Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring

Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected. Machine learning techniques, specifically random forest, were used to analyze patterns that identify physical-aggressive incident with 1-min time resolution. A total of 119 aggression episodes, lasting 7.3 ± 13.1 min for a total of 872 1-min epochs including 132 physical aggression epochs, were collected. The model achieved high precision (80.2%), accuracy (82.0%), recall (85.0%), F1 score (82.4%), and area under the curve (89.3%) to distinguish physical aggression epochs. The sensor-derived feature of vector magnitude (faster triaxial acceleration) was the second contributing feature in the model, and significantly distinguished aggression and non-aggression epochs. If validated in larger samples, this model could provide a practical and efficient solution for remotely detecting and managing aggressive incidents in children.
Supercritical CO2 Extraction of Oil from Dried Avocado (Persea Americana Mill.) Fruit Pulp: Oil Yield, Solubility, and the Oil Characteristics

The purpose of this study was to investigate the applicability of supercritical fluid technology for extraction of avocado oil. The dried avocado fruit pulp (Persea americana Mill.) was extracted by using a semi-pilot supercritical extraction system. Effect of several process parameters, such as CO2 flow rate, particle size, pressure and temperature on total oil yield, free fatty acid, peroxide value, total phenolic and total flavonoid content in oil were assessed. Avocado oil was extracted within the range for flow rate of 10 and 15L/h, particle size of 2.0 and 3.0 mm, temperature of 34, 42 and 50 oC and pressure of 15, 20, 25 and 30 MPa. Kinetic curves clearly exhibited three periods of extraction (constant rate, falling rate and diffusion-controlled). Increasing flow rate, pressure, temperature or reducing particle size brought an increase in the oil yield and extraction rate. Overall, after 150 min of extraction, the oil in dried avocado was almost completely extracted. The oil yield by supercritical CO2 method (58.97%) obtained at 50 oC and 30 MPa was higher than that by Soxhlet method (55.83%). An increase in pressure (at constant temperature) brought an increase in free fatty acid values in oil but decrease in peroxide values and total phenolic content. On the other hand, the effect of temperature on those parameters was opposite. The oil by supercritical CO2 method was of better quality than that by Soxhlet method.
Opinion Formation on Social Networks - The Effects of Recurrent and Circular Influence

We present a generalised complex contagion model for describing behaviour and opinion spreading on social networks. Recurrent interactions between adjacent nodes and circular influence in loops in the network structure enable the modelling of influence spreading on the network scale. We have presented details of the model in our earlier studies. Here, we focus on the interpretation of the model and discuss its features by using conventional concepts in the literature. In addition, we discuss how the model can be extended to account for specific social phenomena in social networks. We demonstrate the differences between the results of our model and a simple contagion model. Results are provided for a small social network and a larger collaboration network. As an application of the model, we present a method for profiling individuals based on their out-centrality, in-centrality, and betweenness values in the social network structure. These measures have been defined consistently with our spreading model based on an influence spreading matrix. The influence spreading matrix captures the directed spreading probabilities between all node pairs in the network structure. Our results show that recurrent and circular influence has considerable effects on node centrality values and spreading probabilities in the network structure.
Current status of preoperative risk assessment for posthepatectomy liver failure in patients with hepatocellular carcinoma

Liver resection is an effective therapeutic option for patients with hepatocellular carcinoma. However, posthepatectomy liver failure (PHLF) remains a major cause of hepatectomy‐related mortality, and the accurate prediction of PHLF based on preoperative assessment of liver functional reserve is a critical issue. The definition of PHLF proposed by the International Study Group for Liver Surgery has gained acceptance as a standard grading criterion. Liver function can be estimated using a variety of parameters, including routine blood biochemical examinations, clinical scoring systems, dynamic liver function tests, liver stiffness and fibrosis markers, and imaging studies. The Child–Pugh score and model for end‐stage liver disease scores are conventionally used for estimating liver decompensation, although the alternatively developed albumin‐bilirubin score shows superior performance for predicting hepatic dysfunction. Indocyanine green clearance, a dynamic liver function test mostly used in Japan and other Asian countries, serves as a quantitative estimation of liver function reserve and helps determine indications for surgical procedures according to the estimated risk of PHLF. In an attempt to improve predictive accuracy, specific evaluation of liver fibrosis and portal hypertension has gained popularity, including liver stiffness measurements using ultrasonography or magnetic resonance elastography, as well as noninvasive fibrosis markers. Imaging modalities, including Tc‐99m‐labeled galactosyl serum albumin scintigraphy and gadolinium‐enhanced magnetic resonance imaging, are used for preoperative evaluation in combination with liver volume. This review aims to provide an overview of the usefulness of current options for the preoperative assessment of liver function in predicting PHLF.
Psychiatric Treatment Conducted via Telemedicine Versus In-Person Modality in Posttraumatic Stress Disorder, Mood Disorders, and Anxiety Disorders: Systematic Review and Meta-Analysis

