefully evaluated according to the bending deformations. These findings are significant for leveraging the practical applications in the ultrathin encapsulation strategies for reliable operations of crystalline Si NM based integrated circuits (ICs).

Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features causing limited code representation. Addressing this gap, our paper introduces the Statement-grained Hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.

A two-way coupled model between polydisperse particle phases with compressible gases and a density-based coupling implicit solution method combining the third-order MUSCL with QUICK spatial discretization scheme and the second-order temporal discretization scheme are constructed based on the discrete-phase model (DPM) and the stochastic wander model (DRWM) in the Eu-lerian-Lagrangian framework in conjunction with a unitary particulate source (PSIC) approach and the SST k-ω turbulence model. The accuracy of the numerical prediction method is verified using previous supersonic nozzle gas-solid two-phase flow experiments. Numerical simulation of two-phase jet of dry powder extinguishing agent gas with pilot-type supersonic nozzle was carried out to analyze the influence of geometrical parameters such as length ratio rL and area ratio rA of main nozzle on the two-phase flow field as well as on the jet performance indexes such as particle mean velocity vp,a, velocity inhomogeneity Φvp, particle dispersion Ψp, particle mean acceleration ap,a and so on. By analyzing the parameters, we indicate the requirements for the combination of jet performance metrics for different flame types such as penetrating, spreading, and dispersing.

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at BS. The expression of the uplink achievable rate is derived and analyzed. Besides, genetic algorithm (GA) is used to optimize the phase shifts of RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS’s reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs, and reduce the demand for the number of BS antennas, which can significantly reduce the hardware costs at the BS.

This paper explores the application of machine learning techniques for automated object grasping. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object gripping capabilities.

China is facing the development requirements of the dual carbon goal and the construction needs of resilient cities. Currently, high-strength composite structural systems are a feasible solution, but their research and application in the construction field are insufficient, especially for the combination forms of concrete above C100 and steel above Q550. In response to this issue, this paper proposes a new high-performance structural system, namely the composite frame - high-strength steel plate wall core tube resilient structure system, which includes high-strength steel plate (Q550) - ultra-high strength concrete (C100) shear walls, replaceable energy dissipation coupling beams, and composite frames. Taking a 200 meter building as an example, this paper designs and establishes calculation models for a conventional reinforced concrete frame-core tube structure and a high-performance structure based on PKPM software, and establishes elastoplastic analysis models for both based on SAUSAGE software, and then conducts dynamic elastoplastic time history analysis and seismic resilience assessment of structures under design basis earthquakes (DBEs), maximum considered earthquake (MCEs) and extremely rare earthquakes (EREs). Research has shown that compared to conventional structures, new high-performance structures can effectively reduce the size of shear walls, reduce the self-weight of the structure, and enhance the space for building use; The story drift ratio has higher redundancy than specification limits, lower plastic damage and overall stiffness degradation of the structure, and better seismic performance; The seismic resilience has significantly improved, especially in the case of casualties, which can better ensure the safety of people's lives and property.

Aberrant estrogen receptor (ER) signaling is a major driver of breast tumor growth and progression. Sigma 2 receptor has long been implicated in breast carcinogenesis based on pharmacological studies, but its molecular identity had been elusive until TMEM97 was identified as the receptor. Herein we report that TMEM97/sigma 2 receptor is highly expressed in ER positive breast tumors and its expression is strongly correlated with ER and progesterone receptor (PR) but not with HER2 status. High expression levels of TMEM97 are associated with reduced overall survival of patients. Breast cancer cells with increased expression of TMEM97 had growth advantage over the control cells under both nutrition limiting and sufficient conditions, while knockdown of TMEM97 expression reduced tumor cell proliferations. When compared to their vector control cells, MCF7 and T47D cells with increased TMEM97 expression presented increased resistance to tamoxifen treatment and also grew better under estrogen depleted conditions. TMEM97/sigma 2 receptor enhanced ERα transcriptional activities and increased the level of transactivated ERα, especially the nuclear soluble and chromatin bounded ERα. Increased TMEM97 also stimulated mTOR/S6K1 signaling pathways in MCF7 and T47D cells. The increased level of active, phosphorylated ERα, and the enhanced resistance to tamoxifen treatment with increased TMEM97 could be blocked by an mTOR inhibitor. Knockdown of TMEM97 expression reduced ERα and mTOR/S6K1 signaling activities, rendering the cells with increased sensitivity to tamoxifen. The observations suggest that TMEM97/sigma 2 receptor is a novel regulator of ERα activities in breast tumor cell growth.

