Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning latent local manifold embeddings for hyperspectral feature extraction

Abstract. Hyperspectral images with an immense number of spectral bands provide abundant discriminant information for accurate land-cover classification in the remote sensing field. However, these narrow and adjacent bands contain a large amount of redundant information. Analyzing these images always requires a huge storage space with expensive computational costs. Furthermore, their high correlation coefficient would lead to the Hughes phenomenon, hindering the improvement of classification performance. We propose a linear semi-supervised hyperspectral feature extraction method L3ME to learn latent local manifold embeddings. Although labeled samples are beneficial to construct learning models, their number is always limited in real-world tasks. The motivation of this paper is to jointly enhance the contributions of labeled and unlabeled samples for learning local manifold structures of hyperspectral images. Features in labeled samples are extracted by two procedures, the adaptive patch alignment framework and integrated intraclass-interclass relationships, from different perspectives. The former aims to solve the problem of the uneven distribution of classes by introducing spectral angle based adaptive parameters. The latter aims to solve the problem of the uneven distribution of samples by constructing several adjacency graphs. The locality preserving projection is capable of preserving the local neighborhood structure of samples. A penalty for sparse regularization is cleverly integrated into the proposed linear discriminant objective function, which is optimized using a novel updating strategy. The convergence of L3ME is proved in detail and analyzed in this paper. Experiments on three typical hyperspectral datasets illustrate the effectiveness of the proposed method over some state-of-the-art techniques. The implementation of L3ME is available at https://github.com/biowby/L3ME.
Computational Assessment of Xanthones from African Medicinal Plants as Aldose Reductase Inhibitors

Diabetes mellitus is a life-threatening non-communicable disease that affects all age groups. Despite the increased attention it has received in recent years, the number of diabetic patients has grown exponentially. These increased cases are attributed to essential enzymes involved in blood glucose regulation. In this study, we attempt to reveal the aldose reductase inhibitory potential of xanthones isolated from African medicinal plants. Ensemble docking, molecular dynamics simulation, density functional theory (DFT), and ADMET methods were employed to identify drug candidates as aldose reductase inhibitors. The ensemble docking results identified mangostenone B, bangangxanthone A, smeathxanthone B, mangostenone A, and allanxanthone B as potent inhibitors against the aldose reductase enzyme. Molecular dynamics studies showed the xanthones established better binding mode and affinities against the enzyme. Moreover, the electronic properties of the xanthones explained their good pharmacological potentials. Therefore, our findings suggest that the hit molecules be investigated in vitro and in vivo for drug development against aldose reductase.
Nodal configurations and voronoi tessellations for triangular spectral elements

By combining the high-order accuracy of spectral expansions with the locality and geometric flexibility of finite elements, spectral elements are an attractive option for the next generation of numerical climate models. Crucial to their construction is the configuration of nodes in an element---casual placement leads to polynomial fits exhibiting Runge phenomena manifested by wild spatial oscillations. I provide high-order triangular elements suitable for incorporation into existing spectral element codes. Constructed from a variety of measures of optimality, these nodes possess the best interpolation error norms discovered to date. Motivated by the need to accurately determine these error norms, I present an optimization method suitable for finding extrema in a triangle. It marries a branch and bound algorithm to a quadtree smoothing scheme. The resulting scheme is both robust and efficient, promising general applicability. In order to qualitatively evaluate these nodal distributions, I introduce the concept of a Lagrangian Voronoi tessellation. This partitioning of the triangle illustrates the regions over which each node dominates. I argue that distant and disconnected regions are undesirable as they exhibit a non-physical influence. Finally, I have discovered a link between point distributions in the simplex and on the hypersphere. Through a simple transformation, a distance metric is defined permitting the construction of Voronoi diagrams and the calculation of mesh norms.
From Diabetes to Atherosclerosis: Potential of Metformin for Management of Cardiovascular Disease

Atherosclerosis is a common cause of cardiovascular disease, which, in turn, is often fatal. Today, we know a lot about the pathogenesis of atherosclerosis. However, the main knowledge is that the disease is extremely complicated. The development of atherosclerosis is associated with more than one molecular mechanism, each making a significant contribution. These mechanisms include endothelial dysfunction, inflammation, mitochondrial dysfunction, oxidative stress, and lipid metabolism disorders. This complexity inevitably leads to difficulties in treatment and prevention. One of the possible therapeutic options for atherosclerosis and its consequences may be metformin, which has already proven itself in the treatment of diabetes. Both diabetes and atherosclerosis are complex metabolic diseases, the pathogenesis of which involves many different mechanisms, including those common to both diseases. This makes metformin a suitable candidate for investigating its efficacy in cardiovascular disease. In this review, we highlight aspects such as the mechanisms of action and targets of metformin, in addition to summarizing the available data from clinical trials on the effective reduction of cardiovascular risks.
A new design approach for the acceleration region of vertical, dilute-phase pneumatic conveyors

