Miracle-dz/UniFAR-FTUs huggingface dataset:
Zur Struktur der Hieroglyphenschrift

Die Hieroglyphenschrift gibt seit dem klassischen Altertum und selbst nach ihrer Entzifferung im frühen 19. Jahrhundert immer wieder Anlaß zur Deutung ihres Sinnes oder zur Analyse ihres Aufbaus. Die Ergebnisse sind außerordentlich verschieden, weil man wegen der Komplexität der Hieroglyphenschrift legitim die verschiedensten Ansatzpunkte zu ihrer Beurteilung wählen kann. Solche Aspekte sind etwa: — der p i k t o g r a p h i s c h e , der sich von der bildenden Kunst her immer bietet; — der s y m b o l i s c h e , der im klassischen Altertum und in Europa bis ins 19. Jahrhundert hinein fast als der einzig mögliche betrachtet wurde; — der h i s t o r i s c h e , der insbesondere die Frage nach der Entstehung der Hieroglyphen­ schrift beinhaltet und den die Ägyptologie im Gefolge der historischen Methoden des 19. Jahr­ hunderts bis zum heutigen Tag mit Vorliebe wahrnimmt; — der f u n k t i o n a l e , der die Bedeutung der Schriftzeichengruppen innerhalb des Textes erfaßt und der nach der Entzifferung der Hieroglyphenschrift durch das fortwährende philologische Studium der Texte ständig an Bedeutung gewann. Der vorliegende Beitrag soll einem besseren Verständnis der Funktionen der hieroglyphi­ schen Schriftzeichen und Schriftzeichengruppen dienen, und zwar vor allem durch eine deutliche Scheidung des historischen und des funktionalen Aspekts. Dadurch sollen Grundlagen für die ­ synchronische Beschreibung der j e w e i l s g e l t e n d e n S c h r i f t s y s t e m e gelegt werden, durch deren Vergleich man dann die diachronischen Zusammenhänge bestimmen kann (zu „Synchronie" und „Diachronie" siehe F. DE SAUSSURE [14]). Die hier entwickelten Ansichten ergaben sich teilweise bei der Fixierung des Informations­ gehaltes der hieroglyphischen Schriftzeichen im Spruch 335 a der altägyptischen Sargtexte (siehe [ 5 ] ) . Häufigen Diskussionen mit R. GUNDLACH ist manche Klärung zu verdanken.
Macalester Journal of Physics and Astronomy Macalester

Abstract The Photoemission Electron Microscope (PEEM) is a full-field electron microscope that utilizes the photoelectric effect to image a surface. Due to a spatial resolution on the order of 10 nanometers and its ability to image both the morphology of a surface and its band structure, it is a useful tool for understanding the properties of materials for use in electronic devices. To correct for random sample misalignment and the experimental frame of reference in the spectroscopy mode of the PEEM, the 3D dataset must be rotated in both the momentum and energy coordinates which requires pixel calibration and energy alignment. I have created custom Python scripts to both automate this process and standardize the calibration and correction procedure to streamline data analysis for users of the PEEM. Graphene was utilized as an initial calibration material due to its distinct electronic band structure. The 6 Dirac cones of graphene were used as iso-energy points to align the frames on the energy axis and a series of matrix operations were utilized to rotate the image in the momentum axis to correct for sample misalignment. I used the corrected dataset to estimate the Fermi velocity and compared it to theoretical predictions.
Cross-Border Payments for Central Bank Digital Currencies via Universal Payment Channels *