The LED light source is an important light source for indoor visible light communication. It has the characteristics of a large divergence angle and a high transmission rate. Therefore, using LED as the light source for visible light full-duplex communication can not only satisfy the lighting requirements but also transmit information at high speed. To analyze the factors that affect the optical power of the indoor single-light source visible light communication uplink receiving end, an indoor visible light communication model was established, and mathematical calculation formulas were derived based on the model establishment, and the maximum movable range formula of the retroreflective end under specific conditions was derived; The reliability of the formula was verified with the help of Zemax simulation software. Theoretical analysis and experimental results show that increasing the movement range of the retroreflective end can be achieved by increasing the lens diameter, reducing the focal length, and increasing the link distance.

Despite the fact that SMA restrainers exhibit a superelastic strain capacity of 7%, this capacity appears inadequate for isolated bridges due to the typically greater than 20cm relative dis-placements between girders during intense seismic events. In order to perform such a stroke, a SMA restrainer of greater than 3 metres in length might be required. In order to reduce the length of restrainers, a novel honeycomb damper constructed from superelastic shape memory alloy (SMA) is proposed. The proposed device, denoted as the superelastic SMA honeycomb damper (SHD), is comprised of steel plates to prevent the SMA plane from collapsing and superelastic SMA honeycomb to provide self-centering capability. By incorporating the large strain capacity of SMA and the geometrically nonlinear deformation of honeycomb structures, SHD has been developed to satisfy the requirements of bridge restrainers with large strokes. It is capable of functioning as a restrainer and energy dissipation device when subjected to dynamic tension and compression loads. The SHD was initially investigated from a theoretical perspective. Following this, a mul-ti-cell SHD specimen was manufactured. The specimen underwent axial tensile and compressive experiments in order to examine the mechanical properties of SHDs. Finally, experimental results were investigated through numerical simulation analyses of the SHDs using a three-dimensional high-fidelity finite element model. Additionally, a method for enhancing SHD was proposed. The findings indicate that SHD is capable of exhibiting superior self-centering capability and sta-ble hysteretic responses when subjected to earthquakes.

This study aims to evaluate the combined assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data for predicting short-duration heavy rainfall. The WRFDA 4.3 framework is utilized to establish the data assimilation interface. The structural characteristics of this joint assimilation approach are analyzed using a typical heavy rainfall event in the Kaifeng region of Central China. The findings indicate the following: (1) The joint assimilation of FY-4A AGRI and ground-based MWR data effectively corrects the initial moisture content in model simulations. Data assimilation significantly improves the simulation of 24-hour accumulated rainfall. Furthermore, the joint assimilation of AGRI radiance and MWR data outperforms assimilating either of them individually; (2) In the heavy rainfall stage, from the low level to the high level, strong upward movement occurs in the center of the rainstorm. The water vapor in the process of this rainstorm mainly comes from the westward air transport in front of the high trough. The water vapor is continuously transported to the heavy rainfall belt through this channel, providing sufficient water vapor for the maintenance of the rainstorm; (3) The combination of the interaction between high and low level vorticity and divergence in the atmosphere and the vertical upward movement, the emergence of low level jet and the generation of unstable energy in the atmospheric environment jointly lead to the occurrence of the rainstorm process; and (4) The terrain-sensitive experiment results show that, the existence of the topography of Taihang Mountain changes the precipitation in Kaifeng area, and reducing the terrain height will lead to a 50%-60% decrease in the overall precipitation of the precipitation center, and the precipitation range will also be greatly reduced. An increase of more than 50% in terrain height will lead to an increase in precipitation center, precipitation range and precipitation intensity, and an increase of 10%-20% in overall precipitation; An increase of more than 75% in the terrain height will cause the rain belt to shift eastward by about 0.5°E, and the precipitation center will shift significantly eastward. When the terrain height increased by more than 100%, the precipitation did not continue to increase, which was basically the same as the control test. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance and ground-based microwave radiometer in numerical weather models.

