Complex Variables Research Articles

Analisis Faktor Yang Mempengaruhi Kepuasan Pegawai Dinas Pangan: Pendekatan Menggunakan Algoritma C4.5

The level of satisfaction is an important measure in evaluating the extent to which the needs and expectations of a person or group are met by a product, service, or experience. The concept is often used in a business context to measure how well a product or service meets customer expectations. The level of satisfaction can be measured through various methods such as surveys, interviews or analysis of consumer behavior data. The results of this satisfaction level evaluation provide valuable insights for companies in improving the quality of their products or services, as well as maintaining customer loyalty. Therefore, the author will conduct a study on the level of employee satisfaction Department of food using machine learning approach with C4.5 method. This study aims to explore the patterns and factors that significantly affect the level of employee satisfaction in the context of the Department of food. The C4.5 method was chosen because of its ability to handle complex and diverse data, as well as being able to provide insight into the relationship of complex and non-linear variables.

Integrating Convolutional Attention and Encoder–Decoder Long Short-Term Memory for Enhanced Soil Moisture Prediction

Soil moisture is recognized as a crucial variable in land–atmosphere interactions. This study introduces the Convolutional Attention Encoder–Decoder Long Short-Term Memory (CAEDLSTM) model to address the uncertainties and limitations inherent in traditional soil moisture prediction methods, especially in capturing complex temporal dynamics across diverse environmental conditions. Unlike existing approaches, this model integrates convolutional layers, an encoder–decoder framework, and multi-head attention mechanisms for the first time in soil moisture prediction. The convolutional layers capture local spatial features, while the encoder–decoder architecture effectively manages temporal dependencies. Additionally, the multi-head attention mechanism enhances the model’s ability to simultaneously focus on multiple key influencing factors, ensuring a comprehensive understanding of complex environmental variables. This synergistic combination significantly improves predictive performance, particularly in challenging climatic conditions. The model was validated using the LandBench1.0 dataset, which includes multiple high-resolution datasets, such as ERA5-land, ERA5 atmospheric variables, and SoilGrids, covering various climatic regions, including high latitudes, temperate zones, and tropical areas. The superior performance of the CAEDLSTM model is evidenced by comparisons with advanced models such as AEDLSTM, CNNLSTM, EDLSTM, and AttLSTM. Relative to the traditional LSTM model, CAEDLSTM achieved an average increase of 5.01% in R2, a 12.89% reduction in RMSE, a 16.67% decrease in bias, and a 4.35% increase in KGE. Moreover, it effectively addresses the limitations of traditional deep learning methods in challenging climates, including tropical Africa, the Tibetan Plateau, and Southeast Asia, resulting in significant enhancements in predictive accuracy within these regions, with R2 values improving by as much as 20%. These results underscore the capabilities of CAEDLSTM in capturing complex soil moisture dynamics, demonstrating its considerable potential for applications in agriculture and water resource monitoring across diverse climates.

Population pharmacokinetic analysis identifies an absorption process model for mycophenolic acid in patients with renal transplant.

The pharmacokinetics (PKs) of mycophenolic acid (MPA) exhibit considerable complexity and large variability. We developed a population pharmacokinetic (popPK) model to predict the complex PK of MPA by examining an absorption model. Forty-two patients who had undergone renal transplantation were included in this study. popPK analysis, incorporating several absorption models, was performed using the nonlinear mixed-effects modeling program NONMEM. The MPA area under the concentration-time curve at 0-12 h (AUC0-12) was simulated using the final model to calculate the recommended dose. The PK of MPA was adequately described using a two-compartment model incorporating sequential zero- and first-order absorption with lag time. Total body weight, renal function (RF), and posttransplantation day (PTD) were included as covariates affecting MPA PK. The final model estimates were 7.56, 11.6 L/h, 104.0 L, 17.3 L/h, 169.0 L, 0.0453, 0.283, and 1.95 h for apparent nonrenal clearance, apparent renal clearance, apparent central volume of distribution, apparent intercompartmental clearance, apparent peripheral volume of distribution, absorption half-life, lag time, and duration of zero-order absorption, respectively. Simulation results showed that a dose regimen of 500-1000 mg twice daily is recommended during the early posttransplantation period. However, dose reduction could be required with increased PTD and decreased RF. The complex PK of MPA was explained using an absorption model. The developed popPK model can provide useful information regarding individual dosing regimens based on PTD and RF.

