Miracle-dz/UniFAR-FTUs huggingface dataset:
Repulsive Chemotaxis and Predator Evasion in Predator Prey Models with Diffusion and Prey Taxis

The role of predator evasion mediated by chemical signaling is studied in a diffusive preypredator model when prey-taxis is taken into account (model A) or not (model B) with taxis strength coefficients χ and ξ respectively. In the kinetic part of the models it is assumed that the rate of prey consumption include functional responses of Holling, Bedington-DeAngelis or Crowley–Martin. Existence of global-in-time classical solutions to model A is proved in space dimension n = 1 while to model B for any n ≥ 1. The Crowley-Martin response combined with bounded rate of signal production preclude blow-up of solution in model A for n ≤ 3. Local and global stability of a constant coexistence steady state which is stable for ODE and purely diffusive model are studied along with mechanism of Hopf bifurcation for Model B when χ exceeds some critical value. In model A it is shown that prey taxis may destabilize the coexistence steady state provided χ and ξ are big enough. Numerical simulation depict emergence of complex space-time patterns for both models and indicate existence of solutions to model A which blow-up in finite time for n = 2.
Composite entanglement topology and extensional rheology of symmetric ring-linear polymer blends

Extensive molecular simulations are applied to characterize the equilibrium dynamics, entanglement topology, and nonlinear extensional rheology of symmetric ring-linear polymer blends with systematically varied ring fraction $\phi_R$. Chains with degree of entanglement $Z\approx14$ mixed to produce 10 well-entangled systems with $\phi_R$ varying from neat linear to neat ring melts. Primitive path analysis are used to visualize and quantify the structure of the composite ring-linear entanglement network. We directly measure the quantity of ring-linear threading and linear-linear entanglement as a function of $\phi_R$, and identify with simple arguments a ring fraction $\phi_R\approx0.4$ where the topological constraints of the entanglement network are maximized. These topological analyses are used to rationalize the $\phi_R$-dependence of ring and linear chain dynamics, conformations, and blend viscosity. Complimentary simulations of startup uniaxial elongation flows demonstrate the extensional stress overshoot observed in recent filament stretching experiments, and characterize how it depends on the blend composition and entanglement topology. The overshoot is driven by an overstretching and recoil of ring polymer conformations that is caused by the convective unthreading of rings from linear chains. This produces significant changes in the entanglement structure of blends that we directly visualize and quantify with primitive path analyses during flow.
On the quantization of $C^{\infty}(\mathbb R^d)$

An infinitesimal deformation of $C^{\infty}(\mathbb R^d)$ has a vanishing primary obstruction if and only if its skew form is Poisson, and therefore is integrable to a full deformation of $C^{\infty}(\mathbb R^d)$ by the work of Kontsevich. This is further equivalent to having its coefficients satisfy a set of $\binom{d}{3}$ nonlinear partial differential equations. The single such equation in the case $d=3$ suggests that a big bang might be localized to an instant in time and point in space. This note also reexamines the Basic Universal Deformation Formula (UDF) which asserts that if $D_1, D_2$ are commuting derivations of an associative algebra $\mathcal{A}$ over the rationals then exponentiation of $D_1\smile D_2$ provides a full deformation of $\mathcal{A}$. It gives further applications to quantization and to UDFs with non-commuting derivations.
A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures

This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp. This architecture allows the approach to rely on traded control instead of direct control: we use hand gestures to specify the goal objects for a sequential manipulation task, the robot then autonomously generates a grasping or a retrieving motion using trajectory optimization. The perception module relies on the model-based tracker to precisely track the 6D pose of the objects and makes use of a state of the art learning-based object detection and segmentation method, to initialize the tracker by automatically detecting objects in the scene. Goal objects are identified from user hand gestures using a trained a multi-layer perceptron classifier. After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i.e., one that does not use prediction) which shows that using intent prediction allows to bring down the overall task execution time.
Fair Decision Rules for Binary Classification

