Fusion Based AER System Using Deep Learning Approach for Amplitude and Frequency Analysis

Automatic emotion recognition from Speech (AERS) systems based on acoustical analysis reveal that some emotional classes persist with ambiguity. This study employed an alternative method aimed at providing deep understanding into the amplitude–frequency, impacts of various emotions in order to aid in the advancement of near term, more effectively in classifying AER approaches. The study was undertaken by converting narrow 20 ms frames of speech into RGB or grey-scale spectrogram images. The features have been used to fine-tune a feature selection system that had previously been trained to recognise emotions. Two different Linear and Mel spectral scales are used to demonstrate a spectrogram. An inductive approach for in sighting the amplitude and frequency features of various emotional classes. We propose a two-channel profound combination of deep fusion network model for the efficient categorization of images. Linear and Mel- spectrogram is acquired from Speech-signal, which is prepared in the recurrence area to input Deep Neural Network. The proposed model Alex-Net with five convolutional layers and two fully connected layers acquire most vital features form spectrogram images plotted on the amplitude-frequency scale. The state-of-the-art is compared with benchmark dataset (EMO-DB). RGB and saliency images are fed to pre-trained Alex-Net tested both EMO-DB and Telugu dataset with an accuracy of 72.18% and fused image features less computations reaching to an accuracy 75.12%. The proposed model show that Transfer learning predict efficiently than Fine-tune network. When tested on Emo-DB dataset, the propȯsed system adequately learns discriminant features from speech spectrȯgrams and outperforms many stȧte-of-the-art techniques.

Social Network Evolution: The Case of UK Companies Before and After Brexit

The Brexit referendum has impacted both the UK and the EU economies in several ways. The uncertainty around Brexit highlighted the importance of a relationships network between directors of companies to access information and resources that are necessary for optimal decision making. It is difficult to develop informed business and economy policies without a deep understanding of the magnitude of Brexit on business-to-business relationships with EU-based firms. This study aims to analyze the impact of the passage of the Brexit referendum on the evolution of board interlock networks. The study uses network analysis to measure the evolution of UK-EU directors’ relationships over the Brexit period, predominantly between the 2010 and 2020 period. The study models the structural changes in dynamic networks by converting this evolving network into static graphs on yearly basis. The analysis indicates that links formation in the UK is affected negatively by the Brexit referendum. It also has a negative impact on forming a new link with potential companies’ directors in the EU, but it shows a rising tendency for shared affiliation bias analysis. Interestingly, the contradicted trend in 2007, the number of directors’ connection in consumer service and food & drug sectors was decreasing in the UK while rocketing in the EU. Doi: 10.28991/ESJ-2022-06-01-01 Full Text: PDF

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

Mott–Hubbard insulating state for the layered van der Waals $$\hbox {FePX}_3$$ (X: S, Se) as revealed by NEXAFS and resonant photoelectron spectroscopy

A broad family of the nowadays studied low-dimensional systems, including 2D materials, demonstrate many fascinating properties, which however depend on the atomic composition as well as on the system dimensionality. Therefore, the studies of the electronic correlation effects in the new 2D materials is of paramount importance for the understanding of their transport, optical and catalytic properties. Here, by means of electron spectroscopy methods in combination with density functional theory calculations we investigate the electronic structure of a new layered van der Waals $$\hbox {FePX}_3$$ FePX 3 (X: S, Se) materials. Using systematic resonant photoelectron spectroscopy studies we observed strong resonant behavior for the peaks associated with the $$3d^{n-1}$$ 3 d n - 1 final state at low binding energies for these materials. Such observations clearly assign $$\hbox {FePX}_3$$ FePX 3 to the class of Mott–Hubbard type insulators for which the top of the valence band is formed by the hybrid Fe-S/Se electronic states. These observations are important for the deep understanding of this new class of materials and draw perspectives for their further applications in different application areas, like (opto)spintronics and catalysis.

Foot-and-mouth disease virus 3A hijacks Sar1 and Sec12 for ER remodeling in a COPII-independent manner

Positive-stranded RNA viruses modify host organelles to form replication organelles (ROs) for their replication. Enteroviral 3A protein has been demonstrated to be highly associated with the COPI pathway, in which factors work on the ER-to-Golgi intermediate and the Golgi. However, Sar1, a COPII factor exerting coordinated action at endoplasmic reticulum (ER) exit sites, rather than COPI factors, is required for foot-and-mouth disease virus (FMDV) replication. Therefore, we thought that deep understanding of FMDV 3A was the key to explaining the differences and to unlocking the secret of FMDV RO formation. In this study, FMDV 3A was confirmed as a peripheral membrane protein capable of modifying the ER into vesicle-like structures, which were neither COPII vesicles nor autophagosomes. When the C-terminus of 3A was truncated, it would be located at the ER without vesicular modification. This change was revealed by mGFP and APEX2 fusion constructs observed by fluorescence microscopy and electron tomography, respectively. Referring to other 3A truncation, the minimal region for modification was aa 42–92. Furthermore, we found that the remodeling was related to two COPII factors, Sar1 and Sec12. Both interacted with 3A, but their binding domains on 3A were different. Finally, we hypothesized that the N-terminus of 3A would interact with Sar1 as its C-terminus simultaneously interacted with Sec12, which possibly would enhance Sar1 activation. On the ER membrane, two active Sar1 were connected by 3A with regions of aa 42–59 and aa 76–92, causing curvature of the membrane. This mechanism is distinct from the traditional COPII pathway and should be crucial for FMDV RO formation.

