Search Results (526)

Under the background of global climate change, lichens as a staple fallback food source for the endangered Yunnan snub-nosed monkey (Rhinopithecus bieti) exert a critical influence on the survival of Yunnan snub-nosed monkey populations through their distribution dynamics. This study focused on the contiguous habitats of the Yunnan snub-nosed monkey in the southern Hengduan Mountains. By species distribution models (SDMs) and landscape pattern analysis, we investigated the changes in suitable habitats of lichens under four Representative Concentration Pathway (RCP) scenarios and their implications for the habitat utilization of the Yunnan snub-nosed monkey until 2050. The results indicate that the current suitable habitat for lichen spans approximately 16,821.96 km², with highly suitable habitats predominantly located in Deqin County and Weixi County. Altitude and vegetation type emerged as primary factors influencing lichen distribution. The overlap rate of suitable habitats between lichens and the Yunnan snub-nosed monkey is 72.24%. Furthermore, the Yunnan snub-nosed monkey exhibits a preference for selecting habitats characterized by the largest patch index (LPI) of lichen distribution. By 2050, the suitable habitat for lichen is projected to marginally increase in the southern Hengduan Mountains, particularly under the RCP 6.0 scenario, by 22.20% compared to the current expansion. However, both the suitable habitat and the LPI of lichen face potential decline within the habitat of the Yunnan snub-nosed monkey. Therefore, we recommend conducting a quantitative investigation into the correlation between the actual productivity of lichen radiata and the population dynamics of Yunnan snub-nosed monkey as a priority. This research will offer a more precise scientific foundation for conservation decision-making for Yunnan snub-nosed monkey. Full article

Species distribution models (SDMs) have become an important tool to inform conservation and pest surveillance programs about the potential biological invasion of insect pests. Nonetheless, to be operational, SDMs need to incorporate multiple environmental covariates and a representative number of occurrence points depicting the species’ ecological niche. The algorithm of choice, model of choice, and comparison can also have a great effect on the final prediction output. We created a dataset based on previously published records, plus 36 new occurrences and 37 environmental predictors, to generate the first global ensemble distribution model for Aulacaspis yasumatsui. We employed a strategy that aggregates SDMs with the best performance (i.e., greater accuracy) from six different algorithms, resulting in an averaged and weighted model, i.e., the ensemble model. We then selected models from algorithms whose true skill statistic (TSS) was above 0.5 in order to map the potential global distribution of A. yasumatsui. Our results suggest that covariate selection and the individual model algorithms used in the ensemble may be more important for achieving an accurate SDM than the number of occurrence points. Full article

Breast cancer (BC) is the most frequently diagnosed malignancy in women. Currently, BC is treated with a holistic and multidisciplinary approach from diagnostic, surgical, radio-oncological, and medical perspectives, and advances including in early detection and treatment methods have led to improved outcomes for patients in recent years. Yet, BC remains the second most common cause of cancer-related deaths among women and there is an array of gaps to achieve optimal care. To close gaps in cancer care, here we describe a collaborative Request For Proposals (RFP) framework supporting independent initiatives for metastatic breast cancer (MBC) patients and aiming at improving their quality of care. We set up a collaborative framework between Pfizer and Sharing Progress in Cancer Care (SPCC). Our model is based on an RFP system in which Pfizer and SPCC worked together ensuring the independence of the funded projects. We developed a three-step life cycle RFP. The collaborating framework of the project was based on an RFP with a USD 1.5 million available budget for funding independent grants made available from Pfizer and managed in terms of awareness, selection, and monitoring by SPCC. Our three-step model could be applicable and scalable to quality improvement (QI) initiatives that are devoted to tackling obstacles to reaching optimal care. Through this model, seven projects from five different European countries were supported. These projects covered a range of issues related to the experience of patients with MBC: investigator communication, information, and shared decision-making (SDM) practices across Europe; development, delivery, and evaluation of a scalable online educational program for nurses; assessment of disparities among different minority patient groups; development of solutions to improve compliance or adherence to therapy; an information technology (IT) solution to improve quality of life (QoL) of patients with MBC and an initiative to increase awareness and visibility of MBC patients. Overall, an average of 171 healthcare professionals (HCPs) per project and approximately 228,675 patients per project were impacted. We set up and describe a partnership model among different stakeholders within the healthcare ecosystem―academia, non-profit organizations, oncologists, and pharmaceutical companies―aiming at supporting independent projects to close gaps in the care of patients with MBC. By removing barriers at different layers, these projects contributed to the achievement of optimal care for patients with MBC. Full article

