Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood and Product-Chain: Blood and its Products Supply Chain Management based on Blockchain Approach

— This paper provides a novel implementation of blockchain technology, and data is stored in a decentralized distributed ledger to assist information protection in blood supply chain management and prevent data loss or identity theft. The present blood supply is used exclusively from the blood of volunteers (known as donors), making blood and its derivatives one of the significant roles in treating diseases today. In particular, depending on the type of product extracted from the blood (e.g., red blood cells, white blood cells, platelets, plasma). They require different procedures and storage environments (e.g., time, temperature, humidity). However, the current blood management processes are done manually - where all medical staff does all data entry. Additionally, data about the complete blood donation process (e.g., blood donors, blood recipients, blood inventories) is held centrally and is challenging to examine accurately. Therefore, ensuring centralized data security is extremely difficult because of stealing personal information or losing data. In this study, we present the blockchain technology-based blood management process and offer Blood and Product-Chain, a decentralized distributed ledger that stores data to address these restrictions. Specifically, we target two main contributions: i) we design the Blood and Product-Chain model to manage all relevant information about blood and its products based on blockchain technology, and ii) we implement the proof-of-concept of Blood and Product-Chain by Hyperledger Fabric and evaluate this in the two scenarios (i.e., data creation and data access).
Estimating effects of temperature and moisture on Cn2 in the damp unstable boundary layer for visible, infrared, radio, and millimeter wavelengths

Data collected over barren and vegetated ground surfaces were used to obtain estimates of Cn2 for the damp unstable boundary layer. These data consisted of latent and sensible heat fluxes. Results from this study show that moisture effects on Cn2 can be larger than generally reported in the literature. Results presented emphasize the relative contributions of temperature and moisture to Cn2 for visible, infrared, radio, and millimeter wavelengths.
Rate of convergence of Shepard's global interpolation formula

Given any data points xl, . , x,, in RW and values f(x). f(x") of a function f, Shepard's global interpolation formula reads as follows: Sof (x) =Ef (xi) Wi (X, Wi (X = I X Xi I -P/ElX xjl |P. where | I denotes the Euclidean norm in WR. This interpolation scheme is stable, but if p > 1, the gradient of the interpolating function vanishes in all data points. The interpolation operator Spq is defined by replacing the values f(xi) in Spf by Taylor polynomials of f of degree q E= N. In this paper, we investigate the approximating power of Spq for all values of p, q and s.
Flower-shaped ZnO nanocrystallite aggregates synthesized through a template-free aqueous solution method for dye-sensitized solar cells

Hierarchically structured flower-shaped aggregates composed of ZnO nanocrystals were synthesized through a template-free aqueous solution method. The synthesized nanocrystallite aggregates were demonstrated to be promising photoanode materials for dye-sensitized solar cells (DSSCs). Compared with commercially available ZnO nanoparticles (ZnONPs), the flower-like aggregates (ZnONFs), each having an overall dimension of 400–600 nm, exhibited similar dye loading but higher light-scattering ability, which led to a substantial increase in the light-harvesting efficiency of resulting cells. The unique morphology of ZnONFs also boosted the absorbed photon-to-electric current generation efficiency. Consequently, DSSCs constructed from ZnONFs showed significantly improved photocurrent and achieved an overall conversion efficiency of 4.42%, which was 47% higher than that attained by ZnONP-based cells.
Grad student life: Cost of Living @UBC

—This paper describes an article that utilizes animated and interactive visualizations to present data about UBC computer science (CS) graduate (grad) students’ financial circumstances and the problems that these grad students face in the expensive city of Vancouver, British Columbia. The visualizations focus on 5 main tasks involving readers learning about the expenses of CS grad students, the inflation rate in Vancouver, how readers’ expenses compare to CS grad students and how the financial compensation at UBC compares to other universities. The article uses easy-to-read visualizations and applies a simple layout and format to make it
Auger recombination and carrier transport effects in III-nitride quantum well light emitting diodes