Abstract An analysis has been completed for a comprehensive set of pressure drop data from the acceleration region of seven vertical, dilute-phase pneumatic conveyors. The data provided are for materials with terminal velocities from 2.05 to 22.4 m/s conveyed in risers with heights from 3 to 11.2 m and internal diameters from 0.028 to 0.083 m. Particle velocities derived from the pressure drop data were used to develop an equation of motion that includes terms for terminal velocity, and the distance traveled from the feed point. Despite covering a range of conditions, no effect of other variables was identified. Particle velocities were predicted by the equation of motion and used to calculate total pressure gradients that are within 97 Pa/m (95% confidence limits) of the measured values. The voidage correction of gas and terminal velocities recommended by other studies did not improve the pressure drop predictions. The spreadsheet model developed is simpler than the available complex numerical models and provides more accurate results. Acceleration lengths are longer than allowed for in many studies, and erstwhile established-flow data and correlations likely contain acceleration effects.
Models for Third Down Conversion in the National Football League

Abstract Several models are proposed for the probability of converting a third down attempt in the National Football League. The probability, which can depend on the number of yards to go, the strength of the offense, and the strength of the defense, leads to a logistic regression. We approach the problem through a hierarchical Bayes model and estimate parameters by using Markov chain Monte Carlo (MCMC). This MCMC estimation in the context of a hierarchical Bayes model may be relevant in other sports situations where a probability depends on the difference of strengths of the two teams. We find that the statistic "third-down conversion rate" to be a nearly meaningless measure of the efficiency of an offense. Even when this is adjusted for yards to go for a first down, there is little evidence that teams differ in their ability to achieve a first down on a third down conversion.
A Novel Integer Coding Scheme for Geometrically Distributed Low-Entropy Sources

In this paper, a novel encoding scheme for low-entropy monotonic sources producing geometrically distributed integers is proposed. The approach is based on consecutively transferring the most probable integer onto the unary codes of certain capacity that represent other integers in a canonical way. The length of the codes, which directly determines their capacity, is adjusted according to the properties of the source distribution. In this respect, the proposed method can also be considered an extension of Golomb-Rice coding where the Golomb parameter may become a positive proper fraction. Here, we not only provide the encoding and decoding procedures with a parameter selection mechanism, but also empirically investigate as to how much redundancy occurs for different probabilities of source distribution in comparison to a similar approach in the literature.
Study of the Electrical Properties of Thin Silica Layers with a Single Plane of AgNPs Embedded Near the Surface

The present work explores the effect of metal inclusions (silver nanoparticles, AgNPs) on the electrical mechanisms (charge injection, trapping and transport) responsible for dielectric charging in thin silica layers. It is shown that the presence of a plane of AgNPs embedded in a thin SiO2 layer modifies the electrical conduction in the structure. When the AgNPs-plane is embedded near the SiO2 surface, the structure behaviour at low electric field is non-ohmic and does not allow calculation of the classical volume conductivity. Instabilities in the behaviour of the structure are observed for high electric fields. They are most likely due to sequences of breakdown/self-healing phenomena that clearly appear for fields higher than 108 V/m.
The Use of Aminated Wheat Straw for Reactive Black 5 Dye Removal from Aqueous Solutions as a Potential Method of Biomass Valorization

Straw is a by-product from cereal cultivation. Using straw surplus as a biofuel is a popular means of its management. However, before being used for fuel purposes, straw can be successfully used as a sorbent. The present study investigated the sorption properties of wheat straw (Triticum aestivum L.) modified with ammonia water and epichlorohydrin against the reactive dye Reactive Black 5 (RB5). The tested sorbents were characterized based on FTIR, elemental analysis (C/N content), and pHPZC. The scope of the research included, among others, research on the effect of pH (pH 2–11) on the RB5 sorption efficiency, research on sorption kinetics, and determination of the maximum sorption capacity of the tested sorbents. The sorption efficiency of RB5 on the tested sorbents was the highest at pH 2–3. The experimental data from the research on the sorption kinetics of RB5 were best described by a pseudo-second-order model. The introduction of primary amine groups to the structure of sorbents significantly increased their sorption capacity towards RB5. The obtained sorption capacity of the aminated straw and the aminated straw pre-activated with epichlorohydrin was 24.12 mg RB5/g and 91.04 mg RB5/g, respectively, and it was higher by 44.3% and 444.5% compared to the unmodified straw.
Multiple Fault Tolerant Control Strategy for Rotor Field Oriented Induction Motor Drive Fed From CHB Converter With Redundant Cells