Structural variability of rare-earth bromide complexes with acetylurea

Structural variability of rare-earth bromide complexes with acetylurea

Review of Biomass Gasification and Pyrolysis Process Based on Long Short-Term Memory Network

Abstract. With the global energy crisis become escalated day by day, finding an effective way of exploring the renewable energy has become an important direction as its widely resources, comparatively low environment impact. This paper has concluded the biomass pyrolysis and gasification process based on long- and short-term memory (LSTM) method, including the LSTM principle, current application, faced challenges and future development. LSTM as a kind of special recurrent neural network, which could effectively solve the gradient vanish and explosion problem in time-series data. Research shows that, LSTM has an excellent performance in terms of reaction condition and optimization, which could be able to capture the relationship between complex variables, the pyrolysis efficiency could be improved, and prediction accuracy could over 90%. During the data preprocessing and model training stage, it is vital to make sure the quality of data, including data normalisation, outlier handling and time series splitting, etc. Through these steps, a better prediction accuracy could be achieved by LSTM. At the same time, the real application cases of LSTM used in biomass pyrolysis process have been discussed, it is shown that the combination of LSTM and other machine learning techniques could obviously improve the system stability and product quality. In addition, this paper has also looked forward to the enhancement of model explainability and the prospect of uncertainty quantification, which is helpful to improve the LSTM application and practical value.

The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.

Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. In histopathology, ML algorithms could serve as a support to the pathologist to avoid misclassifications and overcome interobserver variability. Diagnostic platforms based on biopsy-based transcripts serve as a supplement to a kidney biopsy, especially in cases where histopathologic diagnosis is inconclusive. ML models based on radiologic evaluation or gene signature in peripheral blood may be useful in cases where kidney biopsy is contraindicated in addition to other non-invasive biomarkers. The implementation of ML-based diagnostic methods is usually slow and undertaken with caution considering ethical and legal issues. In summary, the approach to the diagnosis of rejection should be individualized and based on all available diagnostic tools (including ML-based), leaving the responsibility for over- and under-treatment in the hands of the clinician.

Rapid and Specific Detection of Bacillus cereus Using Phage Protein-Based Lateral Flow Assays.

Rapid and precise diagnostic techniques are essential for identifying foodborne pathogens, including Bacillus cereus (B. cereus), which poses significant challenges to food safety. Traditional detection methods are limited by long incubation times and high costs. In this context, gold nanoparticle (AuNP)-based lateral flow assays (LFAs) are emerging as valuable tools for rapid screening. However, the use of antibodies in LFAs faces challenges, including complex production processes, ethical concerns, or variability. Here, we address these challenges by proposing an innovative approach using bacteriophage-derived proteins for pathogen detection on LFAs. We used the engineered endolysin cell-wall-binding domain (CBD) and distal tail proteins (Dit) from bacteriophages that specifically target B. cereus. The protein-binding properties, essential for the formation of efficient capture and detection biointerfaces in LFAs, were extensively characterized from the microstructural to the LFA device level. Machine-learning models leverage knowledge of the protein sequence to predict advantageous protein orientations on the nitrocellulose membrane and AuNPs. The study of the biointerface binding quantified the degree of attachment of AuNPs to bacteria, providing, for the first time, a microscopic model of the number of AuNPs binding to bacteria. It highlighted the binding of up to one hundred 40 nm AuNPs per bacterium in conditions mimicking LFAs. Eventually, phage proteins were demonstrated as efficient bioreceptors in a straightforward LFA prototype combining the two proteins, providing a rapid colorimetric response within 15 min upon the detection of 105 B. cereus cells. Recombinantly produced phage binding proteins present an opportunity to generate a customizable library of proteins with precise binding capabilities, offering a cost-effective and ethical alternative to antibodies. This study enhances our understanding of phage protein biointerfaces, laying the groundwork for their utilization as efficient bioreceptors in LFAs and rapid point-of-care diagnostic assays, thus potentially strengthening public health measures.

