Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning for outcome predictions of patients with trauma during emergency department care

Objectives To develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments. Methods This was a retrospective prognostic study using deidentified patient visit data from years 2007 to 2014 of the National Trauma Data Bank. The predictive model intelligence building process is designed based on patient demographics, vital signs, comorbid conditions, arrival mode and hospital transfer status. The mortality prediction model was evaluated on its sensitivity, specificity, area under receiver operating curve (AUC), positive and negative predictive value, and Matthews correlation coefficient. Results Our final dataset consisted of 2 007 485 patient visits (36.45% female, mean age of 45), 8198 (0.4%) of which resulted in mortality. Our model achieved AUC and sensitivity-specificity gap of 0.86 (95% CI 0.85 to 0.87), 0.44 for children and 0.85 (95% CI 0.85 to 0.85), 0.44 for adults. The all ages model characteristics indicate it generalised, with an AUC and gap of 0.85 (95% CI 0.85 to 0.85), 0.45. Excluding fall injuries weakened the child model (AUC 0.85, 95% CI 0.84 to 0.86) but strengthened adult (AUC 0.87, 95% CI 0.87 to 0.87) and all ages (AUC 0.86, 95% CI 0.86 to 0.86) models. Conclusions Our machine learning model demonstrates similar performance to contemporary machine learning models without requiring restrictive criteria or extensive medical expertise. These results suggest that machine learning models for trauma outcome prediction can generalise to patients with trauma across the USA and may be able to provide decision support to medical providers in any healthcare setting.
Electrostatics as the Driving Force Behind the Catalytic Function of the Monoamine Oxidase A Enzyme Confirmed by Quantum Computations

While the function of enzymes has been well-known to researchers for decades, the driving force behind it is still a hotly debated topic. Herein, we report significant evidence for electrostatics being that driving force, using a simple, computationally inexpensive, multiscale model of monoamine oxidase A and phenylethylamine. We found that electrostatics provided by the enzyme substantially enhances the reaction by all the considered criteria (lowering the energy barrier, increasing charge transfer, decreasing the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gap, increasing the dipole moment). The catalytic effect can be rationalized by the stabilizing interaction between the dipole moment of the reacting moiety and the electric field exerted by the charged environment. Both the dipole moment and the electric field are perceivably larger in the transition state as compared to the state of reactants; hence the transition state is stabilized to a larger extent and bett.
Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Abstract Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.
Knockdown of Long Non-coding RNA TUG1 Suppresses Osteoblast Apoptosis in Particle-induced Osteolysis by Up-regulating BMP-7

B ackground: Periprosthetic osteolysis may lead to the failure of hip arthroplasty. The role of long non-coding RNA TUG1 in inducing periprosthetic osteolysis after hip arthroplasty remains unknown. M ethods: Clinical tissues were obtained from the patients undergoing hip arthroplasty. Mouse osteoblast cell line MC3T3-E1 was simulated by CoCrMo metal particles (CoPs). Real-time PCR was performed to determine the expression of TUG1 and BMP-7. Western blot was performed to determine the BMP-7 protein expression. Cell apoptosis was determined using flow cytometry. RNA pull-down was performed to determine the interaction between TUG1 and BMP-7. In vivo experiments were performed to verify the role of TUG1. R esults : Overexpressed TUG1 and down-regulated BMP-7 was observed in both clinical periprosthetic tissues and CoPs-stimulated osteoblasts. Knockdown of TUG1 significantly inhibited the apoptosis of CoPs-stimulated osteoblasts by targeting BMP-7. In vivo experiments verified that knockdown of TUG1 increased bone mineral density of the mouse osteolysis model. C onclusion: Knockdown of TUG1 inhibited the apoptosis of CoPs-stimulated osteoblasts by negatively regulating BMP-7, which might provide a new insight in treating periprosthetic osteolysis after hip arthroplasty. K eywords: periprosthetic osteolysis; TUG1; BMP-7; osteoblast apoptosis
Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

Abstract The study offers a new method of collection and processing of meteorological data from the meteorological service based on observations and correction of numerical weather forecast errors using a new prediction algorithm. This algorithm vastly increases the accuracy of the short-term forecast of outdoor air temperature, which is subject to uncertainty due to the stochastic nature of atmospheric processes. Processing of temperature data using Kalman filter provides the decrease in predicted temperature errors. The main setup methods of Kalman filter have been examined. The article also describes the implementation of accuracy improving algorithm of predicted temperature using Python.
Investigation of Optical Properties of ZnO Nanorods Grown on Different Substrates

