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Abstract Background A substantial proportion of patients with metastatic clear cell renal cell carcinoma (ccRCC) cannot derive benefit from immune checkpoint inhibitor (ICI) plus anti-angiogenic agent combination therapy, making identification of predictive biomarkers an urgent need. The members of pleckstrin homology-like domain family A (PHLDA) play critical roles in multiple cancers, whereas their roles in ccRCC remain unknown. Methods Transcriptomic, clinical, genetic alteration and DNA methylation data were obtained for integrated analyses from TCGA database. RNA sequencing was performed on 117 primary tumors and 79 normal kidney tissues from our center. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis were performed to explore transcriptomic features. Data from three randomized controlled trials (RCT), including CheckMate025, IMmotion151, JAVELIN101, were obtained for validation. Results Members of PHLDA family were dysregulated in pan-cancer. Elevated PHLDA2 expression was associated with adverse clinicopathologic parameters and worse prognosis in ccRCC. Aberrant DNA hypomethylation contributed to up-regulation of PHLDA2. An immunosuppressive microenvironment featured by high infiltrates of Tregs and cancer-associated fibroblasts, was observed in ccRCC with higher PHLDA2 expression. Utilizing data from three RCTs, the association of elevated PHLDA2 expression with poor therapeutic efficacy of ICI plus anti-angiogenic combination therapy was confirmed. Conclusions Our study revealed that elevated PHLDA2 expression regulated by DNA hypomethylation was correlated with poor prognosis and immunosuppressive microenvironment, and highlighted the role of PHLDA2 as a robust biomarker for predicting therapeutic efficacy of ICI plus anti-angiogenic agent combination therapy in ccRCC, which expand the dimension of precision medicine.

Developing antibacterial and biocompatible coatings remains a challenge. A promising new thought is addressed to control the ion release behaviors through the design of the phases, structures, and morphologies of the products. In this study, TiO2-CuxO/Ag coatings were prepared via co-sputtering combined with annealing, the changes brought by different annealing temperatures were characterized, the physical, electrochemical, and antibacterial properties were concerned, and the relationship between biological performance and ions-releasing behavior was investigated. Results showed that the TiO2-CuxO/Ag coatings performed the morphologies of larger Ag particles seeded like islands on the dense CuO, such features improved the hydrophilicity and corrosion resistance of the Ti substrate. Rising the annealing temperature further enhanced the surface roughness, hydrophilicity, Young's modulus, hardness, and dynamic coefficient of friction of the coatings. Within the initial 3 h of soaking, both metal ions contributed to playing the antibacterial roles and produced a synergistic enhancement effect to satisfy the reduction of Staphylococcus aureus to 99.9%. In the next 3–120 h soaking, Ag releasing was suppressed and Cu releasing maintained its slow and stable trend, which made sure that the MC3T3-E1 cells could proliferate and adhere on the negatively charged coating surface, and copper oxide nanoparticles were phagocytized by cells. Among all TiO2-CuxO/Ag coatings, the M500 sample displayed superior performances on the surface roughness, contact angle, mechanical Young's modulus, and surface hardness, also realized the aim of killing pathogenic bacteria first and then activating cell survival, which enabled it to be potentially applied in clinical treatment.

Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments.

Abstract We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models.

Climate zones play a significant role in shaping the forest ecosystems located within them by influencing multiple ecological processes, including growth, disturbances, and species interactions. Therefore, delineation of current and future climate zones is essential to establish a framework for understanding and predicting shifts in forest ecosystems. In this study, we developed and applied an efficient approach to delineate regional climate zones in the northeastern United States and maritime Canada, aiming to characterize potential shifts in climate zones and discuss associated changes in forest ecosystems. The approach comprised five steps: climate data dimensionality reduction, sampling scenario design, cluster generation, climate zone delineation, and zone shift prediction. The climate zones in the study area were delineated into four different orders, with increasing subzone resolutions of 3, 9, 15, and 21. Furthermore, projected climate normals under Shared Socioeconomic Pathways 4.5 and 8.5 scenarios were used to predict the shifts in climate zones until 2100. Our findings indicate that climate zones characterized by higher temperatures and lower precipitation are expected to become more prevalent, potentially becoming the dominant climate condition across the entire region. Thes changes are likely to alter regional forest composition, structure, and productivity. In short, such shifts in climate underscore the significant impact of environmental change on forest ecosystem dynamics and carbon sequestration potential.

