Search Results (452)

Phyllostachys edulis (Moso bamboo) is a subtropical bamboo species endemic to China and holds substantial economic and ecological value. Ongoing climate change is reshaping the geographic distribution of its suitable habitats. To reveal these climate-driven dynamics, we calibrated the maximum entropy (MaxEnt) species distribution model using the ENMeval R package version 4.5.2 and evaluated model robustness via spatially independent block cross-validation. We identified nine critical environmental predictors to model spatiotemporal variations in suitable habitats for Moso bamboo, examining both present-day climate and four future periods under four Shared Socioeconomic Pathway (SSP) scenarios. Model performance was optimized at regularization multiplier (RM) of 1.5 with the feature class combination LQHPT, yielding a spatial validation AUC of 0.9104 and robust predictive capacity. Environmental controls were dominated by mean temperature of the coldest quarter (bio11, 63.9% relative contribution) and precipitation of the driest quarter (bio17, 31.2%), while soil clay and organic carbon content modulated suitability at local scales. Under current conditions, the total suitable habitat area encompasses approximately 218.88 × 10⁴ km², with the majority distributed throughout the subtropical zone of southeastern China. Under nearly all future scenarios, high-suitability habitats showed a general contracting trend of 20.94%–95.29% relative to the current baseline, while moderate-suitability habitats exhibited a fluctuating expansion trend, reaching a peak increase of 47.90% in the 2030s under SSP585, but contracted by 18.65% in the 2090s. Meanwhile, habitat centroids underwent small-scale oscillatory shifts in multiple directions and remained entirely within Hunan Province across all periods. MESS analysis confirmed that 90.60%–99.82% of China’s land area fell within climate-analogous ranges, indicating low extrapolation risk for core habitat projections. Core climate refugia with high cross-scenario stability were primarily concentrated in the traditional bamboo-producing regions of southeastern China. This study provides a scientific basis for the sustainable management of Moso bamboo resources, biodiversity conservation, and ecological risk prevention in the context of ongoing climate change. Full article

Objective: We aimed to investigate whether ultrasound-based texture analysis can differentiate liver metastases according to the histopathological origin of the primary tumor and to evaluate the quantitative texture characteristics of metastases originating from colorectal, pancreatic, and breast cancer. Materials and Methods: This prospective study included 75 patients with biopsy-proven liver metastases, comprising 25 colorectal adenocarcinoma, 25 pancreatic ductal adenocarcinoma, and 25 invasive ductal breast carcinoma metastases. Conventional B-mode ultrasound images were obtained prior to treatment. The largest metastatic lesion in each patient was manually segmented using a whole-lesion two-dimensional region of interest (ROI). Histogram-based texture analysis was performed using an in-house MATLAB-based software package (version R2021a; MathWorks, Natick, MA, USA). Extracted parameters included intensity-based metrics, dispersion measures, entropy, uniformity, and percentile values. Texture features were compared among the three groups using appropriate statistical tests. Results: Significant differences were observed among metastatic lesions according to their primary tumor origin. Significant differences were observed in the mean, median, minimum, maximum, most frequent gray-level values, root-mean-square level, root-sum-of-squares level, entropy, and all evaluated percentile parameters among groups (all p < 0.05). Pancreatic cancer metastases consistently demonstrated the highest intensity-related histogram values and percentiles, whereas breast cancer metastases exhibited the lowest values. Colorectal metastases were generally of intermediate intensity. Entropy values were significantly higher in colorectal and pancreatic metastases than in breast cancer metastases (p < 0.05), suggesting greater structural heterogeneity. No significant differences were observed for kurtosis, skewness, uniformity, or size distribution parameters (all p > 0.05). Conclusions: Ultrasound-based tissue analysis revealed distinct quantitative features among liver metastases originating from colorectal, pancreatic, and breast cancer. Density-related parameters, percentiles, and entropy show the potential to differentiate metastatic lesions based on their primary tumor origin, thus serving as a non-invasive biomarker. Full article