As an outstanding information carrier, skyrmion is increasingly widely used in devices based on complex geometries. To achieve the skyrmion-based spintronic devices, a reasonable and feasible nanotrack is essential. In this paper, we conducted a study on the current-driven skyrmion movement in a circular-ring-shaped nanotrack. Our results suggest that the asymmetry of the inside and outside boundary of the circular ring changed the stable position of the skyrmion, causing it to move like the skyrmion Hall effect when driven by currents. Moreover, the asymmetric boundaries have advantages in enhancing or weakening the skyrmion Hall effect. Additionally, we also compared the skyrmion Hall effect from the asymmetric boundary of circular ring nanotracks with that from inhomogeneous DMI. It is found that the skyrmion Hall effect caused by the circular ring is significantly greater than that caused by the inhomogeneous DMI. These results contribute to our understanding of the skyrmion dynamics in confined geometries and offer an alternative method for controlling the skyrmion Hall effect of skyrmion-based devices.

The airflow carried by artificial aeration provides oxidant O2 for microbial growth and mineral dissolution in heap bioleaching. To investigate the effect of forced aeration rate on bioleaching of low-grade copper sulfide ores, column bioleaching experiments with six columns were conducted in the aeration range of 0~150 L/h at 30~45 ℃. The results showed that when the aeration rate was higher than 95 L/h, the proportions of heap settlement and fine particles mass were much greater than that without aeration. The final total Fe concentration and bacterial cell numbers in pregnant leach solution increased with the increase of aeration rate.The final concentrations of Cu2 + in the mother liquor under aeration conditions of 60-150 L / h were 1.411, 1.432, 1.498, 1.491 and 1.583 g / L, respectively, which were higher than 1.345 g / L under non-aeration conditions. When the aeration rate was 150 L / h, the final Cu leaching rate was 80.1 %, which was 11.4 % higher than that without aeration ( 68.7 % ). Furthermore, in the early stage of leaching, copper leaching rate with 95 L/h aeration rate remained the highest. 结论Artificial aeration accelerates the bioleaching of low-grade copper sulfide ores by improving porosity structure, temperature range of bacterial activity and microbial catalyzed dissolution of targeted minerals.

With increasing demand from unmanned driving and robotics, more attention has been paid to point cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features about reflectivity of the point cloud to maximizing the use of information in point cloud. Secondly, we enhanced the distance features and raw features that we collectively refer to them as self-features of the key points in Set Abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show the enhancement method proposed in this paper has excellent performance.

Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

Recently, the triple-network convergence system (TNCS) has emerged from the deep integration of the power grid, transportation network, and information network. Fault recovery research in the TNCS is important since this system's complexity and interactivity can expand the faults scale and increase faults impact. Currently, fault recovery focuses primarily on single power grids and cyber-physical systems, but there are certain shortcomings, such as ignoring uncertainties including generator start-up failures and the occurrence of new faults during recovery, energy supply-demand imbalances leading to system security issues and communication delay caused by network attacks. In this study, we propose a recovery method based on the improved TD3 algorithm, factoring in shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay and an improved Actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefit in power grid failure scenarios and demonstrates a strong resilience against communication delay caused by DoS attacks.

Background: Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide. Aerobic glycolysis precedes obtainment of oncogenic mutations and loss of tumor suppressors, promoting the progress of CRC. Although numerous biomarkers have been identified to be associated with prognosis and survival, a glycolysis-related gene signature in CRC has not been explored. Methods: mRNA expression profiling data in a group of CRC patients (n = 540) was extracted from the Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GESA) was performed to identify gene sets that were significantly different between CRC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction. Results: Five glycolysis-related genes (ENO3, GPC1, P4HA1, IDUA, ANKZF1) were identified to be significantly associated with overall survival (AUC=0.754). Based on the five‑gene signature, patients with CRC were divided into high and low‑risk subgroups. CRC patients with a low-risk score had better survival benefits than those with a high one (P &lt; 0.001). Conclusions: A five-gene signature associated with glycolysis for predicting the outcome of CRC patients was generated, serving as a valuable prognosis model with high efficiency and potential targeted therapy of CRC patients.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

