Miracle-dz/UniFAR-FTUs huggingface dataset:
Propagation 3D de fissures cohésives X-FEM

Une strategie innovante d'etude quasi-statique de problemes de propagation de fissures a ete proposee dans Code_Aster, couplant des modeles de zones cohesives et la methode des elements finis etendus (XFEM). Elle a ete validee sur des benchmarks issus de la litterature, pour la plupart des experiences realisees sur des eprouvettes de beton. La capacite de la methode a predire des trajets de fissurations complexes pour des problemes tridimensionnels a ainsi ete illustree. Une extension a la dynamique a ete realisee et validee pour les trajets de fissurations droits, les premiers resultats sur trajets courbes etant prometteurs.
Flow Capacity Analysis For Mpl Katowice Pyrzowice Terminals With The Indication Of Zones Requiring Architectonic Changes

Abstract In order to improve the quality of passenger service at an airport terminal it is necessary to continuously monitor the level of the services rendered. Devising a strategy for the process of modernization or extension of an airport, with a view to maintaining or elevating the standards offered, requires a skilled determination of the investment commencement time. The rationale behind such investments is a deliberate prognosis of changes in the numbers of passengers visiting the airport in order to avoid exceeding maximum capacity at peak times. The launch of construction works while maintaining continuity of operations at the airport is connected with a conscious utilization of any available spatial reserves within the existing structures. Analyses of flows of users receiving services in various zones of a building, in consideration of the limit values set for the appropriate standards, are some of the tools that facilitate their proper design. Design that includes the statistical trends of changes in the amount of users receiving service requires a responsible and conscious decision making around the shaping of relations between spatial arrangements and auxiliary technologies. The paper presents different methods of working with large quantities of information which the designer must analyze and comprehend. The aim is to make sound investment and design decisions that would precede and prevent the loss of efficiency and performance in relation to the satisfaction of users.
Influenza A Viral Burst Size from Thousands of Infected Single Cells Using Droplet Quantitative PCR (dqPCR)

An important aspect of how viruses spread and infect is the viral burst size, or the number of new viruses produced by each infected cell. Surprisingly, this value remains poorly characterized for influenza A virus (IAV), commonly known as the flu. In this study, we screened tens of thousands of cells using a microfluidic method called droplet quantitative PCR (dqPCR). The high-throughput capability of dqPCR enabled the measurement of a large population of infected cells producing progeny virus. By measuring the fully assembled and successfully released viruses from these infected cells, we discover that the viral burst sizes for both the seasonal H3N2 and the 2009 pandemic H1N1 strains vary significantly, with H3N2 ranging from 101 to 104 viruses per cell, and H1N1 ranging from 101 to 103 viruses per cell. Some infected cells produce average numbers of new viruses, while others generate extensive number of viruses. In fact, we find that only 10% of the single-cell infections are responsible for creating a significant portion of all the viruses. This small fraction produced approximately 60% of new viruses for H3N2 and 40% for H1N1. On average, each infected cell of the H3N2 flu strain produced 709 new viruses, whereas for H1N1, each infected cell produced 358 viruses. This novel method reveals insights into the flu virus and can lead to improved strategies for managing and preventing the spread of viruses. Author summary Viruses infect and exploit host cells to reproduce and spread. The viral burst size, or the number of viral particles released from an infected cell, plays a critical role in understanding infection dynamics and overall viral fitness. However, accurately determining burst size for many single cells using conventional laboratory methods can be challenging. Here, we introduce dqPCR, a droplet microfluidic method for the rapid measurement of influenza virus numbers produced by thousands of individual cells. Our findings revealed that only a small proportion of infected cells are responsible for producing a significant portion of the total viral population. By utilizing this method in future studies, we can gain a deeper understanding of the role of diversity in rapidly evolving viruses.
MetaWorm: An Integrative Data-Driven Model Simulating C. elegans Brain, Body and Environment Interactions

The behavior of an organism is profoundly influenced by the complex interplay between its brain, body, and environment. Existing data-driven models focusing on either the brain or the body-environment separately. A model that integrates these two components is yet to be developed. Here, we present MetaWorm, an integrative data-driven model of a widely studied organism, C. elegans. This model consists of two sub-models: the brain model and the body & environment model. The brain model was built by multi-compartment models with realistic morphology, connectome, and neural population dynamics based on experimental data. Simultaneously, the body & environment model employed a lifelike body and a 3D physical environment, facilitating easy behavior quantification. Through the closed-loop interaction between two sub-models, MetaWorm faithfully reproduced the realistic zigzag movement towards attractors observed in C. elegans. Notably, MetaWorm is the first model to achieve seamless integration of detailed brain, body, and environment simulations, enabling unprecedented insights into the intricate relationships between neural structures, neural activities, and behaviors. Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, MetaWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment.
Evidencing Distant and Local Influences of Industrial and Other Metal Sources in PM2.5 in a Medium City