Growing interest in digital forms of payments have led central banks around the world to explore the possibility of issuing a new type of central bank money, known as Central Bank Digital Currency (CBDC) . While we anticipate many central banks will implement some form of a digital ledger, it is unlikely that they all adopt the same stack of technologies and protocols due to, for example, different governance, market requirements, technology providers, and levels of monitoring and compliance standards. These differences would limit the interoperability of CBDC networks, which could add friction to future cross-border CBDC payments. Critically, the Bank of International Settlements (BIS) is analyzing these issues as well as evidenced in its recent analysis of a multiple CBDC (mCBDC) arrangement. In this paper, we will focus on the challenges of cross-border payments in the emerging age of CBDCs. We will also envision and evaluate scenarios of how cryptographic interoperability protocols for CBDCs could allow digital money to be moved between these networks. Specifically, we propose cross-border CBDC payment routes called Universal Payment Channels (UPC) that could allow instant transfer of funds across CBDC ledgers. We further discuss the technical trade-offs policymakers will need to consider prior to choosing the right interoperability method. As with all considerations regarding CBDC, these decisions will be based upon the unique policy objectives of the central bank and how it envisions the future CBDC ecosystem. Finally, through the paper, we will analyze some of the broader opportunities and challenges of cross-border CBDC payments going forward.
A Bayesian approach to regional income inequality in Europe using EU-SILC data

Measurement and/or sampling issues concerning high incomes in survey data can introduce signiﬁcant biases for measuring levels of inequality. This paper introduces a new approach to account for these ’missing rich’ issues in studying the income distributions of EU-member states and of the European Union as a whole using EU-SILC data. A general parametric framework for introducing high-income under-reporting and/or undersampling corrections to parametric distribution models is proposed with detailed derivations for cases where analytical probability density functions, cumulative distribution functions, and Generalized Lorenz curves may be obtained. Speciﬁc derivations are presented for two common corrections from the literature. Namely, a right-truncation is exploited as a modelling assumption to allow for corrections for undersampling under the belief that a proportion of the top-incomes population is not represented in the data as in Jorda and Nino-Zarazua (2019). Additionally, a linear progressive under-reporting (LPU) assumption of Bourguignon (2018) is simultaneously introduced as a speciﬁc case of this framework to allow for scenarios where only individuals above a certain income threshold under-report their income in the data and the magnitude of under-reporting is lin-early increasing with incomes. Under these speciﬁc corrections, the framework is taken as a model for each state’s data on equivalized disposable household incomes as available on the European Union’s Statistics on Income and Living Conditions (EU-SILC) surveys. In doing so, two approaches are explored. Firstly, exact inference is allowed for through the analytically derived likelihood function in the speciﬁc case of right-truncation and LPU under a 4-parameter Generalized Beta distribution of the second kind (GB2) model for the population’s income distribution. Secondly, because under alternative assumptions for the ’missing rich’ corrections or under some data formats such as grouped data with non-reported or random bounds an analytically tractable likelihood function might be unavailable or prohibitively costly
Scaling Switch-driven Flow Control with Aquarius

As datacenter networks support more diverse applications and faster link speeds, effective end-to-end congestion control becomes increasingly challenging due to the inherent feedback delay. To address this issue, switch-driven per-hop flow control (FC) has gained popularity due to its natural flow isolation, timely control loop, and ability to handle transient congestion. However, the ideal FC requires impractical hardware resources, and the state-of-the-art approximation approach still demands a large number of queues that exceeds common switch capabilities, limiting scalability in practice. In this paper, we propose Aquarius, a scalable solution for per-hop FC that maintains satisfactory flow isolation with a practical number of queues. The key idea of Aquarius is to take independent control of different flows within the same queue, discarding the traditional practice of managing traffic collectively within the same queue. At its core, Aquarius applies a contribution-aware pausing mechanism on congested switches to enable individual control decisions for arriving flows, and uses an opportunistic re-assigning strategy on upstream switches to further isolate congested and victim flows. Experimental results demonstrate that Aquarius maintains comparable performance with 4 × fewer queues, and achieves 5.5 × lower flow completion times using the same number of queues, compared to existing solutions.
Visualization tool for X-ray scanner for sTGC detector production quality control

The ATLAS experiment at the Large Hadron Collider has an ambitious program of the detector upgrade to meet an expected rise of accelerator luminosity. The first large system which supposed to be installed in 2019 is the New Small Wheel (NSW) for ATLAS muon spectrometer. In order to ensure high quality and reliability of NSW chambers an X-ray scanning technique is being developed. One of the main components of the X-ray scanner is a special software visualization tool which would allow a fast and clear representation of scanning results and an identification of possible chamber defects.
THE EFFECTIVENESS OF USING COOPERATION READING AND COMPOSITION (CIRC) TOWARDS WRITING ACHIEVEMENT ACROSS STUDENTS’ SELF -EFFICACY AT SMAN 16 PEKANBARU

