Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood Oxygenation Level-Dependent Signal Changes in Resting Cerebral Perfusion and Relationship between Caffeine-Induced

BACKGROUND AND PURPOSE: Recent interest has emerged in the use of pharmacologic methods to maximize blood oxygenation level-dependent (BOLD) signal intensity changes in functional MR imaging (fMRI). Adenosine antagonists, such as caffeine and theophylline, have been identified as potential agents for this purpose. The present study was designed to determine whether caffeine-induced decreases in cerebral perfusion result in enhanced BOLD responses to visual and auditory stimuli. METHODS: MR imaging was used to measure resting cerebral perfusion and stimulus-induced BOLD signal intensity changes in 19 patients. We evaluated the relationship between resting cerebral perfusion and the magnitude of BOLD signal intensity induced by visual and auditory stimulation under caffeine and placebo conditions. RESULTS: The data showed that changes in resting cerebral perfusion produced by caffeine are not a consistent predictor of BOLD signal intensity magnitude. Although all cerebral perfusion was reduced in all study participants in response to caffeine, only 47% of the participants experienced BOLD signal intensity increase. This finding was independent of the participants’ usual caffeine consumption. CONCLUSION: The data presented herein show that the relationship between resting cerebral perfusion and the magnitude of BOLD signal intensity is complex. It is not possible to consistently enhance BOLD signal intensity magnitude by decreasing resting perfusion with caffeine. Future studies aimed at evaluating the relationship between perfusion and BOLD signal intensity changes should seek a means to selectively modulate known components of the neural and vascular responses independently.
Edinburgh Research Explorer The evolution, diversity and host associations of rhabdoviruses

Metagenomic studies are leading to the discovery of a hidden diversity of RNA viruses. These new viruses are poorly characterized and new approaches are needed predict the host species these viruses pose a risk to. The rhabdoviruses are a diverse family of RNA viruses that includes important pathogens of humans, animals, and plants. We have discovered thirty-two new rhabdoviruses through a combination of our own RNA sequencing of insects and searching public sequence databases. Combining these with previously known sequences we reconstructed the phylogeny of 195 rhabdovirus sequences, and produced the most in depth analysis of the family to date. In most cases we know nothing about the biology of the viruses beyond the host they were identiﬁed from, but our dataset provides a powerful phylogenetic approach to predict which are vector-borne viruses and which are speciﬁc to vertebrates or arthropods. By reconstructing ancestral and present host states we found that switches between major groups of hosts have occurred rarely during rhabdovirus evolution. This allowed us to propose seventy-six new likely vector-borne vertebrate viruses among viruses identiﬁed from vertebrates or biting insects. Based on currently available data, our analysis suggests it is likely there was a single origin of the known plant viruses and arthropod-borne vertebrate viruses, while vertebrate-and arthropod-speciﬁc viruses arose at least twice. There are also few transitions between aquatic and terrestrial ecosystems. Viruses also cluster together at a ﬁner scale, with closely related viruses tending to be found in closely related hosts. Our data therefore suggest that throughout their evolution, rhabdoviruses have occasionally jumped between distantly related host species before spreading through related hosts in the same environment. This approach offers a way to predict the most probable biology and key traits of newly discovered viruses.
Polynomial Interpolation and Cubic Spline to Determine Approximate Function of COVID-19 Tweet Dataset

As we know theoretically if we are going to construct a polynomial interpolation function through a mapped base, we create an approximation function. In this study, we try to build an approximation function using all sample data available. The approximation function obtained represents the data whose graph goes through a given set of data points. We determine the value of a function at different points and specific intervals using the interpolation model. The first derivative of the function is obtained to find the growth rate of tweet data. The experimental data is a crawling tweet with the keyword COVID-19. Then we get the amount of data per time duration representing a value of the function at a node. The interpolation includes such as Lagrange, Newton’s divided difference, and cubic spline. In this study, we compared polynomial interpolation with cubic splines to obtain optimal results. With the functional approach obtained, a pattern of tweets related to COVID-19 can be seen from its graph that passes through the given data points. The graph and the estimated values obtained show that the cubic spline is the optimal interpolation as an approximation function. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.
MIT Open Access Articles The Renaissance of Bacillosamine and Its Derivatives: Pathway Characterization and Implications in Pathogenicity

: Prokaryote-speci ﬁ c sugars, including N , N ′ - diacetylbacillosamine (diNAcBac) and pseudaminic acid, have experienced a renaissance in the past decade because of their discovery in glycans related to microbial pathogenicity. DiNAcBac is found at the reducing end of oligosaccharides of N-and O-linked bacterial protein glycosylation pathways of Gram-negative pathogens, including Campylobacter jejuni and Neisseria gonorrhoeae . Further derivatization of diNAcBac results in the nonulosonic acid known as legionaminic acid, which was ﬁ rst characterized in the O-antigen of the lipopolysaccharide (LPS) in Legionella pneumophila . Pseuda-minic acid, an isomer of legionaminic acid, is also important in pathogenic bacteria such as Helicobacter pylori because of its occurrence in O-linked glycosylation of ﬂ agellin proteins, which plays an important role in ﬂ agellar assembly and motility. Here, we present recent advances in the characterization of the biosynthetic pathways leading to these highly modi ﬁ ed sugars and investigation of the roles that each plays in bacterial ﬁ tness and pathogenicity.
Use of Ontological Knowledge for Multi-Criteria Comparison of Complex Information Objects

We propose ontology-based formal model of complex information object (CIO) as an element of decision making by intelligent information systems. The main stages of CIO comparison with similar structure based on the use of knowledge from domain are considered. We use various evaluations of semantic proximity and semantic similarity to match CIO properties and their values with requirements of user task that are formalized as CIO reference model. The basis for the reference CIO construction is the natural language task description compared with domain ontology that defines the CIO structure. Domain ontology is used also as a source of comparison criteria that can be constructed from various combinations of characteristics of ontology classes and individuals used in CIO elements, and we propose an algorithm for recursive generation of the CIOs comparison criteria set. Decision that we retrieve is a CIO that is the most similar to this reference model according to these criteria but current significance of them is defined as hierarchy by experts for actual environment.
Spatial Distribution of Wave Pressures on Seawave Slot-cone Generator

acceptance number 166 SPATIAL DISTRIBUTION OF WAVE PRESSURES ON SEAWAVE SLOT- CONE GENERATOR 1 Author: Vicinanza, Diego 2 Author: Margheritini, Lucia 3 Author: Frigaard, Peter Breakwaters Coastal structures Dynamic response Inlets Long waves Overtopping Wave generation
LLM Augmented Hierarchical Agents

Solving long-horizon, temporally-extended tasks using Reinforcement Learning (RL) is challenging, compounded by the common practice of learning without prior knowledge (or tabula rasa learning). Humans can generate and execute plans with temporally-extended actions and quickly learn to perform new tasks because we almost never solve problems from scratch. We want autonomous agents to have this same ability. Recently, LLMs have been shown to encode a tremendous amount of knowledge about the world and to perform impressive in-context learning and reasoning. However, using LLMs to solve real world problems is hard because they are not grounded in the current task. In this paper we exploit the planning capabilities of LLMs while using RL to provide learning from the environment, resulting in a hierarchical agent that uses LLMs to solve long-horizon tasks. Instead of completely relying on LLMs, they guide a high-level policy, making learning significantly more sample efficient. This approach is evaluated in simulation environments such as MiniGrid, SkillHack, and Crafter, and on a real robot arm in block manipulation tasks. We show that agents trained using our approach outperform other baselines methods and, once trained, don't need access to LLMs during deployment.
Eukaryotic-like ribosomal RNA in Asgard archaea

Affiliations: 4 NASA Center for the Origin of Life, Georgia Institute of Technology, Atlanta, GA 30332-0400, USA School of Biological Sciences, Georgia Institute of Technology, North Avenue, Atlanta, GA 30332, USA School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Dr, Atlanta, GA 30332, USA Department of Integrative Biology, The University of Texas at Austin, 2415 Speedway #C0930, 5 Austin, TX 78712, USA 6 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Dr, 7 Atlanta, GA 30332, USA 8
Tuning HeLI-OTS for Guarani-Spanish Code Switching Analysis

This article describes a system created for the first subtask of the GUA-SPA - Guarani-Spanish Code Switching Analysis shared task held as part of the IberLEF 2023 evaluation campaign. The system was based on the HeLI-OTS off-the-shelf language identifier and ad-hoc rules for detecting named entities, unknown languages, other tokens, and words with mixed Spanish Guarani language. With our system, we attained the second position in the subtask with an F-score of 0.9139.
Analysis of Various Techniques and Methodologies for Heart Disease Prediction a Review

: One of the most important organs for the normal operation of our body is the heart. A WHO investigation revealed that cardiovascular diseases consistently account for 31% of deaths worldwide (CVDs). Additionally, low-and middle-income nations like India account for more than 75% of these deaths. Predicting whether or whether CVDs will occur inside a mortal body is the key challenge. There are many medical devices that can be used to diagnose heart diseases, but they have drawbacks such as being extremely expensive and not being able to accurately predict cardiac conditions (10). Age, coitus, blood pressure, cholesterol, blood sugar, and diabetes, as well as other lifestyle factors including rotundness, eating unhealthy foods, not exercising as much, smoking, drinking alcohol, etc. In this proposed research work the review of the different prediction techniques and methodologies have been enlightened with various research literatures. This will give the analysis of the different technologies .
A REVIEW OF LESSONS FROM COMBINED MODELLING APPROACHES IN ASSESSING ENERGY AND CLIMATE POLICIES

