Miracle-dz/UniFAR-FTUs huggingface dataset:
UNCERTAINTY QUANTIFICATION OF AN AUTONOMOUS SURFACE VEHICLE BY MULTI-FIDELITY GAUSSIAN PROCESS

A multi-ﬁdelity Gaussian process (MF-GP) is presented for the forward uncertainty quantiﬁcation (UQ) of the performance of an autonomous surface vehicle (ASV) subject to uncertain operating conditions. The ASV is a shallow water autonomous multi-purpose platform (SWAMP), designed for the acquisition of the environmental parameters in the extremely shallow waters of wetlands. The quantity of interest (QoI) is the hydrodynamic resistance of the SWAMP subject to variable payload and longitudinal position of its center of mass. The QoI is assessed by a linear potential-ﬂow solver coupled with the rigid body equations of motion. Multiple ﬁdelity levels are deﬁned based on the computational grid size and the level of coupling between hydrodynamic loads and motions. The MF-GP is based on a low-ﬁdelity surrogate, corrected with an additive function, representing the error between higher and lower ﬁdelity solutions. The MF-GP provides the prediction with the associated uncertainty. The latter is used to adaptively train the MF-GP, adding points where the prediction uncertainty is maximum. Finally, the UQ of the QoI is performed by Monte Carlo sampling on the MF-GP surrogate. The ﬁrst four statistical moments, the 95th percentile, and the probability density function of the QoI are assessed. MF-GP is compared to its single-ﬁdelity (high-ﬁdelity based) counterpart, showing overall better results
Investigation of local properties of the Fe-Si alloy subjected to mechanical and laser cutting

: The eﬀective design of energy-saving electric motors with eﬃciency class IE4 and higher requires the use of material characteristics that take into account the core shaping process. Therefore, it becomes necessary to use analytical or numerical models that take into account the change of local properties of Fe-Si material. The aim of the work is to indicate a useful analytical model for estimating the local magnetic permeability of the material, as well as to understand the reasons for these changes. For this purpose, low-loss ferromagnetic materials cut with a guillotine and a laser were tested. Rectangular samples, cut at an angle of 0 degrees in relation to the rolling direction, were subjected to macroscopic and microscopic examinations. Finally, the main reasons for changes in material characteristics for both cutting technologies were indicated. Therefore, the proposed model takes into account not only the cutting technology used, but also the current width of the tested strip, for which the material characteristics are to be determined. The parameters of the analytical model are determined on the basis of a limited number of measurements carried out on samples of a simple geometric shape.
Estimation of leaf nitrogen content in winter wheat based on continuum removal and discrete wavelet transform

ABSTRACT Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC 2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV 2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat.
Baseline studies of the clay minerals society source clays: Layer-charge determination and characteristics of those minerals containing 2:1 layers

The layer charge is perhaps the single most significant characteristic of 2:1 layer phyllosilicates. Layer charge affects cation-retention capacity and adsorption of water, and various polar organic molecules. The effects of layer charge on the sorptive properties of organo-clays were illustrated by Lee et al. (1990). It is generally agreed that the classification of 2:1 silicate clays, which is a continuing problem, may be resolved by taking into account the magnitude of the layer charge (Bailey et al. , 1971; Malla and Douglas, 1987) Studies on structural chemistry also confirm the importance of the layer charge for the characterization of the 2:1 phyllosilicates (Newman and Brown, 1987). Layer charge involves charge per [O20(OH)4] and the sum of the tetrahedral and octahedral charges. Cation exchange capacity (CEC) results not only from the layer charge, but also pH-dependent edge charges. If the molecular mass ( M ) or formula weight of the phyllosilicates and the layer charge are known, the CEC due to interlayer charge (ξ) can be calculated from the following equation: \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[Interlayer\ CEC\ (cmol_{c}kg^{{-}1})\ =\ 10^{5}\ {\times}\ {\xi}/\mathit{M}\] \end{document}(1) The molecular mass (Table 1⇓) is without dimension, because it is a relative property. The mean molecular mass of the 2:1 phyllosilicates varies from 370–390 The total CEC exceeds the interlayer CEC, because of charge at crystal edges. For smectites, the edge charge is between 10 and 30% and on average it is ~20% of the total CEC (Lagaly, 1981). Non-exchangeable cations such as K+ in illite or mica are excluded from the total apparent CEC, but they are included in the layer charge. View this table: Table 1. Approximate layer charge of 2:1 phyllosilicates. Traditionally, the layer charge is calculated from the structural formula of homoionic pure clay mineral specimens (Ross and Hendricks, 1945). From the occupancy of elements in the tetrahedral and octahedral sheets, …
Estimating and managing broad risk of chronic wasting disease spillover among cervid species

