ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods

Intelligent transportation system (ITS) collects numerous data for analysis of the transportation system. The data can be used for providing services for travellers and traffic controllers in the ITS and optimizing it, for the purpose of making the transportation more efficient and safer. Due to the wide and flexible employment of video cameras in visual surveillance system (VSS), mature edge-cloud resource scheduling for data transmission and analysis, and the fast development of deep learning, computer vision (CV) methods have been employed in the visual-based ITS services successfully. In this paper, we discuss the edge-cloud surveillance resource scheduling for the CV methods and review the deep learning-based CV methods in the VSS, including detection, classification, and tracking methods, for better understanding of the relationship between the CV-based ITS services and these methods. We experimentally compare several state-of-the-art deep learning-based methods, which have been successfully applied in the CV fields under the ITS scenario, on their performance, inference speed, computational quantity, and model size. According to the comparisons, we propose four main challenges of the deep learning-based CV methods applied in the services, as a discussion of the future research directions. Code are available at https://github.com/PRIS-CV/DL-CV-ITS.
Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition

Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human-machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%.
Association between asthma, chronic bronchitis, emphysema, chronic obstructive pulmonary disease, and lung cancer in the US population

Lung cancer is one of the primary causes of death with poor life expectancy after diagnosis. History of past respiratory diseases such as asthma, chronic obstructive lung disease (COPD), emphysema, and chronic bronchitis can increase the risk of lung cancer. Very few studies are available to simultaneously assess multiple respiratory diseases and lung cancer. The objective of this study was to investigate correlations between asthma, emphysema, chronic bronchitis, and chronic obstructive lung disease with lung cancer in the US adult population. This was a cross-sectional study using data from a total of 23,523 adult participants from the National Health Examination and Nutrition Survey (NHANES) datasets for seven cycles ranging from 2003-2004 to 2015-2016. To analyze the data, specialized weighted complex survey logit regressions were conducted. Linear logit regression models using only main-effects were constructed first to assess the correlation between the selected demographic and lifestyle variables and asthma, emphysema, chronic bronchitis, and COPD. A second set of linear, main-effects logit regression models were constructed to examine the correlation between lung cancer and asthma, emphysema, chronic bronchitis, COPD when corrected for the selected covariates. The study identified positive correlations between emphysema, chronic bronchitis, COPD, and lung cancer. No correlation between asthma and lung cancer was established. Of the covariates studied, race/ethnicity, marital status, highest educational level, age, family income to poverty ratio, and lifetime smoking were also found to be correlated with the presence of lung cancer. Correlations between the covariates gender, body mass index, alcohol consumption, and country of birth and lung cancer were not found. The study established statistically significant correlations between lung cancer and the lung diseases emphysema, chronic bronchitis, and COPD. The lack of association between asthma and lung cancer may arise from the timeline of diagnosis asthma or type of lung cancer. The study also established significant correlations between lung cancer and several of the covariates included in the analysis. It also established correlations between the covariates and the lung diseases asthma, emphysema, chronic bronchitis, and COPD.
Temporal Patternization of Power Signatures for Appliance Classification in NILM

Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks-namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted.
A Quantitative Approach to Screen for Nephrotoxic Compounds In Vitro

Nephrotoxicity due to drugs and environmental chemicals accounts for significant patient mortality and morbidity, but there is no high throughput in vitro method for predictive nephrotoxicity assessment. We show that primary human proximal tubular epithelial cells (HPTECs) possess characteristics of differentiated epithelial cells rendering them desirable to use in such in vitro systems. To identify a reliable biomarker of nephrotoxicity, we conducted multiplexed gene expression profiling of HPTECs after exposure to six different concentrations of nine human nephrotoxicants. Only overexpression of the gene encoding heme oxygenase-1 (HO-1) significantly correlated with increasing dose for six of the compounds, and significant HO-1 protein deregulation was confirmed with each of the nine nephrotoxicants. Translatability of HO-1 increase across species and platforms was demonstrated by computationally mining two large rat toxicogenomic databases for kidney tubular toxicity and by observing a significant increase in HO-1 after toxicity using an ex vivo three-dimensional microphysiologic system (kidney-on-a-chip). The predictive potential of HO-1 was tested using an additional panel of 39 mechanistically distinct nephrotoxic compounds. Although HO-1 performed better (area under the curve receiver-operator characteristic curve [AUC-ROC]=0.89) than traditional endpoints of cell viability (AUC-ROC for ATP=0.78; AUC-ROC for cell count=0.88), the combination of HO-1 and cell count further improved the predictive ability (AUC-ROC=0.92). We also developed and optimized a homogenous time-resolved fluorescence assay to allow high throughput quantitative screening of nephrotoxic compounds using HO-1 as a sensitive biomarker. This cell-based approach may facilitate rapid assessment of potential nephrotoxic therapeutics and environmental chemicals.
Self-Powered Signal Transduction of Ion-Selective Electrodes to an Electronic Paper Display

