Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectories of air parcel motions in Mars' atmosphere computed using HYSPLIT

TRAJECTORIES OF AIR PARCEL MOTIONS IN MARS’ ATMOSPHERE COMPUTED USING HYSPLIT by David Bruggeman An analysis of the advection of air parcels in the Martian atmosphere during the 2001 global dust storm through the use of three-dimensional trajectories is presented. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, wellknown for trajectory, dispersion, and deposition modeling, and originally developed for Earth was modified for Mars to provide forward and backward trajectories. The custom HYSPLIT for Mars uses meteorological input generated by the NASA Ames Mars General Circulation Model (MGCM). The 2001 global dust storm was the earliest on record (Ls ~ 180°) and originated from local dust storms around the Hellas basin as the storm expanded asymmetrically to the east. Trajectories near Hellas and Claritas Fossae correspond with dust transport detected using satellite imagery. Forward trajectories at Ls = 184° from Hellas show flow to the south, transporting dust around the south polar cap, while after Ls = 188° there is an eastward shift in propagation. Air parcel trajectories intersecting the surface during the dust storm may indicate the processes involved with global dust storms contributing to dust layers in the polar regions. Backward trajectories from Claritas Fossae reveal the dust activity in this region was the result of local dust storm activity instead of the propagation of dust eastward from the Hellas region.
SDTU-Net: Stepwise-Drop and Transformer-Based U-Net for Subject-Sensitive Hashing of HRRS Images

As a new integrity authentication technology, subject-sensitive hashing has the ability to achieve subject-sensitive authentication for high-resolution remote sensing (HRRS) images and can provide a security guarantee for their subsequent use. However, existing research on subject-sensitive hashing focuses on improving the structure of the deep neural network of the algorithm to improve the algorithm's performance, which makes it necessary to reconstruct the training dataset or modify the network structure in the face of different integrity authentication requirements. In this article, we delve into the impact of dropout on subject-sensitive hashing and propose a stepwise-drop mechanism to address the robustness and tampering-sensitivity requirements of subject-sensitive hashing. On this basis, a network named stepwise-drop and transformer-based U-net (SDTU-net) is proposed for subject-sensitive hashing of HRRS images. SDTU-net can use our proposed stepwise-drop mechanism to determine the drop rate of different network layers, which makes it possible to adjust the algorithm performance without changing network structure and training data. Experiments show that our SDTU-net based subject-sensitive hashing has better overall performance compared with existing algorithms, especially at medium and low thresholds. Our approach solves the problem that the existing algorithms cannot balance robustness and tamper sensitivity at low thresholds.
Evaluation of different evapotranspiration products in the middle Yellow River Basin, China

Actual evapotranspiration (ET a ) is a central process in the climate system and a nexus of the water and energy cycles. This study assesses the hydrological performance of the four categories of ET a products (i.e., land surface models (LSMs), reanalysis, model tree ensemble, and diagnostic models (DMs)) for use in the middle Yellow River Basin (MYRB) using water balance methods. The results show the following. (1) The water storage changes significantly at annual scale and cannot be neglected when calculating the reference ET a by the water balance methods. ( 2) ET a from LSMs, considering the precipitation input, exhibits the best performance in capturing the reference ET a variation. The MET ET a (AET JUNG ), based on eddy covariance, has fair performance with a small underestimation, followed by the DMs, including MODIS and ZhangKe. Poor performance is found in reanalysis ET a (JRA55), due to overestimations precipitation and radiation. ( 3) The reference ET a showed decreased and then increased trend. ET a from the LSMs-Noah model captures the trend well, followed by the LSMs-variable infiltration capacity model. Our results are not only meaningful for better understanding ET a variability in the MYRB, but also significant for improving global ET a products models9 performance in semi-arid and semi-humid regions.
A Dual-Branch Neural Network for Phase-Aware Speech Bandwidth Extension

