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This study investigates the kinetic modeling of CH4/H2/Air mixture with nanosecond pulse discharge (NSPD) by varying H2/CH4 ratios from 0 to 20% at ambient pressure and temperature. A validated version of the plasma and chemical kinetic mechanisms was used. Two numerical tools, ZDPlasKin and CHEMKIN, were combined to analyze the thermal and kinetic effects of NSPD on flame speed enhancement. The addition of H2 and plasma excitation increased flame speed. The highest improvement (35%) was seen with 20% H2 and 1.2 mJ plasma energy input at ϕ = 1. Without plasma discharge, a 20% H2 blend only improved flame speed by 14% compared to 100% CH4. The study found that lean conditions at low flame temperature resulted in significant improvement in flame speed. With 20% H2 and NSPD, flame speed reached 37 cm/s at flame temperature of 2040 K at ϕ = 0.8. Similar results were observed with 0% and 5% H2 and a flame temperature of 2200 K at ϕ = 1. Lowering the flame temperature reduced NOx emissions. Combining 20% H2 and NSPD also increased the flammability limit to ϕ = 0.35 at a flame temperature of 1350 K, allowing for self-sustained combustion even at low temperatures. [ABSTRACT FROM AUTHOR]

Byline: Alexander. Robinson, Aniket. Rali, Zubair. Shah, Travis. Abicht, Eric. Hockstad, Andrew. Sauer National trends suggest that less than one in four patients experiencing in-hospital cardiac arrest (IHCA) in the [...]

The aim of the present study was to explore the difference between the compliment responses used by male and female EFL speakers of District Sargodha. The target population was all master degree holders of Sargodha District in English Language and Literature. The sample population was selected conveniently from the students of M. Phil. of the Department of English, UOL, Sargodha Campus. Discourse Completion Test (DCT) was used as a tool for the collection of data. which contained compliments and the responses to those compliments in five different social situations. The responses to the compliments were designed following the taxonomies of Pomerantz (1978) and Holmes (1995). The findings reveal that there exists a gender-based difference with respect to complement response. [ABSTRACT FROM AUTHOR]

Purpose: Septic shock (SS) manifests with profound circulatory and cellular metabolism abnormalities and has a high in-hospital mortality (25%-50%). Congestive heart failure (CHF) patients have underlying circulatory dysfunction and compromised cardiac reserve that may place them at increased risk if they develop sepsis. Outcomes in patients with CHF who are admitted with SS have not been well studied. Materials and Method: Retrospective cross sectional secondary analysis of the Nationwide Readmission Database (NRD) for 2016 and 2017. ICD-10 codes were used to identify patients with SS during hospitalization, and then the cohort was dichotomized into those with and without an underlying diagnosis of CHF. Results: Propensity match analyses were performed to evaluate in-hospital mortality and clinical cardiovascular outcomes in the 2 groups. Cardiogenic shock patients were excluded from the study. A total of 578,629 patients with hospitalization for SS were identified, of whom 19.1% had a coexisting diagnosis of CHF. After propensity matching, 81,699 individuals were included in the comparative groups of SS with CHF and SS with no CHF. In-hospital mortality (35.28% vs 32.50%, P < .001), incidence of ischemic stroke (2.71% vs 2.53%, P=.0032), and acute kidney injury (69.9% vs 63.9%, P =.001) were significantly higher in patients with SS and CHF when compared to those with SS and no CHF. Conclusions: This study identified CHF as a strong adverse prognosticator for inpatient mortality and several major adverse clinical outcomes. Study findings suggest the need for further investigation into these findings' mechanisms to improve outcomes in patients with SS and underlying CHF. [ABSTRACT FROM AUTHOR]

• A multi-gene genetic algorithm is proposed to examine LFS behavior in NH 3 /H 2 /O 3 /air. • IDT decreases significantly with increasing H 2 , temperature, and pressure up to 10 atm. • LFS increases with rising H 2 concentration and initial temperature. • LFS decreases with increasing pressure, with less pressure influence beyond 5 atm. • O 3 addition enhances LFS in NH 3 /H 2 /air mixtures under normal conditions. The primary aim of this study is to develop and validate a novel multi-gene genetic programming approach for accurately predicting Laminar Flame Speed (LFS) in ammonia (NH 3)/hydrogen (H 2)/air mixtures, a key aspect in the advancement of carbon-free fuel technologies. Ammonia, particularly when blended with hydrogen, presents significant potential as a carbon-free fuel due to its enhanced reactivity. This research not only investigates the effects of hydrogen concentration, initial temperature, and pressure on LFS and Ignition Delay Time (IDT) but also explores the impact of oxidizing agents like ozone (O 3) in augmenting NH 3 combustion. A modified reaction mechanism was implemented and validated through parametric analysis. Main findings demonstrate that IDT decreases with higher hydrogen concentrations, increased initial temperature, and initial pressure, although the influence of pressure decreases above 10 atm. Conversely, at lower temperatures (below 1200 K) and higher hydrogen concentrations (30 % and 50 %), the dominance of H 2 chemistry can negatively impact initial pressure. LFS increases with higher temperature and hydrogen concentration, but decreases under elevated pressure, with its effect becoming negligible above 5 atm. An optimized equivalence ratio (Φ) range of 1.10 – 1.15 is identified for efficient combustion. Introducing ozone into the oxidizer notably improves LFS in NH 3 /H 2 /air mixtures, with the addition of 0.01 ozone mirroring the effect of a 10 % hydrogen addition under normal conditions. The study's fundamental contribution is the development of a multi-gene genetic algorithm, showcasing the correlation between predicted LFS values and actual values derived from chemkin simulations. The successful validation of this methodology across various case studies underscores its potential as a robust tool in zero-carbon combustion applications, marking a significant stride in the field. [ABSTRACT FROM AUTHOR]

