Miracle-dz/UniFAR-FTUs huggingface dataset:
A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract

Within the human respiratory tract (HRT), virus diffuses through the periciliary fluid (PCF) bathing the epithelium. But virus also undergoes advection: as the mucus layer sitting atop the PCF is pushed along by the ciliated cell’s beating cilia, the PCF and its virus content are also pushed along, upwards towards the nose and mouth. While many mathematical models (MMs) have described the course of influenza A virus (IAV) infections in vivo, none have considered the impact of both diffusion and advection on the kinetics and localization of the infection. The MM herein represents the HRT as a one-dimensional track extending from the nose down towards the lower HRT, wherein stationary cells interact with IAV which moves within (diffusion) and along with (advection) the PCF. Diffusion was found to be negligible in the presence of advection which effectively sweeps away IAV, preventing infection from disseminating below the depth at which virus first deposits. Higher virus production rates (10-fold) are required at higher advection speeds (40 μm/s) to maintain equivalent infection severity and timing. Because virus is entrained upwards, upper parts of the HRT see more virus than lower parts. As such, infection peaks and resolves faster in the upper than in the lower HRT, making it appear as though infection progresses from the upper towards the lower HRT, as reported in mice. When the spatial MM is expanded to include cellular regeneration and an immune response, it reproduces tissue damage levels reported in patients. It also captures the kinetics of seasonal and avian IAV infections, via parameter changes consistent with reported differences between these strains, enabling comparison of their treatment with antivirals. This new MM offers a convenient and unique platform from which to study the localization and spread of respiratory viral infections within the HRT.
Potential global geographical distribution of Lolium temulentum L. under climate change

Invasive alien plants posed a significant threat to natural ecosystems, biodiversity, agricultural production, as well as human and livestock health. Lolium temulentum, an annual invasive alien weed with fibrous roots, can reduce wheat production and cause economic losses. Moreover, the consumption of grains or cereal products mixed with darnel can cause dizziness, vomiting, and even death. Therefore, darnel is regarded as one of ″the worst weeds around the world″. In the present study, we predicted the potential global geographical distribution of L. temulentum using an optimal MaxEnt model, based on occurrence records and related environmental variables. The mean AUC, TSS, and KAPPA were 0.95, 0.778, and 0.75, indicating the MaxEnt model accuracy was excellent. The significant environmental variables, including the mean temperature of coldest quarter (bio 11), precipitation of coldest quarter (bio 19), temperature annual range (bio 7), and annual precipitation (bio 12), produced a great impact on the potential global geographical distribution of L. temulentum. Under the current climate, L. temulentum was primarily distributed in south-eastern Asia, Europe, and south-eastern North America. The widest total suitable habitat was distributed in Asia, covering nearly 796 × 104 km2. By the 2050s, the potential geographical distribution of L. temulentum was expected to decrease in the Northern Hemisphere, and shrink gradually in southern America, Africa, and Oceania. Moreover, the distribution center of L. temulentum was expected to shift from Asia to Europe. Based on these predictions, changes in the suitable habitats for L. temulentum between Europe and Asia warrant close attention to prevent further spread.
Quantification of wake unsteadiness for low-Re flow across two staggered cylinders

This work performs a numerical investigation on the two-dimensional flow across two circular cylinders in staggered arrangement at Re = 100. The seaparting distances between the centers of the cylinders are D/d = 4–10 with ΔD/d = 2 and T/d = 0.0–2.0 with ΔT/d = 0.5 in the streamwise and transverse directions, respectively, in which d is the cylinder diameter. Although the low-Re flow across staggered cylinders has been studied in a number of works, the authors mainly concerned about the identification and transition of various flow patterns. In this work, our objective is to quantitatively reveal the characteristics of flow unsteadiness as affected by the two separating distances. The flow unsteadiness is assessed from several aspects, including the spatial distributions and temporal variations of instantaneous flow patterns, fluctuating characteristic quantities, and fluctuating flow in the gap and in the near- and far-wake regions. To investigate the inherent instability of the flow, the global linear stability and sensitivity analysis is further carried out to demonstrate the unstable mode of perturbation growth and the critical flow patterns that destabilize the flow. The numerical results reveal that the wake flow between the two centerlines and beside the upstream cylinder is the most intensely perturbed. The flow around the downstream cylinder exhibits great fluctuation as perturbed by the destabilized shear layer of the upstream cylinder. The flow downstream of both cylinders shows multiple peak fluctuation of velocity because of the complex interactions between the destabilized shear layer and the wake vortices, resulting in the bidirectional transverse propagation of fluctuation. The stability analysis demonstrates that the unstable mode of perturbation growth is more significant in the far-wake region as the two cylinders are placed in proximity; the sensitivity analysis shows that the gap flow is crucial for the flow destabilization at small D, while the wake flow of cylinder-B is more significant for large D.
Hybrid Model for Energy Consumption Forecasting in Buildings Stocks in Tropical Regions