This paper proposes a dual-branch method to speech bandwidth extension in spectral domain. The network architecture proposed in this study includes a magnitude branch and a complex branch. The former is intended to provide an approximate estimation of the magnitude of high-frequency speech signals, while the latter is designed in parallel and capable of estimating phase information as well as residual magnitudes, which can account for compensation of the magnitude branch. To capture long-term time-frequency dependencies more effectively, both branches of our network employ convolutional recurrent networks(CRN). The proposed method outperforms the baselines in terms of Log-Spectral Distance (LSD), Perceptual Evaluation of Speech Quality (PESQ), and Scale-Invariant Signal-to-Noise Ratio (SI-SNR).
Assessing Medicinal Plant Extracts in Strengthening Anti-Dandruff Shampoo: An Analysis Using Multi-Criteria Decision-Making Techniques

Currently, more than half of the global population grapples with dandruff. The presence of the Malassezia fungus significantly contributes to this condition by potentially triggering the production of cytokines in keratinocytes, the cells responsible for synthesizing keratin and activating inflammatory pathways. Given the distress caused by dandruff, considerable efforts are underway to find effective treatments. Numerous studies centered on medicinal plants suggest promising efficacy in addressing this issue. This particular research focuses on conducting a clinical trial to explore the effectiveness of zinc L-pyrrolidone carboxylic acid and pirocton olamine, combined with extracts from six medicinal plants, for dandruff therapy. In this study, a combination of methanolic extracts from alternatives, along with pirocton olamine and zinc-PCA in the form of shampoo, was applied to a group of 30 individuals experiencing dandruff. This treatment regimen was carried out over a span of two months to assess its effectiveness in managing dandruff. Notably, 15 patients with persistent dandruff experienced significant relief within the initial two weeks. Another 12 patients noticed dandruff reduction after 28 days, the majority experienced relief within the first few weeks, and by the end of the fifth week, those remaining also expressed satisfaction with the treatment. These results strongly highlight the impressive effectiveness of medicinal plant extracts in alleviating dandruff, accompanied by minimal side effects, albeit requiring extended treatment duration. The research indicates that blending medicinal plant extracts with both natural and chemical compounds in pharmaceutical formulations shows potential for enhancing the effectiveness of dandruff treatments.
Wave based modeling of airborne flanking transmission across suspended ceilings

In this paper, a wave based model is developed to assess the flanking sound transmission between two adjacent rooms through the plenum of suspended ceilings. The model accounts for the modal behaviour of the rooms, the plenum and the suspended ceilings. The plenum can either be empty or partly filled with an absorbent layer. Results are compared with a classical three-room model that assumes diffuse sound fields in both rooms and the plenum. At very low frequencies, the sound transmission is determined by the modal behaviour of the global system. At middle frequencies, the sound field in the plenum is two-dimensional. As a result, the three-room model overestimates the airborne flanking transmission in this frequency range. The transmission loss decreases at a cut-on frequency that is determined by the height of the plenum and above which the sound field in the plenum is three-dimensional.
Plant-Polyphenol-Mediated Synthesis of Magnetic Biocompatible Iron Oxide Nanoparticles for Diagnostic Imaging and Management of Neurodegenerative Diseases