The management of infectious wildlife diseases often involves tackling pathogens that infect multiple host species. Chronic wasting disease (CWD) is a prion disease that can infect most cervid species. CWD was detected in reindeer (Rangifer tarandus) in Norway in 2016. Sympatric populations of red deer (Cervus elaphus) and moose (Alces alces) are at immediate risk. However, the estimation of spillover risk across species and implementation of multispecies management policies are rarely addressed for wildlife. Here, we estimated the broad risk of CWD spillover from reindeer to red deer and moose by quantifying the probability of co‐occurrence based on both (1) population density and (2) habitat niche overlap from GPS data of all three species in Nordfjella, Norway. We describe the practical challenges faced when aiming to reduce the risk of spillover through a marked reduction in the population densities of moose and red deer using recreational hunters. This involves setting the population and harvest aims with uncertain information and how to achieve them. The niche overlap between reindeer and both moose and red deer was low overall but occurred seasonally. Migratory red deer had a moderate niche overlap with the CWD‐infected reindeer population during the calving period, whereas moose had a moderate niche overlap during both calving and winter. Incorporating both habitat overlap and the population densities of the respective species into the quantification of co‐occurrence allowed for more spatially targeted risk maps. An initial aim of a 50% reduction in abundance for the Nordfjella region was set, but only a moderate population decrease of less than 20% from 2016 to 2021 was achieved. Proactive management in the form of marked population reduction is invasive and unpopular when involving species of high societal value, and targeting efforts to zones with a high risk of spillover to limit adverse impacts and achieve wider societal acceptance is important.
Calcitonin Gene-Related Peptide Systemic Effects: Embracing the Complexity of Its Biological Roles—A Narrative Review

The calcitonin gene-related peptide (CGRP) is a neuropeptide widely distributed throughout the human body. While primarily recognized as a nociceptive mediator, CGRP antagonists are currently utilized for migraine treatment. However, its role extends far beyond this, acting as a regulator of numerous biological processes. Indeed, CGRP plays a crucial role in vasodilation, inflammation, intestinal motility, and apoptosis. In this review, we explore the non-nociceptive effects of CGRP in various body systems, revealing actions that can be contradictory at times. In the cardiovascular system, it functions as a potent vasodilator, yet its antagonists do not induce arterial hypertension, suggesting concurrent modulation by other molecules. As an immunomodulator, CGRP exhibits intriguing complexity, displaying both anti-inflammatory and pro-inflammatory effects. Furthermore, CGRP appears to be involved in obesity development while paradoxically reducing appetite. A thorough investigation of CGRP’s biological effects is crucial for anticipating potential side effects associated with its antagonists’ use and for developing novel therapies in other medical fields. In summary, CGRP represents a neuropeptide with a complex systemic impact, extending well beyond nociception, thus offering new perspectives in medical research and therapeutics
Intergenerational consequences of migration: Socio-economic, Family and Cultural Patterns of Stability and Change in Turkey and Europe

Migration is a life-changing experience not only for the migrants themselves but also for those left behind. Using a novel 'origins of migration' approach, this fascinating book reveals the true impact of migration across multiple aspects of migrants' lives by comparing three generations of Turkish migrants to Europe to their non-migrant counterparts in Turkey. Working with unique and exciting information on 2000 Turkish families and their 50,000 family members, the authors examine three generations-worth of complex migration trajectories and dense webs of transnational connections, taking in migration, settlement, return migration, friendships, marriages and visits. They evaluate change and continuity over 50 years, from the first labour migration of the 1960s to the present. Whilst in socio-economic terms migrants have gained from migration, and their attitudes and practices show some adjustment to the European context, the picture also reveals substantial continuity with their peers who remained in their country of origin.
Contour Artifact Removal for Expanded HDR Content

