Miracle-dz/UniFAR-FTUs huggingface dataset:
EPR Investigations to Study the Impact of Mito-Metformin on the Mitochondrial Function of Prostate Cancer Cells

Background: Mito-metformin10 (MM10), synthesized by attaching a triphenylphosphonium cationic moiety via a 10-carbon aliphatic side chain to metformin, is a mitochondria-targeted analog of metformin that was recently demonstrated to alter mitochondrial function and proliferation in pancreatic ductal adenocarcinoma. Here, we hypothesized that this compound may decrease the oxygen consumption rate (OCR) in prostate cancer cells, increase the level of mitochondrial ROS, alleviate tumor hypoxia, and radiosensitize tumors. Methods: OCR and mitochondrial superoxide production were assessed by EPR (9 GHz) in vitro in PC-3 and DU-145 prostate cancer cells. Reduced and oxidized glutathione were assessed before and after MM10 exposure. Tumor oxygenation was measured in vivo using 1 GHz EPR oximetry in PC-3 tumor model. Tumors were irradiated at the time of maximal reoxygenation. Results: 24-hours exposure to MM10 significantly decreased the OCR of PC-3 and DU-145 cancer cells. An increase in mitochondrial superoxide levels was observed in PC-3 but not in DU-145 cancer cells, an observation consistent with the differences observed in glutathione levels in both cancer cell lines. In vivo, the tumor oxygenation significantly increased in the PC-3 model (daily injection of 2 mg/kg MM10) 48 and 72 h after initiation of the treatment. Despite the significant effect on tumor hypoxia, MM10 combined to irradiation did not increase the tumor growth delay compared to the irradiation alone. Conclusions: MM10 altered the OCR in prostate cancer cells. The effect of MM10 on the superoxide level was dependent on the antioxidant capacity of cell line. In vivo, MM10 alleviated tumor hypoxia, yet without consequence in terms of response to irradiation.
Physical, Chemical, and Antioxidant Characterization of Nano-Pomegranate Peel and Its Impact on Lipid Oxidation of Refrigerated Meat Ball

Pomegranate peel (CPP), enriched with bioactive constituents, had potent antioxidant features. Therefore, it is worth finding out functional and antioxidant features of the nanoscale pomegranate peel. The nanoscale of pomegranate peel was prepared by ultrafine grinding in a ball mill for 45 min (NPP45) and 90 min (NPP90). The physical (SEM, TEM, FTIR, and XRD) and chemical characteristics (phenolics, flavonoids, DPPH scavenging activity, FRAP, and reducing power) of nanoparticles were studied. The quality aspects of cold stored (5 ± 2°C) meatballs formulated with 0.5% (W/W) of nano-peel powder were evaluated. Similarly, FTIR spectra and XRD patterns were recorded for nano and crude pomegranate peel samples. Generally, grinding the crude peel for 45 and 90 min enhanced its scavenging activity, reducing power, FRAP, total phenolic, and flavonoid by a range of 12.58 to 20.37 and 20.57% to 35.18%, respectively. The addition of crude/nanosized peel to the meat ball diminish ( p < 0.05 ) formation of thiobarbituric acid (TBARS), peroxide (PV), and volatile nitrogen and kept the sensory attributes up to 9 days of cold storage. No significant differences were noticed in PV and TBARS of meatballs formulated with 0.5% NPP90 and 0.1% BHT, which suggests the potential use of nanoscale pomegranate peel as natural substitutes to BHT in meat products.
MIS 3–2 PALEO-WINTER TEMPERATURE RECONSTRUCTIONS OBTAINED FROM STABLE WATER ISOTOPE RECORDS OF RADIOCARBON-DATED ICE WEDGES OF THE BATAGAY ICE COMPLEX (YANA UPLAND, EASTERN SIBERIA)

ABSTRACT The recently formed Batagay mega-thaw slump exposes permafrost deposits to a depth of up to 92 m below ground surface and provides insight into the climate record in the region of the most severe continental climate in the Northern Hemisphere. Radiocarbon and stable isotope data were obtained to verify the age of ice wedges in the Batagay yedoma (upper Ice Complex in the local cryostratigraphy), and to deduce paleoclimate information. A millennial-scale resolution paleotemperature record from radiocarbon-dated ice wedges spans Marine Isotope Stages (MIS) 3–2. The Batagay mean January air paleo-temperature during the 44.9–42 and 30.8–27.1 cal ka BP periods was stable cold has been reconstructed to about –51 ± 3°C each.
Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters, and Modular Pipelines

