Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying groundwater discharge using thermal imagery and conventional groundwater exploration techniques for estimating the nitrogen loading to a meso-scale inland estuary

This dissertation presents the results of a study to evaluate groundwater inflow and nutrient loadings to the Great Bay Estuary, New Hampshire. The evaluation of inflow was accomplished independently by two methods: one, used thermal imagery, and the other, piezometric mapping. The thermal imagery method assessed groundwater that was observed to discharge within the intertidal zone of an inland estuary. The groundwater piezometric mapping method used the monitoring wells around the bay to create an overall piezometric map of the near-bay area. Groundwater discharge was evaluated with respect to flow, concentration, and ultimately nitrogen loading to coastal waters. The results represent a snapshot for these variables, examined by a thermal infrared aerial survey in the spring of 2000, and water quality, specific discharge, and piezometric surface maps in the summer of 2001. Monitoring wells upgradient of the Great Bay were analyzed for nitrogen as an indicator of potential discharge source waters. Total groundwater discharge to the estuary was calculated as 24.2 cubic feet per second (cfs) with an average of 0.81 +/0.89 mg dissolved inorganic nitrogen (DIN)/L, with a maximum value of 2.7 mg DIN/L (n = 20). Nutrient concentrations, averaging 0.83 +/1.34 mg DIN/L, with a maximum value of 10.2 mg DIN/L, were observed in upgradient bedrock groundwater analyzed from 192 monitoring wells. Nutrient loading was calculated to be 19.3 +/21.2 tons of N per year for the total Great Bay Estuary, covering nearly 144 miles of shoreline. The groundwater derived nutrient loading accounts for approximately 5% of the total non-point source load to the estuary. The thermal imagery method was found to be an effective and affordable alternative to conventional groundwater exploration approaches.
Computational prediction of structure, function and interaction of aphid salivary effector proteins

Similar to plant pathogens, phloem-feeding insects such as aphids deliver effector proteins inside their hosts that act to promote host susceptibility and enable feeding and infestation. Despite exciting progress towards identifying and characterizing effector proteins from these insects, their functions remain largely unknown. The recent ground-breaking development in protein structure prediction algorithms combined with the availability of proteomics and transcriptomic datasets for agriculturally important pests, such as the aphid Myzus persicae (green peach aphid), provides new opportunities to explore the structural and functional diversity of effector repertoires. In this study, we sought to gain insight into the the M. persicae effector repertoire by predicting and analysing the structures of a set of 71 effector candidate proteins. We used two protein structure prediction methods, AlphaFold and OmegaFold, which produced mutually consistent results. We observed a wide continuous spectrum of sizes and structures among the effector candidates, from disordered proteins to globular enzymes. We made use of the structural information and state-of-the-art computational methods to predict M. persicae effector protein properties, including function and interaction with host plant proteins. Overall, our investigation provides novel insights into the structure, function, and interaction prediction of aphid effector repertoires and will guide the necessary experimental characterization to address new hypotheses.
REVIEW ARTICLE USE‐WEAR ANALYSIS: THE STATE OF THE ART

This paper is a review of the recent developments in use-wear analysis. The focus is on some key issues: polish formation, quantification of use-wear, residue analysis and the way these related themes have affected methods of use-wear analysis. A brief history of the development of use-wear analysis is presented and a discussion of blind tests, expert systems (explained at length in the ‘Appendix’) and how use-wear analysis can be incorporated into the chaine operatoire approach to the interpretation of Stone Age sites.
Crystal structure of a Class A β-lactamase from Nocardia cyriacigeorgica

Nocardia are gram-positive bacteria from the actinobacterial class. Some Nocardia species can infect humans and are usually considered opportunist pathogens as they often infect immunocompromised patients. Albeit, their clinical incidence is low, many Nocardia species are nowadays considered emerging pathogens. Primary sites of infection of Nocardia are the skin or the lungs but dissemination to other body parts is very frequent. These disseminated infections are very difficult to treat and thus, tackled with multiple classes of antibiotics, on top of the traditional treatment targeting the folate pathway. ß-lactams are often included in the regimen but many Nocardia species present moderate or strong resistance to some of this drug class. Genomics, microbiological, and biochemical studies have reported the presence of class A ß-lactamases (ABL) in a handful of Nocardia species but no structural investigation of Nocardia ß-lactamases has been performed yet. In this study, we report the expression, purification, and preliminary biochemical characterization of the ABL from a Nocardia cyriacigeorgica (BlaNcyr) clinical strain. We described, as well, the crystallization and the very high-resolution crystal structure of BlaNcyr. The protein sequence and structural analysis demonstrate that BlaNcyr belongs to ß-lactamase of class A1 and attest to very high conservation between ABL from other human pathogenic Nocardia species. In addition, the presence of one molecule of citrate tightly bound in the catalytic site of the enzyme is described. This structure may provide a solid basis for future drug development to specifically target Nocardia ß-lactamases.
Using Evolutionary Algorithms to Induce Oblique Decision Trees

