Miracle-dz/UniFAR-FTUs huggingface dataset:
Monte Carlo simulations for the analysis of texture and strain measured with Bragg edge neutron transmission

The transmission spectrum of cold and thermal neutrons displays sudden intensity changes, known as Bragg edges, whose appearance is connected to the crystallographic structure of the investigated sample. However, many difficulties in interpreting and understanding the detected signal exist, in particular when results are evaluated quantitatively. We measured strain and texture properties of different samples utilizing the Bragg edge method on the CONRAD and VSANS instruments at Helmholtz Centre Berlin as well as on ENGIN-X at ISIS, UK. The different results will be compared and validated with conventional diffraction measurements and theoretical considerations. According to this, a Bragg edge neutron transmission simulation was developed to include instrument effects together with sample characteristics. Therefore, a neutron ray-tracing simulation package was applied to emulate the employed instruments in combination with the Bragg edge transmission technique. A new sample module has been created to realize energy-dependent transmission simulations based on neutron cross sections calculations. Towards the analysis of the conducted experiments the virtual sample features the expected strain and texture parameters. The results of the simulation are in good agreement with the experiments and will be discussed with regard to the prospects and limitations of this measurement method and the utilized instruments.
The CFHT north galactic pole faint galaxy survey

As part of a study of large-scale structure at intermediate redshifts, a catalog a faint galaxies has been derived for a 2.2 deg 2 area (maximum angular extent ∼ 2 o .5 × 2 o .5) near the north Galactic pole. The photometric and astrometric calibration methods for the catalog are described in detail; the data comprising the catalog are found to be complete to limiting magnitudes J ≃ 24 and F ≃ 23. Galaxy counts and color distributions have been obtained from the catalog
Analysis of Ventricular Wall Motion Using Color-Coded Tissue Doppler Imaging System

We developed a new color Doppler system by which Doppler signals associated with tissue motion can be determined, and called it the tissue Doppler imaging (TDI) system. Using high-speed scanning, the frame rate was 26–38 F/s, and the pulse repetition frequency was 4.5–6.0 kHz. Under these conditions, the lowest measurable velocity was improved to 2 mm/s. Wall motion toward the transducer was coded as red, and that away from the transducer, as blue. To examine the accuracy and validity of the measured velocity of ventricular wall motion, we performed in vitro and in vivo studies. The results demonstrate that the present TDI system accurately represents the tissue velocity, and is applicable for creating two-dimensional images of the ventricular wall motion in real time, facilitating the visual assessment of abnormal ventricular wall motion.
ML4Science: Psychology takes on ML

—The adoption of asynchronous video interviews (AVIs) in job interviews ultimately involves machines in employment processes. The role and powers of computational methods to analyse these negotiations, often sprinkled with deception, need to be transparently studied and assessed. This paper explores the capacity of low-and high-level features extracted from interviews’ transcripts and audio recordings to align with human-rated performance and deception levels.
Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras

The increase in security threats and a huge demand for smart transportation applications for vehicle identification and tracking with multiple non-overlapping cameras have gained a lot of attention. Moreover, extracting meaningful and semantic vehicle information has become an adventurous task, with frameworks deployed on different domains to scan features independently. Furthermore, approach identification and tracking processes have largely relied on one or two vehicle characteristics. They have managed to achieve a high detection quality rate and accuracy using Inception ResNet and pre-trained models but have had limitations on handling moving vehicle classes and were not suitable for real-time tracking. Additionally, the complexity and diverse characteristics of vehicles made the algorithms impossible to efficiently distinguish and match vehicle tracklets across non-overlapping cameras. Therefore, to disambiguate these features, we propose to implement a Ternion stream deep convolutional neural network (TSDCNN) over non-overlapping cameras and combine all key vehicle features such as shape, license plate number, and optical character recognition (OCR). Then jointly investigate the strategic analysis of visual vehicle information to find and identify vehicles in multiple non-overlapping views of algorithms. As a result, the proposed algorithm improved the recognition quality rate and recorded a remarkable overall performance, outperforming the current online state-of-the-art paradigm by 0.28% and 1.70%, respectively, on vehicle rear view (VRV) and Veri776 datasets.
Extremely Lightweight PUF-based Batch Authentication Protocol for End-Edge-Cloud Hierarchical Smart Grid

