Miracle-dz/UniFAR-FTUs huggingface dataset:
Ionic Conductance of Solid Electrolytes at the Meso- and Nanoconfinement

Author(s): Martinez, Joseph Alex | Advisor(s): Siwy, Zuzanna S | Abstract: This thesis describes a novel approach for probing ionic conductivity of silica ionogel electrolytes on the meso- and nanoscale. These properties are important to know for the development of solid-state energy-storage devices that implement nanostructures. Silica ionogels are of specific interest because they maintain a fluidic state on the nanoscale, allowing for a high conductivity of liquid electrolytes, but on the macroscale, they maintain the stability benefits of solid electrolytes. Using single mesopores in polymer films to restrict the geometry of the ionogel to the desired size, we were able to measure the conductivity using two methods. The first method uses liquid electrolytes and makes measurements similar to how the conductivity in liquid electrolytes is found, the other method probes the solid electrolyte in a way more representative to that of energy storage devices. We concluded that the electrochemical properties of ionogel are not significantly affected by constriction to the mesoscale, meaning they can be useful in the implementation of mesoscale solid-state energy-storage devices.
Lumen Catheter Adventitia Stent Media Catheter Lumen Adventitia Stent Media FibroticCalcification Stent

We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels. The proposed approach is applied to IVUS and OCT images. Both qualitative and quantitative results show superior performance of the proposed method compared to a number of alternative segmentation techniques.
Advancements in Human Breast Cancer Targeted Therapy and Immunotherapy

Human breast cancer treatment regimens have evolved greatly due to the significant advances in understanding the molecular mechanisms and pathways of the common subtypes of breast cancer. In this review, we discuss recent progress in breast cancer targeted therapy and immunotherapy as well as ongoing clinical trials. We also highlight the potential of combination therapies and personalized approaches to improve clinical outcomes. Targeted therapies have surpassed the hormone receptors and the human epidermal growth factor receptor 2 (HER2) to include many other molecules in targetable pathways such as the epidermal growth factor receptor (EGFR), poly (adenosine diphosphate-ribose) polymerase (PARP), and cyclin-dependent kinase 4/6 (CDK4/6). However, resistance to targeted therapy persists, underpinning the need for more efficacious therapies. Immunotherapy is considered a milestone in breast cancer treatments, including the engineered immune cells (CAR-T cell therapy) to better target the tumor cells, vaccines to stimulate the patient's immune system against tumor antigens, and checkpoint inhibitors (PD-1, PD-L1, and CTLA4) to block molecules that mediate immune inhibition. Targeted therapies and immunotherapy tested in breast cancer clinical trials are discussed here, with special emphasis on combinatorial approaches which are believed to maximize treatment efficacy and enhance patient survival.
Mechanisms of Memory Updating: State Dependency vs. Reconsolidation

Reactivating a memory trace has been argued to put it in a fragile state where it must undergo a stabilization process known as reconsolidation. During this process, memories are thought to be susceptible to interference and can be updated with new information. In the spatial context paradigm, memory updating has been shown to occur when new information is presented in the same spatial context as old information, an effect attributed to a reconsolidation process. However, the integration concept holds that memory change can only occur when reactivation and test states are the same, similar to a state-dependent effect. Thus, in human episodic memory, memory updating should only be found when state is the same across the study, reactivation, and test sessions. We investigated whether memory updating can be attributed to state dependency in two experiments using mood as a state. We found evidence of memory updating only when mood was the same across all sessions of the experiments, lending support to the integration concept and posing a challenge to a reconsolidation explanation.
Experimental investigation and theoretical for the performance improvement of MPPT technique with PV systems connected to the grid

Many researchers have provided theoretical analysis of photoelectric (PV) energy systems connected to the electrical grid. This paper is concerned with experimental preparation in order to validate more and remains a major challenge in the field of practical research. In addition, the biggest problem with the PV energy system is how to get the utmost value out of the energy under a changing solar radiation. To take full power of the PV array output energy dependent on the change in solar radiation, Maximum Power Point Tracking (MPPT) techniques are used in the PV systems. The experimental and theoretical application of MPPT technology for grid connected PV system is suggested. This is done to track maximum energy under rapidly changing weather conditions in two states of PV radiation changes: the change of solar radiation in ramp profile and the width under change step. This article introduces the MPPT of a grid-connected PV system as a simulation and experimentally to track the speed of PV radiation change in different operating conditions. Through the results, an increase and correspondence in the output energy is shown using the MPPT technology between the experimental and theoretical side. Simulation results show the feasibility and power of control systems. Finally, a simulation result was compared with the experimental results for energy, current, and voltage coming out of the PV array as well as the electrical grid.
Minimally Invasive Surgery vs. Open Surgery for Infectious Spondylodiscitis: A Systematic Review and Meta-Analysis