Gallium nitride based light emitting diodes (LEDs) have established as powerful devices, that are well suited for general lighting. Despite the progress within the recent years the so-called “efficiency droop” is still a central issue of nitride-based LED research. Up to now, no widely accepted explanation is available for the reduction of the internal quantum efficiency with increasing injection current. We report on a novel mechanism contributing to efficiency droop, that combines two of the previously reported effects: Auger recombination and carrier leakage. A sophisticated Auger model, that takes account of the overlap of the wave functions, is extended to model the energy transfer towards the third involved carrier. This carrier is assumed to be expelled from the well and regenerated in the continuum carrier population, where it can contribute to carrier leakage. A physics-based simulation of a quantum well LED employing a semi-classical approach has been carried out to demonstrate the impact of this effect. Depending on the parametrization, the inclusion of Auger expulsion reduces the Auger coefficient up to 50% when compared to a standard Auger model, which could explain the discrepancy between calculated and experimentally extracted Auger coefficients.
Dilations of $V$-bounded stochastic processes indexed by a locally compact group

It is proved that a stochastic process (i.e., a Hubert space valued function) indexed by a locally compact group is V-bounded (i.e., weakly har- monizable in an appropriate sense) if, and only if, it can be expressed as an orthogonal projection of a process whose covariance function R satisfies R(s,t) = p(t_1s) + p(st~1) for some continuous positive-definite function p. The result generalizes a well-known theorem due to H. Niemi, and depends on the noncommutative Grothendieck type inequality of G. Pisier. Let G be a locally compact (not necessarily Abelian) group and H a (complex) Hubert space. In analogy with the case G = R and H = Lg(f2,^,F) = {/ G L2\JfdP = 0} for a probability space (Q,A,P), we call any function : G —> H a (stochastic) process. A process 4>: G —► H is called V-bounded, if it is weakly continuous and the set {f (i>ds\p, G Ll(G), \\s\\' : C*(G) —» H whose Fourier transform i> equals (p. Here 4> is defined by the formula $(s) = $**(w(s)) where $** : C*(G)** —► H is the bitranspose of $ and a; : G —► C*(G)** C L(HJ) is the representation of G corresponding to the universal representation of the group C*-algebra C*(G) of G. For a detailed discussion of these matters we refer to (6). In (4), H. Niemi proved that in the case G = R a process is V-bounded if, and only if, it is an orthogonal projection of a stationary process. For subsequent developments of the theme one may, e.g., consult (1) and its bibliography. In this note we extend Niemi's result to the noncommutative setting. Our treatment is also in part influenced by (1 and 2), Let us begin with a general lemma, which explains the role of 2-majorizability, inherent in the subject.
Predictive Distributions in the Presence of Measurement Errors

We consider a hierarchical linear regression model where the regression parameters for the units have a multivariate normal distribution whose parameters are unknown. Several replications are available for each unit. The design matrices for the units need not be the same. A complicating feature of the model is that each observation is subject to measurement error. The objective of the paper is to derive the predictive distribution of the “true” value of the response at a given design point. A Bayesian treatment is given to the problem. In addition to standard prior distributions, other prior distributions are considered. The calculations are done with the Gibbs sampler. An example is discussed in detail.
Evolving Submerged Meridional Circulation Cells within the Upper Convection Zone Revealed by Ring-Diagram Analysis

Using the local helioseismic technique of ring-diagram analysis applied to Michelson Doppler Imager (MDI) Dynamics Program data from the Solar and Heliospheric Observatory, we have discovered that the meridional flow within the upper convection zone can develop additional circulation cells whose boundaries wander in latitude and depth as the solar cycle progresses. We report on the large-scale meridional and zonal flows that we observe from 1996 to 2001. In particular, we discuss the appearance and evolution of a submerged meridional cell during the years 1998-2001, which arose in the northern hemisphere and disrupted the orderly poleward flow and symmetry about the equator that is typically observed. The meridional flows in the southern and northern hemispheres exhibit striking asymmetry during the past four years of the advancing solar cycle. Such asymmetry and additional circulation cells should have profound impact on the transport of angular momentum and magnetic field within the surface layers. These flows may have a significant role in the establishment and maintenance of the near-surface rotational shear layer.
Analysis and identification of Hammerstein model non-linear delay systems using block-pulse function expansions