Mobile integrated electrochemical sensors normally require a power supply for operation. Unfortunately, the practice of discarding batteries associated with these devices runs counter to our desire for a sustainable world. Self-powered sensing concepts that draw the energy directly from the measurement itself would overcome this limitation. Potentiometric sensors for the measurement of pH, many electrolytes, and gases are ubiquitous in analytical practice. However, in potentiometry, the voltage is acquired in the absence of current flow, making it seemingly impossible to draw power. Fortunately, it has been recently established that transient currents may be tolerated across potentiometric measurement cells to charge a capacitive or electrochromic element such as Prussian blue integrated in the measurement cell and whose absorbance then directly follows the potential changes in a reversible manner. We have shown here that commercial electronic paper (e-paper), widely used to make electronic ink and ebook readers, can directly be driven by a potentiometric measurement cell in a reversible manner at mild potentials of >100 mV typical for such sensors. The capacitance of the e-paper pixel studied here was found to be 0.53 μF mm-2, 30 times smaller than that of Prussian blue films. The colorimetric absorbance of the e-paper was also more stable (observed drift over 2 h corresponding to 0.76 mV h-1) and reproducible (corresponding to 1 mV standard deviation). The e-paper pixel was directly driven by a polymeric pH electrode as a model system. Choosing a basic inner solution (pH 12.9) behind the membrane gave sufficiently positive cell potentials for driving visible absorbance change in a sample pH range of 4-10, while a more acidic pH of 3.4 and alternating the connections to the e-paper were more suited for more basic samples of pH > 10. This convenient and cost-effective approach makes it possible to directly drive an optical display from the potentiometric measurement itself and should be suitable for moderate sensing membrane resistances of less than about 100 kΩ, depending on the area of the chosen pixel.
Rigorous Assessment of Cl- -Based Anolytes on Electrochemical Ammonia Synthesis

Many challenges in the electrochemical synthesis of ammonia have been recognized with most effort focused on delineating false positives resulting from unidentified sources of nitrogen. However, the influence of oxidizing anolytes on the crossover and oxidization of ammonium during the electrolysis reaction remains unexplored. Here it is reported that the use of analytes containing halide ions (Cl- and Br- ) can rapidly convert the ammonium into N2 , which further intensifies the crossover of ammonium. Moreover, the extent of migration and oxidation of ammonium is found to be closely associated with external factors, such as applied potentials and the concentration of Cl- . These findings demonstrate the profound impact of oxidizing anolytes on the electrochemical synthesis of ammonia. Based on these results, many prior reported ammonia yield rates are calibrated. This work emphasizes the significance of avoiding selection of anolytes that can oxidize ammonium, which is believed to promote further progress in electrochemical nitrogen fixation.
State of the art and challenges of security SLA for cloud computing

There are users and organizations that resist adopting cloud computing solutions, due to concerns about the security and privacy of their data. A Service Level Agreement (SLA) can be used to address these concerns, increasing trust in the purchased services through the clear description of the guarantees offered by the provider to the subscribers. For this purpose, the authors performed a literature systematic mapping to enumerate existing solutions and open issues in security SLAs in cloud computing. This review is presented in this paper as well as an analysis of the state of art. This paper also presents a classification of the selected papers and a discussion about management of security SLAs in clouds. (C) 2016 Elsevier Ltd. All rights reserved.
On the cooperation of meta-heuristics for solving many-objective problems: An empirical analysis including benchmark and real-world problems

