A Multimodal Approach for Dementia Detection from Spontaneous Speech with Tensor Fusion Layer

Alzheimer's disease (AD) is a progressive neurological disorder, meaning that the symptoms develop gradually throughout the years. It is also the main cause of dementia, which affects memory, thinking skills, and mental abilities. Nowadays, researchers have moved their interest towards AD detection from spontaneous speech, since it constitutes a time-effective procedure. However, existing state-of-the-art works proposing multimodal approaches do not take into consideration the inter- and intra-modal interactions and propose early and late fusion approaches. To tackle these limitations, we propose deep neural networks, which can be trained in an end-to-end trainable way and capture the inter- and intra-modal interactions. Firstly, each audio file is converted to an image consisting of three channels, i.e., log-Mel spectrogram, delta, and delta-delta. Next, each transcript is passed through a BERT model followed by a gated self-attention layer. Similarly, each image is passed through a Swin Transformer followed by an independent gated self-attention layer. Acoustic features are extracted also from each audio file. Finally, the representation vectors from the different modalities are fed to a tensor fusion layer for capturing the inter-modal interactions. Extensive experiments conducted on the ADReSS Challenge dataset indicate that our introduced approaches obtain valuable advantages over existing research initiatives reaching Accuracy and F1-score up to 86.25% and 85.48% respectively.Comment: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) - Oral Presentatio

Building Ontology for Production Scheduling Systems: Towards a Unified Methodology

In this paper we consider the use of ontology as the basis for structuring and simplifying the process of constructing real-time problem-solving tools, focusing specifically on the task of production scheduling. In spite of the commonality in production scheduling system requirements and design, different scheduling environments invariably present different challenges (e.g. different constraints, different objectives, different domain structures, etc.). The proposed methodology for building ontology used for production scheduling systems represent a synthesis of extensive word in developing constraint-based scheduling models for a wide range of applications in manufacturing and production planning. Since the effective modelling is one of the most important and difficult steps in the development of reliable information systems and taking into consideration the fact that the general problem of the production scheduling in the industries is very difficult and still unsolved, one can easily estimate the merit of this methodology

Evaluating the Integrated Measurement and Evaluation System IMES: A Success Story

This case study serves to illustrate an integrated and practical methodology for evaluating advanced information database systems. The goal of the integration is to create a top-down evaluation process that reduces user and data requirements to a standard evaluation structure. In this framework, the evaluation of the Integrated Measurement and Evaluation System IMES was implemented by the Energy Policy Unit of the National Technical University of Athens. Evaluation team members successfully followed the proposed evaluation methodology

Design and testing of sorbents for CO2 separation of post-combustion and natural gas sweetening applications

In post-combustion processes, sorbents with both high capacity and selectivity are required for reducing the cost of carbon capture. Although physisorbents have the advantage of low energy consumption for regeneration, it remains a challenge to obtain both high capacity and sufficient CO2/N2 selectivity at the same time. A novel N-doped hierarchical carbon has been developed, which exhibits record-high Henry’s law CO2/N2 selectivity among physisorptive carbons while having a high CO2 adsorption capacity. Specifically, the synthesis involves the rational design of a modified pyrrole molecule that can co-assemble with the soft Pluronic template via hydrogen bonding and electrostatic interactions to give rise to mesopores followed by carbonization. The low-temperature carbonization and activation processes allow for the development of ultra-small pores (d2 affinity. Furthermore, the described work provides a strategy to initiate the development of rationally-designed porous conjugated polymer structures and carbon-based materials for various potential applications. In addition to post-combustion capture, natural gas sweetening is another topic of interest. Natural gas, having the lowest carbon intensity compared to coal and petroleum, is projected to increase in production and consumption in the coming decades. However, a drawback associated with natural gas is that it contains considerable amounts of CO2 at the recovery well, making on-site CO2 capture necessary. Solid sorbents are advantageous over traditional amine scrubbing due to their relatively low regeneration energies and non-corrosive nature. However, it remains a challenge to improve the sorbent’s CO2 capacity at elevated pressures relevant to natural gas purification. A series of porous carbons have been developed, which were derived from an intrinsic 3D hierarchical nanostructured polymer hydrogel, with simple and effective tunability over the pore size distribution. The optimized surface area reached a record-high of 4196 m2 g-1 among carbon-based materials. This high surface area along with the abundant micro/narrow mesopores (1.94 cm3 g-1 with d \u3c 4 nm) results in a record-high CO2 capacity (28.3 mmol g-1 at 25 °C and 30 bar) among carbons. This carbon also showed reasonable CO2/CH4 selectivity and excellent cyclability. In addition, this work for the first time combines experimental studies with first-principle molecular simulations for high-pressure CO2 adsorption on porous sorbents. It was found that at elevated pressures, the CO2 density in the adsorbed phase is significantly enhanced in the micro- and narrow mesopores with quantitative values provided for CO2 density. Furthermore, surface nitrogen functionalities have a trivial contribution to the CO2 uptake at high pressures. These findings emphasize the importance of being able to tune a sorbent’s pore size to achieve high CO2 uptake. Thus, the simulation studies help in our understanding of our sorbent’s superior performance as well as provides useful insight into future sorbent development

Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered