Miracle-dz/UniFAR-FTUs huggingface dataset:
User Tailored Jass AI

Ratings in competitive games are very important. On the one hand it gives an estimation about ones own skill relative to other players and on the other hand it is an opportunity to offer a skill based matchmaking. In this thesis, we will discuss the selection of a suitable rating system and evaluate the effect of such in connection with the bots and human players. To ensure a good game experience on the WebApp we implemented a login system and a user page where the users can access the statistics based on their game plays. Furthermore we extended the WebApp with a matchmaking system where the users can choose between three different bot strengths. The system will then assign bots in the chosen bot strength to the player.
A Parsimonious Macroeconomic ABM for Labor Market Regulations

The literature on macroeconomic agent-based models (MABMs) has gained growing attention since the early 2000s. Most MABMs dealing with market regulations have been focusing on the financial market. In contrast, only a small number of MABMs investigate the effects of labor market regulations. In this paper, we provide a parsimonious yet extendable agent-based model that focuses on labor market dynamics within a macroeconomic framework, suitable to analyze labor market regulations such as minimum wages and employment protection legislations. The model is stock-flow-consistent and small-scaled, i.e., there are only workers and firms interacting in the goods and in the labor market. There are two different types of workers, namely skilled and unskilled, and firms produce according to a CES production function. This allows for substitutability between the two types of workers. A one-factor-at-a-time (OFAT) sensitivity analysis is performed to gain insights into the mechanisms and patterns produced by the model. Results show that the model is sensitive to the minimum wage parameter and that for reasonable values of the minimum wage, income inequality decreases, while aggregate consumption rises. Overall, the results suggest that the model can be used to further investigate aggregate and distributional effects of labor market regulations.
W&M ScholarWorks W&M ScholarWorks

Proposed hypersonic vehicles would be able to travel at five to ten times the speed of sound, but there are still many problems that need to be solved to construct a functioning vehicle. One such problem involves shocks created in the engine isolator when the vehicle reaches high speeds. These shocks must be contained to the isolator to maximize performance and avoid potential failure. This project attempts to track the location of the leading shock given images of airflow from ground tests of engines using random forests and convolutional neural networks. When the models are trained and tested on data from the same facility, the average root mean squared testing error is approximately 18-20mm. However, the models struggle to adapt to the difference in lighting conditions and increase in noise when tested on data from a different facility, leading to much higher error.
In-Vitro Antidiabetic Activity of Ethanolic Extract of Barleria cuspidata F.Heyne Ex Nees by Using α-Amylase and α- Glucosidase Inhibition Assay

Background: Barleria cuspidata, Heyne ex nees belongs to Family: Acanthaceae Common name Pido-Kanta-shediyo Telugu nameRadhamonoharam Tamil NameMancat – cemmulli Barleria cuspidata, Heyne ex nees has been reported to have anti-diabetic activity Aims and Objective: To evaluate In vitro anti-diabetic activity of aqueous and ethanolic leaves extracts of BarleriaCuspidata, Heyne ex nees α-Amylase Inhibitory Assay and αGlucosidase Inhibition Assay. Materials and Methods: 6. 25μg,12. 5μg,25μg, 50μg, 100μg concentration of acarbose aqueous and ethanolic leaves extracts of BarleriaC uspidata, Heyne ex nees were used for the study. The absorbance values were taken in a spectrophotometer at 540nm and 400nm for α-Amylase and α-Glucosidase enzyme respectively. Results and Discussion: The extracts exhibited significant inhibition of α-Amylase, and αGlucosidase enzyme in a dose-dependent manner. The ethanolic extract exhibited more inhibitory activity when compared with aqueous extract. Conclusion: Results show considerable α-Amylase inhibitory activity as well as αGlucosidase inhibitory activity The present findings indicate that aqueous and ethanolic leaves e xtract Barleria cuspidata, Heyne ex nees have in vitro antidiabetic activity. V.GOMATHI*1, R.MOHIT RAHUL2
Integration of complete elemental mass-balanced stoichiometry and aqueous-phase chemistry for bioprocess modelling of liquid and solid waste treatment systems – Part 5: Ionic speciation