Background: Telemedicine has played a vital role in providing psychiatric treatment to patients during the rapid transition of services during the COVID-19 pandemic. Furthermore, the use of telemedicine is expected to expand within the psychiatric field. The efficacy of telemedicine is well described in scientific literature. However, there is a need for a comprehensive quantitative review that analyzes and considers the different clinical outcomes and psychiatric diagnoses. Objective: This paper aimed to assess whether individual psychiatric outpatient treatment for posttraumatic stress disorder, mood disorders, and anxiety disorders in adults using telemedicine is equivalent to in-person treatment. Methods: A systematic search of randomized controlled trials was conducted using recognized databases for this review. Overall, 4 outcomes were
Dy4, Dy5, and Ho2 Complexes of an N3O2 Aminophenol Donor: A Dy5-µ3-Peroxide Single Molecule Magnet

The reactivity of the new flexible potentially pentadentate N3O2 aminophenol ligand H4Lr (2,2′-((pyridine-2,6-diylbis(methylene))bis(azanediyl))diphenol) towards different dysprosium salts and holmium(III) nitrate was investigated. Accordingly, this reactivity seems to greatly depend on the metal ion and salt employed. In this way, the reaction of H4Lr with dysprosium(III) chloride in air leads to the oxo-bridged tetranuclear complex [Dy4(H2Lr)3(Cl)4(μ3-O)(EtOH)2(H2O)2]·2EtOH·H2O (1·2EtOH·H2O), while the same reaction just changing the chloride salt by the nitrate one renders the peroxo-bridged pentanuclear compound [Dy5(H2Lr)2(H2.5Lr)2(NO3)4(µ3-O2)2]·2H2O (2·2H2O), where both peroxo ligands seem to come from the fixation and reduction of atmospheric oxygen. However, if holmium(III) nitrate is used instead of dysprosium(III) nitrate, no evidence of a peroxide ligand is observed, and the dinuclear complex {[Ho2(H2Lr)(H3Lr)(NO3)2(H2O)2](NO3)} 2.5H2O (3·2.5H2O) is isolated. The three complexes were unequivocally characterized by X-ray diffraction techniques, and their magnetic properties were analyzed. Thus, while the Dy4 and Ho2 complexes do not show magnet-like behavior even in the presence of an external magnetic field, 2·2H2O is a single molecule magnet, with an Ueff barrier of 61.2 K (43.2 cm−1). This is the first homonuclear lanthanoid peroxide SMM, which also shows the highest barrier among the reported 4f/3d peroxide zero field SMMs to date.
Using Grapefruit and Tomato Waste in O/W Type Chicken Meat Emulsion

: Utilizing food waste is crucial for several reasons, including preserving valuable ingredients, lowering formulation costs, taking advantage of its functional properties, and preserving the environment. Grapefruit waste (GW) and Tomato waste (TW) are used in oil-in-water (O/W) emulsion formulation to investigate their contributions to emulsion stability, textural parameters, and steady shear rheological properties of the model system. O/W emulsions were formed with four different concentrations (0.5%, 1%, 2%, and 3%) of GW and TW, and chicken breast meat and corn oil were the main components of emulsions. Both food wastes increased emulsion stability (ES) in most concentrations compared to the control. The best ES of 86.35 was observed at a concentration of 1% TW emulsion. Emulsions were subjected to texture profile analysis as both row and heat-treated. Heat-treated emulsions had higher hardness values (241.2-518.6 g) than the row emulsions (149.7-247.2 g). All emulsions were found to have pseudoplastic character and exhibit shear thinning behavior. All emulsion samples' apparent viscosity fit well with the Ostwald-de-Waele model (R 2 >0.9). Food waste samples used in the study improved the chicken-type O/W emulsion properties in terms of emulsion stability and textural properties.
Adverse Childhood Experiences and Mental Health among Students Seeking Psychological Counseling Services