Penelitian ini membuktikan Efektivitas Penggunaan Cooperation Reading and Composition (CIRC) terhadap Prestasi Menulis di Seluruh Self-Efficacy Siswa. Peneliti melakukan penelitian ini di SMAN 16 Pekanbaru. Tujuan penelitian ini, peneliti ingin membuktikan, adakah pengaruh dari CIRC dalam membangkitkan Self-efficacy siswa dalam menulis. Yang dimana populasi untuk penelitian ini yaitu siswa kelas sebelas, dan untuk sample nya siswa kelas XI MIPA-1 nd XI IPS-2. Dan untuk teknik pengambilan sampelnya peneliti menggunakan metode purposive. Jenis penelitian yang peneliti gunakan yaitu quasi-experimental design. Untuk penelitian ini, peneliti akan mengukur Self-efficacy siswa dengan menggunakan metode CIRC dalam pembelajaran menulis. Dan untuk hasilnya menunjukkan bahwa tidak ada pengaruh metode ini dalam meningkatkan self-efficacy siswa dalam pembelajaran menulis. Kata
M ACHINE L EARNING FOR D ATA A NALYSIS IN F OOTBALL : A S URVEY OF M ETHODS AND P ROBLEMS

Machine learning is growing exponentially, and its applications are gaining more traction in the sports analysis community in recent years. The application of machine learning methods on spatiotemporal data in sports like football is getting attention from football clubs, academics, and amateur analysts and is the focus of this survey. This survey analyses and identifies current trends in research papers and literature to determine current and future applications in football analytics using spatiotemporal data.
AN INTEGRATED ELECTRONIC-SKIN PATCH FOR REAL-TIME AND CONTINUOUS MONITORING OF A PANEL OF BIOMARKERS COMBINED WITH DRUG DELIVERY

i Publications.1 Chapter One 3 Introduction.3 1.1 Inflammatory Biomarkers.3 1.2 Association of Inflammatory Biomarkers with Diseases and Wound Status.4 1.2. 1 C-reactive Protein (CRP). 4 1.2. 2 Interlukin-6 (IL-6). 5 1.2. 3 Interlukin-10 (IL-10). 5 1.3 Sweat Biofluid Collection Via Microfluidic Channel.6 1.3. 1 Sweat Biofluid.6 1.3. 2 Microfluidic Channel.7 1.4 Sweat pH and Skin Temperature Monitoring . 8 1.4. 1 Sweat pH . 8 1.4. 2 Skin Temperature . 9 1.5 Strain-insensitive Structure . 9 1.6 Drug Delivery . 10
Predation potential and prey-stage preference of two mirid bugs on different stages of Plutella xylostella

The predation rate and prey-stage preference of two mirid predatory bugs Macrolophus pygmaeus Rambur and Nesidiocoris tenuis (Reuter) (Hemiptera Miridae), were investigated on the diamondback moth (DBM) Plutella xylostella (L.) (Lepidoptera Plutellidae), a noxious pest of cruciferous plants. In particular, predation capacity of each predatory stages of both predators was studied on the first, second and third larval instars of DBM. There were no significant differences in predation between the predators on eggs, second and third instar larvae of DBM. Among the different life stages of both predators, females had the highest predation on eggs and all larval instars of DBM. Preference indices for both mirid species indicated that there was a preference toward eggs, first instar and second instar larvae versus first instar, second instar and third instar larvae, respectively. The data of the current study suggest that M. pygmaeus and N. tenuis are effective predators of DBM particularly against eggs and first instar larva. It would therefore be worth to investigate how these mirids can be incorporated in biological control programs of DBM.
In silico analysis of antibiotic resistance genes in Lactiplantibacillus plan‐ tarum subsp. plantarum Kita‐3