ZnO nanorods arrays are synthesized over the different substrates namely; Indium Tin Oxide (ITO), Kapton Tape (KT), Polyethylene terephthalate (PET), Porous Silicon (PS) and Silicon (Si) using modified chemical bath deposition (MCBD) method at 95 ºC for 4 h. The MCBD is the air bubbles inside growth solution during CBD process. The ZnO nano-seed layers are coated on different substrates using RF magnetron sputtering technique. The optical properties (transmittance, reflectance and energy band gap) and surface morphology of ZnO nanorods grown on different substrates have been investigated in details by using UV-Visible Spectrometer and Field emission scanning electron microscopy (FESEM), respectively. The results found that the morphology and diameter of ZnO nanorods is closely concerned to the nature of substrates. Also it is indicated that the substrate has strong and important impact on the growth, optical properties, Egand quality of synthesized ZnO nanorods (NRs). The higher transmittance has been observed for ZnO NRs grown over KT substrates and is about (~ 33 %). The average transmittance decreases sharply near UV region at wavelength around 393 nm for ZnO nanorods grown on ITO substrate. However, for PET and KT substrates, the transmittance decreases sharply near visible region around 401nm and 498 nm, respectively. Besides, the ZnO NRs grown on PS substrate have the strong reflectance characteristics after approximately 395 nm, and then decreases in the wavelength range of 410 nm to 700 nm. On the other hand, the strong reflectance property of ZnO NRs grown on Si substrate is observed at 400 nm. Also the minimum and maximum Egare obtained for ZnO nanorods that fabricated on the KT substrate and porous silicon substrate, respectively.
MAX-DOAS measurements of tropospheric NO2 and HCHO in Nanjing and the comparison to OMI observations

Abstract. In this paper, we present long term observations of atmospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground based MAX-DOAS measurements were performed from April 2013 to February 2017. The MAX-DOAS measurements of NO2 and HCHO vertical column densities (VCDs) are used to validate OMI satellite observations over Nanjing. The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient (R) of 0.91. However, OMI observations are on average a factor of 3 lower than the MAX-DOAS measurements. Replacing the a priori NO2 profiles by the MAX-DOAS profiles in the OMI NO2 VCD retrieval would increase the OMI NO2 VCDs by ~ 30 % with correlation nearly unchanged. The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99. We developed a new technique to assemble the source contribution map using backward trajectory analysis. The age weighted backward propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time. The reconstructed NO2 fields show a distinct agreement with OMI satellite observations. However, due to the short atmospheric lifetime of HCHO, the backward propagated HCHO data does not show a strong spatial correlation with the OMI HCHO observations. The result shows the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta, indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants. The MAX-DOAS data are also used to evaluate the effectiveness of air pollution control measures implemented during the Youth Olympic Games 2014. The MAX-DOAS data show a significant reduction of ambient aerosol, NO2 and HCHO (30 %–50 %) during the Youth Olympic Games. Our results provide a better understanding of the transportation and sources of pollutants in over the Yangtze River Delta as well as the effect of emission control measures during large international event, which are important for the future design of air pollution control policies.
The impact of plant water status on the gas exchange, berry composition and wine quality of Chasselas grapes in Switzerland

