Miracle-dz/UniFAR-FTUs huggingface dataset:
Qualitative Methods in Inverse Scattering Theory: An Introduction

Functional Analysis and Sobolev Spaces. - Ill-Posed Problems. - Scattering by an Imperfect Conductor. - The Inverse Scattering Problem for an Imperfect Conductor. - Scattering by an Orthotropic Medium. - The Inverse Scattering Problem for an Orthotropic Medium. - The Factorization Method. - Mixed Boundary Value Problems. - A Glimpse at Maxwell? s Equations.
Real-Time Energy Management for a Hybrid Electric Vehicle Based on Heuristic Search

To solve air pollution problems and reduce energy costs, hybrid electric vehicles (HEV) are applied for larger scale as a superior solution and their energy management strategies (EMS) are essential for achieving higher fuel efficiency. This paper proposes a reinforcement learning (RL) based real-time EMS for a HEV, which requires no prior knowledge of the future driving conditions. First, considering that the practical driving conditions are mutative, an online recursive Markov Chain model is constructed, which can depict the stochastic environment more accurately. Then, a novel RL framework, heuristic search, is introduced, which means using extra knowledge in the search process. It aims to find an optimal action for a specific state rather than learn a policy for an entire driving cycle like traditional RL algorithms. Consequently, the learning process is significantly efficient and real-time optimization can be realized. Moreover, a Q-table is learned offline, and is used online as a heuristic function to direct the search process, which can accelerate the convergence rate and improve the real-time performance further. In the simulation, the effectiveness of the recursive Markov Chain model and the superiority of the heuristic function are verified. Then, a comparative simulation of heuristic search, Q-learning, dynamic programming (DP) and equivalent consumption minimization strategy is conducted and evaluated. The proposed method consumes 1.63% more fuel compared with DP in a totally unknown cycle, which demonstrates its control performance and real-time capability.
AVALIAÇÃO DA SUSTENTABILIDADE DA PRODUÇÃO SUINÍCOLA

O estudo tem por objetivo identificar o nível de sustentabilidade de uma propriedade rural que desenvolve a atividade suinícola a partir do Sistema de Gestão e Avaliação da Sustentabilidade da Suinocultura. Metodologicamente a pesquisa é descritiva, realizada por meio de estudo de caso junto a uma propriedade rural do município de São Carlos-SC. As etapas para a realização do estudo foram: identificação dos indicadores; realização de entrevista com o produtor rural; aplicação do constructo de indicadores e métricas denominado Sistema de Gestão e Avaliação da Sustentabilidade da Suinocultura – SIGEASS. Os resultados permitem observar as extremidades positivas e negativas da atividade suinícola. Os resultados demonstram a partir do SIGEASS, as externalidades negativas nos indicadores ambientais de avaliação do solo, energia, água e ar/ efeito estufa, indicando a necessidade da implementação de melhorias nestes aspectos da avaliação, enquanto a remuneração do capital investido e da mão de obra apresentaram externalidades positivas.
Introduction to Linear Algebra with Applications

Abstract theory is essential to understanding how linear algebra is applied. Each concept is fully developed presenting natural connections between topics giving students a working knowledge of the theory and techniques for each module covered. Applications have been carefully chosen to highlight the utility of linear algebra in order to see the relevancy of the subject matter in other areas of science as well as in mathematics. Ranging from routine to more challenging, each exercise set extends the concepts or techniques by asking the student to construct complete arguments. End of chapter True/False questions help students connect concepts and facts presented in the chapter. Examples are designed to develop intuition and prepare students to think more conceptually about new topics as they are introduced. Students are introduced to the study of linear algebra in a sequential and thorough manner through an engaging writing style gaining a clear understanding of the theory essential for applying linear algebra to mathematics or other elds of science. Summaries conclude each section with important facts and techniques providing students with easy access to the material needed to master the exercise sets.
INOVASI PROSES DI SUATU INDUSTRI SANDAL