The performance of state-of-the-art evolutionary algorithms in solving many-objective problems varies ac-cording to different problem characteristics, which poses a challenge for many-objective optimization. In this study, we analyze the cooperative hyper-heuristic (HH-CO) for many-objective optimization. HH-CO tackles the challenge of dynamically finding the best MOEA (multi-objective evolutionary algorithm) for applying and, at the same time, exploiting the MOEAs cooperation for a given problem instance. This recently proposed hyper-heuristic (HH) showed results competitive to stand-alone MOEAs and a state-of-art hyper-heuristic. Our goal is to identify what leads HH-CO towards its competitive results and distinguishes it from other state-of -art hyper-heuristics. To answer those questions, we observed the choices made by HH-CO and a state-of-art HH. In addition, we analyzed how those choices are related to the quality of MOEAs applied stand-alone. Furthermore, we evaluated scenarios where HH-CO presented better and worse results and identified the main reasons for these outcomes. Overall, HH-CO presented better results in 80% of instances. We concluded that the greedy selection heuristic employed by HH-CO could be improved. Still, the positive influence of the cooperative migration procedure surpasses HH-CO deficiencies for most problem instances. Finally, we evaluated the capabilities of both strategies on a real-world problem. They achieved very similar hypervolume results, without a significant difference to the best MOEA, but better than some state-of-the-art MOEAs.
Affective Image Captioning for Visual Artworks Using Emotion-Based Cross-Attention Mechanisms

Within the museum community, the automatic generation of artwork description is expected to accelerate the improvement of accessibility for visually impaired visitors. Captions that describe artworks should be based on emotions because art is inseparable from viewers' emotional reactions. By contrast, artworks typically do not have unique interpretations; thus, it is difficult for systems to reflect the specified emotions in captions precisely. Most existing methods attempt to leverage predicted emotion labels from images to generate emotion-oriented captions; however, they do not allow users to specify arbitrary emotions. In this paper, we aim to build a model that generates emotion-conditioned captions that describe visual art. We propose an affective visual encoder, which integrates emotion attributes and cross-modal joint features of images into visual information over all encoder blocks. Moreover, we introduce affective tokens that fuse grid- and region-based image features to cover both contextual and object-level information. We validated our method on the ArtEmis dataset, and the results demonstrated that our method outperformed baseline methods on all metrics in the emotion-conditioned task.
[Characteristics and Clinical Application of Commonly Used Wound Dressings]

The pathological mechanism of wound healing is complicated and affected by multiple factors. Modern wound dressings are widely used in the clinical management of wound healing and have achieved good therapeutic effects. Clinically, wounds are often caused by different etiologies. However, there are few reviews focus on the selection of reasonable dressings for different types of wounds. This study mainly focuses on the characteristics of commonly used wound dressings and summarizes the characteristics of the most commonly used wound dressings in clinical practice and their effects. The advantages and disadvantages of pathology wounds: diabetic foot ulcers, pressure injuries, burns, and leg ulcers are reviewed. This study aims to provide references for the development and clinical selection of wound dressings for scientific researchers and first-line nursing staff who are engaged in wound dressings.
Clustered Regularly Interspaced short palindromic repeats-Based Microfluidic System in Infectious Diseases Diagnosis: Current Status, Challenges, and Perspectives

Mitigating the spread of global infectious diseases requires rapid and accurate diagnostic tools. Conventional diagnostic techniques for infectious diseases typically require sophisticated equipment and are time consuming. Emerging clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) detection systems have shown remarkable potential as next-generation diagnostic tools to achieve rapid, sensitive, specific, and field-deployable diagnoses of infectious diseases, based on state-of-the-art microfluidic platforms. Therefore, a review of recent advances in CRISPR-based microfluidic systems for infectious diseases diagnosis is urgently required. This review highlights the mechanisms of CRISPR/Cas biosensing and cutting-edge microfluidic devices including paper, digital, and integrated wearable platforms. Strategies to simplify sample pretreatment, improve diagnostic performance, and achieve integrated detection are discussed. Current challenges and future perspectives contributing to the development of more effective CRISPR-based microfluidic diagnostic systems are also proposed.
State-of-the-art of classical SPH for free-surface flows