In recent years, machine learning has begun automating decision making in fields as varied as college admissions, credit lending, and criminal sentencing. The socially sensitive nature of some of these applications together with increasing regulatory constraints has necessitated the need for algorithms that are both fair and interpretable. In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on two different measures of classification parity: equality of opportunity and equalized odds. Column generation framework, with a novel formulation, is used to efficiently search over exponentially many possible rules. When combined with faster heuristics, our method can deal with large data-sets. Compared to other fair and interpretable classifiers, our method is able to find rule sets that meet stricter notions of fairness with a modest trade-off in accuracy.
Bound‐state soliton and rogue wave solutions for the sixth‐order nonlinear Schrödinger equation via inverse scattering transform method

In this work, inverse scattering transform for the sixth‐order nonlinear Schrödinger equation with both zero and nonzero boundary conditions at infinity is given, respectively. For the case of zero boundary conditions, in terms of the Laurent's series and generalization of the residue theorem, the bound‐state soliton is derived. For nonzero boundary conditions, using the robust inverse scattering transform, we present a matrix Riemann–Hilbert problem of the sixth‐order nonlinear Schrödinger equation. Then, based on the obtained Riemann–Hilbert problem, the rogue wave solutions are derived through a modified Darboux transformation. Besides, according to some appropriate parameters choices, several graphical analysis are provided to discuss the dynamical behaviors of the rogue wave solutions and analyze how the higher‐order terms affect the rogue wave.
Adversarially Robust Kernel Smoothing

We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy. We adapt the integral operator using supremal convolution in convex analysis to form a novel function majorant used for enforcing robustness. Our method is simple in form and applies to general loss functions and machine learning models. Exploiting a connection with optimal transport, we prove theoretical guarantees for certified robustness under distribution shift. Furthermore, we report experiments with general machine learning models, such as deep neural networks, to demonstrate competitive performance with the state-of-the-art certifiable robust learning algorithms based on the Wasserstein distance.
Accurate model for the primordial black hole mass distribution from a peak in the power spectrum

We examine the shape of the primordial black hole mass distribution arising from a peak in the primordial power spectrum. In light of improvements to the modelling, we revisit the claim that the effects of critical collapse produce a distribution that is not described by the commonly assumed lognormal, showing that this conclusion remains valid, particularly for narrow peaks where the shape of the mass distribution is insensitive to the peak properties and critical collapse determines a minimum width. We propose some alternative models that may better describe the shape, both for the narrow peak case and for much broader peaks where the effect of the peak shape is significant. We highlight the skew-lognormal and a generalised model motivated by the physics of critical collapse as the best of these possible alternatives. These models can be used as an accurate and fast approximation to the numerically calculated mass distribution, allowing for efficient implementation in an MCMC analysis. We advocate the use of one of these two models instead of the lognormal with sufficiently accurate data, such as future LIGO-Virgo observations, or when considering strongly mass dependent constraints on the PBH abundance.
COUPLED TRANSPORT/HYPERELASTIC MODEL FOR HIGH ENERGY DENSITY

A new development in aerospace technology involves the creation of aircraft that can undergo large changes in the shape of their wings and control surfaces. This technology, called morphing aircraft, does away with several performance compromises by allowing the aircraft to adapt to a wide variety of speed and altitude conditions. One of the challenges associated with the development of morphing aircraft is the creation of a skin that can allow for the in-plane stretching necessary for morphing but possess enough out-of-plane flexural rigidity to handle aerodynamic forces.A new class of high energy density active materials based upon biological processes is being developed to address this problem. These materials utilize the controlled transport of charge and fluid into micron-scale inclusions. The inclusions are phase separated from the surrounding matrix by a selectively-permeable membrane. Selective stimulation of the membrane enables bulk deformation in a process referred to in the plant kingdom as nastic movements. The particular material considered in this work utilizes biological transport mechanisms to generate an osmotic gradient across the membrane.The purpose of this work is to develop a physics-based computational model of the nastic material that couples ion and solvent fluxes generated by the biological transporters to a finite element analysis of the surrounding matrix. This model is to act as a feedback loop for material synthesis efforts. The processes occurring in the biotransport system are complex and highly coupled to one another. The numerical solution of the resulting transport model and its coupling with the finite element analysis are key challenges in creating a viable model. The resulting model has been compared to experiment and is capable of predicting material response over a wide range of configurations and transport components. A series of parametric studies is performed to determine the relative importance of the material parameters and provide guidance to experimental efforts.
Aqueous phase removal of ofloxacin using adsorbents from Moringa oleifera pod husks