This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that at least in some cases this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolution strategy and a simple genetic algorithm on public domain and artificial data sets. The empirical results suggest that the EAs quickly find competitive classifiers, and that EAs scale up better than traditional methods to the dimensionality of the domain and the number of instances used in training.
1 Using AdaBoost for Place Labeling and Topological Map Building Óscar Martı́nez

Indoor environments can typically be divided into places wi th different functionalities like corridors, kitchens, offices, or seminar rooms . We believe that the ability to learn such semantic categories from sensor data or in maps enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot in teraction, path-planning, exploration, or localization. In this work, we first propose an app roach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from vision and laser range data into a strong classifier. We furthermore present two main applications of this approach . Firstly, we show how our approach can be utilized by a moving robot for robust online cla ssification of the poses traversed along its path using a hidden Markov model. Secondly, we intr oduce a new approach to learn topological maps from geometric maps by applying our semant ic classification procedure in combination with probabilistic labeling. Experimental re sults obtained in simulation and with real robots demonstrate the effectiveness of our approach i n various environments.
Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a wide variety of infectious disease agents. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. Previously, this immune profiling approach or “immunosignature” has been implemented using a purely statistical evaluation of pattern binding, with no regard for information contained in the amino acid sequences themselves. Here, a neural network is trained on immunoglobulin G binding to 122,926 amino acid sequences selected quasi-randomly to represent a sparse sample of the entire combinatorial binding space in a peptide array using human serum samples from uninfected controls and 5 different infectious disease cohorts infected by either dengue virus, West Nile virus, hepatitis C virus, hepatitis B virus or Trypanosoma cruzi. This results in a sequence-binding relationship for each sample that contains the differential disease information. Processing array data using the neural network effectively aggregates the sequence-binding information, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared to the raw binding data. Because the neural network model is trained on all samples simultaneously, the information common to all samples resides in the hidden layers of the model and the differential information between samples resides in the output layer of the model, one column of a few hundred values per sample. These column vectors themselves can be used to represent each sample for classification or unsupervised clustering applications such as human disease surveillance. Author Summary Previous work from Stephen Johnston’s lab has shown that it is possible to use high density arrays of near-random peptide sequences as a general, disease agnostic approach to diagnosis by analyzing the pattern of antibody binding in serum to the array. The current approach replaces the purely statistical pattern recognition approach with a machine learning-based approach that substantially enhances the diagnostic power of these peptide array-based antibody profiles by incorporating the sequence information from each peptide with the measured antibody binding, in this case with regard to infectious diseases. This makes the array analysis much more robust to noise and provides a means of condensing the disease differentiating information from the array into a compact form that can be readily used for disease classification or population health monitoring.
Controller and Trajectory Optimization for a Quadrotor UAV with Parametric Uncertainty

In this work, we exploit the recent notion of closed-loop state sensitivity to critically compare three typical controllers for a quadrotor UAV with the goal of evaluating the impact of controller choice, gain tuning and shape of the reference trajectory in minimizing the sensitivity of the closed-loop system against uncertainties in the model parameters. To this end, we propose a novel optimization problem that takes into account both the shape of the reference trajectory and the controller gains. We then run a large statistical campaign for comparing the performance of the three controllers which provides some interesting insight for the goal of increasing closed-loop robustness against parametric uncertainties.
Investigation of atmospheric instability for communication experiments with the ESA's geostationary satellite ARTEMIS

The investigations of the atmospheric turbulence instability are carried out. The observations of different stars and different positions of stars in the sky performed with the AZT-2 telescope (diameter of 0.7 m) of the Main Astronomical Observatory, Kyiv, Ukraine, and at the Optical Ground Station (the 1.0-m telescope) of ESA at Canary Islands are described. Short exposures (40 ms) with CCD cameras in focal plane of objectives with the filters were used. The calculations of middle positions of star images were performed. The deviation of star’s image positions from the middle position was also performed. The atmospheric attenuation and FWHM (Full Width Half Maximum) function were calculated using the MIDAS/ROMAFOT software package. The results of analyses of experimental data obtained due to observations in different regions are also presented.
BIDDING BEHAVIOR AND DECISION COSTS IN FIELD EXPERIMENTS

