The immobile lifestyle of plants determines their strong environmental adaptability. There fore, plants need to continuously modulate their metabolism to adapt to different light conditions. Sucrose non-fermenting 1-related protein kinase 1 ( SnRK1) in plant cells can be activated by energy stress in plants, regulating a series of biochemical reactions of carbon and nitrogen metabolism to adapt to energy stress. This review systematically summarizes the metabolic pathways regulated by SnRK1 under energy stress conditions through transcriptional regulation and post-translational phos-phorylation modification, and concludes the general rules of SnRK1 function for carbon and nitrogen metabolism, including SnRK1 inhibits sucrose synthesis and photosynthetic efficiency; SnRK1 represses nitrogen assimilation and nitrogen signal transduction, coordinating a carbon and nitrogen balance; SnRK1 promotes gluconeogenesis to maintain sugar metabolism homeostasis; as well as, SnRK1 pro motes autophagy and amino acid oxidation metabolism. These summarized results indicate that SnRK1 is the core regulatory element of plant carbon and nitrogen metabolism regulation, which will be a useful reference for functional analysis of SnRK1 in crops.

The influenza virus genome consists of eight genetic segments of varying lengths, with a total ength of approximately 14 ~ 16 kb. Due to the special molecular genetic mechanism of the virus, it undergoes rapid mutations through gene point mutation and genome rearrangement, which leads to changes in its biological infection characteristics and poses a continuous threat to health. Therefore, accurate prediction of natural avian influenza virus spillovers is crucial for public health. This paper, employs a combination of convolutional neural network ( CNN) and recurrent neural network ( RNN) to represent viral genome sequences. The model’s transferability on both specific group datasets and entire datasets was evaluated. The experimental results demonstrate excellent prediction performance of the specific group model on the respective datasets, with AUROC exceeding 0 966 and AUPR values surpassing 0 876. However, its generalization ability is limited. Conversely, except for the H9N2 group, the global model performs well with AUROC and AUPR values reaching 1.000 across all groups. Based on ablation experiments, it was found that attention mechanism and sequence embed ding method significantly impact model performance while further testing its generalization ability reveals AUROC values above 0 984 and AUPR values over 0 941 for transfer predictions respectively. Visualizing the attention weight matrix provides biological interpretability for the model. The high performing deep learning prediction model can be effectively utilized for early warning systems against cross species infections caused by avian influenza viruses.

This paper discusses the utilization of computer vision technology to capture a series of structural vibration images. The resulting sub-pixel vibration displacement of the monitoring nodes are then obtained by Digital Image Correlation ( DIC) technology, the fast Bayesian FFT method is then adopted to identify the dynamic modal parameters of the tested structure. To assess the precision and reliability of the dynamic modal parameter identified for a structure, a combination of computer vision based vibration test and fast Bayesian FFT methodology is conducted in this paper. A 5 6 m steel truss is adopted as an example to extract its vibration displacement data from captured videos for the undamaged and other 5 damaged conditions of the test structure. The size of the errors and reasons in the identified displacement of monitoring nodes at various locations in the truss are analyzed in this study. Additionally, the fast Bayesian FFT method is adopted to evaluate the accuracy and uncertainty of the identified dynamic modal parameters by investigating the vibration video data extracted from different monitoring points in the tested structure. The results from the proposed method show that the utilization of DIC technology and fast Bayesian FFT can accurately identify dynamic modal parameters of the test structure.

With the rapid development of quantum computers, post-quantum cryptography has become a research hotspot. Lattice cryptography has become the mainstream in post-quantum cryptography due to its balanced performance, solid security foundation, and rich functions. Pre-image sampling is the core algorithm in lattice cryptography and is widely used in the construction of many advanced cryptography schemes. Hash-and-Sign digital signature on lattice is its simplest and most direct application. Technically, pre-image sampling algorithms are divided into GPV and Peikert. The former is characterized by high output quality, but the algorithm can usually only be executed serially; the latter sup ports parallel operations, but the output quality is poor. This article applies non-spherical Gaussian technology to the Peikert sampling algorithm on the NTRU lattice, aiming to improve its efficiency. Specifically, two parameter modes were selected. Compared with the Peikert sampling algorithm on the original NTRU lattice, mode 1 can improve the security strength of digital signatures based on this sampling algorithm and reduce the size of the signature; mode 2 does not reduce security. Under the premise, the signature size can be further reduced. Experimental results show that in mode 1, the security is improved by about 18% ~ 20% and the signature size is reduced by about 15% ; in mode 2,the security remains unchanged, but the signature size is reduced by about 30% ~ 35% .