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), and glioblastoma tumors (GB), represent a spectrum of disorders characterized by progressive neuronal degeneration. This gradual deterioration presents significant challenges in early diagnosis and treatment. As a solution, iron oxide nanoparticles (IONPs) have emerged as promising materials due to their diminutive size and inherent biocompatibility, which facilitates bypassing the blood-brain barrier (BBB), thereby serving as effective targeted nanocarriers for neuroprotective therapeutics, offering efficient delivery to the brain. Furthermore, IONPs demonstrate efficacy as T1/T2 contrast agents (CAs), offering a novel diagnostic tool for various NDDs, emphasizing GB. Despite the expanding potential of IONPs in the management of NDDs, a clear gap exists in developing environmentally sustainable synthesis methods. Such methods must adhere to ecological standards and enable the production of nanoparticles capable of efficiently crossing the BBB. This is essential for precisely delivering and monitoring therapeutic interventions in diseases such as AD, PD, and GB. This review systematically explores the potential of green-synthesized IONPs in the context of NDD treatment. In this review, we emphasize different functionalization techniques for IONPs, tackling how these functionalized IONPs can be effective therapeutic approaches for NDDs. Moreover, we discuss biosafety and biocompatibility by covering a range of in vitro biocompatibility studies to analyze the safety of green-synthesized IONPs for the human body. Furthermore, our thorough analysis discussed the current state of green-synthesized IONPs using plant extracts polyphenols, which also act as neuroprotective agents, as proved by studies mentioned in the review.
Fecal and oral microbiome analysis of snakes from China reveals a novel natural emerging disease reservoir

Introduction The gastrointestinal tract and oral cavity of animal species harbor complex microbial communities, the composition of which is indicative of the behavior, co-evolution, diet, and immune system of the host. Methods This study investigated the microbial composition in snakes from varying altitudinal ranges by assessing the fecal and oral bacterial communities in Protobothrops mucrosquamatus, Elaphe dione, and Gloydius angusticeps from Sichuan Province, China, using metagenomic sequencing. Results and discussion It was revealed that Bacteroidetes, Proteobacteria, Firmicutes, and Fusobacteria were the core microbial phyla in fecal samples across all three species, while Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes were the core microbial phyla in oral samples across all three species. Notably, the dominance of Armatimonadetes was documented for the first time in the feces of all three species. Comparative analysis of the microbiomes of the three species indicated distinct microbiological profiles between snakes living at low- and high-altitude regions. Furthermore, 12 to 17 and 22 to 31 bacterial pathogens were detected in the oral and fecal samples, respectively, suggesting that snakes may serve as a novel reservoir for emerging diseases. Overall, this study provides a comparative analysis of the fecal and oral microbiomes in three snake species. Future investigations are anticipated to further elucidate the influence of age, genetics, behavior, diet, environment, ecology, and evolution on the gut and oral microbial communities of snakes.
Towards Energy-Efficient Intelligent Edge Computing

This research focuses on energy-efficient edge computing in the context of intelligent edge computing. With the increasing demand for computation and data processing at edge network nodes, the study explores the use of artificial intelligence (AI) techniques, specifically reinforcement learning, to enhance energy management. By conducting a comparative analysis of algorithms, including the Markov Decision Process, Q-learning, Fair heuristic, and Random heuristic, on the basis of waiting time of servers, response time, and energy consumption, the research aims to identify the most effective approach for optimizing the intelligent edge system. Simulations using the iFogSim simulator kit provide empirical evidence for performance evaluation. The findings highlight the advantages of AI-based schemes and emphasize the need for further advancements in intelligent edge computing applications.
A Constrained Two-Link Brachiating Robot for Power Line Inspection

This paper introduces a cascaded control approach for a fully actuated two-link brachiating powerline inspection robot. The robot operates under the constraints of internal and external factors such as friction and wind drag. The control system combines Proportional-Integral-Derivative (PID) and Computed Torque Control (CTC) techniques to achieve precise control. The robot's mechanical design is created using SolidWorks, transformed into a Matlab Simulink environment. An optimal trajectory is generated through the use of MATLAB's optimization toolbox. The control architecture, leveraging the robot's actuation capabilities, effectively tracks trajectories while maintaining stability. It also compensates for internal constraints and mitigates external disturbances imposed on the Simulink model. The effectiveness of this control approach is demonstrated through simulations within the Simulink environment and visualization facilitated by SimScape multibody simulation. This study represents a significant advancement in the integration of mechanical CAD models and symbolic tools with SimScape, potentially contributing to the process of control system development by reducing the complexity of equation derivation and approximation techniques.
Biases and Fairness in Deep Learning Models: A Survey on Inculcating Fairness in Generative Models

