Miracle-dz/UniFAR-FTUs huggingface dataset:
Mercury in Kansas Fish: Levels, Patterns, and Risk-Based Safe Consumption Limits for Mercury Sensitive Individuals

In wet environments a portion of ambient inorganic mercury (Hg) is transformed to methylmercury (MeHg); a neurotoxin that readily concentrates in fish. Public health entities monitor Hg levels in fish to inform consumers of the types and amounts of fish that are safe to eat. In this study, monitoring data collected from stream and lakes across Kansas were used to summarize fillet tissue total mercury (THg = MeHg + Hg) levels in 36 fish species and hybrids. Data associated with eleven food fish taxa (genus and species) were presented in detail and statewide risk-based monthly safe consumption limits (SCLs) protective of Hg sensitive individuals were calculated, including two size categories (< or ≥ 508 mm) for larger growing taxa. Linear mixed effects models (LMEMs) were used to generate least squares (LS) mean fish THg values for between habitat (lakes and streams) and within habitat (waterbody size, ecoregion, and major river basin) categorical variables, and for individual sample sites. Sample site LS means were used to identify potential fish THg hotspots among lakes (LS means ≥ 90th percentile) and streams (Getis-Ord Gi* statistic). Across all samples (n = 2,106) a more than 200-fold difference existed between the highest (2.1 mg/kg) and lowest THg (0.01 mg/kg) concentrations, but most samples were below the Kansas Department of Health and Environment's (KDHE) human health screening level (HSL) (0.23 mg/kg). Statewide safe consumption limits ranged from 1 meal/month in ≥ 508 mm flathead catfish to 8 meals/mo. in crappie spp. Least squares mean fish THg was significantly higher in streams than lakes, and significantly higher in small lakes (< 10 surface acres) than larger lakes. Stream size did not affect fish THg levels. In both lakes and streams, ecoregion and major river basin significantly affected fish THg levels with higher levels more prevalent in eastern geographic areas. Lakes identified as potential fish THg hotspots were widely distributed throughout the study area, but all stream hotspot sites were clustered in the southeast portion of the state.
MOF-Based Mycotoxin Nanosensors for Food Quality and Safety Assessment through Electrochemical and Optical Methods

Mycotoxins in food are hazardous for animal and human health, resulting in food waste and exacerbating the critical global food security situation. In addition, they affect commerce, particularly the incomes of rural farmers. The grave consequences of these contaminants require a comprehensive strategy for their elimination to preserve consumer safety and regulatory compliance. Therefore, developing a policy framework and control strategy for these contaminants is essential to improve food safety. In this context, sensing approaches based on metal-organic frameworks (MOF) offer a unique tool for the quick and effective detection of pathogenic microorganisms, heavy metals, prohibited food additives, persistent organic pollutants (POPs), toxins, veterinary medications, and pesticide residues. This review focuses on the rapid screening of MOF-based sensors to examine food safety by describing the main features and characteristics of MOF-based nanocomposites. In addition, the main prospects of MOF-based sensors are highlighted in this paper. MOF-based sensing approaches can be advantageous for assessing food safety owing to their mobility, affordability, dependability, sensitivity, and stability. We believe this report will assist readers in comprehending the impacts of food jeopardy exposure, the implications on health, and the usage of metal-organic frameworks for detecting and sensing nourishment risks.
Rapid Convolutional Neural Networks for Gram-Stained Image Classification at Inference Time on Mobile Devices: Empirical Study from Transfer Learning to Optimization

Despite the emergence of mobile health and the success of deep learning (DL), deploying production-ready DL models to resource-limited devices remains challenging. Especially, during inference time, the speed of DL models becomes relevant. We aimed to accelerate inference time for Gram-stained analysis, which is a tedious and manual task involving microorganism detection on whole slide images. Three DL models were optimized in three steps: transfer learning, pruning and quantization and then evaluated on two Android smartphones. Most convolutional layers (≥80%) had to be retrained for adaptation to the Gram-stained classification task. The combination of pruning and quantization demonstrated its utility to reduce the model size and inference time without compromising model quality. Pruning mainly contributed to model size reduction by 15×, while quantization reduced inference time by 3× and decreased model size by 4×. The combination of two reduced the baseline model by an overall factor of 46×. Optimized models were smaller than 6 MB and were able to process one image in <0.6 s on a Galaxy S10. Our findings demonstrate that methods for model compression are highly relevant for the successful deployment of DL solutions to resource-limited devices.
Topological derivative for the nonlinear magnetostatic problem

