Miracle-dz/UniFAR-FTUs huggingface dataset:
Obesity-Related Kidney Disease: Current Understanding and Future Perspectives

Obesity is a serious chronic disease and an independent risk factor for the new onset and progression of chronic kidney disease (CKD). CKD prevalence is expected to increase, at least partly due to the continuous rise in the prevalence of obesity. The concept of obesity-related kidney disease (OKD) has been introduced to describe the still incompletely understood interplay between obesity, CKD, and other cardiometabolic conditions, including risk factors for OKD and cardiovascular disease, such as diabetes and hypertension. Current therapeutics target obesity and CKD individually. Non-pharmacological interventions play a major part, but the efficacy and clinical applicability of lifestyle changes and metabolic surgery remain debatable, because the strategies do not benefit everyone, and it remains questionable whether lifestyle changes can be sustained in the long term. Pharmacological interventions, such as sodium-glucose co-transporter 2 inhibitors and the non-steroidal mineralocorticoid receptor antagonist finerenone, provide kidney protection but have limited or no impact on body weight. Medicines based on glucagon-like peptide-1 (GLP-1) induce clinically relevant weight loss and may also offer kidney benefits. An urgent medical need remains for investigations to better understand the intertwined pathophysiologies in OKD, paving the way for the best possible therapeutic strategies in this increasingly prevalent disease complex.
Strategy for Constructing Phosphorus-Based Flame-Retarded Polyurethane Elastomers for Advanced Performance in Long-Term

Polyurethane elastomer (PUE), which is widely used in coatings for construction, transportation, electronics, aerospace, and other fields, has excellent physical properties. However, polyurethane elastomers are flammable, which limits their daily use, so the flame retardancy of polyurethane elastomers is very important. Reactive flame retardants have the advantages of little influence on the physical properties of polymers and low tendency to migrate out. Due to the remarkable needs of non-halogenated flame retardants, phosphorus flame retardant has gradually stood out as the main alternative. In this review, we focus on the fire safety of PUE and provide a detailed overview of the current molecular design and mechanisms of reactive phosphorus-containing, as well as P-N synergistic, flame retardants in PUE. From the structural characteristics, several basic aspects of PUE are overviewed, including thermal performance, combustion performance, and mechanical properties. In addition, the perspectives on the future advancement of phosphorus-containing flame-retarded polyurethane elastomers (PUE) are also discussed. Based on the past research, this study provides prospects for the application of flame-retarded PUE in the fields of self-healing materials, bio-based materials, wearable electronic devices, and solid-state electrolytes.
A Novel Stochastic Unit Commitment Characterized by Closed-Loop Forecast-and-Decision for Wind Integrated Power Systems

In general, the day-ahead wind power prediction and the probability distribution of relevant forecast error critically determines the solution of stochastic unit commitment (SUC) applied for wind-integrated power systems. However, in a conventional open-loop forecast-and-decision (OFD) framework, the accuracy-oriented forecaster independently computes the predictive information and unidirectionally pass them to the SUC model. In order to intrinsically upgrade the solution quality of SUC, this article proposes a closed-loop forecast-and-decision framework-based SUC (CFD-SUC) model where the predicted wind power scenarios are elaborately expressed as functions of adjustable parameters of wind power forecast (WPF) models. Thus, compared to the conventional OFD framework-based SUC (OFD-SUC) model that only optimizes dispatching variables, the additional searching space spanned by the WPF model parameters enables the CFD-SUC model existing superior solutions that can further remarkably reduce the risk cost-based objective function. A customized small-step iteration-based solving method (SSISM) is then proposed to find optimal solution of the CFD-SUC model. Statistical tests implemented on two systems verify the efficiency and effectiveness of SSISM. Moreover, the scheduling results derived from the CFD-SUC model are statistically predominant for risk operation of the system, compared with those from the OFD-SUC model.
A Comparative Study on Estimation Methodologies of Carbon Sequestration Amount by Vegetation for Environmental Impact Assessment on Development Projects