The absence of transferable antibiotic resistance genes is required for the safety of commercial probiotics. Previous studies have found that antibiotic resistance genes on plasmids in Lactobacilli make them unsafe for food purposes due to the genes’ ability to transfer to pathogenic microorganisms. In contrast, bacteria from the Lactobacillaceae family are widely used as a probiotic. This study assessed the antibiotic susceptibility of Lactiplantibacillus plantarum subsp. plantarum Kita‐3 (previously known as Lactobacillus plantarum K‐3) isolated from Halloumi cheese using eight antibiotics. Genome sequencing was performed using the Illumina NovaSeq 6000 sequencing platform to detect the presence of antibiotic resistance genes on chromosomes and plasmids. L. plantarum subsp. plantarum Kita‐3 was resistant to clindamycin, streptomycin, and chloramphenicol but susceptible to tetracycline, ampicillin, kanamycin, erythromycin, and ciprofloxacin. Genome sequencing of L. plantarum subsp. plantarum Kita‐3 verified the presence of tetracycline, fluoroquinolones, β‐lactamase resistance genes, and multidrug resistance efflux. Kita‐3 had no transposable elements, gene transfer agents, plasmid‐related functions, or intact prophages. Overall, this study produced the antibiotic resistance profile of L. plantarum subsp. plantarum Kita‐3 to assess the risk of transferring antibiotic resistance genes to other bacteria. The study provides essential data on the safe use of L. plantarum subsp. plantarum Kita‐3 as probiotics.
Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing

The optimization of the electrode manufacturing process is important for upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing energy demand. In particular, LIB manufacturing is very important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery application conditions by proposing a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. This ML pipeline allows the inverse design of the process parameters to adopt in order to manufacture electrodes for energy or power applications. The latter work is an analogy to our previous work that supported the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical pseudo-two-dimensional model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic ML model to implement fast bi-objective optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.
Research on Building Facade Feature Information Extraction

: As one of the main components of the urban interior, buildings play a pivotal role in the construction of smart cities. In order to segment the structural characteristics contained in building facades more accurately and efficiently and intelligently, this paper constructs a small building facade dataset based on on-board LiDAR point clouds, and improves it based on the original PointNet++ network by adding a multi-headed attention mechanism module in model feature extraction; in model training, an extraction function is introduced after the training function for unlabelled In terms of model training, an extraction function is introduced after the training function to extract features from the unlabelled building façade data, and the extraction results are finally given labels and saved to the training data set, so as to achieve the effect of enhancing the training sample data, and by improving the model structure and model training method, the effective extraction of building façade features is achieved. The final accuracy of the proposed method is 88.2% and the average intersection-to-merge ratio (mIoU) is 77.1%, which is 1% and 3.3% higher than the original PointNet++ network respectively.
PRIMARY: JURNAL PENDIDIKAN GURU

Published: 27 April 2023 27 April 2023 Abstract: Teachers are professional educators with the main task of educating, teaching, guiding, directing, training, assessing, and evaluating students in every teaching and learning process in schools. In the learning process, various skills are needed for the continuity of the learning process, one of which is the skill of making lesson plans. The skill of a teacher in making lesson plans determines the learning outcomes that will be accepted by students. This study aims to analyze the effect of RADEC-based workshop activities in improving the skills of elementary school teachers to make good lesson plans. This study uses the One Group pretest posttest method with a sample of elementary school teachers in Purwakarta Regency. The instrument used is the result of adaptation of the assessment of the learning implementation plan in the PPG program. There are seven indicators used in the instrument used. The results showed that the RADEC model had an impact on teachers in developing learning planning skills. The average n-gain score obtained from this study was 58 which was categorized as quite effective.
Finite Element Modeling and Simulation Analysis of Non-Pyrotechnics Separation Device

The research object of this paper is non-pyrotechnics separation device based on SMA. The non-pyrotechnics separation device with different buffer materials is simulated and analyzed by setting accurate simulation parameters. The results show that when the cushion material is TC4, the maximum acceleration of the housing is 269.3g and the separation of the device is completed at 11ms. When the cushion material is rubber, the maximum acceleration of the housing is 122 g, and the device is separated at 12.56 ms.
Nondeterminism and Language Design in Deep Inference: A Proof Theoretic Approach to Logic Programming