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures. [ABSTRACT FROM AUTHOR]

Cardiac implantable electronic devices, including implantable cardioverter-defibrillators and therapy, are part of guideline-indicated treatment for a subset of patients with heart failure with reduced ejection fraction. Current technological advancements in cardiac implantable electronic devices have allowed the detection of specific physiological parameters that are used to forecast clinical decompensation through algorithmic, multiparameter remote monitoring. Other recent emerging technologies, including cardiac contractility modulation and baroreflex activation therapy, may provide symptomatic or physiological benefits in patients without indications for cardiac resynchronization. Our goal in this state-of-the-art review is to describe the new commercially available technologies, their purported mechanisms of action, and the evidence surrounding their clinical roles, limitations and future directions. Finally, we underline the need for standardized workflow and close interdisciplinary management of this population to ensure the delivery of high-quality care. [ABSTRACT FROM AUTHOR]

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications. [ABSTRACT FROM AUTHOR]

This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures. [ABSTRACT FROM AUTHOR]

We explore the effectiveness of serious games for visuospatial abilities among older adults with cognitive impairment by conducting a systematic review. Out of 548 identified publications, seven randomized controlled trials (RCTs) were included in this review. According to a meta-analysis of four RCTs, there is no statistically significant difference (p=0.28) in visuospatial abilities between serious game and control groups. Further, the included RCTs noted no statistically significant difference in the visuospatial ability when comparing serious games to conventional exercise (one study) and other serious games (two studies). One RCT demonstrated a statistically significant effect of serious games on the visuospatial ability when compared with conventional cognitive training. This review could not prove the effectiveness of serious games in enhancing visuospatial abilities for older adults with cognitive impairment. Thus, serious games should not be offered or used for enhancing visuospatial abilities amongst the elderly population with cognitive impairment. More robust RCTs are needed to make firm conclusions on the efficacy of serious games. [ABSTRACT FROM AUTHOR]

Children go through varied emotions such as happiness, sadness, and fear. At times, it may be difficult for children to express their emotions. Detecting and understanding the unexpressed emotions of children is very important to address their needs and prevent mental health issues. In this paper, we develop an artificial intelligence (AI) based Emotion Sensing Recognition App (ESRA) to help parents and teachers understand the emotions of children by analyzing their drawings. We collected 102 drawings from a local school in Doha and 521 drawings from Google and Instagram. Four different experiments were conducted using a combination of the two datasets. The deep learning model was trained using the Fastai library in Python. The model classifies the drawings into positive or negative emotions. The model accuracy ranged from 55% to 79% in the four experiments. This study showed that ESRA has the potential in identifying the emotions of children. However, the underlying algorithm needs to be trained and evaluated using more drawings to improve its current accuracy and to be able to identify more specific emotions. [ABSTRACT FROM AUTHOR]

We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either “vaccine-critical” (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or “other” (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning based language models, BERT and BERTweet, and implemented four classical AIbased models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures. [ABSTRACT FROM AUTHOR]

This article intended to carry out a systematic review on the effectiveness of serious games for language processing among older adults with cognitive impairment. Out of 548 retrieved records, six randomized controlled trials (RCTs) eventually met the eligibility criteria. A meta-analysis of four studies showed that serious games are more effective than no/passive interventions in enhancing language processing among older adults with cognitive impairment (p=0.008). Further, a statistically significant effect of serious games on language processing when compared with conventional cognitive activities and conventional exercises was reported in two RCTs. Other RCTs found that exergames are as effective as computerized cognitive training games in improving language processing. Serious games should be offered or used as complementary (i.e., not a substitute) to the current interventions. For there to be definitive conclusions about the efficacy of serious games on language processing more trials are needed. [ABSTRACT FROM AUTHOR]