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Regulating the binding energy between the catalysts and reaction intermediate species is crucial for electrocatalytic processes. As according to the Sabatier principle, excessively strong binding energies can be detrimental. The presence of oxygen vacancy defects is beneficial, leading to a relatively lower binding energy. In this study, ZnO catalysts with varying oxygen vacancy contents were successfully synthesized with different solvents and hydrothermal treatment temperatures. The oxygen vacancy content of prepared samples was characterized using X-ray photoelectron spectroscopy and electron paramagnetic resonance, while the electrocatalytic CO2reduction reaction (CO2RR) experiments were performed in a flow cell. A proportional relationship was observed between the CO production rate and oxygen vacancy content. As a result, the catalyst with a hydrothermal temperature of 180 °C for 3 h (ZnO-EG-180-3) exhibited the highest oxygen vacancy content, accompanied by a high CO yield. The sample was stable in CO2RR for over 50 h at a constant current of 150 mA, maintaining a stable CO faradaic efficiency (FE) exceeding 90%, and the CO yield was more than 2625 μmol·h–1·cm–2, while lower than 10% for H2. Density functional theory calculations confirmed that the presence of oxygen vacancies in ZnO catalysts reduces the binding energy of the *CO intermediate, facilitating *CO desorption, mitigating the poisoning effect of *CO on active sites, and promoting the generation of CO generation.

Front-running attacks have been a major concern on the blockchain. Attackers launch front-running attacks by inserting additional transactions before upcoming victim transactions to manipulate victim transaction executions and make profits. Recent studies have shown that front-running attacks are prevalent on the Ethereum blockchain and have caused millions of US dollars loss. It is the vulnerabilities in smart contracts, which are blockchain programs invoked by transactions, that enable the front-running attack opportunities. Although techniques to detect front-running vulnerabilities have been proposed, their performance on real-world vulnerable contracts is unclear. There is no large-scale benchmark based on real attacks to evaluate their capabilities. We make four contributions in this paper. First, we design an effective algorithm to mine real-world attacks in the blockchain history. The evaluation shows that our mining algorithm is more effective and comprehensive, achieving higher recall in finding real attacks than the previous study. Second, we propose an automated and scalable vulnerability localization approach to localize code snippets in smart contracts that enable front-running attacks. The evaluation also shows that our localization approaches are effective in achieving higher precision in pinpointing vulnerabilities compared to the baseline technique. Third, we build a benchmark consisting of 513 real-world attacks with vulnerable code labeled in 235 distinct smart contracts, which is useful to help understand the nature of front-running attacks, vulnerabilities in smart contracts, and evaluate vulnerability detection techniques. Last but not least, we conduct an empirical evaluation of seven state-of-the-art vulnerability detection techniques on our benchmark. The evaluation experiment reveals the inadequacy of existing techniques in detecting front-running vulnerabilities, with a low recall of $\leq$≤ 6.04%. Our further analysis identifies four common limitations in existing techniques: lack of support for inter-contract analysis, inefficient constraint solving for cryptographic operations, improper vulnerability patterns, and lack of token support.

Application Programming Interfaces (APIs), which encapsulate the implementation of specific functions as interfaces, greatly improve the efficiency of modern software development. As the number of APIs grows up fast nowadays, developers can hardly be familiar with all the APIs and usually need to search for appropriate APIs for usage. So lots of efforts have been devoted to improving the API recommendation task. However, it has been increasingly difficult to gauge the performance of new models due to the lack of a uniform definition of the task and a standardized benchmark. For example, some studies regard the task as a code completion problem, while others recommend relative APIs given natural language queries. To reduce the challenges and better facilitate future research, in this paper, we revisit the API recommendation task and aim at benchmarking the approaches. Specifically, the paper groups the approaches into two categories according to the task definition, i.e., query-based API recommendation and code-based API recommendation. We study 11 recently-proposed approaches along with 4 widely-used IDEs. One benchmark named APIBench is then built for the two respective categories of approaches. Based on APIBench, we distill some actionable insights and challenges for API recommendation. We also achieve some implications and directions for improving the performance of recommending APIs, including appropriate query reformulation, data source selection, low resource setting, user-defined APIs, and query-based API recommendation with usage patterns.