In this study, we deduced the best estimation methodology for amount of carbon sequestration by vegetation, through the case study using the data obtainable from the environmental assessment procedure. Our results showed that the estimation methodology using the national vegetation map was the best for the strategic environmental assessment, whileas those using the vegetation growth equation were applicable for environmental impact assessment procedure. Furthermore, we found that the amount of carbon sequestration by farmland and/or grassland, not by vegetation, was not negligible. Therefore, we concluded that the area of farmland and/or grassland need to be taken into account during the landuse planning.
Luminance-Preserving Visible and Near-Infrared Image Fusion Network with Edge Guidance

Near-infrared (NIR) images and visible (VIS) images can provide mutually complementary information for each other, thus the fusion of the two modalities can create images of high quality even in adverse conditions. However, the luminance of NIR and VIS images may be inconsistent in some regions, resulting in color distortion and unrealistic appearance in the fused images. The existing methods perform poorly at luminance retention. Aiming at the problem and based on deep learning framework, we propose an edge-guided method which can be applied to the image fusion network. Edge maps are utilized as prior knowledge of images to boost the performance of the neural network. Additionally, we propose a luminance-preserving loss function combined with max-edge loss to further improve the image quality. Experimental results show the superiority of our method.
Liquid-Crystal-Driven Reconfigurable Intelligent Surface With Cognitive Sensors for Self-Sustainable Operation

For the first time, this article describes the concept of a sustainable reconfigurable intelligent surface (RIS) for random wireless propagation channel management at millimeter-wave (mmWave) bands. Reconfigurable beam scanning capabilities are obtained by modifying the permittivity of liquid-crystal (LC) molecules. In order to configure a self-sustainable feedback loop for the proposed RIS operation, a lens-based beamforming circuit is embedded in the LC-driven reconfigurable beamforming surface. This sensing circuit enables the acquisition of channel estimation information without additional hardware implementation and signal processing costs. The proposed RIS is verified by link-level field trials for near-field indoor and far-field outdoor environments, satisfying the 5G new radio specifications for proof-of-concept purposes. This proposed RIS is expected to be applied in energy-efficient wireless channel management for mmWave radios.
Influence of Thermal Treatment and Acetic Acid Concentration on the Electroactive Properties of Chitosan/PVA-Based Micro- and Nanofibers

This study presents, for the first time, a comprehensive investigation of the influence of pre- and post-fabrication parameters for the electroactive properties of electrospun chitosan/PVA-based micro- and nanofibers. Chitosan/PVA fibers were fabricated using electrospinning, characterized, and tested as electroactive materials. Solutions with different acetic acid contents (50, 60, 70, and 80 v/v%) were used, and the rheological properties of the solutions were analyzed. Characterization techniques, such as rheology, conductivity, optical microscopy, a thermogravimetric analysis, differential scanning calorimetry, a tensile test, and FT-IR spectroscopy, were utilized. Fiber mats from the various solutions were thermally treated, and their electroactive behavior was examined under a constant electric potential (10 V) at different pHs (2–13). The results showed that fibers electrospun from 80% acetic acid had a lower electroactive response and dissolved quickly. However, thermal treatment improved the stability and electroactive response of all fiber samples, particularly the ones spun with 80% acetic acid, which exhibited a significant increase in speed displacement from 0 cm−1 (non-thermally treated) to 1.372 cm−1 (thermally treated) at a pH of 3. This study sheds light on the influence of pre- and post-fabrication parameters on the electroactive properties of chitosan/PVA fibers, offering valuable insights for the development of electroactive materials in various applications.
SPAWNING BIOMASS OF PACIFIC SARDINE (SARDINOPS SAGAX), FROM 1994-2004 OFF CALIFORNIA