To address the influence of multi-source probabilistic uncertain parameters on the dynamic transmission error (DTE) of harmonic drives, this paper proposes a robust DTE modeling and multi-objective optimization method. First, a Chebyshev surrogate model is constructed by integrating the measured static transmission error (STE) probability model, system dynamic equations, and identified nominal parameters. Prototype validations show a prediction mean absolute percentage error (MAPE) of 8.14% and a mean absolute error (MAE) of 7.761″. Meanwhile, compared to the original dynamic equations, the surrogate model reduces the single-evaluation time from 0.147 s to 0.000003 s (a 49,000-fold acceleration), effectively overcoming the efficiency bottleneck of numerical integration in dynamic response evaluation. Secondly, to achieve the collaborative optimization of system transmission accuracy and anti-disturbance robustness, a Chebyshev–AMP–MOPSO algorithm integrating a diversity entropy state-driven weight and a pyramid-hierarchical dual-track search strategy is proposed, which improves upon the issues of local convergence and uneven solution set distribution in the classical MOPSO and NSGA-II algorithms. On this basis, parameter optimization under three decision preferences was completed. The accuracy-first scheme reduces the DTE mean by 3.67%, the robustness-first scheme reduces the standard deviation by 9.36%, and the balanced scheme improves both. Finally, comparative tests on five prototypes show the actual dynamic parameters’ deviation (D_i) relative to the theoretical optimal configuration exhibits a consistent corresponding trend with measured DTE means. Prototypes with the minimum (D_i = 0.365) and maximum (D_i = 0.474) deviations yield the lowest and highest measured means, respectively, matching theoretical optimization expectations. Full article

The Weibull distribution is one of the most versatile and widely applied continuous probability distributions in reliability engineering, survival analysis, wind-energy modelling, and extreme-value theory. Classical parameter estimation relies on maximum likelihood estimation or method-of-moments using integer-order moments, both of which may suffer from instability or high variance in small to moderate samples. This paper develops a rigorous framework for estimating Weibull parameters by combining the maximum-entropy principle with fractional-order moment constraints, i.e., constraints of the form $E\left[X^r\right]$ for non-integer $r>0$. A central theme of the paper is symmetry: we show that the maximum-entropy density subject to a finite set of fractional-moment constraints uniquely recovers the Weibull family, and that the underlying moment-matching system, while not symmetric in every sense considered in an earlier draft (see Remark 3), possesses a precisely characterised duality under joint rescaling and relabelling of the exponent pair, together with a log-moment map whose local curvature is strictly positive and increasing with exponent spacing rather than symmetric about a fixed midpoint. We derive closed-form expressions relating the Lagrange multipliers to the shape and scale parameters, establish new sound theoretical results on the symmetric behaviour of the moment-ratio function and its sensitivity, and propose a numerically stable algorithm for solving the resulting moment-matching system. Extensive Monte Carlo experiments demonstrate that the proposed maximum-entropy fractional-moment estimator achieves a bias and root-mean-square error that are comparable to, and for small-to-moderate samples somewhat better than, maximum likelihood estimation, with the size of the advantage depending on how closely the chosen exponent pair tracks the true shape parameter. Applications to real wind-speed data and composite-material fatigue life data illustrate the practical utility of the method. The paper contributes both to the information-theoretic foundations of distribution fitting and to applied statistical methodology, with symmetry serving as both a diagnostic tool and a unifying structural principle throughout. Full article

Xinjiang’s distinctive geography and climate provide important habitats for Falconidae species, yet their climate-driven habitat shifts and conservation gaps remain poorly understood. Using 2731 validated occurrence records and 26 environmental predictors, including bioclimatic, land-use, topographic, hydrological, anthropogenic, and Normalized Difference Vegetation Index (NDVI) predictors, we applied an optimized Maximum Entropy (MaxEnt) framework to project suitable habitats for seven falconid species under current conditions and three Shared Socioeconomic Pathway scenarios (1–2.6, 2–4.5, and 5–8.5) for 2041–2060, 2061–2080 and 2081–2100. Barycenter migration analysis, the Habitat Quality module of the Integrated Valuation of Ecosystem Services and Tradeoffs framework, and protected-area overlays were further integrated to identify conservation priorities. All models showed high discriminatory performance, with mean areas under the receiver operating characteristic curve exceeding 0.90. Current suitable habitats were mainly concentrated along river corridors and mountain foothills in the southern Altai, central-western Tianshan and northern Kunlun regions. Future responses were strongly species-specific. By 2081–2100 under Shared Socioeconomic Pathway 5–8.5, suitable habitat increased by 154.6% for Falco peregrinus and 79.5% for Falco tinnunculus but declined by 81.1% for Falco vespertinus; Falco subbuteo also showed overall expansion, with its suitable-habitat barycenter shifting by up to 291.9 km. Mean relative habitat quality was 0.514 [standard deviation = 0.185], while suitable habitat outside protected areas ranged from 35,346 to 304,866 km² among species, and high-quality priority conservation gaps reached 102,489 km² for Falco cherrug. These results demonstrate that climatic suitability does not necessarily correspond to high habitat quality or adequate protection and support species-specific, climate-adaptive conservation strategies for falconids in Xinjiang. Full article