Assessing the Impact of Environmental Conditions on Reflectance Values in Inland Waters Using Multispectral UAS Imagery

This study investigates the impact of environmental conditions on reflectance values obtained from multispectral Unmanned Aerial System (UAS) imagery in inland waters, focusing on sun glint, cloud glint, wind-generated waves, and cloud shading projections. Conducted in two reservoirs with differing water qualities, UAS platforms equipped with MicaSense Altum and DJI Phantom 4 Multispectral sensors were used to collect multispectral images. The results show that sun glint significantly increases reflectance variability as solar elevation rises, particularly beyond 54°, compromising data quality. Optimal flight operations should occur within a solar elevation angle range of 25° to 47° to minimize these effects. Cloud shading introduces complex variability, reducing median reflectance. Wind-generated waves enhance sun glint, increasing variability across all spectral bands, while cloud glints amplify reflectance non-uniformly, leading to inconsistent data variability. These findings underscore the need for precise correction techniques and strategic UAS deployment to mitigate environmental interferences. This study offers valuable insights for improving UAS-based monitoring and guiding future research in diverse aquatic environments.

Complex stochastic optimal control foundation of quantum mechanics

Abstract Recent studies have extended the use of the stochastic Hamilton-Jacobi-Bellman (HJB) equation to include complex variables for deriving quantum mechanical equations. However, these studies often assume that it is valid to apply the HJB equation directly to complex numbers, an approach that overlooks the fundamental problem of comparing complex numbers when finding optimal controls. This paper explores the application of the HJB equation in the context of complex variables. It provides an in-depth investigation of the stochastic movement of quantum particles within the framework of stochastic optimal control theory. We obtain the complex diffusion coefficient in the stochastic equation of motion using the Cauchy-Riemann theorem, considering that the particle’s stochastic movement is described by two perfectly correlated real and imaginary stochastic processes. During the development of the covariant form of the HJB equation, we demonstrate that if the temporal stochastic increments of the two processes are perfectly correlated, then the spatial stochastic increments must be perfectly anti-correlated, and vice versa. The diffusion coefficient we derive has a form that enables the linearization of the HJB equation. The method for linearizing the HJB equation, along with the subsequent derivation of the Dirac equation, was developed in our previous work [V. Yordanov, Scientific Reports 14, 6507 (2024)]. These insights deepen our understanding of quantum dynamics and enhance the application of stochastic optimal control theory to quantum mechanics.

Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges. Method: This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets. Result: Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4% Conclusion: The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

Assessing the diversity and species composition in macroinvertebrate assemblages thriving in shallow water macroalgal habitats: Structural complexity is not always better

The assemblages of motile and sessile macroinvertebrates [mainly arthropods (peracarids), mollusks and annelids] were studied in three shallow macroalgal habitats from the NW Mediterranean Sea. Habitats were respectively dominated by three macroalgae: Ericaria mediterranea, a canopy-forming fucoid with repeatedly ramified cylindrical branches and spine-like projections; Cystoseira compressa, a canopy-forming fucoid with occasionally ramified compressed branches; and Ellisolandia elongata, a turf-forming, repeatedly branched, geniculate calcified alga. A total of 175,800 macroinvertebrate specimens, belonging to 228 taxa were collected. Macroinvertebrate assemblages in E. mediterranea habitat showed the highest abundances and number of taxa (104,412 specimens and 184 taxa), followed by those from C. compressa (49,128 specimens and 157 taxa) and E. elongata habitats (22,260 specimens and 144 taxa). Annelids were the most diverse group (43% of taxa) and amphipods the most abundant (78% of individuals). Macroinvertebrate abundances, alpha-diversity and species composition significantly differed among habitats and/or sites, while the number of taxa did not show significant differences neither among sites nor among habitats. Despite the simpler morphology of E. elongata respect to fucoid species, E. elongata habitat registered the lowest macrofauna abundance values but the highest alpha-diversity. Amphipods were the most abundant group in fucoid algae habitats (82–85%), while bivalves dominated in E. elongata one. Annelid polychaetes were the third more abundant group. Macroinvertebrate assemblages thriving in E. elongata habitat were always different to those found in fucoid algae habitats. Amphipods Protohyale schmidtii, Jassa herdmani, Jassa morinoi and Stenothoe gallensis were the most abundant species in fucoid algae habitats, while several species of Mytilidae, together with Caprella liparotensis, S. gallensis and nematodes dominated in E. elongata turfs. Differences observed are explained according to differences in algal morphological complexity and natural variability.