One of the main sources of uncertainty in carbon budgets is that continental landscapes are made up of a heterogeneous mosaic of ecosystems. Here the authors put forward an integrative framework to improve estimates of land-atmosphere carbon exchange based on the accumulation of carbon in the landscape as constrained by its lateral export through rivers.

Abstract Background Timber harvesting and industrial wood processing laterally transfer the carbon stored in forest sectors to wood products creating a wood products carbon pool. The carbon stored in wood products is allocated to end-use wood products (e.g., paper, furniture), landfill, and charcoal. Wood products can store substantial amounts of carbon and contribute to the mitigation of greenhouse effects. Therefore, accurate accounts for the size of wood products carbon pools for different regions are essential to estimating the land-atmosphere carbon exchange by using the bottom-up approach of carbon stock change. Results To quantify the carbon stored in wood products, we developed a state-of-the-art estimator (Wood Products Carbon Storage Estimator, WPsCS Estimator) that includes the wood products disposal, recycling, and waste wood decomposition processes. The wood products carbon pool in this estimator has three subpools: (1) end-use wood products, (2) landfill, and (3) charcoal carbon. In addition, it has a user-friendly interface, which can be used to easily parameterize and calibrate an estimation. To evaluate its performance, we applied this estimator to account for the carbon stored in wood products made from the timber harvested in Maine, USA, and the carbon storage of wood products consumed in the United States. Conclusion The WPsCS Estimator can efficiently and easily quantify the carbon stored in harvested wood products for a given region over a specific period, which was demonstrated with two illustrative examples. In addition, WPsCS Estimator has a user-friendly interface, and all parameters can be easily modified.

ABSTRACT Alicyclobacillus acidoterrestris, which has strong acidophilic and heat-resistant properties, can cause spoilage of pasteurized acidic juice. The current study determined the physiological performance of A. acidoterrestris under acidic stress (pH 3.0) for 1 h. Metabolomic analysis was carried out to investigate the metabolic responses of A. acidoterrestris to acid stress, and integrative analysis with transcriptome data was also performed. Acid stress inhibited the growth of A. acidoterrestris and altered its metabolic profiles. In total, 63 differential metabolites, mainly enriched in amino acid metabolism, nucleotide metabolism, and energy metabolism, were identified between acid-stressed cells and the control. Integrated transcriptomic and metabolomic analysis revealed that A. acidoterrestris maintains intracellular pH (pHi) homeostasis by enhancing amino acids decarboxylation, urea hydrolysis, and energy supply, which was verified using real-time quantitative PCR and pHi measurement. Additionally, two-component systems, ABC transporters, and unsaturated fatty acid synthesis also play crucial roles in resisting acid stress. Finally, a model of the responses of A. acidoterrestris to acid stress was proposed. IMPORTANCE Fruit juice spoilage caused by A. acidoterrestris contamination has become a major concern and challenge in the food industry, and this bacterium has been suggested as a target microbe in the design of the pasteurization process. However, the response mechanisms of A. acidoterrestris to acid stress still remain unknown. In this study, integrative transcriptomic, metabolomic, and physiological approaches were used to uncover the global responses of A. acidoterrestris to acid stress for the first time. The obtained results can provide new insights into the acid stress responses of A. acidoterrestris, which will point out future possible directions for the effective control and application of A. acidoterrestris.