Algorithms for the analysis and identification of non-linear delay systems described by a Hammerstein model, which consists of a single-value non-linearity followed by a linear time-delayed plant, are presented. By using the finite-dimensional block-pulse expansion, the solution of a Hammerstein model non-linear dynamic system with time delay is reduced to the solution of a set of linear algebraic equations. In addition, through the block-pulse expansions of the measured input-output data, parameters of both the linear time-delayed plant and the polynomial approximation of the non-linear gain are estimated using the least-squares method. Examples are given to illustrate the utility of this method.
A note on the scale parameter of the dirichlet process

This paper gives an interpretation for the scale parameter of a Dirichlet process when the aim is to estimate a linear functional of an unknown probability distribution. We provide exact first and second posterior moments for such functionals under both informative and noninformative prior specifications. The noninformative case provides a normal approximation to the Bayesian bootstrap.
Disambiguating Voynich Manuscript transliterations with word embeddings

—Voynich manuscript is a 15 th century document written in unknown language and script. Consequently, reading and transliterating it to electronic form is equivocal and leads to ambiguities. We present a way to disambiguate said uncertainties using word embeddings models, which achieves accuracies up to 86% on artificially corrupted texts of the same size.
Machine Learning Project 2-Predicting Energy Expenditure

—The goal of this study is to develop a model capable of predicting individual energy expenditure from physiological and environmental variables, as easy to measure as possible. Experimental data was collected and has already been used before to train machine learning models: however, we found a problem of overfitting on subjects in this previous work. While proving that claim in this report, we also propose a new data processing pipeline as well as a rectified model, that we could not fully test by lack of data
Novel Gemini Surfactant for Binding Eu(III)-Polyoxometalate into Hydrogels and Polymer Latexes

The incorporation of rare-earth ions into polymer matrices can lead to useful materials in various fields such as biomarkers, lasers, luminescent devices, optical storage materials, and so on. Methods of incorporation are either extremely simple, such as mixing the polymer and the ion of interest in adequate solvents, or more sophisticated such as synthesizing predesigned monomers that contain the rare-earth ion or binding the ion on an already formed polymer chain. Cationic gemini surfactants represent a class of surfactants that can be used to incorporate metal-oxygen cluster compounds by means of strong electrostatic interactions. In this study, first, a novel cationic gemini surfactant having double bonds on both side chains was designed and prepared. After characterization, the surfactant was used to synthesize hydrogels with different degrees of crosslinking and also as a surfmer in emulsion polymerization of methyl methacrylate. The resulted polymer matrices were able to bind europium-polyoxometalate Na9[EuW10O36]. 32H2O. In case of luminescent lanthanide ions, changing the microenvironment around the metal ion also changes the intensity of some emission peaks as well as other luminescent parameters. Investigation of emission spectra of Eu3+ indicates a decrease in the symmetry of the microenvironment, when the polyanions pass from water to latex, to surfactant solution, and to hydrogel.
Cu/CuOx@C Composite as a High-Efficiency Electrocatalyst for Oxygen Reduction Reactions

Among clean energy transformation devices, fuel cells have gained special attention over the past years; however, advancing appropriate non-valuable metal impetuses to halfway supplant the customary Pt/C impetus is still in progress. In this paper, we propose a specific electrocatalyst in the formula of highly-active Cu species, associated with coated carbon (Cu@C-800), for oxygen reduction reaction (ORR) through post-treatment of a self-assembled precursor. The optimized catalyst Cu@C-800 showed excellent ORR performance (i.e., the onset potential was 1.00 V vs. RHE, and half-wave potential of 0.81 V vs. RHE), high stability, resistance to methanol, and high four-electron selectivity. The enhancement is attributed to the synergy between the carbon matrix and a high explicit surface region and rich Cu nano-species.
Undergraduate Thesis MAJOR IN MATHEMATICS and BUSINESS ADMINISTRATION Introduction to Time Series and Forecasting

Time series analysis allows complex processes to be expressed in simple terms to understand how these processes were generated and to predict future values. SARIMA models assume that the observations of a process depend on the previous observations and the variation between them to give an expression of the underlying data generating process. To find the SARIMA model that better fits our data we introduce the Box and Jenkins method, based on three iterative steps: model identification, parameter estimation and fitness check. Once we have identified the most appropriate fitting model, we use it to forecast future values. We have followed this methodology to find the model that best fits the Spanish unemployment series from 2002 to the first quarter of 2022 and to forecast the next 8 observations.
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI

