Miracle-dz/UniFAR-FTUs huggingface dataset:
First case report of isolation of Micrococcus lylae from urinary catheter of a 50-year-old woman suffering from malignancy.

INTRODUCTION Catheter-associated urinary tract infection (CAUTI) is the most common healthcare-associated infection which tends to cause increased length of morbidity, and mortality of patients, in addition to increased bacterial resistance to antibiotics. METHODOLOGY In the present study, urinary catheters were collected from a 50-year-old woman suffering from malignancy, bedridden, and having urinary incontinence. These catheters were processed in laboratory for isolation of bacteria using standard procedures. RESULTS Microbiological examination of the urinary catheters by biochemical, physiological, and VITEK 2 compact system revealed bacterial infection caused by Micrococcus lylae, a Gram-positive microorganism belonging to the family Micrococcacea. These Gram-positive bacteria were found to be susceptible to streptomycin, erythromycin, cefotaxime, neomycin, kanamycin, vancomycin, azithromycin, chloramphenicol, and tetracycline. Bacterial species were confirmed using 16s rRNA sequencing. CONCLUSIONS The sequences were found to have 99% similarity with Micrococcus lylae. This is the first report of isolation of Micrococcus lylae from the urinary catheter.
Synchronization behaviors of coupled neurons under electromagnetic radiation

Based on an improved neuronal model, in which the effect of magnetic flux is considered during the fluctuation and change of ion concentration in cells, the transition of synchronization is investigated by imposing external electromagnetic radiation on the coupled neurons, and networks, respectively. It is found that the synchronization degree depends on the coupling intensity and the intensity of external electromagnetic radiation. Indeed, appropriate intensity of electromagnetic radiation could be effective to realize intermittent synchronization, while stronger intensity of electromagnetic radiation can induce disorder of coupled neurons and network. Neurons show rhythm synchronization in the electrical activities by increasing the coupling intensity under electromagnetic radiation, and spatial patterns can be formed in the network under smaller factor of synchronization.
Artificial Intelligence and the Remodeling of the Arab Clan Culture: A Comprehensive Analysis

For public organizations looking to assure fairness and meritocracy, the clan system—which refers to the employment and advancement of people based on their family or social connections—has long been a problem. By increasing efficiency, decreasing bias, and facilitating improved decision-making, artificial intelligence (AI) has the potential to drastically alter the clan system of public institutions. This study employs a qualitative methodology to offer insightful explanations of how AI can influence the clan system in public organizations. Qualitative research can aid in the development of responsible and efficient AI systems that support equality, empowerment, and effectiveness by examining the experiences and viewpoints of people participating in hiring and promotion processes. The study finds that artificial intelligence can lessen the impact of clan ties in the hiring process and guarantee that the best individuals are chosen for the position. Additionally, AI-powered tools can be used to find any irregularities in the hiring process and offer suggestions for improvement. AI can be used to evaluate performance and make promotion decisions in addition to recruitment. AI can assist in ensuring that promotions are based on merit rather than clan allegiance by evaluating employee performance data and spotting patterns and anomalies. Additionally, AI can offer insight into the knowledge and training that will best increase performance, enabling public agencies to give their staff members specialized training and development opportunities. In general, this study concludes that the use of these technologies is ethical and transparent. It is important to ensure that they are applied ethically and transparently to avoid unintended consequences and ensure that they benefit all people equally. In addition, the study emphasizes the importance of considering the cultural and social context in which AI is implemented to ensure that it is consistent with community values and norms.
Bubble shape oscillations of finite amplitude

Shape oscillations arising from the spherical instability of an oscillating bubble can be sustained in a stationary acoustic field. Describing such a steady state requires that nonlinear saturation effects are accounted for to counteract the natural exponential growth of the instability. In this paper, we analyse the establishment of finite-amplitude bubble shape oscillations as a consequence of nonlinear interactions between spherical and non-spherical modes. The set of coupled dynamical equations describing the volume pulsation and the shape oscillations is solved using a perturbation technique based on the Krylov–Bogoliubov method of averaging. A set of first-order differential equations governing the slowly varying amplitudes and phases of the different modes allows us to reproduce the exponential growth and subsequent nonlinear saturation of the most unstable, parametrically excited, shape mode. Solving these equations for steady-state conditions leads to analytical expressions of the modal amplitudes and derivations of the conditionally stable and absolutely stable thresholds for shape oscillations. The analysis of the solutions reveals the existence of a hysteretic behaviour, indicating that bubble shape oscillations could be sustained for acoustic pressures below the classical parametric threshold.
An Approach to Macromodeling of MEMS for Nonlinear Dynamic Simulation