The problem which will be discussed is assembly lines in the shop floor, especially in sewing department. This department used line process system, that arised problems such as process jam, delay, wasting time of workers and too much holding materials. This research introduced process innovation, i.e. forming work-groups among workers. There are two alternative ideas which will be assessed. First, work-groups by process. Second, work-groups by complete line. After choosing the best idea, then the process was followed up with line balancing method with COMSOAL (Computer Method of Sequencing for Assembly Lines). The result showed that the second idea (complete line work groups) is better than the first one. Efficiency improvement is 29.2 % compared by the efficiency before the application of line balancing. Abstract in Bahasa Indonesia : Masalah yang akan dibahas adalah proses kerja pada lantai produksi di bagian jahit yang memberlakukan line process system. Proses ini menimbulkan kemacetan, keterlambatan, arus bolak-balik antar pekerja dan penumpukan material yang mengganggu produktivitas. Pada penelitian ini akan dilakukan inovasi proses pada sistem alur kerja dengan cara membentuk kelompok kerja. Ada dua ide alternatif kelompok kerja yang akan diuji, yaitu pengelompokan berdasarkan proses dan pengelompokan berdasarkan line utuh. Setelah terpilih ide alternatif terbaik, dilakukan keseimbangan lintasan dengan metode COMSOAL (Computer Method of Sequencing for Assembly Lines). Hasil penelitian menunjukkan ide terbaik adalah pengelompokan dengan line utuh. Setelah dilakukan keseimbangan lintasan, diperoleh peningkatan efisiensi sebesar 29,2% dibandingkan kondisi sebelum diterapkan keseimbangan lintasan. Kata kunci: inovasi proses, keseimbangan lintasan.
The natural radial element method

In this work an innovative numerical approach is proposed, which combines the simplicity of low‐order finite elements connectivity with the geometric flexibility of meshless methods. The natural neighbour concept is applied to enforce the nodal connectivity. Resorting to the Delaunay triangulation a background integration mesh is constructed, completely dependent on the nodal mesh. The nodal connectivity is imposed through nodal sets with reduce size, reducing significantly the test function construction cost. The interpolations functions, constructed using Euclidian norms, are easily obtained. To prove the good behaviour of the proposed interpolation function several data‐fitting examples and first‐order partial differential equations are solved. The proposed numerical method is also extended to the elastostatic analysis, where classic solid mechanics benchmark examples are solved. Copyright © 2013 John Wiley & Sons, Ltd.
State of the Art in Monte Carlo Ray Tracing for Realistic Image Synthesis

This full day course will provide a detailed overview of state of the art in Monte Carlo ray tracing. Recent advances in algorithms and available compute power have made Monte Carlo ray tracing based methods widely used for simulating global illumination. This course will review the fundamentals of Monte Carlo methods, and provide a detailed description of the theory behind the latest techniques and algorithms used in realistic image synthesis. This includes path tracing, bidirectional path tracing, Metropolis light transport, scattering equations, irradiance caching and photon mapping.
Formation of discs around super-massive black hole binaries

Abstract We model numerically the evolution of 104M⊙ turbulent molecular clouds in near-radial infall onto 106M⊙, equal-mass supermassive black hole binaries, using a modified version of the SPH code gadget-3. We investigate the different gas structures formed depending on the relative inclination between the binary and the cloud orbits. Our first results indicate that an aligned orbit produces mini-discs around each black hole, almost aligned with the binary; a perpendicular orbit produces misaligned mini-discs; and a counter-aligned orbit produces a circumbinary, counter-rotating ring.
Unsupervised Learning Methods to Explore Automotive Design