Cardiac Imaging for the Assessment of Left Atrial Mechanics Across Heart Failure Stages

The left atrium (LA) is emerging as a key element in the pathophysiology of several cardiac diseases due to having an active role in contrasting heart failure (HF) progression. Its morphological and functional remodeling occurs progressively according to pressure or volume overload generated by the underlying disease, and its ability of adaptation contributes to avoid pulmonary circulation congestion and to postpone HF symptoms. Moreover, early signs of LA dysfunction can anticipate and predict the clinical course of HF diseases before the symptom onset which, particularly, also applies to patients with increased risk of HF with still normal cardiac structure (stage A HF). The study of LA mechanics (chamber morphology and function) is moving from a research interest to a clinical application thanks to a great clinical, prognostic, and pathophysiological significance. This process is promoted by the technological progress of cardiac imaging which increases the availability of easy-to-use tools for clinicians and HF specialists. Two-dimensional (2D) speckle tracking echocardiography and feature tracking cardiac magnetic resonance are becoming essential for daily practice. In this context, a deep understanding of LA mechanics, its prognostic significance, and the available approaches are essential to improve clinical practice. The present review will focus on LA mechanics, discussing atrial physiology and pathophysiology of main cardiac diseases across the HF stages with specific attention to the prognostic significance. Imaging techniques for LA mechanics assessment will be discussed with an overlook on the dynamic (under stress) evaluation of the chamber.

Type 2 Inflammation in Eosinophilic Esophagitis: From Pathophysiology to Therapeutic Targets

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease of the esophagus characterized clinically by symptoms related to esophageal dysfunction and histologically by eosinophil-predominant inflammation, whose incidence is rising. It significantly affects patients’ quality of life and, if left untreated, results in fibrotic complications. Although broad consensus has been achieved on first-line therapy, a subset of patients remains non-responder to standard therapy. The pathogenesis of EoE is multifactorial and results from the complex, still mostly undefined, interaction between genetics and intrinsic factors, environment, and antigenic stimuli. A deep understanding of the pathophysiology of this disease is pivotal for the development of new therapies. This review provides a comprehensive description of the pathophysiology of EoE, starting from major pathogenic mechanisms (genetics, type 2 inflammation, epithelial barrier dysfunction, gastroesophageal reflux, allergens, infections and microbiota) and subsequently focusing on the single protagonists of type 2 inflammation (involved cells, cytokines, soluble effectors, surface proteins and transcription factors) that could represent present and future therapeutic targets, while summarizing previous therapeutic approaches in literature.

A Data-driven Platform for Simulating Vehicular Fog Computing Environment

<div>Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of compute-intensive vehicular applications such as cooperative driving. Concerning the spatio-temporal variation in the vehicular traffic flows and the demand for edge computing capacity generated by connected vehicles, vehicular fog computing (VFC) has been proposed as a cost-efficient deployment model that complements stationary fog nodes with mobile ones carried by moving vehicles. Accessing the feasibility and the applicability of such hybrid topology, and further planning and managing the networking and computing resources at the edge, require deep understanding of the spatio-temporal variations in the demand and the supply of edge computing capacity as well as the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. To meet such requirements, we propose in this paper an open platform for simulating the VFC environment and for evaluating the performance and cost efficiency of capacity planning and resource allocation strategies under diverse physical conditions and business strategies. Compared with the existing edge/fog computing simulators, our platform supports the mobility of fog nodes and provides a realistic modeling of vehicular networking with the 5G and beyond network in the urban environment. We demonstrate the functionality of the platform using city-scale VFC capacity planning as example. The simulation results provide insights on the feasibility of different deployment strategies from both technical and financial perspectives.</div>

Radiation tolerance of GaN: the balance between radiation-stimulated defect annealing and defect stabilization by implanted atoms

Realization of radiation-hard electronic devices able to work in harsh environments requires deep understanding the processes of defect formation/evolution occurring in semiconductors bombarded by energetic particles. In the present work we address such intriguing radiation phenomenon as high radiation tolerance of GaN and analyze structural disorder employing advanced co-irradiation schemes where low and high energy implants with different ions have been used. Channeling analysis revealed that the interplay between radiation-stimulated defect annealing and defect stabilization by implanted atoms dominates defect formation in the crystal bulk. Furthermore, the balance between these two processes depends on implanted species. In particular, strong damage enhancement leading to the complete GaN bulk amorphization observed for the samples pre-implanted with fluorine ions, whereas the co-irradiation of the samples pre-implanted with such elements as neon, phosphorus, and argon ions leads to a decrease of the damage.

A Data-driven Platform for Simulating Vehicular Fog Computing Environment

<div>Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of compute-intensive vehicular applications such as cooperative driving. Concerning the spatio-temporal variation in the vehicular traffic flows and the demand for edge computing capacity generated by connected vehicles, vehicular fog computing (VFC) has been proposed as a cost-efficient deployment model that complements stationary fog nodes with mobile ones carried by moving vehicles. Accessing the feasibility and the applicability of such hybrid topology, and further planning and managing the networking and computing resources at the edge, require deep understanding of the spatio-temporal variations in the demand and the supply of edge computing capacity as well as the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. To meet such requirements, we propose in this paper an open platform for simulating the VFC environment and for evaluating the performance and cost efficiency of capacity planning and resource allocation strategies under diverse physical conditions and business strategies. Compared with the existing edge/fog computing simulators, our platform supports the mobility of fog nodes and provides a realistic modeling of vehicular networking with the 5G and beyond network in the urban environment. We demonstrate the functionality of the platform using city-scale VFC capacity planning as example. The simulation results provide insights on the feasibility of different deployment strategies from both technical and financial perspectives.</div>