Aims: The aim of this research was to study the physiological and agronomical behaviors (leaf gas exchange, plant vigor, mineral supply, and yield components) of the Chasselas grapevine subjected to different water regimes during the growing season. The resulting grape and wine qualities were also determined.Methods and results: Adult vines of Vitis vinifera L. cv. Chasselas (clone 14/33-4, grafted onto 5BB) were subjected to different water regimes (various levels of irrigation) during the growing season. Physiological indicators were used to monitor the plant water status [the predawn leaf (. Leaf photosynthesis (A) and transpiration (E), stomatal conductance (gs), vulnerability to cavitation, yield parameters, berry composition at harvest, and organoleptic quality of wines were analyzed over a period of eight consecutive years between 2009 and 2016, under the relatively dry conditions of the Canton of Wallis, Switzerland.In non-irrigated vines, the progressively increasing water deficit observed over the season reduced the leaf gas exchange (A and E) and gs. The intrinsic water use efficiency (WUEi, A/gs) increased over the season and was greater in vines that had suffered water restriction than in irrigated vines. The rise in WUEi was correlated with an increase in d13C in the must sugars at harvest. Vulnerability to cavitation (embolism phenomenon) increased with increasing water deficit in the non-irrigated vines with covered soils. A decrease in plant vigor was observed in the vines that had been subjected to water restrictions over multiple years. Moderate water stress during fruit ripening was favorable for sugar accumulation in berries and lowered the contents of total and malic acidity in the musts and the content of available nitrogen (YAN). Overall, the organoleptic characteristics and quality of Chasselas wines were little influenced by the vine water regimes, with the exception of the hot, dry season in 2009 (and, to a lesser degree, in 2011). In those years, the quality of the wines from the irrigated vines, which had not suffered any water stress, received a better appreciation. Bitterness was generally greater in samples from the non-irrigated vineyards that had suffered from drought than in samples from the irrigated vines. No significant differences in the aroma and wine structure were measured during the study period, regardless of the vine irrigation status.Conclusions: The physiological behavior (gas exchange, plant vigor, and mineral supplies) and grape ripening in Chasselas vines were largely dependent on the water supply conditions in the vineyard during the growing season.Significance and impact of the study: Vine water status is a key factor in leaf gas exchange, canopy water use efficiency, berry composition and, lastly, wine quality.
DFT study of L-alanines Crystal, Molecule and Three Linear Molecules for Optoelectronic Behavior

L-alanine requires more insight in terms of the optoelectronic properties as the organic devices. For that an electronic structure of the L-alanine using the density of state (DOS) and band structure have been calculated by the density functional theory (DFT) using the plane wave method. The unit cell crystal, single molecule and hydrogen bonding approach were investigated first in this work using GGAPAW. The molecular orbitals were also examined using BL3YP functional for the hydrogen bonding between the amine and the carboxylic groups and found the bandgap energy comparable to the GGA-PAW case. The indirect band gap was obtained in the band-structure calculation. The effect of three linear molecules in x, y and z direction in a crystal was investigated and the significant variations found in DOS in all three cases of L-alanine. The present study predicted the longer length wire or tube structures are most useful as the outcome to make the good organic optoelectronic material. Keywords—L-alanine, Density of states, DFT, Energy bandgap, optoelectronics behavior
Social Groups Based Content Caching in Wireless Networks

The unprecedented growth of wireless mobile traffic, mainly due to multimedia traffic over online social platforms has strained the resources in the mobile backhaul network. A promising approach to reduce the backhaul load is to proactively cache content at the network edge, taking into account the overlaid social network. Known caching schemes require complete knowledge of the social graph and mainly focus on one-to-one interactions forgoing the prevalent mode of content sharing among circles of 'friends'. We propose Bingo, a proactive content caching scheme that leverages the presence of interest groups in online social networks. The mobile network operator (MNO) can choose to incrementally deploy Bingo at select network nodes (base stations, packet core, data center) based on user profiles and revenue numbers. We approximate the group memberships of users using the available user-content request logs without any prior knowledge of the overlaid social graph. Bingo can cater to the evolving nature of online social groups and file popularity distribution for making caching decisions. We use synthetically generated group structures and simulate user requests at the base station for empirical evaluation against traditional and recent caching schemes. Bingo achieves up to 30%-34% gain over the best baseline.
Environment Aware Text-to-Speech Synthesis

This study aims at designing an environment-aware text-to-speech (TTS) system that can generate speech to suit speciﬁc acoustic environments. It is also motivated by the desire to leverage massive data of speech audio from heterogeneous sources in TTS system development. The key idea is to model the acoustic environment in speech audio as a factor of data variability and incorporate it as a condition in the process of neural network based speech synthesis. Two embedding extractors are trained with two purposely constructed datasets for characterization and disentanglement of speaker and environment factors in speech. A neural network model is trained to generate speech from extracted speaker and environment embeddings. Objective and subjective evaluation results demonstrate that the proposed TTS system is able to effectively disentangle speaker and environment factors and synthesize speech audio that carries designated speaker characteristics and environment attribute. Audio samples are available online for demonstration 1 .
Polygon Area Decomposition Using a Compactness Metric