Although black-box models can accurately predict outcomes such as weather patterns, they often lack transparency, making it challenging to extract meaningful insights (such as which atmospheric conditions signal future rainfall). Model explanations attempt to identify the essential features of a model, but these explanations can be inconsistent: two near-optimal models may admit vastly different explanations. In this paper, we propose a solution to this problem by constructing uncertainty sets for explanations of the optimal model(s) in both frequentist and Bayesian settings. Our uncertainty sets are guaranteed to include the explanation of the optimal model with high probability, even though this model is unknown. We demonstrate the effectiveness of our approach in both synthetic and real-world experiments, illustrating how our uncertainty sets can be used to calibrate trust in model explanations.
Towards Building a Legal Virtual Assistant Based on Knowledge Graphs

Knowledge graphs (KGs) are gaining more and more attention nowadays due to their usefulness in many applications. Virtual assistants (VAs) can have a beneficial role in providing a conversational user interface (UI) for KGs. The legal domain touches a crucial aspect of our lives and yet access to legal professionals remains an obstacle. The use of KG-based VAs in the legal domain can help alleviate such a problem, democratizing legal knowledge to anyone, anywhere, and anytime. In this paper, we present our industrial experience in building a VA on top of a legal KG about laws related to labor. We discuss several use cases and lessons learned from what we have done. A demo video to give a glimpse of our KG-based VA is available online at https://youtu.be/yNOsXQaK89E.
Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding

To develop fast and integrated spectrometry, we present a new method of compressed sensing spectrometry based on electro-optical transmittance coding. A LiNbO3 electro-optic modulator was applied to a directly compressed measurement code in the spectral dimension. Subsequently, a sequential forward floating selection algorithm was employed to select the coding measurement matrix, and sparsity adaptive matching pursuit was used as a solver algorithm. The principle was analyzed, and the measurement system was built for verification experiments. The experimental results reveal that the spectral reconstruction relative error is in the order of 10−2, and the full width at half maximum of the spectral measurement is as high as 1.2 nm. The spectral resolution can reach approximately 0.4 nm in the visible light band range (0.38–0.78 μm) with 1024 spectral channels. The compressed ratio of the compressed sensing spectrometry is up to 1:29. Furthermore, the spectral signal measurement time is only 0.25 ms. The study demonstrates a novel method, which exhibits high precision, speed, high compressed ratio and hyperspectral resolution.
Bridging Human–Computer Interaction and Natural Language Processing

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one that does not scale well. Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour to quickly build or extend a taxonomy to better fit information needs.
Identification of Key Regulatory Factors of Molecular Marker TGS377 on Chromosome 1 and Its Response to Cold Stress in Tomato

Low temperature, as a kind of stress factor, often leads to tomato growth stagnation or yield reduction or even no harvest in production. At present, numerous genes have been shown to be involved in the regulation of cold resistance in the tomato. Complex regulatory mechanisms responding to low temperature stress in the tomato are still unclear in their details. In this study, six accessions of tomato (‘NL-7’, ‘NL-15’, ‘NL-18’, ‘NL-21, ‘NL-37’, and ‘NL-67’) with different cold tolerance were selected to detect the response to low temperature. The results showed that ‘NL-15’, ‘NL-18’, and ‘NL-21’ tomato accessions had cold tolerance under 8 °C/6 °C (day/night) for 15-day treatments. The TGS377 molecular marker, closely related to cold tolerance, was located on chromosome 1. The potential factors were identified and bioinformatics analysis within 50 kb upstream and downstream of TGS377. Fifteen genes were identified, and their structural analysis and functional annotation were also performed. The expression levels of Solyc01g008480 and Solyc01g150104 in the cold-sensitive tomato accessions (‘NL-7’, ‘NL-37’, and ‘NL-67’) were higher than that in the cold-tolerant accessions (‘NL-15’, ‘NL-18’ and ‘NL-21’). The expression levels of Solyc01g008390 and Solyc01g008410 in the cold-tolerant tomato ‘NL-18’ accession was significantly higher than that in the cold-sensitive accessions (‘NL-15’, ‘NL-18’, and ‘NL-21’). The results suggested that these genes may be involved in regulating low temperature response in the tomato, which lays a foundation for the search of potential cold response regulators in the tomato.
A Survey of 3D Indoor Localization Systems and Technologies

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.