Chemically activated and carbonized adsorbents were prepared from Moringa oleifera pod husks (MOP), characterized and evaluated for their ability to remove a common antibiotic - ofloxacin (OFX) from aqueous solution. The pulverized precursor was steeped in a saturated ammonium chloride solution for a day to give the chemically activated adsorbent (AMOP). A portion of AMOP was pyrolyzed in a muffle furnace at 623 K for 30 min to furnish its carbonized analogue (CMOP). The adsorbents showed favorable physicochemical attributes. The effects of operational parameters such as initial OFX solution pH and concentration, adsorbent dosage, temperature and contact time on OFX removal were investigated. At equilibrium, optimal removal efficiencies of 90.98% and 99.84% were achieved at solution pH 5 for AMOP and CMOP, respectively. The equilibrium adsorption data fitted into both the Langmuir and Freundlich isotherms. Gibbs free energy change (ΔGo), enthalpy change (ΔHo) and entropy change (ΔSo) indicated that the adsorption of OFX was feasible, spontaneous, exothermic and occurred via the physisorption mode. Adsorption kinetics obeyed the Blanchard pseudo-second-order model. The results may find applications in the adsorptive removal of micro-contaminants of pharmaceutical origin from wastewater.
The Combined Use of Fractional Urate and Potassium Excretion in the Diagnosis of Diuretic-Induced Hyponatremia

Introduction Thiazide and loop-diuretics are among the most widely used drugs in the therapy of hypertension and chronic heart failure. Furthermore, hyponatremia is the most prevalent electrolyte imbalance affecting up to 25-30% of hospitalized patients while syndrome of inappropriate antidiuresis (SIAD) is involving approximately 35% of hyponatraemic inpatients. Clinical and laboratoristic algorithms support the differential diagnosis of hypotonic hyponatremia in actual guidelines of SIAD, but a potential bias is represented by the misleading clinical assessment of the extracellular volume status in diuretic-treated patients where the necessity of withdrawal of the therapy is mandatory. We investigated the role of fractional uric acid and potassium excretion (FEUA and FEK) in the differential diagnosis of hypotonic hyponatremia in SIAD and diuretic-treated patients. Methods Thirty-six SIAD, 30 thiazide-induced hyponatremia (TIH), and 32 diuretic-induced hyponatremia (DIH) patients were investigated calculating FEUA and FEK values in receiver operating characteristic (ROC) curve analysis to improve the diagnostic approach of hypotonic hyponatremia. Results The combination of the two investigated markers showed different significative results generating patterns useful to discriminate among the three different hyponatremic groups. Conclusion The fractional uric acid and potassium excretion could be considered as new markers in the diagnostic approach of hyponatremic diuretic-treated patients where classical algorithms could fail.
Increased Adherence to Infection Control Practices Among Medical Laboratory Technicians During the COVID-19 Pandemic: A Self-Reported Survey Study