Araucaria angustifolia (Bertol.) Kuntze, a critically endangered tree species, plays an irreplaceable ecological role in its native habitats. Under global climate change, identifying the drivers governing its geographic distribution and assessing climate-related threats can provide scientific guidance for the long-term conservation and habitat restoration of this species. In this study, a total of 287 valid occurrence records from 27 countries were compiled. Combined with 14 screened environmental variables, an optimized Maximum Entropy (MaxEnt) model was used to predict the potential suitable habitats of A. angustifolia under historical climate conditions (1970–2000), as well as under low-emission (SSP126) and high-emission (SSP585) scenarios for the future periods of 2050, 2070, and 2090. Under historical climatic conditions, the average training AUC value from 10 replicate model runs was 0.979, indicating excellent and reliable model performance. Globally, the species has 1.91 × 10⁶ km² of moderately suitable habitat and 0.95 × 10⁶ km² of highly suitable habitat, with a total suitable habitat area of 2.86 × 10⁶ km², accounting for only 1.92% of the global terrestrial area. Mean annual temperature (bio1), mean temperature of the coldest quarter (bio11), and annual temperature range (bio7) are the dominant environmental variables shaping the distribution of A. angustifolia, followed by annual precipitation (bio12). Under future climate scenarios, the overall suitable habitats of A. angustifolia exhibit a slight contracting trend, whereas their spatial distribution patterns remain relatively stable. Full article

Climate and land-use change are shifting the spatial distribution and habitat suitability of many plant species, particularly those with narrow ecological niches and limited ranges. Dracaena cochinchinensis (Lour.) S.C.Chen is a medicinally important and nationally protected plant species distributed in tropical and subtropical southern China. Predicting its habitat suitability under current and future climate scenarios is essential for understanding its responses to environmental change and guiding conservation and resource management. In this study, an optimized maximum entropy model was used to predict the current habitat suitability of D. cochinchinensis in China and to assess changes in habitat suitability under future climate scenarios in the 2090s. The model showed reliable predictive performance for estimating species distribution. Under current conditions, suitable habitats were concentrated in southern China, particularly southern Yunnan, Guangxi, Guangdong, Hainan, Taiwan, and parts of Fujian, covering 23.93 × 10⁴ km² (8.31% of the study area). Mean temperature of the driest quarter (Bio9), temperature annual range (Bio7), and precipitation seasonality (Bio15) were the dominant predictors, highlighting the importance of thermal conditions in shaping species distribution, whereas lithology (Lith) and soil type (ST) were the major non-climatic contributors influencing habitat suitability. Under future scenarios, suitable habitats showed spatial redistribution rather than uniform expansion or contraction. The spatial comparison among future scenarios identified stable habitats in the central and southwestern parts of Hainan Island, south-central Yunnan, and western Guangxi, with habitat losses mainly occurring in coastal South China and limited expansion in southwestern regions. Habitat centroid shifts were scenario dependent, with an eastward shift under SSP1-2.6 and northwestward shifts under SSP3-7.0 and SSP5-8.5, with migration distances ranging from 60.09 to 165.77 km. These results indicate that climate change may substantially reorganize the distribution pattern of D. cochinchinensis in southern China. Therefore, future conservation planning should prioritize current high-suitability areas, potential macroclimatically stable areas, and emerging suitable habitats to support long-term preservation and sustainable utilization of nationally protected medicinal species. Full article