Infectious spondylitis is estimated to affect 4–24 patients per million per year, with male:female ratio of 1.5:2.1. Contributing to 0.15%–5% of all osteomyelitis cases, until date, the treatment of infectious spondylitis is still very challenging in order to minimalize the mortality and morbidity of these patients [1,2]. Surgical intervention is needed in patients unresponsive to antibiotic treatments, those with spinal instability, neurological deficit, soft tissue abscess involvement, and significant spinal destruction. In order to ensure infection eradication, open surMinimally Invasive Surgery vs. Open Surgery for Infectious Spondylodiscitis: A Systematic Review and Meta-Analysis
The Distribution and Role of the CFTR Protein in the Intracellular Compartments

Cystic fibrosis is a hereditary disease that mainly affects secretory organs in humans. It is caused by mutations in the gene encoding CFTR with the most common phenylalanine deletion at position 508. CFTR is an anion channel mainly conducting Cl− across the apical membranes of many different epithelial cells, the impairment of which causes dysregulation of epithelial fluid secretion and thickening of the mucus. This, in turn, leads to the dysfunction of organs such as the lungs, pancreas, kidney and liver. The CFTR protein is mainly localized in the plasma membrane; however, there is a growing body of evidence that it is also present in the intracellular organelles such as the endosomes, lysosomes, phagosomes and mitochondria. Dysfunction of the CFTR protein affects not only the ion transport across the epithelial tissues, but also has an impact on the proper functioning of the intracellular compartments. The review aims to provide a summary of the present state of knowledge regarding CFTR localization and function in intracellular compartments, the physiological role of this localization and the consequences of protein dysfunction at cellular, epithelial and organ levels. An in-depth understanding of intracellular processes involved in CFTR impairment may reveal novel opportunities in pharmacological agents of cystic fibrosis.
Shelf Life Extension of Greenhouse Tomatoes Using Ozonation in Combination with Packaging under Refrigeration

ABSTRACT Greenhouse tomatoes were exposed to gaseous ozone for 1 h at different concentrations (0.5, 1, and 2 mg/L), packaged in low-density polyethylene (LDPE) bags and stored at 4 ± 1 °C for 49 days. Peel firmness, color parameter, browning index values, weight loss, acidity, total viable count, psychrotrophs, yeasts and molds, and sensory analyses were carried out. Results showed significant differences on parameters determined, depending on storage time and treatment (p < 0.05). Sensory evaluation showed that ozonated tomatoes (especially at 2 mg/L) retained a fresh character compared to untreated samples, for an extended period of ca. 15 days.
Internet Addiction Scale: A Parametric Study through the EFA and Polychoric Correlation Matrices

The purpose of the study is to determine the degree of Internet addiction in engineering students. It takes as a theoretical reference the scale proposed by Young (1998), which establish the criteria and ranges of addiction. The instrument is in Likert format with responses ranging from 1 (rarely) to 5 (always). The participants were 306 students of the Engineering career enrolled in the different school grades of the Technological Institute of Tierra Blanca, Veracruz in the 2020−2021 school year. For data analysis, the statistical technique of exploratory factor analysis with extraction of principal components and Varimax rotation is used. In addition, for instruments designed with Likert scales, it is necessary to use the Polychoric Correlation matrices for factor analysis (Richaud, 2005; Ogasawaras, 2011). The main findings are: a factorial structure of six factors was obtained, which explains 63.1 % of the total variance and is in accordance with Young's proposal (1998), however the indicators are integrated differently in each factor. The level of internet addiction of the students does not fall within the normal range; the highest percentage is mild, which does not generate an alert in which immediate attention should be paid. It was also found that there is no difference in internet addiction in relation to gender.
Consumer vs. High-End 3D Printers for Guided Implant Surgery—An In Vitro Accuracy Assessment Study of Different 3D Printing Technologies