Soil salinity is one of the major environmental challenges facing global agriculture today, and it is of great importance to conduct research on salt stress response mechanisms in order to make plants grow better. The plant specific homologous structural domain leucine zipper ( HDZIP) family transcription factor HAT1 regulates multiple stress responses in Arabidopsis, and so far, it is not clear whether HAT1 is involved in regulating plant responses to salt stress. This paper focuses on the regulatory role of HAT1 in response to salt stress in Arabidopsis, with the aim of expanding the molecular regulatory network of salt stress. This study demonstrates that the EIN3HAT1CSDs molecular module regulates the plant response to salt stress. Salt stress induces the accumulation of EIN3, which inhibites the expression of HAT1, thereby weakening the transcriptional inhibition of HAT1 on downstream genes CSD1 and CSD2 and activating the antioxidant system to remove excess reactive oxygen species to enhance the salt tolerance of plants. This signaling pathway provides a new potential target for improving plant salt tolerance through molecular breeding.

Parametric level set topology optimization addresses numerical complexities associated with traditional level set methods. Since topological optimization using level sets requires introducing more advanced functions to construct topological models, most research on parametric level set topology optimization has been limited to two dimensions. This article extends the foundation of two-dimensional parametric level set topology optimization to three dimensions. By incorporating the concept of point clouds, it tackles the representation challenges of three-dimensional topological configurations. The algorithm is validated through typical numerical examples involving cantilever beams and simply supported beams, demonstrating its ability to address issues that arise when introducing higher-dimensional functions in three-dimensional level sets. Additionally, the article discusses the application of the SIMP ( Solid Isotropic Material with Penalization) multi-material interpolation model for multi-material topology optimization, providing valuable insights for further research in this field.

UAV-aided communication; wireless-powered communication; convex optimization; energy harvesting

Since late 2019, the widespread outbreak of the novel coronavirus has had a severe impact on public health and social order. Machine learning based prediction methods have the capability to determine the infectivity phenotype and pandemic risk of coronaviruses. Presently, six classes of coronaviruses that infect humans have been identified. These viruses exhibit significant differences in their genomic sequences, and the continuous genetic variation in these viruses has resulted in a decline in the performance of machine learning models, potentially causing issues related to learned forgetting. This study, based on an incremental learning model framework, employed a One class SVM algorithm for continuous discrimination of novel coronavirus subgroups. Furthermore, a combined strategy of parameter sharing and knowledge distillation to adapt a backpropagation ( BP) neural network for continuous learning and prediction of the human infecting phenotype of coronaviruses was employed. The results indicate that the One class SVM, with a combination of balancing parameters v at 0.92, 0. 81, 0.24, 0.11, 0.55, and 0. 2, achieved the optimal classification performance for the six virus classes. It was found that the prediction model achieved the best performance when the number of hidden layer nodes was increased to 6, with a maximum Index of Agreement ( IAC) value of 0 903 5 and a maxi mum Bias Total ( BT) value of – 0.039 9. This effectively suppressed the learning amnesia trend in the network model, with the model’s predictive performance being close to that of joint data training( IAC: 0 923 6 ) . This performance was significantly better than that of neural networks without knowledge distillation ( IAC: 0.776 4) . Moreover, in comparison to other incremental methods, our approach outperformed sample-based methods such as ESRIL ( IAC: 0.866 2) and model parameter based methods like CCLL ( IAC: 0. 885 3) . This research holds important implications for public health applications.