In this paper, we introduce a new three-parameter lifetime distribution that is obtained via a power transformation of the modified bimodal exponential model. The inclusion of an additional shape parameter significantly enhances the flexibility of the baseline distribution, allowing it to capture a wide range of distributional characteristics, including skewness, heavy tails, and varying hazard rate shapes such as increasing, decreasing, and non-monotonic forms. Several important structural properties of the proposed model are derived, including explicit expressions for the probability density function, cumulative distribution function, moments, and moment generating function. Entropy measures such as Rényi entropy, Shannon entropy, and cumulative residual entropy are also obtained. Key reliability characteristics, including the survival function, hazard rate function, cumulative hazard function, reversed hazard rate, and mean residual life function, are investigated in detail. A theoretical result on the modality of the distribution is established, demonstrating its ability to exhibit both unimodal and bimodal shapes. Parameter estimation is carried out using maximum likelihood estimation along with several alternative methods. A comprehensive simulation study is conducted to evaluate the performance of the estimators under different parameter settings. Finally, the applicability and effectiveness of the proposed distribution are demonstrated through the analysis of real datasets from reliability and environmental studies. Comparative results based on goodness-of-fit measures indicate that the proposed model provides a superior fit compared to several existing competing distributions. Full article

This study investigates pre-seismic ionospheric Total Electron Content (TEC) disturbances preceding the Mw 6.9 Northern Aegean Sea earthquake of 24 May 2014 using observations from 13 Global Navigation Satellite System (GNSS) stations. A pronounced negative TEC disturbance was identified on 22 May 2014, approximately two days before the earthquake, under comparatively quiet solar and geomagnetic conditions. Station-wise Z-score analysis, which expresses the TEC departure from the reference mean in units of standard deviation, revealed significant negative deviations across the network, while inter-station correlations indicated a temporally coherent but spatially heterogeneous ionospheric response. To characterize the disturbance beyond peak-based measures, the TEC Anomaly Significance Index (TASI) was developed by integrating the mean absolute Z-score, coefficient of variation, and Shannon entropy. TASI showed strong agreement with the maximum absolute Z-score ranking (Spearman’s $\rho =0.89$, $p<0.001$) while providing greater sensitivity to cumulative and persistent anomaly behaviour. It exhibited stronger associations than maximum $\mid Z\mid$ for six of the seven evaluated temporal descriptors, particularly those representing anomaly duration and consecutive persistence. Among the analyzed GNSS stations, KASI recorded the highest TASI despite not being the nearest station to the epicenter, indicating that anomaly significance was not governed solely by epicentral distance. The proposed framework provides a multidimensional, network-based approach for characterizing potential pre-seismic ionospheric disturbances and establishes a basis for future multi-event and control-period validation. Full article

This study proposes an entropy-regularized likelihood inference framework for lifetime distributions under progressive Type-II censoring. By incorporating Shannon entropy directly into the classical likelihood function, the proposed approach aims to alleviate the information loss caused by censoring and improve the finite-sample stability of parameter estimation. Entropy-regularized maximum likelihood estimators (ERMLEs) are developed for the Exponential, Weibull, Gamma, and Lognormal lifetime distributions. Distribution-specific regularization parameters are selected by minimizing the average mean squared error across a comprehensive Monte Carlo simulation study covering different sample sizes, censoring rates, and progressive censoring schemes. Estimation performance is evaluated using bias, mean squared error, and the relative reduction in MSE achieved by ERMLE. The proposed methodology is further illustrated using two progressively Type-II censored real datasets from engineering reliability and biomedical survival analysis. Model adequacy is assessed through goodness-of-fit statistics with corresponding p-values, bootstrap confidence intervals, and graphical comparisons. The simulation results show that entropy regularization substantially improves estimation accuracy for the Exponential, Weibull, and Gamma distributions, particularly under moderate and heavy censoring, whereas only negligible improvements are observed for the Lognormal distribution. In the engineering reliability application, the Weibull distribution provides the best overall fit, while the Weibull and Gamma models exhibit the most satisfactory performance for the bladder cancer remission data. Across both applications, ERMLE yields parameter estimates and fitted models that are highly consistent with those of the classical MLE while providing stable estimation under progressive censoring. Overall, the proposed framework demonstrates that the effectiveness of entropy regularization is distribution-dependent rather than universal and provides practical guidance for selecting suitable estimation strategies in reliability and survival analysis. Full article