Smart grid is gradually replacing traditional grid with two-way communication and improved management. Besides the efficiency and reliability it brings, the smart grid is inevitably fraught with rampant physical and cyber-attack. Although several physical unclonable function (PUF)-based schemes have been proposed, they are unsuited to the end-edge-cloud hierarchical smart grid. This paper proposes a PUF-based batch authentication and key agreement protocol, which protects both meters and gateways and provides end-to-end authentication between meters and the server. By offloading heavy operations from field devices to the server, the computation overhead is reduced substantially. Moreover, we innovatively devolve batch authentication and access control to the gateway, which additionally decreases downlink communication and signaling cost, and is superior to most recent schemes. Our protocol is proved by Tamarin under extended Dolev–Yao adversary and the Real-or-Random model and is evaluated to be secure against various attacks. Using extremely lightweight operations, our protocol is implemented on the MSP430FR5969 microcontroller.
Optimal Consumption for General Diffusions with Absorbing and Reflecting Barriers

Two stochastic control problems of the storage or inventory type are considered for general diffusion processes. In the absorption problem, a diffusion process is controlled by subtracting a nondecreasing withdrawal process. The controlled process is absorbed if it reaches zero. The objective function to be maximized is the expected discounted value of the withdrawals plus a discounted penalty for absorption. In the reflection problem, the process can be controlled by subtracting a withdrawal process and adding a deposit process, and the controlled process must be nonnegative. One seeks to maximize an expected discounted weighted difference in withdrawals and deposits. The value function is computed, and a necessary and sufficient condition for the existence of an optimal policy is given. When they exist, optimal policies are found to be the minimal processes which keep the controlled process inside an interval.
Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.
Overview on Common Genes Involved in the Onset of Glioma and on the Role of Migraine as Risk Factor: Predictive Biomarkers or Therapeutic Targets?

Simple Summary Mutations in hundreds of genes have been identified in gliomas, and most relevant discoveries showed specific gene alterations as potential risk factors for brain tumor onset. The aim of this study was to clarify the function of these genes in triggering or modulating brain tumors, to early diagnosing brain tumor onset in patients affected by a simple headache. We confirmed a significant modulation of CLOCK, BMLA1 and NOTCH genes in glioma, particularly praising NOTCH genes family to be good as potentially attractive therapeutic targets for glioblastoma, strengthening the protective role observed in clinical trials for brain tumors. The investigation of these genes could suggest potential therapeutic targets for treating brain tumors, open up the possibility of personalized treatments that can target each brain tumor’s specific genetic abnormality. Abstract Gliomas are relatively rare but fatal cancers, and there has been insufficient research to specifically evaluate the role of headache as a risk factor. Nowadays, gliomas are difficult to cure due to the infiltrative nature and the absence of specific adjuvant therapies. Until now, mutations in hundreds of genes have been identified in gliomas and most relevant discoveries showed specific genes alterations related to migraine as potential risk factors for brain tumor onset. Prognostic biomarkers are required at the time of diagnosis to better adapt therapies for cancer patients. In this review, we aimed to highlight the significant modulation of CLOCK, BMLA1 and NOTCH genes in glioma onset and development, praising these genes to be good as potentially attractive therapeutic markers for brain tumors. A improved knowledge regarding the role of these genes in triggering or modulating glioma maybe the key to early diagnosing brain tumor onset in patients affected by a simple headache. In addition, investigating on these genes we can suggest potential therapeutic targets for treating brain tumors. These considerations open up the possibility of personalized treatments that can target each brain tumor’s specific genetic abnormality.
Optimisation of a High-Throughput Model for Mucus Permeation and Nanoparticle Discrimination Using Biosimilar Mucus