Within this paper, we investigate the application of un-supervised learning to image generation, with the intent to create new car designs as a mixture of k existing cars. To accomplish this, we encode images of existing cars into a lower-dimensional latent space, create a new latent vector that represents a convex combination of the existing cars, and finally decode the combined latent vector into a new image. We investigated various methods of compressing vehicles into a lower-dimensional space, including Principal Component Analysis (PCA), a Convolutional Neural Network-based Variational Autoencoder (VAE), and a Resnet18-based VAE. Our methods were evaluated both by their ability to reconstruct a single image of a car, and their ability to design new cars as a mixture of multiple existing cars. The generated images were assessed both on their apparent visual quality and their Frechet Inception Distance to the original dataset of real car images.
Investigation of Cell Ratio Objects by Magnetic Resonance Imaging （MRI）

: The physical model of cell ratio object is used for calculation of additional magnetic fields caused by the interaction of magnatic moments. There have been calculated the additional magnetic fields caused by the interaction of magnetic moments. The spatial density reconstruction of the modeled object with additional magnetic fields was obtained by MRI. The pseudo-inversion matrix method is used for spatial density reconstruction of investigated object. The calculation of additional magnetic fields while reconstruction of small objects gives more precise and full information about investigated objects.
Anatomopathological characterization of pneumonia in guinea pig produced by Streptococcus pneumoniae in intensive breeding

Objective. Characterize macroscopically and microscopically the lesions caused by Streptococcus pneumoniae in samples of guinea pig lung ( Cavia porcellus ) of intensive breeding in Lima. Materials and methods. The necropsy of 138 guinea pigs with clinical signs of pneumonia was performed, and the sample of the affected lungs was taken. Bacterial isolation was performed for the identification of Streptococcus pneumoniae , using blood agar and biochemical tests; Histopathological processing was performed using the hematoxylin eosin (HE) staining of tissue samples preserved in 10% formalin. Results. Of the total lungs diagnosed with pneumonia at necropsy, 17.4% (24/138) was positive for Streptococcus pneumoniae . Of these, 75% (18/24) showed macroscopic characteristics of interstitial pneumonia and 20.87% (5/24) macroscopic characteristics of suppurative bronchopneumonia and 4.17% (1/24) of fibrinous bronchopneumonia. In the microscopic evaluation it was found that 54.17% presented both interstitial bronchopneumonia, 37.50% interstitial pneumonia, and 8.33% of them correspond to bronchopneumonia. Conclusions. Interstitial bronchopneumonia, interstitial pneumonia and bronchopneumonia with a diagnosis of Streptococcus pneumoniae are lesion patterns found in intensive rearing systems in Lima.
Synthesis and Characterization of Spinel Ferrite Co 0.8 Fe 2.2 O 4 Nanoparticle

Cobalt ferrite Co 0.8 Fe 2.2 O 4 nanoparticles were prepared using the sol-gel auto combustion process. The effects of calcination temperature on structural, magnetic, and electrical properties were studied. The cubic spinel phase fashioning of ferrite structure was confirmed using Fourier Transform-Infrared Spectroscopy (FT-IR) and X-ray Diffraction Patterns (XRD). The size of the formed crystallite of ferrite samples is ranged from 24.530 to 49.067 nm and it is found to be dependent on calcination. According to the images, which were taken by a Field Emission-Scanning Electron Microscope (FE-SEM), the particle size increases with raising the calcination temperature. Energy Dispersive Spectrum (EDS) was used to confirm the presence of Co, Fe, and O in all samples. A Vibrating Sample Magnetometer (VSM) was used to study the magnetic properties such as coercivity, saturation magnetization, and remanence field for the as-burnt and calcined samples. All samples exhibited ferrimagnetic behavior. As the calcination temperature rises, saturation magnetization (𝑀 𝑠 ), remanent magnetization (𝑀 𝑟 ) , and squareness ratio (𝑀 𝑟 / 𝑀 𝑠 ) increased. This behavior is related to the spin canting and disturbance in the surface spin. At room temperature, the dielectric loss factor (𝜀′′) , dielectric loss angle (𝑡𝑎𝑛𝛿) , dielectric constant (𝜀′) , and the conductivity 𝜎 𝑎𝑐 of all samples were examined as a function of frequency using the LCR meter. The changes in dielectric properties have been characterized at frequencies ranged from 50Hz to 2MHz based on Koop's theory, Maxwell-Wagner polarization, and electron hopping. As frequency rose, all-dielectric properties exhibited natural behavior.
First report of Caenorhabditis brenneri (Nematoda: Rhabditida) isolated from the cadaver of Philippinella moellendorffi (Stylommatophora: Ariophantidae), a terrestrial slug in the Philippines

