Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid algorithm of lattice Boltzmann method and finite difference–based lattice Boltzmann method for viscous flows

In this paper, a hybrid algorithm of the lattice Boltzmann method (LBM) and the finite difference LBM (FDLBM) is proposed. The conventional LBM is known for its high computational efficiency, whereas the finite difference method can guarantee both the accuracy of the description of the complex geometry and the high resolution of the flow field by a local refined anisotropic body‐fitted mesh. The hybrid algorithm of the LBM and FDLBM takes advantage of the merits of both the methods. Four benchmark flow problems are simulated to verify the hybrid algorithm, namely, the lid‐driven cavity flow, the flow past a static circular cylinder, the flow past an impulsively started cylinder, and the flow past 2 cylinders in tandem configuration. Results from these simulations show that the hybrid method proposed in the present work works very well for viscous flows.
Programming theorems on enumerator

This paper deals with the examination of the programming patterns best known by programmers: the programming theorems. It is a significant issue that in what way these patterns can be formulated in order to solve a relatively broad spectrum of problems using a small number of patterns. In this paper, the well known programming theorems are applied to the processing of enumerators. To this end, the robustness of patterns gained this way will be presented, and it will be also pointed out how the programs thus constructed can be implemented in the modern object-oriented programming environments: in language C++, Java and C#.
A LOCAL TIME STEPPING SOLVER FOR 1D BLOOD FLOW: APPLICATION TO THE ADAN MODEL

We present a nite volume solver for one-dimensional blood ow simulations in networks of elastic and viscoelastic vessels, featuring high-order space-time accuracy and local time stepping (LTS). The solver is built on: (i) a high-order nite-volume type numerical scheme (3, 5); (ii) a high-order treatment of the numerical solution at internal vertexes of the network, often called junctions (6, 7); (iii) an accurate LTS strategy (4). After verifying the accuracy of the proposed methodology by empirical convergence tests, we apply the LTS scheme to the Anatomically Detailed Arterial Network model (ADAN) (1, 2). Such model comprises 2142 arterial vessels, reaching an unprecedented level of detail in the anatomical description of the human arterial vasculature, with vessel lengths ranging from less than a millimeter up to tens of centimeters. We examine the gain in execution time with respect to standard global time stepping for serial and parallel versions of the solver when applied to two model settings, corresponding to purely elastic and to elastic/viscoelastic vessels. Moreover, we illustrate the eect of using a simple p-adaptivity approach and discuss the advantages and implications of using high-order schemes for one-dimensional blood ow solutions in complex networks. The proposed methodology can be extended to any other hyperbolic system for which network applications are relevant.
Validity boundary of orbital-free molecular dynamics method corresponding to thermal ionization of shell structure

With $^{6}\mathrm{Li}\text{D}$ as an example, we show that the applicable region of the orbital-free molecular dynamics (OFMD) method in a large temperature range is determined by the thermal ionization process of bound electrons in shell structures. The validity boundary of the OFMD method is defined roughly by the balance point of the average thermal energy of an electron and the ionization energy of the lowest localized electronic state. This theoretical proposition is based on the observation that the deviation of the OFMD method originates from its less accurate description to the charge density in partially ionized shells, as compared with the results of the extended first-principles molecular dynamics method, which well reproduces the charge density of shell structures.
Spectral content of cloud cavitation about a sphere

The physics and spectral content of cloud cavitation about a sphere are investigated in a variable-pressure water tunnel using dynamic surface pressure measurement and high-speed imaging. Experiments are conducted using a polyvinyl chloride sphere at a Reynolds number of $1.5\times 10^{6}$ with cavitation numbers, $\unicode[STIX]{x1D70E}$ , ranging from inception to supercavitation. Three distinct shedding regimes are identified: a uni-modal regime for $\unicode[STIX]{x1D70E}>0.9$ and two bi-modal regimes for $0.9>\unicode[STIX]{x1D70E}>0.675$ and $0.675>\unicode[STIX]{x1D70E}>0.3$ . For small cavity lengths ( $\unicode[STIX]{x1D70E}>0.9$ ), Kelvin–Helmholtz instability and transition to turbulence in the overlying separated boundary layer form the basis for cavity breakup and coherent vortex formation. At greater lengths ( $\unicode[STIX]{x1D70E}<0.9$ ), larger-scale shedding ensues, driven by coupled re-entrant jet formation and shockwave propagation. Strong adverse pressure gradients about the sphere lead to accumulation and radial growth of re-entrant flow, initiating breakup, from which, in every case, a condensation shockwave propagates upstream causing cavity collapse. When the shedding is most energetic, shockwave propagation upstream may cause large-scale leading edge extinction. The bi-modal response is due to cavity shedding being either axisymmetric or asymmetric. The two bi-modal regimes correspond to $\unicode[STIX]{x1D70E}$ ranges where the cavity and re-entrant jet either remain attached or become detached from the sphere. There is a distinct frequency offset at transition between regimes in both shedding modes. Despite the greater cavity lengths at lower $\unicode[STIX]{x1D70E}$ values, the second bi-modal regime initially exhibits shorter shedding periods due to increased cavity growth rates. The second regime persists until supercavitation develops for $\unicode[STIX]{x1D70E}<0.3$ .
Status of SPES Facility for Acceleration of High Intensity Protons and Production of Exotic Beams