Nonintrusive load monitoring (NILM) techniques are increasingly becoming a key instrument for identifying the power consumption of individual appliances based on a single metering point. Particularly, deep learning (DL) models are gaining interest in this regard. However, the challenges brought by the NILM datasets and the nonavailability of common experimental guidelines tend to compromise comparison, research transparency, and replicability. The limited adoption of efficient research instruments and lack of best practices guidelines contribute in huge part to this problem, where no features, encouraging standardized formats for benchmarking, and results sharing are offered. To address these issues, we first present a brief overview of recent best practices for DL and highlight how deep NILM research can benefit from these practices. Furthermore, we suggest a novel open-source toolkit leveraging these practices, i.e., Deep-NILMTK. The proposed toolkit offers a common testing bed for NILM algorithms independently of the underlying deep learning framework with a modular NILM pipeline that can easily be customized. Furthermore, Deep-NILMTK introduces the concept of experiment templating to offer predesigned experiments allowing to enhancing research efficiency. Leveraging this concept and DL best practices, we present a case study of creating an online NILM benchmark repository1 considering eight of the most popular deep NILM algorithms. All sources relative to the tool are made publicly available on Github2 along with the corresponding documentation.
Controlled Release of Tea Tree Oil from a Chitosan Matrix Containing Gold Nanoparticles

Chitosan is a biopolymer that, due to its versatile bioactive properties, has applications in several areas, including food, medicine and pharmaceuticals. In the field of tissue engineering, chitosan can be used, for example, as a dressing to treat wounds or dermal damage, such as burns or abrasions. This work deals with the controlled release of tea tree oil from chitosan-based polymeric films and droplets containing gold nanoparticles (AuNP). AuNPs were successfully incorporated into the chitosan matrix using two different approaches. Both solutions were loaded with tea tree oil, and from these solutions, it was possible to obtain drop-cast films and droplets. The controlled release of oil in water was performed both in the films and in the droplets. The addition of AuNP in the controlled release system of melaleuca oil favored a release time of around 25 h. A series of experiments was carried out to investigate the effects of different reaction temperatures and acetic acid concentrations on the formation of AuNPs in the presence of chitosan. For this purpose, images of the AuNP films and droplets were obtained using transmission electron microscopy. In addition, UV-vis spectra were recorded to investigate the release of tea tree oil from the different samples.
Concepts of thermodynamics

A-6. Calculus Fundamentals. Mathematics, like thermodynamics, is a discipline based upon postulates and definitions; if the postulates and definitions are not thoroughly understood, little progress will be made in either subject. In this review, emphasis is laid upon certain aspects of the calculus that are often misunderstood and that are of importance in the study of thermodynamics. (But it should be remarked that the pure mathematician would quarrel with several of the statements to -follow on the grounds that they are too restrictive for his purposes. } a. Set. A collection of particular things.
Informed grounded theory: A symbiosis of philosophy, methodology, and art.

ABSTRACT The methodological paper illustrates the informed grounded theory (informed GT) methodology by briefly demonstrating a case study about synchronous video communications and teacher presence in distance education. Unlike Glaser, B. G. , & Strauss, A. L. (1967). The discovery of grounded theory. Aldine. GT, the literature review is not delayed until the end but is ongoing and integrated throughout the research activity. After the research, the literature review could revisit the theory to make the findings transferable and sustainable. The power of informed GT as a methodology is that it is heterogeneous, combining inductive, deductive, and abductive reasoning in an iterative research process with an initial and ongoing literature review. This process created the theory of tele-proximity and an explanatory model storyline. The storyline can change and improve by integrating more data and literature in the future. Deduction started with the sensitising concept of proximity found in the literature, mapping the territory, identifying criticisms, and formulating research questions. Induction was used to construct the findings from informants’ perceptions to create a theoretical frame with categories that expanded on the Community of inquiry model and its concept of teacher presence. Abductive reasoning was used when plausible reasons were generated from interdisciplinary literature on the value of social cues and embodied cognition in synchronous video communications. Finally, a grounded theory of tele-proximity was constructed with the concept of tele-teacher presence. In practice, this process was messy. The main challenge was staying grounded and moving between abductive, inductive, and deductive reasoning to avoid getting stuck in crude deductions, making wild guesses during abductive reasoning, or generating explanations that were not a good fit for the data.
Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images