The daily egg production method (DEPM) has been used to estimate the spawning biomass of Pacific sardine (Sardinops sagax) since 1986. In this paper, we document the current DEPM procedure using as an example the 2004 survey which incorporates the new procedures adopted since 1997. An adaptive allocation survey design used for sardine eggs has been successfully implemented. Yet, other issues associated with estimating spawning biomass of Pacific sardines remain. We also examine the time series of DEPM spawning biomass estimates and associated parameters from 1994 to 2004 and compare them to the spawning stock biomass (SSB) estimates derived from stock assessment models. The spawning biomass estimates off California increased from less than 10,000 mt in 1986 to 118,000 mt in 1994, to nearly 300,000 mt in 2004 and have fluctuated during the recent years. The spatial distribution of Pacific sardine eggs varied with sea surface temperature. The average fish weight doubled in the last 10 years, as has the reproductive rate.
Ex Vivo Fecal Fermentation of Human Ileal Fluid Collected After Wild Strawberry Consumption Modulates Human Microbiome Community Structure and Metabolic Output and Protects Against DNA Damage in Colonic Epithelial Cells

revealed significant treatment-specific changes in microbiome community structure in terms of alpha but not beta diversity at 24 h. Nutri-kinetic analysis revealed a significant increase in the concentration of gut microbiota catabolites, including 3-(4hydroxyphenyl)propionic acid, 3-(3-hydroxyphenyl)propanoic acid and benzoic acid. Furthermore, post-berry ileal fermentates (24 h) significantly (p<0.01) decreased DNA damage (% Tail DNA, COMET assay) in both HT29 cells (~45%) and CCD 841 CoN cells (~25%) compared to untreated controls. Conclusions: Post berry consumption fermentates exhibited increased overall levels of (poly)phenolic metabolites which retained their bioactivity, reducing DNA damage in colonocytes.
Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

Currently, rolling bearings operate in harsh environments, resulting in acquired signals with a low signal-to-noise ratio. In light of this, this paper proposes an improved variational modal decomposition(VMD) combined with refine composite multi-scale fuzzy entropy (RCMFE) and linear support vector machine (LSVM) for fault diagnosis. Firstly, the sailfish optimization (SFO) algorithm is employed to optimize the important parameter combinations in the VMD algorithm, using the envelope entropy as its objective function. The analysis includes both simulated and real measured signals with varying signal-to-noise ratios. The results demonstrate that, compared to traditional manual parameter setting and empirical modal decomposition methods, this approach effectively addresses the parameter setting issue of VMD in the signal decomposition process. Additionally, it successfully eliminates noise to extract the fault characteristic signal hidden within the original signal. Secondly, the RCMFE algorithm is introduced to overcome the problem of commonly used dimensioned and dimensionless indicators being influenced by load and speed when used as characteristic indicators. By analyzing the influence of load and speed on the RCMFE value, the results demonstrate its strong stability as a feature indicator, unaffected by these factors. For the intelligent classification of failure type and damage degree, LSVM is chosen as the classification method. Analysis results indicate that the distribution characteristics of RCMFE values align better with LSVM compared to the common radial basis function support vector machine, resulting in a significant improvement in diagnosis accuracy.
Electrically Erasable Wide-Bandgap Charge-Trap Memory With an Electric-Flux-Modulating Counter Electrode