AI-generated images are now widely used in various industries, including the art industry, advertising, healthcare, etc. The generative models used in making this happen are only getting incrementally powerful. However, as with any technology, these models are not immune to the inherent biases which are inadvertently introduced due to the discrimination reflected in the data. These biases in data can lead to undesirable consequences, such as reinforcing stereotypes, discrimination or perpetuating inequalities. Hence ‘Fairness’ in generative models is an increasingly crucial concept. This review paper discusses the biases and their types that percolate the deep learning models and are reflected in their results. The paper then presents a comprehensive overview of FairGAN and FairGAN+, two recently proposed generative models that aim to generate fair, accurate and diverse data, including their strengths and limitations. FairGAN incorporates fairness constraints into the objective function to generate fair data. FairGAN+ improves upon FairGAN by using multiple discriminators to achieve both fairness and accuracy in the generated data while also promoting diversity. Overall, FairGAN and FairGAN+ are promising approaches to addressing fairness in generative models. Further developments in this area are expected to lead to more effective techniques for generating fair and accurate data.
Diabetic Retinopathy Detection Using Convolutional Neural Network

In persons with diabetes, diabetic retinopathy (DR), a serious eye condition, can cause blindness. Improved patient outcomes and the prevention of vision loss are possible with early DR identification. Convolutional neural networks (CNN), for example, have demonstrated considerable potential in automating the identification of DR. This paper's goal is to investigate CNN's effectiveness in DR detection. The suggested methodology entails using a publicly accessible collection of retinal pictures to train a CNN model. In order to categorize images as having no DR, mild DR, moderate DR, or severe DR, the model is trained. A number of metrics, including sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve, are used to assess the model's performance. The study's findings demonstrated that the CNN model had a DR detection accuracy of 84.26%. The model had a sensitivity and a specificity of 81.34% and 78.67%, respectively. The ROC curve's area under it showed good performance in DR detection at 0.82. The performance of the CNN model was also evaluated in comparison to that of other established machine learning techniques like Random Forest and Support Vector Machines. The CNN model performed more accurately and sensitively than these conventional techniques. The findings of this study show that CNN has a lot of potential for automated DR detection. With the purpose of increasing the precision and effectiveness of DR diagnosis, the proposed methodology can be included into current clinical workflows. The study also emphasizes the significance of enhancing medical imaging's capacity for disease identification and diagnosis through the use of deep learning techniques.
Comparison of Machine Learning Classifiers on Integrated Transcriptomic Data

Omics data are being generated for different conditions, and can be a valuable resource for building novel predictive models for medical diagnosis. Given the reduced number of samples in each dataset, the application of Machine Learning (ML) models requires data integration. At the same time, multiple ML models are available, and the best option for data integration is not known. These challenges have been addressed typically in restricted settings, i.e., for one single disease at a time. However, a thorough comparison of models on integrated data, for different conditions, is still missing. In this paper we confront 7 classifiers on integrated data for 6 diseases, over 14 datasets. We compared the models on single and integrated datasets, employing different pre-processing techniques. We also evaluated the effect of feature selection, analyzing the robustness and relevance of the features extracted. We observed that, even if integration slightly reduces predictive power, the models are still able to produce good classifications. When testing generalization abilities on new datasets, sometimes the performance decreases drastically, depending on the disease studied.
Aspiration thrombectomy versus stent retriever thrombectomy as a first-line approach for cardiogenic cerebral embolism and cryptogenic stroke in large vessels of the anterior circulation

