Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Monitoring System for Elderly Patients Using Intelligent Task Mapping Mechanism in Closed Loop Healthcare Environment

The ageing population’s problems directly impact countries’ socio-economic structure, as more resources are required to monitor the aged population’s health. The growth in human life expectancy is increasing due to medical technologies and nutritional science innovations. The Internet of Things (IoT) is the connectivity of physical objects called things to the Internet. IoT has a wide range of health monitoring applications based on biomedical sensing devices to monitor health conditions. This paper proposes elderly patients’ health monitoring architecture based on an intelligent task mapping approach for a closed-loop IoT healthcare environment. As a case study, a health monitoring system was developed based on the proposed architecture for elderly patients’ health monitoring in the home, ambulance, and hospital environment. The system detects and notifies deteriorating conditions to the authorities based on biomedical sensors for faster interventions. Wearable biomedical sensors are used for monitoring body temperature, heart rate, blood glucose level, and patient body position. Threshold and machine learning-based approaches were used to detect anomalies in the health sensing data. The proposed architecture’s performance analysis is evaluated in terms of round trip time, reliability, task drop rate, and latency performance metrics. Performance results show that the proposed architecture of the elderly patient health monitoring can provide reliable solutions for critical tasks in IoT environments.
A Novel Meshing Method Based on Adaptive Size Function and Moving Mesh for Electromagnetic Finite Element Analysis

A moving meshing algorithm with mesh adaptive size function was proposed in this paper with regard to the modeling speed and solution accuracy of electromagnetic equipment in the optimization design process. In the proposed method, a mesh size function that considers curvature, feature size, and distance gradient restrictions is constructed, which can obtain high quality meshes and avoid excessive iteration; when the finite element mesh domain is deformed, only the mesh nodes close to the moving boundary are allowed to move, and the theory of force-balance is used combined with the second-order boundary projection algorithm to perform iterative optimization of the mesh node positions. The proposed method has the advantages of keeping the original mesh structure and minimum mesh deformation as well as speed up the convergence, save time in the finite element meshing, and ensure the quality of the generated mesh. Then, the proposed method was applied to a 37 kw motor for electromagnetic analysis, and the results obtained proved the accuracy of the algorithm; finally, the effectiveness of the mesh movement algorithm in three-dimensional space was tested by moving the sphere inside the cylinder.
Business Models for Distributed-Simulation Orchestration and Risk Management

Nowadays, industries are implementing heterogeneous systems from different domains, backgrounds, and operating systems. Manufacturing systems are becoming more and more complex, which forces engineers to manage the complexity in several aspects. Technical complexities bring interoperability, risk management, and hazards issues that must be taken into consideration, from the business model design to the technical implementation. To solve the complexities and the incompatibilities between heterogeneous components, several distributed and cosimulation standards and tools can be used for data exchange and interconnection. High-level architecture (HLA) and functional mockup interface (FMI) are the main international standards used for distributed and cosimulation. HLA is mainly used in academic and defense domains while FMI is mostly used in industry. In this article, we propose an HLA/FMI implementation with a connection to an external business process-modeling tool called Papyrus. Papyrus is configured as a master federate that orchestrates the subsimulations based on the above standards. The developed framework is integrated with external heterogeneous components through an FMI interface. This framework is developed with the aim of bringing interoperability to a system used in a power generation company.
From ECG signals to images: a transformation based approach for deep learning

Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).
Mixed Convection of Silica-Molybdenum Disulphide/Water Hybrid Nanoliquid over a Rough Sphere

A steady combined convective motion over a rough sphere with hybrid nanoparticles is analyzed. We have considered silica (SiO2) and molybdenum disulphide (MoS2) nanoadditives which are added in H2O to form MoS2–SiO2/H2O hybrid nanoliquid. The partial differential equations describing the boundary layer flow characteristics are reduced into non-dimensional form with appropriate non-similar reduction. It should be noted that the governing equations have been written using the conservation laws of mass, momentum and energy. These considered equations allow simulating the analyzed phenomenon using numerical techniques. Implicit finite difference approximation and technique of Quasilinearization are utilized to work out the dimensionless control equations. The influence of various physical characteristics included in this challenge, such as the velocity fields and temperature patterns, is investigated. The study of border gradients is performed, which deals with the skin friction and energy transport strength. The plots of computational outcomes are considered, which ascertain that velocity distribution reduces, whilst coefficient of friction at the surface, energy transport strength and temperature distribution augment for enhancing values of hybrid nanofluid. For enhancing magnitude of combined convection parameter, dimensionless velocity distribution, surface drag coefficient and energy transport strength enhance, while temperature distribution diminishes. High impact of hybrid nanofluid on energy transport strength and the surface friction compared to the host liquid and mono nanofluid in presence/absence of surface roughness is shown. Velocity distribution enhances for rising values of velocity ratio parameter. Enhancing values of frequency parameter rise the friction at the surface and energy transport strength. It is also examined that the hybrid nanofluid has a maximum temperature for the blade-shaped nanoparticles and has a low temperature for the spherical-shaped nanoparticles.
Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined.
Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.
HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