An effective electrical erasing operation in the charge-trap memory (CTM) based on wide bandgap (WBG) semiconductors is demonstrated by realizing an electric flux-modulating counter electrode (<inline-formula> <tex-math notation="LaTeX">${E} _{\text {F}}\textit {MC}_{\text {E}}$ </tex-math></inline-formula>). A fundamental limitation of gate-bias erasing operation in WBG CTMs is overcome by depositing a top Al metal electrode on the bottom-gate top-contact thin-film transistor memories. The function of <inline-formula> <tex-math notation="LaTeX">${E} _{\text {F}}\textit {MC}_{\text {E}}$ </tex-math></inline-formula> is both experimentally and theoretically investigated through device fabrications and finite-element numerical simulation. Both pentacene- and In-Ga-Zn oxide based CTMs incorporating the <inline-formula> <tex-math notation="LaTeX">${E} _{\text {F}}\textit {MC}_{\text {E}}$ </tex-math></inline-formula> show repeatable and stable electrical programming and erasing. The proposed concept of <inline-formula> <tex-math notation="LaTeX">${E} _{\text {F}}\textit {MC}_{\text {E}}$ </tex-math></inline-formula> therefore improves the applicability of WBG semiconductors in future memory technologies.
Driving Maneuver Detection at Intersections for Connected Vehicles: A Micro-Cluster-Based Online Adaptable Approach

Real-time detection of oncoming vehicle maneuvers at intersections is essential for connected autonomous vehicles (CAVs) to plan safe paths and driving strategies. Most existing methods use supervised learning methods to construct behavior detection models and assume that most data have labels. Real data collected by the onboard sensor as a data stream is unstable, and there are outliers, concept drift, and evolution problems, potentially decreasing the detection accuracy. To this end, we propose a micro-cluster-based online adaptable (MCOA) approach. The framework consists of four parts: initial model construction, new class detection, classification using k-nearest neighbor (k-NN), and online update. First, k-means clustering is performed on the maneuvering behavior data, and cluster features are derived to obtain a set of micro-clusters (MCs) to establish the initial model. Second, we analyze the instances stored in the data block to detect new classes and use the k-NN to classify the incoming instances. Finally, the model is updated online using an update strategy based on error-driven representative learning, a time-effect function, and a local decision boundary. A driving simulator is used to collect experimental data consisting of left turns (LT), right turns (RT), and going straight (GS) to establish and evaluate the model. The results show that the proposed model achieves higher detection accuracy for early-stage intersection maneuvers and has stronger adaptability to new classes than benchmark algorithms.
Deep Learning Meets Particle Swarm Optimization For Aortic Valve Calcium Scoring From Cardiac Computed Tomography

Aortic stenosis is the most common primary valvular pathology requiring surgical or transcatheter intervention in developed countries. Quantification of aortic valve calcification with cardiac computed tomography (CCT) is used for assessment of aortic stenosis severity, disease progression and prediction of major cardiovascular events. The calcium deposits, however, commonly appear in different regions of the aorta and heart, leading to false-positive regions, and to an incorrectly calculated aortic valve calcium score. We tackle the issue of pruning such false-positive regions from CCT scans, and introduce a particle swarm optimization (PSO) algorithm for this task. In our approach, PSO optimizes the radius while benefiting from the evolved position of a sphere which would embrace those calcifications that are positioned near the aortic valve. Our experimental study, performed over 30 non-contrast CCT scans, showed that our results are in strong agreement with the experienced human reader, and indicate the potential of PSO in data-driven pruning of false-positive calcifications which are positioned in other parts of the aorta and heart. Additionally, PSO outperformed a geometrical-based approach for this task.
Exploring the Spectral Line Broadening of the Bulk Motions in the High Mass Star Forming Region with Radiative Transfer Simulations

The Davis–Chandrasekhar–Fermi (DCF) method is widely used to indirectly estimate the strength of magnetic fields in star-forming regions. However, recent developments in this method have primarily focused on improving the measurement of angular dispersion of the field, neglecting other physical quantities, especially turbulence velocity. Most DCF studies tend to overlook or fail to acknowledge the influence of bulk motions on the linewidth, and directly obtain the turbulence velocity based on the non-thermal linewidth. Therefore, to explore the contributions of bulk motions to the linewidth, we conducted radiative transfer simulations using a rotating and infalling envelope–disk model to a high-mass star formation region, IRAS18360-0537. The main conclusion from our work is that the bulk motions contribute significantly to the linewidth and cannot be fully eliminated by simply deducing velocity gradients. Hence, fully attributing the observed non-thermal velocity dispersion derived from fitting a spectral line profile to the turbulence can result in significantly overestimated magnetic field strength and may yield unscientific results of star-forming regions.
Subcutaneous, Oral, and Intranasal Immunization of BALB/c Mice with Leishmania infantum K39 Antigen Induces Non-Protective Humoral Immune Response