Book of the 18 International Conference on Heavy Metals in the Environment, 12 to 15 September 2016, Ghent, Belgium This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. – 515 – 18th International Conference on avy Metals in the Environment 12 to 15 September 2016, Ghent, Belgium ICHMET 2016 Proceedings of the 18 International Confer nce on Heavy Metals in the Environment, 12 to 15 September 2016, Ghent, Belgium
Gene expression differences associated with intrinsic hindfoot muscle loss in the jerboa, Jaculus jaculus

Vertebrate animals that run or jump across sparsely vegetated habitats, such as horses and jerboas, have reduced the number of distal limb bones, and many have lost most or all distal limb muscle. We previously showed that nascent muscles are present in the jerboa hindfoot at birth and that these myofibers are rapidly and completely lost soon after by a process that shares features with pathological skeletal muscle atrophy. Here, we apply an intra- and inter-species approach, comparing jerboa and mouse muscles, to identify gene expression differences associated with the initiation and progression of jerboa hindfoot muscle loss. We show evidence for reduced Hepatocyte Growth Factor (HGF) and Fibroblast Growth Factor (FGF) signaling and an imbalance in nitric oxide signaling; all are pathways that are necessary for skeletal muscle development and regeneration. We also find evidence for phagosome formation, which hints at how myofibers may be removed by autophagy or by non-professional phagocytes without evidence for cell death or immune cell activation. Last, we show significant overlap between genes associated with jerboa hindfoot muscle loss and genes that are differentially expressed in a variety of human muscle pathologies and rodent models of muscle loss disorders. All together, these data provide molecular insight into the mechanism of evolutionary and developmental muscle loss in jerboa hindfeet.
ANALISIS EKSISTENSI TRAVELING WAVE FRONT PADA MODEL MATEMATIKA REASSORTMENT VIRUS INFLUENSA H5N1 DAN H1N1-p

Reassortment dari virus influensa adalah transmisi dari lebih dari satu virus pada satu species yang dapat terjadi pada satu maupun beberapa lokasi , transmisi virus dapat terjadi karena kontak individual pada masing-masing lokasi atau pada lokasi silang. Jika penyebaran virus pada multilokasi terjadi pada multi species dan multi strain maka analisa traveling wave front dapat dilakukan berdasarkan pada model sistem persamaan differensial parsial-integral. Pada makalah ini merupakan hasil penelitian dengan mengembangkan model penyebaran virus pada 2 lokasi dengan setiap lokasi mempunyai potensi untuk terjadinya reassortment virus influensa H5N1 dengan H1N1-p , pergerakan individual secara lokal maupun global sangat mempengaruhi konstruksi model penyebaran virus yang dibangun, oleh karena itu analisa terhadap eksistensi traveling wave front dalam rangka untuk mengetahui potensi terjadinya penyebaran secara luas.
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows

Modern language models excel at integrating across long temporal scales needed to encode linguistic meaning and show non-trivial similarities to biological neural systems. Prior work suggests that human brain responses to language exhibit hierarchically organized "integration windows" that substantially constrain the overall influence of an input token (e.g., a word) on the neural response. However, little prior work has attempted to use integration windows to characterize computations in large language models (LLMs). We developed a simple word-swap procedure for estimating integration windows from black-box language models that does not depend on access to gradients or knowledge of the model architecture (e.g., attention weights). Using this method, we show that trained LLMs exhibit stereotyped integration windows that are well-fit by a convex combination of an exponential and a power-law function, with a partial transition from exponential to power-law dynamics across network layers. We then introduce a metric for quantifying the extent to which these integration windows vary with structural boundaries (e.g., sentence boundaries), and using this metric, we show that integration windows become increasingly yoked to structure at later network layers. None of these findings were observed in an untrained model, which as expected integrated uniformly across its input. These results suggest that LLMs learn to integrate information in natural language using a stereotyped pattern: integrating across position-yoked, exponential windows at early layers, followed by structure-yoked, power-law windows at later layers. The methods we describe in this paper provide a general-purpose toolkit for understanding temporal integration in language models, facilitating cross-disciplinary research at the intersection of biological and artificial intelligence.
Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience

Neural systems network-based representations are useful tools to analyze numerous phenomena in neuroscience. Probabilistic graphical models (PGMs) give a concise and still rich representation of complex systems from different domains, including neural systems. In this paper we analyze the characteristics of a bidirectional relationship between networks-based representations and PGMs. We show the way in which this relationship can be exploited introducing a number of methods for the solution of classification, inference and optimization problems. To illustrate the applicability of the introduced methods, a number of problems from the field of neuroscience, in which ongoing research is conducted, are used.
GreenABR+: Generalized Energy-Aware Adaptive Bitrate Streaming

Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This paper proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization.
Vertical Propagation of Middepth Zonal Currents Associated With Surface Wind Forcing in the Equatorial Indian Ocean

The vertical propagation of middepth (200–1,500 m) zonal velocity in the equatorial Indian Ocean (EIO) and its relationship with surface wind forcing are investigated. We focus on semiannual time scales, using in situ mooring observations and an oceanic reanalysis product that can reasonably well reproduce the structure and magnitude of the observed middepth zonal velocity in the EIO. The pronounced semiannual cycle in observed middepth zonal currents indicates a clear upward phase propagation with particularly robust signatures at depths of 500–1,000 m between 3°S to 3°N, 70 to 85°E. The main characteristics of the middepth currents are consistent with vertically propagating first-meridional-mode Rossby wave at semiannual period. Theory suggests that equatorial Kelvin waves, forced by zonal wind variations in the western half of the basin, transfer energy downward and eastward, reflecting into Rossby waves at the eastern boundary. These reflected Rossby waves transfer energy downward and westward, contributing greatly to observed phase propagation at middepths in the EIO.
EUROCAT – epidemiologijsko praćenje prirođenih mana u Europi

Registri prirođenih mana osnivaju se zato da bi se uspostavio nadzor nad pojavnoscu prirođenih mana koje imaju sve vecu važnost u strukturi morbiditeta i mortaliteta dojencadi. U Europi vec 25 godina djeluje EUROCAT - mreža regionalnih registara - koja godisnje nadzire oko1.2 milijuna porođaja. Ciljevi registra su prikupljanje osnovnih epidemologijskih informacija o prirođenim manama, rano otkrivanje izloženosti teratogenima, pojave nakupljanja u vremenu i prostoru, te evaluacija ucinkovitosti organizacije i primjene primarne prevencije i prenatalnog probira na razini populacije. EUROCAT se pokazao kao djelotvorna mreža i infrastruktura za istraživanja na podrucju etiologije i prevencije prirođenih mana. Hrvatski registar, sa sredisnjicom u Referentnom centru Ministarstva zdravstva za pracenje kongenitalnih anomalija Republike Hrvatske, djeluje od 1983. godine i obuhvaca cetiri regije – Varaždin, Rijeku, Pulu i Koprivnicu. Sirenje registra na ostala podrucja Hrvatske znacajno bi poboljsalo pracenje kvalitete i planiranje zdravstvene zastite u trudnoci i ranom djetinjstvu.
Relating Generalization in Deep Neural Networks to Sensitivity of Discrete Dynamical Systems

The ability of deep neural networks to generalize over a large diversity of function modeling tasks, remains a key mystery of the field. While the workings of the network are fully known, it remains unclear which specific properties are necessary and/or sufficient for the observed generalization. In this paper, we approach the characterization of this generalization by studying the ability to learn the evolution of discrete dynamical systems. Our findings reveal a strong correlation between the number of examples needed for generalization and the sensitivity of the dynamical systems to perturbations of the initial state
Measurement of the 𝑡̄𝑡 cross section and its ratio to the 𝑍 production cross section using 𝑝𝑝 collisions at

The inclusive top-quark-pair production cross section 𝜎 𝑡̄𝑡 and its ratio to the 𝑍 -boson production cross section have been measured in proton–proton collisions at √ 𝑠 = 13 . 6 TeV, using 29 fb −1 of data collected in 2022 with the ATLAS experiment at the Large Hadron Collider. Using events with an opposite-charge electron-muon pair and 𝑏 -tagged jets, and assuming Standard Model decays, the top-quark-pair production cross section is measured to be 𝜎 𝑡̄𝑡 = 850 ±3(stat . )±18(syst . )±20(lumi . ) pb. The ratio of the 𝑡̄𝑡 and the 𝑍 -boson production cross sections is also measured, where the 𝑍 -boson contribution is determined for inclusive 𝑒 + 𝑒 − and 𝜇 + 𝜇 − events in a ﬁducial phase space. The relative uncertainty on the ratio is reduced compared to the 𝑡̄𝑡 cross section, thanks to the cancellation of several systematic uncertainties. The result for the ratio, 𝑅 𝑡̄𝑡 ∕ 𝑍 = 1 . 145 ± 0 . 003(stat . ) ± 0 . 021(syst . )±0 . 002(lumi . ) is consistent with the Standard Model prediction using the PDF4LHC21 PDF set.
A Review of Nano Additives Used in Lubricants to Improve Tribological Performance