Recent years have seen a marked rise in the number of students accessing University Psychological Counseling (UPC) services, and their concerns have been increasingly severe. This study aimed to examine the impact of cumulative adverse childhood experiences (ACEs) on mental health in students who had approached counseling services (N = 121) and students who had no experience with counseling services (N = 255). Participants completed an anonymous online self-report questionnaire measuring exposure to adverse childhood experiences (ACE-Q), psychological distress (General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9), personality traits (PID-5), and coping strategies. We found that students who approached UPC services scored higher on cumulative ACEs than the non-counseling group. While ACE-Q score was a direct positive predictor of PHQ-9 (p < 0.001), it did not predict GAD-7. Moreover, the results supported the existence of a mediation effect of avoidance coping, detachment, and psychoticism on the indirect effects of ACE-Q score on PHQ-9 or GAD-7. These results underlined the importance of screening for ACEs in a UPC setting because it can help identify students at higher risk for developing mental and physical health problems and provide them with early interventions and support.
Top-N Knowledge Concept Recommendations in MOOCs Using a Neural Co-Attention Model

Massive Open Online Courses (MOOCs), which provide learners with a large-scale, open-access learning opportunity, have drawn a lot of attention recently. The amount of information available on MOOCs is increasing, making it challenging for learners to choose the best course materials and leading to low learning efficiency and high dropout rates. To address these problems, Recommendation Systems (RSs) have been researched as a direct approach of delivering educational content to learners while also attracting their interest. However, a course often consists of various learning concepts, each of which covers a different topic. Explicitly proposing courses can cause the lack of learners’ attention to a particular knowledge concept. We introduce a Top-N Knowledge Concept Recommendations in MOOCs using a Neural Co-Attention Model, called NCO-A, that integrates significant heterogeneous data with recommendations based on knowledge concepts. The NCO-A model’s effectiveness has been proven by extensive experiments on three real-world datasets.
Research on Intelligent Identification of Transmission Line by UAV 3D Lidar

In order to solve the problems of long inspection period and high operation intensity of high voltage transmission lines, we propose a method for UAV 3D lidar intelligent identification of transmission line defection. First of all, the method uses the automatic classification to resample the 3D point cloud in the corridor, which is processed in a pyramidal manner. Secondly, the graded data is automatically divided into four categories: ground, quilt, pole tower and guide wire, which are combined with human workers to interpret and identify other categories such as low power lines and highways. Finally, the safety defects of wires and surrounding objects under current working conditions are extracted according to the relevant safety bureau regulations. The experimental results show that the proposed method can effectively identify many kinds of important defects and greatly improve the quality and efficiency of the existing high-voltage transmission line inspection process.
Sedation with volatile anesthetics in the intensive care unit: a new option with old agents

Since December 2019, when the first cases were described in China, the coronavirus disease 2019 (COVID-19) pandemic has impacted health systems around the world. A significant number of patients have the severe form of the disease, requiring admission to the intensive care unit (ICU). (1) The shortage of beds, equipment and drugs represented an even greater challenge in the management of these patients. The improvised use of operating rooms, which served as ICU beds, and the use of anesthesia equipment for sedation and mechanical ventilation have been described and were employed as heroic measures in the management of these patients. (2-4) In this context, the use of volatile anesthetics (VAs) has reappeared as an option for the sedation of critically ill patients. (3) The use of VAs in the ICU has been described for more than 2 decades and is mainly used in Europe and Canada;(5) however, the equipment to administer VAs was only recently approved for use in Brazil. The main VAs used as sedatives in the ICU are sevoflurane and isoflurane. The development of equipment with compact vaporizers adapted for mechanical ventilators in ICUs made it possible to use these agents as an option for sedation. Among the main advantages of using VAs rather than opioids in critically ill patients are earlier awakening, lower use of opioids and shorter time on mechanical ventilation. Other reported benefits of VAs are bronchodilator effects and improved oxygenation, especially in patients with acute respiratory distress syndrome (ARDS). Among the contraindications and limitations of VAs are a personal or family history of malignant hyperthermia, suspected or confirmed intracranial hypertension, severe hemodynamic instability and significant pulmonary secretion with the need for frequent aspiration due to the risk of system obstruction. (5) Three meta-analyses showed that compared to venous sedation, the use of VAs in the ICU resulted in faster awakening and extubation times. (6-8) More recently, Meiser et al., in a multicenter noninferiority study of isoflurane compared to propofol, showed that isoflurane was an effective and safe option. Additionally, in the isoflurane group, opioid consumption was lower. (9) Experimental studies have shown that sevoflurane has the ability to reduce lung inflammation in ARDS models. (10,11) Jabaudon et al., in a randomized study, demonstrated that compared with midazolam, the use of sevoflurane in patients with ARDS for a period of 48 hours was related to improved oxygenation and reduced markers of lung epithelial lesions. (12) The use of VAs in the ICU has been more frequently reported in populations of surgical patients. Although there are no contraindications for VAs use in other populations of critically ill patients (e.g., patients with sepsis), further studies are needed.
Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Simple Summary Positron emission tomography is currently considered the non-invasive reference standard for determining whether lung cancer also affects thoracic lymph nodes (staging). However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. This study aimed to predict the positron emission tomography result from traditional contrast-enhanced computed tomography and test new feature extraction strategies. As input, we compared traditional (hand-crafted) imaging biomarkers (radiomics) with novel features derived from pre-trained neural networks. This hybrid approach yielded better performance than using both feature sources alone. In conclusion, both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive lymph nodal staging in lung cancer. Abstract Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional “hand-crafted” radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865–0.878), SBS 35.8 (34.2–37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.
Diffusion Tensor Imaging Noise Removal for Tissue Fiber Tracking