In 2010 the SPES project [1] has entered in the construction phase at Laboratori Nazionali di Legnaro (LNL), Italy. The new high power cyclotron is being assembled and tested by BEST Cyclotron Systems (BCSI) company [2] in Canada and the installation at LNL site is scheduled for fall 2014. Such machine is able to deliver two simultaneous proton beams in the energy range of 3570 MeV and current varying within 250-500 μA and the facility has been designed in order to serve at the same time two different experimental areas. The three main uses of the high power beams are: production of radioactive beams (RIBs) by ISOL technique, production of radioisotopes for research purpose and high intensity neutron beams generation. The configuration of the facility and the further capabilities as multipurpose experimental laboratory will be presented.
Telemetry Systems for Diabetes Mellitus

Diabetes mellitus (DM) is a chronic disease affecting large numbers of people worldwide and this number is continuously increasing. Self-management is essential for attaining optimal long-term glucose control, and requires careful recording of food intake, glycemic values, insulin doses and other information. Until now, values have usually been recorded in a paper diary. Mobile healthcare and telemedicine provide new possibilities of care models for chronic diseases. There is a big potential to increase adherence to selfmanagement of DM with the use of smartphones and digital therapeutics interventions. This thesis focuses on two issues: The first one is the development of a mobile application (app) for DM control and selfmanagement and presents the results of long-term usage of this system in the Czech Republic. The development was based on cooperation with both clinicians and patients. The mobile app was available free-of-charge on Google Play Store from the middle of 2014 until 2019 and suitable users who downloaded the app were selected for the long-term analysis. The second part of this thesis describes framework for calculation of glycemic variability for Continuous Glucose Monitoring (CGM) data. The Matlab framework implements important measures of glycemic variability and was used for CGM data extraction in several clinical studies. A few requirements for further extension of the functionality such as data filtering or time spent in hypoglycemia have emerged from them. The thesis shows that the usability of a diabetes mellitus self-management smartphone mobile app and web-based systems could be satisfactory and promising. Nonetheless, some better ways to motivate people with diabetes to participate in self-management are needed. Further studies involving a larger number of patients are needed in order to assess the effect on long-term diabetes management.
Karhunen-Loève transform for Fragile Watermarking

This technical report presents a fragile watermarking technique useful to discover image manipulations at block level. The proposed algorithm is based on the Karhunen-Loève linear transform (KLT): it modifies some of the KLT coefficients obtained from a secret base defined by a secret key image so that these coefficients contain a binary pseudo-random message. A genetic algorithm (GA) is used to compensate for rounding errors introduced by inverse transforming in the integer pixel domain.
Optimizing Automata Learning via Monads.

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design, prove correct, and implement a wide class of such optimizations. The former perspective on monads allows us to develop a new algorithm and accompanying correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that our approach allows us to uniformly capture existing algorithms, develop new ones, and add optimizations. The latter perspective allows us to effortlessly translate the theory into practice: we provide a Haskell library implementing our general framework, and we show experimental results for two specific instances: non-deterministic and weighted automata.
Longitudinal parameters measurement at BEPC II storage rings

A new upgrade to BEPC II has been deployed to improve the luminosity and maximum energy. After the upgrade, the maximum beam energy will reach to 2.8 GeV, and the optimized luminosity at 2.35 GeV will be tripled through squeezing the bunch length and the vertical beta function at the interaction point β y *. Therefore, one more RF cavity will be added to each ring to increase the RF voltage to 3.3 MV. In order to study the feasibility of this upgrade and the longitudinal beam characteristics after the upgrade, some beam-based experiments were carried out at the electron and positron rings of BEPC II. Bunch length and its variation with RF gap voltage are measured, shows that it can be squeezed by approximately 30% at 3.3 MV compared to the normal 1.65 MV operation voltage. Bunch synchronous phases at different RF voltages are also obtained by fitting the variation of bunch centroid with RF voltage at low current. The longitudinal broadband coupling impedance is measured with streak camera, real part impedance is obtained from synchronous phase shift with current, and the imaginary part from bunch lengthening with current. Energy loss per turn at 2.474 GeV is 363.2 ± 14.5 keV by measuring synchrotron tune varies with RF voltages.
Evaluating the Impact of Transition spaces on the Indoor comfort conditions of a Naturally Ventilated Institution Building

