Disparate Distribution Research Articles

Novel approach to explore hydrogen trapping sites in aluminum: Integrating Muon spin relaxation with first-principles calculations

This paper provides a novel technique to explore hydrogen trapping in aluminum alloys by combining muon spin relaxation and first-principles calculations. Zero-field muon spin relaxation experiments were conducted on Al–Mn, Al–Cr, Al–Fe, and Al–Ni alloys, resulting in temperature-dependent variations of the dipole field widths (Δ) that elucidated four distinct peaks for the prepared alloys. Our first-principles calculations have clarified that atomic configurations of the muon trapping, which correspond to the Δ peaks below 200 K, are consistent with hydrogen trapping sites in proximity to a solute and solute-vacancy pair. However, significant deviations from this linear relationship were observed for the fourth Δ peaks above 200 K in Al–Mn, Al–Cr, Al–Fe, and Al–Ni alloys. This discrepancy can be interpreted by considering the disparate distribution functions of muon and hydrogen within the tetrahedral site, wherein two of the four Al atoms are substituted by the solute element and vacancy (solute-vacancy pair).

A qualitative approach to describe the viscosity of flowable concrete made with manufactured sand containing different microfines

The concrete made with different manufactured sands can exhibit complex rheological properties. The prediction of viscosity is crucial in concrete placement, but remains challenging given the varied content and characteristics of microfines in the sands. This paper studies the influence of tuff (TF) and limestone (LS) microfines with disparate particle size distributions on the pseudo-Newtonian viscosity of concrete prepared with crushed sand. The concrete had a fixed water-to-binder mass ratio of 0.32 and polycarboxylate ether superplasticizer (PCE) dosage of 0.14 % by mass of biner. Test results showed that the microfines in manufactured sand can reduce the aggregate-to-excess cement paste ratio, leading to lower contact interactions between the aggregate grains. But the ratio of total powder particles and entrained air to excess interstitial solutions increased for the fluid paste matrix of the concrete, which caused greater flow resistance related to the powder contacts and air bubble deformations. A qualitative approach correlating the volume ratio of aggregate grains to excess cement pastes and that of total powder particles and entrained air to excess interstitial solutions was proposed to describe the pseudo-Newtonian viscosity of concrete, regardless of the different microfine additions in manufactured sand.

Association between physicians’ maldistribution and core clinical competency of resident physicians: a nationwide cross-sectional study

ObjectivesWith physician maldistribution recognised as a global issue, Japan implemented the physician uneven distribution (PUD) index as a strategic measure. Currently, there is a lack of objective assessment of core...

Indigenized Characterization Factors for Health Damage Due to Ambient PM2.5 in Life Cycle Impact Assessment in China.

Life cycle assessment (LCA) is a broadly used method for quantifying environmental impacts, and life cycle impact assessment (LCIA) is an important step as well as a major source of uncertainties in LCA. Characterization factors (CFs) are pivotal elements in LCIA models. In China, the health loss due to ambient PM2.5 is an important aspect of LCIA results, which, however, is generally assessed by adopting CFs developed by global models and there remains a need to integrate localized considerations and the latest information for more precise applications in China. In this study, we developed indigenized CFs for LCIA of health damage due to ambient PM2.5 in China by coupling the atmospheric chemical transport model GEOS-Chem, exposure-response model GEMM containing Chinese cohort studies, and the latest local data. Results show that CFs of four major PM2.5 precursors all exhibit significant interregional variation and monthly differences in China. Our results were generally an order of magnitude higher and show disparate spatial distribution compared to CFs currently in use, suggesting that the health damage due to ambient PM2.5 was underestimated in LCIA in China, and indigenized CFs need to be adopted for more accurate results in LCIA and LCA studies.

A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions

Operating conditions reflect the mission evolution of rotating machinery in specific application scenarios. The monitoring data under different operating conditions exhibit disparate statistic distributions in terms of amplitude and frequency properties. In the modern operation and maintenance, domain adaptation approaches, which align the feature distributions of source and target domains, are commonly employed to address cross-domain diagnosis challenges. However, the availability of the target domain is serendipitous rather than guaranteed. To meet the engineering requirements, a domain feature decoupling network (DFDN) is proposed to achieve the fault diagnosis of rotating machinery under unseen operating conditions. The core concept of DFDN is to decouple the extracted features into domain-related features and fault-related features. These features are then adaptively integrated into domain-invariant features using a significance fusion method, thereby enabling the feature learning and knowledge transfer from seen source domains to unseen target domains. Furthermore, a self-learning joint optimization strategy is developed for DFDN to facilitate the efficient acquisition of generalized diagnosis knowledge. Two bearing datasets from different test rigs are considered to assess the domain generalization performance of DFDN. The experimental results demonstrate the superiority and potential of this method in coping with zero-shot fault diagnosis under unseen operating conditions.