Abstract Gastropod-associated nematodes have been previously studied and documented worldwide, with some species forming host-specific association as obligate parasites of molluscs while others form intermediate and temporary association. Philippinella moellendorffi from Imelda, Zamboanga Sibugay, Philippines, were collected, washed and maintained in the laboratory until death. Cadavers were placed on nutrient agar to allow nematode proliferation. Nematode pure culture was obtained using one gravid female for propagation. Morphology and molecular analyses (18S ribosomal DNA (rDNA) and D2–D3 expansion segments of 28S rDNA) were employed as diagnostic tools in identifying the nematode species isolated from P. moellendorffi. The newly isolated nematode was identified as Caenorhabditis brenneri, thus designated as C. brenneri strain IZSP from the Philippines. This is the first record of C. brenneri isolated from the terrestrial slug P. moellendorffi.
Deep Learning for Assisting Annotation and Studying Relationship between Cancers using Histopathology Whole Slide Images

Cancer has been the leading cause of death across the globe and there has been a constant push and research from the scientific community as a whole towards assisting its diagnosis. Particularly the last decade has seen widespread use of computer vision and AI towards tackling cancer diagnosis using both radiological (non-invasive, ex: X-ray, CT ) and pathological modalities (invasive ex: histopathology). A histopathologic Whole Slide Image (WSI) represents digitized image of tissue sample characterized by a large size of up to 109 pixels at maximum resolution. It is considered the gold standard for cancer diagnosis. The routine diagnosis involves experts called pathologists to analyse a slide containing the tissue samples under a microscope. The process is often subjective to cognitive load and the diagnosis is prone to inter and intra pathologist errors. With the digitization of tissue samples as WSI, computerassisted diagnosis would be impactful to address the above issues especially with the advent of deep learning. For this we require models to be trained with large amounts of annotated data as well as understanding the cancer manifestation across organs. In this thesis work, we address two major issues with the help of deep learning techniques. (1) assisting the whole slide image annotation using an expert in the loop and (2) understanding the relationship between cancers and bring to light commonalities of cancer patterns between certain pairs of organs. A typical process of slide diagnosis under a microscope by experts involves exhaustive analysis in scanning across the slides, searching for anomalous/tumorous regions present. Owing to the large dimensions of the histopathology WSI, visually searching for clinically significant regions (patches) is a tedious task for a medical expert. Sequential analysis of several such images further increases the workload resulting in poor diagnosis. A major impediment to automate this task using a deep learning model is the requirement of large amounts of annotated data of WSI patches which is a laborious process and involves exhaustive search for anomalous regions. To tackle this issue, the first part of the thesis work proposes a novel CNN-based, expert feedback-driven interactive learning technique to mitigate this issue. The proposed method seeks to acquire labels of the most informative patches in small increments with multiple feedback rounds to maximize the throughput. It requires the expert to query a patch of interest from a slide and provide feedback to a set of unlabelled patches chosen using the proposed sampling strategy from a ranked list. The proposed technique is applied in a setting that assumes there exists a large cohort of unannotated slides, almost eliminating the need of annotated data upfront; instead learns with an expert involvement. We discuss several strategies for sampling the right set of samples to be labelled by the expert to minimise the expert feedback and maximise the throughput. The
Optimization of Liquid Polyphenol Extraction from Coffee Pulp