Cotton cultivation and sustaining its productivity is challenging in temperate arid regions around the globe. Exploring suitable cotton cultivation areas to improve productivity in such climatic regions is essential. Thus, this study explores the ecologically suitable areas of cotton cultivation using the Maxent model, having 375 distribution points of long-staple cotton and various factors, including 19 climatic factors, 2 terrain factors, and 6 soil factors in Xinjiang. The area under the curve (AUC) of the predicted results was greater than 0.9, indicating that the model's predictions had fairly high accuracy. However, the main environmental factors that affect cotton's growth are the lowest temperature in the coldest month, the hottest month, the precipitation in the driest season, and the monthly average temperature difference. Further, the temperature factors contribute 71%, while the contribution ratio of terrain and soil factors is only 22%. The research shows that the current planting area is consistent with the predicted area in many areas of the study. Still, some areas, such as the Turpan region, northwest of Bayingolin Mongol Autonomous Prefecture, are supposed to be suitable for planting cotton, but it is not planted. The current potential distribution area of long-staple cotton is mainly located in Aksu Prefecture and the northern part of the Kashgar Prefecture region. The climatic prediction shows that the growing area of long-staple cotton may expand to southern Altay, central Aksu, and Bortala Mongol Autonomous Prefecture. This study will be helpful for cotton cultivation suitability areas in Xinjiang and other regions with similar environments.

Mitochondrial hypochlorite (ClO-) plays important and often contradictory roles in maintaining the redox balance of mitochondria. Abnormal ClO- levels can induce mitochondrial inactivation and further cause cell apoptosis. Herein. we have developed an anthracene carboxyimide-based fluorescent probe mito-ACS for imaging mitochondrial ClO- in living cells. This probe exhibits some distinctive features as excellent resistance to photobleaching, high selectivity and sensitivity, as well as good water solubility. Mito-ACS showed a noticeable fluorescence response toward ClO-with a fast response (within 6 s) and a low detection limit (23 nM). Moreover, the introduction of triphenylphosphonium makes the probe soluble in water and selectively localizes to mitochondria. Furthermore, mito-ACS was successfully applied to image mitochondria ClO- in living cells with low toxicity. Remarkably. the less used fluorophore anthracene carboxyimide exhibiting excellent photostability and desirable optical properties provides a promising application prospect in biological systems.

Due to the influence of background conditions, lighting conditions, occlusion issues and the image resolution, how to extract robust person features is one of the difficulties in ReID research. Vision in Transformers (ViT) has achieved significant results in the field of computer vision. However, the existing problems still limit its application in ReID due to slow extraction of person features and difficulty in utilizing local features of people. To solve the mentioned problems, we utilize Pyramid Vision Transformer (PVT) as the backbone of feature extraction and propose a PVT-based ReID method in conjunction with other studies. Firstly, some improvements suitable for ReID are used on the PVT backbone, and we establish a basic model by using powerful methods verified on CNN-based ReID. Secondly, in an effort to further promote the robustness of the person features extracted by the PVT backbone, two new modules are designed. (1) The local feature clustering (LFC) is recommend to enhance the robustness of person features by calculating the distance between local features and global feature to select the most discrete local features and clustering them. (2) The side information embeddings (SIE) are used to encode non-visual information and send it into the network for training to reduce its impact on person features. Finally, the experiments show that PVTReID has achieved excellent results in ReID datasets and are 20% faster on average than CNN-based ReID methods.

To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems and accounting for other load-influencing factors, such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system.

This paper proposes a Grouped Contrastive Learning of self-supervised Sentence Representation (GCLSR), which can learn an effective and meaningful representation of sentences. Previous works maximize the similarity between two vectors to be the objective of contrastive learning, suffering from the high-dimensionality of the vectors. In addition, most previous works have adopted discrete data augmentation to obtain positive samples and directly employed contrastive framework of computer vision to perform contrastive training, which could hamper contrastive training because text data is discrete and sparse compared with image data. To address those issues, we propose a grouped contrastive learning framework, i.e., GCLSR, which divides the high-dimensional feature vector into several groups and respectively computes the groups’ contrastive losses to make use of more local information, eventually obtaining a more fine-grained sentence representation. In addition, in GCLSR, we design a new self-attention mechanism and a continuous as well as partial word vector augmentation (PWVA). For the discrete and sparse text data, the usage of self-attention could help model focus the informative words by measuring the importance of every word in a sentence. By using the PWVA, GCLSR can obtain high-quality positive samples used for contrastive learning. Experimental results demonstrate that our proposed GCLSR achieves an encouraging result on the challenging datasets of the standard semantic textual similarity (STS) task and transfer task.