Regional Differences in American Indian/Alaska Native Chronic Respiratory Disease Disparity: Evidence from National Survey Results.

American Indian/Alaska Native (AI/AN) persons in the US experience a disparity in chronic respiratory diseases compared to white persons. Using Behavioral Risk Factor Surveillance System (BRFSS) data, we previously showed that the AI/AN race/ethnicity variable was not associated with asthma and/or chronic obstructive pulmonary disease (COPD) in a BRFSS-defined subset of 11 states historically recognized as having a relatively high proportion of AI/AN residents. Here, we investigate the contributions of the AI/AN variable and other sociodemographic determinants to disease disparity in the remaining 39 US states and territories. Using BRFSS surveys from 2011 to 2019, we demonstrate that irrespective of race, the yearly adjusted prevalence for asthma and/or COPD was higher in the 39-state region than in the 11-state region. Logistic regression analysis revealed that the AI/AN race/ethnicity variable was positively associated with disease in the 39-state region after adjusting for sociodemographic covariates, unlike in the 11-state region. This shows that the distribution of disease prevalence and disparity for asthma and/or COPD is non-uniform in the US. Although AI/AN populations experience this disease disparity throughout the US, the AI/AN variable was only observed to contribute to this disparity in the 39-state region. It may be important to consider the geographical distribution of respiratory health determinants and factors uniquely impactful for AI/AN disease disparity when formulating disparity elimination policies.

Revealing the microbial composition changes and relationship with Fusarium caused by rot disease in the Crocus sativus L.

PurposeRot disease caused by Fusarium poses a formidable threat to the growth of saffron (Crocus sativus L.), resulting in substantial damage to both yield and quality. It is paramount to delve into the root causes of rot disease in saffron to optimize both yield and quality. Existing preventive and treatment modalities have exerted deleterious effects on corms and the natural environment. Consequently, the quest for efficacious and eco-friendly methods such as biological control agents has become an urgent imperative.MethodsThe disparate distribution of microbial communities between rhizospheric microorganisms and saffron serves as the foundational exploration for uncovering the underlying causes of rot disease. Samples from various saffron organs and rhizosphere soil were gathered, and the sequencing data from the microbial communities were interpreted using 16S rRNA and ITS gene sequencing methods. This facilitated an in-depth examination of the composition and changes of microorganisms in both healthy and diseased saffron plants.ResultsThe findings indicated rot disease reduced the abundance and diversity of microorganisms in saffron, and the fungal co-occurrence networks were less stable and their communities were more sensitive to rot disease than the bacterial community. Fusarium was the predominant genus in diseased samples, accounting for 99.19% and 89.77% of the communities in diseased leaves and corms. With corms and leaves displaying heightened susceptibility to infection compared to other plant organs. Some of the beneficial bacterial taxa enriched in the diseased plants were also identified in networks, they showed an antagonistic relationship with Fusarium, suggesting a potential for these bacteria to be used in biologically based control strategies against rot disease. These insights could prove invaluable for the development of biocontrol agents aimed at combating this plant ailment.ConclusionThese findings significantly advance our understanding of saffron-microbiome interactions and could provide fundamental and important data for improving saffron yield and quality in the process of sustainable development.

Global species hotspots and COI barcoding cold spots of marine Gastropoda

With the on-going efforts in digitising museum collections, increased participation of citizen scientists, and greater accessibility to research data, accurately determining global patterns of diversity has become more achievable. Here, we used occurrence records from the Global Biodiversity Information Facility, with annotation of authoritative taxonomy, to evaluate the taxonomic richness of marine gastropods and to identify global species hotspots for this group. We also reviewed the availability of genetic resources within hotspots to detect potentially important regions where reference sequences for identifying these organisms are wanting. We find 33,268 unique and valid species under 3291 genera belonging to 380 gastropod families that have been recorded from 1662 to 2023. Globally, only 12.1% of reported species are linked to a COI barcode, whilst 27.1% of the families are represented with complete mitogenomes. Georeferenced records show the wide albeit disparate distribution of observations and species counts. The compiled dataset, published by organisations that are restricted to 55 countries, reveal a seeming lack of local submissions, but nonetheless demonstrates the growing contribution of citizen science platforms. We present 28 marine provinces across the globe as putative gastropod species hotspots and call for further work and stronger involvement, particularly within COI barcoding cold spots, to address the observed genetic reference inequity. Lastly, we highlight the important and relevant role of open and inclusive science to biodiversity monitoring and research.

