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Bletilla striata , an important traditional Chinese medicine resource, holds high medicinal and ornamental value. However, unscientific habitat selection for its cultivation has led to low yields and poor quality as medicinal materials in China. The optimized MaxEnt model is a powerful tool for analyzing the potential impacts of environmental factors on species distribution and predicting habitat changes under climate change. It offers great significance for the protection and development of B. striata in China. Based on 269 B. striata distribution records in China and 15 major environmental factors, this study simulated the distribution patterns of potentially suitable B. striata habitats under four different climate change scenarios (SSP1.26, SSP2.45, SSP3.70, and SSP5.85) and three time periods (the current period, 2050s, and 2070s). The analysis was conducted using the MaxEnt model which exhibited high predictive accuracy and minimal overfitting. Solar radiation, annual temperature range and mean diurnal range were revealed as the dominant factors affecting B. striata distribution, and their thresholds were ≤ 16,265.39 kJ/m 2 ·d −1 , ≤ 39.7 ℃ and ≤ 12.6 ℃, respectively. The results showed that the total potentially suitable B. striata habitats in China were 30.07 × 10 5 km 2 under current climate conditions, mainly distributed in 14 provinces or regions in southern China. Under future climate change conditions, the predicted potentially suitable B. striata habitats will decrease significantly over time, and the centroid of the predicted potentially suitable habitats at all levels will shift northward. The research results can guide future B. striata resource conservation, variety selection, and cultivation.

The ecological balance and agricultural productivity of northeastern China are seriously threatened by the long-term invasion and spread of Asteraceae plants, which have severely disrupted the region’s biodiversity and ecosystem stability. Ambrosia artemisiifolia L., Ambrosia trifida L., and Erigeron canadensis L. are Class 1 malignant invasive species widely distributed across northeastern China. In this context, we selected 36 predictor variables and utilized the MaxEnt model to investigate the influence of current climate on their distribution patterns. Using future climate data, we projected shifts in the distribution dynamics of these three Asteraceae species for two time periods (2041–2060 and 2061–2080) under three climate change scenarios (SSP126, SSP245, and SSP585). The MaxEnt model demonstrated a good predictive impact, with an average area under the curve (AUC) of 0.918. Currently, the three Asteraceae species are primarily found in the southern part of northeastern China. However, due to future climatic changes, their distribution centroids are gradually shifting southwest, leading to an increase in the area of highly suitable zones for these species. Moreover, trend analysis revealed that the potential distribution changes of highly suitable zones for the three Asteraceae species in the southwestern northeastern China are likely to experience an increasing invasive trend under various future climate models. This study provides initial insights into the distribution dynamics of Asteraceae species in northeastern China under climate change, enabling the formulation of plans for managing and preventing the risks and impacts of invasive species.

Hydrocera triflora is a perennial herb found in southeastern and southern Asia. In China, it is only found in Hainan Province. With global climate change, studying the impact of climate change on the distribution of H. triflora can provide a theoretical basis for the scientific protection of this species. In this study, the MaxEnt model was used to predict the potential distribution area of H. triflora in China under historical, current, and future periods based on 66 distribution points and 12 environmental variables. The results were as follows: (i) The main environmental variables affecting the distribution of H. triflora were precipitation in the coldest month and in the wettest quarter, with elevation also being a significant factor. (ii) Over the past three periods, the last interglacial, last glacial maximum, and mid-Holocene, the suitable area for H. triflora initially decreased and then increased. The suitable area reached the lowest value in the last glacial maximum period, at only 27.03 × 104 km2. (iii) The current potential distribution area is 67.81 × 104 km2, and the optimal area is mainly distributed in the Guangxi, Guangdong, and Hainan provinces. (iv) Under future climate scenarios, the potential distribution area of H. triflora is projected to increase by 11.27~90.83 × 104 km2. It is expected to reach a maximum value (158.64 × 104 km2) in 2081~2100 under the SSP-585 climate scenario, with the distribution centroid shifting to higher latitudes. The newly gained optimal habitats will provide potential areas for introduction and ex situ conservation of this species.

In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model. The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training. This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation. Finally, the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid. The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense. The effectiveness of the proposed method is demonstrated through comprehensive experiments.

L. chinensis is a National Grade II Protected Animal in China, and predicting its suitable habitats in the middle and lower reaches of the Yangtze River is crucial for developing effective conservation strategies. This study uses methods such as the MaxEnt model to predict suitable habitats for L. chinensis in the region, providing a scientific basis for species conservation. The model results show that the AUC value of MaxEnt is 0.989, indicating excellent predictive performance. Under current climatic conditions, the area of highly suitable habitats for L. chinensis in the middle and lower reaches of the Yangtze River is 75.1 × 103 km2, accounting for 8.8% of the total area of the region. However, under the SSP2-4.5 climate scenario, the predicted results indicate a general decline in suitable habitats for the species. This change signals that the survival of L. chinensis will face significant risks in the future, highlighting the urgent need to develop and implement scientifically effective conservation measures.