High Dynamic Range (HDR) displays are being widely adopted in the consumer electronics market, pushing up the demand of HDR content. However, HDR contents converted from their Standard Dynamic Range (SDR) counterparts often suffer from the contour artifact phenomenon. To address this issue, we formulate a sensitive region aware weighted gradient minimization process to remove the contour artifact. In order to assign reasonable weights to the image gradients in the objective function, we use features composed of local smoothness and gradient directions to highlight the risk regions, and then propose an expansion ratio to precisely locate the artifact. Solving the objective function gives improved HDR images with the contour artifacts minimized and texture preserved. Experimental results demonstrate the effectiveness of our method by the measurement of the objective metrics and visual comparison.
Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.
Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-Aware Semantic Segmentation in Point Clouds

Uncertainty-aware semantic segmentation of the point clouds includes predictive uncertainty estimation and uncertainty-guided model optimization. One key challenge in the task is the efficiency of pointwise predictive distribution establishment. The widely used Monte Carlo (MC) dropout establishes the distribution by computing the standard deviation of samples using multiple stochastic forward propagations, which is time-consuming for tasks based on point clouds containing massive points. Hence, a framework embedded with neighborhood spatial aggregation (NSA)-MC dropout, a variant of MC dropout, is proposed to establish distributions in just one forward pass. Specifically, our method uses the one-time stochastic inference of a point with neighbors to approximate the point’s repeated stochastic inferences, outputting pointwise distribution via the prediction variance of neighbors. Based on this, uncertainties acquire from the predictive distribution. The aleatoric uncertainty is integrated into the loss function to suppress noise, preventing the model from overfitting. Besides, the predictive uncertainty quantifies the prediction confidence. Experiments show that our plug-and-play NSA-MC dropout significantly improves the segmentation performance of backbones, ranging from multilayer perceptron (MLP)-, convolution-, and attention-based to transformer-based networks, without introducing additional parameters. Besides, it is several times faster than MC dropout to quantify the results’ credibility, and the inference time does not establish a coupling relation with the sampling times.
Automatic Verification for Handwritten Based on Gray Level Co-occurrence Matrix (GLCM) Properties and Seven Moments

In recent years, the need for community personality verification has increased dramatically, and biometrics are becoming very important in many daily applications. Biometrics work to verify individuals based on measurable data for their descriptions and characteristics. Biometric systems have thus been able to verify or identify a person. Handwritten signatures are recognized in all societies, and because they are biometric, it has made signature verification an important biometric process. In this paper, several models are collected from hand signatures for a person to study the characteristics of the parity matrix for each of these signatures to find common factors between the characteristics of these models. The practical aspect summarizes the removal of all empty spaces outside the frame of the signature image to be followed by the process of unifying the dimensions and then drawing four characteristics of the matrix of the dialogues. The process is conducted on 100 samples to sign the person to draw the qualities of his signature and then find the common characteristics among those four attributes of the signatures group. The tests on 10 people proved that adopting plastic qualities clearly distinguishes people's manual signatures. More than 80% of the signatures of people who were approved for the test were found. Thus supporting the possibility of being adopted as a biometric standard for personal verification, Matlab has been adopted to implement the software.
Elucidating the Impact of Biosolids-Derived Antimicrobials on Denitrifying Microbial Community Function and Structure in Agricultural Soil

Elucidating the Impact of Biosolids-Derived Antimicrobials on Denitrifying Microbial Community Function and Structure in Agricultural Soil by Ryan Michael Holzem Department of Civil and Environmental Engineering Duke University Date:_______________________ Approved: ___________________________ Claudia Gunsch, Supervisor ___________________________ Heather Stapleton ___________________________ Rytas Vilgalys ___________________________ Lingchong You ___________________________ Francis de los Reyes III An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Civil and Environmental Engineering in the Graduate School of Duke University 2014 Copyright by Ryan Michael Holzem 2014
Sensor Selection Based on Sparse Sensing in the Presence of Sensor Position Error