Whether rationality of economic behavior increases with expected payoffs and decreases with the cognitive cost it takes to formulate an optimal strategy remains an open question. We explore these issues with field data, using individual bids from sealed-bid auctions in which we sold nearly $10,000 worth of sportscards. Our results indicate that stakes do indeed matter, as high-priced ($70) cards produced more of the theoretically predicted strategic behavior than did lower-priced ($3) cards. We find additional evidence consistent with the importance of cognitive costs, as subjects more experienced with sportscard auctions exhibited a greater tendency to behave strategically than did less experienced bidders.
eGFR-TKis in adjuvant treatment of lung cancer: to give or not to give?

: Epidermal growth factor receptor-tyrosine-kinase inhibitors (EGFR-TKIs) brought a significant revolution in the treatment of non-small-cell lung cancer (NSCLC). In a short period of time, EGFR-TKIs became the standard of treatment for mutation-positive, advanced stage non-squamous NSCLC. In recent years, second-and third-generation EGFR-TKIs are emerging, further widening the clinical use. However, the question of EGFR-TKIs efficiency in the treatment of early stage NSCLC still remains open. Early clinical trials failed to approve the use of EGFR-TKIs in adjuvant setting. The majority of these early trials were performed in unselected NSCLC populations and without standardized biomarker identification. One should certainly not rely solely on these results and dismiss the use of EGFR-TKIs as adjuvant therapy. Many important questions are still unanswered. Most important issues such as stage heterogeneity (IA–IIIA), timing (after or concomitantly with chemotherapy), and type of administration (monotherapy or combination) need to be answered in near future. Adjuvant TKIs in the treatment of lung cancer might offer significant number of advancements. Having in mind the significant duration of response observed in advance disease setting, there could be place for prolongation of response in adjuvant setting potentially, leading to improvement in survival. TKIs could offer less-toxic adjuvant treatment with better efficiency than chemotherapy. However, there is a chronic lack of randomized controlled trials in this field, leading to inability to draw any scientifically sound conclusion with regard to the adjuvant treatment. For now, the use of EGFR-TKIs outside clinical trial setting is not recommended. The purpose of this review is to evaluate current and available data.
Annual chronology and climate response in Abies guatemalensis Rehder (Pinaceae) in Central America

The continued expansion of dendroclimatology into Mesoamerica requires the identification and evaluation of species whose rings can be precisely dated and then statistically compared with precipitation and temperature variability in order to make inferences about past climate. Here, we establish the basis for using Abies guatemalensis Rehder (Pinaceae) for climate reconstruction in Central America. Annual crossdating in this montane species is demonstrated at high-elevation sites in the Sierra de los Cuchumatanes in western Guatemala. We find that ring width is most strongly influenced by early growing season moisture conditions, controlled by late dry season rainfall, and negatively correlated with growing season temperature. Our chronology is also significantly negatively correlated with eastern tropical Pacific sea surface temperature anomalies. Our confirmation of annual chronology and the identification of a climatic signal in this species now allow its use in local and regional paleoclimate reconstructions, as well as ecological studies.
The Ascender System: Automated Site Modeling from Multiple Aerial Images

The Ascender system acquires, extends, and refines 3D geometric site models from calibrated aerial imagery. To acquire a new site model, an automated building detector is run on one image to hypothesize potential building rooftops. Supporting evidence is located in other images via epipolar line segment matching in constrained search regions. The precise 3D shape and location of each building is then determined by multiimage triangulation under geometric constraints of 3D orthogonality, parallelness, colinearity, and coplanarity of lines and surfaces. Projective mapping of image intensity information onto these polyhedral building models results in a realistic site model that can be rendered using virtual “fly-through” graphics. As new images of the site become available, model extension and refinement procedures are performed to add previously unseen buildings and to improve the geometric accuracy of the existing 3D building models. In this way, the system gradually accumulates evidence over time to make the site model more complete and more accurate. An extensive performance evaluation of component algorithms and the full system has been carried out. Two-dimensional building detection accuracy, as well as accuracy of the three-dimensional building reconstruction, are presented for a representative data set.
Annealed Importance Sampling for Neural Mass Models

Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution.
Initial stages of the riming process on ice crystals