COPYRIGHT © The Author(s) Published under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0) Where aqueous ionic chemistry is combined with biological chemistry in a bioprocess model, it is advantageous to deal with the very fast ionic reactions in an equilibrium sub-model, as was frequently mentioned in the preceding papers in this series. This last paper in the series presents details of how such an equilibrium speciation sub-model can be implemented, based on well-known open-source aqueous chemistry models. Specific characteristics of the speciation calculations which can be exploited to reduce the computational burden are highlighted. The approach is illustrated using the ionic equilibrium sub-model of a plant-wide wastewater treatment model as an example. Integration of complete elemental mass-balanced stoichiometry and aqueous-phase chemistry for bioprocess modelling of liquid and solid waste treatment systems – Part 5: Ionic speciation
Upper Campanian Ammonites from the Gschliefgraben (Ultrahelvetic, Upper Austria)

The bulk of this fauna is also known or has close allies in the Upper Campanian Bostrychoceras po­ lyplocum Zone faunas of northern Europe and the USSR, to which stratigraphic horizon it is referr­ ed. The P. (Hypophylloceras) and S. cala are exotic elements, but both are known from the Maastrichtian of the same area. Z u s a m m e n f a s s u n g : Im Gschliefgraben (Ultrahelvetikum, Oberösterreich) konnten in den letzten Jahren beachtliche Aufsammlungen von Ammoniten des Campans gemacht werden. Diese liegen in drei Erhaltungszuständen vor, sodaß auf drei verschiedene Horizonte geschlossen werden kann: a) Relativ wenig deformierte Steinkerne mit Resten der Aragonit schale treten in großen dunklen Kalkkonkretionen auf (Pachydiscus ((PJ) perfidus). b) Aus spröden dunkelgrauen, bioturbaten Kalken stammt ein unbestimmbarer Texanitine. c) Die Hauptmasse der hier beschriebenen Fauna ist in Form zusammengedrückter Skulpturstein­ kerne erhalten. Das Gesteine ist ein hellgrauer, poröser Kalk bis Mergelkalk. Die Formen der Horizonte a und c sind in das Obercampan zu stellen. Großteils sind es Repräsen­ tanten der traditionellen Bostrychoceras polyplocum-Zone, der höchsten Ammonitenzone im nord­ westeuropäischen Obercampan. Die meisten Arten sind aus Irland, BRD, DDR, Polen und den USSR bekannt. Phylloceras (Hypophylloceras), Saghalinites cala, Anapachydiscus arrialoorensis und Poly­ ptychoceras (Polyptychoceras) cf. pseudogaultinum sind jedoch neu auch für diesen Raum. Anapa­ chydiscus arrialoorensis ist ein naher Verwandter des A. vistulensis BLASZKIEWICZ, 1980, der in Polen und Deutschland vorkommt. Das Fehlen von Hoplitoplacenticeras und Scaphites spiniger (SCHLÜTER, 1872) läßt darauf schließen, daß die Gschliefgrabenfauna einem engbegrenzten Hori­ zont des oberen Obercampans entstammt. Nicht einstufbar ist die Lage mit dem unbestimmbaren Texaniten. Alle Funde stammen aus einem tektonisch stark gestörten Bereich, der durch Erdrutsch­ tätigkeit jeden Zusammenhang in sich verloren hat. * Geological Collections, University Museum, Oxford; OX1 3PW, U.K. ** Geologisch-Paläontologische Abteilung, Naturhistorisches Museum, A-1014 Wien, Austria. ©Verein zur Förderung der Paläontologie am Institut für Paläontologie, Geozentrum Wien
robmixglm : An R Package for Robust Analysis Based on Mixtures

A difficulty in statistical analysis is the presence of outliers, or observations which Ąt poorly to the assumed statistical model. Robust methods may be used to down weight these observations. One method is to assume that the data consists of standard and outlier observations with different distributions and Ąt these as a mixture model. For generalized linear models the outliers are assumed to be from an overdispersed model, constructed either by including a random effect in the linear predictor or where the distribution includes a scale parameter varying it. The use of the robmixglm R package is demonstrated on three examples, demonstrating how outliers may be identiĄed. An advantage of this approach is that it is likelihood based, allowing all the associated methods. This is demonstated using a package for model selection.
A numerical framework for simulating the atmospheric variability of supermicron marine biogenic ice nucleating particles