As we all know, there are many ways to express emotions. Among them, facial emotion recognition, which is widely used in human–computer interaction, psychoanalysis of mental patients, multimedia retrieval, and other fields, is still a challenging task. At present, although convolutional neural network has achieved great success in face emotion recognition algorithms, it has a rising space in effective feature extraction and recognition accuracy. According to a large number of literature studies, histogram of oriented gradient (HOG) can effectively extract face features, and ensemble methods can effectively improve the accuracy and robustness of the algorithm. Therefore, this paper proposes a new algorithm, HOG-ESRs, which improves the traditional ensemble methods to the ensembles with shared representations (ESRs) method, effectively reducing the residual generalization error, and then combining HOG features with ESRs. The experimental results on the FER2013 dataset show that the new algorithm can not only effectively extract features and reduce the residual generalization error, but also improve the accuracy and robustness of the algorithm, the purpose of the study being achieved. The application of HOG-ESRs in facial emotion recognition is helpful to solve the symmetry of edge detection and the deficiency of related methods in an outdoor lighting environment.
Corporate Reputation of Family-Owned Businesses: Parent Companies vs. Their Brands

The reputation of companies is one of their key success factors. It is therefore necessary to value this intangible asset. In order to detect possible threats quickly, continuous monitoring of corporate reputation plays an important role in this valuation process. Family businesses are an ideal object for reputation management research, as through their brands they integrate tradition and addressability at the same time. The main aim of the paper is to discuss the issue of innovative approaches to the online reputation management. We performed an in-depth analysis of online reputation through an Advanced sentiment analysis on the significant sample of ten largest family-owned businesses in the world. Taking into account all relevant determinants of reputation such as Google as well as major social networks, namely Facebook, Twitter, YouTube, and LinkedIn. As there is a noticeable difference between the marketing communication of the parent company and the marketing communication of the brand owned by the company, the findings of the analyses will provide a better insight into the issue of sustainable brand development. By identify good practices, as well as highlighting weaknesses, our research has the ambition to contribute to the shift of knowledge in the field of reputation management.
MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks

High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) inference approach wherein multiple heterogeneous datasets are fused using MKL paradigm. We formulate the GRN learning problem as a supervised classification problem, whereby genes regulated by a specific transcription factor are separated from other non-regulated genes. A parallel execution architecture is devised to learn a large scale GRN by decomposing the initial classification problem into a number of subproblems that run as multiple processes on a multi-processor machine. We evaluate the approach in terms of increased speedup and inference potential using genomic data from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The results thus obtained demonstrate that the proposed method exhibits better classification accuracy and enhanced speedup compared to other state-of-the-art methods while learning large scale GRNs from multiple and heterogeneous datasets.
Spatial Dependence of the Dipolar Interaction between Quantum Dots and Organic Molecules Probed by Two-Color Sum-Frequency Generation Spectroscopy