The article proposes a postfault control strategy for a rotor field oriented induction motor (RFO-IM) drive fed by a cascaded H-bridge multilevel converter (CHB-MLC) with redundant cells in each phase. The objective of the control strategy is to maintain the torque and speed of the RFO-IM in postfault operation and to maximize the converter's power capability by adding a maximum number of power cells during postfault operation. Furthermore, maximum line voltage is utilized from the redundant cells without increasing the dc-link voltage of power cells, and the motor's operating point in the voltage plane is analyzed and determined for various loading conditions during prefault and postfault operation. In the postfault operation, a common mode voltage of fundamental frequency is also added depending on the motor's loading condition and postfault configuration to distribute power evenly among the remaining healthy cells. Theoretical and graphical analyses include the equation of the operating point in the voltage plane, traversal of the operating point from prefault to postfault operation, and selection of common mode voltage of fundamental frequency based on the fault configuration and the motor's loading. The simulation and experimental data are used to validate the proposed fault-tolerant control approach.
Star formation and galaxy evolution of the Local Universe based on HIPASS

This thesis investigates the star formation and galaxy evolution of the nearby Local Volume based on Neutral Hydrogen (HI) studies. A large portion of this thesis consists of work with the Northern extension of the HI Parkes All Sky Survey (HIPASS). HIPASS is an HI survey of the entire Southern sky up to a declination of +25.5 degrees (including the Northern extension) using the Parkes 64-metre radio telescope. I have also produced a catalogue of the optical counterparts corresponding to the galaxies found in Northern HIPASS. From this optical catalogue, we also conclude that we did not find any isolated dark galaxies. The other half of my thesis consists of work with the SINGG and SUNGG projects. SINGG is the Survey for Ionization in Neutral Gas Galaxies and SUNGG is the Survey of Ultraviolet emission in Neutral Gas Galaxies. Both SINGG and SUNGG are selected from HIPASS and are star formation studies in the H-alpha and ultraviolet (UV), respectively. My work in the SINGG-SUNGG collaboration is mostly based on SUNGG. Using the results of SUNGG, I measured the local luminosity density and the cosmic star formation rate density (SFRD) of the Local Universe. Using far-infrared (FIR) observations from IRAS, the FIR luminosity density was also calculated. Combining the FUV luminosity density and the FIR luminosity density, the bolometric SFRD of the Local Universe was estimated. This thesis also includes the discovery of one of the nearest drop-through ring galaxies, NGC 922, which is a factor of three closer than the infamous Cartwheel galaxy.
The role of magnesium deficiency in the pathogenesis of hypertension and the influence of magnesium supplementation on blood pressure – literature review

Magnesium is a macronutrient that is very important for the proper functioning of the human body. Deficiency of this element is crucial in the development of many disorders in the proper functioning of many organ systems. The optimal level of magnesium has a significant influence on the functioning of the cardiovascular system, among others. Although the factors showing the mechanism of lowering blood pressure by magnesium ions are not fully understood, the influence of the correct concentration of magnesium in the human body on cardiovascular protection is beyond doubt. Magnesium supplementation produces positive effects in the functioning of blood vessels and in maintaining blood pressure within the normal range. In our article, we focused on the role of magnesium in maintaining normal blood pressure at the molecular, cellular and tissue levels, as well as on the impact of magnesium supplementation on reducing blood pressure.
Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches

Single-stranded DNA (ssDNA) binding proteins (SSBs) are critical in maintaining genome stability by protecting the transient existence of ssDNA from damage during essential biological processes, such as DNA replication and gene transcription. The single-stranded region of telomeres also requires protection by ssDNA binding proteins from being attacked in case it is wrongly recognized as an anomaly. In addition to their critical roles in genome stability and integrity, it has been demonstrated that ssDNA and SSB–ssDNA interactions play critical roles in transcriptional regulation in all three domains of life and viruses. In this review, we present our current knowledge of the structure and function of SSBs and the structural features for SSB binding specificity. We then discuss the machine learning-based approaches that have been developed for the prediction of SSBs from double-stranded DNA (dsDNA) binding proteins (DSBs).
Google Cardboard 3D-VR: Dispositivo de realidad virtual para el aprendizaje inmersivo en el entrenamiento policial.

