Improved preconcentration workflow for organic explosive traces in aqueous samples using solvent-assisted dispersive solid-phase extraction.

The present study deals with the development of a solvent-assisted dispersive solid phase extraction method for the extraction of HMX, RDX, and TNT from aqueous samples. Benzophenone and methanol were selected as explosives sorbent and dispersive solvent respectively. Extraction parameters like pH, extraction time, amount of sorbent, volume and type of the disperser solvent and centrifuge time were optimized. Dispersion of 0.5 mL dispersive solution (4% (w/v) benzophenone in methanol) was performed by injection into the 5 mL aqueous sample (pH=7) using a 1.0 mL syringe. After centrifuge, the extracted explosives were analyzed by high performance liquid chromatography with ultraviolet detection (HPLC-Uv). The results indicated that the linear ranges with the correlation coefficients of 0.99 ≤ R2 were 1.6-204.6 µg L-1, 1.4-213.7 µg L-1 and 1.3-225.9 µg L-1 for HMX, RDX and TNT respectively. The limit of detection and limit of quantification obtained for each explosive were: 0.3 µg L-1 and 0.8 µg L-1 for HMX, 0.3 µg L-1 and 0.9 µg L-1 for RDX and 0.2 µg L-1 and 0.7 µg L-1 for TNT. Finally, the practical applicability of the developed method was evaluated for the extraction of some organic explosives in water samples followed their determination by HPLC-Uv.

Variable-order porous media equations: Application on modeling the S&P500 and Bitcoin price return.

This article reveals a specific category of solutions for the 1+1 variable order (VO) nonlinear fractional Fokker-Planck equations. These solutions are formulated using VO q-Gaussian functions, granting them significant versatility in their application to various real-world systems, such as financial economy areas spanning from conventional stock markets to cryptocurrencies. The VO q-Gaussian functions provide a more robust expression for the distribution function of price returns in real-world systems. Additionally, we analyzed the temporal evolution of the anomalous characteristic exponents derived from our study, which are associated with the long-term (power-law) memory in time series data and autocorrelation patterns.

Sandpiles subjected to sinusoidal drive.

This paper considers a sandpile model subjected to a sinusoidal external drive with the period T. We develop a theoretical model for the Green's function in a large T limit, which predicts that the avalanches are anisotropic and elongated in the oscillation direction. We track the problem numerically and show that the system additionally shows a regime where the avalanches are elongated in the perpendicular direction with respect to the oscillations. We find a crossover between these two regimes. The power spectrum of avalanche size and the grains wasted from the parallel and perpendicular directions are studied. These functions show power-law behavior in terms of the frequency with exponents, which run with T.

Edwards-Wilkinson depinning transition in fractional Brownian motion background.

There are various reports about the critical exponents associated with the depinning transition. In this study, we investigate how the disorder strength present in the support can account for this diversity. Specifically, we examine the depinning transition in the quenched Edwards-Wilkinson (QEW) model on a correlated square lattice, where the correlations are modeled using fractional Brownian motion (FBM) with a Hurst exponent of H.We identify a crossover time [Formula: see text] that separates the dynamics into two distinct regimes: for [Formula: see text], we observe the typical behavior of pinned surfaces, while for [Formula: see text], the behavior differs. We introduce a novel three-variable scaling function that governs the depinning transition for all considered H values. The associated critical exponents exhibit a continuous variation with H, displaying distinct behaviors for anti-correlated ([Formula: see text]) and correlated ([Formula: see text]) cases. The critical driving force decreases with increasing H, as the host medium becomes smoother for higher H values, facilitating fluid mobility. This fact causes the asymptotic velocity exponent [Formula: see text] to increase monotonically with H.

Minimally invasive approach in symptomatic aberrant right subclavian artery treatment.

INTRODUCTION AND IMPORTANCE: Anomalous right subclavian artery (ARSA) represents an uncommon anatomical deviation concerning the genesis of the right subclavian artery. As the predominant embryological irregularity of the aortic arch, it is clinically recognized as arteria lusoria (AL). CASE PRESENTATION: This study, describe the instance of a 22-year-old female exhibiting a non-aneurysmal, symptomatic anomalous right subclavian artery (ARSA) coursing posteriorly to the esophagus, as evidenced by thoracic computed tomography (CT) imaging. CLINICAL DISCUSSION: As an attractive option, a minimally invasive surgical method was used to treat the patient, and the anomalous vessel was closed from the closest location to its origin in the aortic arch during a short time thoracoscopic surgery. DISCUSSION, CONCLUSION: Compared to the common surgical methods to treat this anomaly, the complications and morbidity resulting from this method are much less and the length of stay in the hospital is shorter and the results are acceptable.

