Miracle-dz/UniFAR-FTUs huggingface dataset:
The causal properties of sounds predict their recognition and misidentification

One way to study human sound recognition is to investigate the reasons why sounds are sometimes misheard as coming from the wrong source. Understanding this cognitive process can not only help prevent undesirable sound confusions (e.g., auditory display design) but can also promote useful confusions (e.g., Foley effects, cognitive reappraisal for misophonia). We tested the hypothesis that sounds are more confusable if their source events share causal properties. In Expi.1, listeners assessed causal properties of everyday sounds (ESC-50 Dataset) by judging their actions (e.g., tapping), materials (e.g., metal), and causal agents (e.g., machines). Causal similarity between sounds was measured by the distance between their causal properties. In Exp. 2, new listeners identified these sounds with 90% accuracy. Using the distances obtained in Exp. 1, misidentifications were predicted with 91% sensitivity and 89% specificity. The causal properties that had the largest effect on recognition accuracy were predominantly actions (according to a Ridge regression). Additional experiments with our own recorded and synthesized sounds show that spectral degradation and temporal manipulation can cause significant changes in causal properties and source assignment. Altogether, these findings show that causal properties provide unique explanatory power for sound recognition and misidentification. [Work supported by REAM. ]
Analyzing And Detecting Risk Factors For The Diagnosis Of Angina Pectoris With Machine Learning

Aim: To classify angina pectoris (AP) in women by applying the Bagged CART approach, which is one of the machine learning (ML) methods, to the open-access AP dataset. Another aim is to reveal the risk factors associated with AP in women through modeling. Materials and Methods: In the current study, modeling was done with the Bagged CART technique utilizing an open-access data set containing the factors associated with AP. Model results were assessed with accuracy (ACC), sensitivity (Sen), balanced accuracy (BACC), positive predictive value (PPV), specificity (Spe), negative predictive value (NPV), and F1-score performance criteria. In addition, a 5-fold cross-validation approach was applied in the modeling phase. Finally, variable importance was derived with modeling. Results: ACC, BACC, Sen, Spe, PPV, NPV, and F1-score from Bagged CART modeling were 98.5%, 98.5%, 99.0%, 98.0%, 98.0%, 99.0%, and 98.5%, respectively. Depending on the variable importance values calculated for the input variables investigated in the current study, age, family history of myocardial infarction: yes, the average number of cigarettes smoked per day smoking status: current, family history of angina: yes, hypertensive condition: moderate, smoking status: ex, hypertensive condition: mild, family history of stroke: yes, whether the woman has diabetes: yes were obtained as the most important variables associated with AP. Conclusion: With the ML model used, the AP dataset was classified successfully, and the associated risk factors were revealed. ML models can be used as clinical decision support systems for early diagnosis and treatment.
First-principle Calculation of Effects of Sb Doping on Electrical Conductivity of SnO_2 Transparent Film

In order to investigate the effect of Sb doping on the electrical conductivity of SnO2, the First-principle calculation of plane wave pseudopotential technology based upon the Density Function Theory(DFT) was performed. The property changes of SnO2, which Sb doping causes, were studied, such as crystal structure, energy band structure and the state density of the charge. The calculated results revealed that the SnO2 semiconductor with Sb doping has high conductivity. With the increase of doping concentration, the degeneracy of energy band structure was enhanced, and the energy level of shallow donor impurity is shifted away from the bottom of conduction band.
Sodium depletion and secondary hyperaldosteronism in outpatients with an ileostomy: a cross-sectional study

Abstract Objective Patients with an ileostomy may experience postoperative electrolyte derangement and dehydration but are presumed to stabilise thereafter. We aimed to investigate the prevalence of sodium depletion in stable outpatients with an ileostomy and applied established methods to estimate their fluid status. Methods We invited 178 patients with an ileostomy through a region-wide Quality-of-Life-survey to undergo outpatient evaluation of their sodium and fluid status. The patients delivered urine and blood samples, had bioelectrical impedance analysis performed and answered a questionnaire regarding dietary habits. Results Out of 178 invitees, 49 patients with an ileostomy were included; 22 patients (45%, 95% CI, 31–59%) had unmeasurably low urinary sodium excretion (<20 mmol/L), indicative of chronic sodium depletion, and 26% (95% CI, 16–41%) had plasma aldosterone levels above the reference value. Patients with unmeasurably low urinary sodium excretion had low estimated glomerular filtration rates (median 76, IQR 63–89, mL/min/1.73m2) and low venous blood plasma CO2 (median 24, IQR 21–26, mmol/L), indicative of chronic renal impairment and metabolic acidosis. Bioelectrical impedance analysis, plasma osmolality, creatinine and sodium values were not informative in determining sodium status in this population. Conclusion A high proportion of patients with an ileostomy may be chronically sodium depleted, indicated by absent urinary sodium excretion, secondary hyperaldosteronism and chronic renal impairment, despite normal standard biochemical tests. Sodium depletion may adversely affect longstanding renal function. Future studies should investigate methods to estimate and monitor fluid status and aim to develop treatments to improve sodium depletion and dehydration in patients with an ileostomy. IMPACT AND PRACTICE RELEVANCE STATEMENT Sodium depletion in otherwise healthy persons with an ileostomy was identified in a few publications from the 1980s. The magnitude of the problem has not been demonstrated before. The present study quantifies the degree of sodium depletion and secondary hyperaldosteronism in this group, and the results may help guide clinicians to optimise treatment. Sodium depletion is easily assessed with a urine sample, and sequelae may possibly be avoided if sodium depletion is detected early and treated. This could ultimately help increase the quality of life in patients with an ileostomy.
Firefly with Levy Based Feature Selection with Multilayer Perceptron for Sentiment Analysis