Given the tunability of their optical properties over the UV–Visible–Near IR spectral range, ligand-capped quantum dots (QDs) are employed for the design of optical biosensors with low detection threshold. Thanks to non-linear optical spectroscopies, the absorption properties of QDs are indeed used to selectively enhance the local vibrational response of molecules located in their vicinity. Previous studies led to assume the existence of a vibroelectronic QD–molecule coupling based on dipolar interaction. However, no systematic study on the strength of this coupling has been performed to date. In order to address this issue, we use non-linear optical Two-Color SumFrequency Generation (2C-SFG) spectroscopy to probe thick QD layers deposited on calcium fluoride (CaF2) prisms previously functionalized by a self-assembled monolayer of phenyltriethoxysilane (PhTES) molecules. Here, 2C-SFG is performed in Attenuated Total Reflection (ATR) configuration. By comparing the molecular vibrational enhancement measured for QD–ligand coupling and QD– PhTES coupling, we show that the spatial dependence of the QD–molecule interactions (∼1/r3, with r the QD–molecule distance) is in agreement with the hypothesis of a dipole–dipole interaction.
An Improved Sub-Model PLSR Quantitative Analysis Method Based on SVM Classifier for ChemCam Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative and quantitative analysis. Component analysis is a significant issue for the LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam and SuperCam on the Mars 2020 rover. The partial least squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by the ChemCam science team. We innovatively used a support vector machine (SVM) classifier to select the corresponding sub-model. Then conventional regression approaches partial least squares regression (PLSR) was utilized as a sub-model to prove that our selecting method was feasible, effective, and well-performed. For eight oxides, i.e., SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, and K2O, the modified SVM-PLSR blended sub-model method was 34.8% to 62.4% lower than the corresponding root mean square error of prediction (RMSEP) of the full model method. In order to avoid that SVM classifiers classifying the spectrum into an incorrect class, an optimized method was proposed which worked well in the modified PLSR blended sub-models.
A Depth‐Resolved In‐Situ Study of the Reduction and Oxidation of Ni‐Based Anodes in Solid Oxide Fuel Cells

The stability of Ni‐YSZ anodes as part of solid oxide fuel cells (SOFCs) towards redox cycling is an important issue for successfully introducing the technology. Detailed knowledge of the NiO‐Ni transitions and their impact on the mechanical integrity of the whole system is necessary to improve the overall stability. In the present paper, a unique in‐situ X‐ray diffraction setup is presented which allows monitoring of the local structural changes during processing of SOFCs. With this setup technological SOFCs – a half cell and a full cell – were studied with respect to NiO‐Ni transitions in repeated reduction‐oxidation cycles, under conditions relevant for SOFC application. It was found that the redox kinetics is a function of the sample depth. Ni particles further away from the surface were reduced/oxidized at a slower rate than particles close to the surface.
Application and evaluation of knowledge graph embeddings in biomedical data

Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, “knowledge graphs”. The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.
Electron Symmetry Breaking during Attosecond Charge Migration Induced by Laser Pulses: Point Group Analyses for Quantum Dynamics

Quantum simulations of the electron dynamics of oriented benzene and Mg-porphyrin driven by short (<10 fs) laser pulses yield electron symmetry breaking during attosecond charge migration. Nuclear motions are negligible on this time domain, i.e., the point group symmetries G = D6h and D4h of the nuclear scaffolds are conserved. At the same time, the symmetries of the one-electron densities are broken, however, to specific subgroups of G for the excited superposition states. These subgroups depend on the polarization and on the electric fields of the laser pulses. They can be determined either by inspection of the symmetry elements of the one-electron density which represents charge migration after the laser pulse, or by a new and more efficient grouptheoretical approach. The results agree perfectly with each other. They suggest laser control of symmetry breaking. The choice of the target subgroup is restricted, however, by a new theorem, i.e., it must contain the symmetry group of the time-dependent electronic Hamiltonian of the oriented molecule interacting with the laser pulse(s). This theorem can also be applied to confirm or to falsify complementary suggestions of electron symmetry breaking by laser pulses.
Development of a gas chromatograph for trace level measurement of peroxyacetyl nitrate using chemical amplification

A gas chromatographic technique for trace level detection of peroxyacetyl nitrate (PAN) that relies on postcolumn chemical amplification as a means of improving the detection limit has been developed. The GC detector is based on luminol chemiluminescence detection of NO 2 . PAN eluting from the chromatographic column is thermally decomposed, resulting in a free-radical chain reaction in which NO is converted to NO 2 , using CO as a chain carrier. The system operates at optimized carrier gas NO and CO concentrations of 6 ppm and 8%, respectively
Desorption of CO2 from MDEA and Activated MDEA Solutions

A packed column was used for investigating the desorption rate of CO{sub 2} from aqueous methyldiethanolamine (MDEA) and activated MDEA solutions. Experiments were conducted within the temperature range 30--70 C, the concentration of MDEA was 4.28 kmol/m{sup 3}, and the concentration of piperazine (PZ) was 0.10 kmol/m{sup 3} for aqueous activated MDEA solutions. Experimental data confirmed that the kinetics model of absorption CO{sub 2} into aqueous MDEA and activated MDEA solutions can be applicable to the situations in which desorption occurs, and the desorption rate of model predictions agree well with that of experimental determination.