ADVANCED PREDICTIVE MODELING FOR THE BIOFIRE FILMARRAY SYSTEM IN INFECTIOUS DISEASE DIAGNOSTICS

The Bio Fire Film Array System has emerged as a powerful tool in clinical diagnostics, offering rapid and multiplexed detection of infectious pathogens. This study presents the development of a comprehensive mathematical model aimed at predicting the accuracy and sensitivity of the BioFire FilmArray System under diverse conditions. Key factors such as sample type, pathogen load, specimen characteristics, and environmental variables were systematically analyzed to elucidate their impact on the system's performance. The model incorporates a range of parameters including sample volume, specimen complexity, microbial diversity, and system-specific variables to generate probabilistic estimates of diagnostic outcomes. By leveraging large datasets encompassing diverse clinical scenarios and pathogen profiles, the model achieves robustness and generalizability, thereby facilitating its applicability across different healthcare settings and populations. Validation of the model against independent datasets demonstrates its efficacy in accurately predicting the performance of the BioFire FilmArray System across a spectrum of conditions. Overall, this mathematical model represents a significant advancement in optimizing the utility of the BioFire FilmArray System for infectious disease diagnosis. Its integration into clinical practice holds promise for improving patient care, guiding sample processing protocols, and informing decision-making processes in infectious disease management.

AnimeSketchNet: Integrating traditional sketching and anime art via an innovative dataset

Abstract. Recent advancements in text-to-image synthesis have shown promise, yet many models struggle with generalizability and adaptability, particularly in specialized genres like anime sketches. To overcome these challenges, we introduce AnimeSketchNet, a novel framework designed to enhance model performance by effectively capturing the complexity and stylistic variability unique to anime. AnimeSketchNet leverages a diverse collection of anime sketch styles with varying levels of detail, enabling the model to learn and adapt to the distinct artistic style of anime. Our dual-branch architecture, featuring a decoupled attention mechanism, seamlessly assimilates textual descriptions along with visual features, producing high-quality anime sketch that are both detailed and faithful the reference sketch. Experimental results demonstrate that AnimeSketchNet surpasses existing state-of-the-art models in both qualitative and quantitative evaluations. This work significantly advances text-to-image synthesis by offering a powerful and flexible tool tailored specifically to the unique demands of anime art.

Robust brain MRI image classification with SIBOW-SVM

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) is the most effective technique for brain tumor detection, generating comprehensive brain scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning methods, such as Convolutional Neural Networks (CNNs), have demonstrated significantly higher accuracy than manual screening. However, deep learning-based image classification methods, including CNNs, face challenges in estimating class probabilities without proper model calibration (Guo et al., 2017; Minderer et al., 2021). In this paper, we propose a novel brain tumor image classification method called SIBOW-SVM, which integrates the Bag-of-Features model with SIFT feature extraction and weighted Support Vector Machines. This new approach can effectively extract hidden image features, enabling differentiation of various tumor types, provide accurate label predictions, and estimate probabilities of images belonging to each class, offering high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive image datasets. To benchmark our method, we apply SIBOW-SVM to a public dataset of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results demonstrate that the new method outperforms state-of-the-art techniques, including CNNs, in terms of uncertainty quantification, classification accuracy, computational efficiency, and data robustness.