—Sentimental Analysis (SA) has recently received a lot of attention in decision-making because it can extract and analyze sentiments from web-based reviews made by customers. In this case, SA has been used as a Sentiment Classification (SC) problem, in which reviews are typically labeled as positive or negative depending upon online reviews. By combining FS (Feature Selection) and categorization, this work proposes an effective SA method for internet reviews. FireFly (FF) and Levy Flights (FFL) algorithms have been used for extracting features of web-based reviews, and also the Multilayer Perceptron (MLP) framework has been used to categorize the emotions. A standard DVD database displayed the efficacy of the FF-MLP model on the testing. The outcome shows that the suggested FF-MLP system accomplishes enhanced performance with maximum sensitivity of 98.97%, specificity of 93.67%, accuracy of 97.97%, F-score of 98.75, and kappa of 93.32%.
Approaches to evidence synthesis in international development

This paper discusses the spectrum of synthesis methods available to generate, explore and test theory, their value to the field of international development and innovations required to make better use of the primary research available. It goes further than setting substantive priorities for international development impact and learning. It addresses current advances and priority gaps in the methods for research before considering the substantive and methodological direction of evidence synthesis for impact evaluation in international development, particularly as this relates to the work of CEDIL. This scope encompasses methods for all stages in the process, from setting the question to appraising and synthesising the findings. It describes existing methods for synthesis, including how methods vary and the guidance and standards available. It then considers how well existing methods match the field of international development and the latest innovations emerging or required before providing a research agenda for advancing synthesis methods. In particular, it argues for clearer distinctions between syntheses produced as public goods, and those tailored to specific circumstances; and strengthening knowledge systems through greater use of maps to navigate existing and missing evidence, harmonised outcomes and measures, and advances in automation technologies. Improved methods and guidance are required for synthesising formative research and investigating contextual factors. Engaging stakeholders and working across academic disciplines support the production of policy-relevant syntheses and inspire methods development.
WATERSHED-BASED RIVER REGION DETECTION IN LOW CONTRAST IMAGE

WATERSHED-BASED RIVER REGION DETECTION IN LOW CONTRAST IMAGE Dongshun Cui, Shuigen Wang, M. R. Anjum, Wenzheng Wang and Baojun Zhao School of Information and Electronics, Beijing Institute of Technology, Beijing, China. Contact: engr.muhammadrizwan@gmail.com ABSTRAC : In this paper, a novel river region detection method based on watershed for low contrast image is introduced. The watershed transformation is a popular image segmentation algorithm for grey-scale image, which finds “catchment basins” and “watershed ridge lines” by treating the image as a surface where light pixels are high and dark pixels are low. The original image is firstly preprocessed by finding its Local Binary Patterns (LBP) map and calculating a Euclidean Distance (ED) map for binary result of the LBP map. Then Watershed segmentation algorithm is exploited. Finally, a region merging is been applied and we get the river region. We have compared our method with the existing threshold-based algorithms. Experimental results with images from Google Earth demonstrate that the metric proposed in this paper can always achieve a better performance.
Observations of the Early Evolution of an Explosive Oceanic Cyclone during ERICA IOP 5. Part I: Synoptic Overview and Mesoscale Frontal Structure

Abstract The structural evolution of the extratropical cyclone that occurred during intensive observation period 5 (IOP 5) of the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) is presented. In the present paper, the focus will be on the synoptic-scale development of the cyclone and the mesoscale frontal structure in the vicinity of the surface low center at a time early in its period of rapid deepening. Within this context the much higher resolution analyses of the three-dimensional wind field of the circulation center are discussed in the companion paper. The surface low center appeared initially as one of at least two mesoscale circulation centers along a preexisting low-level frontal zone approximately aligned with the Gulf Stream. Development occurred in association with the approach of a synoptic-scale upper-level trough from the west. As in the ERICA IOP 4 cyclone, a bent-back warm front-representing the extension of the warm front to the west of its point of intersection wit.
Suspension TRAPping Filter (sTRAP) Sample Preparation for Quantitative Proteomics in the Low µg Input Range Using a Plasmid DNA Micro-Spin Column: Analysis of the Hippocampus from the 5xFAD Alzheimer’s Disease Mouse Model