Mucosal vs. cutaneous advancement flaps for the treatment of chronic anal fissures: a randomized clinical trial.

PURPOSE: The aim of this study was to compare two surgical treatment methods for chronic anal fissures (CAF), mucosal advancement flap anoplasty (MAFA) and cutaneous advancement flap anoplasty (CAFA). METHODS: A randomized, blinded clinical trial was conducted on patients with CAF refractory to medical treatment referred to a tertiary-level hospital between January 2021 and December 2022. The patients were assigned to two groups by block randomization and were compared in terms of outcome, pain reduction, and complications. RESULTS: There were 30 patients (male to female ratio 2:3, median age 42 years [range 25-59 years]). Both techniques reduced anal pain significantly (p = 0.001); however, there were no significant differences between MAFA and CAFA groups in recurrence, duration of healing, postoperative pain, and postoperative bleeding. No patient suffered from fecal incontinence (Wexner score = 0) or flap necrosis postoperatively. Only two patients in the MAFA group (1 and 3 months after surgery) and one patient in the CAFA group (2 months after surgery) had recurrence (total recurrence rate = 10%, healing rate = 90%). All of the patients were satisfied with their surgical results. CONCLUSION: Mucosal and cutaneous anal advancement flap techniques are effective and comparable surgical procedures for the treatment of chronic anal fissures with minimal complications, fast healing process, and minimal postoperative pain and complications. CLINICAL TRIAL ID: IRCT20120129008861N4 ( www.irct.ir ).

Relation between the degree and betweenness centrality distribution in complex networks.

The centrality measures, like betweenness b and degree k in complex networks remain fundamental quantities helping to classify them. It is realized from Barthelemy's paper [Eur. Phys. J. B 38, 163 (2004)10.1140/epjb/e2004-00111-4] that the maximal b-k exponent for the scale-free (SF) networks is η_{max}=2, belonging to SF trees, based on which one concludes δ≥γ+1/2, where γ and δ are the scaling exponents for the distribution functions of the degree and the betweenness centralities, respectively. This conjecture was violated for some special models and systems. Here we present a systematic study on this problem for visibility graphs of correlated time series, and show evidence that this conjecture fails in some correlation strengths. We consider the visibility graph of three models: two-dimensional Bak-Tang-Weisenfeld (BTW) sandpile model, one-dimensional (1D) fractional Brownian motion (FBM), and 1D Levy walks, the two latter cases are controlled by the Hurst exponent H and the step index α, respectively. In particular, for the BTW model and FBM with H≲0.5, η is greater than 2, and also δ<γ+1/2 for the BTW model, while the Barthelemy's conjecture remains valid for the Levy process. We assert that the failure of the Barthelemy's conjecture is due to large fluctuations in the scaling b-k relation resulting in the violation of hyperscaling relation η=γ-1/δ-1 and emergent anomalous behavior for the BTW model and FBM. Universal distribution function of generalized degree is found for these models which have the same scaling behavior as the Barabasi-Albert network.

Stochastic and deterministic dynamics in networks with excitable nodes.

Networks of excitable systems provide a flexible and tractable model for various phenomena in biology, social sciences, and physics. A large class of such models undergo a continuous phase transition as the excitability of the nodes is increased. However, models of excitability that result in this continuous phase transition are based implicitly on the assumption that the probability that a node gets excited, its transfer function, is linear for small inputs. In this paper, we consider the effect of cooperative excitations, and more generally the case of a nonlinear transfer function, on the collective dynamics of networks of excitable systems. We find that the introduction of any amount of nonlinearity changes qualitatively the dynamical properties of the system, inducing a discontinuous phase transition and hysteresis. We develop a mean-field theory that allows us to understand the features of the dynamics with a one-dimensional map. We also study theoretically and numerically finite-size effects by examining the fate of initial conditions where only one node is excited in large but finite networks. Our results show that nonlinear transfer functions result in a rich effective phase diagram for finite networks, and that one should be careful when interpreting predictions of models that assume noncooperative excitations.