Inland waters receive large quantities of dissolved organic carbon (DOC) from soils and act as conduits for the lateral transport of this terrestrially derived carbon, ultimately storing, mineralizing, or delivering it to oceans. The lateral DOC flux plays a crucial role in the global carbon cycle, and numerous models have been developed to estimate the DOC export from different landscapes. We reviewed 34 published models and compared their characteristics to identify challenges in model applications and opportunities for future model development. We classified these models into three types: indicator-driven, hydrology-forced, and process-based DOC export simulation models. They differ mainly in their environmental inputs, simulation approaches for soil DOC production, leaching from soils to inland waters, and transit through inland waters. It is essential to consider landscape characteristics, climate conditions, available data, and research questions when selecting the most appropriate model. Given the substantial assumptions associated with these models, sufficient measurements are required to benchmark estimates. Accurate accounting of terrestrially derived DOC export to oceans requires incorporating the DOC produced in aquatic ecosystems and deposited with rainwater; otherwise, global export estimates may be overestimated by 40.7%. Additionally, improving the representation of mineralization and burial processes in inland waters allows for more accurate accounting of carbon sequestration through land ecosystems. When all the inland water processes are ignored or assuming DOC leaching is equivalent to DOC export, the loss of soil carbon through this lateral flux could be underestimated by 43.9%.

Forest ecosystems are a critical component of the global carbon cycle, which stores carbon in both vegetation biomass and soil organic matter. Timber harvesting can laterally move the carbon stored in forest sectors to harvested wood products (HWPs) and thus create an HWPs carbon pool. The carbon stored in HWPs is allocated to end-use wood products (e.g., paper, furniture), landfills (e.g., waste wood materials), and charcoal (e.g., non-energy use biochar). Environmental change is predicted to have far-reaching effects on the carbon stored in HWPs by altering the timber supply. In addition, technological advancement in the wood industry accelerates the carbon inflow rate by promoting processing efficiency and reduces the outflow rate by creating innovative wood products with a longer service life. Socioeconomic factors such as population and household income also contribute to the carbon stock changes in wood products by expanding or reducing the demand. Given numerous factors that are correlated with the size of HWPs carbon pool, an advanced and comprehensive understanding of these factors on modifying the HWPs carbon storage is essential to modeling and predicting the carbon stored in HWPs, atmospheric CO2 concentration, and global warming; therefore, we reviewed, summarized, and discussed the function of these factors in regulating the carbon stored in HWPs.

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

Abstract Prostaglandin E2 (PGE2) is a potent lipid mediator of inflammation that modulates immune cell function by binding to unique G protein‐coupled receptors (EP receptors). PGE2 production increases during microbial infection and inflammation. In this study, we assessed the effect of PGE2 on the phagocytosis of bacteria by neutrophils, which are key players during infection and inflammation. We also looked for specific EP receptor signaling pathways that contributed to the neutrophil phagocytic activity. PGE2 (50–1000 ng/ml) inhibited the phagocytosis of Escherichia coli by HL‐60 human neutrophils in a concentration‐dependent manner. Inhibition of neutrophil phagocytosis by PGE2 correlated with increased intracellular cyclic adenosine monophosphate (cAMP) production, and forskolin, an adenosyl cyclase agonist, confirmed the inhibitory effect of cAMP stimulation on neutrophil phagocytosis. The expression of EP2 receptors by HL‐60 cells was confirmed by western blot analysis, and selective agonism of EP2 receptors mimicked the inhibition of phagocytosis by PGE2. The EP2 receptor antagonist AH‐6089 partially blocked the inhibition of neutrophil phagocytosis PGE2. Specific inhibition of phosphatase and tensin homolog (PTEN) enzyme attenuated the inhibition of neutrophil phagocytosis by PGE2, and both PGE2 and increased intracellular cAMP increased neutrophil PTEN activity, which was associated with decreased PTEN phosphorylation. The results support negative regulation of the antimicrobial activity of neutrophils (i.e., phagocytosis), which has important implications for the future management of bacterial infections.