The method of linear normal mode summation is utilized here to construct reduced order macromodels to perform the nonlinear dynamic analysis of microelectromechanical devices and systems (MEMS). The validity of the approach is illustrated with the example of a doubly clamped beam under electrostatic actuation. Using the reduced order macromodel, it is possible to observe nonlinear effects such as the frequency shift due to a DC bias voltage, and the amplitude-dependence of resonance frequency. The undamped dynamic pull-in voltage is found to be about 9% less than the quasi-static pull-in voltage. The dynamic simulation results of the macromodel are compared with those of the complete model, and it is noted here that linear normal modes provide an adequate basis to carry out the dynamic analysis with enormous computational advantage while not losing accuracy. The simulation results are also validated with the experimentally measured data for the dynamic and static pull-in voltages, and the time to pull in at voltages beyond the pull-in voltage.
Analytic calculations of the spectra of cosmogenic neutrinos

We present an analytic derivation of the diffuse spectra for high energy neutrinos (E>PeV), produced by the propagation of ultra-high energy protons in astrophysical photon backgrounds (cosmogenic neutrinos). We address several scenarios of cosmological transport based on homogeneous distribution of sources with a generic cosmological evolution. By utilizing recent cross-section models, we compute the local neutrino emissivity and finally the expected neutrinos flux at redshift 𝑧 = 0. Beyond its pedagogical value, this result yields essential insights into estimating the uncertainties associated with these predictions. Furthermore, it serves as a reliable benchmark for more advanced calculations conducted using modern numerical codes such as SimProp and CRPropa.
A quantitative detection method for stress corrosion cracks in turbine discs using a BPSO-RBFNN model

Stress corrosion cracks are a huge potential security problem for steam turbine discs, and the quantitative detection of the initial cracks is significant for estimating the safety factor and the service life of the turbine discs. In this paper, the initial corrosion cracks are detected using the ultrasonic phased array, and a binary particle swarm optimization-radial basis function neural network (BPSO-RBFNN) model is proposed for the quantitative evaluation of small cracks in the early corrosion stage. The echoes reflected from the small cracks with different depths and orientation are acquired using the ultrasonic phased array. From these echo signals, some related features in the time domain, frequency domain and about nonlinearity are extracted. A quantitative analysis model based on the BPSO algorithm and the RBFNN is investigated for the feature optimization and the quantitative evaluation of the crack depth and orientation. The results showed that this method was effective in this quantitative evaluation work.
TiO2 and Ag2O Decorated GeTe Monolayer as a Highly Sensitive Material for SF6 Decomposition Species Detection

The effective detection and maintenance of electrical equipment failures in circuit maintenance is crucial in reducing public property losses. Therefore, the research and development of a highly sensitive SF6 decomposition detection device is of great significance. Here, in this article, the adsorption behavior and sensing characteristics of TiO2, Ag<inline-formula> <tex-math notation="LaTeX">$_{{2}}\text{O}$ </tex-math></inline-formula> doped GeTe monolayer for SF6 decomposition species (<inline-formula> <tex-math notation="LaTeX">$\text{H}_{{2}}\text{S}$ </tex-math></inline-formula>, SO2) have been studied through first-principles analysis. The results show that the doping of Ag<inline-formula> <tex-math notation="LaTeX">$_{{2}}\text{O}$ </tex-math></inline-formula> and TiO2 enhances the conductivity of GeTe monolayer by 14.9% and 16.8%, respectively, and increases the adsorption energy of the doped GeTe monolayer for <inline-formula> <tex-math notation="LaTeX">$\text{H}_{{2}}\text{S}$ </tex-math></inline-formula> and SO2 by 214% (TiO2–GeTe–<inline-formula> <tex-math notation="LaTeX">$\text{H}_{{2}}\text{S}$ </tex-math></inline-formula>), 184% (TiO2–GeTe–SO2), 361% (Ag<inline-formula> <tex-math notation="LaTeX">$_{{2}}\text{O}$ </tex-math></inline-formula>–GeTe–<inline-formula> <tex-math notation="LaTeX">$\text{H}_{{2}}\text{S}$ </tex-math></inline-formula>), and 191% (Ag<inline-formula> <tex-math notation="LaTeX">$_{{2}}\text{O}$ </tex-math></inline-formula>–GeTe–SO2), respectively. The adsorption energy with proper size makes both doped GeTe monolayers highly sensitive and selective with a rapid recovery time, which theoretically solves the problem of industrial recycling of sensing materials. The research results provide a theoretical basis for the material selection of a highly selective sensor for detecting SF6 decomposition products.
Point2Lane: Polyline-Based Reconstruction With Principal Points for Lane Detection