Immune-mediated necrotizing myopathy following vaccination with the AstraZeneca (AZD1222) COVID-19 vaccine: A case report and brief review.

Immune-Mediated Necrotizing Myopathy (IMNM) after vaccination has been reported previously, however it is rare after COVID-19 vaccination. We report the first case of IMNM two weeks after vaccination with the AstraZeneca (AZD1222) COVID-19 vaccine. There was a probable temporal relationship between the COVID-19 vaccination and the development of IMNM due to lack of known causative factors for IMNM. This may have been due to 1) autoimmunity directly caused by the vaccine, 2) exacerbation of autoimmunity triggered by the vaccine or 3) autoimmune syndrome triggered by the vaccine adjutants. Further studies are needed to assess the underlying mechanisms.

Factors influencing international collaboration on the prevention of COVID-19.

OBJECTIVES: COVID-19 has spread rapidly throughout the world, which has highlighted the importance of collaboration between countries to prevent further transmission of the virus. This review aims to identify the factors that influence international collaboration between policymakers for COVID-19 prevention and consider strategies to manage pandemics in the future. STUDY DESIGN: A scoping review was conducted using the Arksey and O'Malley framework for scoping reviews. METHODS: A literature search was performed across PubMed, Google Scholar, Scopus and Embase databases using relevant keywords. The initial search identified 1010 articles; after selection criteria were applied, 28 studies were included in the review. RESULTS: Most of the selected articles were literature reviews, and China had the greatest contribution of articles to this study. The following seven key categories influencing international collaboration were identified: political, structure, infrastructure, leadership and governance, knowledge and information sharing, community engagement, and process/action. CONCLUSION: Leadership and governance was the most important factor identified in international collaboration between countries. In addition, knowledge and information sharing were seen to help avoid repetition of negative situations experienced in other countries. Moreover, controlling COVID-19 on a global scale is more likely to be achieved when there are sufficient structures and resources and when appropriate communication between countries, health systems and communities is used. This collaboration can also greatly benefit low- and middle-income countries where resources and expertise are often limited.

EventHD: Robust and efficient hyperdimensional learning with neuromorphic sensor.

Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Hyper-Dimensional Computing (HDC) has shown promising results in enabling efficient and robust cognitive learning. In this study, we exploit HDC as an alternative computational model that mimics important brain functionalities toward high-efficiency and noise-tolerant neuromorphic computing. We present EventHD, an end-to-end learning framework based on HDC for robust, efficient learning from neuromorphic sensors. We first introduce a spatial and temporal encoding scheme to map event-based neuromorphic data into high-dimensional space. Then, we leverage HDC mathematics to support learning and cognitive tasks over encoded data, such as information association and memorization. EventHD also provides a notion of confidence for each prediction, thus enabling self-learning from unlabeled data. We evaluate EventHD efficiency over data collected from Dynamic Vision Sensor (DVS) sensors. Our results indicate that EventHD can provide online learning and cognitive support while operating over raw DVS data without using the costly preprocessing step. In terms of efficiency, EventHD provides 14.2× faster and 19.8× higher energy efficiency than state-of-the-art learning algorithms while improving the computational robustness by 5.9×.

Memory-inspired spiking hyperdimensional network for robust online learning.

Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivate their combination. With the help of the classical psychological model on memory, we propose SpikeHD, the first framework that fundamentally combines Spiking neural network and hyperdimensional computing. SpikeHD generates a scalable and strong cognitive learning system that better mimics brain functionality. SpikeHD exploits spiking neural networks to extract low-level features by preserving the spatial and temporal correlation of raw event-based spike data. Then, it utilizes HDC to operate over SNN output by mapping the signal into high-dimensional space, learning the abstract information, and classifying the data. Our extensive evaluation on a set of benchmark classification problems shows that SpikeHD provides the following benefit compared to SNN architecture: (1) significantly enhance learning capability by exploiting two-stage information processing, (2) enables substantial robustness to noise and failure, and (3) reduces the network size and required parameters to learn complex information.

A self-organized critical model and multifractal analysis for earthquakes in Central Alborz, Iran.