Electric energy consumption forecasting is a relevant issue to design and implement public policies related to energy generation and distribution at urban scale. This problem has been addressed from different standpoints, including traditional time series analysis and machine learning techniques, focused on the prediction of future energy demand according to metered data. This work proposes a hybrid model, based on computer simulation results from the City Energy Analyst toolbox and metered environmental and energy consumption data from a representative set of buildings located in Singapore. Such model is intended to provide reliable energy demand forecasts by using regression models. Three different regression methods were evaluated: a traditional SARIMA model and both NARX and Recurrent neural network architectures. The results obtained using this approach point out that RNN models provide accurate forecasts for 1, 12 and 24 hours, outperforming NARX based models, while the SARIMA is, in general, unable to represent the electrical demand time series patterns.
Design of a bidirectional DC/DC converter for a hybrid electric drive system with dual-battery storing energy

Electric vehicles are projected to play an important role in the present and ensuing transportation as global environmental and energy issues become more serious. Looking into the growing popularity of electric vehicles, we need to pay even more attention to battery energy storage systems. Electric vehicles in the traditional sense rely on a battery to provide electricity. Influential requirements (acceleration and braking) will deteriorate the battery, or it may not be feasible to use it at all in some instances, due to the battery’s compactness. This paper proposes a Bidirectional DC/DC Converter topology and investigates its operation modes. The proposed converter can be used in hybrid electric vehicles. Hybrid electric vehicles use this converter type to connect a primary battery (ES1), an extra battery (ES2), and an adjustable voltage bus. Both step-up (i.e., Dual-source low-voltage powering mode) and step-down (i.e., energy-regenerating high-voltage dc-link mode) modes of operation are possible with the proposed converter, allowing bidirectional power flow. Furthermore, the model can handle the flow of electricity between any two low-voltage sources. For the proposed Bidirectional DC/DC Converter, two ways of power transmission, circuit design, and operation modes are analyzed. A MATLAB-Simulink model of the proposed, and the system is developed based on a Digital Signal Processor flow chart, and the results of the simulation as well as the performance of the system are discussed.
Automatic Multiple Document Text Summarization using Wordnet and Agility Tool

The number of web pages on the World Wide Web is increasing very rapidly. Consequently, search engines like Google, AltaVista, Bing etc. provides a long list of URLs to the end user. So, it becomes very difficult to review and analyze each web page manually. That’s why automatic text sumarization is used to summarize the source text into its shorter version by preserving its information content and overall meaning. This paper proposes an automatic multiple documents text summarization technique called AMDTSWA, which allows the end user to select multiple URLs to generate their summarized results in parallel. AMDTSWA makes the use of concept based segmentation, HTML DOM tree and concept blocks formation. Similarities of contents are determined by calculating the sentence score and useful information is extracted for generating a comparative summary. The proposed approach is implemented by using ASP.Net and gives good results.
Exploring opportunities for AI supported medication error categorization: A brief report in human machine collaboration

Understanding and mitigating medication errors is critical for ensuring patient safety and improving patient care. Correctly identifying medication errors in the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports can be difficult because of the complexities of medication error concepts. We took a user-centered design approach to support the medication error categorization workflow process with artificial intelligence (AI). We developed machine learning models to categorize medication error terms. The average F1-score, precision, recall, and area under the precision recall curve for 18 Medical Dictionary for Regulatory Activities (MedDRA) Lower Level Term (LLT) relating to medication errors were 0.88, 0.92. 0.85, and 0.83 respectively. We developed a framework to help evaluate opportunities for artificial intelligence integration in the medication error categorization workflow. The framework has four attributes: technical deployment, process rigidity, AI assistance, and frequency. We used the framework to compare two AI integration opportunities and concluded that the quality assurance (QA) opportunity to be a more feasible initial option for AI integration. We then extended these insights into the development and user testing of a prototype application. The user testing identified the highlighting and commenting capabilities of the application to be more useful and sliders and similar report suggestions to be less useful. This suggested that different AI interactions with human highlighting should be explored. While the medication error quality assurance prototype application was developed for supporting the review of direct FAERS reports, this approach can be extended to assist in the workflow for all FAERS reports.
COMPARATIVE ANALYSIS OF 3D POINT CLOUDS GENERATED FROM A FREEWARE AND TERRESTRIAL LASER SCANNER