In this work, we observed that a nonlinear line could be expressed with a set of linear lines. We propose a novel lane detection method with polyline-based reconstruction based on this hypothesis. We define the optimal principal points with a new metric, the principal score, to generate the polyline. According to the principal score, we select principal points having a high influence on lane reconstruction and simply reproduce the target lane by connecting them. Additionally, conventional methods predict a fixed number of parameters to express each lane. However, this can limit an ability to represent a lane curvature and cause inaccurate detection results. Therefore, we set the number of principal points to be dynamically changed depending on the lane curvature to solve this problem. This allows the model to make flexible detection results reflecting the characteristics of each lane. We also propose a training strategy with a new piece-wise linear equation-based loss function. With this strategy, the model is fine-tuned to predict the principal points representing the curved parts of the lane well. Last, we propose a spatial context-aware feature flip fusion module to exploit the symmetric property of road images. This module helps the model selectively utilize the spatial context in the flipped feature map based on the lane density. We effectively reduce the adverse effects, especially the false positives of the existing feature flip fusion module misaligned on asymmetrical images. The experiments show that the proposed method provides competitive lane detection results compared to state-of-the-art methods.
Information Retrieval Using Non - negative Matrix Factorization

The Vector Space Model (VSM) is a conventional information retrieval model, which represents a document collection by a term-by-document matrix. Since term-by-document matrices are usually high-dimensional and sparse, they are susceptible to noise and are also difficult to capture the underlying semantic structure. Dimensionality reduction is a way to overcome these problems. Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are popular techniques for dimensionality reduction based on matrix decomposition, however they contain both positive and negative values in the decomposed matrices. In the work described here, we use Non-negative Matrix Factorization (NMF) for dimensionality reduction of the vector space model. Since matrices decomposed by NMF only contain non-negative values, the original data are represented by only additive, not subtractive, combinations of the basis vectors. This characteristic of parts-based representation is appealing because it reflects the intuitive notion of combining parts to form a whole. Using MEDLINE collection, we experimentally showed that NMF offers great improvement over the vector space model.
Ge-implantation-enhanced defect-state absorption in C-band silicon waveguide photodiode monitors

We demonstrate Ge-implantation-enhanced defect-state absorption in the C-band in a 225-μm-long silicon waveguide photodiode, with a responsivity of ~54.8 mA/W/mm and a 1 nA dark current upon -3 V bias after a 10-minute 350°C annealing.
Effect of Modifier Form on Mechanical Properties of Hypoeutectic Silumin

Aluminum–silicon alloys require modification due to their coarse-grained microstructures and resulting low strength properties. So far, research into the modification process has focused on the use of various chemical components and technological processes, the tasks of which are to refine the microstructure and, thus, increase the mechanical properties of the alloy. In this paper, the answer to the question of whether the form of the modifier influences the modification effect of the hypoeutectic silumin will be found. The tests were carried out using the popular silumin AlSi7Mg. To answer our research question, the alloy was modified under comparable conditions using the following elements: Ti, B, and master alloys AlTi1.5 and AlB1.5. Modifiers in the form of Sr and master alloy AlSr1.5 were also used. All mentioned modifiers were produced and introduced into the liquid alloy in the form of a powder and a rod. Master alloys AlSr1.5 were also produced via cooling from the liquid state through cooling in air and the second variant at a speed of 200 °C/s (in the form of powder and a thin strip). The microstructure and mechanical properties were analyzed based on the following measures: tensile strength, elongation, and hardness of silumin. Based on the conducted research, it was found that the form of the modifier also affects the modification effect visible in the form of changes in the microstructure and mechanical properties. For the powder-modified alloy, greater fineness in the eutectic phase (α and B phases) and an increase in all analyzed mechanical properties were obtained.
Kalman on dynamics and contro, Linear System Theory, Optimal Control, and Filter

Rudolf Emil Kalman (R.E.K.) passed away on July, 2nd, 2016. Among contemporary economists Kalman is mainly remembered for his filter, an algorithm that allows recursive estimation of unobserved time varying variables in a system. However, he has also a key part on the whole of recursive macroeconomic theory as is notably expressed by Lars Ljungqvist's and Thomas Sargent's book [Ljunqvist and Sargent, 2012]. Our paper is a contribution to show the links between Kalman's works on filtering, linear quadratic optimal control, and system theory. We also provide a model on cooperative advertising to show that Kalman's works on dynamics and control can be useful in macroeconomics as in microeconomics, a domain where his contributions seem to be unfortunately less used.
Investigating the Physical Mechanisms of Blazar Emission: A Multi-Zone Emission Model

Blazars are a type of active galactic nuclei that exhibit highly variable, non-thermal emission across the electromagnetic spectrum. This study presents an expanding emission model to investigate the physical processes leading to the blazar multi-wavelength emission. We explore the role of external photon fields in producing their high-energy emission and study the particle acceleration timescale to model their flaring activity. We aim to investigate the correlations between the flux at different wavelengths to gain insight into the physical properties of these intriguing astrophysical objects
Aloe-Emodin Ameliorates Cecal Ligation and Puncture-Induced Sepsis