The topological derivative represents the sensitivity of a domain-dependent functional with respect to a local perturbation of the domain and is a valuable tool in topology optimization. Motivated by an application from electrical engineering, we derive the topological derivative for an optimization problem which is constrained by the quasilinear equation of two-dimensional magnetostatics. Here, the main ingredient is to establish a sufficiently fast decay of the variation of the direct state at scale 1 as $|x|\rightarrow \infty$. In order to apply the method in a bi-directional topology optimization algorithm, we derive both the sensitivity for introducing air inside ferromagnetic material and the sensitivity for introducing material inside an air region. We explicitly compute the arising polarization matrices and introduce a way to efficiently evaluate the obtained formulas. Finally, we employ the derived formulas in a level-set based topology optimization algorithm and apply it to the design optimization of an electric motor.
The Effect of Proline on the Freeze-Drying Survival Rate of Bifidobacterium longum CCFM 1029 and Its Inherent Mechanism

Amino acids, which are important compatible solutes, play a significant role in probiotic lyophilization. However, studies on the functions of Bifidobacterium during freeze-drying are limited. Therefore, in this study, we compared the freeze-drying survival rate of Bifidobacterium longum CCFM 1029 cultivated in different media containing different kinds of compatible solutes. We found that the addition of 21 g/L proline to the culture media substantially improved the freeze-drying survival rate of B. longum CCFM 1029 from 18.61 ± 0.42% to 38.74 ± 1.58%. Interestingly, this change has only been observed when the osmotic pressure of the external culture environment is increased. Under these conditions, we found that proline accumulation in this strain increased significantly. This change also helped the strain to maintain its membrane integrity and the activity of some key enzymes during freeze-drying. Overall, these results show that the addition of proline can help the strain resist a tough environment during lyophilization. The findings of this study provide preliminary data for producers of probiotics who wish to achieve higher freeze-drying survival rates during industrial production.
Potential functions on Grassmannians of planes and cluster transformations

With a triangulation of a planar polygon with $n$ sides, one can associate an integrable system on the Grassmannian of 2-planes in an $n$-space. In this paper, we show that the potential functions of Lagrangian torus fibers of the integrable systems associated with different triangulations glue together by cluster transformations. We also prove that the cluster transformations coincide with the wall-crossing formula in Lagrangian intersection Floer theory.
The Cahn-Hilliard Equation and the Allen-Cahn Equation on Manifolds with Conical Singularities

We consider the Cahn-Hilliard equation on a manifold with conical singularities. We first show the existence of bounded imaginary powers for suitable closed extensions of the bilaplacian. Combining results and methods from singular analysis with a theorem of Clément and Li we then prove the short time solvability of the Cahn-Hilliard equation in Lp-Mellin-Sobolev spaces and obtain the asymptotics of the solution near the conical points. We deduce, in particular, that regularity is preserved on the smooth part of the manifold and singularities remain confined to the conical points. We finally show how the Allen-Cahn equation can be treated by simpler considerations. Again we obtain short time solvability and the behavior near the conical points.
Classification-Based Diverse Workflows Scheduling in Clouds

Cloud workflow scheduling is a typical combinatorial optimization problem and becomes more challenging due to the increasing diversity of workflows. However, current research employs the same scheduling strategy on diverse workflows. In fact, a scheduling strategy may perform well on one workflow but poorly on other workflows owning to the unique characteristics of each workflow. Therefore, in practical applications, selecting suitable scheduling strategies for diverse workflows is a critical issue. To solve it, this paper investigates a diverse workflows scheduling problem and presents a classification-based workflow scheduling framework, which includes workflow parser, workflow classifier, workflow scheduler, resource manager and workflow status tracker, to manage and schedule diverse workflows using suitable strategies. Based on the framework, we propose a classification-based workflow scheduling algorithm (CWSA) to optimize the economic cost of workflow execution under deadline constraints. We conduct the experiments using diverse workflow instances randomly generated from five types of real-world workflows to evaluate the proposed CWSA approach. The results demonstrate the superiority of CWSA compared with the state-of-the-art approaches. Note to Practitioners—Diverse workflows (i.e., many workflows with various types, such as Montage, LIGO and Cybernetics) in clouds are widespread. How to efficiently schedule them in cloud is very important. This paper formulates the diverse workflows scheduling problem and proposes a CWSA to solve it. The basic idea of CWSA is to select a suitable scheduling strategy for each workflow. Specifically, in CWSA, we design a classification neural network architecture that consists of a graph neural network and a fully connected neural network to classify each workflow to its suitable deadline distribute strategy by its characteristics and deadline constraint. Then CWSA obtains the sub-deadlines of tasks and assigns tasks to appropriate VMs (Virtual Machines). Furthermore, as an important factor in workflow scheduling, the transmission time between dependent tasks is introduced into the graph neural network, which improves the classification accuracy.
Improved geodesics for the reduced curvature-dimension condition in branching metric spaces