The consumption of cheese in the People's Republic of China is increasing rapidly. Little is known about the microbiota, the presence of antibiotic-resistant bacteria, or the distribution of antibiotic resistance genes (ARGs) in commercially produced cheeses sold in China. This information is important for evaluating quality and safety. This study was conducted using 16S rRNA gene sequencing to assess the metagenomics of 15 types of cheese. Fourteen bacterial genera were detected, and Lactococcus, Lactobacillus,and Streptococcuswere dominant based on number of sequence reads. Multidrug-resistant lactic acid bacteria (i.e., resistant to two or more types of antibiotic) were isolated from most of the types of cheese. Of these isolates, 100 and 91.7% were resistant to streptomycin and sulfamethoxazole, respectively, and genes involved in acquired resistance to streptomycin (strB)and sulfonamides (sul2)were detected with high frequency. To analyze the distribution of ARGs in the cheeses overall, 309 ARGs from eight categories and nine transposase genes were profiled. A total of 169 ARGs were detected in the 15 cheeses; their occurrence and abundance varied significantly between cheeses. Our study revealed diverse bacteria and ARGs in cheeses sold in China. The risks associated with multidrug resistance among dominant lactic acid bacteria are of great concern.

Antibiotic resistance genes (ARGs) are emerging contaminants that pose a health risk to humans worldwide. Little information on ARGs in bee honey is available. This study profiles ARGs in bee honey samples produced in China, the biggest producer in the world. Of 317 known ARGs encoding resistance to 8 classes of antibiotics, 212 were found in collected honey samples by a real-time quantitative polymerase chain reaction approach. Occurrence frequencies of genes providing resistance to FCA (fluoroquinolone, quinolone, florfenicol, chloramphenicol, and amphenicol) and aminoglycosides were 21.0% and 18.5%, respectively. Frequencies of genes encoding efflux pumps were 42.5% and those of destructase genes 36.6%, indicating that these two mechanisms were predominant for resistance. Nine plasmid-mediated quinolone resistance genes were detected. Of the nine transposase genes known to be involved in antibiotic resistance, eight were found in the samples examined, with tnpA-4, tnpA-5, and tnpA-6being more abundant. The abundance of the transposase genes was associated with genes conferring resistance to tetracyclines (r= 0.648, p< 0.01), macrolide–lincosamide–streptogramin B (r= 0.642, p< 0.01), FCA (r= 0.517, p< 0.01), and aminoglycosides (r= 0.401, 0.01 < p< 0.05). This is the first study on the abundance and diversity of ARGs in Chinese bee honey products. These findings suggest that bee honey may be a significant source of ARGs that might pose threat to public health. Further research is required to collect more samples in diverse geographic regions in China to make a more comprehensive judgment of ARG in bee honey.

A series of novel zinc-polyurethane copolymers, which contained zinc cations in the form of polymeric salt and can be exchanged with the sodium ions in sea water, was prepared. As the ion exchange reaction proceeded, the zinc-polyurethane copolymers became soluble and leached out by the flow of sea water. The self-polishing rates of marine antifouling coatings formulated with the polymers proved to be easily controlled by the amount of 2,2-bis(hydroxymethyl) propionic acid. Marine field test revealed that zinc-polyurethane antifouling coatings had excellent efficiency in preventing marine biofouling for more than 12 months. These results proved that the zinc-polyurethane copolymers were a good candidate for the preparation of long-efficiency and controllable marine antifouling coatings.

Bio-organic fertilizers enriched with plant growth-promoting microbes (PGPMs) have been widely used in crop fields to promote plant growth and maintain soil microbiome functions. However, their potential effects on N2O emissions are of increasing concern. In this study, an in situmeasurement experiment was conducted to investigate the effect of organic fertilizer containing Trichoderma guizhouense(a plant growth-promoting fungus) on soil N2O emissions from a greenhouse vegetable field. The following four treatments were used: no fertilizer (control), chemical fertilizer (NPK), organic fertilizer derived from cattle manure (O), and organic fertilizer containing T guizhouense(O+T, referring to bio-organic fertilizer). The abundances of soil N cycling-related functional genes (amoA) from ammonium-oxidizing bacteria (AOB) and archaea (AOA), as well as nirS, nirK,and nosZ,were simultaneously determined using quantitative PCR (qPCR). Compared to the NPK plot, seasonal total N2O emissions decreased by 11.7% and 18.7% in the O and O+T plots, respectively, which was attributed to lower NH4+-N content and AOB amoAabundance in the O and O+T plots. The nosZabundance was significantly greater in the O+T plot, whilst the AOB amoAabundance was significantly lower in the O+T plot than in the O plot. Relative to the organic fertilizer, bio-organic fertilizer application tended to decrease N2O emissions by 7.9% and enhanced vegetable yield, resulting in a significant decrease in yield-scaled N2O emissions. Overall, the results of this study suggested that, compared to organic and chemical fertilizers, bio-organic fertilizers containing PGPMs could benefit crop yield and mitigate N2O emissions in vegetable fields.