Sepsis remains a major challenge owing to its severe adverse effects and high mortality, against which specific pharmacological interventions with high efficacy are limited. Mitigation of hyperactive inflammatory responses is a key factor in enhancing the likelihood of survival in patients with sepsis. The Aloe genus has several health benefits, including anti-inflammatory properties. The toxicological implications of aloe-emodin (AE), extracted from various Aloe species, remain uncertain in clinical contexts. However, AE has been shown to inhibit inflammatory responses in lipopolysaccharide-induced mice, indicating its potential as a therapeutic approach for sepsis treatment. Nonetheless, there is a paucity of data regarding the therapeutic benefits of AE in the widely recognized cecal ligation and puncture (CLP)-induced sepsis model, which is commonly used as the gold standard model for sepsis research. This study demonstrates the potential benefits of AE in the treatment of CLP-induced sepsis and investigates its underlying mechanism, along with the efficacy of postoperative AE treatment in mice with CLP-induced sepsis. The results of this study suggest that AE can mitigate sepsis in mice by diminishing systemic inflammation and regulating the gut microbiota. The study provides novel insights into the molecular mechanisms underlying the anti-inflammatory effects of AE.
Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter

The power circuit of AC voltage controller capable of operating at a leading, lagging, and unity power factor is studied by a lot of researchers in the literature. Circuits working with high switching frequency are known as power factor correctors (PFCs). The single-phase boost converter has become the most popular topology for power factor correction (PFC) in general purpose power supplies. Power factor correction circuit provides conventional benefits to electric power systems. The benefits are the reduction of power factor penalty and utility bill and power loss. Therefore, a boost converter power factor correction scheme is presented in this paper. A PI, fuzzy logic PI and fractional order PI (FOPI) controllers are used to fix an active shaping of input current of the circuit and to improve the power factor. The controller parameters (coefficients) are optimized using the Particle Swarm Optimization (PSO) algorithm. Average current mode control (ACMC) method is used in the circuit. The converter circuit consists of a single-phase bridge rectifier, boost converter, transformer and load. A mathematical model of the plant is required to design the PI controller. A model for power factor correction circuit is formed in MATLAB/Simulink toolbox and a filter is designed to reduce THD value. The proposed model is simulated using a combination of PI, fuzzy logic and FOPI controllers. The control scheme is applied to 600 Watt PFC boost converter to get 400 Volt DC output voltage and 0.99 power factor. The input voltage is 230 VRMS with 50 Hz. The combination of FOPI and PI controller has the best solution to control the power factor according to PI and fuzzy controllers.
Supernova Cosmology and How to Talk about It: New Approaches to Cosmological Parameter Inference with Type Ia Supernovae and an Assessment of the Education and Public Outreach Program of the Dark Energy Survey

The discovery of the accelerating expansion of the Universe launched a new chapter in modern cosmology. Evidence for this accelerating expansion was first observed using Type Ia supernovae, which are brilliant, standardizable explosions that can be detected at large distances and used to infer cosmological parameters. New surveys are being designed to detect thousands of Type Ia supernovae, ushering in an era where parameter inference is no longer limited by statistics, but by systematic uncertainties. One of these systematics which is not well understood is the progenitor and progenitor environment, which can be investigated by studying properties of the supernova host galaxy. In this dissertation, I use the three-year sample of photometrically-classified and spectroscopically-confirmed Type Ia supernovae from Sloan Digital Sky Survey-II Supernova Survey to explore correlations between supernova luminosity and host-galaxy mass, metallicity, and star-formation rate. Observations suggest that such correlations should be incorporated to improve the standardization of Type Ia supernova luminosities. As such, new techniques for parameter inference will need to accommodate increasingly large samples of supernovae and a variety of standardization models. In this dissertation, I also introduce the BAyesian hierarchical Modeling with BIased Simulations (BAMBIS) algorithm, a novel approach to parameter inference using Type Ia supernovae which can, in principle, include systematics such as host-galaxy correlations in a robust statistical framework. In addition to offering new scientific research opportunities, the quest to understand the evolution of the cosmos brings excellent opportunities for astronomers to engage in science education and public outreach (EPO). I present an analysis of the Dark Energy Survey EPO program, a unique large-scale astronomy EPO initiative organized and led entirely by professional astronomers. In this analysis, I detail the development of the EPO program as well as analyze the strengths and weaknesses of a subset of specific initiatives. I also discuss scientists' reported methods of communicating science with the public. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Physics & Astronomy First Advisor Masao Sako This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/2640