Miracle-dz/UniFAR-FTUs huggingface dataset:
The Conceptual Framework of My Computer-Assisted Drawings: Reflections on Physics, Buddhism and Transactional Psychology

The artist’s holistic view of reality is presented by reference to the common ground that exists between three areas of knowledge: particle physics, Theravada Buddhism and transactional psychology. A number of the artist’s computer-assisted drawings and a related relief are then described, showing how this conceptual framework has influenced their genesis, form and structure.
Raman study of phase transformation from diamond structure to wurtzite structure in the silicon nanowires

Hexagonal silicon has emerged as an exciting material due to its novel vibrational and electronic properties. Synthesis of the wurtzite silicon nanowires (w-SiNWs) is studied here using metal assisted chemical etching (MACE) technique. Stress induced in the SiNWs during wet chemical etching is attributed to formation of the w-SiNWs. Presence of the w-SiNWs is revealed by first-order and second-order Raman spectra. The effect of variation of deposition time of silver (catalyst) is explicitly studied for growth of w-SiNWs. The deposition time enhances the density of SiNWs in an island of vertically aligned SiNWs. Absorption coefficient studies of the w-SiNWs are also conducted using UV–vis spectroscopy as a function of deposition time. Increase in the absorption coefficient in SiNWs is noticed with increasing deposition time. The prominent quantum confinement along with stress and porosity is shown to be mainly responsible for the transformation from diamond structure to wurtzite structure in the silicon nanowires.
Fault Detection in a Mixed Setting

In this note, we study the problem of fault detection in linear time-invariant (LTI) systems which are simultaneously effected by two classes of unknown inputs: Noises having fixed spectral densities and unknown finite energy disturbances. This problem is formulated as a mixed filtering problem. Necessary and sufficient conditions for local optimality are presented. Moreover, it is shown that suboptimal solutions can be computed by solving a convex minimization problem with a set of linear matrix inequality (LMI) constrains. A numerical example is given to illustrate the advantage of the mixed approach as compared to existing techniques which are based on optimization of and criteria.
Numerical modelling of river flow (numerical schemes for one type of nonconservative systems

In this paper we propose a new numerical scheme to simulate the river flow in the presence of a variable bottom surface. We use the finite volume method, our approach is based on the technique described by D. L. George for shallow water equations. The main goal is to construct the scheme, which is well balanced, i.e. maintains not only some special steady states but all steady states which can occur. Furthermore this should preserve nonnegativity of some quantities, which are essentially nonnegative from their physical fundamental, for example the cross section or depth. Our scheme can be extended to the second order accuracy.
A Bidirectional 2DLPP Method and Its Application in Face Recognition

To improve the robustness for the face recognition method of illumination,and pose external factors,this paper modifies the two-dimensional locality preserving projections (2DLPP) and gives a bidirectional 2DLPP (Bidirectional 2DLPP) algorithm. The difference with 2DLPP is that the bidirectional feature matrix is obtained by seeking the projection matrix in the column direction after the projection matrix in the row direction,thus the purpose of the dimensionality reduction is achieved. The experimental results show that this method has some robustness and higher recognition rate for the variation of illumination and pose.
Seasonal Upwelling Conditions Modulate the Calcification Response of a Tropical Scleractinian Coral

Natural processes such as upwelling of deeper-water masses change the physical-chemical conditions of the water column creating localized ocean acidification events that can have an impact on the natural communities. This study was performed in a coral reef system of an archetypical bay within the Tayrona National Natural Park (PNNT) (Colombia), and aimed to quantify net calcification rates of a foundational coral species within a temporal context (6 months) taking into account the dynamics of seasonal upwelling that influence the study area. Net calcification rates of coral fragments were obtained in situ by the alkalinity anomaly technique in short-term incubations (~2.5 h). We found a significant effect of the upwelling on net calcification rates (Gnet) (p < 0.05) with an 42% increase in CaCO3 accretion compared to non-upwelling season. We found an increase in total alkalinity (AT) and dissolved inorganic carbon (DIC) with decreased aragonite saturation (Ωara) for the upwelling months, indicating an influence of the Subtropical Under Water mass (SAW) in the PNNT coral community. Significant negative correlations between net calcification with temperature and Ωara, which indicates a positive response of M. auretenra with the upwelling conditions, thus, acting as “enhancer” of resilience for coral calcification.
Joint modelling of the order-dependent parts supply strategies sequencing, kitting and batch supply for assembly lines: insights from industrial practice