In this work, Polyvinyl alcohol ( PVA) was crosslinked via Boric acid( H3 BO3 ) and prepared to be the hydrogel film. MPP is used to improve the flame retardant properties of the PVA hydrogel films. The experimental results showed that the PVA / MPP hydrogel films could achieve UL94 V0 rating with a 20 wt% addition of MPP which could effectively reduce the HRR and the smoke emission of the PVA hydrogel films. The flame retardant mechanism of the PVA / MPP hydrogel films is a synergistic effect of gasphase with condensed phase flame retardant mechanism. MPP decomposed into melamine and phosphoric acid, then melamine converted to N2 and CO2; Phosphoric acid promoted the PVA hydrogels formation of a continuous and dense char layer which hinders the exchange of oxygen and heat with the outside. At the same time, because of the interaction be tween MPP and PVA, the mechanical properties of PVA are maintained while the flame retardancy of PVA is improved.

With the integration of hyperspectral sensors and drones, drone hyperspectral remote sens ing technology has become one of the most important research directions in the field of remote sensing. Compared to traditional satellite remote sensing technology, drone remote sensing technology offers ad vantages such as low cost, simplicity of operation, high spatial resolution, and high temporal resolu tion. Researchers can flexibly acquire hyperspectral images of study areas. Hyperspectral sensors pro vide more spectral details, and the combination of drone technology and hyperspectral technology of fers researchers an effective technical means for remote sensing related research. In recent years, it has received widespread attention. This article summarizes the characteristics and development status of drones, sensors, and hyperspectral technology. It also describes the preparation work before the flight of drone hyperspectral remote sensing technology and the processing of the collected data. This article focuses on the application of drone hyperspectral remote sensing technology in fields such as hydrology, agriculture and forestry, natural disasters, and marine environments. It also introduces the main types and basic parameters of sensors carried by drones. Additionally, it points out the current challenges, opportunities, and development prospects of using drone hyperspectral technology.

The spaceborne microwave scatterometer, characterized by its high spatiotemporal resolution and all-weather observational capability, serves as a pivotal tool for observing the sea surface wind fields with multiple bands, polarizations, and viewing angles. It has emerged as a crucial means to acquire high-resolution sea surface wind field data over expansive regions, playing a central role as the primary satellite sensor for global sea surface wind field observations. Consequently, investigating its intricacies holds significant academic significance. This paper offers a comprehensive and systematic examination of the advancements made by scholars globally in the domain of sea surface wind field inversion methods. Emphasizing the evolution of geophysical model functions, the optimization of fuzzy solution removal schemes, and the application of neural network algorithms and deep learning in ocean remote sensing, it provides a nuanced exploration of this interdisciplinary field. By delving into these topics, this paper not only furnishes valuable insights for advancing sea surface wind field retrieval technology but also presents novel perspectives for future research and applications within the realm of ocean remote sensing.

Peptide hormones play indispensable roles in regulating plant growth, development, and environmental adaptation. GLVs peptide family has been demonstrated to participate in regulating various processes such as root meristem development, root gravitropism, lateral root initiation, and plant immunity. However, it remains unclear whether members of GLVs family are also involved in regulating the development of aerial parts of a plant. We examined the expression patterns of all 11 GLV genes, and found that some members are expressed in leaves, with GLV2 showing more expression than other GLVs. Using a CRISPRCas9 gene editing approach, we generated a glv2 mutant. Com pared to the wild type, glv2 exhibited curled and elongated leaf phenotypes. Detailed examinations via histological sections and scanning electron microscopy analyses revealed that leaf curling in the glv2 mutant primarily resulted from the abnormal development of the spongy tissues. Existing researches demonstrate that GLV family members act primarily as the ligands of the receptor kinase family, RGIs. We also found that most RGI family members are expressed in leaves. A quintuple mutant of RGIs exhibited abnormal leaf curl phenotype similar to glv2. Qrt-PCR analysis identified a number of auxin-related genes which are up regulated in glv2 leaves, suggesting the potential activation of auxin signaling as a contributing factor to the altered leaf morphology in the glv2 mutant. In summary, our results suggest that GLV2, likely perceived by RGIs, modulates plant leaf mesophyll cell development by regulating auxin signaling, thereby influencing leaf morphogenesis.