We present a framework for estimating concentrations of episodically elevated high-temperature marine ice nucleating particles (INPs) in the sea surface microlayer and their subsequent emission into the atmospheric boundary layer. These episodic INPs have been observed in multiple ship-based and coastal field campaigns, but the processes controlling their ocean concentrations and transfer to the atmosphere are not yet fully understood. We use a combination of empirical constraints and simulation outputs from an Earth system model to explore different hypotheses for explaining the variability of INP concentrations, and the occurrence of episodic INPs, in the marine atmosphere. In our calculations, we examine the following two proposed oceanic sources of high-temperature INPs: heterotrophic bacteria and marine biopolymer aggregates (MBPAs). Furthermore, we assume that the emission of these INPs is determined by the production of supermicron sea spray aerosol formed from jet drops, with an entrainment probability that is described by Poisson statistics. The concentration of jet drops is derived from the number concentration of supermicron sea spray aerosol calculated from model runs. We then derive the resulting number concentrations of marine high-temperature INPs (at 253 K) in the atmospheric boundary layer and compare their variability to atmospheric observations of INP variability. Specifically, we compare against concentrations of episodically occurring high-temperature INPs observed during field campaigns in the Southern Ocean, the Equatorial Pacific, and the North Atlantic. In this case study, we evaluate our framework at 253 K because reliable observational data at this temperature are available across three different ocean regions, but suitable data are sparse at higher temperatures. We find that heterotrophic bacteria and MBPAs acting as INPs provide only a partial explanation for the observed high INP concentrations. We note, however, that there are still substantial knowledge gaps, particularly concerning the identity of the oceanic INPs contributing most frequently to episodic high-temperature INPs, their specific ice nucleation activity, and the enrichment of their concentrations during the sea–air transfer process. Therefore, targeted measurements investigating the composition of these marine INPs and drivers for their emissions are needed, ideally in combination with modeling studies focused on the potential cloud impacts of these high-temperature INPs. Published by Copernicus Publications on behalf of the European Geosciences Union. 848 I. Steinke et al.: Simulating the atmospheric variability of supermicron marine biogenic INPs
Deep Recurrent Modelling of Stationary Price Formation Using the Order Flow

In this paper we present a deep recurrent model based on the order ﬂow for the stationary modelling of the directional movements of high-frequency prices. The formation of prices seen on the price chart of a stock or currency at any timescale are driven by the order ﬂow, which is the microsecond streams of orders arriving at the exchange. In our experiments, we use high-frequency Bitcoin data to train our models. We train a deep recurrent neural network on the order ﬂow to model the directional price movement given a sequence of order ﬂow. We show that without any retraining, the model is temporally stable even as Bitcoin trading shifts into an extremely volatile ”bubble trouble” period. The signiﬁcance of the result is shown by benchmarking against existing state-of-the-art models for modelling price formation using deep learning. As such the secondary contribution of this paper is also a comparative study between the stationarity of existing deep models in literature.
Phenolic Rich Extract Of Finger Millet Bran Attenuates Lung Inflammation And Fibrosis In A Mouse Model Of Ovalbumin Induced Asthma