In this paper, we consider the problem of partitioning a polygon into a set of connected disjoint sub-polygons, each of which covers an area of a specific size. The work is motivated by terrain covering applications in robotics, where the goal is to find a set of efficient plans for a team of heterogeneous robots to cover a given area. Within this application, solving a polygon partitioning problem is an essential stepping stone. Unlike previous work, the problem formulation proposed in this paper also considers a compactness metric of the generated sub-polygons, in addition to the area size constraints. Maximizing the compactness of sub-polygons directly influences the optimality of any generated motion plans. Consequently, this increases the efficiency with which robotic tasks can be performed within each sub-region. The proposed problem representation is based on grid cell decomposition and a potential field model that allows for the use of standard optimization techniques. A new algorithm, the AreaDecompose algorithm, is proposed to solve this problem. The algorithm includes a number of existing and new optimization techniques combined with two post-processing methods. The approach has been evaluated on a set of randomly generated polygons which are then divided using different criteria and the results have been compared with a state-of-the-art algorithm. Results show that the proposed algorithm can efficiently divide polygon regions maximizing compactness of the resulting partitions, where the sub-polygon regions are on average up to 73% more compact in comparison to existing techniques.
A Wireless Intrusion Detection System for 802.11 WPA3 Networks

Wi-Fi (802.11) networks have become an essential part of our daily lives; hence, their security is of utmost importance. However, Wi-Fi Protected Access 3 (WPA3), the latest security certification for 802.11 standards, has recently been shown to be vulnerable to several attacks. In this paper, we first describe the attacks on WPA3 networks that have been reported in prior work; additionally, we show that a deauthentication attack and a beacon flood attack, known to be possible on a WPA2 network, are still possible with WPA3. We launch and test all the above (a total of nine) attacks using a testbed that contains an enterprise Access Point (AP) and Intrusion Detection System (IDS). Our experimental results show that the AP is vulnerable to eight out of the nine attacks and the IDS is unable to detect any of them. We propose a design for a signature-based IDS, which incorporates techniques to detect all the above attacks. Also, we implement these techniques on our testbed and verify that our IDS is able to successfully detect all the above attacks. We provide schemes for mitigating the impact of the above attacks once they are detected. We make the code to perform the above attacks as well as that of our IDS publicly available, so that it can be used for future work by the research community at large.
Implementation of machine learning techniques to predict impact parameter and transverse spherocity in heavy-ion collisions at the LHC

Machine learning techniques have been quite popular recently in the high-energy physics community and have led to numerous developments in this field. In heavy-ion collisions, one of the crucial observables, the impact parameter, plays an important role in the final-state particle production. This being extremely small (i.e. of the order of a few fermi), it is almost impossible to measure impact parameter in experiments. In this work, we implement the ML-based regression technique via Gradient Boosting Decision Trees (GBDT) to obtain a prediction of impact parameter in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV using A Multi-Phase Transport (AMPT) model. After its successful implementation in small collision systems, transverse spherocity, an event shape observable, holds an opportunity to reveal more about the particle production in heavy-ion collisions as well. In the absence of any experimental exploration in this direction at the LHC yet, we suggest an ML-based regression method to estimate centrality-wise transverse spherocity distributions in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV by training the model with minimum bias collision data. Throughout this work, we have used a few final state observables as the input to the ML-model, which could be easily made available from collision data. Our method seems to work quite well as we see a good agreement between the simulated true values and the predicted values from the ML-model.
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons

In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items. Thus a uniform querying strategy over users may not be optimal. To address this issue, we propose an elimination-based active sampling strategy, which estimates the ranking of items via noisy pairwise comparisons from users and improves the users' average accuracy by maintaining an active set of users. We prove that our algorithm can return the true ranking of items with high probability. We also provide a sample complexity bound for the proposed algorithm which is better than that of non-active strategies in the literature. Experiments are provided to show the empirical advantage of the proposed methods over the state-of-the-art baselines.
Photocatalytic Hydrogen Production Under Near-UV Using Pd-Doped Mesoporous TiO2 and Ethanol as Organic Scavenger

Photocatalysis can be used advantageously for hydrogen production using a light source (near-UV light), a noble metal-doped semiconductor and an organic scavenger (2.0 v/v% ethanol). With this end, palladium was doped on TiO2 photocatalysts at different metal loadings (0.25 to 5.00 wt%). Photocatalysts were synthetized using a sol-gel method enhancing morphological properties with a soft template precursor. Experiments were carried out in the Photo-CREC Water II reactor system developed at CREC-UWO (Chemical Reactor Engineering Centre- The University of Western Ontario) Canada. This novel unit offers hydrogen storage and symmetrical irradiation allowing precise irradiation measurements for macroscopic energy balances. Hydrogen production rates followed in all cases a zero-order reaction, with quantum yields as high as 30.8%.