This study examined the habitat suitability of Kentucky bluegrass (Poa pratensis L.) in Iran’s Fars province, a region characterized by diverse climatic conditions and significant ecological challenges. Utilizing a multi-technique approach that included species distribution models (SDMs) based on machine learning algorithms, geographic information systems (GIS), and remote sensing, we analyzed environmental factors such as climate variables, soil properties, and water availability to understand their influence on habitat suitability. The results indicated that Kentucky bluegrass shows a strong preference for areas near water sources, and its distribution is significantly affected by soil salinity and texture. Among the models tested, Random Forest (RF) and Support Vector Machines (SVMs) demonstrated the highest predictive accuracy. Based on the RF model, the most suitable habitats were identified in the counties of Sepidan, Beyza, Bavanat, Pasargad, and Abadeh. At the same time, areas with lower suitability included Eqlid, Marvdasht, Zarghan, and Arsanjan. Although this study primarily focused on current distribution patterns, the findings provide important insights into the ecological preferences and adaptive capacities of Kentucky bluegrass. These insights are essential for the development of targeted conservation strategies in transitional climate zones. Future studies are recommended to explore the species’ response to future climate scenarios, enhancing its resilience against global climate change. Full article

Inclusive education aims to ensure not only academic development but also social participation among students with intellectual disabilities. However, research consistently shows that students with intellectual disabilities are prone to social exclusion in secondary school settings. While theoretical frameworks increasingly highlight the importance of contextual and systemic factors—particularly classroom social dynamics—empirical studies on teachers’ practices for fostering participation remain scarce. This qualitative study investigates how secondary school teachers in inclusive classrooms perceive and enact their role in promoting social participation. Semi-structured interviews were conducted with 30 teachers from various German secondary schools. The data were analyzed using qualitative content analysis based on the social dynamics management (SDM) framework, which distinguishes between universal, selected, and indicated intervention levels. The results reveal that teachers use a wide range of strategies across all three levels. In addition to the categories proposed by the SDM framework, two further areas were identified inductively: (1) teachers’ pedagogical beliefs and (2) internal and external cooperation. These findings suggest a need to expand the SDM model and provide guidance for the professional development of teachers aiming to promote inclusive classroom environments. Full article

Sustainable tourism development has emerged as a strategic priority across ASEAN countries, yet the role of green innovation and environmentally responsible investment in shaping tourism outcomes remains underexplored. Existing studies often overlook the spatial interdependencies that characterize regional integration and cross-border environmental dynamics. This study investigates how green patents and green foreign direct investment (FDI) influence sustainable tourism development, both within and across ASEAN nations. Drawing on endogenous growth theory, ecological modernization, and FDI spillover frameworks, the analysis employs a Spatial Durbin Model (SDM) using panel data from 2000 to 2023. The findings reveal that green innovation and green FDI significantly enhance tourism development, with notable spatial spillover effects that benefit neighboring countries. These effects are most pronounced in leading ASEAN economies, where institutional capacity and absorptive readiness amplify the impact of green practices. The relationship is further shaped by economic growth, human capital, and political stability, while environmental degradation and inflation pose constraints. The study underscores the nonlinear and regionally heterogeneous nature of green tourism development, offering policy insights for fostering inclusive, resilient, and environmentally sustainable tourism strategies across ASEAN. Full article

In the context of the digital economy reshaping the global competitive landscape, digital industry clusters have become the key driving force to overcome the diminishing returns of traditional inputs and realize sustainable economic development in the digital era. However, the internal mechanisms and spatial effects through which digital industrial clusters drive high-quality development and thereby foster sustainable regional economic growth remain unclear. Based on China’s provincial panel data from 2012 to 2023, this study constructs time-fixed spatial Durbin model and mediation effect model to systematically examine the impact mechanism of digital industry clusters on high-quality economic development, and to analyze their direct effects, spatial spillover effects and mediation transmission effects. The following effects have been found: (1) digital industry clusters can directly promote the high-quality development of the region’s economy (0.070), and can also significantly promote the high-quality development of the region’s economy through the mediating effect of innovative talent agglomeration (0.021); (2) the spatial spillover effect of digital industry clusters consists of the negative siphoning effect of innovative talent and positive technology diffusion and driving effect, which makes the total effect of digital industry clusters on neighboring regions uncertain; (3) Technology-intensive areas, as well as the eastern and northeastern regions, have effectively transformed the advantages of digital industry clusters into momentum for high-quality economic development, whereas central and western regions have not yet fully unleashed the driving effect of digital industry on the high-quality development of the economy, due to the constraints of the industrial structure, innovation factors and infrastructure. Based on the empirical results, the article suggests accelerating the construction of digital industry innovation hubs, establishing cross-regional technology sharing platforms, constructing a negative externality compensation mechanism for talent loss areas, and implementing differentiated regional development strategies. The study addresses a gap in existing research by analyzing the spatial mediation effects of digital industrial agglomeration on high-quality economic development. It extends theoretical insights into industrial clustering within the digital economy and offers actionable policy pathways for developing countries to promote sustainable economic growth through digital industrial clusters. Full article