The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.
Spatiotemporal patterns of male and female white‐tailed deer on a hunted landscape

Abstract Resource selection in sexually dimorphic ungulates is at least partially explained by sex‐specific resource requirements and risk aversion strategies. Females generally spend more time in areas with less risk and abundant, high‐quality forage due to their smaller body size. However, demographically variable responses to risk are context dependent, and few have concurrently quantified male and female behavior within areas with the same resource base. We captured 111 (54 males, 57 females) adult white‐tailed deer (Odocoileus virginianus) from 2009 to 2018 on a site in South Carolina, USA, where hunters were the primary source of adult mortality. We fit each deer with a GPS collar programmed to collect locations at 30‐min intervals. Upon collar recovery, we analyzed the data to estimate sex‐ and time‐specific selection for, and distance to, various cover types. While both sexes generally avoided risky areas (i.e., sites hunted more frequently) during the day, females (p = .41) were more likely than males (p = .16) to use risky areas containing abundant food resources during the day, where p = probability of selection. Our findings indicate that female white‐tailed deer may be forced to utilize high risk areas during high risk periods due to their smaller body size and increased nutritional demands, whereas larger males are better able to forgo foraging opportunities during risky periods to mitigate risk; however, our study design left room for the possibility that our observations were driven by innate sex‐specific patterns in white‐tailed deer. Nonetheless, our study contributes information to the literature by describing sex‐specific resource selection by diel period on a site where sexes shared the same resources and were presented with the same landscape of risk.
Determination of multi‐step constant current based fast charging method for Li‐NMC battery cells using multi‐swarm particle swarm optimization algorithm

Thanks to today's developing technology, lithium‐ion batteries in commercial use have become important components in energy storage systems and electric vehicles. In lithium‐ion battery‐based systems, it is especially important that the electrochemical structures of electric vehicle batteries and rapid charging process as allowed by the manufacturers. In other respects, it is necessary to define the best quality, fastest and healthiest charging method within the limited time period determined in line with user requests. In this study, an intelligent charging algorithm is designed and adapted to the problem for optimizing the charging current capacity and charging time for INR 18650 cylindrical type 2000 mAh Li‐NMC battery cells, taking into account the manufacturer's notes and the characteristics of the battery cells. To determine the optimum fast charging method by completing the total charging time within 30 min, the multi swarm‐particle swarm optimization technique with both fixed and dynamic internal weights has been used. The algorithm with dynamic state variables has showed an average improvement of 0.03% and a maximum of 1.08% in the cellular chargeable capacity value, according to the test data consisting of 11 pulses and 1 A constant current. Otherwise, the algorithm with dynamic internal weights has achieved an average improvement of 1.14% and a maximum of 2.21% when compared to the fast charging information on the manufacturer's information page using the constant current‐constant voltage method. Considering the 50‐minute fast charging data in the manufacturer's information notes, the algorithm with dynamic weights improved the charging time by approximately 54.4%. As a result of running the dynamic weighted algorithm, a maximum improvement of 0.06% in terminal voltage and 0.92% in the state of charge value has been observed. When hundreds of cells in electric vehicle battery blocks and mobile energy storage systems come together, this increase provides significant energy savings. Similar to the increase in charging capacity, an improvement in equilibrium voltage, charging efficiency and state of charge has been also detected at the end of the charging process. Consequently, the method in the study has provided high charging capacity in a shorter time, while keeping the energy efficiency at high levels.
Delay decomposition approach to delay-dependent stability for singular time-delay systems [Brief Paper]

This study deals with the problem of delay-dependent stability analysis for singular time-delay systems. By employing the delay decomposition approach, an improved delay-dependent stability criterion is established in terms of linear matrix inequalities (LMIs), which ensures that the singular time-delay system is regular, impulse free and asymptotically stable. This stability criterion, which is obtained without using model transformation and bounding technique for cross-product terms, is much less conservative and includes some existing results as its special cases. Numerical examples are given to illustrate the effectiveness of the proposed method.
The Dyadic Effects of Self-Efficacy on Quality of Life in Advanced Cancer Patient and Family Caregiver Dyads: The Mediating Role of Benefit Finding, Anxiety, and Depression

