Reproducibility of cerebral blood flow, oxygen metabolism, and lactate and N-acetyl-aspartate concentrations measured using magnetic resonance imaging and spectroscopy.

In humans, resting cerebral perfusion, oxygen consumption and energy metabolism demonstrate large intersubject variation regardless of methodology. Whether a similar large variation is also present longitudinally in individual subjects is much less studied, but knowing the time variance in reproducibility is important when designing and interpreting longitudinal follow-up studies examining brain physiology. Therefore, we examined the reproducibility of cerebral blood flow (CBF), global cerebral metabolic rate of oxygen (CMRO2), global arteriovenous oxygen saturation difference (A-V.O2), and cerebral lactate and N-acetyl-aspartate (NAA) concentrations measured using magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques through repeated measurements at 6 h, 24 h, 7 days and several weeks after initial baseline measurements in young healthy adults (N = 26, 13 females, age range 18-35 years). Using this setup, we calculated the correlation, limit of agreement (LoA) and within-subject coefficient of variation (CoVWS) between baseline values and the subsequent repeated measurements to examine the longitudinal variation in individual cerebral physiology. CBF and CMRO2 correlated significantly between baseline and all subsequent measurements. The strength of the correlations (R2) and reproducibility metrics (LoA and CoVWS) demonstrated the best reproducibility for the within-day measurements and generally declined with longer time between measurements. Cerebral lactate and NAA concentrations also correlated significantly for all measurements, except between baseline and the 7-day measurement for lactate. Similar to CBF and CMRO2, lactate and NAA demonstrated the best reproducibility for within-day repeated measurements. The gradual decline in reproducibility over time should be considered when designing and interpreting studies on brain physiology, for example, in the evaluation of treatment efficacy.

Smartphone Apps for Domestic Violence Prevention: A Systematic Review.

Smartphone applications or apps are increasingly being produced to help with protection against the risk of domestic violence. There is a need to formally evaluate their features. OBJECTIVE: This study systematically reviewed app-based interventions for domestic violence prevention, which will be helpful for app developers. METHODS: We overviewed all apps concerning domestic violence awareness and prevention without language restrictions, collating information about features and limitations. We conducted searches in Google, the Google Play Store, and the App Store (iOS) covering a 10-year time period (2012-2022). We collected data related to the apps from the developers' descriptions, peer reviewed research articles, critical reviews in blogs, news articles, and other online sources. RESULTS: The search identified 621 potentially relevant apps of which 136 were selected for review. There were five app categories: emergency assistance (n = 61, 44.9%), avoidance (n = 29, 21.3%), informative (n = 29, 21.3%), legal information (n = 10, 7.4%), and self-assessment (n = 7, 5.1%). Over half the apps (n = 97, 71%) were released in 2020-22. Around a half were from north-east America (n = 63, 46.3%). Where emergency alerts existed, they required triggering by the potential victim. There was no automation. Content analysis showed 20 apps with unique features, including geo-fences, accelerometer-based alert, shake-based alert, functionality under low resources, alert auto-cancellation, anonymous communication, and data encryption. None of the apps deployed artificial intelligence to assist the potential victims. CONCLUSIONS: Apps currently have many limitations. Future apps should focus on automation, making better use of artificial intelligence deploying multimedia (voice, video, image capture, text and sentiment analysis), speech recognition, and pitch detection to aid in live analysis of the situation and for accurately generating emergency alerts.

Feature selection enhancement and feature space visualization for speech-based emotion recognition.

Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied principle component analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Berlin Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study.

Towards a component-based system model to improve the quality of highly configurable systems.