Los dispositivos de realidad virtual (VR) son más asequibles y accesibles, por lo que revisten una importancia en la educación, ya que, llevan el aprendizaje a otro nivel de aplicación y potencialización de la experiencia, o sea, un aprendizaje inmersivo por la acción y de la aplicación de lo experimentado, tanto en la formación y/o capacitación presencial, a distancia y virtual (online y offline). Para explicar esto desde un punto de vista metodológico, se hizo necesario aplicar una revisión sistemática de literatura (RSL), para rastrear los avances en el desarrollo de aplicaciones educativas en enseñanza y aprendizaje. El objetivo fue desarrollar una herramienta de aprendizaje inmersivo que sea útil para el entrenamiento policial, al recrear la visión experiencial de casos en la vida real adecuados para la formación y capacitación en procedimientos policiales, utilizando Google Cardboard 3D-VR, un dispositivo electrónico móvil integrado con gafas y auriculares de bajo costo, que ofrece experiencias de realidad virtual envolventes a través de un smartphone a toda clase de usuario, sin que sean necesarios movimientos espaciales e interacción con las manos. Para lograr que se dé una innovación educativa, la investigación que muestra este tipo de dispositivos móviles es una alternativa eficaz para el entrenamiento, como herramienta tecnológica de reciente introducción al entorno educativo, frente a la cual se encontraron resultados satisfactorios, que pueden establecer replicas en diversos ámbitos de aplicación.
Dynamic ROI Extraction for Palmprints using MediaPipe Hands

Hand-based biometric traits have been widely used in recognition systems. Dynamic region of interest extraction is an important preprocessing step for these systems to avoid recognition performance degradation. In this study, a dynamic region of interest extraction method that can be used for palm vein, palmprint, and dorsal hand vein has been proposed using Google’s MediaPipe Hands framework. Since 3 biometric traits focus on nearly the same region that contains biometric information on the images, this study aims to show that the proposed extraction method can be utilized for these traits on mobile biometric applications. This method has been implemented on IIT Delhi Touchless Palmprint Database and 93% accuracy was obtained. The average processing time per image for ROI extraction was recorded as 2.64 seconds. With this study, a paradigm for future studies on hand biometrics has been created and the required processing time for a dynamic extraction has been reduced considerably.
Triple treatment of high-risk prostate cancer. A matched cohort study with up to 19 years follow-up comparing survival outcomes after triple treatment and treatment with hormones and radiotherapy

Abstract Purpose: To evaluate the efficacy of a triple treatment strategy, including surgery, on high risk prostate cancer comparing long-term survival outcome with a cohort receiving standard radiotherapy with endocrine therapy. Materials and methods: This study compared two cohorts in survival outcomes, matched on the year of diagnosis and age. In both groups there was a curative intention to treat localized high-risk prostate cancer (one or more of Gleason score 8–10, PSA 20–50 or stage T3), diagnosed between 1995–2010, follow-up at the end of 2014. Triple treatment group: 153 patients treated primarily with radical prostatectomy with neoadjuvant endocrine treatment, and a majority with adjuvant radiotherapy. Standard radiotherapy group: 702 patients with a treatment of either external radiotherapy or high dose brachytherapy combined with external beam therapy, both modalities in combination with neoadjuvant endocrine therapy. Results: The prostate-cancer-specific mortality was 10% for the triple treatment group and 15% for the standard radiotherapy group during the period, HR = 2.01 (1.17–3.43), p = 0.011. The corresponding overall mortality was 26% vs 29%, HR = 1.54 (1.09–2.17), p = 0.015. High Gleason score was the dominating risk factor for early death due to the disease. Clinical T-stage was not an independent risk factor for death in this population. Conclusion: Adding surgery in a multimodal treatment model in high-risk prostate cancer showed significantly better survival outcome compared with the current standard of radiotherapy. Surgery in this group is, therefore, compelling and that also includes a clinical T3-stage of the disease. The study is limited by possible selection bias for the two treatment models.
Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks

The study of physiological changes recorded by wearable devices during physical exercises belongs to very important research topics in neurology for the detection of motion disorders or monitoring of the fitness level during sports activities. This paper contributes to this area with studies of the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes. Experimental data sets include 296 segments of their total length of 60 hours, recorded on a home exercise bike under different motion conditions. Wearable instruments with oximetric, heart rate, accelerometric, and thermal camera sensors were used to fill the own database of signals recorded with selected sampling frequencies. The proposed methodology includes fundamental signal and image processing methods for signal analysis and machine learning tools for labeling image components and detecting facial temperature changes. Results show the minimal effect of mask wearing on blood oxygen concentration but its substantial influence on the breathing frequency and the heart rate. The use of a respirator substantially increased the respiratory rate for the given set of experiments under the load. This indicates how wearable sensors, computational intelligence, and machine learning can be used for motion monitoring and data analysis of signals recorded in different conditions.