Background: The adherence of medical laboratory technicians (MLT) to infection control guidelines is essential for reducing the risk of exposure to infectious agents. This study explored the adherence of MLT towards infection control practices during the COVID-19 pandemic. Method: The study population consisted of MLT (n = 444) who worked in private and government health sectors in Jordan. A self-reported survey was used to collect data from participants. Findings: More than 87% of the participants reported adherence to hand-washing guidelines and using personal protective equipment (PPE) when interacting with patients (74.5%), and handling clinical samples (70.0%). Besides, 88.1%, 48.2%, and 7.7% reported wearing of lab coats, face masks, and goggles, at all times, respectively. The majority reported increased adherence to infection control practices during the COVID-19 pandemic. This includes increased PPE use at the workplace (94.2%), increased frequency of disinfection of laboratory surfaces (92.4%) and laboratory equipment (86.7%), and increased frequency of handwashing/use of antiseptics (94.6%). Having a graduate degree was significantly associated with increased adherence of participants to the daily use of goggles/eye protection (p = 0.002), and the use of PPE while handling clinical samples (p = 0.011). Having work experience of >10 years was associated with increased adherence to the use of PPE while handling clinical samples (p = 0.001). Conclusion: MLT reported very good adherence with most assessed infection control practices. In addition, they reported increased conformity with infection control guidelines during the COVID-19 pandemic.
Vertebral Compression Fractures—The First Manifestations in the Elderly Acute Lymphoblastic Leukemia

Background: Acute lymphoblastic leukemia (ALL) is reported to be the most common malignancy in children, but is less common in adults. Though vertebral compression fractures have been reported as initial signs of ALL in children, to the best of our knowledge, there have not been any previous reports of vertebral compression fractures as an initial manifestation of ALL in elderly people. Case Presentation: Here we reported a 65-year-old man who was admitted with a 1-month history of progressive chest and back pain and worsening pain after heavy lifting. Considering the histories of osteoporosis, minor trauma and normal peripheral blood counts, it was firstly misdiagnosed as spinal osteoporotic compression fractures which were caused by low-energy injuries. The patient was definitely diagnosed as ALL after bone marrow biopsy. Conclusion: ALL must be considered in the differential diagnosis with acute vertebral compression fracture. Considering that bony lesions may precede clinical findings, knowledge of radiographic and orthopedic appearances of leukemia are important in order to initiate earlier treatment to avoid the progressive damage and improve their survival rates. we recommend bone marrow biopsy in vertebroplasty to avoid misdiagnosis.
A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden

Objective Tumor-associated macrophages (TAMs) within the tumor immune microenvironment (TiME) of solid tumors play an important role in treatment resistance and disease recurrence. The purpose of this study was to investigate if nanoradiomics (radiomic analysis of nanoparticle contrast-enhanced images) can differentiate tumors based on TAM burden. Materials and Methods In vivo studies were performed in transgenic mouse models of neuroblastoma with low (N = 11) and high (N = 10) tumor-associated macrophage (TAM) burden. Animals underwent delayed nanoparticle contrast-enhanced CT (n-CECT) imaging at 4 days after intravenous administration of liposomal-iodine agent (1.1 g/kg). CT imaging-derived conventional tumor metrics (tumor volume and CT attenuation) were computed for segmented tumor CT datasets. Nanoradiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing 900 radiomic features (RFs). RF selection was performed under supervised machine learning using a nonparametric neighborhood component method. A 5-fold validation was performed using a set of linear and nonlinear classifiers for group separation. Statistical analysis was performed using the Kruskal–Wallis test. Results N-CECT imaging demonstrated heterogeneous patterns of signal enhancement in low and high TAM tumors. CT imaging-derived conventional tumor metrics showed no significant differences (p > 0.05) in tumor volume between low and high TAM tumors. Tumor CT attenuation was not significantly different (p > 0.05) between low and high TAM tumors. Machine learning-augmented nanoradiomic analysis revealed two RFs that differentiated (p < 0.002) low TAM and high TAM tumors. The RFs were used to build a linear classifier that demonstrated very high accuracy and further confirmed by 5-fold cross-validation. Conclusions Imaging-derived conventional tumor metrics were unable to differentiate tumors with varying TAM burden; however, nanoradiomic analysis revealed texture differences and enabled differentiation of low and high TAM tumors.
Hyperoside Attenuate Inflammation in HT22 Cells via Upregulating SIRT1 to Activities Wnt/β-Catenin and Sonic Hedgehog Pathways