ABSTRACT Parts can be supplied from warehouses to assembly lines via several production-order-independent and -dependent parts supply strategies. Order-dependent parts supply strategies sequencing, kitting and batch supply share enough similarities that allow joint modelling in picking order planning and execution, whereas line stocking, just-in-time, just-in-sequence and just-in-sequence kit supply require separate modelling. Joint modelling is the precondition for setting up a software system in industrial practice, covering multiple parts supply strategies efficiently. With similar input–output relations and process steps, joint algorithms can be used. This work presents insights from a software system for sequencing, kitting and batch supply implemented in the automotive industry. The main process steps modelled are order-dependent part requests determination, bundling of part requests to picking orders, scheduling and release, and picking and transportation execution. Given the prevalence of assembly lines, sharing knowledge about successful modelling of parts supply strategies is crucial both for practitioners and researchers.
Topics on Regularization of Parameters in Multivariate Linear Regression

Topics on Regularization of Parameters in Multivariate Linear Regression. (December 2011) Lianfu Chen, B.S., University of Science & Technology of China; M.S., Texas A&M University Chair of Advisory Committee: Dr. Mohsen Pourahmadi My dissertation mainly focuses on the regularization of parameters in the multivariate linear regression under different assumptions on the distribution of the errors. It consists of two topics where we develop iterative procedures to construct sparse estimators for both the regression coefficient and scale matrices simultaneously, and a third topic where we develop a method for testing if the skewness parameter in the skew-normal distribution is parallel to one of the eigenvectors of the scale matrix. In the first project, we propose a robust procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for the correlations of the response variables. Robustness to outliers is achieved using heavy-tailed t distributions for the multivariate response, and shrinkage is introduced by adding to the negative log-likelihood 1 penalties on the entries of both the regression coefficient matrix and the precision matrix of the responses. Taking advantage of the hierarchical representation of a multivariate t distribution as the scale mixture of normal distributions and the EM algorithm, the optimization problem is solved iteratively where at each EM iteration suitably modified multivariate regression with covariance estimation (MRCE) algorithms proposed by Rothman, Levina and Zhu are used. We propose two new optimization algorithms for the penalized likelihood, called MRCEI and MRCEII, which differ from MRCE in the way that the tuning parameters for the two matrices are selected. Estimating the degrees of freedom when penalizing the en-
Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.
On the response of the tropical atmosphere to large-scale deforestation

SUMMARY Recent studies on the Amazon deforestation problem predict that removal of the forest will result in a higher surface temperature, a significant reduction in evaporation and precipitation, and possibly significant changes in the tropical circulation. Here, we discuss the basic mechanisms contributing to the response of the tropical atmosphere to deforestation. A simple linear model of the tropical atmosphere is used in studying the effects of deforestation on climate. It IS suggested that the impact of large-scale deforestation on the circulation of the tropical atmosphere consists of two components: the response of the tropical circulation to the negative change in precipitation (heating), and the response of the same circulation to the positiue change in surface temperature. Owing to their different signs, the changes in predicted temperature and precipitation excite competing responses working in opposite directions. The predicted change in tropical circulation determines the change, if any, in atmospheric moisture convergence, which is equivalent to the change in run-off. The dependence of run-off predictions on the relative magnitudes of the predicted changes in precipitation and surface temperature implies that the predictions about run-off are highly sensitive, which explains, at least partly, the disagreement between the different models concerning the sign of the predicted change in Amazonian run-off.
Hepatic Insulin Resistance Model in the Male Wistar Rat Using Exogenous Insulin Glargine Administration