Coffee pulp is one of the most abundant residues in the coffee agroindustry. Its inadequate handling and disposition produce contamination of water and soil. However, this waste contains high levels of tannins and other phenolic compounds, which could be extracted, getting added value. A vertical rotational composite design was used to optimize procedures for obtaining total polyphenols and tannins, based on temperature and concentration of sodium hydroxide in the extraction solution, at a fixed time of 30 minutes of contact. The optimal values for the extraction of total polyphenols were obtained at a temperature of 55.8°C, using sodium hydroxide solutions of 2.55 M, achieving the extraction of 116.59 mg GAE/g (mg of gallic acid equivalent per gram of pulp). These values are significantly higher than those reported in other studies. The optimum conditions for the extraction of tannins were a maximum temperature of 64.14°C for the test and concentrations of sodium hydroxide of 2.58 M, which obtained the extraction of 66.07 g GAE per gram of pulp in tannins. Most favorable values for antioxidant capacity were close to those obtained for both polyphenols and tannins. The increase in the concentration of sodium hydroxide of the extraction solution above 2.7 M caused the reduction of the contents of total polyphenols, and tannins. Results show the possibility of valorizing coffee pulp by extracting tannins and other polyphenols and reveal possible uses of process streams. These would allow the formulation of strategies framed within the concepts of green chemistry, circular economy and biorefineries.
RF Accelerator Technology R&D: Report of AF7-rf Topical Group to Snowmass 2021

Accelerator radio frequency (RF) technology has been and remains critical for modern high energy physics (HEP) experiments based on particle accelerators. Tremendous progress in advancing this technology has been achieved over the past decade in several areas highlighted in this report. These achievements and new results expected from continued R&D efforts could pave the way for upgrades of existing facilities, improvements to accelerators already under construction (e.g., PIP-II), well-developed proposals (e.g., ILC, CLIC), and/or enable concepts under development, such as FCC-ee, CEPC, C3, HELEN, multi-MW Fermilab Proton Intensity Upgrade, future Muon Colloder, etc. Advances in RF technology have impact beyond HEP on accelerators built for nuclear physics, basic energy sciences, and other areas. Recent examples of such accelerators are European XFEL, LCLS-II and LCLS-II-HE, SHINE, SNS, ESS, FRIB, and EIC. To support and enable new accelerator-based applications and even make some of them feasible, we must continue addressing their challenges via a comprehensive RF R&D program that would advance the existing RF technologies and explore the nascent ones.
Content based User Preference Modeling in Music Generation

Automatic music generation (AMG) has been an emerging research topic in AI in recent years. However, generating user-preferred music remains an unsolved problem. To address this challenge, we propose a hierarchical convolutional recurrent neural network with self-attention (CRNN-SA) to extract user music preference (UMP) and map it into an embedding space where the common UMPs are in the center and uncommon UMPs are scattered towards the edge. We then propose an explainable music distance measure as a bridge between the UMP and AMG; this measure computes the distance between a seed song and the user's UMP. That distance is then employed to adjust the AMG's parameters which control the music generation process in an iterative manner, so that the generated song will be closer to the user's UMP in every iteration. Experiments demonstrate that the proposed UMP embedding model successfully captures individual UMPs and that our proposed system is capable of generating user-preferred songs.
Computational cognitive methods for precision psychiatry

When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health. Tasks allow highly specific computational and learning processes to be probed. After a brief introduction into computational psychiatry more broadly, this chapter explores how tasks can be used for precision psychiatry. It is argued that identification of disease mechanisms via tasks can facilitate the development of targeted interventions and the targeted administration of therapies. However, the clinical use of tasks currently face issues around reliability and robustness. This chapter describes and discusses four reasons for this: time, strategy, noise and research setting, and describes how these are at least partially amenable to computational techniques. Huys: Precision psychiatry COMPUTATIONAL PSYCHIATRY
Time is Life

The affirmative statement of the existence of extraterrestrial life is tentatively raised to the status of a principle. Accordingly, Fermi's question is answered and the anthropic principle is shown to be falsifiable. The time-scale for the development of life on Earth and the age of the universe are the fundamental quantities upon which the arguments are framed.