In this note we show that in metric measure spaces satisfying the reduced curvature-dimension condition CD*(K,N) we always have geodesics in the Wasserstein space of probability measures that satisfy the critical convexity inequality of CD*(K,N) also for intermediate times and in addition the measures along these geodesics have an upper-bound on their densities. This upper-bound depends on the bounds for the densities of the end-point measures, the lower-bound K for the Ricci-curvature, the upper-bound N for the dimension, and on the diameter of the union of the supports of the end-point measures.
Lagrangian Engel Structures

We study the geometry of Engel structures, which are 2-plane fields on 4-manifolds satisfying a generic condition, that are compatible with other geometric structures. A \em{Lagrangian} Engel structure is an Engel 2-plane field on a symplectic 4-manifold for which the 2-planes are Lagrangian with respect to the symplectic structure. We solve the equivalence problems for Lagrangian Engel structures and use the resulting structure equations to classify homogeneous Lagrangian Engel structures. This allows us to determine all compact, homogeneous examples. Compact manifolds that support homogeneous Lagrangian Engel structures are diffeomorphic to quotients of one of a determined list of nilpotent or solvable 4-dimensional Lie groups by co-compact lattices.
HSV-2 Meningitis in a Young Woman Presenting with Acute Tonsillitis

Herpes simplex virus type 2 (HSV‐2) is a common cause of viral meningitis, and the leading etiological agent of recurrent Mollaret’s meningitis. HSV‐2 is the second most common cause of adult viral meningitis in developed countries,[1] occurring even in the absence of clinical genital herpes. [2] In contrast, there are very few case reports of HSV‐2 meningitis from India[3] and this kind of evidence has rarely been documented. We report a case of a young, immunocompetent woman presenting to the hospital with Acute Tonsillitis and later developing meningitis due to HSV‐2 during the course of the hospital stay.
Rigged configurations as tropicalizations of loop Schur functions

We conjecture an explicit formula for the image of a tensor product of Kirillov-Reshetikhin crystals $\bigotimes_{i=1}^m B^{1, s_i}$ under the Kirillov-Schilling-Shimozono bijection. Our conjectured formula is piecewise-linear, where the shapes are given by the tropicalization of cylindric loop Schur functions and the riggings are given by the tropicalization of loop Schur functions. We prove that our formula changes the riggings by the correct amount based upon the time evolution of the corresponding box-ball system. We show that our formula is correct under the column splitting portion of the Kirillov-Schilling-Shimozono bijection and for $B^{1, s}$.
Discrete Aleksandrov solutions of the Monge-Ampere equation

We make two relaxations of the Oliker-Prussner method for the Dirichlet problem for the Monge-Ampere equation. First we relax the convexity requirement and consider mesh functions which are only discrete convex. The second relaxation consists in using a finite stencil. The discrete nonlinear equations are solved with a damped Newton's method. We give two proofs of convergence of the resulting scheme for right-hand side a density, on domains which are convex and not necessarily strictly convex, under the assumption that the boundary data has a continuous convex extension. The first proof is based on the notion of Aleksandrov solution while the second uses viscosity solutions. For more information see https://ejde.math.txstate.edu/conf-proc/26/a2/abstr.html
Biochemical and histopathological evaluations of chronic renal failure rats treated with pluripotent human stem cells

stem cells is the current research approach in the treatment of chronic renal failure (CRF). In the present study, the histopathological and biochemical techniques were used to evaluate stem cells’ (SCs) role in treatment of CRF. Sixty-four rats were divided into eight groups. Group I (GI): rats were injected with doxorubicin (15 mg/kg) to initiate CRF. GII-GVII: rats were injected with doxorubicin and treated with SCs (1x10 6 MSCs or/and 2x10 4 HSCs/rat) with/without growth factors extract (200 µL/rat) and/or immunosuppressor (cyclosporine A, 5 mg/kg/day). GVIII: rats treated with PBS (100 µL/kg/day). Levels of creatinine, urea and uric acid were increased in rats sera after injection with doxorubicin, while blood electrolyte levels of Na, K, P and Mg were decreased. Also, histopathological abnormalities such as hyalinized blood vessels, degenerated hyalinized glomerulus tubules and cell debris in the lumen and degeneration of renal tissues were observed in these rats. After treatment with SCs, all these parameters restore their normal values with regeneration of the damaged cells as demonstrated in histopathology of the treated groups. It can be concluded that, the use of SCs in treatment of kidney diseases is a promising approach and needs more efforts.
$\ast$-operator frame for $End_{\mathcal{A}}^{\ast}(\mathcal{H})$

In this paper, a new notion of frames is introduced: $\ast$-operator frame as generalization of $\ast$-frames in Hilbert $C^{\ast}$-modules introduced by A. Alijani and M. A. Dehghan \cite{Ali} and we establish some results.
A path integral approach to the Langevin equation