Transition spaces connect between the interior space and the exterior environment and it acts as an intermediate or a third space. A well-designed transition space acts as a microclimate modifier for the interior spaces. The current study focuses on the role of transition spaces in creating a comfortable thermal environment in classrooms of an educational institute, located in a region with hot humid climate. The aim of the study is to investigate the impact of transition spaces upon the creation of indoor thermal comfort. For this study, the quantitative data was collected through field measurements in terms of air temperature on a typical summer day. Further, the field data was also collected from different orientations and various classrooms within the campus of the institution. Adaptive thermal comfort metrics was used to compare the performance on the built geometry. The spatial distribution of adaptive thermal comfort was simulated using IESVE software. Further, new scenarios were proposed and developed for the built geometry by altering the built parameters like aspect ratio and the depth of the transition space. The indoor thermal comfort performance of the proposed scenarios was compared with the existing conditions so as to establish a relationship between the built parameters and comfort conditions. A significant improvement was evident in the comfort level of indoor spaces, which suggest that the transition spaces optimize the indoor space comfort levels by reducing the indoor temperature by about 0.5°C to 2°C. Thus, the study proves that the changes in the aspect ratio and corridor width significantly impacts the indoor comfort. The current study outcomes can further help the designers to understand that the best efficient transition space has a positive impact upon a healthy learning space.
Utilization of ELVs in architecture and construction sector - An implication towards sustainability

In a contemporary scenario, the ‘reduce, reuse, and recycle’ is extended to 10 ‘R’s as per the studies conducted earlier. Agriculture, industry, packing, transportation, human settlements and other sectors’ resources are either utilised as alternatives or partial substitutes in the construction industry. In current study, the authors investigate different applications of materials and components recovered or deconstructed from the End-of-Life Vehicles (ELV) in architecture and civil industries. The authors used thematic analysis in this study. The methods used in building the envelope and interiors and the manufacturing of construction materials were used to categorise the application of secondary resources, mined from the scrapped vehicles. The findings suggest that re-contextualizing the disassembled ELV components necessitates thinking skills that transcend the standard frames of reference and ways to convert the concepts into real-world situations. Upcycling or upgrading the secondary resources, derived from ELVs, to manufacture products for the construction sector is supported by interdisciplinary, multi-disciplinary or trans-disciplinary approaches.
DEEP VISUAL RECOMMENDATION SYSTEM

Recommendation system is a filtering system that predicts ratings or preferences that a user might have. Recommendation system is an evolved form of our trivial information retrieval systems. In this paper, we present a technique to solve new item cold start problem. New item cold start problem occurs when a new item is added to a shopping website like Amazon.com. There is no metadata for this item, no ratings and no reviews because it’s a new item in the system. Absence of data results in no recommendation or bad recommendations. Our approach to solve new item cold start problem requires only an image of a new item. A deep learning architecture is used to extract feature vector from an image. Using a distance metric, the distance between various image feature vectors are calculated. Finally, the model recommends most similar items to the users.
Robust Lightweight Object Detection

Robust Lightweight Object Detection By Siddharth Kumar Object detection is a very challenging problem in computer vision and has been a prominent subject of research for nearly three decades. There has been a promising increase in the accuracy and performance of object detectors ever since deep convolutional networks (CNN) were introduced. CNNs can be trained on large datasets made of high resolution images without flattening them, thereby using the spatial information. Their superior learning ability also makes them ideal for image classification and object detection tasks. Unfortunately, this power comes at the big cost of compute and memory. For instance, the Faster R-CNN detector required 180 billion FLOPs for training, and has over 100 million parameters. In this project, we explore the popular state-of-the-art object detectors and present their contributions and shortcomings. Then we explore the recent lightweight detectors which try to address the issue of high resource requirements by building leaner models. Building upon the contributions of the state-of-the-art object detectors, and recent developments in CNN training, we propose our own lightweight detector. We proposed a novel CNN block, to improve the inter-channel dependency in feature maps, called the inter-channel dependency block (ICDB). Through experiments on benchmark datasets we demonstrated our model attains better accuracy compared to the previous methods. Three benchmarking datasets PASCAL VOC 2007, KITTI and COCO have been used to demonstrate that our model scales well to different scenarios.
Comparison of Sampling Methods for Predicting Wine Quality Based on Physicochemical Properties