Smoothed Particle Hydrodynamics (SPH) is the most widely established mesh-free method which has been used in several fields as astrophysics, solids mechanics and fluid dynamics. In the particular case of computational fluid dynamics, the model is beginning to reach a maturity that allows carrying out detailed quantitative comparisons with laboratory experiments. Here the state-of-the-art of the classical SPH formulation for free-surface flow problems is described in detail. This is demonstrated using dam-break simulations in 2-D and 3-D. The foundations of the method will be presented using different derivations based on the method of interpolants and on the moving least-squares approach. Different methods to improve the classic SPH approach such as the use of density filters and the corrections of the kernel function and its gradient are examined and tested on some laboratory cases.
Biosensor Characterization from Cratylia mollis Seed Lectin (Cramoll)-MOF and Specific Carbohydrate Interactions in an Electrochemical Model

Biosensors are small devices known for their selectivity, high specificity and sensitivity to the respective analyte, at low concentrations. We developed an electrochemical biosensor using the crystalline polymer MOF-[Cu3 (BTC)2 (H2 O)2 ]n to characterize Cratylia mollis seed lectin (Cramoll) and its interaction with free carbohydrate (glucose) and carbohydrates on the surface of rabbit erythrocytes. The electrochemical potentials presented by the exponential curves that vary from 96 to 142 mV in relation to concentrations of 10 to 20 mM of glucose are decisive for the use of the system containing gold electrode/MOF/Cramoll for the characterization of biological models due to its high sensitivity. As well as the kinetic behavior presented in the cyclic voltammograms, with a cathodic current response of 0.000 3 A for a glucose concentration of 15 mM. These results were due to the high specificity of Cramoll under these conditions, promoting stability of surface charges at the Cramoll/electrode interface. This phenomenon facilitates the monitoring of the interaction with free glucose present in the electrolyte medium by potentiometric and amperometric methods and with carbohydrates present on the surface of rabbit erythrocytes through the potentiometric method. Through scanning electron microscopy (SEM) it was possible to observe Cramoll immobilized on the MOF surface, proving the specificity of the ligand (glucose-lectin) through the morphological lectin changes in this process. This electrochemical model, Cramoll/MOF biosensor, is effective for evaluating free lectin/carbohydrate or in the erythrocyte membrane.
Self-Reported Cannabis Use and HIV Viral Control among Patients with HIV Engaged in Care: Results from a National Cohort Study

Background: The association between cannabis use and HIV-1 RNA (viral load) among people with HIV (PWH) engaged in care is unclear. Methods: We used data collected from 2002 to 2018 on PWH receiving antiretroviral therapy (ART) enrolled in the Veterans Aging Cohort Study. Generalized estimating equations were used to estimate associations between self-reported past-year cannabis use and detectable viral load (>= 500 copies/mL), with and without adjustment for demographics, other substance use, and adherence. Results: Among 2515 participants, 97% were male, 66% were Black, the mean age was 50 years, and 33% had detectable HIV viral load at the first study visit. In unadjusted analyses, PWH with any past-year cannabis use had 21% higher odds of a detectable viral load than those with no past-year use (OR = 1.21; 95% CI, 1.07-1.37). However, there was no significant association between cannabis use and viral load after adjustment. Conclusions: Among PWH engaged in care and receiving ART, cannabis use is associated with decreased adherence in unadjusted analyses but does not appear to directly impact viral control. Future studies are needed to understand other potential risks and benefits of cannabis use among PWH.
Supervised learning in automatic channel selection for epileptic seizure detection

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40% sensitivity and 89.24% specificity with a. mean detection delay of 2.63 s for the test set. The area under the ROC curve (AUC) is 96.94%, that is comparable to the current state-of-the-art with AUC of 96.29%. (C) 2017 The Authors. Published by Elsevier Ltd.