Subject This study aims to compare the clinical efficacy of aspiration thrombectomy and stent retriever thrombectomy as first-line approaches for anterior circulation large vessel cardiogenic cerebral embolism and cryptogenic stroke. Method This retrospective observational study included patients with anterior circulation large vessel cardiogenic cerebral embolism and cryptogenic stroke treated with endovascular therapy. Patients were grouped according to the first-line approach they received: aspiration thrombectomy or stent retriever thrombectomy. The primary outcome measure was the change in the National Institute of Health Stroke Scale (NIHSS) score from preoperative to immediate postoperative and from preoperative to discharge. Secondary indicators included the rate of favorable prognosis at discharge [Modified Rankin Scale (mRS) score ≤ 2], successful vessel recanalization rate [modified Treatment in Cerebral Ischemia (mTICI) score ≥ 2b], time from successful femoral artery puncture to successful vessel recanalization, and perioperative complications. Result The study included 127 cases, with 1 case withdrawal after enrollment due to a stroke of another determined cause, with 83 in the aspiration thrombectomy group and 43 cases in the stent retriever thrombectomy group. The change in NIHSS score from preoperative to immediate postoperative was 5 (1, 8) in the aspiration thrombectomy group and 1 (0, 4.5) in the stent retriever thrombectomy group. The change from preoperative to discharge was 8 (5, 12) in the aspiration thrombectomy group and 4 (0, 9) in the stent retriever thrombectomy group. The aspiration thrombectomy group exhibited significantly better prognosis rates and shorter time from successful femoral artery puncture to successful vessel recanalization. There were no significant differences between the two groups in terms of successful vessel recanalization rates and perioperative complications. Conclusion As a first-line approach for anterior circulation large vessel cardiogenic cerebral embolism and cryptogenic stroke, aspiration thrombectomy leads to better improvement in neurological functional deficits and prognosis rates compared to stent retriever thrombectomy.
The Performance of Cellulose Composite Membranes and Their Application in Drinking Water Treatment

Water scarcity and water pollution have become increasingly severe, and therefore, the purification of water resources has recently garnered increasing attention. Given its position as a major water resource, the efficient purification of drinking water is of crucial importance. In this study, we adopted a phase transition method to prepare ZrO2/BCM (bamboo cellulose membranes), after which we developed IP-ZrO2/BC-NFM (bamboo cellulose nanofiltration membranes) through interfacial polymerization using piperazine (PIP) and tricarbonyl chloride (TMC). Subsequently, we integrated these two membranes to create a combined “ultrafiltration + nanofiltration” membrane process for the treatment of drinking water. The membrane combination process was conducted at 25 °C, with ultrafiltration at 0.1 MPa and nanofiltration at 0.5 MPa. This membrane combination, featuring “ultrafiltration + nanofiltration,” had a significant impact on reducing turbidity, consistently maintaining the post-filtration turbidity of drinking water at or below 0.1 NTU. Furthermore, the removal rates for CODMN and ammonia nitrogen reached 75% and 88.6%, respectively, aligning with the standards for high-quality drinking water. In a continuous 3 h experiment, the nanofiltration unit exhibited consistent retention rates for Na2SO4 and bovine serum protein (BSA), with variations of less than 5%, indicating exceptional separation performance. After 9 h of operation, the water flux of the nanofiltration unit began to stabilize, with a decrease rate of approximately 25%, demonstrating that the “ultrafiltration + nanofiltration” membrane combination can maintain consistent performance during extended use. In conclusion, the “ultrafiltration + nanofiltration” membrane combination exhibited remarkable performance in the treatment of drinking water, offering a viable solution to address issues related to water scarcity and water pollution.
Healthcare Safety Management: The Model of Fmea/Fmeca in Anatomic Pathology Service

Andrea Ianni1*, Roberto Virgili2, Michela Piredda3, Maria Grazia De Marinis3 and Tommasangelo Petitti1 1Research Unit in Hygiene, Statistics and Public Health (Dir. Prof. T. Petitti), Campus Bio-Medico University, Rome, Italy 2Anatomic and Surgical Pathology Service and Unit, Campus Bio-Medico University Hospital, Rome, Italy 3Nursing Research Unit (Dir. Prof. M.G. De Marinis), Campus Bio-Medico University Medical School, Rome, Italy