Speech emotion recognition using spectrogram based neural structured learning

Human emotions are extremely crucial in our daily life. Emotion analysis based solely on auditory data is difficult due to the lack of visible visual information on human faces. Thus, a unique emotion recognition system based on robust characteristics and machine learning from the audio speech is reported in this paper. Audio details are used as input to the person-independent emotion recognition system, from which the spectrogram values are extracted as features. The generated features are then used to train and understand the emotions via Neural Structured Learning (NSL), a fast and accurate deep learning approach. During studies on an emotion dataset of audio speeches, the proposed approach of integrating spectrogram and NSL produced improved recognition rates compared to other known models. The system can be used in smart environments like homes or clinics to provide effective healthcare, music recommendations, customer support, and marketing, among several other things. As a result, rather than processing data and making judgments from far distant data sources, the decision-making could be made closer to where the data lives. The Toronto Emotional Speech Set (TESS) dataset that contains 7 emotions has been used for this research. The algorithm is successfully tested with the dataset with an accuracy of ~97%

Advances in materials informatics: A review

Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. Conversely, DL, an advancement of ML, employs mathematical neural networks to automatically extract features and handle intricate data at the cost of data size and computational complexity. This work aims to provide a state-of-the-art understanding of the tools, data sources and techniques used in MI and their benefits and challenges. We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven materials discovery. The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. As a specific example, understanding of materials properties using microstructural images is reviewed. Future demands and research prospects in materials science utilizing materials informatics have also been comprehensively analyzed

Addressing climate change with behavioral science:A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors

Addressing climate change with behavioral science:A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Rapid detection of invasive Mycobacterium chimaera disease via a novel plasma-based next-generation sequencing test

Abstract Background There is an ongoing outbreak of Mycobacterium chimaera infections among patients exposed to contaminated heater-cooler devices used during cardiac surgery. Recognition of M. chimaera infection is hampered by its long latency and non-specific symptoms. Standard diagnostic methods using acid-fast bacilli (AFB) culture often require invasive sampling, have low sensitivity, and can take weeks to result. We describe the performance of a plasma-based next-generation sequencing test (plasma NGS) for the diagnosis of M. chimaera infection. Methods We conducted a retrospective study of 10 patients with a history of cardiac surgery who developed invasive M. chimaera infection and underwent testing by plasma NGS between February 2017 and April 2018. Results Plasma NGS detected M. chimaera in 9 of 10 patients (90%) with invasive disease in a median of 4 days from specimen collection, including all 8 patients with disseminated infection. In 7 of these 9 cases (78%), plasma NGS was the first test to provide microbiologic confirmation of M. chimaera infection. In contrast, AFB cultures required a median of 20 days to turn positive, and the median time for confirmation of M. chimaera was 41 days. Of 24 AFB blood cultures obtained in this cohort, only 4 (17%) were positive. Invasive procedures were performed in 90% of cases, and in 5 patients (50%), mycobacterial growth was achieved only by culture of these deep sites. Conclusions Plasma NGS can accurately detect M. chimaera noninvasively and significantly faster than AFB culture, making it a promising new diagnostic tool