Due to ever-evolving software developments processes, companies are motivated to develop desired quality products quickly and effectively. Industries are now focusing on the delivery of configurable systems to provide several services to a wide range of customers by making different configurations in a single largest system. Nowadays, component-based systems are highly demanded due to their capability of reusability and restructuring of existing components to develop new systems. Moreover, product line engineering is the major branch of the component-based system for developing a series of systems. Software product line engineering (SPLE) provides the ability to design several software modifications according to customer needs in a cost-effective manner. Researchers are trying to tailor the software product line (SPL) process that integrates agile development technologies to overcome the issues faced during the execution of the SPL process such as delay in product delivery, restriction to requirements change, and exhaustive initial planning. The selection of suitable components, the need for documentation, and tracing back the user requirements in the agile-integrated product line (APL) models still need to improve. Furthermore, configurable systems demand the selected features to be the least dependent. In this paper, a hybrid APL model, quality enhanced application product line engineering (QeAPLE) is proposed that provides support for highly configurable systems (HCS) by evaluating the dependency of features before making the final selection. It also has a documentation and requirement traceability function to ensure that the product meets the desired quality. Two-fold assessments are undertaken to validate the suggested model, with the proposed model being deployed on an active project. After that, we evaluated the proposed model performance and effectiveness using after implementing it in a real-world environment and compared the results with an existing method using statistical analysis. The results of the experimental study proofs that the proposed model is practically and statistically significant as compared to the existing method in terms of effectiveness and participants' performance. Hence, the statistical results of the comparative analysis show that the proposed model improved ease of understanding and adaptability, required effort, high-quality achievement, and version management are significant i.e., more the 50% as compared to the exiting method i.e., less than 50%. The proposed model offers to assist in the development of a highly configurable system that achieves the needed quality. Therefore, the proposed model manages the variation identification, versions control, components dependency for correct selection of components, and validation activities from domain engineering to application engineering.

Identifying the evidence of speech emotional dialects using artificial intelligence: A cross-cultural study.

The advancement in technology especially in the field of artificial intelligence has opened up novel and robust ways to reanalyze the many aspects of human emotional behavior. One of such behavioral studies is the cultural impact on the expression and perception of human emotions. In-group advantage makes it easy for the people of the same cultural group to perceive each other's emotions accurately. The goal of this research is to re-investigate human behavior regarding expression and perception of emotions in speech. The theoretical basis of this research is grounded on the dialect theory of emotions. For the purpose of this study, six datasets of audio speeches have been considered. The participants of these datasets belong to six different cultural areas. A fully automated, machine learning-based framework i.e. Support Vector Machine (SVM) is used to carry out this study. The overall emotion perception for all six cultural groups supports in-group advantage, whereas emotion wise analysis partially supports the In-group advantage.

Measuring the impact of social drive across social media forums: a case study of COVID-19.

Users considered Social media forums like Facebook, Twitter and blogs as the most prominent social networks in the present age, where users share their views quickly in words and respond to feedback from other users within no time. This study aims to: measure the impact of influencing factors on a particular community when social media forums promote it using a machine learning model. In this research work, we performed an association rule-based method to measure the impact of COVID-19 influencing factors on adolescence when they promoted it on social media. The proposed method gave a remarkable output when we compared it with the different existing approaches. It works well in all respective fields we observed. Last but not least, when compared with survey and official results, the proposed method predicts well, and the obtained results are pretty promising.

An Accurate Deep Learning Model for Clinical Entity Recognition From Clinical Notes.

The growing use of electronic health records in the medical domain results in generating a large amount of medical data that is stored in the form of clinical notes. These clinical notes are enriched with clinical entities like disease, treatment, tests, drugs, genes, and proteins. The extraction of clinical entities from clinical notes is a challenging task as clinical notes are written in the form of natural language. The extraction of clinical entities has many useful applications such as clinical notes analysis, medical data privacy, decision support systems, and disease analysis. Although various machine learning and deep learning models are developed to extract clinical entities from clinical notes, developing an accurate model is still challenging. This study presents a novel deep learning-based technique to extract the clinical entities from clinical notes. The proposed model uses local and global context to extract clinical entities in contrast to existing models that use only global context. The combination of CNN, Bi-LSTM, and CRF with non-complex embedding (proposed model) outperforms existing models by a margin of 4-10% and 5-12% in terms of F1-score on i2b2-2010 and i2b2-2012 data. The accurate detection of clinical entities can be helpful in the privacy preservation of medical data that increases the user's and medical organization's trust in sharing medical data.