Asthma is a serious global health risk characterized by chronic airway inflammation of lungs with infiltration of inflammatory cells, enhanced mucus secretion and narrowing of airways that leads to poor respiratory functions. The clinical symptoms of asthma includes recurrent episodes of cough, shortness of breath, wheezing and chest tightness. One of the pathophysiology of asthma was mediated by overproduction of pro-inflammatory asthmatic leukotrienes through arachidonic acid pathway catalyzed phospholipase A2 (PLA2) and 5-lipoxygenase (5-LOX) enzymes. The available conventional therapies to treat asthma may induce severe side effects on health and high cost. To overcome the disadvantages to treat asthma and to improve the current therapeutic strategy, we used natural products as an alternative therapy. Finger millet (Eleusine coracana) has been used by folk and traditional medicinal practitioners to treat various inflammatory conditions. Previously, we have found the promising anti-inflammatory phytochemicals in finger millet bran (Fmb). Hence, aim of the present study was to identify and characterize anti-asthmatic phytochemicals from Fmb and the objective was to test its anti-inflammatory activity in ovalbumin induced asthma mouse model. Phytochemical analysis of Fmb revealed the presence of phenolics, saponins and anthraquinone. Among all the extracted fractions, finger millet bran methanol extract (FbMe) possessed strong in vitro anti-inflammatory activity by inhibiting pro-inflammatory PLA2 and 5-LOX enzymes activity. Further, in vivo anti-inflammatory activity of FbMe was evaluated in an ovalbumin induced asthma mouse model. FbMe (50 mg/kg) significantly reduced the infiltration of inflammatory cells, lung fibrosis by reducing the deposition of collagen in the tissue. It also inhibited the PLA2 as well as 5-LOX enzymes activity in collected BAL fluid. This finding concludes the presence of strong anti-inflammatory and anti-fibrotic phenolic compounds in the FbMe, which attenuates the lung inflammation and fibrosis probably via inhibition of PLA2 and 5-LOX enzymes activity.
Exponential stabilization of clamped Timoshenko beam with a tip mass

In this paper we consider the stabilization problem of a clamped beam with torque and force actuation on a mass in the other side of the beam. We show how to derive the model starting from the Principle of Least Action and we rewrite it as the interconnection between a 1 dimensional distributed parameter port-Hamiltonian system and a finite dimensional port-Hamiltonian system. Therefore, we propose a control law that allow to exponentially stabilise the origin of the closedloop system. In this preliminary paper we only sketch the theoretical proofs, but we give the procedure to compute the exponential bound of the system’s state. Finally, we provide some numerical simulations testing the closed-loop behaviour with different choices of the control parameters.
Integration of complete elemental mass-balanced stoichiometry and aqueousphase chemistry for bioprocess modelling of liquid and solid waste treatment systems – Part 4: Aligning the modelled and measured aqueous phases

COPYRIGHT © The Author(s) Published under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0) Completely mass-balanced biological, physical and chemical process stoichiometry ensures that the CHONPS material and charge content entering and exiting bioprocess system models is conserved, which is a requirement for pH prediction in integrated physical, chemical and biological process models. Bioprocesses transform the material content from reactants to products, exchanging material between the aqueous, gaseous and solid phases, which cause pH changes in the aqueous phase. By measuring the material content of the aqueous phase, the progress of bioprocesses can be monitored. Alkalinity is an important aqueousphase property that can be used to track aqueous-phase changes caused by physical, chemical and biological processes. Alkalinity is a stoichiometry property of the components in solution (i.e., a linear function of the amounts present). Its uptake from, and release to, the aqueous phase can both be modelled with bioprocess stoichiometry, and measured in physical bioprocess systems, and so aid in linking the modelled and measured aqueous-phase compositions. Changes in the concentrations of components containing the elements C, H, O, N, P and S result in changes in six weak acid/bases systems in the aqueous phase, all of which affect the total alkalinity. These are: inorganic carbon (IC), ortho-phosphate (OP), free and saline ammonia (FSA), volatile fatty acids (VFA), free and saline sulphide (FSS) and the water itself. Characterization of the aqueous phase to quantify the material content of the aqueous phase containing these six weak acid/base systems using the 5-point titration method is described. While several alkalinity titration based methods are available for anaerobic digestion bioprocess monitoring, only the 5-point titration is sufficiently accurate for aqueousphase characterization to quantify the aqueous-material content for pH prediction in bioprocess models. Integration of complete elemental mass-balanced stoichiometry and aqueousphase chemistry for bioprocess modelling of liquid and solid waste treatment systems – Part 4: Aligning the modelled and measured aqueous phases
Origami: A High-Performance Mergesort Framework