Real time population data is crucial for urban planning, resource management, and the sustainable development of society. In order to effectively enhance existing population estimation methods based on geospatial big data, this study comprehensively compares and analyzes the population simulation performance of different open geospatial datasets, and develops a comprehensive approach integrating remote sensing and emerging social media user data to achieve high precision rapid estimation of population at the county level. Taking Chinese counties as the experimental area, multiple linear regression and geographically weighted regression methods are employed to comprehensively evaluate the population modeling capability of various geospatial remote sensing data. The data utilized include Tencent Location Based Service ( LBS) data, Amap Point of Interest ( POI) data, nighttime light remote sensing data, and land use / cover data derived from remote sensing. The research findings indicate that, in estimating population distribution, Tencent location data and POI data outperform remotely sensed land use / cover data and nighttime light satellite data, with population simulation accuracies of 81 .6% , 70 .8% , 68 .8% , and 63 .0% , respectively. Furthermore, the comprehensive use of multisource geospatial data can achieve an overall population simulation accura cy of 85 .4% . The research results and discoveries can provide data and technical support for popula tion related policies in China.

Previous studies have shown that the quasi maximum exponential likelihood estimation based on high frequency data can improve the estimation accuracy of GARCH model, but few studies have derived the corresponding test statistic for this estimator. In this paper, a portmanteau Q test statistic is proposed based on the asymptotic property of quasi maximum exponential likelihood estimation of GARCH model based on high-frequency data. The theoretical correctness of the test statistic is vali dated through simulation in this paper, and specific applications are provided by using the data of the CSI 300, CSI 500, and SSE 50 indices. The results show that when the model is adequate, the distribution of the test statistic proposed in this paper more closely follows the theoretically derived distribution, which is better than the results of the test statistic based on low-frequency data. Moreover, the statistic is able to capture high-frequency residual autocorrelation due to the inclusion of high-frequency information. While for low-frequency residual autocorrelation, the statistic can also identify model non-sufficiency when the correlation is stronger, which is useful for order identification in GARCH model. Empirical research also indicates that the test statistic can identify the effective utilization of high-frequency information by the models based on high-frequency data, demonstrating a certain degree of practicality.

Net primary productivity ( NPP) of vegetation is an important component of the surface carbon cycle, and the accurate assessment of NPP is of great significance to the correct understanding of ecosystem energy transformation and the evaluation of ecosystem health. While existing studies have reviewed NPP assessment from perspectives such as light use efficiency, ecosystem process simulation, and remote sensing data-driven approaches, there remains a need for further refinement in reviewing specific drug molecules, and the relationship trend between different descriptors and the three performance indicators were preliminarily explored. Secondly, six machine learning models including Random Forest, Extreme Gradient Boosting ( XGB) , Gradient Boosting Decision Tree, Light Gradient Boosting Machine, Backpropagation Neural Network, and Support Vector Regression six machine learning algorithms with eight descriptors and three performance evaluation criteria ( Adsorption selectivity SS-IBU/ N2 ,Adsorption capacity NS-IBU/ N2 and Tradeoff value TSN) for big data training and mining, were used to establish quantitative relationships. The results show that the prediction accuracy of the six ML algorithms is N > TSN > S . For S , XGB showed the best prediction ( R2 = 0.83) . Subsequently, based on the XGB model, the SHaple Additive explanation ( SHAP) method was used to explain and analyze the importance of MOF descriptors to performance indicators. The total energy generated during MOF adsorption is considered to be the key influencing factor, and it shows a positive correlation trend with both TSN and NS-IBU . Finally, combined with toxicological analysis, a series of high-performance MOF materials were recommended and designed. This work, from molecular level, high-throughput computing to big data mining, systematically studied the adsorption and delivery mechanism of ibuprofen drug molecules in MOF, which provides theoretical guidance for drug delivery materials. calculation methods. In this study, typical NPP assessment models are systematically reviewed according to the classification of climate productivity model, physiological and ecological process model, and light energy use model. Focusing on the structure and driving parameters of each model, this paper discusses and analyzes the characteristics and applicability of each model, and makes an overview of the key issues of the models-development, pointing out that future research needs to integrate the perspectives of multiple disciplines, give full play to the advantages of the new earth observation technology, and further deepen the scale conversion related to NPP assessment.