Neuroinflammation plays important roles in the pathogenesis and progression of altered neurodevelopment, sensorineural hearing loss, and certain neurodegenerative diseases. Hyperoside (quercetin-3-O-β-D-galactoside) is an active compound isolated from Hypericum plants. In this study, we investigate the protective effect of hyperoside on neuroinflammation and its possible molecular mechanism. Lipopolysaccharide (LPS) and hyperoside were used to treat HT22 cells. The cell viability was measured by MTT assay. The cell apoptosis rate was measured by flow cytometry assay. The mRNA expression levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-α (TNF-α) were determined by quantitative reverse transcription polymerase chain reaction. The levels of oxidative stress indices superoxide dismutase (SOD), reactive oxygen species (ROS), catalase (CAT), glutathione (GSH), and malondialdehyde (MDA) were measured by the kits. The expression of neurotrophic factor and the relationship among hyperoside, silent mating type information regulation 2 homolog-1 (SIRT1) and Wnt/β-catenin, and sonic hedgehog was examined by western blotting. In the LPS-induced HT22 cells, hyperoside promotes cell survival; alleviates the level of IL-1β, IL-6, IL-8, TNF-α, ROS, MDA, Bax, and caspase-3; and increases the expression of CAT, SOD, GSH, Bcl-2, BDNF, TrkB, and NGF. In addition, hyperoside upregulated the expression of SIRT1. Further mechanistic investigation showed that hyperoside alleviated LPS-induced inflammation, oxidative stress, and apoptosis by upregulating SIRT1 to activate Wnt/β-catenin and sonic hedgehog pathways. Taken together, our data suggested that hyperoside acts as a protector in neuroinflammation.
X-ray diffraction and infrared spectroscopy analyses on the crystallinity of engineered biological hydroxyapatite for medical application

Biological hydroxyapatite (BHAp) derived from thermally-treated fish bones was successfully produced. However, the obtained biological HAp was amorphous and thus making it unfavorable for medical application. Consequently, this research exploits and engineers the crystallinity of BHAp powders by addition of CaCO3 and investigates its degree of crystallinity using XRD and IR spectroscopy. On XRD, the HAp powders with [Ca]/[P] ratios 1.42, 1.46, 1.61 and 1.93 have degree of crystallinity equal to 58.08, 72.13, 85.79, 75.85% and crystal size equal to 0.67, 0.74, 0.75, 0.72 nm, respectively. The degree of crystallinity and crystal size of the obtained calcium deficient biological HAp powders increase as their [Ca]/[P] ratio approaches the stoichiometric ratio by addition of CaCO3 as source of Ca2+ ions. These results show the possibility of engineering the crystallinity and crystal size of biological HAp by addition of CaCO3. Moreover, the splitting factor of PO4 vibration matches the result with % crystallinity on XRD. Also, the area of phosphate-substitution site of PO4 vibration shows linear relationship (R2 = 0.994) with crystal size calculated from XRD. It is worth noting that the crystallinity of the biological HAp with [Ca]/[P] ratios 1.42 and 1.48 fall near the range 60-70% for highly resorbable HAp used in the medical application.
Enhanced Drug Permeation into Human Keloid Tissues by Sonophoresis-Assisted Microneedling

Keloids are disfiguring pathological scars that could cause pain and pruritus. The conventional treatments, such as bolus injection of drugs or surgery, are invasive and require a personal visit to clinic. Microneedle (MN) technology has great potential to offer a self-administered and minimally invasive treatment of keloids. However, drugs delivered using MNs suffer from limited penetration in keloid tissue. This study demonstrates enhanced drug penetration in human keloid scar tissue by combining MN and sonophoresis.