energy visions while considering the complex hourly dynamic of power supply and demand poses by renewable energies. Lack of a holistic approach causes inaccuracies in determining the timing of investment in new technologies, generation capacity, exploring trade-offs, projecting the actual CO 2 emissions, estimating needed capacity and storage. The combined modelling approaches allow for a more in-depth analysis of the interactions of different policies. Our work shows that the soft-linking of the energy economy model with the techno-economic capacity expansion model can create a feedback loop in the system that are not accounted for in the separate modelling structures or any existing combined modelling approaches. Although there are some trade-offs when combining models, and some uncertainty as to which policies a specific combined modelling approach can inform, in general, combined modelling approaches with effective feedback between the models will be able to address more holistic policy questions than the separate models will be able to address. Furthermore, though there are significant strengths in linking models, one must be careful to ensure that the linkages mutually enhance the different models being incorporated and do not exacerbate existing model weaknesses.
Robust estimation of parallel robot dynamic parameters with interval analysis

This paper deals with the application of interval analysis for outer bounding the physical parameters of parallel robots. The robot is modeled with classical Lagrange equation which leads to an inverse dynamic model linear with respect to the parameters. Assuming the error additive on the input (motor torque), the problem is termed as a constraint propagation one and the solution is performed with a preconditioned Gauss-Seidel contractor modified in order to be used with over-determined linear systems. Experimental results are exhibited for a fully parallel robot with 4 degrees of freedom.
Operations Research Letters

We consider a problem where agents have private positions on a line, and public approval preferences over two facilities, and their cost is the maximum distance from their approved facilities. The goal is to decide the facility locations to minimize the total and the max cost, while incentivizing the agents to be truthful. We design a strategyproof mechanism that is simultaneously 11-and 5-approximate for these two objective functions, thus improving the previously best-known bounds of 2 n + 1 and 9. © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons .org /licenses /by-nc -nd /4 .0/).
MIT Open Access Articles Q-learning with nearest neighbors

We consider model-free reinforcement learning for inﬁnite-horizon discounted Markov Decision Processes (MDPs) with a continuous state space and unknown transition kernel, when only a single sample path under an arbitrary policy of the system is available. We consider the Nearest Neighbor Q-Learning (NNQL) algorithm to learn the optimal Q function using nearest neighbor regression method. As the main contribution, we provide tight ﬁnite sample analysis of the convergence rate. In particular, for MDPs with a d -dimensional state space and the discounted factor � 2 (0 , 1) , given an arbitrary sample path with “covering time” L , we establish that the algorithm is guaranteed to output an " -accurate estimate of the optimal Q-function using
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Principal component Analysis (PCA) is one of the most frequently used multivariate statistical methods. Especially, it is used on the purpose of dimension reduction and obtaining uncorrelated variables. However, classic PCA (CPCA) is sensitive to outlier. Because it is based on classic covariance or correlation matrices influenced by outliers. Therefore, CPCA can give fallacious results in data sets which have outliers. In this study, the robust PCA (RPCA) methods to solve this problem of CPCA are introduced in literature. Moreover, we bring forward a proposal to ROBPCA algorithm which is one of these methods.
Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data

Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can have high performance but can be deemed untrustworthy as they lack trans-parency, which makes them unreliable for taking high-stakes decisions during mission planning. We address this by proposing Neural Networks with Uncertainty Quantification in Terrain Classification. We enable our Neural Networks with Monte Carlo Dropout, DropConnect, and Flipout in time series-capable architectures using only proprioceptive data as input. We use Bayesian Optimization with Hyperband for efficient hyper-parameter optimization to find optimal models for trustworthy terrain classification.
Response of Growing Male New Zealand White Rabbits to Feeding on Two Sources of Green Berseem Fodder