Harnessing Ascidians as Model Organisms for Environmental Risk Assessment

Environmental Risk Assessment (ERA) often relies on a restricted set of species as bio-indicators, introducing uncertainty when modeling complex environmental variables. This may lead to oversimplified or erroneous risk assessments. Ascidians, marine filter-feeding sessile chordates, are valuable models for scientific research in various biological fields such as stem cell biology, embryogenesis, regeneration, innate immunity, and developmental biology. Their global distribution, sensitivity to pollutants, high abundance, mass sexual reproduction, and habitation in coastal areas impacted by anthropogenic pollution make them excellent indicators for monitoring marine pollution and global environmental changes, including biological invasions and species diversity diminution cases. Despite their potential as environmental bioindicators, ascidians remain underutilized in ERAs (≤0.13% of ERA studies), particularly in the field of chemical pollution impact assessment, primarily due to a lack of standardization. This underrepresentation poses a challenge for accurate modeling, especially in models relying on a broad range of species (e.g., Species Sensitivity Distributions). Given these constraints, expanding the use of ascidians in ERAs could improve the comprehension and precision of environmental changes and their assessments. This underscores the necessity for future research to establish standardized testing protocols and choose the most suitable ascidian species for inclusion in ERAs.

A self-organizing map clustering adaptive artificial neural network model for analysis critical success factors in different phases of MTO projects case study: electrical equipment manufacturer and supplier in Iran

PurposeThe purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.Design/methodology/approachThis study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.FindingsCluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.Research limitations/implicationsThe main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.Practical implicationsIn this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.Originality/valueThis paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

GAMBUT field measurement of emissions from a tropical peatland fire experiment: from ignition to spread to suppression

Background Accurate quantification of emissions from peatland wildfire is crucial for understanding their feedback to the atmospheric and Earth system. However, current knowledge on this topic is limited to a few laboratory and field studies, which report substantial variability in terms of the fire emission factors (EFs). Aims We aim to understand how emissions vary across the life cycle of a peatland fire. Methods In August/September 2018, we conducted the largest and longest to-date field-scale experimental burn on a tropical peatland in Sumatra, Indonesia. Field measurements of gas emissions from the fire experiment were conducted using an open-path Fourier transform infrared spectroscopy to retrieve mole fractions of 11 gas species. Key results For the first time, we calculated and reported EFs from 40 measurement sessions conducted over 2 weeks of burning, encompassing different fire stages (e.g. ignition, smouldering spread, and suppression) and weather events (e.g. rainfall). Our findings provide field evidence to indicate that EFs vary significantly among fire stages and weather events. We also observed that the heterogeneous physicochemical properties of peatland site (e.g. moisture content) influenced the EFs. We also found that modified combustion efficiency was highly sensitive to complex field variables and could introduce large uncertainties when determining the regimes of a peat fire. Conclusions and implications Further studies to investigate peat fire emissions are needed, and more comprehensive mapping of peatland heterogeneity and land use for emissions inventories, accounting for spatial and temporal variability in EFs since the initiation of a fire event is required.

Effective identification of debris-covered glaciers in Western China using multiple machine-learning algorithms

Debris-covered glaciers (DCGs) represent a substantial portion of global glaciers, but their automatic identification remains a challenge due to environmental complexity and glacier type variability. This study focuses on 30 glacial areas across Western China, representing continental, sub-continental, and maritime glaciers. Using four machine-learning algorithms—Random Forests (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM)—we integrated multiple environmental variables, including surface reflectance, normalized indices, surface temperature (ST), and topography, to classify DCGs through a three-process approach. Process 1 distinguished overall ice/snow cover from other land features, while Processes 2 and 3, both building on results of Process 1, further separated ice from snow cover and differentiated debris from other land features, respectively. Results showed that RF generally outperformed the other algorithms, with median Matthews correlation coefficient (MCC) values exceeding 0.999 in Process 1 and reaching 0.612 and 0.784 in Processes 2 and 3, respectively, and the average MCC value for DCGs identification reached 0.819. Regional modeling significantly improved the accuracy of DCG identification in Processes 2 and 3, particularly in continental glacier regions, with a notable increase in MCC values. Additionally, the independent binary-class models outperformed the integrated multi-class model in Processes 2 and 3, further validating the necessity of process-specific modeling. Comparison with the GGI18 glacier inventory showed a high level of consistency with the DCGs identified by the optimal algorithms, with an R2 value of 0.971. Despite uncertainties in maritime glacier regions, the proposed method demonstrates robustness and high spatiotemporal generalization. This approach, combining machine learning and environmental variables, offers a reliable tool for automated DCG identification and can significantly aid future glacier inventory compilation and monitoring efforts.