In many applications of wireless sensor networks (WSNs), sensor positions are often not known exactly. The existence of sensor position error (SPE) may significantly impact system performance if not appropriately modeled or considered. In this article, we address the important sensor selection problem in the presence of SPE for general nonlinear measurement models considering independent and correlated measurement noise cases. The sensor selection problem is formulated in the framework of sparse sensing, in which the number of activated sensors is minimized subject to certain predetermined performance constraints. To facilitate the challenging convex relaxation of nonconvex constraints for the case with independent measurement noise, we prove that the Fisher information matrix (FIM) remains additive even in the presence of SPE. For correlated measurement noise, quadratic inequality constraints are introduced and two suboptimal solvers are proposed. The first solver uses matrix decomposition to transform the quadratic constraint into linear matrix inequalities (LMIs), while the second solver iteratively performs a linearization procedure on the quadratic constraint to obtain a reduced dimension of LMI, thus decreasing the computational complexity significantly. The proposed algorithms are compared in terms of their computational complexity quantitatively and experimentally with suboptimal greedy approaches and existing algorithms ignoring SPE. The results show the importance and necessity of considering SPE when implementing sensor selection and demonstrate the effectiveness of the proposed three sensor selection solvers.
Processes of Synthesis and Characterization of Silver Nanoparticles with Antimicrobial Action and their Future Prospective

The discovery of novel therapies is required due to the stark rise in microbial resistance to currently available conventional antibiotics, which poses a significant obstacle to the effective management of infectious diseases. Nanomaterials between 1 and 100 nm in size have recently become effective antibacterial agents. In particular, several classes of antimicrobial nanomaterials and nanosized carriers for antibiotic delivery have demonstrated their efficacy for treating infectious diseases, including antibiotic-resistant ones, in vitro and in animal models. Because of their high surface area-to-volume ratios, these materials can provide better therapy than conventional drugs and have new mechanical, chemical, electrical, optical, magnetic, electro-optical, and magneto-optical properties. So, nanoparticles have been proven to be fascinating in the fight against bacteria. In this chapter, we will go into detail about the various characteristics of microorganisms and how they differ across each strain. The toxicity mechanisms change depending on the stain. Even the effectiveness of nanomaterials to treat different bacteria and their defence mechanisms varies depending on strains, particularly the composition of cell walls, the makeup of the enzymes, and other factors. As a result, a perspective on nanomaterials in the microbial world, a method to combat drug resistance by tagging antibiotics in nanomaterials, as well as predictions for their future in science.
ФІЗИЧНІ, ХІМІЧНІ ТА ІНШІ ЯВИЩА, НА ОСНОВІ ЯКИХ МОЖУТЬ БУТИ СТВОРЕНІ СЕНСОРИ PHYSICAL, CHEMICAL AND OTHER PHENOMENA, AS THE BASES OF SENSORS

It has been carried out sensing and calculating probabilities and oscillator strengths of a number of radiative transitions in the spectra of heavy Rydberg atoms of alkaline elements on the basis of new relativistic model potential method in the framework of gauge-invariant perturbation theory and relativistic energy approach. It has been shown that that a new approach provides a precise accounting exchange-correlation effects, including effect of essentially non-Coulomb grouping of Rydberg levels, pressure continuum. There are received precise data on energy and spectroscopic parameters energy, radiation width, amplitude transitions, the lifetime) for Rydberg atoms Rb, Cs, Fr, particularly, transitions nS1⁄2→n’P1⁄2,/2 (n=5,6; n’=10-70), nP1⁄2,3.2→n’D /2, /2 (n=5,6; n’=10-80) in the Rb, Cs spectra; for Rydberg Fr there are in most first calculated and predicted spectroscopic data on transition amplitude, lifetime of Rydberg states and transitions 7S1⁄2-nP1⁄2,/2, 7P1⁄2,3.2-nD /2, /2 (n=20-80). Data obtained can be used in carrying out new types of radiative quantum sensors and frequency standards.
41.3: A flat‐panel holographic‐optical‐element system for holographic augmented reality display with a beam expander

We demonstrate a compact design for holographic augmented reality displays using two holographic optical elements (HOEs). In the method, the two HOEs, which are properly set in a flat‐panel form, function as a beam expander and see‐through eyepiece simultaneously. There is no traditional optical element in the system, resulting in a compact and lightweight design. The feasibility of this design is experimentally verified.