Suspension TRAPping filter (sTRAP) is an attractive sample preparation method for proteomics studies. The sTRAP protocol uses 5% SDS that maximizes protein solubilization. Proteins are trapped on a borosilicate glass membrane filter, where SDS is subsequently removed from the filter. After trypsin digestion, peptides are analyzed directly by LC-MS. Here, we demonstrated the use of a low-cost plasmid DNA micro-spin column for the sTRAP sample preparation of a dilution series of a synapse-enriched sample with a range of 10–0.3 µg. With 120 ng tryptic peptides loaded onto the Evosep LC system coupled to timsTOF Pro 2 mass spectrometer, we identified 5700 protein groups with 4% coefficient of variation (CoV). Comparing other sample preparation protocols, such as the in-gel digestion and the commercial Protifi S-TRAP with the plasmid DNA micro-spin column, the last is superior in both protein and peptide identification numbers and CoV. We applied sTRAP for the analysis of the hippocampal proteome from the 5xFAD mouse model of Alzheimer’s disease and their wildtype littermates, and revealed 121 up- and 54 down-regulated proteins. Protein changes in the mutant mice point to the alteration of processes related to the immune system and Amyloid aggregation, which correlates well with the known major Alzheimer’s-disease-related pathology. Data are available via ProteomeXchange with the identifier PXD041045.
Bigel Formulations of Nanoencapsulated St. John’s Wort Extract—An Approach for Enhanced Wound Healing

This study aimed to develop a semisolid vehicle for topical delivery of nanoencapsulated St. John’s wort (SJW) extract, rich in hyperforin (HP), and explore its wound-healing potential. Four nanostructured lipid carriers (NLCs) were obtained: blank and HP-rich SJW extract-loaded (HP-NLC). They comprised glyceryl behenate (GB) as a solid lipid, almond oil (AO), or borage oil (BO) representing the liquid lipid, along with polyoxyethylene (20) sorbitan monooleate (PSMO) and sorbitan monooleate (SMO) as surfactants. The dispersions demonstrated anisometric nanoscale particles with acceptable size distribution and disrupted crystalline structure, providing entrapment capacity higher than 70%. The carrier exhibiting preferable characteristics (HP-NLC2) was gelled with Poloxamer 407 (PM407) to serve as the hydrophilic phase of a bigel, to which the combination of BO and sorbitan monostearate (SMS) organogel was added. The eight prepared bigels with different proportions (blank and nanodispersion-loaded) were characterized rheologically and texturally to investigate the impact of the hydrogel-to-oleogel ratio. The therapeutic potential of the superior formulation (HP-NLC-BG2) was evaluated in vivo on Wistar male rats through the tensile strength test on a primary-closed incised wound. Compared with a commercial herbal semisolid and a control group, the highest tear resistance (7.764 ± 0.13 N) was achieved by HP-NLC-BG2, proving its outstanding wound-healing effect.
Development of an Electronic Nose for Harmful Gases with Prediction Modeling Using Machine Learning

—Harmful gases can inflict damage, accident, or even death on a human being. The safety and security of human beings are essential to the community. This study aims to develop an electronic nose with prediction modeling using machine learning. With the use of the sensor substitution technique, the system can predict the gas by utilizing a minimal number of sensors. The system, which is designed to predict Ammonia, Ethanol, and Isobutylene, relies on Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN) to ensure the prediction models are accurate. As a result, for Ammonia, Ethanol, and Isobutylene, using SVM, GPR, and FFNN model attain value of 1.00 for R 2 . The RMSE and MAE has a low result value indicates that the SVM, GPR, and FFNN model is performing well and can be used to make decisions regarding the concentration levels of harmful gases. This study shows that the system can predict the presence of gases within it using machine learning.
Sleep Quality Should Be Assessed in Inpatient Rehabilitation Settings: A Preliminary Study