This study evaluates the accuracy of drill guides fabricated in medical-grade, biocompatible materials for static, computer-aided implant surgery (sCAIS). The virtually planned drill guides of ten completed patient cases were printed (n = 40) using professional (Material Jetting (MJ)) and consumer-level three-dimensional (3D) printing technologies, namely, Stereolithography (SLA), Fused Filament Fabrication (FFF), and Digital Light Processing (DLP). After printing and post-processing, the drill guides were digitized using an optical scanner. Subsequently, the drill guide’s original (reference) data and the surface scans of the digitized 3D-printed drill guide were superimposed to evaluate their incongruencies. The accuracy of the 3D-printed drill guides was calculated by determining the root mean square (RMS) values. Additionally, cast models of the planned cases were used to check that the drill guides fitted manually. The RMS (mean ± SD) values for the accuracy of 3D-printed drill guides were—MJ (0.09 ± 0.01 mm), SLA (0.12 ± 0.02 mm), FFF (0.18 ± 0.04 mm), and DLP (0.25 ± 0.05 mm). Upon a subjective assessment, all drill guides could be mounted on the cast models without hindrance. The results revealed statistically significant differences (p < 0.01) in all except the MJ- and SLA-printed drill guides. Although the measured differences in accuracy were statistically significant, the deviations were negligible from a clinical point of view. Within the limits of this study, we conclude that consumer-level 3D printers can produce surgical guides with a similar accuracy to a high-end, professional 3D printer with reduced costs.
CARLA Simulated Data for Rare Road Object Detection

Labeled data are paramount for modern, deep learning object detection models. However, such data are not always available, either due to time and financial constraints or due to the rarity of certain objects. In this paper, we show that the CARLA simulator can be used effectively to provide automatic annotations for custom street-view objects, boosting datasets for objects with few labels. We evaluate our models on real world images and show that low-shot training data expanded by synthetic images rendered in CARLA can provide better performance than training models with low-shot examples alone. To overcome the sim-to-real domain gap, we perform domain randomization by taking advantage of CARLA's diverse simulations of weather conditions, actors, and maps. We train detectors on CARLA-generated images of two different object classes and evaluate them on publicly available datasets. We provide access to our synthetic fire hydrant33https://www.kaggle. com/xinhez/synthetic-fire-hydrants and crosswalk44https://www.kaggle. com/buvision/synthetic-crosswalks datasets as well as provide step-by-step instructions55https://www.github. com/xinhez/simulation-for-detection to generate custom datasets in CARLA.
A Highly Active Cp*Ir Complex with an Anionic N,N-donor Chelate Ligand Catalyzes Robust Regeneration of NADH under Physiological Conditions

A highly active [N^N¯] iridium complex [Cp*Ir(pba)Cl] (3, Cp*= pentamethylcyclopentadiene, pba = 4-(picolinamido)benzoic acid) has been obtained with anionic ligand, and exhibited the most robust performances for cofactor NADH regeneration.
SERALOC: SLAM on semantically annotated radar point-clouds

Localization for Autonomous Driving is mainly researched on camera and LiDAR data, rarely on radar data. Applying a point cloud pose graph SLAM on a modern 77GHz automotive radar sensor-set, we realize direct radar sub-map scan matching for consistent mapping and ego vehicle localization at low speeds in parking scenarios. A spatiotemporal activation maps pre-filters clutter and noise. Matching locally overlapping radar sub-maps in open-loop sections results in a locally stiffer but drift corrected pose graph architecture. Integrating point cloud segmentation of 3d radar detections yields semantically consistent scan matching and increased static map accuracy. Our results reveal how semantic information, temporal filtering, and Normal Distribution Matching can outperform the accuracy of direct radar mapping and localization. Our approach SERALOC yields a new benchmark of 0.4 m absolute pose error (APE) over a distance of 624 m, measured on real-world test data against differential GPS data.
Poly(Lactic Acid)/Graphite Nanoplatelet Nanocomposite Filaments for Ligament Scaffolds

The anterior cruciate ligament (ACL) is one of the most prone to injury in the human body. Due to its insufficient vascularization and low regenerative capacity, surgery is often required when it is ruptured. Most of the current tissue engineering (TE) strategies are based on scaffolds produced with fibers due to the natural ligament’s fibrous structure. In the present work, composite filaments based on poly(L-lactic acid) (PLA) reinforced with graphite nanoplatelets (PLA+EG) as received, chemically functionalized (PLA+f-EG), or functionalized and decorated with silver nanoparticles [PLA+((f-EG)+Ag)] were produced by melt mixing, ensuring good filler dispersion. These filaments were produced with diameters of 0.25 mm and 1.75 mm for textile-engineered and 3D-printed ligament scaffolds, respectively. The resulting composite filaments are thermally stable, and the incorporation of graphite increases the stiffness of the composites and decreases the electrical resistivity, as compared to PLA. None of the filaments suffered significant degradation after 27 days. The composite filaments were processed into 3D scaffolds with finely controlled dimensions and porosity by textile-engineered and additive fabrication techniques, demonstrating their potential for ligament TE applications.
A predictive viscosity model for aqueous electrolytes and mixed organic-inorganic aerosol phases