Most small- and medium-sized manufacturers face challenges in adopting artificial intelligence (AI) in production systems due to limited domain expertise and challenges in making interrelated decisions. This decision-making process can be characterized as sequential decision-making (SDM), in which guidance on the decision order is valuable. This study proposes a data-driven SDM framework to identify an effective order of key decision elements for AI adoption, aiming to rapidly reduce uncertainty at each decision stage. The framework employs a Q-learning-based reinforcement learning approach, using conditional entropy as the reward function to quantify uncertainty. Based on a review of 55 studies applying AI to milling processes, the proposed model identifies the following decision order that minimizes cumulative uncertainty: sensor, data collection interval, data dimension, AI technique, data type, and data collection period. To validate the model, we conduct simulations of 4000 SDM episodes under rule-based constraints using the number of corrected episodes as a performance metric. Simulation results show that the proposed model generates decision orders with no corrections and that knowing the relative order between two elements is more effective than knowing exact positions. The proposed data-driven framework is broadly applicable and can be extended to AI adoption in other manufacturing domains. Full article

Species distribution models (SDMs) grapple with uncertainty. To address this, a parameter-optimized MaxEnt model was used to predict habitat suitability for Elsholtzia densa, a predominant agricultural weed on the Tibetan Plateau. Through multiparameter optimization with 149 occurrence points and three climate variable sets, we systematically evaluated how the three MaxEnt extrapolation approaches (Free Extrapolation, Extrapolation with Clamping, No Extrapolation) influenced model outputs. The results showed the following: (1) Model optimization using the Kuenm R package version (1.1.10) identified seven critical bioclimatic variables (Feature Combinations = LQTH, Regularization Multipliers = 2.5), with optimized models demonstrating high accuracy (Area Under Curve > 0.9). (2) Extrapolation approaches exhibited negligible effects on variable selection, though four bioclimatic variables “bio1 (annual mean temperature)”, “bio12 (annual precipitation)”, “bio2 (mean diurnal range)”, and “bio7 (temperature annual range)” predominantly drove model predictions. (3) Current high-suitability areas are clustered in the eastern and southern regions of the Tibetan Plateau, and with Free Extrapolation yielding the broadest current distribution. Climate change projections suggest habitat expansion, particularly under conditions of No Extrapolation. (4) Multivariate Environmental Similarity Surface (MESS) and Most Dissimilar Variable (MoD) are not affected by the extrapolation method, and extrapolation risk analyses indicate that future climate anomalies are mainly concentrated in the western and southern parts of the Tibetan Plateau and that future warming will further increase the unsuitability of these regions. (5) Variance analysis showed that the extrapolation methods did not significantly affect the 10-replicate results but influenced the parameter and emission scenarios, with No Extrapolation methods showing minimal variance changes. Our findings validate that multiparameter optimization improves species distribution model robustness, systematically characterizes extrapolation impacts on distribution projections, and provides a conceptual framework and early warning systems for agricultural weed management on the Tibetan Plateau. Full article