Dissolved organic carbon (DOC) can be initially moved from soils to inland waters with surface runoff, and then mineralized, buried, or eventually delivered to the coastal ocean. This land-to-ocean phase of the DOC flux must be accounted for to comprehensively understand the global carbon cycle. To estimate the terrestrial-aquatic DOC leaching, calculating the product of the riverine DOC concentration and the corresponding river discharge measured at the watershed outlet is a common method. However, it is challenging to frequently and exactly record riverine DOC concentrations, thus the relationship between DOC concentrations and discharges (C-Q relationship) are established and used to interpolate the time-series of DOC concentrations. We found that the widely used time-dependent and time-independent C-Q regression models are weak in representing their altered relationship when the discharge is extremely high, which was named as diminishing marginal effect. In this study, we evaluated the performance of two C-Q regression models and discussed possible reasons for the diminishing marginal effect. We suggest that repeated and long-term measurements of the DOC concentration are required to adequately analyze their relationships, especially during the early spring and seasons with heavy precipitations.

Alicyclobacillus acidoterrestris is a major concern in fruit juice industry due to its spoilage potential of acidic fruit juice. Quantifying the expression levels of functional genes by real-time quantitative polymerase chain reaction (RT-qPCR) is necessary to elucidate the response mechanisms of A. acidoterrestris to acid stress. However, appropriate reference genes (RGs) for data normalization are required to obtain reliable RT-qPCR results. In this study, eight novel candidate RGs were screened based on transcriptome datasets of A. acidoterrestris under acid stress. The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. This study revealed for the first time that A. acidoterrestris had different suitable RGs under different acid conditions, with implications for further deciphering the acid response mechanisms of this spoilage-causing bacterium.

Greenland is a strongly autonomous subnational island jurisdiction (SNIJ) within the Kingdom of Denmark. This paper takes its point of departure in studies of politics in small island territories to ask to what extent Greenland matches findings from other small island states and SNIJs in terms of personalisation of politics, party performance, and political cleavages that do not follow left-right divides. Even though Greenland possesses a strongly multiparty system, supported by elections involving party-list proportional representation within a single multimember constituency, a single political party, Siumut, has led the government for all but a brief period since the advent of Greenlandic autonomy in 1979. By considering Greenland’s political ecosystem, spatially and personally conditioned aspects of voter behaviour, and coalition-building processes, paying particular attention to the 24 April 2018 parliamentary elections, we argue that it is inappropriate to study Greenland as a monolithic political unit or to draw oversimplified analogies with party politics from large state Western liberal democracies. Instead, Greenlandic politics must be understood in relation to the island territory’s particular historical, geographical, and societal characteristics as well as its electoral system.

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, the adaptive LASSO method enjoys the oracle property of selecting temperature-sensitive variables. After the temperature-sensitive variable selection, the XGBoost algorithm is further adopted to model and predict thermal errors. Since the XGBoost algorithm is decision tree based, it has natural advantages to address the multicollinearity and provide interpretable results. Furthermore, based on the experimental data from the Vcenter-55 type 3-axis vertical machining center, the proposed algorithm is compared with benchmark methods to demonstrate its superior performance on prediction accuracy with 7.05 μm (over 14.5% improvement), robustness with 5.61 μm (over 12.9% improvement), worst-case scenario predictions with 16.49 μm (over 25.0% improvement), and percentage errors with 13.33% (over 10.7% improvement). Finally, the real-world applicability of the proposed model is verified through thermal error compensation experiments.

The modeling and compensation method is a common method for reducing the influence of thermal error on the accuracy of machine tools. The prediction accuracy and robustness of the thermal error model are two key performance measures for evaluating the compensation effect. However, it is difficult to maintain the prediction accuracy and robustness at the desired level when the ambient temperature exhibits strong seasonal variations. Therefore, a year-round thermal error modeling and compensation method for the spindle of machine tools based on ambient temperature intervals (ATIs) is proposed in this paper. First, the ATIs applicable to the thermal error prediction models (TEPMs) under different ambient temperatures are investigated, where the C-Means clustering algorithm is utilized to determine ATIs. Furthermore, the prediction effect of different numbers of ATIs is analyzed to obtain the optimal number of ATIs. Then, the TEPMs corresponding to different ATIs in the annual ambient temperature range are established. Finally, the established TEPMs of ATIs are used to predict the experimental data of the entire year, and the prediction accuracy and robustness of the proposed ATI model are analyzed and compared with those of the low and high ambient temperature models. The prediction accuracies of the ATI model are 20.6% and 41.7% higher than those of the low and high ambient temperature models, respectively, and the robustness is improved by 48.8% and 62.0%, respectively. This indicates that the proposed ATI method can achieve high prediction accuracy and robustness regardless of the seasonal temperature variations throughout the year.