Molybdenum carbide-based catalysts have been utilized widely in hydrogen production from methanol steam reforming (MSR) because of their high selectivity to H2and low reaction temperature. However, the catalytic performance and stability for MSR are highly vulnerable to the method and condition of catalyst preparation. Here, ZnPt/MoC catalysts synthesized by different processing techniques were used for the production of hydrogen from MSR at low temperature with emphasis on the influence of hydrogen treatment on the catalytic performance and stability. These ZnPt/MoC catalysts were analyzed fully using several characterization techniques. From the results, it was found that reducing the ZnPt/MoC catalyst precursor before the carburization facilitated the formation of the α-MoC1 – xphase and enhanced the platinum dispersion on the catalyst surface, which resulted in increased catalytic performance and stability for MSR. Furthermore, after treating with flowing hydrogen at 200–400 °C, more α-MoC1 – xphase and highly dispersed platinum particles were obtained in the modified catalysts. The optimal ZnPt/MoC-p3–200 catalyst exhibited outstanding catalytic performance for MSR and extremely low CO selectivity at low reaction temperature. This study provides a perspective on the design and synthesis of stable and highly active molybdenum carbide-based catalysts for MSR.

ABSTRACTCooperative adaptive cruise control (CACC) vehicles need vehicle-to-vehicle (V2 V) communication to achieve CACC function. When a CACC vehicle follows a manual-driven vehicle (MDV) without V2 V communication, it needs degenerate to adaptive cruise control (ACC). By using real experiments, California PATH program indicated that ACC vehicles are apt to be unstable, which may have negative influence on fuel consumption and traffic emissions. Hence, this paper studies the impacts of the mixed CACC-MDV traffic on fuel consumption and emissions, by taking into consideration partial degenerations from stable CACC vehicles to unstable ACC vehicles. To deal with this, microscopic simulations were adopted by using car-following models. Then, an appropriate emission model was used for evaluating the emission impacts under different CACC market penetration rates (MPRs). In order to obtain reliable evaluation results, the models validated by PATH program using real experimental data were employed as the CACC and ACC car-following models. In addition, we also analytically investigated stability of the mixed traffic flow under different CACC MPRs, in order to explore its relationship with the emission impacts. The results show that the fuel consumption and emissions firstly increase and then decrease with the increase of the CACC MPR. This means the mixed traffic under some ranges of CACC MPRs will produce more fuel consumption and emissions, compared with the full MDVs traffic. It indicates that stability situations of the mixed traffic qualitatively influence the impact trend of CACC MPRs on fuel consumption and emissions. Then, V2 V communication equipments on MDVs are not only encouraging but also essential to avoid the deterioration of fuel consumption and emissions of the mixed traffic flow.ImplicationsFrom a novel perspective, emission impacts of mixed CACC-HDVs flow are studied by considering partial degeneration from stable CACC to unstable ACC. It showed that stability of the mixed traffic flow qualitatively influences the impact trend of emissions with respect to CACC market penetration rates (MPR). Full V2 V communication environment is essential to reduce fuel consumption and emissions in the mixed traffic flow.

The oxidative coupling of methane (OCM) as a promising single-step method towards olefins has attracted significant attention in efficient methane conversion and utilization. Here, we report a promising porous MnOx-Na2WO4/S-1 catalyst. The microporous silicalite-1 support ensures a uniform distribution of the elements Na, W and Mn on the catalyst surface, resulting in more active sites. In addition, the catalyst with weakly bonded and easily reduced lattice oxygen favors the generation of oxygen defect sites on the MnOx, further promoting the formation of electrophilic species (peroxide and superoxide). Hence, a high ratio of (O2-+ O-)/O2-was achieved to boost the OCM performance. Furthermore, the MnOx-Na2WO4catalyst supported by S-1 exhibited the ability to inhibit the loss of active components. As a result, an excellent C2yield of 24.2% was achieved at 760 ℃ for OCM, accompanied by 43.1% CH4conversion and 56.2% C2selectivity. Moreover, the catalyst exhibited excellent stability without any deactivation in 50 hours.