Metabolic diseases are a worldwide health problem. Insulin resistance (IR) is their distinctive hallmark. For their study, animal models that provide reliable information are necessary, permitting the analysis of the cluster of abnormalities that conform to it, its progression, and time-dependent molecular modifications. We aimed to develop an IR model by exogenous insulin administration. The effective dose of insulin glargine to generate hyperinsulinemia but without hypoglycemia was established. Then, two groups (control and insulin) of male Wistar rats of 100 g weight were formed. The selected dose (4 U/kg) was administered for 15, 30, 45, and 60 days. Zoometry, a glucose tolerance test, insulin response, IR, and the serum lipid profile were assessed. We evaluated insulin signaling, glycogenesis and lipogenesis, redox balance, and inflammation in the liver. Results showed an impairment of glucose tolerance, dyslipidemia, hyperinsulinemia, and peripheral and time-dependent selective IR. At the hepatic level, insulin signaling was impaired, resulting in reduced hepatic glycogen levels and triglyceride accumulation, an increase in the ROS level with MAPK-ERK1/2 response, and mild pro-oxidative microenvironmental sustained by MT, GSH, and GR activity. Hepatic IR coincides with additions in MAPK-p38, NF-κB, and zoometric changes. In conclusion, daily insulin glargine administration generated a progressive IR model. At the hepatic level, the IR was combined with oxidative conditions but without inflammation.
Global Research Assessment of CRISPR: A Scientometric Analysis of Literature Published in Scopus

The study aimed to review the CRISPR research trend by covering major historic milestones in its research development from 2005 to date. The data from Scopus considered for this evidence-based scientometric study. Nine thousand four hundred eighty-nine data were extracted and analyzed for publications, sources, authors, affiliations, countries, and keywords based on different scientometric tools using MS Excel and other scientometric software viz. Biblioshiny (RStudio), VOSviewer. The findings showed the content and research direction of the area and have helped us identify the current situation and find the most productive teams in this field. The co-citation analysis identified the highly cited reference. The cluster analysis and timeline helped to recognize the structure and evolution of this research.
Flow Measurement in Support of Carbon Capture, Utilisation and Storage

Carbon Capture, Utilisation and Storage (CCUS) is a key United Kingdom Government strategy for reducing carbon dioxide (CO 2 ) emissions to combat the potentially catastrophic effects of climate change. The UK aims to capture and store 10 million tonnes of CO 2 each year by 2030. Across the entire CCUS value chain, each of the stages require accurate measurement of CO 2 at temperatures, pressures, flow rates and fluid phases that must be validated through a credible traceability chain for flow. This traceability chain would provide the underpinning confidence in meter performance, financial and fiscal transactions and, critically, environmental compliance. The UK-adopted version of the EU Emissions Trading System (EU ETS) has specified an uncertainty value for CO 2 flow measurement that must be adhered to. Accordingly, the provision of accurate and traceable flow measurement of CO 2 in the UK and internationally will be essential for the successful operation of CCUS. Unfortunately, there are currently no CO 2 flow measurement facilities in the world that are capable of traceable flow calibrations of gas phase, liquid/dense phase and supercritical phase CO 2 that replicate real-world CCUS conditions. The absence of traceable CO 2 gas and liquid flow measurement facilities and accompanying national or international flow measurement standards could seriously impede the widespread deployment of CCUS. These significant barriers could potentially jeopardise the successful implementation of CCUS projects worldwide, not least because these will be governed by legislation and environmental regulations requiring traceable measurement. This paper presents an overview of the current traceability chain for CO 2 flow measurement in the UK and globally. Current challenges will be detailed along with potential solutions and opportunities for the measurement community.
Vibrational Radar Backscatter Communications Theory and Bound

Vibrational radar backscatter communication (VRBC) utilizes millimeter-wave radar vibrometry to receive message signals from vibrating surfaces. So long as multiple transponding surfaces are separable by the system radar in angle and/or range, VRBC can simultaneously detect, isolate, and decode messages from these multiple sources. For anything-to-vehicle communications, VRBC is therefore a scalable, low-latency approach which leverages existing automotive radars to increase situational awareness without requiring any additional use of the RF spectrum. In this paper, we describe the modeling and processing of VRBC signals. We show that knowledge of surface resonances and coding constraints can be used to improve system performance by incorporating them into a Markov model. We provide insight into the rate capabilities of VRBC by providing a theoretical upper bound that incorporates the presence of intersymbol interference (ISI). Simulation results compare our upper bound to achievable communication rates using single symbol vs sequence detection maximum likelihood methods for a given symbol length, set of symbol excitations, and VRBC radar pulse repetition interval (PRI).
Membrane Potential-requiring Succinate Dehydrogenase Constitutes the Key to Propionate Oxidation and Is Unique to Syntrophic Propionate-oxidizing Bacteria