Abstract. In the recent past, several heritage structures have faced destruction due to both human-made incidents and natural calamities that have caused a great loss to the human race regarding its cultural achievements. In this context, the importance of documenting such structures to create a substantial database cannot be emphasised enough. The Clock Tower of Dehradun, India is one such structure. There is a lack of sufficient information in the digital domain, which justified the need to carry out this study. Thus, an attempt has been made to gauge the possibilities of using open source 3D tools such as VSfM to quickly and easily obtain point clouds of an object and assess its quality. The photographs were collected using consumer grade cameras with reasonable effort to ensure overlap. The sparse reconstruction and dense reconstruction were carried out to generate a 3D point cloud model of the tower. A terrestrial laser scanner (TLS) was also used to obtain a point cloud of the tower. The point clouds obtained from the two methods were analyzed to understand the quality of the information present; TLS acquired point cloud being a benchmark to assess the VSfM point cloud. They were compared to analyze the point density and subjected to a plane-fitting test for sample flat portions on the structure. The plane-fitting test revealed the planarity of the point clouds. A Gauss distribution fit yielded a standard deviation of 0.002 and 0.01 for TLS and VSfM, respectively. For more insight, comparisons with Agisoft Photoscan results were also made.
High-throughput fabrication and semi-automated characterization of oxide thin film transistors

High throughput experimental methods are known to accelerate the rate of research, development, and deployment of electronic materials. For example, thin films with lateral gradients in composition, thickness, or other parameters have been used alongside spatially-resolved characterization to assess how various physical factors affect the material properties under varying measurement conditions. Similarly, multi-layer electronic devices that contain such graded thin films as one or more of their layers can also be characterized spatially in order to optimize the performance. In this work, we apply these high throughput experimental methods to thin film transistors (TFTs), demonstrating combinatorial channel layer growth, device fabrication, and semi-automated characterization using sputtered oxide TFTs as a case study. We show that both extrinsic and intrinsic types of device gradients can be generated in a TFT library, such as channel thickness and length, channel cation compositions, and oxygen atmosphere during deposition. We also present a semi-automated method to measure the 44 devices fabricated on a 50 mm×50 mm substrate that can help to identify properly functioning TFTs in the library and finish the measurement in a short time. Finally, we propose a fully automated characterization system for similar TFT libraries, which can be coupled with high throughput data analysis. These results demonstrate that high throughput methods can accelerate the investigation of TFTs and other electronic devices.
Seismic and Aseismic Fault Growth Lead to Different Fault Orientations

Orientations of natural fault systems are subject to large variations. They often contradict classical Coulomb failure theory as they are misoriented relative to the regional Andersonian stress field. This is ascribed to local effects of structural or stress heterogeneities and reorientations of structures or stresses on the long term. To better understand the relation between fault orientation and regional stresses, we simulate spontaneous fault growth and its effect on the stress field. Our approach incorporates earthquake rupture dynamics, viscoelastoplastic brittle deformation and a rate‐ and state‐dependent friction formulation in a continuum mechanics framework. We investigate how strike‐slip faults orient according to local and far‐field stresses during their growth. We identify two modes of fault growth, seismic and aseismic, distinguished by different fault angles and slip velocities. Seismic fault growth causes a significant elevation of dynamic stresses and friction values ahead of the propagating fault tip. These elevated quantities result in a greater strike angle relative to the maximum principal regional stress than that of a fault segment formed aseismically. When compared to the near‐tip time‐dependent stress field the fault orientations produced by both growth modes follow the classical failure theory. We demonstrate how the two types of fault growth may be distinguished in natural faults by comparing their angles relative to the original regional maximum principal stress. A stress field analysis of the Landers‐Kickapoo fault suggests that an angle greater than ∼25° between two faults indicates seismic fault growth.
Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRMCNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRMCNN method was validated by visualizing output features and analyzing the performance of different geographic objects.
Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks

Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused on the overall prevalent action being executed, but we argue that bimanual human motion cannot always be described sufficiently with a single action label. We present a system for frame-wise action classification and segmentation in bimanual human demonstrations. The system extracts symbolic spatial object relations from raw rgb-d video data captured from the robot's point of view in order to build graph-based scene representations. To learn object-action relations, a graph network classifier is trained using these representations together with ground truth action labels to predict the action executed by each hand. We evaluated the proposed classifier on a new rgb-d video dataset showing daily action sequences focusing on bimanual manipulation actions. It consists of 6 subjects performing 9 tasks with 10 repetitions each, which leads to 540 video recordings with 2 hours and 18 minutes total playtime and per-hand ground truth action labels for each frame. We show that the classifier is able to reliably identify (action classification macro $F_1$-score of 0.86) the true executed action of each hand within its top 3 predictions on a frame-by-frame basis without prior temporal action segmentation.
Improved photocatalytic decomposition of methyl ethyl ketone gas from indoor air environment by using TiO2/graphene oxide

This study investigates the potential application of synthesized TiO2-graphene (Ti/GO) for the decomposition of methyl ethyl ketone (MEKT) in an indoor air environment. XRD and UV-vis studies on the materials confirmed that GO is successfully reduced by TiO2. The effect of various parameters such as catalyst dose, the inlet concentration of MEKT, relative humidity (RH) and gas flowrate were investigated. TiO2-graphene showed greatly improved photocatalytic degradation of the MEKT gas (96.8%) from the indoor air, as compared to commercial TiO2 (32.7%) due to its lower band gap energy, larger specific surface area and the synergistic effect of the graphene nanosheet. The relative humidity was a key factor for the removal of MEKT by photocatalytic oxidation under visible light. The possible photocatalytic mechanism for the removal of MEKT from indoor environments using TiO2-graphene has been discussed in detail. The results are crucial for designing high-performing TiO2-graphene photocatalysts for the degradation of volatile compounds (VOCs) from indoor environments.
Simple Distributed Programming for Scalable Software-defined Networks

Simple Distributed Programming for Scalable Soware-Dened Networks Soheil Hassas Yeganeh Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2015 Soware Dened Networking (SDN) aims at simplifying the process of network programming, by decoupling the control and data planes. is is oen exemplied by implementing a complicated control logic as a simple control application deployed on a centralized controller. In practice, for scalability and resilience, such simple control applications are implemented as complex distributed applications, that demand signicant eorts to develop, measure, and optimize. Such complexities obviate the benets of SDN. In this thesis, we present two distributed control platforms, Kandoo and Beehive, that are straightforward to program. Kandoo is a two-layer control plane: (i) the bottom layer is a colony of controllers with only local state, and (ii) the top layer is a logically centralized controller that maintains the network-wide state. Controllers at the bottom layer run only local control applications (i.e., applications that can function using the state of a single switch) near datapath. ese controllers handle frequent events and shield the top layer. Using Kandoo, programmers ag their applications as either local or non-local, and the ooading process is seamlessly handled by the platform. Our evaluations show that a network controlled by Kandoo has an order of magnitude lower control channel consumption compared to centralized controllers. We demonstrate that Kandoo can be used to adapt and optimize Transform Control Protocol (TCP) in a large-scale High-Performance Computing (HPC) datacenter with a low network overhead. Beehive is a more generic proposal built around a programming model that is similar
Modelling the Influence of Regional Identity on Human Migration

Human migration involves the relocation of individuals, households or moving groups between geographical locations. Aggregate spatial patterns of movement reflect complex interactions among motivations (such as distance, identity, economic opportunities, etc.) that influence migration behaviour and determine destination choice. Gravity models and radiation models are often used to study different types of migration at various spatial scales. In this paper, we propose that human migration models can be improved by embedding regional identities into the model. We modify the existing human migration gravity model by adding an identity parameter based on three different sets of Dutch identity regions. Through analysis of the Dutch internal migration data between 1996 and 2016, we show that adding the identity parameter has a significant effect on the distance distribution. We find that individuals are more likely to move towards municipalities located within the same identity region. We test the impact of regional identity by comparing randomly spatially clustered and optimised identity regions to show that the effects we attribute to regional identity could not be attributed due to chance. Finally, our finding shows that cultural identity should be taken into account and has broad implications on the practice of modelling human migration patterns at large. We find that people living in Dutch municipalities are 3.89 times as likely to move to a municipality when it is located within the same historic identity region. Including these identity regions in the migration model decreases the deviation of the model by 10.7%.