In deep inference, in contrast to traditional proof-theoretic methodologies, inference rules can be applied at any depth inside logical expressions. This makes it possible to design deductive systems that are tailored for computer science applications and otherwise provably not expressible. With deep inference, we can simulate analytic proofs in traditional deductive formalisms, and also construct much shorter analytic proofs. However, deep applicability of inference rules causes a greater nondeterminism in proof construction. This thesis studies the problem of dealing with nondeterminism in proof search while preserving the shorter proofs. By redesigning the deductive systems, some redundant rule applications are prevented. By introducing a new technique which reduces nondeterminism, it becomes possible to obtain a more immediate access to shorter proofs without breaking proof theoretic properties such as cut-elimination. Different implementations presented allow to perform experiments and observe the performance improvements. Within a computation-as-proof-search perspective, we use these deductive systems to develop a common proof-theoretic language for planning and concurrency.
Hierarchical Contextualization of Movement Primitives

Learning from Demonstration (LfD) is a widely used approach in robot learning. However, applying the learned motions from expert data to different environments still poses challenges. This is because the robot’s working space is often more complex than the demonstrated environment, especially in cases where there is interaction between the robot and humans. Therefore, it is crucial to adjust the learned movement based on the environment. This thesis proposes a hierarchical approach that combines reinforcement learning with the existing framework of Probabilistic Movement Primitives (ProMP), enabling that the robot maintains a similar movement pattern to the demonstration while avoiding obstacles. Specifically, this method regards the position and time of predicted via points as the policy representation of the upper layer, and generates a trajectory similar to the demonstrations in the lower layer. Different from previous work, this paper proposes a more flexible time modulation method. The method is evaluated on the LASA handwriting dataset. The results show that the algorithm is able to avoid obstacles that do not exist in the demonstration and maintain a high similarity.
Bicarbonate enrichment may mitigate oxidative stress in two octocorals and their photosynthetic symbionts

BICARBONATE ENRICHMENT MAY MITIGATE OXIDATIVE STRESS IN TWO OCTOCORALS AND THEIR PHOTOSYNTHETIC SYMBIONTS Jeffrey Golladay, M.S. Department of Biological Sciences Northern Illinois University, 2018 Neil W. Blackstone, Director The ecological communities built by corals are some of the most biodiverse in the world, and the current rate of climate change threatens their long-term persistence, with mass coral bleaching events becoming more frequent. Ocean acidification (OA) is one consequence of increased atmospheric CO2 that results in changes in seawater carbon chemistry with the potential to impact organisms that rely on seawater dissolved inorganic carbon (DIC) for calcification, photosynthesis, or both. Corals are unique animals that participate in a symbiotic relationship with dinoflagellate algae of the genus Symbiodinium spp. , in which the algal symbionts translocate most of the products of photosynthesis to their host coral. To fuel photosynthesis, the algal symbionts must obtain inorganic carbon from seawater via their coral host. As such, symbionts may be particularly sensitive to the changes in the seawater DIC pool that will result from ongoing OA. Projected increases in seawater bicarbonate (HCO3 ) ion concentration associated with OA may act to supplement symbiont photosynthesis, and could potentially mitigate that effects of other stressors that lead to photoinhibition and subsequent production of reactive oxygen species (ROS). ROS may ultimately trigger coral bleaching. The research presented here focuses on the effects of ocean acidification, specifically bicarbonate enrichment, on the photo-physiological stress response of two representative octocoral species, Sympodium sp. and Sarcothelia sp. , and their symbionts. Bicarbonate enrichment experiments were carried out and fluorescence microscopy was used to visualize the production of ROS in hospite (within the host) in light stressed colonies of both octocoral species. Additional bicarbonate enrichment experiments were conducted in which oxygen metabolism was measured in colonies of Sympodium sp. The results presented here suggest that bicarbonate enrichment may act to alleviate oxidative stress resulting from photoinhibition, possibly by supplementing symbiont photosynthesis under saturating irradiance. NORTHERN ILLINOIS UNIVERSITY DE KALB, ILLINOIS