B. Chen, E. Hsu Biomedical Engineering, Duke University, Durham, NC, United States, Center for In Vivo Microscopy, Duke University, Durham, NC, United States Introduction Due to the large dataset size (minimum of seven images) and the intensity attenuation nature of diffusion encoding, diffusion tensor imaging (DTI), particularly at high resolution, has been hampered by long acquisition time and low signal to noise ratio (SNR). The noise level in the diffusion weighted images has a direct impact on the accuracy of diffusion tensor computation [1]. Noise removal techniques may be used to improve the SNR of diffusion tensor imaging without increasing the acquisition time. While noise removal techniques such as low-pass filtering can be readily used to improve the SNR, they necessarily incur blurring at tissue borders that may lead to errors in the diffusion tensor computation and fiber tracking. Partial differential equation based diffusion filter (PDE filter) has been used to remove image noise through anisotropic smoothing with edge preservation feature [2]. The challenges in extending scalar PDE filtering for DTI are (a) direct de-noise of eigenvector fields cannot be done accurately on a component-by component basis, and (b) the diffusion tensor eigenvector field is subject to both image noise and diffusion tensor goodness-of-fit errors. In this study, we developed a vector based PDE filter with norm conservation constrains and compared its performance with other widely used noise removal techniques.
Computational methods for ill-posed problems of gravitational gasodynamics

Abstract A new numerical model of a galaxy interaction in gasodynamical approach is considered. In this model the equations of a gas dynamics with a cold function and the Poisson equation for a gravity potential are solved. A total system of equations is illposed. It allows us to describe a gravitational instability of astrophysical processes. As an example, the results of the collapse simulation are presented. The gas dynamic system of equations is solved by the Fluids-in-Cell method with the energy balance correction. Using the 3D Cartesian simulation a rotation, a self-consistent gravitational field, a complex central body geometry and a gas temperature were taken into account. A parallel implementation of the method for the simulation of various galaxy interaction scenarios was used in the model. Also, a research of the numerical model application range for various collision velocities and a mass relation of gas and star components was made here.
Visual Design Space Exploration using Contextual Self-Organizing Maps

Self-organizing maps (SOMs) and contextual maps are methods of visualizing high dimensional data in a low dimensional space. SOMs have previously been applied to visualize characteristics of optimization problems by generating maps of the component variables to compare interactions and relationships between design variables. In this paper, SOMs and contextual maps are explored as a visualization method to directly visualize the design space. Using the techniques described in the paper, high dimensional datasets are reduced to a 2D, human readable, visual map. Preliminary results show that the topology of three optimization functions using varying dimensionality can be clustered and visualized using contextual maps, and information can be gathered from these clusters including objective function values and variability amongst differing areas of the design space. This paper focuses on the use of contextual maps to extract valuable information such as modality and curvature to aid in future work such as selection of appropriate optimization algorithm and initial point for a solution run.