Zelkova schneideriana (Ulmaceae), an endemic relict species of the Tertiary in China, has experienced a sharp decline in population due to habitat fragmentation, poor natural regeneration, and anthropogenic disturbances. It is currently listed as a category II national key protected wild plant and categorized as Vulnerable by the International Union for Conservation of Nature (IUCN). To explore its response mechanisms to climate change, this study integrates 11 species distribution models (SDMs) to comprehensively predict its suitable habitat distribution patterns. Key environmental variables were identified as Bio06 (minimum temperature of the coldest month, 21.57%), Bio02 (mean diurnal range, 19.81%), Bio17 (precipitation of the driest quarter, 13.52%), Bio15 (precipitation seasonality, 8.32%), Bio07 (temperature annual range, 8.15%), Bio12 (annual precipitation, 6.58%), and elevation (6.57%), collectively contributing approximately 85%. Spatiotemporal analysis revealed that during historical glacial periods, suitable habitats were significantly restricted, and highly suitable zones were absent under extreme climatic conditions, suggesting the presence of potential glacial refugia. Under current climatic conditions, highly suitable habitats have expanded notably. However, under the high-emission scenario (SSP585) in the future, the suitable range is projected to shrink considerably, with a drastic reduction in highly suitable areas. Moreover, the suitability centroid is expected to shift markedly toward higher elevations in the northeast, indicating a potential adaptation strategy of Z. schneideriana toward mountainous regions in Hunan, Hubei, and Chongqing. These findings provide quantitative guidance for the formulation of targeted conservation strategies for Z. schneideriana and offer methodological insights for predicting suitable habitats and managing related relict plant species under the threat of climate change. Full article

As China advances toward its “Dual Carbon” goals, clarifying the role of the digital economy (DE) in reducing urban carbon emissions is of growing importance. This study uses panel data from 274 Chinese prefecture-level cities (2011–2022) and applies benchmark regression, the Spatial Durbin Model (SDM), two-regime SDM, threshold analysis, and mediation effect modeling to examine the impact of the DE on carbon emission intensity (CEI) and its spatial spillover effects. Results show that the DE significantly reduces CEI through both direct and indirect channels. Spatial analysis reveals that the DE’s spillover effect is most pronounced within a 500 km range. Regionally, the DE has a stronger inhibitory effect on CEI in eastern and western regions, while its effect in the central region is weaker or even reversed, likely due to reliance on carbon-intensive industries. Resource-based cities exhibit stronger spatial spillovers than non-resource-based ones, suggesting greater potential for DE-driven low-carbon transitions. A threshold effect is also identified at a DE index value of 0.0326, beyond which the marginal benefits decline. Pathway analysis indicates that while the DE improves production efficiency, it does not significantly promote green, high-value-added transformation, partially masking its carbon reduction effects. These findings highlight the need for tailored regional strategies to enhance the low-carbon potential of the DE. Full article

Extreme climate events—such as heatwaves, floods, and droughts—are increasingly affecting ecosystems, with the global average temperature projected to rise by up to 3 °C (IPCC, 2023) due to anthropogenic greenhouse gas emissions. These changes pose critical challenges to food security, as evidenced by 733 million people facing hunger in 2024. In response, crop modeling considering different climate change scenarios has become a valuable tool to guide the development of climate-resilient agricultural strategies. Despite its nutritional importance and capacity to thrive across diverse environments, Ipomoea batatas (sweet potato) remains understudied in terms of potential spatial distribution forecasting, particularly in regions of high agrobiodiversity such as northwestern South America. Therefore, in this study we modeled the projected distribution of wild and landrace sweet potato genepools in the northern Andes under four future timeframes using seven machine learning algorithms. Our results predicted a 50% reduction in the climatically suitable range for the wild genepool by 2081, coupled with an average altitudinal shift from 1537 to 2216 m above sea level (a.s.l.). For landraces, a 36% reduction was projected by 2080, with a shift from 62 to 1995 m a.s.l. By the end of the century, suitable zones for both wild and cultivated genepools are expected to converge in high-altitude regions such as the Colombian Massif, with additional remnants of wild populations near the mountain range of Farallones de Cali. This modeling approach provides essential insights into the spatial dynamics of I. batatas under climate change, highlighting the need for ex situ conservation planning in vulnerable regions as well as assisted migration to more suitable areas. Future research should integrate edaphic and biotic interaction data to better approach the realized niche of the species and understand potential responses under a niche conservatism assumption, as well as genomic data to account for the species’ intrinsic adaptative potential, overall informing conservation, germplasm mobilization, and pre-breeding strategies that may ultimately secure the role of sweet potato in resilient food systems. Full article