This paper proposes a method to construct a one-dimensional discrete chaotic system. First, we define a generalized distance function to control the boundedness of the one-dimensional discrete system. Based on Marotto’s theorem, one-dimensional discrete systems are proven to be chaotic in the sense of Li–Yorke, and the corresponding chaos criterion theorem is proposed. The system can be distributed uniformly by adjusting the parameters. In this paper, we propose an image encryption scheme based on a uniformly distributed discrete chaotic system and DNA encoding. DNA encoding and decoding rules are determined by plain text. The experimental results demonstrate that our encryption algorithm has a large key space, high key sensitivity, and fast encryption speed and can resist differential and statistical attacks.

Ozone is a typical hazardous pollutant in Earth’s lower atmosphere, but the phyllosphere and its microbiome are promising for air pollution remediation. Despite research to explore the efficiency and mechanism of ozone phylloremediation, the response and role of the phyllosphere microbiome remains untouched. In this study, we exposed Euonymus japonicus to different ozone levels and revealed microbial successions and roles of the phyllosphere microbiome during the exposure. The low-level exposure (156 ± 20 ppb) induced limited response compared to other environmental factors. Fungi failed to sustain the community richness and diversity, despite the stable ITS concentration, while bacteria witnessed an abundance loss. We subsequently elevated the exposure level to 5000~10,000 ppb, which considerably deteriorated the bacterial and fungal diversity. Our results identified extremely tolerant species, including bacterial genera (Curtobacterium, Marmoricola, and Microbacterium) and fungal genera (Cladosporium and Alternaria). Compositional differences suggested that most core fungal taxa were related to plant diseases and biocontrol, and ozone exposure might intensify such antagonism, thus possibly influencing plant health and ozone remediation. This assumption was further evidenced in the functional predictions via a pathogen predominance. This study shed light on microbial responses to ozone exposure in the phyllosphere and enlightened the augmentation of ozone phylloremediation through the microbial role.

The loading of dissolved organic carbon (DOC) from soils to inland waters and ultimate transport to the ocean is a critical flux pathway in the terrestrial biosphere carbon cycle. Fires can significantly affect this flux through biogeochemical processes related to oxidation and mobilization of DOC in the soil. Therefore, in order to accurately estimate and model terrestrial carbon storage and export to the marine environment, we need to better understand the effects of fire on DOC flux. In this analysis, we compiled available observational data sets from seven watersheds across the conterminous United States generally spanning the years 1999–2019. We used these data sets to examine the effects of fire on riverine DOC concentration draining a watershed to study both the immediate impacts and the post-fire recovery patterns. Our results suggest that these fires result in an immediate decrease in riverine DOC concentration draining the watershed by 26 ± 15%, and the time required for DOC concentrations to recover to pre-fire levels was estimated to be, on average, approximately 9 months. During recovery, DOC concentration was 24 ± 11% lower than the long-term average for the watershed. In addition, the larger the proportion of the watershed that burned, the greater the concentration decrease and the longer the time period for post-fire recovery.