For deep-penetrating geochemistry, there is a frontier issue as to how to explore ore-bearing information deep in covered areas, while it is, too, important to investigate the geochemical migration of ore-forming elements for the development of exploration geochemistry. In this study, a 380-day migration column experiment was conducted with surface soil and pure Cu powder collected from the Yujiashan study area in Wuhan City, Hubei Province taken as the object of research to simulate the migration process of ore-forming elements under natural conditions. The results showed obvious vertical migration of the ore-forming elements and conversion of mobile metal forms in the process; further, according to 6 groups of simulation experiments that took 45 days to conduct on migration column samples and the surface oil and Cu powder collected from the Pulang mining area in Yunnan Province, the vertical migration of the ore-forming element was still visible, and the exogenous substance (pure Cu powder) could enhance the anomaly without changing the basic migration mechanism. This indicates that Cu might migrate in a stable manner in soil cover in the Pulang mining area, and its migration was mostly driven by the transformation of Fe-Mn oxide bound state and organic bound state; finally, a multifractal analysis was conducted, revealing that the ore-forming elements presented a nonlinear and complex structure during their upward migration, which caused anomalies in the covered area, and multiple parameters exerted a certain indicative effect on different source intensities and properties of soil. In this study, laboratory simulations were performed to summarize the active-state evolution pattern and migration mechanism of ore-forming elements with a view to providing technical support and theoretical guidance for geochemical prospecting in the covered region.

Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

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

In order to solve the problem of environmental pollution caused by a large number of discarded hickory husks in Lin'an, and to make them a clean energy source that can be recycled, in this paper,using crushed hickory husks as raw material, single factor test was carried out on a hydraulic plunger forming machine with different moisture content and length-diameter ratio of forming sleeve.The change of axial pressure and radial force in the forming sleeve is measured by the sensor, and the microstructure of the biomass moulding fuel briquette was observed by stereomicroscope. The results showed that the hickory husks could be compressed and formed at room temperature. When the moisture content of the hickory husks is 14% and the length-diameter ratio of the forming sleeve is 4.5, the forming quality is the best, and the relaxation density of the formed briquette is up to 0.98g /cm3. When the moisture` content is the same, the forming pressure increases with the increase of the length-diameter ratio of the forming sleeve. When the length-diameter ratio of molding sleeve is the same, the molding pressure increases first and then decreases with the increase of water content.

In recent years, due to the low energy utilization of the whole life-cycle of buildings and the di-minished indoor and outdoor comfort of buildings and the impact on the surrounding environ-ment caused by the carbon emissions of the building life-cycle, the establishment of a whole life- cycle carbon emission model for buildings to improve their energy utilization at all stages of their life-cycle has received unprecedented attention. In China, the construction industry has become a major carbon emitter, and traditional houses, as a green development building type, are an effec-tive way to achieve carbon neutrality in buildings, but the existing green building evaluation system lacks quantitative indicators of carbon emissions in the building life-cycle. The exclusive evaluation indexes for regional and characteristic buildings, such as traditional houses, are ne-glected; This study explored the formula for calculating the life-cycle carbon emissions model of traditional residential houses under a carbon emission intervention and used real measurements of the physical environment and comparative analysis of the limitations of existing local stand-ards; The results showed that the carbon emission calculation formulas for each stage of the life-cycle of traditional residential houses must be established by combining the selection of local building materials, and the materials for sustainable energy and carbon sinks of green systems. Based on the results of indoor and outdoor heat and humidity environmental tests of residential houses and an analysis of the limitations of existing evaluation indexes due to locality, four im-portant factors affecting locality green buildings in terms of energy utilization, site ecology and landscape, land saving and material saving were extracted; on this basis, the whole life-cycle carbon emission model of traditional residential houses is constructed by combining the existing green building evaluation standards with the carbon emission indexes of the life-cycle of tradi-tional residential houses.

Aiming at the prediction of environmental vibrations induced by elevated high-speed railway, a machine-learning method is developed by combining random forest algorithm and Bayesian optimization, which using the dataset from on-site experiments . When it comes to achieving a rapid and effective prediction of environmental vibration, there is few research on com-parisons and verifications of different algorithms, and neither on parameter tuning and optimi-zation of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations caused by high-speed trains running on bridge, and then the en-vironmental vibration characteristics are analyzed in view of ground accelerations and weighted vibration levels. Subsequently, three machine-learning algorithms of linear regression, support vector machine and random forest are developed by using experimental database, and their prediction performance are discussed. Finally, two optimization models for the hyperparameter set of random forest algorithm are further compared. It turns out that the integrated random forest algorithm has higher accuracy in predicting environmental vibration than linear regression and support vector machine; the Bayesian optimization has excellent performance and high efficiency in achieving efficient and in-depth optimization of parameters, and can be combined with the RF machine learning algorithm to effectively predict the environmental vibrations induced by the high-speed railway.