We study the Langevin equation with both a white noise and a colored noise. We construct the Lagrangian as well as the Hamiltonian for the generalized Langevin equation which leads naturally to a path integral description from first principles. This derivation clarifies the meaning of the additional fields introduced by Martin, Siggia and Rose in their functional formalism. We show that the transition amplitude, in this case, is the generating functional for correlation functions. We work out explicitly the correlation functions for the Markovian process of the Brownian motion of a free particle as well as for that of the non-Markovian process of the Brownian motion of a harmonic oscillator (Uhlenbeck-Ornstein model). The path integral description also leads to a simple derivation of the Fokker-Planck equation for the generalized Langevin equation.
Phytopharmacological and multi-elemental profiling of potential zingiberaceae species

Zingiberaceae family are used in medicine to treat a variety of human maladies, lessen inflammation, relieve motion sickness, and enhance digestion. In the current study seven Zingiberaceae species, including Zingiber zerumbet, Kaempferia rotunda, Kaempferia galanga, Curcuma zedeoaria, Zingiber rubens, Costus speciosus and Hedychium flavescens were assessed for phytochemical and multi-element composition. All seven species showed a considerable variation proving to be superior in many ways when compared to the nutritional worth of near commercial species, such as ginger and turmeric. The outcome emphasizes the phytochemical and elemental value of lesser-known but equally significant plants with strong therapeutic potential, and also indicates the need to encourage cultivation and protect genetic resources.
Nonoverlapping (delta, gamma)-approximate pattern matching

Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (δ,γ)-distance to give an approximate pattern matching, where the local and the global distances do not exceed δ and γ, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.
On the superconvergence of a hydridizable discontinuous Galerkin method for the Cahn-Hilliard equation

We propose a hydridizable discontinuous Galerkin (HDG) method for solving the Cahn-Hilliard equation. The temporal discretization can be based on either the backward Euler method or the convex-splitting method. We show that the fully discrete scheme admits a unique solution, and we establish optimal convergence rates for all variables in the $L^2$ norm for arbitrary polynomial orders. In terms of the globally coupled degrees of freedom, the scalar variables are superconvergent. Another theoretical contribution of this work is a novel HDG Sobolev inequality that is useful for HDG error analysis of nonlinear problems. Numerical results are reported to confirm the theoretical convergence rates.
UrbanEnQoSPlace: A Deep Reinforcement Learning Model for Service Placement of Real-Time Smart City IoT Applications

Multi-access Edge Computing (MEC) enables IoT applications to place their services in the edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-efficiency. Previous works consider compute requirements, while the IoT and latency/bandwidth per-flow communicate requirements are largely ignored. Moreover, the Smart City domain presents unique challenges – modeling the Urban Smart Things (USTs – urban IoT clients), their connectivity with MEC network, diverse resource requirements (compute, communicate, and IoT) of application services, modeling the federation of multiple MEC providers in a city, which we consider in this article. To address these research gaps, we propose: i) <italic>UrbanEnQoSMDP</italic> – formulation for energy and QoS (latency) optimized service placement for a set of applications in the ‘Urban IoT-Federated MEC-Cloud’ architecture to satisfy applications’ compute, per-flow communicate, and IoT requirements; ii) <italic>‘’<inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives><mml:math><mml:mi>ε</mml:mi></mml:math><inline-graphic xlink:href="bansal-ieq1-3218044.gif"/></alternatives></inline-formula>-greedy with mask”</italic> policy for apriori satisfaction of IoT requirements by shortlisting suitable USTs; iii) <italic>UrbanEnQoSPlace</italic> – proposed multi-action Deep Reinforcement Learning (DRL) model, designed from Dueling Deep-Q Network, that uses the proposed policy to solve the UrbanEnQoSMDP for simultaneously placing all services of an application. Extensive simulation results illustrate efficacy and scalability of proposed model against state-of-the-art DRL algorithms (better convergence, higher rewards, lesser runtime; proposed policy w.r.t fewer violations).
Defects in Cohomological Gauge Theory and Donaldson-Thomas Invariants

Donaldson-Thomas theory on a Calabi-Yau can be described in terms of a certain six-dimensional cohomological gauge theory. We introduce a certain class of defects in this gauge theory which generalize surface defects in four dimensions. These defects are associated with divisors and are defined by prescribing certain boundary conditions for the gauge fields. We discuss generalized instanton moduli spaces when the theory is defined with a defect and propose a generalization of Donaldson-Thomas invariants. These invariants arise by studying torsion free coherent sheaves on Calabi-Yau varieties with a certain parabolic structure along a divisor, determined by the defect. We discuss the case of the affine space as a concrete example. In this case the moduli space of parabolic sheaves admits an alternative description in terms of the representation theory of a certain quiver. The latter can be used to compute the invariants explicitly via equivariant localization. We also briefly discuss extensions of our work to other higher dimensional field theories.