Recent a few years have witnessed the rapid expansion of the peer-to-peer lending marketplace. As a new field of investment and a novel channel of financing, it has drawn extensive attention throughout the world. Many investors have shown great enthusiasm for this field. However, investors are at the disadvantage of information asymmetry, which is a key issue in this marketplace that is unavoidable and can lead to moral hazard or adverse selection. In this paper, we propose an L 1 / 2 -regularized weighted logistic regression model for default prediction of peer-to-peer lending loans from investors’ perspective, which can reduce the impact of information asymmetry in the process of loan decision. Rather than solely focus on the accuracy of the prediction, we take into consideration the different risk preferences of different investors. We try to find a trade-off between the risk of losing principal and that of losing potential investment opportunities on the basis of investors’ risk preferences. Meanwhile, due to the nature of peer-to-peer lending loans, we add an L 1 / 2 -regularization term to reduce the chance of overfitting. Xu’s algorithm for L 1 / 2 -regularization problems is applied to solve our model. We perform training, in-sample test, and out-of-sample test with data from LendingClub. Numerical experiments demonstrate that regularization could enhance out-of-sample the area under the Precision–Recall curve (AUPRC). By applying the proposed model, the risk-averse investors could apply a higher penalty factor to lower the risk of losing principal at the cost of the loss of some potential investment opportunities according to their own risk preferences. This model can help investors reduce the impact of information asymmetry to a great extent.

Reductions in fire frequency (RFF) are known to occur in the area adjacent to the rigid-boundary of simulated forest landscapes. Few studies, however, have removed those edge effected regions (EERs), and many others may, thus, have misinterpreted their simulated forest conditions within those unidentified edges. We developed three methods to detect and remove EERs with RFF and applied them to fire frequency maps of 2900 × 2900 grids developed using between 1000 and 1200 fire-year maps. The three methods employed different approaches: scanning, agglomeration, and division, along with the consensus of two and three of those methods. The detected EERs with RFF ranged in mean width from 5.9 to 17.3 km, and occupied 4.9 to 21.3% of the simulated landscapes. Those values are lower than the 40 km buffer width, which occupied 47.5% of the simulated landscape, used in a previous study in this area that based buffer width on length of the largest fire. The maximum width of the EER covaried with wind predominance, indicating it is not possible to prescribe a standard buffer width for all simulation studies. The three edge detection methods differ in their optimality, with the best results provided by a consensus of the three methods. We suggest that future landscape forest simulation studies should, to ensure their results near the rigid boundary are not misrepresentative, simulate an appropriately enlarged study area and then employ edge detection methods to remove the EERs with RFF.

This paper studies the peer-to-peer lending and loan application processing of LendingClub. We tried to reproduce the existing loan application processing algorithm and find features used in this process. Loan application processing is considered a binary classification problem. We used the area under the ROC curve (AUC) for evaluation of algorithms. Features were transformed with splines for improving the performance of algorithms. We considered three classification algorithms: logistic regression, buffered AUC (bAUC) maximization, and AUC maximization.With only three features, Debt-to-Income Ratio, Employment Length, and Risk Score, we obtained an AUC close to 1. We have done both in-sample and out-of-sample evaluations. The codes for cross-validation and solving problems in a Portfolio Safeguard (PSG) format are in the Appendix. The calculation results with the data and codes are posted on the website and are available for downloading.

In order to meet the demand for mechanisation of sunflower segmented harvesting and tray insertion for drying, an intelligent tray insertion dryer was designed and developed. The machine integrates the functions of disc picking, disc flipping, bar clipping, lifting and inserting, and uses SolidWorks for accurate mechanism design and simulation, incorporating the YOLOv5 model for efficient recognition of flower discs and the LeGO-LOAM algorithm for accurate navigation and map building. In the experiment, 81 sunflower samples were collected to analyse data on disc diameter, plant height, rod diameter and stalk diameter, and to verify the recognition accuracy of the YOLOv5 model in different directions. The results showed that the precision of disc recognition was 95.54%, accuracy was 89.94%, recall was 95.54% and F1 value was 0.89. Using the LeGO-LOAM algorithm tested at different path lengths, the root-mean-square error of the navigational build trajectory was 0.15 m, with a standard deviation of 0.10 m. This technological integration improves the operational efficiency and supports the mechanisation of sunflower insertion tray drying. [ABSTRACT FROM AUTHOR]