Using the physicochemical properties of wine to predict quality has been done in numerous studies. Given the nature of these properties, the data is inherently skewed. Previous works have focused on handful of sampling techniques to balance the data. This research compares multiple sampling techniques in predicting the target with limited data. For this purpose, an ensemble model is used to evaluate the different techniques. There was no evidence found in this research to conclude that there are specific oversampling methods that improve random forest classifier for a multi-class problem.
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs

In this thesis, we present KLARAPTOR (Kernel LAunch parameters RAtional Program estimaTOR), a freely available tool to dynamically determine the values of kernel launch parameters of a CUDA kernel. We describe a technique for building a helper program, at the compile-time of a CUDA program, that is used at run-time to determine near-optimal kernel launch parameters for the kernels of that CUDA program. This technique leverages the MWP-CWP performance prediction model, runtime data parameters, and runtime hardware parameters to dynamically determine the launch parameters for each kernel invocation. This technique is implemented within the KLARAPTOR tool, utilizing the LLVM Pass Framework and NVIDIA Nsight Compute CLI profiler. We demonstrate the effectiveness of our approach through experimentation on the PolyBench benchmark suite of CUDA kernels.
Over-actuated vehicles path tracking algorithms

The emergence of autonomous vehicles in the public transport sector has sparked increasing interest and recognition as a solution in the transportation industry. Consequently, the significance of pathtracking algorithms has escalated, accompanied by heightened demands on these control systems. This thesis aims to present a design of a path-tracking algorithm tailored explicitly for over-actuated vehicles, which deviate from conventional platforms. To begin, the thesis provides a comprehensive overview of the kinematics and dynamics regarding the motion of over-actuated vehicles, incorporating the derivation of relevant mathematical models. These models serve as the foundation for designing and evaluating the proposed algorithms. Furthermore, the thesis explores the distinctions between reactive algorithms and those incorporating a lookahead component of the reference path. The primary algorithm proposed is augmented with additional features and subsequently assessed across diverse driving scenarios. Performance and driving comfort comparisons are conducted between the developed algorithms and a Stanleyinspired controller. Lastly, the thesis concludes with the integration of the algorithm into a validation platform, enabling real hardware platform validation. This integration serves as a pivotal step in assessing the algorithm’s functionality.
Bounds for the Clayton copula

We provide two upper bounds on the Clayton copula Cθ(u1,.,un) if θ > 0 and n ≥ 2 and a lower bound in the case θ ∈ [-1,0) and n ≥ 2. The obtained bounds provide a nice probabilistic interpretation related to some negative dependence structures and also allow defining three new two-dimensional copulas which tighten the classical Frechet–Hoeffding bounds for the Clayton copula when n = 2.
Fostering European SMEs' internationalization using big data: the BIZMAP application

This paper proposes a decision-making tool (BIZMAP) that enables European small and medium-sized enterprises (SMEs) to visualize the most economically attractive European regions for the internationalization of their business activities. Building on more than 80 variables coming from seven different open access databases, we take advantage of big data and machine learning methods to include the most relevant ones in a standard gravity model of trade. In the end, we implement an interactive data visualization tool inside our BIZMAP application. Depending on the sector and the home country, we provide SMEs with a ranking of most promising European countries. Importantly, BIZMAP not only enables SMEs to understand what are the main drivers of this score but also offers the possibility to compare the 281 European regions with each other. Hence, by reducing information uncertainty abroad, BIZMAP is likely to improve the SMEs’ analysis of new markets through the visualization of harmonized territorial attractiveness indicators.
Interpretable machine learning models to predict short-term postoperative outcomes following posterior cervical fusion

By predicting short-term postoperative outcomes before surgery, patients who undergo posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that reduce the likelihood of unfavorable outcomes. We developed machine learning models for predicting short-term postoperative outcomes and incorporate these models into an open-source web application in this study. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify patients who underwent PCF surgery. Prolonged length of stay, non-home discharges, and readmissions were the three outcomes that were investigated. To predict these three outcomes, machine learning models were developed and incorporated into an open access web application. A total of 6277 patients that underwent PCF surgery were included in the analysis. The most accurately predicted outcome in terms of the area under the receiver operating characteristic curve (AUROC) was the non-home discharges with a mean AUROC of 0.812, and the most accurately predicting algorithm in terms of AUROC was the LightGBM algorithm with a mean AUROC of 0.766. The following URL will take users to the open access web application created to provide predictions for individual patients based on their characteristics: https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NSQIP-PCF. Machine learning techniques have a significant potential for predicting postoperative outcomes following PCF surgery. The development of predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis as the amount of data in spinal surgery keeps growing. Here, we present predictive models for PCF surgery that are meant to accomplish the aforementioned goals and make them publicly available.