Territorial space evaluation is the basic requirement for promoting the high quality development of territorial space, and it has important value and significance. Applying the knowledge map ping analysis method based on Citespace software, the current research status of China’s land space e valuation is explored in terms of basic theory, research content, evaluation indexes and evaluation methods, etc. The results show that: the number of land space evaluation publications increased rap idly after 2018, most of them are intra region cooperation among the authors of the publications, and dual evaluation, comprehensive evaluation, and triple space are the hot spots of the research; the research content is focused on the evaluation of the suitability of territorial space development, evaluation of the carrying capacity of resources and environment, evaluation of the development potential of territorial space and evaluation of the sensitivity of ecological environment, and the evaluation index system is also constructed around these four aspects; the evaluation methods are diversified and applicable to different types of territorial space evaluation. Future research should be carried out in the three major aspects of improving the research scale, optimizing the index system and upgrading the e valuation technology, so as to enrich the theoretical and methodological system of land space evaluation research.

The coordinated development of urban agglomerations is an essential requirement for achieving high-quality regional economic growth. Examining the network characteristics of Chinese urban agglomerations from a multi-dimensional perspective holds significant importance in advancing the co ordinated development of these agglomerations. This article establishes urban agglomeration information, transportation, and population networks based on data from information search indices, train schedules, and Baidu migration indices. By employing social network analysis methods, the spatial network connectivity characteristics of 19 urban agglomerations in China are examined. The research findings are as follows: ①Scale Characteristics: The ranking of network sizes among the 19 urban ag glomerations does not vary significantly across different factor flows. Higher levels of economic development correspond to larger network sizes of urban agglomerations. ②Element Connections: Urban agglomerations exhibit hierarchical and differential characteristics in their information, transportation, and population networks. The information network is primarily characterized by strong connections, while the transportation and population networks are mainly characterized by moderate to weak connections. Spatially, the information network displays a “ diamond” shaped pattern, while the transportation and population networks exhibit a “ two horizontal and three vertical” pattern. ③Cluster Characteristics: Information connections between urban agglomerations are the closest, followed by transportation and population connections. The impact of geographical proximity on different element networks varies, with the overall pattern showing a higher impact on the population network followed by the transportation network and then the information network. ④Urban Agglomeration Types: Utilizing net work indicators, the identified types of urban agglomerations are classified into four categories: radiative, siphon, balanced, and peripheral. These types demonstrate heterogeneous characteristics across different element networks. In the information network, the urban agglomerations in the eastern, central, and western regions exhibit distinct network characteristics, with a trend of “ radiation type dominance by eastern urban agglomerations and siphon type dominance by western cities” .

Membership inference attacks in deep learning refer to inferring whether a given sample belongs to the training dataset of a target model. Due to the presence of privacy-sensitive information in the training dataset, defending against membership inference attacks is crucial for privacy protection. This paper begins by defining membership inference attacks and elucidating the underlying reasons causing such attacks. Subsequently, existing defense algorithms are comprehensively reviewed. Finally, a novel defense mechanism is proposed, delineating the defensive approach adopted in this paper. Compared to state-of-the-art defenses against membership inference attacks, this method offers superior trade-offs between preserving member privacy and maintaining model utility. Detailed explanations of the employed techniques are provided to facilitate a better understanding of membership inference attacks and their defenses, thereby furnishing valuable insights for mitigating privacy risks in training datasets and striking a balance between model utility and privacy security.

Precision components are widely used in various industries, including national defense, medical devices, aerospace, and electronics. They are among the fundamental elements for realizing the grand vision of “ Made in China 2025. ” However, during the processing of precision components, surface defects such as cracks, pitted surfaces, and scratches are inevitable, which leads to a decline in the quality and precision of the components. Furthermore, these surface defects will affect the performance and lifespan of the equipment. Therefore, effective detection of surface defects is crucial. To address the issues of missed and false detections of metal surface defects and to improve detection accuracy, this paper proposes an improved YOLOv5s model for surface defect detection in metal components. First, considering that metal surface defects often present as streaks or spots which express as relatively simple types, a lightweight GhostC3 module is designed by generating feature maps using fewer parameters and resulting in reduced computational complexity. Second, given the characteristics of defects such as pitted surfaces and rolled in scale which are generally small and unevenly distributed, a Concat-BiFPN module based on a bidirectional feature pyramid network was designed. This module makes full use of the ability of BiFPN to fuse features at different scales, and improves the accuracy and stability of small target detection. Finally, the SIoU loss function is utilized, which considers the shape and spatial relationships of targets to better capture the positional relationships of different targets among the image, thus resulting in enhancing the precision of target localization. Ablation and comparative experiments on the NEU DET dataset demonstrate that the proposed method achieves higher multi class average precision with a significantly reduced number of parameters.