Multi-physical field coupling modeling of microwave heating and reduction behavior of zinc oxide

Microwave heating provides a cleaner pyrometallurgical method for separating zinc from electric arc furnace dust (EAFD, a solid waste generated during steel production with excellent dielectric properties). Despite its undisputed heating efficiency, the impact of microwave heating characteristics on the zinc removal process from EAFD remains unclear. This paper focuses on ZnO, one of the primary components of EAFD, and develops an electromagnetic-thermal-chemical reaction model to analyze the effects of microwave power and graphite addition on heating behavior, reduction reactions, field distributions, and reaction kinetics. The results indicate that the heat generated from the interaction between microwaves and materials exhibits inherent non-uniformity, leading to the localized thermal effect and making the error of general temperature measurement methods. Increasing microwave power and adding graphite enhance heating efficiency and promote the reduction reaction but also result in significant internal-external temperature disparity and worse temperature distribution. At 1000 W, with 1.2 times the stoichiometric amount of graphite addition, the temperature measurement error reaches approximately 200 °C, potentially affecting the kinetic results to a certain degree. The non-isothermal kinetics results from simulations indicate that the localized thermal region exhibits a lower average activation energy of 47.5 kJ/mol compared to experimental results, suggesting that the lower activation energy of the ZnO reduction reaction during microwave heating is primarily caused by the localized thermal effect rather than the non-thermal effect. Additionally, in the energy distribution, the higher proportion of return loss during heating underscores the importance of a well-designed microwave heating system.

Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye

Today’s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

The global pattern of epiphytic liverwort disparity: insights from Frullania

The geographical and ecological patterns of morphological disparity are crucial to understand how species are assembled within communities in the context of the evolutionary history, morphological evolution and ecological interactions. However, with limited exceptions, rather few studies have been conducted on the global pattern of disparity, particularly in early land plants. Here we explored the spatial accumulation of disparity in a morphologically variable and species rich liverwort genus Frullania in order to test the hypothesis of latitude disparity gradient. We compiled a morphological data set consisting of eight continuous traits for 244 currently accepted species, and scored the species distribution into 19 floristic regions worldwide. By reconstructing the morphospace of all defined regions and comparisons, we identified a general Gondwana-Laurasia pattern of disparity in Frullania. This likely results from an increase of ecological opportunities and / or relaxed constraints towards low latitudes. The lowest disparity occurred in arid tropical regions, largely due to a high extinction rate as a consequence of paleoaridification. There was weak correlation between species diversity and disparity at different spatial scales. Furthermore, long-distance dispersal may have partially shaped the present-day distribution of Frullania disparity, given its frequency and the great contribution of widely distributed species to local morphospace. This study not only highlighted the crucial roles of paleoenvironmental changes, ecological opportunities, and efficient dispersal on the global pattern of plant disparity, but also implied its dependence on the ecological and physiological function of traits.

Multi-Scenario analysis of rooftop greening regulation on runoff effects based on adaptive Evaluation: A case study of Macau, China

The reduction in internal ecological spaces within urban environments and the expansion of impervious surfaces have been identified as factors significantly increasing the susceptibility of cities to flood disasters. Rooftop greening, an essential component of urban stormwater management strategies, offers a versatile solution for augmenting permeable surfaces and delivering ecological services while seamlessly integrating with existing urban infrastructure. This approach has been proven effective in enhancing urban resilience against rainfall-induced flooding events. Hence, the precise identification of suitable building rooftops for green infrastructure within urbanized regions, combined with the simulation of runoff regulation effects brought about by these rooftop greening interventions, holds substantial practical significance. This study innovatively constructs a technical method for measuring the runoff control performance of urban-scale green roofs from identification, evaluation to simulation. Taking the Macao Special Administrative Region as an example, a green roof suitability evaluation index system that is consistent with urban characteristics has been constructed, integrating machine learning, comprehensive evaluation, SWMM simulation methods to evaluate the suitability of roof greening in urban areas and simulate the impact of multi-senarios green roof construction types on runoff regulation. Finally, a comprehensive strategy for rooftop greening construction in Macau is proposed, with the aim of amplifying the runoff regulation capabilities of such initiatives. The main findings of the research include the following: (1) The research area exhibits a notably high aggregate potential for rooftop greening, with suitable rooftop areas comprising approximately 34% of the total rooftop expanse. Notably, construction vintage and roof elevation are identified as the most substantive constraints on rooftop greening. (2) Discernible differences in rooftop greening construction scale exert a significant influence on runoff regulation capacity, with the magnitude of greening positively correlated with its runoff regulation efficacy. Distinct greening typologies elicit varying impacts on runoff regulation, with semi-intensive rooftop greening surpassing extensive approaches in runoff regulation performance. Disparate distribution patterns of rooftop greening engender dissimilar runoff regulation effects, with rooftop greening layouts situated distant from drainage outlets facilitating runoff mitigation, while those in close proximity to drainage outlets enhance peak flow reduction effects.