High-throughput permeation models are essential in drug development for timely screening of new drug and formulation candidates. Nevertheless, many current permeability assays fail to account for the presence of the gastrointestinal mucus layer. In this study, an optimised high-throughput mucus permeation model was developed employing a highly biorelevant mucus mimic. While mucus permeation is primarily conducted in a simple mucin solution, the complex chemistry, nanostructure and rheology of mucus is more accurately modelled by a synthetic biosimilar mucus (BSM) employing additional protein, lipid and rheology-modifying polymer components. Utilising BSM, equivalent permeation of various molecular weight fluorescein isothiocyanate-dextrans were observed, compared with native porcine jejunal mucus, confirming replication of the natural mucus permeation barrier. Furthermore, utilising synthetic BSM facilitated the analysis of free protein permeation which could not be quantified in native mucus due to concurrent proteolytic degradation. Additionally, BSM could differentiate between the permeation of poly (lactic-co-glycolic) acid nanoparticles (PLGA-NP) with varying surface chemistries (cationic, anionic and PEGylated), PEG coating density and size, which could not be achieved by a 5% mucin solution. This work confirms the importance of utilising highly biorelevant mucus mimics in permeation studies, and further development will provide an optimal method for high-throughput mucus permeation analysis.
Finite element methods for high speed flows

An explicit finite element based solution procedure for solving the equations of compressible viscous high speed flow is presented. The method uses domain splitting to advance the solution with different timesteps on different portions of the mesh. For steady inviscid flows, adaptive mesh refinement procedures are successfully employed to enhance the definition of discontinuities. Preliminary ideas on the application of adaptive mesh refinement to the solution of problems involving steady viscous flow are presented. Sample timings are given for the performance of the finite element code on modern supercomputers.
A Hybrid Human-In-The-Loop Framework for Fact Checking

Online misinformation is posing a serious threat for the modern society. Assessing the veracity of online information is a complex problem which nowadays is addressed by heavily relying on trained fact-checking experts. This solution is not scalable and, also due the importance of the problem the issue gained the attention of the scientific community, which proposed many AI-based automatic solutions. Despite the efforts made, the effectiveness of such approaches is not yet enough to allow them to be used without supervision. In this position paper, we propose a hybrid human-in-the-loop framework for fact-checking: we address the misinformation issue by relying on a combination of automatic AI methods, crowdsourcing ones, and experts. We study the single components of the frameworks as well as their interactions, and we propose an interleaving of the different components which we believe will serve as useful starting point for the future research towards effective and scalable fact-checking.
Argumentation for Knowledge Base Inconsistencies in Hybrid Intelligence Scenarios

Hybrid Intelligence (HI) is the combination of human and machine intelligence, expanding human intellect instead of replacing it. In HI scenarios, inconsistencies in knowledge bases (KBs) can occur for a variety of reasons. These include shifting preferences, user’s motivation and or external conditions (for example, available resources and environment can vary over time). Argumentation is a potential method to address such inconsistencies as it provides a mechanism for reasoning with conflicting information, with natural explanations that are understandable to humans. In this paper, we investigate the capabilities of Argumen-tation in representing and reasoning about knowledge of both human and artificial agents in the presence of inconsistency. Moreover, we show how Argumentation enables Explainability for addressing problems in Decision-Making and Justification of an opinion. In order to investigate the applicability of Argumentation in HI scenarios, we demonstrate a mapping of two specific HI scenarios to Argumentation problems. We analyse to what extent of Argumentation is applicable by clarifying the practical inconsistency types of the HI scenarios that Argumentation can address. These include inconsistencies related to recommendations and decision making. We then model particularly the presentation of conflicting information for each scenario based on the form of argument representation.
Biocueing for body awareness and emotion regulation for patients with borderline personality disorder: An exploratory multiple baseline design

Emotion dysregulation is considered to be the hallmark of borderline personality disorder (BPD) and may stem from poor body awareness. Biocueing – i.e. providing real-time feedback, in everyday life, on physiological arousal from bodily measurements – shows a promising perspective in augmenting existing treatment approaches for BPD. Aims of this study were to develop a biocueing protocol using a smartwatch application designed specifically for patients with BPD and evaluate its potential efficacy in improving body awareness and emotion regulation using a multiple baseline design. Nine participants with BPD, admitted to residential dialectic behavior therapy (DBT), received 3 weeks of biocueing preceded by baseline measurements that varied randomly (1-3 weeks), and 2 weeks follow-up. Participants reported daily on body awareness, and emotion regulation was evaluated at four different assessment points. Results showed that biocueing has good acceptance and feasibility when added to DBT. No significant effect was found on daily reported body awareness. No reliable change was found on self-reported body awareness and emotion regulation. Concluding interviews revealed an added value of biocueing for interoceptive awareness not captured by the current outcome measure. Thus, more studies with innovative methods for measuring body awareness are needed to examine the role of biocueing for patients with BPD.
Reviewing Theoretical and Generalizable Text Network Analysis: Forma Mentis Networks in Cognitive Science