This paper is devoted to a phenomenological study of the earthquakes in central Alborz, Iran. Using three observational quantities, namely the weight function, the quality factor, and the velocity model in this region, we develop a modified dissipative sandpile model which captures the main features of the system, especially the average activity field over the region of study. The model is based on external stimuli, the location of which is chosen (I) randomly, (II) on the faults, (III) on the low active points, (IV) on the moderately active points, and (V) on the highly active points in the region. We uncover some universal behaviors depending slightly on the method of external stimuli. A multi-fractal detrended fluctuation analysis is exploited to extract the spectrum of the Hurst exponent of the time series obtained by each of these schemes. Although the average Hurst exponent depends slightly on the method of stimuli, we numerically show that in all cases it is lower than 0.5, reflecting the anti-correlated nature of the system. The lowest average Hurst exponent is found to be associated with the case (V), in such a way that the more active the stimulated sites are, the lower the average Hurst exponent is obtained, i.e. the large earthquakes are more anticorrelated. Moreover, we find that the activity field achieved in this study provide information about the depth and topography of the basement, and also the area that can potentially be the location of the future large events. We successfully determine a high activity zone on the Mosha Fault, where the mainshock occurred on May 7th, 2020 (M[Formula: see text] 4.9).

Self-similar but not conformally invariant traces obtained by modified Loewner forces.

The two-dimensional Loewner exploration process is generalized to the case where the random force is self-similar with positively correlated increments. We model this random force by a fractional Brownian motion with Hurst exponent H≥1/2≡H_{BM}, where H_{BM} stands for the one-dimensional Brownian motion. By manipulating the deterministic force, we design a scale-invariant equation describing self-similar traces which lack conformal invariance. The model is investigated in terms of the "input diffusivity parameter" κ, which coincides with the one of the ordinary Schramm-Loewner evolution (SLE) at H=H_{BM}. In our numerical investigation, we focus on the scaling properties of the traces generated for κ=2,3, κ=4, and κ=6,8 as the representatives, respectively, of the dilute phase, the transition point, and the dense phase of the ordinary SLE. The resulting traces are shown to be scale invariant. Using two equivalent schemes, we extract the fractal dimension, D_{f}(H), of the traces which decrease monotonically with increasing H, reaching D_{f}=1 at H=1 for all κ values. The left passage probability (LPP) test demonstrates that, for H values not far from the uncorrelated case (small ε_{H}≡H-H_{BM}/H_{BM}), the prediction of the ordinary SLE is applicable with an effective diffusivity parameter κ_{eff}. Not surprisingly, the κ_{eff}'s do not fulfill the prediction of SLE for the relation between D_{f}(H) and the diffusivity parameter.

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning.

Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization. Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information in high-dimensional space. GrapHD defines an encoding method representing complex graph structure while supporting both weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.

Combined Molecular and Biochemical Identification of Alpha Toxin in Local Isolated Clostridium novyi from the Sheep Liver.

Clostridium novyi (C. novyi) causes deadly Black disease in sheep and rarely in other animals. Alpha toxin (α-toxin), the most apparent pathogen of this disease, is produced by C. novyi type B. Economic damages of C. novyi include sheep mortality costs, depreciation of affected farms, and health problems with infected carcasses. The identification of C. novyi and isolation of its pathogens by conventional methods is a time-consuming process, necessitating a simple and rapid method for isolating and detecting pathogenic C. novyi. Therefore, this study aimed to molecularly identify α-toxin in local C. novyi isolates from the sheep livers. In this study, 75 livers suspected of Black disease were sampled. The samples of the liver were cultured under anaerobic conditions. Some of the cultured colonies were used in biochemical tests. For molecular confirmation, the DNA of isolates was extracted, and the isolates were confirmed by the polymerase chain reaction (PCR) on the liver tissue and cultured samples using specific α-toxin primers. The PCR on α-toxin produced a band in the range of 609 bp, indicating that the samples belonged to C. novyi. According to the results, of 75 isolates, 18 isolates were confirmed as C. novyi. C. novyi type B was isolated from the liver and confirmed by biochemical and molecular characterization. The PCR assay ensured a sensitive and specific tool for the detection of C. novyi in the samples.

Self-repelling bipedal exploration process.