Testing exponentiality against increasing failure rate alternatives is central to reliability theory and lifetime data analysis. This paper develops the Adaptive Cumulative Residual Entropy Test under Proportional Hazards (Adaptive CRE-PH Test), a scale-invariant nonparametric procedure that unifies a Tsallis-entropy departure functional with a proportional-hazards transformation. For each tuning value, the fixed-$q$ statistic admits a normalized-spacing representation. Under exponentiality, the spacing proportions follow a Dirichlet distribution, yielding exact finite-sample means, covariances, and a joint null characterization of the adaptive maximum. Joint asymptotic normality and consistency under fixed alternatives are established for the complete dependent score vector. Structural analysis of the score family motivates a prespecified moderate grid governed by endpoint stability, directional diversity, and multiplicity economy, rather than retrospective power optimization. Monte Carlo experiments demonstrate accurate size control, explicitly quantify calibration stability, and show power close to the best fixed-$q$ component under linear failure rate, Makeham, and Weibull alternatives. A dedicated power experiment confirms substantial detection capability against an IFRA-but-not-IFR benchmark, showing that the broader population sign condition has practical as well as theoretical relevance. Additional DFR and bathtub experiments clarify directional specificity: rejection provides evidence against exponentiality in the IFR-sensitive direction but does not, without shape-specific inference, establish a globally increasing hazard. Three real-data applications illustrate the practical importance of distinguishing formal directional inference from exploratory Q–Q and total-time-on-test diagnostics. Full article

Pholidota chinensis is a valuable medicinal epiphytic orchid in China. While its ethnomedicinal importance is recognized, the environmental constraints governing its distribution and its response to future climate change remain insufficiently understood. This study employed the Maximum Entropy (MaxEnt) model to identify dominant environmental drivers and project current (1970–2000) and future (2041–2060, 2061–2080) potential climatically suitable habitats under SSP245 and SSP370 scenarios. Using 184 filtered occurrence records and 11 screened environmental variables, the model achieved high discrimination performance (mean AUC = 0.9614 ± 0.0092). Annual precipitation and precipitation of the driest quarter were identified as principal predictors, reflecting a coupled water-temperature niche. Future projections suggested an overall expansion of climatically suitable areas, particularly under SSP245, and a northwestward shift in suitability centroids. However, these projections represent potential climatic suitability rather than confirmed future distribution, as establishment depends on dispersal capacity, host availability, and microhabitat conditions. These findings provide a scientific basis for conservation prioritization and climate-adaptive cultivation planning. Full article

A fiber Bragg grating (FBG)-based measurement method is proposed for outer-ring fault detection in rolling bearings. To acquire fault-related responses in a near-source manner, a circumferential groove was machined on the bearing outer ring, and an FBG sensor was embedded and bonded to directly measure the dynamic strain of the outer ring. The acquired strain signal was first decomposed using local mean decomposition (LMD), and the product function components were selected for reconstruction according to a correlation coefficient–kurtosis weighted criterion. Maximum correlated kurtosis deconvolution (MCKD) was then applied to enhance the weak periodic impulsive components associated with the outer-ring fault. Experiments were conducted on an NJ205EM cylindrical roller bearing with an artificial outer-ring defect at a rotational speed of 600 r/min and a sampling frequency of 8 kHz. The results show that the raw FBG signal contains the outer-ring fault characteristic frequency, but the fault-related component is weak and easily affected by surrounding spectral components. After LMD-MCKD processing, the fault characteristic frequency and its harmonics become more distinguishable in the envelope spectrum. A comparison with the conventional minimum entropy deconvolution (MED) method further confirms the effectiveness of the proposed method. The fault feature amplitude ratio and the corresponding local signal-to-noise ratio increase from 12.257 and 21.767 dB for MED to 16.985 and 24.555 dB for the proposed LMD-MCKD method, respectively. These results demonstrate that the proposed FBG-based near-source strain measurement combined with LMD-MCKD processing provides an effective approach for weak outer-ring fault detection in rolling bearings. Full article