Innovation and entrepreneurship education is one of the hot areas of higher education research and practice in China in recent years. Internet innovation and entrepreneurship can be taken as a new direction which can also provide a new development opportunity and change path for the theoretical research and application practice of contemporary innovation and entrepreneurship education in China. Based on the analysis of the concepts, characteristics and development status of Internet innovation and entrepreneurship education in China, this paper focuses on the implementation strategies of Internet innovation and entrepreneurship education, including the basic contents of curriculum setting, teaching method reform, teacher team building, Internet innovation and entrepreneurship practice. Finally, the paper further discusses how to improve the problems involved in Internet innovation and entrepreneurship education in China.

Rivers are of increasing concern as sources of atmospheric methane (CH4), while estimates of global CH4emissions from rivers are poorly constrained due to a lack of representative measurements in tropical and subtropical latitudes. Measurements of complete CH4flux components from subtropical rivers draining agricultural watersheds are particularly important since these rivers are subject to large organic and nutrient loads. Two-year measurements of CH4fluxes were taken to assess the magnitude of CH4emissions from the Lixiahe River (a tributary of the Grand Canal) draining a subtropical rice paddy watershed in China. Over the two-year period, annual CH4emissions averaged 29.52 mmol m–2d–1, amounting to 10.78 mol m–2yr–1, making the river a strong source of atmospheric CH4. The CH4emissions from rivers during the rice-growing season (June–October) accounted for approximately 70% of the annual total, with flux rates at 1–2 orders of magnitude greater than those for rice paddies in this area. Ebullition contributed to 44–56% of the overall CH4emissions from the rivers and dominated the emission pathways during the summer months. Our data highlight that rivers draining agricultural watersheds may constitute a larger component of anthropogenic CH4emissions than is currently documented in China.

Overexpression of coactivator associated arginine methyltransferase 1 (CARM1) is associated with various diseases including cancer. Therefore, CARM1 has emerged as an attractive therapeutic target and a drug response biomarker for anticancer drug discovery. However, the development of conventional CARM1 inhibitors has been hampered by their limited clinical efficacy, acquired resistance, and inability to inhibit nonenzymatic functions of CARM1. To overcome these challenges, new strategies such as isoform-selective inhibitors, dual-acting inhibitors, targeted protein degradation technology (e.g., PROTACs), and even activators, are essential to enhance the anticancer activity of CARM1 modulators. In this perspective, we first summarize the structure and biofunctions of CARM1 and its association with cancer. Next, we focus on the recent advances in CARM1 modulators, including isoform-selective CARM1 inhibitors, dual-target inhibitors, PROTAC degraders, and activators, from the perspectives of rational design, pharmacodynamics, pharmacokinetics, and clinical status. Finally, we discuss the challenges and future directions for CARM1-based drug discovery.

It is unclear whether cesarean delivery increases the risk of allergic diseases in offspring.

Manure application rather than biochar amendment increased acidic soil respiration.Biochar application promoted soil C sequestration in the tea plantation.Manure application stimulated CO2emissions by altering ecological clusters’ abundance.Gemmatimonadetesmight be the dominant hosts for C-cycling genes.

Photocatalytic Non-oxidative coupling of methane (NOCM) is a green method to convert methane into high value-added products such as ethane and ethylene by using photogenerated electron-holes. Compared with oxidative coupling of methane (OCM), besides C2 products, H2is also produced in NOCM process. However, the effect of the photogenerative carriers on the yield and selectivity of photocatalytic NOCM products is still unclear. We tested Au1-ZnO/TiO2synthesized by NaBH4reduction and photodeposition in a flowing gas reactor at atmospheric pressure without external heat source. The Au1-ZnO/TiO2-N sample showed a superior C2H6yield of 0.015 mmol/h accompanied by a small amount of H2production (1.32 μmol/h). Meanwhile, Au1-ZnO/TiO2-p120 sample shows much higher H2yield (0.012 mmol/h) and a lower C2H6yield (4.96 μmol/h). Through varies characterizations, we demonstrated that the selectivity of C2H6and H2are highly related to photogenerative carriers. We propose that defects affect the degree of peroxidation in photocatalytic NOCM by controlling the concentration of photogenerated holes.