Self-clustered GAN for precipitation nowcasting

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets.

Epidemiological approaches to multivariable models of health inequity: A study of race, rurality, and occupation during the COVID-19 pandemic

PurposeMethods for assessing the structural mechanisms of health inequity are not well established. This study applies a phased approach to modeling racial, occupational, and rural disparities on the county level. MethodsRural counties with disparately high rates of COVID-19 incidence or mortality were randomly paired with in-state control counties with the same rural-urban continuum code. Analysis was restricted to the first six months of the pandemic to represent the baseline structural reserves for each county and reduce biases related to the disruption of these reserves over time. Conditional logistic regression was applied in two phases—first, to examine the demographic distribution of disparities and then, to examine the relationships between these disparities and county-level social and structural reserves. ResultsIn over 200 rural county pairs (205 for incidence, 209 for mortality), disparities were associated with structural variables representing economic factors, healthcare infrastructure, and local industry. Modeling results were sensitive to assumptions about the relationships between race and other social and structural variables measured at the county level, particularly in models intended to reflect effect modification or mediation. ConclusionsMultivariable modeling of health disparities should reflect the social and structural mechanisms of inequity and anticipate interventions that can advance equity.

Shallow crustal deformation in the Yunnan-Myanmar and surrounding areas by regionally weighted interpolation of GPS measurements data

Yunnan-Myanmar and its surrounding areas are the areas directly affected by the collision of the Eurasian Plate and the Indian Plate. In view of the uneven distribution of GPS stations in Yunnan and Myanmar, this paper employs the method of regionally weighted interpolation. In areas with a high concentration of GPS stations, a combination of Voronoi cell weighting function and Gaussian function weighting is utilized, while in regions with sparse GPS coverage, a combination of Voronoi cell weighting function and quadratic function weighting is employed. Utilizing the rigid-body movement uniform strain model, we calculate the horizontal strain rates of the crust in Yunnan-Myanmar and adjacent areas, followed by an in-depth analysis of model performance and the activity of primary faults. The results show that: (1) We found that Yunnan Myanmar strain has obvious boundary characteristics. (2) Upon scrutinizing the model, it was ascertained that the model delineated in this study possesses an expansive search range, enabling it to yield more precise outcomes, particularly in regions characterized by the disparate distribution of GPS stations; (3) Through the analysis of the profiles of horizontal GPS velocity field and maximum shear strain rates of the main faults in the study area, we found that the direction of the maximum shear strain rate is consistent with the direction of the main faults in the study area, and that the slip and maximum shear strain rate of the major faults increase and decrease along the tectonic direction.

ASSESSING THE ROLE OF CLIMATE FINANCE IN SUPPORTING DEVELOPING NATIONS: A COMPREHENSIVE REVIEW

Climate finance plays a critical role in supporting developing nations to mitigate and adapt to the impacts of climate change. This comprehensive review examines the multifaceted role of climate finance in assisting developing countries in their efforts to address climate change challenges. The review begins by providing an overview of the global climate finance landscape, highlighting key sources of climate finance, including public funds, private investments, and innovative financing mechanisms. It then explores the allocation and distribution of climate finance, analyzing trends, disparities, and challenges in accessing and utilizing funds among developing nations. Furthermore, the review assesses the effectiveness of climate finance in supporting mitigation and adaptation activities in developing countries. It examines case studies and best practices to illustrate how climate finance has been utilized to finance renewable energy projects, enhance resilience in vulnerable communities, and promote sustainable development. Additionally, the review evaluates the governance and institutional frameworks governing climate finance, including the role of international financial institutions, national governments, and civil society organizations in mobilizing and disbursing funds. Moreover, the review examines the challenges and opportunities associated with scaling up climate finance to meet the growing needs of developing nations. It discusses issues such as financial transparency, accountability, and the alignment of climate finance with national development priorities. Additionally, the review explores innovative financing mechanisms, such as green bonds, climate insurance, and carbon pricing, as potential avenues for mobilizing additional resources for climate action in developing countries. In conclusion, this comprehensive review underscores the importance of climate finance in supporting developing nations to address climate change challenges and achieve sustainable development goals. It highlights the need for enhanced international cooperation, policy coherence, and investment mobilization to accelerate climate action and build resilience in the most vulnerable regions. By leveraging climate finance effectively and efficiently, developing countries can transition to low-carbon, climate-resilient economies while advancing their development objectives.
 Keywords: Assess, Climate, Finance, Developing Nations, Support.