The handsome cross grasshopper Oedaleusdecorus (Germar), which was a minor pest in the 19th and first half of the 20th century, in the late 20th and early 21st centuries, became one of the emerging regional pests in the Asian part of its range, especially across the intermountain basins of the mountains of South Siberia, Mongolia and North and Northeast China. This is why it is necessary to identify the main trends of possible changes in its distribution. Several sets of data on the species distribution (1895–1960, 1961–2023, and all data) are analysed and several various approaches to modelling (graphic, maximum entropy and multidimensional ellipsoid) are utilised. The models produced for modern conditions describe well the known geographical and ecological distributions of the species in the Asian part of its range. Graphic models characterising the species’ distribution within a system of life zones and major ecosystem types show its general preference for dry steppes and semideserts. The models demonstrate apparent opportunities for this species to pierce northwards (at least up to 58° N in West Siberia, 62–63° N in East Siberia) in the future. This means that in the future, there is a certain probability that some individuals will migrate to these more northern regions. In the context of global warming, further northwards and northeastward shifts in the range boundaries of O. decorus can be predicted. In addition, there have been some changes in its population distribution and abundance, especially in the optimal parts of its population system where O. decorus normally colonises all applicable habitats; its average abundance is relatively high and it is common even during depressions. These shifts may be justified by both climatic changes (especially northwards) and some transformations of human activities (mainly for eastwards relocation). Full article

When marine chillers operate under complex marine conditions, they suffer from severe cross-equipment feature distribution shifts, scarce labeled fault samples in the target domain, industrial vibration noise mixed in sensor signals, and difficulties in accurately identifying subtle faults with varying severity levels. To tackle these issues, this paper improves upon the domain difference perception network (DDPN) and proposes a dual-hybrid attention feature discriminant domain-Adversarial network (DAFDAN) to realize intelligent fault diagnosis across different equipment and working conditions under few-shot scenarios. The proposed method constructs a dual-branch feature encoder consisting of a source domain compressor and a target domain extender to accommodate the distinct sensor dimensions of two heterogeneous chiller types. A hybrid attention module is formed by integrating squeeze-and-excitation efficient channel attention (SE-ECA, a module for screening channel-wise features) and spatial attention, which adaptively amplifies time-series features sensitive to faults and suppresses irrelevant noise. Residual connections (shortcut paths in deep neural networks to mitigate the vanishing gradient problem during deep-layer training) are introduced to optimize feature transmission. A dual-layer domain alignment framework is built with gradient reversal layers and maximum mean discrepancy (MMD). Combined with adversarial training (a training paradigm that learns domain-agnostic features through a game between a feature extractor and a domain discriminator), the framework achieves joint optimization of implicit feature confusion and explicit distance constraints. Meanwhile, a five-stage progressive training strategy is designed, which activates multiple loss functions, including weighted cross-entropy, mean square error (MSE), binary cross-entropy (BCE), and Kullback–Leibler (KL) divergence stage by stage. Class weighting and early stopping strategies are adopted to alleviate sample imbalance and model overfitting. In this paper, the public ASHRAE RP-1043 centrifugal chiller dataset is used as the source domain, and time-series measurement data collected from a self-developed laboratory marine screw chiller serves as the target domain. Verification experiments are carried out covering one normal steady-state operating condition and 15 gradient faults falling into five major categories with different severity degrees. Results from ablation experiments (controlled-variable comparative experiments that quantify the independent contribution of each component by comparing model performance with or without a specific module/loss), multi-algorithm comparisons, and confusion matrix visualization demonstrate that the cross-domain fault diagnosis accuracy of the proposed DAFDAN approaches is 100%, outperforming mainstream transfer learning algorithms such as support vector machine (SVM), deep neural network (DNN), MMD, correlation alignment (CORAL), and domain-adversarial neural network (DANN). Multiple ablation experiments verify that the three core components—hybrid attention, adversarial training, and semi-supervised learning—jointly boost the model’s diagnosis accuracy and operational stability. The loss curves of the complete five-stage training process converge smoothly. The confusion matrix reveals zero misjudgments and zero false alarms across all 16 refined operating states, enabling precise identification of subtle incipient faults of all severity levels. This study proves that DAFDAN can effectively address the pain points of few-shot cross-equipment fault diagnosis for marine chillers and provides a reliable algorithmic reference for the intelligent operation and maintenance of ship refrigeration equipment. Full article