Wireless sensor networks are usually applied in hostile areas where nodes are easy to be moni-tored and captured by adversary. Key distribution is an essential primitive to provide most of security mechanism. However, the characteristic of limited resources of sensors restricts the direct use of conventional key distribution schemes. In this paper, a complete security key distribution scheme based on asymmetric cryptography technology is proposed in both static and mobile scenarios. Mutual authentication is guaranteed using challenge-response mechanism. The per-formance evaluation and security analysis show that the proposed scheme with low complexity not only provides better security for wireless sensor networks, but also reduces storage overhead and key exposure risks.

Discrete distribution estimation is a fundamental statistical tool, which is widely used to perform data analysis tasks in various applications involving sensitive personal information. Due to privacy concerns, individuals may not always provide their raw information, which leads to unpredictable biases in the final results of estimated distribution. Local Differential Privacy (LDP) is an advanced technique for privacy protection of discrete distribution estimation. Currently, typical LDP mechanisms provide same protection for all items in the domain, which imposes unnecessary perturbation on less sensitive items and thus degrades the utility of final results. Although, several recent works try to alleviate this problem, the utility can be further improved. In this paper, we propose a novel notion called Item-Oriented Personalized LDP (IPLDP), which independently perturbs different items with different privacy budgets to achieve personalized privacy protection. Furthermore, to satisfy IPLDP, we propose the Item-Oriented Personalized Randomized Response (IPRR) based on the observation that the sensitivity of data shows an inverse relationship with the population size of respective individuals. Theoretical analysis and experimental results demonstrate that our method can provide fine-grained privacy protection and improve data utility simultaneously.

The SARS coronavirus 2 (SARS-CoV-2) is the causative agent of the 2019 coronavirus disease (COVID-19) pandemic that has executed 6.9 million people and infected over 765 million. It’s become a major worldwide health alarm and is also known to cause abnormalities in various systems, including the hematologic system. COVID-19 infection primarily affects the lower res-piratory tract and can lead to a cascade of events, including a cytokine storm, intravascular thrombosis, and subsequent complications such as arterial and venous thromboses. COVID-19 can cause thrombocytopenia, lymphopenia, and neutrophilia, which are associated with worse out-comes. Prophylactic anticoagulation is essential to prevent complication and death rate associated with the virus's effect on the coagulation system. It is crucial to recognize these complications early and promptly start therapeutic anticoagulation to improve patient outcomes. While rare, COVID-19-induced disseminated intravascular coagulation exhibits some similarities to DIC induced by sepsis. LDH, D-dimer, ferritin, and CRP biomarker are often increase in serious COVID-19 cases and poor prognosis. Understanding the pathophysiology of the disease and identifying risk factors for adverse outcomes is critical for effective management of COVID-19.

Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city's overall functionality. In this study, we apply machine learning to elucidate these biases within urban spatial perception research, utilizing a three-step methodology: objective mapping, subjective perception analysis, and perception deviation assessment. Our findings reveal that machine learning can expose hidden patterns within this research field, bearing substantial implications for urban planning and design. Of particular note, the study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Moreover, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. These findings underscore the importance of integrating both objective mapping and subjective perspectives in urban architectural functionality classification.

Existing methods for 3D human pose estimation mainly divide the task into two stages. The first stage identifies the 2D coordinates of the human joints in the input image, namely the 2D human joint coordinates. The second stage uses the results from the first stage as input to recover the depth information of human joints from the 2D human joint coordinates to achieve 3D human pose estimation. However, the recognition accuracy of the two-stage method relies heavily on the results of the first stage and includes too many redundant processing steps, which reduces the inference efficiency of the network. To address these issues, we propose the EDD, a fully End-to-end 3D human pose estimation method based on transformer architecture with Dual Decoders. By learning multiple human poses, the model can directly infer all 3D human poses in the image using a pose decoder, and then further optimize the recognition result using a joint decoder based on the kinematic relations between joints. With the attention mechanism, this method can adaptively focus on the most relevant features to the target joint, effectively overcoming the feature misalignment problem in the human pose estimation task and greatly improving the model performance. Any complex post-processing step, such as non-maximum suppression, is eliminated, further improving the efficiency of the model. The results show that the method achieves an accuracy of 87.4% on the MuPoTS-3D dataset, significantly improving the accuracy of end-to-end 3D human pose estimation methods.