A porous polyvinyl chloride composite membrane with good filter effect for cooling water in continuous casting was designed and prepared. Physical-chemistry properties of the porous polyvinyl chloride composite materials, including surface microstructure, wettability (for water and oil) and permeability coefficient, were investigated. Filtration effect of the porous polyvinyl chloride composite tube filter was enhanced compared with the traditional filter due to the adjustment of wettability and permeability for water and oil. It was found that particle size and pore size distribution for exine and intine of the composite tube are almost the same. Particle size is about 50–120 μm while pore diameter is about 20–60 μm. Water droplets exhibit good wettability on the porous composite materials, while the oil droplets are not wetted. Due to the oleophilic/hydrophobic characteristics passage of water and adsorption of oil droplets, the porous PVC composite filter tube possesses good filter effect. As a result, water quality is remarkably improved after filtration by using the new filter.

A multifunctional microwave modulator is developed for electromagnetic protection, infrared stealth and operating temperature monitoring over wide temperature ranges for the first time.Microwave modulator achieves the integration of two electromagnetic protection mechanisms, microwave absorption and radiation deflection.Microwave modulator demonstrates the maximum effective bandwidth of 5.2 GHz with a thickness of only 1.5 mm in the temperature range 298–673 K.

Latent factor model is better at capturing global information, but not local information. Therefore, many models combine the nearest neighbor information in the latent factor model to improve the performance in recommender system. However, the current fusion models pay no attention to the relationship between the accuracy of local information and model performance. In addition, the expression of local information is different between explicit neighbors and implicit neighbors, so that explicit feedback and implicit feedback have different values. Current fusion models usually utilize only one kind of feedback to capture the local information, which leads to the insufficiency and inaccuracy for using local information. These exited methods do not effectively learn the deep correlation between the similar users and similar items which all can represent the local information. Utilizing the recommender system of deep learning, we propose a fusion MLP model based on Joint Similar Groups, which utilizes both explicit feedback and implicit feedback in local information and learn the deep correlation between similar users and similar items. Experiments on two datasets show that our modes outperform state-of-art algorithms in explicit recommendation task.

An unknown state of a quantum system Sis usually determined by repeatedly measuring a set of non-commuting observables. The state can also be obtained from the repeated measurements of a single separable observable when the system Sinteracts with an assistant system Ain a known state. In this paper, we study the quantum state tomography of a three-level atom (the system S) interacting with two radiation fields as the assistant system A. We obtain the initial state of Sby repeatedly measuring a separable observable Ô= Ŝz⊗ ̂n1⊗ ̂n2, in which Ŝzis the atom operator, and ̂n1and ̂n2are the photon number operators of the two radiation fields. We achieve the one-to-one mapping Mbetween the initial density matrix of the system Sand the measured results of the single separable observable. We also give a concrete numerical example.

The N-soliton solutions for the integrable Hirota equation describing pulse propagation in optical fibers with higher-order effects are presented by using the Darboux transformation method. As an example, the general one-soliton solution on a cw background is given in its explicit form. Then, two exact analytic solutions that describe (i) modulation instability and (ii) bright pulse propagation on a cw background are discussed in detail. The simulations performed in selected cases show that these soliton solutions can be generated numerically when the involved parameters do not exactly satisfy the required integrability conditions.

Fruit weight is an integral part of fruit quality and directly influences the commodity value and economic return of fruit crops. Despite the importance of fruit weight, its underlying molecular mechanisms remain understudied, especially for perennial fruit tree crops such as cultivated loquat (Eriobotrya japonicaLindl.). Auxin is known to regulate fruit development, but its role and metabolism during the development of loquat fruit remain obscure. In this study, we used a multi-omics approach, integrating whole-genome resequencing-based quantitative trait locus (QTL) mapping with an F1population, population genomics analysis using germplasm accessions, transcriptome analysis, and metabolic profiling to identify genomic regions potentially associated with fruit weight in loquat. We identified three major loci associated with fruit weight, supported by both QTL mapping and comparative genomic analysis between small- and large-fruited loquat cultivars. Comparison between two genotypes with contrasting fruit weight performance by transcriptomic and metabolic profiling revealed an important role for auxin in the regulation of fruit development, especially at the fruit enlargement stage. The multi-omics approach identified homologs of ETHYLENE INSENSITIVE 4(EjEIN4) and TORNADO 1(EjTRN1) as promising candidates for the control of fruit weight. Three single nucleotide polymorphism (SNP) markers were also closely associated with fruit weight. Results from this study provide insights into the genetic and metabolic controls of fruit weight in loquat from multiple perspectives. The candidate genomic regions, genes, and sequence variants will facilitate our understanding of the molecular basis of fruit weight and lay a foundation for future breeding and manipulation of fruit weight in loquat.