Propionate oxidation in Pelotomaculum thermopropionicum is performed under a thermodynamic limit. The most energetically unfavorable reaction in the propionate oxidation pathway is succinate oxidation. Based on previous genomic and transcriptomic analyses, succinate oxidation in P. thermopropionicum under propionate-oxidizing conditions is conducted by the membrane-bound forms of two succinate dehydrogenases (SDHs). We herein examined the activity of SDH, the mechanisms underlying the succinate oxidation reaction in P. thermopropionicum, and the importance of the protein sequences of related genes. SDH activity was highly localized to the membrane fraction. An analysis of the soluble fraction revealed that fumarate reductase received electrons from NADH, suggesting the involvement of membrane-bound SDH in propionate oxidation. We utilized an uncoupler and inhibitors of adenosine triphosphate (ATP) synthase and membrane-bound SDH to investigate whether the membrane potential of P. thermopropionicum supports propionate oxidation alongside hydrogen production. These chemicals inhibited hydrogen production, indicating that membrane-bound SDH requires a membrane potential for succinate oxidation, and this membrane potential is maintained by ATP synthase. In addition, the phylogenetic distribution of the flavin adenine dinucleotide‐binding subunit and conserved amino acid sequences of the cytochrome b subunit of SDHs in propionate-oxidizing bacteria suggests that membrane-bound SDHs possess specific conserved amino acid residues that are strongly associated with efficient succinate oxidation in syntrophic propionate-oxidizing bacteria.
Cochlea-inspired sound visualization method

This study presents the initial study for a new approach to visualize an acoustic sound aimed at mimicking the traveling wave propagation of the basilar membrane within the human cochlea. Typically, a fast Fourier transform (FFT) is required to extract the frequency information from acoustic sound (i.e., voice) for speech recognition. Although this algorithm ensures real-time frequency extraction due to the inherent fast recursive structure, it is necessary to develop a new frequency selectivity technique for advanced speech recognition. We explore the potential of the cochlea-inspired sound visualization to deliver new frequency selectivity by using an image sensor. The experimental prototyping model is fabricated, and we capture images of frequency dependent wave propagation motion using a camera and reproduce 2D images through motion magnification. This approach offers a promising application for speech recognition systems because no FFT is required to extract the frequency information, although there are outstanding technical problems that need to be further examined.
Effect of troxerutin on the expression of genes regulating mitochondrial biogenesis and microRNA-140 in doxorubicin-induced testicular toxicity

Background: Application of doxorubicin (DOX) in cancer patients is limited due to its dose-dependent toxicity to nontarget tissues such as testis and subsequent infertility. Due to limitation of our knowledge about the mechanisms of DOX toxicity in the reproductive system, reduction of DOX-induced testicular toxicity remains an actual and primary clinical challenge. Considering the potentials of troxerutin (TXR) in generating a protective phenotype in many tissues, we aimed to examine the effect of TXR on DOX-induced testicular toxicity by evaluating the histological changes and the expression of mitochondrial biogenesis genes and microRNA-140 (miR-140). Materials and Methods: Twenty-four adult male Wistar rats (250–300 g) were divided in groups with/without DOX and/or TXR. DOX was injected intraperitoneally at 6 consecutive doses over 12 days (cumulative dose: 12 mg/kg). TXR (150 mg/kg/day; orally) was administered for 4 weeks before DOX challenge. One week after the last injection of DOX, testicular histopathological changes, spermatogenesis activity, and expression of mitochondrial biogenesis genes and miR-140 were determined. Results: DOX challenge significantly increased testicular histopathological changes, decreased testicular expression profiles of sirtuin 1 (SIRT-1) and nuclear respiratory factor-2 (NRF-2), and increased expression of miR-140 (P < 0.05 to P < 0.01). Pretreatment of DOX-received rats with TXR significantly reversed testicular histopathological changes, spermatogenesis activity index, and the expression levels of SIRT-1, peroxisome proliferator-activated receptor-γ coactivator 1-alpha (PGC-1α), NRF-2, and miR-140 (P < 0.05 to P < 0.01). Conclusion: Reduction of DOX-induced testicular toxicity following TXR pretreatment was associated with upregulation of SIRT-1/PGC-1α/NRF-2 profiles and better regulation of miR-140 expression. It seems that improving microRNA-mitochondrial biogenesis network can play a role in the beneficial effect of TXR on DOX-induced testicular toxicity.