Objectives: In this preliminary, longitudinal study, our objective was to assess changes in sleep quality during an inpatient stay in a rehabilitation setting in the United States and to relate changes to patients’ demographic and clinical characteristics (i.e., age, gender, BMI, ethnicity, reason for hospitalization, pre-hospital living setting, prior diagnosis of sleep disorders, and mental health status). Methods: A total of 35 patients participated in this preliminary study (age = 61 ± 16 years old, 50% <65; BMI = 30 ± 7 kg/m2; 51% female; 51% Caucasian). The average length of hospitalization was 18 ± 8 days. Reasons for hospitalization included orthopedic-related issues (28%), spinal cord injury (28%), stroke (20%), and other (23%). In this sample, 23% had prior sleep disorders (mostly sleep apnea), and 60% came from an acute care unit. Patients’ sleep quality was assessed using the Pittsburgh sleep quality index (PSQI) at admission and before discharge. Demographic and medical data were collected. Patients’ mental health status was also assessed at the same intervals. Nighttime sound levels and the average number of sleep disturbances were also collected throughout the study (6 months). Results: Our data revealed that most patients had poor sleep (PSQI > 5) at admission (86%) and discharge (80%). Using a repeated ANOVA, a significant interaction was obtained between sleep quality and the presence of a diagnosed sleep disorder [F (1, 33) = 12.861, p = 0.001, η2p = 0.280]. The sleep quality of patients with sleep disorders improved over their stay, while the sleep of patients without such disorders did not. The mean nighttime sound collection level averages and peaks were 62.3 ± 5.1 dB and 86.1 ± 4.9 dB, respectively, and the average number of sleep disturbances was 2.6 ± 1.1. Conclusion: The improved sleep observed in patients with vs. without sleep disorders might be related to the care received for treating such disorders over the stay. Our findings call for the better detection and management of poor sleep in acute inpatient rehabilitation settings. Furthermore, if our findings are replicated in the future, studies on the implementation of quiet times for medical staff, patients, and family should be performed to improve sleep quality in the inpatient rehabilitation setting.
A GPS-based on-board orbit propagator for low earth-orbiting CubeSats

On-board knowledge of satellite position is vital to all space missions. Due to the stringent power budgets of the CubeSat form factor, permanently active on-board GPS receivers for accurate navigation solutions are infeasible. Analytical techniques such as SGP4, which have been used since the 1970s, are often identified as the natural replacements. Unfortunately, due to inherent position errors of more than 1 km RMS, such techniques are unfit for some missions that prioritise precision. This emphasises the need for a new strategy for accurate on-board orbit determination and propagation. In an attempt to conserve power, it is proposed that the GPS receiver is activated intermittently such that a duty cycle of less than 15% (including the time-to-first-fix) is required. The orbit will be estimated during the activated period, after which navigation solutions will be obtained by means of propagation algorithms during the deactivated parts. Two approaches, one based on an analytical method and the other on numerical integration, are designed and implemented in the C programming language. These systems are simulated using actual on-board GPS datasets from SumbandilaSat and NigeriaSat missions. The analytical approach is based on SGP4. TLE parameter offsets are estimated by filtering the difference between the GPS and SGP4 output instantaneous Kepler elements, and an additional time offset is calculated for an in-track correction. Simulations revealed RMS and maximum 3D position errors of 200 m and 1 km, respectively, for a 12.5% duty cycle. The numerical approach employs an Extended Kalman Filter with a 4th order Runge-Kutta integrator for propagation. Reducing the propagator’s orbit dynamics model to only a 9th order JGM-3 geopotential and a Jacchia atmospheric density model for aerodynamic drag still produced fairly accurate results. Simulations revealed RMS and maximum 3D position errors of 60 m and 300 m, respectively, for a 10.7% duty cycle. The proposed system therefore delivers a remarkable accuracy improvement over standard analytical propagators at only a fraction of the power required by permanently active on-board GPS receivers. iii Stellenbosch University https://scholar.sun.ac.za
Non-Parallel Whisper-to-Normal Speaking Style Conversion Using Auxiliary Classifier Variational Autoencoder

This paper is concerned with non-parallel whisper-to-normal speaking-style conversion (W2N-SC), which converts whispered speech into normal speech without using parallel training data. Most relevant to this task is voice conversion (VC), which converts one speaker’s voice to another. However, the W2N-SC task differs from the regular VC task in three main respects. First, unlike normal speech, whispered speech contains little or no pitch information. Second, whispered speech usually has significantly less energy than normal speech and is therefore more susceptible to external noise. Third, in the actual usage scenario of W2N-SC, users may suddenly switch voice modes from whispered to normal speech, or vice versa, meaning that the speaking-style of input speech cannot be assumed in advance. To clarify whether existing VC techniques can successfully handle these task-specific concerns and how they should be modified to better address them, we consider a variational autoencoder (VAE)-based VC method as a baseline and examine what modifications to this method would be effective for the current task. Specifically, we study the effects of 1) a self-supervised training scheme called filling-in-frames (FIF); 2) data augmentation (DA) using noisy speech samples; and 3) an architecture that allows for any-to-many conversions. Through experimental evaluation of the W2N-SC and speaker conversion tasks, we confirmed that, especially in the W2N-SC task, the version incorporating the above modifications works better than the baseline VC model applied as is.