We propose a novel electrolyte additive, 5-acetylthiophene-2-carbonitrile (ATCN) with three functional groups (thiophene, nitrile and carbonyl), to in situ construct a stable cathode interface film that can significantly improve the cycling stability of LiCoO2cathode under high-voltage. Adding 0.2% of ATCN into a base electrolyte, the capacity retention of LiCoO2/Li cell under 4.5 V is enhanced from 53% to 91% after 200 cycles at 1 C, and the cycle number of commercial LiCoO2/graphite pouch cell (34 Ah) with 10% capacity loss at 0.5 C under a cut-off voltage of 4.45 V is increased from 550 to 800. Experimental characterizations and theoretical calculations reveal that ATCN is preferentially oxidized on LiCoO2cathode and utilizes its decomposition intermediates to convert the detrimental components, the hydrogen fluoride and water present in the electrolyte, and the lithium oxide and carbonate resulting from the electrolyte decomposition, into a unique film texture comprised of underneath compacted lithium salts and outer thiophene polymers. The as-constructed film significantly improves the cathode/electrolyte interface stability and the cycling stability of the cell. Such an effective strategy to address the interface instability has never been reported before and paves a new path to improve the energy density of commercial lithium-ion batteries via enhancing cut-off charge voltage.

Noble metal nanoparticles (NPs) exhibit high reactivity towards hydrocarbon reforming, however, suffer from agglomeration and poisoning issues (e.g., carbon deposition). A facile and environmental approach to rejuvenate noble metal catalysts is techniques using thermal redox cycles. Here we demonstrate that in situ thermal oxidative aging and reduction strategy can reverse the sintering of Rh/TiO2catalyst and meanwhile this redox process significantly affects the catalytic performance towards methane dry reforming (DRM). The oxidative aging process has substantial impact on the mobilization of Rh species across TiO2, and thus the degree of oxymetal activation and the Rh NPs size. In the subsequent reductive process, reduction temperature not only influences oxymetal activation but also the electronic structure of Rh NPs, which directly relates to the carbon formation.

CO2reforming of methane (DRM) represents a viable process to convert the two major greenhouse gases (CO2and CH4) into high‐value syngas. Nickel‐based catalysts are effective and economical for such a reaction, but they are limited by deactivation owing to sintering and coking. In this work, a one‐pot hydrothermal method is presented, whereby highly stable nano‐sized Ni particles combined with a highly dispersed Ca promoter is implemented over Ca–Ni–SiO2. The so‐produced catalyst shows excellent performance for DRM at temperature as low as 550 °C, with no deactivation observed after a 20‐h catalytic run. Further characterizations demonstrate that the nickel silicate precursor produces sintering‐resistant Ni particles with an average diameter of 3.6 nm, harboring substantial active sites and with relatively active carbon species. In addition, the high dispersion of Ca promoter significantly increases the adsorption of CO2, which enhances the elimination of carbon deposition. Such a synergistic effect derived from small‐sized Ni active sites and basic promoter remarkably increases activity and stability of the catalyst, and inhibits carbon deposition, with only 0.6% deposited carbon formed. Stabilized nano‐sized Ni particles combined with a highly dispersed Ca promoter prepared via a one‐pot hydrothermal method, demonstrates high coking‐resistance and is active for low‐temperature CO2reforming of methane.

This paper studies the pricing problem of group lending using the strength model and the Monte Carlo simulation method. Simulating the default process of farmers with a Poisson process, this paper describes the default distribution for farmers, and based on which the paper establishes the pricing model of the group lending and obtains the critical number of the defaulted farmers and the default probability for the group. Next, this paper introduces the t-Copula function to describe the default correlation between farmers, and obtains the partially analytical solution of the loan rate for the group lending, and it also provides the Monte Carlo simulation algorithm for the pricing of group lending based on t-Copula function. Finally, this paper carries out the Monte Carlo simulation algorithm on the pricing problem of group lending with three and five peasant households through an example, and discusses the relationship between the loan rate and each of the influencing factors in the pricing model of the group lending.

Considering the question of personal credit rating, this paper proposes a hybrid method for credit assessment based on an improved Support Vector Data Description (SVDD) algorithm combined with the particle swarm optimization (PSO) algorithm. First, the paper carries out data preprocess, and then it solves the two problems: parameters optimization and feature selection at the same time using the PSO algorithm combined with the improved SVDD algorithm and assesses the credit data using the optimized parameters and features. Finally, the method constructed is tested through two data sets in practice, and the results show that the hybrid method constructed in this paper can obtain higher classification accuracy compared with some other existing credit scoring methods.