Abstract. Aerosol viscosity is determined by mixture composition and temperature, with a key influence from relative humidity (RH) in modulating aerosol water content. Aerosol particles frequently contain mixtures of water, organic compounds and inorganic ions, so we have extended the thermodynamics-based group-contribution model AIOMFAC-VISC to predict viscosity for aqueous electrolyte solutions and aqueous organic-inorganic mixtures. For aqueous electrolyte solutions, our new, semi-empirical approach uses a physical expression based on Eyring's absolute rate theory, and we define activation energy for viscous flow as a function of temperature, ion activities, and ionic strength. The AIOMFAC-VISC electrolyte model's ion-specific expressions were simultaneously fitted, which arguably makes this approach more predictive than that of other models. This also enables viscosity calculations for aqueous solutions containing an arbitrary number of cation and anion species, including mixtures that have never been studied experimentally. These predictions achieve an excellent level of accuracy while also providing physically meaningful extrapolations to extremely high electrolyte concentrations, which is essential in the context of microscopic aqueous atmospheric aerosols. For organic-inorganic mixtures, multiple mixing approaches were tested to couple the AIOMFAC-VISC electrolyte model with its existing aqueous organic model. We discuss the best performing mixing models implemented in AIOMFAC-VISC for reproducing viscosity measurements of aerosol surrogate systems. We present advantages and drawbacks of different model design choices and associated computational costs of these methods, of importance for use of AIOMFAC-VISC in dynamic simulations. Finally, we demonstrate the capabilities of AIOMFAC-VISC predictions for phase-separated organic-inorganic particles equilibrated to observed temperature and relative humidity conditions from atmospheric balloon soundings. The predictions for the studied cases suggest liquid-like viscosities for an aqueous electrolyte-rich particle phase throughout the troposphere, yet a highly viscous or glassy organic-rich phase in the middle and upper troposphere.
Interactive 3D-Visualization of Brain Spatial Networks

With the advancement of data acquisition techniques, a huge number of datasets describing different brain functions are generated today building the basis for data-driven and computational approaches to neuroscience. This involves an increasing need for comprehensive and intuitive tools allowing both, exploration and visualisation, of such data. 2D visualisations of graphs are generally described as superior to 3D representations in discovering complex relationships [SBS (cid:3) 13]. However, 3D representations of brain networks in their natural anatomical context can provide neuroscientists with complementary information on the spatial arrangement of a network and are common for the communication of results in publications. Existing software [WTS (cid:3) 13] for 3D graph visualization have difﬁculties obfuscating edges and nodes due to many connections, which creates a chaotic image and makes interrelations too complex to interpret. Moreover, an effective force-directed layout [Kob12] cannot be applied to the network with a ﬁxed node’s location. We have developed a view-dependent algorithm that considers the camera position and observation direction. The proposed method allows connections to be presented in a way the user could see them also during interaction with a low number of intercross-ing. Edges are rendered not as straight lines but as Bezier interpolated symmetrical curves, which are aesthetically pleasing and help to avoid clutter (comparison on Figures 1). Curve’s parameters depend on values projected on a device screen: the distance between the nodes and distance to the central point of the brain volume (both calculated in screen space). A user study showed that this improved the perception of the anatomical context of the network. This implementation is a part of the tool ’Brain Trawler’
The Hackers’ Viewpoint: Exploring Challenges and Beneﬁts of Bug-Bounty Programs

In recent years, bug-bounty programs have garnered popularity and became a signiﬁcant part of the security culture of many organizations. Bug-bounty programs enable these organizations to improve their security posture by harnessing the outside perspective of a diverse crowd of security experts (bug hunters). However, bug-bounty programs also suffer from in-efﬁciencies, such as duplicate and invalid bug reports, which are resource consuming for organizations and bug hunters alike. To address these issues, it is crucial to understand how bug hunters make decisions, what motivates them, and what challenges they face. We present the results of an initial survey conducted among bug hunters to address these questions. We recruited 56 security experts who participate in bug-bounty programs to answer open-ended questions regarding various aspects of their participation in bug-bounty programs. Their responses provide a detailed overview of the motivations of security experts and the challenges that they face.