Phoracantha semipunctata is a destructive invasive alien forest pest worldwide. It primarily damages the eucalyptus via adults, affecting almost all parts of the eucalyptus. Its larvae develop in almost all major tissues of the plant. Phoracantha semipunctata spreads both via the migration of adults and global trade in intercontinental translocation. Currently, this pest has spread to six continents worldwide, except Antarctica, resulting in substantial economic losses. Based on global occurrence data and environmental variables, the potential global geographical distribution of P. semipunctata was predicted using an ensemble model. The centroid shift, overlap, unfilling, and expansion scheme were selected to assess niche dynamics during the global invasion process. Our results indicated that the AUC and TSS values of the ensemble model were 0.993 and 0.917, respectively, indicating the high prediction accuracy of the model. The distribution pattern of P. semipunctata is primarily attributed to the temperature seasonality (bio4), mean temperature of the warmest quarter (bio10), and human influence index variables. The potential geographical distribution of P. semipunctata is primarily in western and southwestern Asia, western Europe, western and southern North America, southern South America, southern Africa, and eastern and southern Oceania. The potential geographical distribution of P. semipunctata showed a downward trend in the 2030s and the 2050s. The distribution centroid showed a general tendency to shift southward from the near-current to future climate. Phoracantha semipunctata has largely conserved its niche during the global invasion process. More attention should be paid to the early warning, prevention, and control of P. semipunctata in the countries and regions where it has not yet become invasive.

The threat posed by poisonous weeds to grassland ecosystems may be exacerbated by climate change mainly driven by carbon dioxide (CO2) emissions. Achnatherum inebrians is a common and poisonous grassland weed that is seriously endangering the sustainable development of prairie animal husbandry in Western China. Understanding the influence of future climate change under different CO2 emission scenarios on the potential distributions of A. inebrians is critical for planning agricultural strategies to manage the continued invasion. An ecological niche model (ENM) was developed using Maxent to predict the potential distribution of A. inebrians under three different CO2 emission scenarios. Occurrence records of A. inebrians were selected utilizing the nearest neighbor method. Six environmental variables, which were identified through principal component analysis, correlation analysis and their contribution rates, were used to perform the ENM. At the same time, considering the uncertainties of predicting future climates, four global circulation models were used for the Maxent projections with average results calculated. Our results demonstrate differential influences of various CO2 emission scenarios on the potential distributions of A. inebrians. Before 2050, high CO2 emission scenarios resulted in a wider potential distribution of A. inebrians, when compared to low CO2 emission scenarios. However, after 2050, the low CO2 emission scenarios were more conducive to an expanded potential distribution. In addition, after 2050, high CO2 emission scenarios maintain the geographical distribution centroids of A. inebrians in lower latitudes, while low CO2 emission scenarios result in distribution centroids rising to higher latitudes. Further, low CO2 emission scenarios resulted in the average potential distribution elevation dropping lower than in high CO2 emission scenarios.

For irregular end milling cutters, the incomplete equality of the pitch angle and helix angle will lead to an uneven mass distribution. The problems of centroid distribution caused by this, and whether it would affect the dynamic characteristics of milling cutters, have not been systematically studied. In this paper, through the proposed mathematical model, the centroid positions of each radial section of four types of end milling cutters, with equal overall eccentricities and different structures, are calculated, respectively. The centroid distribution of end milling cutters is studied and analyzed. Combined with finite element analysis, the vibration mode, frequency, and resonance frequency band of each type of end milling cutter, under the same dynamic excitation, is obtained. Based on a study of the dynamic characteristics of various types of end milling cutters, it is found that the response displacement of the variable pitch variable helix end milling cutter is the smallest, which is 0.043800 mm. With the same level of accuracy, its dynamic performance is the best. On the premise of not changing the overall eccentricity of the end milling cutter, a new idea for the structural design to improve the dynamic characteristics of the end milling cutter is provided.

Troides aeacus is the largest butterfly in China and is highly valued for its ornamental beauty. Due to T. aeacus being classified as a national second-class protected species in China, studying its spatial distribution is crucial for developing effective conservation measures. In this study, a total of 490 distribution points were obtained, and the potential distribution areas of the golden-sheathed T. aeacus were analyzed by using the maximum entropy model (MaxEnt) based on three different greenhouse gas emission scenarios, namely, SSP1-2.6, SSP2-4.5, and SSP5-8.5, in combination with nine important environmental variables. The results indicate that temperature and precipitation are the primary environmental factors influencing the suitable habitat of T. aeacus, with key variables including the minimum temperature of the coldest month (bio6), temperature annual range (bio7), mean temperature of the warmest quarter (bio10), annual precipitation (bio12), precipitation of the coldest quarter (bio19), and slope. The height distribution of T. aeacus in my country is in the area south of the Huaihe River in the Qinling Mountains, with a total area of 270.96 × 104 km2, accounting for 28.23% of the total area of China. According to future climate change conditions, as climate warming progresses, both low- and high-suitability areas show an expansion trend in most scenarios, particularly under the SSP5-8.5 scenario, where highly suitable areas increase significantly while moderately suitable areas gradually shrink. To address future climate change, conservation strategies should focus on protecting highly suitable areas and strengthening the management of marginal habitats to enhance the adaptability and survival chances of T. aeacus.

In order to investigate the maximum stairs-climbing capability of the six-track robot with four swing arms and the centroid position control in the climbing process, firstly, the isolated steps and successive stairs in reality environments were simplified into a model that composed of horizontal planes and vertical planes; afterward, the robots centroid distribution was analyzed, the step-climbing methods and processes were given, and the relation equations of the step height, the arms swing angles and the main track evaluation angle were founded; Lastly, the conditions of the maximum step height and the conditions of climbing successive stairs using the slope-climbing method was deduced. Using the parameters of CUMT-III type coal mine exploring robot prototype, the relation curves of the step height and the robot elevation angle was drawn when the rear swing arms spread, and the 3D relation graphs of the robot elevation angle, rear arms swing angles and the step height were drawn, when the swing arms took some specific swing angles. The maximum theoretical step height of the prototype were obtained, which were compared with the test results in laboratory. This paper will provide theoretical basis for centroid position control in steps and stairs climbing process.