Chatter vibration in machining operations has been identified as one of the major obstacles to improving surface quality and productivity. Therefore, efficiently and accurately predicting stable cutting regions is becoming increasingly important, especially in high-speed milling processes. In this study, on the basis of a predictor–corrector scheme, the following three error correction methods are developed for milling stability analysis: the Correction Hamming–Milne-based method (CHM), the Correction Adams–Milne-based method (CAM) and the Predictor–Corrector Hamming–Adams–Milne-based method (PCHAM). Firstly, we employ the periodic delay differential equations (DDEs), which are usually adopted to describe mathematical models of milling dynamics, and the time period of the coefficient matrix is divided into two unequal subintervals based on an analysis of the vibration modes. Then, the Hamming method and the fourth-order implicit Adams–Moulton method are separately utilized to predict the state term, and the Milne method is adopted to correct the state term. Based on local truncation error, combining the Hamming and Milne methods creates a CHM that can more precisely approximate the state term. Similarly, combining the fourth-order implicit Adams–Moulton method and the Milne method creates a CAM that can more accurately approximate the state term. More importantly, the CHM and the CAM are employed together to acquire the state transition matrix. Thereafter, the effectiveness and applicability of the three error correction methods are verified by comparing them with three existing methods. The results demonstrate that the three error correction methods achieve higher prediction accuracy without sacrificing computational efficiency. Compared with the 2nd SDM, the calculation times of the CHM, CAM and PCHAM are reduced by around 56%, 56% and 58%, respectively. Finally, verification experiments are carried out using a CNC machine (EMV650) to further validate the reliability of the proposed methods, where ten groups of cutting tests illustrate that the stability lobes predicted by the three error correction methods exhibit better agreement with the experimental results. Full article

Some Melanothamnus species have been documented growing epiphytically on other algae in seaweed aquaculture farms as fouling organisms. Such turf-forming Polysiphonia-looking algae were collected from a small (<1.0 km² area) Agarophyton tenuistipitata (Gracilariaceae, Gracilariales) farm on the east coast of the Bay of Bengal and examined for their taxonomy. DNA was extracted from silica gel-preserved specimens, and plastid-encoded rbcL, nuclear-encoded small subunit SSU, large subunit LSU, and universal plastid amplicon (UPA) were amplified and sequenced. Maximum likelihood (ML) and Bayesian inference were performed for the phylogenetic analysis. Four single-locus species delimitation methods (SDMs), namely, the generalized mixed Yule-coalescent (GMYC) method, a Poisson tree processes (PTP) model, the automatic barcode gap discovery (ABGD), and the assemble species by automatic partitioning (ASAP) method, were performed to segregate the putative species from other taxa in the Polysiphonia sensu lato clades. Our results revealed that rbcL had 1.4% interspecific genetic divergence, whereas LSU, UPA, and SSU had 1.6%, 2.5%, and 5.4% genetic divergence, respectively, from the nearest neighbors. Both comparative genetic and distinct morphological data revealed that the collected Bay of Bengal specimens comprise a species new to science. In addition, the above-mentioned SDMs supported the genetic data and segregated our specimens as Melanothamnus coxsbazarensis sp. nov. as a distinct species. Full article

The digital revolution has profoundly influenced the automotive industry, shifting the paradigm from conventional vehicles to smart cars (SCs). The SCs rely on in-vehicle communication among electronic control units (ECUs) enabled by assorted protocols. The Controller Area Network (CAN) serves as the de facto standard for interconnecting these units, enabling critical functionalities. However, inherited non-delineation in SCs— transmits messages without explicit destination addressing—poses significant security risks, necessitating the evolution of an astute and resilient self-defense mechanism (SDM) to neutralize cyber threats. To this end, this study introduces a lightweight intrusion mitigation mechanism based on an adaptive momentum-based deep denoising autoencoder (AM-DDAE). Employing real-time CAN bus data from renowned smart vehicles, the proposed framework effectively reconstructs original data compromised by adversarial activities. Simulation results illustrate the efficacy of the AM-DDAE-based SDM, achieving a reconstruction error (RE) of less than 1% and an average execution time of 0.145532 s for data recovery. When validated on a new unseen attack, and on an Adversarial Machine Learning attack, the proposed model demonstrated equally strong performance with RE < 1%. Furthermore, the model’s decision-making capabilities were analysed using Explainable AI techinques such as SHAP and LIME. Additionally, the scheme offers applicable deployment flexibility: it can either be (a) embedded directly into individual ECU firmware or (b) implemented as a centralized hardware component interfacing between the CAN bus and ECUs, preloaded with the proposed mitigation algorithm. Full article