: This article deals with the prose review of the nano lubricants mixed with different base oils. The synthesis of a few of the nanoparticles and their effect on friction and wear has been analyzed. The characterization has been studied by different methods like Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), Energy Dispersive X-ray Spectroscopy (EDX) It has been studied that the optimum concentration of the nanoparticles in the base oils has given significant improvement in the friction co-efficient, scar diameter, and the increase in the life of rubbing components. Nanocomposites in base oils are promising since they increase tribological features and still they are eco-friendly.
Design of Boost Converter with PI and IP Controllers and PI Observer

-It is well-known that DC/DC boost converters have non-minimum phase characteristics. Using predesigned cascade controller with layered reduced-order proportional-integral observers to maintain required voltage regulation performance of cascade controller for a DC/DC boost converter despite changes in reference input, load and input voltage (PIOs). Fast-inner current control uses integral-proportional control; slow-outer voltage control uses linearized model and uses integral-proportional control. Unifying theoretical study using singular perturbation theory supports anticipated approximation of enhanced PIO system to the nominal closed-loop system using cascade controller without accounting for uncertainty. Singular perturbation theory. In order to test boost converter, computer simulations using MATLAB were conducted. Bode plot analysis of a boost converter is summarised, and it is shown system more stable with proportional-integral observers under load changes, parametric uncertainties and input voltage variations (PIOs). In order to implement boost converter, hardware is required. We discovered output voltage around 2.5 times more than input voltage both cases DC 12V battery and solar panel used to power sources.
Physics-consistency of infinite neural networks

Recent research has highlighted the incorporation of prior physics knowledge into neural networks by construction of special neural activation functions. For this, we consider (i) Gaussian process kernels that adhere to the principles of physics, and (ii) the infinite-width correspondence between neural networks and Gaussian processes. Together, this begs the question for infinite-width neural networks that are consistent with the laws of physics. So construed regression models may find specialized applications such as inverse problems, uncertainty quantification or optimization of or with physical models. These ’surrogate’ models should learn efficiently from limited data while generalizing in a physically sensible manner
PERFUSION IN

: BACKGROUND: Stroke is a clinical diagnosis that refers to sudden onset focal neurological deficit of presumed vascular origin. MR perfusion (MRP) provide a rapid, quantitative, and easily interpretable visual assessment of the penumbra. OBJECTIVE: To study the value of MR perfusion in acute ischemic stroke within 1 st 24hr.
Model-Driven Feature Engineering for Data-Driven Battery SOH Model

—Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and run-time monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improvement. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.
VALUE AUTOMATA WITH FILTERS ∗

We propose to study value automata with ﬁlters, a natural generalization of regular cost automata to nondeterminism. Models such as weighted automata and Parikh automata appear naturally as specializations. Results on the expressiveness of this model oﬀer a general understanding of the behavior of the models that arise as special cases. A landscape of such restrictions is drawn
: the Black and White Differential

With 20 years of PSID data, we document persistent racial diﬀerentials in life-cycle consumption dynamics. Starting from similar positions in the consumption distribution Blacks end up in lower percentiles than Whites. Education, income, and wealth are three key drivers of these diﬀerent dynamics. Blacks tend to save less, and hence have less buﬀer than the Whites to prevent them from falling in the lower part of the consumption distribution.
Construction of Revised TPB Model of Customer Green Be-havior: Environmental Protection Purpose and Ecological Values Perspectives

This study constructs a comprehensive conceptual model of consumer behavioral intentions in green hotels. For the purpose of protecting the environment, based on Schwartz’s values theory, this study identifies ecological values that influence consumers’ green hotel visit intention. This study aims to develop an extended Theory of Planned Behavior (TPB) research model which includes environmental concern and perceived moral obligation to predict consumers’ intention to visit green hotels. The green hotel behavioral intentions will provide insights to better understand the green consumer and to better manage the green hotel.