Understanding of the correlation between the magnetomechancial coupling factors (k) and damping factors (Q^-1) is a key pathway to enhance the magnetomechanical power conversion ef-ficiency in laminated magnetoelectric (ME) composites by engineeringly manipulating the magnetic and mechanical properties of Fe-based amorphous metals. The k and Q^-1 factors of FeSiB amorphous ribbons annealed in air at different temperatures are investigated. It is found that k and Q^-1 factors are affected by both magnetic and elastic properties. The magnetic and elastic properties are equalized in terms of the magnetomechanical power efficiency for the low temperature annealing. The k and Q^-1 of FeSiB-based epoxied laminates with different stacking numbers show that the -3 dB bandwidth and Young’s modulus are equalized in terms of the magnetomechanical power efficiency for the high lamination stacking.

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on context information and large effective receptive field (ERF) is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements on it. First, the CAA-Net, which has a large ERF, is proposed to better merge the context and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.

This paper proposes a novel biologically inspired design for an insect-scale flapping robot. The proposed scheme employs two motors to drive four wings, mimicking the flapping motion of nat-ural four-winged insects. The vehicle incorporates two pairs of flapping wings to generate lift force, with a transmission mechanism featuring flexure hinges instead of pivot joints, resulting in reduced weight and volume. The flexure hinges also exhibit nonlinear stiffness to facilitate smooth wing motion. The vehicle is designed to be symmetrical in structure, with the wings of each section generating greater lift through the clap-and-fling mechanism. The final product weighs 6.2g, pos-sesses a wingspan of 17cm, a one-sided flapping wing angle of 103 degrees, and generates lift that exceeds its weight, attesting to its successful design.

Sulfate attack is one of the main factors affecting the durability of concrete structures. In recent years, multi-walled carbon nanotubes (MWCNTs) have attracted the attention of scholars for their excellent mechanical properties and durability performance. In this paper, the influence of sulfate attack and dry-wet cycles on the performance of multi-walled carbon nanotube-lithium slag concrete (MWCNTs-LSC) with varied MWCNTs content (0wt%, 0.05wt%, 0.10wt% and 0.15wt%) and varied water-cement ratios (0.35, 0.40 and 0.45) were investigated. Besides, scanning electron microscopy (SEM) and X-ray computed tomography (CT) tests were conducted to analyze the microstructure and pore structure of the concrete. The results show that concrete incorporated with MWCNTs could effectively mitigate sulfate attack The resistance to sulfate attack of concrete is negatively related to the water-cement ratio when the dry-wet cycle is fixed. The MWCNTs-LSC showed the best compressive strength at the water-cement ratio of 0.35 and 0.10 wt% MWCNTs. The SEM test results showed that the MWCNTs both filled the pores and cracks within the specimen and formed bridges between the cracks, enhancing the resistance to sulfate attack. The CT test results also showed that the addition of MWCNTs could reduce the porosity of concrete, refine the pore size and inhibit the generation and development of cracks, thus optimizing the internal structure of concrete and improving its resistance to sulfate attack.

To address the problem that it is easy to form coverage blind areas when wireless sensor networks are randomly deployed, an improved coverage optimization algorithm based on improved Salpa swarm Intelligent algorithm (ATSSA) is proposed for wireless sensor networks. Firstly, the pop-ulation is initialized using tent chaotic sequence to enhance the optimization ability of the algo-rithm. Secondly, the T-distribution mutation is added to the update formula of the leaders for improving the ability to jump out of the local optimal value. Finally, an adaptive formula for updating the position of the follower is proposed, which not only guarantees the local searching ability of the algorithm in the late iteration period, but also improves the global searching ability of the algorithm in the early iteration period. The experimental results show that ATSSA algorithm can improve the coverage of the wireless sensor networks and reduce deployment costs com-pared with other algorithms, when it is used in the wireless sensor networks.