Objectives To explore the dyadic interdependence of self-efficacy, benefit finding, anxiety, depression, and QOL in cancer patient (CP) and family caregiver (FC) dyads, and to ascertain the dyadic effects of self-efficacy on quality of life (QOL) in CP-FC dyads. Methods Conducted from November 2014 to December 2015, participants comprised 772 CP-FC dyads. The study surveyed participant characteristics, self-efficacy, benefit finding, anxiety, depression, and QOL. Data were analyzed using Pearson's correlation, T-test, and actor-partner interdependence mediation model (APIMeM). Results CPs' self-efficacy was positively correlated with both their own and FCs' benefit finding and mental component summary (MCS), and negatively associated with anxiety and depression (all Ps < 0.01, |r| = 0.144∼0.432). However, CPs' self-efficacy was only positively correlated with their own physical component summary (PCS) (r = 0.193), but not FCs' PCS. The same profile was identified in FCs' self-efficacy (all Ps < 0.01, |r| = 0.100∼0.468). FCs reported higher levels of self-efficacy and PCS compared to CPs (both Ps < 0.001). Significant positive correlations (r = 0.168–0.437) were identified among all paired variables in CP-FC dyads (all Ps < 0.001). To some extent, dyads' self-efficacy influences dyads' MCS and PCS through improving positive emotions (benefit finding) and relieving negative emotions (anxiety and depression). Conclusions Study findings not only support the dyadic interdependence of self-efficacy, benefit finding, anxiety, depression, and QOL in CP-FC dyads but confirm the hypothesis that dyads' self-efficacy may impact their MCS/PCS via an indirect approach to improve benefit finding and relieve anxiety and/or depression in CP-FC dyads.
Blind signal separation for coprime planar arrays: An improved coupled trilinear decomposition method

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor‐based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.
Branching Brownian motion with selection

In this thesis, branching Brownian motion (BBM) is a random particle system where the particles diffuse on the real line according to Brownian motions and branch at constant rate into a random number of particles with expectation greater than 1. We study two models of BBM with selection: BBM with absorption at a space-time line and the N -BBM, where, as soon as the number of particles exceeds a given number N , only the N right-most particles are kept, the others being removed from the system. For the first model, we study the law of the number of absorbed particles in the case where the process gets extinct almost surely, using a relation between the Fisher-Kolmogorov-Petrovskii-Piskounov (FKPP) and the Briot-Bouquet equations. For the second model, the study of which represents the biggest part of the thesis, we give a precise asymptotic on the position of the cloud of particles when N is large. More precisely, we show that it converges at the timescale log³ N to a Levy process plus a linear drift, both of them explicit, which confirms a prediction by Brunet, Derrida, Mueller and Munier. This study contributes to the understanding of travelling waves of FKPP type under the influence of noise. Finally, in a third part we point at the relation between the BBM and stable point processes.
Overview of BSDF Reconstruction Methods for Rough Surfaces

The work provides an overview of methods aimed to the reconstruction of Bidirectional Scattering Distribution Function (BSDF) for rough surfaces. The elements with rough surfaces are permanently present in our life and widely used in modern optical devices, for exam-ple, in light guiding plates for display illuminating systems, car dashboards, or luminaires. Light scattering by rough surface is an important component in the visual appearance of many materials including water, glass, skin, etc. The problem of the rough surface visualization is complex and contains many different aspects. Accordingly there are many techniques to provide their realistic rendering. In many lighting simulation and optical design tasks it is sufficient and more effective to replace real geometry of rough surface by a surface optical characteristics expressed via BSDF. So, accurate reconstruction of scattering properties of rough surfaces is a significant factor in visualizations tasks and generation of photorealistic images. In some cases, BSDF can be just measured. However, in many cases direct BSDF measurements are impossible if, for example, it is required to define BSDF inside of the material and neither a measuring device detector nor a light source can be placed inside the material. So this results in the development of many approaches for BSDF reconstruction. It started in the end of the last century with the development of many analytical methods based on microfacet models of rough surface such as the Phong, the Ward reflection, the Cook-Torrance models. Nowadays many direct numerical methods of BSDF reconstruction appear, for example, methods based on normals and heights distribution. As a rule, these methods use ray tracing to calculate BSDF. Sizes of microroughness can be small, sufficient to raise a problem which optics wave or ray is more appropriate here. To answer this and other ques-tions related to BSDF reconstruction, an investigation of well-known and effective reconstruct methods was conducted. This paper also presents the study results for eight real samples with different profile parameters of rough surface. The verification is based on numerical comparison with real measured data and visual comparison of images generated using different reconstructed BSDF. Finally, the general recommendations are presented about what methods and for what applications are more appropriate.