Spatial averages have been widely used in scientific research and practical applications since they were proposed. How to estimate the weight coefficient in the spatial average has been a problem studied by many scholars. In the case of missing data, the spatial average is the ratio of the two random variables R = ∑ βi si ri as well as S = ∑ βi si . In this paper, we use the “ Delta method”to derive approximate formulas for the squared bias, variance, and mean squared error of the estimatorr used to estimate the true spatial average, assuming that the weight βi is known. The bias of the estimator r and the source of variance are analyzed, and the spatial average estimated weight to minimize the bias is given. Finally, the obtained results are applied to the global relative abundance data of ammoniating archaea.

As robot technology becomes more widespread, its applications in various fields such as daily life, industry, and services have significantly increased. In practical scenarios, robots performing machining tasks are often influenced by external factors or system errors, leading to deviations from the intended path and affecting task completion. This paper aims to investigate the problem of deviations from the intended trajectory during the motion of robot manipulators, with a focus on optimizing compensation for the deviation distance. The research involves the use of a locally weighted method to adjust the deviation distance, reducing the impact of measurement errors. Additionally, a physical impact function is proposed, considering physical factors such as moment of inertia, dynamic friction, and centrifugal force on the deviation distance. The research findings indicate that when ro bot manipulators are chamfering corners, the pre compensation algorithm for deviation distance re duces the deviation distance error from an initial uncorrected range of approximately 0 .22 mm to 0 .83 mm to a range of 0 .02 mm to 0 .34 mm, with a reduction in deviation fluctuation of about 52 .46% . This is significant for improving work efficiency and reducing deviation errors.

Cluster algebra is an important research object in algebra. Cluster algebra is a kind of com mutative subalgebra of a rational function field which satisfies some mutation rule by taking cluster variables as generators. There are some research results on the cluster algebra A( b, c) of rank 2. In particular, when bc≤4, A( b, c) is of two types of algebras with finite type and affine type. Nowadays, cluster algebra plays an important role in Poisson geometry, representation theory, quantum group and other research fields. This paper studies the algebraic structure of a special class of generalized cluster algebras, mainly by defining sn ( n≥1) to establish the multiplication formula of such generalized cluster algebras, and thus proves that the set of all cluster monomials xpm xqm+1 ( m∈Z) and sn( n≥1) is an integer base of such generalized cluster algebras.

The mixture exponential decay model plays an important role in pharmacokinetics and chemical kinetics. Different from the previous research method which was transformed into a linear model according to Taylor′s development, based on the theory of R-optimal design, this paper uses a nonlinear optimization method, Shengjin formula and interior point method to deduce R-optimal design under the model when the two decay parameters are equal. Meanwhile, the R-optimal design algorithm is given when the two decay parameters are unequal. It is verified by the equivalence theorem that the derived design is R-optimal.

In this paper, we prove the local existence and uniqueness of solutions to the evolutionary model for magnetoviscoelasticity in R2 , R3 . This model consists of an incompressible Navier Stokes, a regularized system for the evolution of the deformation gradient and the Landau Lifshitz Gilbert system for the dynamics of the magnetization. Our approach depends on approximating the system with a sequence of perturbed systems.

To examine the core features of resilience among Chinese students and to compare the differences in psychological resilience networks between first-generation and non-first-generation college students, a survey was conducted on 3 017 college students ( consisting of 2 166 first-generation college students and 851 non-first-generation college students) using the ConnorDavidson Resilience Scale. Network analysis methods were used for network model construction, centrality analysis, and network comparative analysis. The results showed that: ①The item with the highest centrality of resilience network structure was “ I can achieve my goals” ; ②There was no significant difference in the network structure and network connection strength of resilience between first-generation college students and non-first-generation college students; ③Among first-generation college students, “ I will do my best regardless of the outcome” was the item with the highest centrality, whereas among non-first-generation college students, “ I consider myself a strong person” was the item with the highest centrality. Therefore, in the future, the resilience intervention of college students can be classified and accurately implemented, and the items with the highest centrality will be used as the target for the intervention of first-generation college students and non-first-generation college students.