Non-renewable warranty cost analysis for dependent series configuration with distinct warranty periods

A warranty product often has an interval structure, resulting in different warranty periods for each component due to their distinct degradation paths. From the manufacturer’s perspective, it is reasonable to establish appropriate warranty periods for each part to achieve cost savings. This paper introduces a dependent series configuration consisting of a main component and an auxiliary component. The failure of the main component leads to system failure, while the failure of the auxiliary component only causes a random damage to the main component. These two components follow different Wiener degradation paths, and then their first passage times (FPTs) follow disparate inverse Gaussian distributions. Assuming a pre-deterministic warranty period, W, for the main component, and ηW,0<η<1, for the auxiliary component, the warranty period can be divided into two stages: [0,ηW] and [ηW,W]. In this context, the paper proposes warranty claim policies for replacement, objective-oriented imperfect repair (OOIR), and refund. The corresponding warranty costs sustained by manufacturer and consumer within these two stages are considered. Finally, a numerical example is presented to illustrate the warranty costs sustained by manufacturer and consumer under different scenarios, as well as the total warranty costs.

Neural embeddings of urban big data reveal spatial structures in cities

Over decades, many cities have been expanded and functionally diversified by population activities, socio-demographics and attributes of the built environment. Urban expansion and development have led to the emergence of spatial structures of cities. Uncovering cities’ spatial structures is critical to understanding various urban phenomena such as segregation, equity of access, and sustainability. In this study, we propose using a neural embedding model—graph neural network (GNN)—that leverages the heterogeneous features of urban areas and their interactions captured by human mobility networks to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The clustered representations of urban areas show the shared characteristics among urban areas in the same cluster. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the spatial structure in another county, allowing us to make cross-county comparisons and inferences. The findings reveal complex relationships among urban components in cities. Since the identified multifaceted spatial structures capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through an integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities.

Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions. Predominant methodologies either delineate a single dataset distribution into training, validation, and testing subsets or merge datasets from diverse conditions or distributions before their division into the subsets. Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions, evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions, and assessing their performance in entirely distinct settings through three experiments. By evaluating diverse network architectures and training approaches (finetuning versus feature extraction), testing various architectures, employing different training strategies, and amalgamating data, we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1, conducted in a uniform environment, accuracy ranged from 80% to 100% across all models and training strategies, with finetune mode achieving a superior performance of 94% to 99.9% compared to the feature extraction mode at 80% to 92.96%. Experiment 2 underscored a significant performance decline, with accuracy figures between 25% and 60%, primarily at 40%, when the origin of the test data deviated from the train and validation sets. Experiment 3, spotlighting dataset and distribution amalgamation, yielded promising accuracy metrics, notably a peak of 99.6% for ResNet in finetuning mode to a low of 69.9% for InceptionV3 in feature extraction mode. These pivotal findings emphasize that merging data from diverse distributions, coupled with finetuned training on advanced architectures like ResNet and MobileNet, markedly enhances performance, contrasting with the relatively lower performance exhibited by simpler networks like AlexNet. Our results suggest that embracing data diversity and flexible training methodologies are crucial for optimizing weed classification models when disparate data distributions are available. This study gives a practical alternative for treating diverse datasets with real-world agricultural variances.

Cretaceous fossil birds from China

Abstract The explosive radiation of early birds has been well documented by the extraordinary discoveries of the Early Cretaceous fossil birds in China in the past three decades. They have greatly expanded the diversity, disparity and temporal distribution of Mesozoic birds, refining our knowledge of the evolutionary path leading to the characteristic avian body plan, including feathers and powered flight, locomotion and habitat differentiation, diet and digestion, reproduction and development, feather colours and display. In addition, studies on the geological background of the Jehol Biota that produced the majority of the Chinese Cretaceous birds have provided important clues to our understanding of the taphonomy as well as the interaction between deep geological process and biological evolution in northeastern China and East Asia during the Early Cretaceous. Future multidisciplinary studies are expected to advance our knowledge of the palaeogeography of Early Cretaceous birds, what impacted the changes in the diversity of early birds in China, and the roles that early birds played in the Early Cretaceous terrestrial ecosystem.