Recommendations for network studies in learning analytics emphasize that network construction requires careful definitions of nodes, relationships between them, and network boundaries. Thus far, researchers in learning analytics have discussed how to operationalize interpersonal networks in learning settings. Analytical choices used in constructing networks of text have not been examined as much. By reviewing examples of text network analysis in learning analytics we demonstrate that convenience-based decisions for network construction are common, particularly when the ties in the text networks are defined as the co-occurrences of words or ideas. We argue that such an approach is limited in its potential to contribute to theory or generalize across studies. This submission presents an alternative approach to network representations of the text in learning settings, using the concept of Forma Mentis Networks. As reported in previous studies, Forma Mentis Networks are network representations either (1) elicited from individuals through free association tasks that capture valence or (2) constructed by analysts creating shared mental maps derived from text. Forma Mentis Networks is a theory-based and scalable approach complementary to the existing set of tools available for the analysis of teaching and learning.
The 12–14 March 1993 Superstorm: Performance of the NMC Global Medium-Range Model

Abstract The performance of the NMC Global Medium-Range Model in forecasting the March 1993 “superstorm” at various lead times is assessed. The forecasts of mean sea level pressure, 1000–500-mb thickness, 500-mb geopotential height and vorticity, 850-mb wind and streamfunction, and precipitation are described, Forecasts of storm track and intensity are shown, and comparisons with the European Centre for Medium-Range Weather Forecasts and the U.K. Meteorological Office forecasts are made. It is shown that the main features of the storm and the planetary-scale developments leading up to it were accurately predicted as much as five days in advance, especially north of its track, but that there were significant underestimates of the early stages of its development in the Gulf of Mexico.
Investigating the User Experience of Mind Map Software: A Comparative Study based on Eye Tracking

—Software for creating mind maps is currently prevalent, and it should have strong usability and create a good user experience. Usability testing can help to uncover flaws in software's usability and support its optimization. This paper took the mind map software "Xmind" and "MindMaster" as study cases and conducted comparative research on three aspects: effectiveness, efficiency, and satisfaction. The research investigated 20 participants' interactions with the two software. Task completion rate, number of errors, and number of requests for help were collected to evaluate the effectiveness. Eye tracking data and task completion time are collected to evaluate efficiency. System usability, interface quality, and emotional dimensions were collected with subjective scales to assess the software’s user satisfaction. The data together led to a conclusion: each software has a few usability issues. The use of jargon to explain functions was costly to learn and quickly undermined users' confidence in using the software; the interface's simplicity impacted satisfaction, although users tended to evaluate utility tools in terms of their ease of use and ease of learning. These findings could be used to optimize utility software.
High APOBEC3B mRNA Expression Is Associated with Human Papillomavirus Type 18 Infection in Cervical Cancer

The APOBEC3 (A3) proteins are cytidine deaminases that exhibit the ability to insert mutations in DNA and/or RNA sequences. APOBEC3B (A3B) has been evidenced as a DNA mutagen with consistent high expression in several cancer types. Data concerning the A3B influence on HPV infection and cervical cancer are limited and controversial. We investigated the role of A3B expression levels in cervical cancer in affected women positive for infection by different HPV types. Tumor biopsies from cancerous uterine cervix were collected from 216 women registered at Hospital do Câncer II of Instituto Nacional de Câncer, and infecting HPV was typed. A3B expression levels were quantified from RNA samples extracted from cervical biopsies using real-time quantitative PCR. Median A3B expression levels were higher among HPV18+ samples when compared to HPV16+ counterparts and were also increased compared to samples positive for other HPV types. In squamous cell carcinoma, HPV18+ samples also showed increased median A3B expression when compared to HPV Alpha-9 species or only to HPV16+ samples. Our findings suggest that A3B expression is differentially upregulated in cervical cancer samples infected with HPV18. A3B could be potentially used as a biomarker for HPV infection and as a prognostic tool for clinical outcomes in the context of cervical cancer.