Mergesort is a popular algorithm for sorting real-world workloads as it is immune to data skewness, suitable for parallelization using vectorized intrinsics, and relatively simple to multi-thread. In this paper, we introduce Origami , an in-memory merge-sort framework that is optimized for scalar, as well as all current SIMD (single-instruction multiple-data) CPU architectures. For each vector-extension set (e.g., SSE, AVX2, AVX-512), we present an in-register sorter for small sequences that is up to 8× faster than prior methods and a branchless streaming merger that achieves up to a 1.5× speed-up over the naive merge. In addition, we introduce a cache-residing quad-merge tree to avoid bottlenecking on memory bandwidth and a parallel partitioning scheme to maximize thread-level concurrency. We develop an end-to-end sort with these components and produce a highly utilized mergesort pipeline by reducing the synchronization overhead between threads. Single-threaded Origami performs up to 2× faster than the closest competitor and achieves a nearly perfect speed-up in multi-core environments.
Indonesian Journal of Electrical Engineering and Computer Science

Received Sep 1, 2021 Revised Nov 6, 2021 Accepted Dec 2, 2021 Deep learning models have been at the forefront of facial recognition because they deliver improved classification accuracy over traditional ones. Regardless, deep learning models require an extensive dataset for training. To significantly cut down on its training time and dataset volume, pretrained models, have been used although, they are still required to undergo the usual training process for custom facial recognition tasks. This research focuses on an improved facial recognition system that lacks the training and retraining requirements. The system uses an existing deep learning feature extraction model. First, a user stands before a camera-enabled system. After that, the user supplies a unique identification number to fetch a corresponding face image from the database. This process generates two face feature vectors. One from the camera and that retrieved from the database. The cosine distance function determines the similarity value of these vectors. When the cosine distance value falls below a set threshold, the face is recognized and access granted. If the cosine distance of the two vectors gives a value above this threshold, access is denied. The proposed model performs satisfactorily on publicly available datasets.
Consortium blockchain management with a peer reputation system for critical information sharing

Blockchain technology based applications are emerging to establish distributed trust amongst organizations who want to share critical information for mutual benefit amongst their peers. There is a growing need for consortium based blockchain schemes that avoid issues such as false reporting and free riding that impact cooperative behavior between multiple domains/entities. Specifically, customizable mechanisms need to be developed to setup and manage consortiums with economic models and cloud-based data storage schemes to suit various application requirements. In this MS Thesis, we address the above issues by proposing a novel consortium blockchain architecture and related protocols that allow critical information sharing using a reputation system that manages co-operation amongst peers using off-chain cloud data storage and on-chain transaction records. We show the effectiveness of our consortium blockchain management approach for two use cases: (i) threat information sharing for cyber defense collaboration system viz., DefenseChain, and (ii) protected data sharing in healthcare information system viz., HonestChain. DefenseChain features a consortium Blockchain architecture to obtain threat data and select suitable peers to help with cyber attack (e.g., DDoS, Advance Persistent Threat, Cryptojacking) detection and mitigation. As part of DefenseChain, we propose a novel economic model for creation and sustenance of the consortium with peers through a reputation estimation scheme that uses 'Quality of Detection' and 'Quality of Mitigation' metrics. Similarly, HonestChain features a consortium Blockchain architecture to allow protected data sharing between multiple domains/entities (e.g., health data service providers, hospitals and research labs) with incentives and in a standards-compliant manner (e.g., HIPAA, common data model) to enable predictive healthcare analytics. Using an OpenCloud testbed with configurations with Hyperledger Composer as well as a simulation setup, our evaluation experiments for DefenseChain and HonestChain show that our reputation system outperforms state-of-the-art solutions and our consortium blockchain approach is highly scalable
Accounting for Unobserved Country Heterogeneity in Happiness research: Country effects versus region effects

Many empirical studies are indeterminate to whether good formal institutions are conducive to subjective well-being. Possibly, this ambiguity is caused by cross-section models that do not account for unobserved cultural and institutional effects. Using the World Value Survey 1980-2005, this paper supports a positive relation in a country panel framework that accounts for unobserved country heterogeneity. This study also shows that using region dummies in a random effects model appears to be a sufficient substitution for omitted country fixed effects.