Functional traits mediate the elevational patterns of functional diversity and community structure of mosses in a tropical mountain area

Functional diversity refers to the value, range, and distribution of functional traits within a community, such as the morphological, physiological and behavioral characteristics of species that determine the role a species plays in an ecosystem. Assessing the drivers of elevational pattern of functional diversity is crucial to predict the effects of global change on functional diversity and community structure. In our research, we collected mosses species data from 65 sampling sites and measured 11 functional traits that are related to their ecological functions. We applied double canonical correspondence analysis (DCCA) to analyze species responses to environmental conditions based on their traits. We applied generalized additive models with a Gaussian function of variance to determine the elevational patterns of functional trait composition, diversity and community structure. We applied and compared boosted regression trees (BRT) without considering functional traits and piecewise structural equation modelling (SEM) mediated with functional traits to relate environmental variables with functional diversity and functional community structure, respectively. We found the first DCCA axis accounted for the largest dissimilarities between the two groups of species and correlated mainly with the water-utilization traits. Although both functional diversity and community structure displayed negatively skewed pattern along the elevational gradient, habitat complexity and climatic variables contributed differently to their respective patterns. The SEM approach consistently outperformed the BRT approach in explaining the variations in both functional diversity and community structure. Our findings highlight the important role of functional traits in mediating the relationship between the environment and functional diversity metrics. Both biotic and abiotic processes play a significant role in shaping the community assemblages, with environmental variables having distinct impacts. Traits related to structure maintenance, water absorption, cell hydration, and nutrient acquisition were found to be key drivers of functional diversity, while water-utilization traits were mainly responsible for regulating functional community structure.

Swirling Flow Quantification in Helical Stents Using Ultrasound Velocimetry.

Helical stents have been developed to treat peripheral arterial disease (PAD) in the superficial femoral artery (SFA), with the premise that their particular geometry could promote swirling flow in the blood. The aim of this work is to provide evidence on the existence of this swirling flow by quantifying its signatures. This study consists of in vitro and in vivo parts. For the in vitro part, 3 helical stent models of different helicity degrees and 1 straight model were fabricated, and the flow was assessed at the inlet and outlet of each model. For the in vivo part, only 1 patient, treated with the helical stent, was eligible to participate in the study. The stent implanted in the SFA of the patient was evaluated in 2 leg postures (straight and flexed), and flow was assessed in 12 locations along the SFA. The in vivo study was approved by an ethical board (NL80130.091.21) in the Netherlands. High-frame-rate ultrasound was used to acquire data from the regions of interest (ROIs), using microbubbles as contrast agents. After processing the data via a correlation-based algorithm (echo particle image velocimetry or echoPIV), the velocity vector field within each ROI was extracted and analyzed for parameters such as vector complexity and velocity profile skewedness. The results show that in the outlet of the helical stents, when compared with the inlet, the flow vector field is more complex and the velocity profile is more skewed. For the in vivo case, the outcomes demonstrate more complexity and higher variability in the sign of skewedness inside the stent when compared with the flow in the proximal to the stent. Helical stents make the vector field of the flow more complex and the velocity profile more skewed, both of which are signatures of swirling flow. Further studies are needed to evaluate whether these features can benefit patients in terms of patency rates. This study demonstrates that helical stent models alter the blood flow when compared with straight stent models. Particularly, the flow grows more complex and its velocity profile becomes more skewed, both of which hint at the existence of swirling flow inside the helical stent. These observations, alongside with population-based studies that are currently being carriet out, may provide the evidence that helical stents have some advantages over straight stents for the patients.