: A total number of 24 male New Zealand white rabbits aged 4-5 weeks with an average body weight 676.5±64.95g were divided into two equal groups each of contains 12 rabbits. Experimental rabbits were used in a feeding trial that continuous for 49 days. Tested group rabbits were classified as Group 1: rabbits fed on green berseem fodder normal fertilize (BNF) ad libitum and considered as control. Group 2: rabbits fed on green berseem fodder sludge fertilize (BSF) ad libitum . The results showed that BSF was superior in their contents of dry matter, crude protein, nitrogen free extrct, ash, non fiber carbohydrates, gross energy, digestible energy, total digestible nutrient and digestible crude protein in comparison with BNF that that superior in their contents in crude fiber, ether extract, neutral detergent fiber, acid detergent fiber, acid detergent lignin, hemicellulose and cellulose. Heavy metal contents of two Berseem sources were in the same range. Except for crude protein, neutral detergent fiber digestibilities and digestible crude protein the other parameter were not affected (P>0.05) by the source of berseem used (BNF or BSF). All parameters of growth performance were not significantly (P<0.05) affected by the source of berseem used. Sampling time at 45 days of feeding period significantly (P<0.05) decreased heavy metal concentrations in blood plasma includes (zinc, cobalt, cadmium and lead) in comparison with that determined at 15 days. Meanwhile the others includes (iron, manganese, copper, chromium and nickel) were not significantly (P>0.05) affected by sampling time. Values of red blood cells, hematocrit, lymphocyte, monocyte and hemoglobin were not affected by the two sources of berseem, meanwhile, values of white blood cells, granulated and mean cell size were significantly (P<0.05) decreased when rabbits fed BSF comparing to that fed BNF. All values of carcass were not affected (P>0.05) by the tow sources of berseem (BNF or BSF). Heavy metal in kidneys tissues includes (iron, chromium and cadmium) were significantly (P<0.05) decreased in rabbits fed BSF. Meanwhile, it significantly (P<0.05) increased value of cobalt concentration. However, values of zinc, manganese, copper, nickel and lead concentrations were not affected. Heavy metal in liver tissues includes (iron and manganese) significantly (P<0.05) decrease in rabbits fed BSF meanwhile, cobalt concentration significantly (P<0.05) increased. But the other values of heavy metal determined were not affected (P>0.05). All values of heavy metal determined in muscles tissues were not affected (P>0.05). Heavy metal in brain tissues includes (iron and zinc) were significantly (P<0.05) decreased in rabbits fed BSF, meanwhile, copper concentarion significantly (P<0.05) increased. However the other values of heavy metal were not affected (P>0.05). It can be mentioned that rabbits can be fed on Berseem green fodder only without occurring any adverse effect on their performance, digestibility, blood, carcass.
LEED Adopters : Public Procurement and the Private Supply of “ Green ” Buildings

October 18, 2011 DRAFT: DO NOT CITE OR CIRCULATE Governments often use their considerable purchasing power to promote policy objectives. However, it remains unclear whether or when public procurement preferences can stimulate private markets for innovative products and services. We examine municipal government procurement policies that mandate the construction of “green” buildings. We ask whether these policies aimed at government buildings actually influence private-sector building practices; specifically, the adoption of the U.S. Green Building Council's LEED standard. Using a combination of matching methods, panel-data and instrumental variables we find that municipal green building policies not only stimulate the supply of professionals investing in the required expertise, but also have spillover effects that spur private sector demand for green buildings. These findings suggest a link between product compatibility and environmental sustainability; specifically, new environmental standards may suffer from “excess inertia” if adoption requires coordinated investments, for example between builders and real estate professionals. Government procurement policies are one way to break these deadlocks.
PERFORMANCE ANALYSIS OF A VERTICAL AXIS WIND TURBINE

: The increase of environmental awareness and with the shortage of fossil fuels, wind energy is becoming more important than ever. As the market for wind energy grows, wind turbines and wind farms are becoming larger. The shape of the turbine blades plays a crucial role in determining the efficiency and power output of the turbine, as well as its noise and vibration levels. The objective of the project is to Performing a computational fluid dynamic analysis on a single Vertical Axis Wind Turbine (VAWT) with two different blade profiles, elliptical and parabolic. To perform the CFD analysis, a 3D model of the VAWT with the elliptical and parabolic blade profiles was created using a space claim design (CAD) software. The model was then imported into a CFD software package, which used numerical methods to simulate the flow of air around the turbine blades. Examine parameters that affect the performance of these models, such as pressure, velocity, and the way in which the turbine force is projected onto the flow field.
IN-SITU MEASUREMENTS OF LAND-ATMOSPHERE EXCHANGES OF WATER, ENERGY AND CARBON DIOXIDE IN SPACE AND TIME OVER THE HETEROGENEOUS BARRAX SITE DURING SPARC2004

In order to advance our understanding of landatmosphere exchanges of water, energy and CO2 in space and time over heterogeneous land surfaces, an intensive field campaign was carried out at the Barrax agricultural test site in Spain in the period 12-21 July 2004 involving multiple field, satellite and airborne instruments for characterizing the state of the atmosphere, the vegetation and the soil from visible to microwave range of the spectrum. Part of the experimental area is a core site of a 25 km 2 area within which numerous crops are grown - on both irrigated and dry land - alongside fields of bare soil. This campaign was carried out in combination with the EU 6FP EAGLE Project. Emphasis of this paper is on the in-situ measurements of land-atmosphere exchanges of water, energy and CO2 as well as the thermal dynamic states of the atmosphere, the soil and the vegetation.