The characteristics of the capture and freezing of supercooled water droplets by falling ice crystals were quantitatively investigated in a vertical supercooled cloud chamber of 10 m length at temperatures from −5 to −25°C. We present evidence that ice crystals can start the riming process at smaller sizes than previously reported. The results obtained show that the minimum dimension of ice crystals involved in riming is around 60 μm in diameter for hexagonal plates and 30 μm width and 60 μm length for columnar ice crystals. No substantial difference has been observed between the size distribution of the droplets accreted on the crystals and the cloud droplet size distribution, indicating that this process should not produce significant changes in the cloud droplet spectrum. The time elapsed between the ice crystal nucleation and the collection of crystals with droplet accretion was typically around 60 seconds.
93.89% Peak Efficiency 24V-to-1V DC-DC Converter with Fast In-Situ Efficiency Tracking and Power-FET Code Roaming

This paper presents a high-efficiency 24V-to-1V buck converter featuring two techniques to improve efficiency and reliability. First, we propose a fast in-situ efficiency tracking technique, which helps to maintain high power efficiency across load conditions and process, voltage, and temperature variations. Second, we propose a power-FET code roaming technique to avoid excessive aging on specific segments. The proposed converter was fabricated in a 180nm BCD process, achieving a peak power efficiency of 93.89% at 405-mA load current. The tracking technique demonstrates an efficiency improvement of up to 34.74% compared to the baseline without efficiency tracking. In addition, the roaming technique slows down the on-time resistance degradation by 5.8X.
Development of Selection Method for Additional Devices to Supporting Active Monitoring of Healthcare Asset

Asset monitoring is one of the important things in asset management. Monitoring techniques can be done passively or actively. Whereas, active monitoring will push information automatically, where information comes directly from assets. In order to support this technique, it requires additional devices. Basically, not all assets, especially in healthcare, need to use active monitoring. This paper proposes a method for selecting which assets should be deemed necessary to obtain active monitoring. The technique used is asset classification weighted score with some defined parameter. The results of this study have been tested on a sample of around 135 types of assets on healthcare. Based on this method implementation, it can be selected that only about 48 types of assets need to use active monitoring. In the final step, the selection of the appropriate technology for active monitoring is continued.
Application of a Stochastic Deposition and Erosion Model to Reservoir Heterogeneity and Stratigraphic Data

ABSTRAcr In thispaper we consider the dynamics of sedimentation in sedimentary basins and apply fractal models to quantify the dynamics of sedimentation and the resulling basin helerogeneily Firsl we consider porosily varialions in sedimentary basins A stochastic model of deposition and erosion is applied to the spatial and Iemporal variations in deposition and erosion The spatial variability of the topography along strike in thismodel is a Brownian walk This has been widely observed as the spec1ral behavior of topography We show that this variability also is consistent with the spatial distribulion of oil pools in sedimentary basins The lemporal variabilily of sedimenlalion is associaled with Ihe vertical variabilily of porosily Vertical well logs have power law power spectra S proportional to approximately c 3f2 where kis the wave number The vertical variabilily of sedimentation and erosion also can be used 10 model the completeness of the sedimentary record The rate of sedimentation R has a power law dependence on thetime period of sedimentalion T withR oc TJ 76 Atime series with power spectrum Sif oc f 32 wherefis the frequency gives R oc TJ 75 Wealso examine the persistence inthe series of bed thicknesses as a function of depth Forthe model no persistence isobserved Ifthe model fully characterizesthe autocyclic dynamics in sedimentary basins thelack of persislence in thesyn thetic bed sequences suggeslS thai observed persistence and cyclicity inreal bed thickness sequences must bethe result of allocyclic processes
A 50μW 2.45GHz Direct-Conversion RX with On-Chip LO with -84dBm Sensitivity for 1Mb/s GFSK

In this paper, an ultra-low-power 2. 45GHz mixer-first 1ow-F receiver front-end is presented. To have extremely low power consumption, a technique for implementing an on-chip low-power local oscillator is proposed that contains a 9-stage pseudo-differential ring oscillator operating at 119 of the RF frequency. As well as reducing the power consumption, this technique results in having a lower $1/f^{3}$ phase noise corner frequency. The proposed front-end is fabricated in GF22nm FDSOI technology. It consumes $50\mu \mathrm{W}$ and has 2x less power than the state-of-the-art while having competitive calculated sensitivity of -84dBm for 1MHz GFSK demodulation.