In the research of Boolean functions, there is an important problem: given a subset A ={ ( λi , a ) | λi∈Fn , ai∈Z, i = 0, 1, 2, … , m - 1} , find all the Boolean functions f in n variables, such that f^( λi ) = ai for all i = 0, 1, 2, … , m - 1, where f^( λi ) is the Walsh spectral value of the Boolean function f at the address λi . In general, this problem is quite difficult. But if the given set of addresses is a vector subspace of Fn , there is a simple solution. This paper, gives an algorithm called WDC Algorithm, that can solve this problem efficiently in this special case. The WDC Algorithm contains the following three parts: ① constructs all the Boolean functions that satisfying all these conditions;② finds the number of such Boolean functions; and ③ gives a necessary and sufficient condition of the spectral distribution on the subspace to ensure the existence of such Boolean functions. On the other hand, the Bent function is the Boolean function that has the maximal nonlinearity, thus possessing excellent cryptographic properties. This paper uses WDC algorithm to find all 6 - variable Bent functions by means of computer searching, the total number of such functions is 5 425 430 528.

Drug separation / drug loading materials have become one of the significant research objects in controlled drug release and drug preparation technology. In order to select candidates for efficient drug loading from a large number of existing materials and explore their loading mechanisms, 1 000 MOFs materials were extracted from the CoRE-MOF 2019 database for this study, and their adsorptive loading performance the drug ibuprofen was explored by high-throughput calculations. Firstly, the eight structure / energy descriptors of MOFs were analyzed by univariate analysis with the adsorption selectivity ( SSIBU/ N2 ) , adsorption capacity ( NSIBU ) and tradeoff value ( TSN) of MOFs for ibuprofen drug molecules, and the relationship trend between different descriptors and the three performance indicators were preliminarily explored. Secondly, six machine learning models including Random For est, Extreme Gradient Boosting ( XGB) , Gradient Boosting Decision Tree, Light Gradient Boosting Machine, Backpropagation Neural Network, and Support Vector Regression six machine learning algorithms with eight descriptors and three performance evaluation criteria ( Adsorption selectivity SSIBU/ N2 ,Adsorption capacity NSIBU/ N2 and Tradeoff value TSN) for big data training and mining, were used to establish quantitative relationships. The results show that the prediction accuracy of the six ML algorithms is N > TSN > S . For S , XGB showed the best prediction ( R2 = 0。83) . Subsequently, based on the XGB model, the SHaple Additive explanation ( SHAP) method was used to ex plain and analyze the importance of MOF descriptors to performance indicators. The total energy generated during MOF adsorption is considered to be the key influencing factor, and it shows a positive correlation trend with both TSN and NSIBU . Finally, combined with toxicological analysis, a series of high-performance MOF materials were recommended and designed. This work, from molecular level, high-throughput computing to big data mining, systematically studied the adsorption and delivery mechanism of ibuprofen drug molecules in MOF, which provides theoretical guidance for drug delivery materials.

Based on the thermodynamic method, NRTLRK, Aspen plus software was used to simulate isopropyl alcohol production process by transesterification and optimized design of energy saving, which is catalyzed by sodium methanol solid catalyst. The project used 50 000 tons isopropyl alcohol production per year, while the factors of reaction equilibrium, separation difficulty and cost of raw materials are considered for reactor design. The reactive distillation column is set as isopropyl alcohol synthesis reactor. The bottoms flow is 99.99 wt% of isopropyl alcohol. The pressure swing distillation system design is based on the thermal characteristics of flow from the top of the reactive distillation column, which can achieve the goals of recycle the byproducts, methyl acetate, and the raw material, methanol. Heat pump distillation and double effect distillation technologies are adopted in reactive distillation and pressure swing distillation systems, respectively. While the energy consumption values decrease 60.2% and 43.9% respectively, the heat exchange network energy was further integrated, and the final energy consumption was reduced by 59.2% .