Visceral leishmaniasis is a high-burden disease caused by parasites of the Leishmania genus. The K39 kinesin is a highly antigenic protein of Leishmania infantum, but little is known about the immune response elicited by this antigen. We evaluated the humoral immune response of female BALB/c mice (n = 6) immunized with the rK39-HFBI construct, formed by the fusion of the K39 antigen to a hydrophobin partner. The rK39-HFBI construct was administered through subcutaneous, oral, and intranasal routes using saponin as an adjuvant. We analyzed the kinetics of IgG, IgG1, and IgG2a production. The groups were then challenged by an intravenous infection with L. infantum promastigote cells. The rK39-HFBI antigen-induced high levels of total IgG (p < 0.05) in all groups, but only the subcutaneous route was associated with increased production of IgG1 and IgG2a 42 days after immunization (p < 0.05), suggesting a potential secondary immune response following the booster dose. There was no reduction in the splenic parasite load; thus, the rK39-HFBI failed to protect the mice against infection under the tested conditions. The results presented here demonstrate that the high antigenicity of the K39 antigen does not contribute to a protective immune response against visceral leishmaniasis.
MODELING TRIVARIATE CORRELATED BINARY DATA

This paper provides the estimation and the test procedures for the measures of association in the correlated binary data associated with covariates. The generalized linear model (GLM) using the serial dependence and the developed alternative quadratic exponential form (AQEF) procedures are employed for the trivariate binary correlated outcome variables. The log-odds ratios, as measures of association, are estimated and the appropriate tests are suggested. For comparison between the two procedures, we used the simulation study and an application to an ecology problem which involves the estimation of the measures of association and their tests. The over-dispersion criteria is investigated for these procedures. Finally, the deviance and scaled deviance are used as tests for the goodness of fit of the model to determine the best procedure.
Formation mechanism and source apportionment of nitrate in atmospheric aerosols

Atmospheric particulate nitrate is relevant to various atmospheric processes and shows profound impacts on regional air quality and global climate change. Exploring the formation mechanism and sources of nitrate in atmospheric particulate matter is essential for the successful mitigation of nitrate. This review summarises the different formation mechanisms, sources, and source apportionment methods of nitrate. In particular, the current progress of the nitrogen/oxygen (N/O) isotope technique coupled with the Bayesian isotopic mixing model (MixSIAR) is fully depicted. The limitations of the current source apportionment methods are also presented and the promising direction for the source apportionment of nitrate is proposed. As such, this review provides a thorough understanding of nitrate formation mechanisms and sources, which is particularly helpful for mitigating nitrate pollution in polluted cities such as those in East Asia.
Spatial Modelling in Forest Ecology and Management- A Case Study

In this book, GIS-based geomorphological, statistical, and geostatistical methods are used to improve the ecological site description of forest areas. Possibilities and limitations of the regionalization of climatic, soil, and macrofauna point measurements are presented. The resulting maps of indicators provide a broad and precise basis for forest planning and are essential for an integration of ecological and economic aspects of forest management.The applicability for forest practice is exemplified by the delimitation of forest site classes and of forest growth districts as well as the modelling of potential natural woodland communities. A scenario technique was used to simulate the forest stand development in the Harz mountains and to reveal the economic and ecological results of new management guidelines that are favoured by the forest administration. Spatial modelling and GIS-based information and planning systems provide improved data bases that are valuable instruments to describe the consequences of different management guidelines. Thus, they make a major contribution to a fruitful discussion about sustainable forest management.