A self-repelling two-leg (biped) spider walk is considered where the local stochastic movements are governed by two independent control parameters ß_{d} and ß_{h}, so that the former controls the distance (d) between the legs positions, and the latter controls the statistics of self-crossing of the traversed paths. The probability measure for local movements is supposed to be the one for the "true self-avoiding walk" multiplied by a factor exponentially decaying with d. After a transient behavior for short times, a variety of behaviors have been observed for large times depending on the value of ß_{d} and ß_{h}. Our statistical analysis reveals that the system undergoes a crossover between two (small and large ß_{d}) regimes identified in large times (t). In the small ß_{d} regime, the random walkers (identified by the position of the legs of the spider) remain on average in a fixed nonzero distance in the large time limit, whereas in the second regime (large ß_{d}), the absorbing force between the walkers dominates the other stochastic forces. In the latter regime, d decays in a power-law fashion with the logarithm of time. When the system is mapped to a growth process (represented by a height field which is identified by the number of visits for each point), the roughness and the average height show different behaviors in two regimes, i.e., they show a power law with respect to t in the first regime and logt in the second regime. The fractal dimension of the random walker traces and the winding angle are shown to consistently undergo a similar crossover.

Persistent homology of fractional Gaussian noise.

In this paper, we employ the persistent homology (PH) technique to examine the topological properties of fractional Gaussian noise (fGn). We develop the weighted natural visibility graph algorithm, and the associated simplicial complexes through the filtration process are quantified by PH. The evolution of the homology group dimension represented by Betti numbers demonstrates a strong dependency on the Hurst exponent (H). The coefficients of the birth and death curves of the k-dimensional topological holes (k-holes) at a given threshold depend on H which is almost not affected by finite sample size. We show that the distribution function of a lifetime for k-holes decays exponentially and the corresponding slope is an increasing function versus H and, more interestingly, the sample size effect completely disappears in this quantity. The persistence entropy logarithmically grows with the size of the visibility graph of a system with almost H-dependent prefactors. On the contrary, the local statistical features are not able to determine the corresponding Hurst exponent of fGn data, while the moments of eigenvalue distribution (M_{n}) for n≥1 reveal a dependency on H, containing the sample size effect. Finally, the PH shows the correlated behavior of electroencephalography for both healthy and schizophrenic samples.

On the prevalence of flow instabilities from high-fidelity computational fluid dynamics of intracranial bifurcation aneurysms.

High-fidelity computational fluid dynamics (HF-CFD) has revealed the potential for high-frequency flow instabilities (aka "turbulent-like" flow) in intracranial aneurysms, consistent with classic in vivo and in vitro reports of bruits and/or wall vibrations. However, HF-CFD has typically been performed on limited numbers of cases, often with unphysiological inflow conditions or focused on sidewall-type aneurysms where flow instabilities may be inherently less prevalent. Here we report HF-CFD of 50 bifurcation aneurysm cases from the open-source Aneurisk model repository. These were meshed using quadratic finite elements having an average effective spatial resolution of 0.065 mm, and solved under physiologically-pulsatile flow conditions using a well-validated, minimally-dissipative solver with 20,000 time-steps per cardiac cycle Flow instability was quantified using the recently introduced spectral power index (SPI), which quantifies, from 0 to 1, the power associated with velocity fluctuations above those of the driving inflow waveform. Of the 50 cases, nearly half showed regions within the sac having SPI up to 0.5, often with non-negligible power into the 100's of Hz, and roughly 1/3 had sac-averaged SPI > 0.1. High SPI did not significantly predict rupture status in this cohort. Proper orthogonal decomposition of cases with highest SPIavg revealed time-varying energetics consistent with transient turbulence. Our reported prevalence of high-frequency flow instabilities in HF-CFD modelling of aneurysms suggests that care must be taken to avoid routinely overlooking them if we are to understand the highly dynamic mechanical forces to which some aneurysm walls may be exposed, and their prevalence in vivo.

Self-organized criticality in cumulus clouds.

The shape of clouds has proven to be essential for classifying them. Our analysis of images from fair weather cumulus clouds reveals that, in addition to turbulence, they are driven by self-organized criticality. Our observations yield exponents that support the fact the clouds, when projected to two dimensions, exhibit conformal symmetry compatible with c=-2 conformal field theory. By using a combination of the Navier-Stokes equation, diffusion equations, and a coupled map lattice, we successfully simulated cloud formation, and obtained the same exponents.