Solving reaction-diffusion equations 10 times faster

The most popular numerical method for solving systems of reaction-diffusion equations continues to be a low order finite-difference scheme coupled with low order Euler time stepping. This paper extends previous 1D work and reports experiments that show that with high--order methods one can speed up such simulations for 2D and 3D problems by factors of 10--100. A short MATLAB code (2/3D) that can serve as a template is included.\ud \ud This work was supported by the Engineering and Physical Sciences Research Council (UK) and by the MathWorks, Inc

Enhancing Instance-Level Image Classification with Set-Level Labels

Instance-level image classification tasks have traditionally relied on single-instance labels to train models, e.g., few-shot learning and transfer learning. However, set-level coarse-grained labels that capture relationships among instances can provide richer information in real-world scenarios. In this paper, we present a novel approach to enhance instance-level image classification by leveraging set-level labels. We provide a theoretical analysis of the proposed method, including recognition conditions for fast excess risk rate, shedding light on the theoretical foundations of our approach. We conducted experiments on two distinct categories of datasets: natural image datasets and histopathology image datasets. Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods. Notably, our algorithm achieves 13% improvement in classification accuracy compared to the strongest baseline on the histopathology image classification benchmarks. Importantly, our experimental findings align with the theoretical analysis, reinforcing the robustness and reliability of our proposed method. This work bridges the gap between instance-level and set-level image classification, offering a promising avenue for advancing the capabilities of image classification models with set-level coarse-grained labels

Predicting Types of Failures in Wireless Sensor Networks Using an Adaptive Neuro-fuzzy Inference System

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (~92%) for all ANFIS models

COVID-19 Crowd Detection

Object detection was introduced by researchers for face detection. Researchers explain how the detected face is divided into minor frames to be recognized by the algorithm. Due to COVID-19 and government regulations, many people face problems going to shopping centers and shop safely. It has been very hard for both the government and the people to manage social distancing. In our study, we developed a system using Raspberry Pi-4 that will detect the distance between people along with counting the number of distance and mask violations. An error message will appear on the screen in red, showing the total number of distance and mask violations, which could later be used by the customer as statistical evidence for better safety precautions

Bone graft donor site infection with a rare organism, aeromonas hydrophila: A typical location, presentation and organism with 2 years follow-up: Case report

Introduction: Aeromonas are Gram-negative bacilli often causing necrotizing fasciitis or sepsis in immunocompromised patients. Aeromonas Hydrophila is most often found in immunocompromised patients or those with burns or aquatic trauma. When patients present with a discharge and infection on bone graft donor site and progressive sepsis, an Aeromonas hydrophila infection should be considered in the differential diagnosis. Presentation of Case: We report here a rare case of Aeromonas hydrophila with surgical site sep- sis/infection in an immunocompromised 69 years old female, with several comorbids. Here we are reporting infection on donor surgical graft site, sparing major surgical site with the implant. After getting culture report of exudates from the wound that grew A. hydrophila, immediate wound debridement and antibiotic beads insertion was performed with appropriate antimicrobial therapy and regular wound dressing. She was followed for around 2 years. Discussion: This is the first report to our knowledge of A. Hydrophila infection in bone graft donor site. Aeromonas most often cause gastrointestinal and soft tissue infections, and bacteremia in immuno- compromised patients. Early surgical intervention is essential to reducing mortality in deep soft tissue infections caused by this organism. Aeromonas have shown resistance to penicillin but are sensitive to other broad-spectrum antibiotics. Conclusion: Early suspicion, diagnosis, and treatment with potent antibiotics are needed to prevent any further complications resulting from infection by this emerging aggressive pathogen

Pan-urologic cancer genomic subtypes that transcend tissue of origin

AbstractUrologic cancers include cancers of the bladder, kidney, prostate, and testes, with common molecular features spanning different types. Here, we show that 1954 urologic cancers can be classified into nine major genomic subtypes, on the basis of multidimensional and comprehensive molecular characterization (including DNA methylation and copy number, and RNA and protein expression). Tissue dominant effects are first removed computationally in order to define these subtypes, which reveal common processes—reflecting in part tumor microenvironmental influences—driving cellular behavior across tumor lineages. Six of the subtypes feature a mixture of represented cancer types as defined by tissue or cell of origin. Differences in patient survival and in the manifestation of specific pathways—including hypoxia, metabolism, NRF2-ARE, Hippo, and immune checkpoint—can further distinguish the subtypes. Immune checkpoint markers and molecular signatures of macrophages and T cell infiltrates are relatively high within distinct subsets of each cancer type studied. The pan-urologic cancer genomic subtypes would facilitate information sharing involving therapeutic implications between tissue-oriented domains.</jats:p

ZnO hollow spheres arrayed molecularly-printed-polymer based selective electrochemical sensor for methyl-parathion pesticide detection

A highly sensitive electrochemical-based detector was fabricated to selectively sense methyl-parathion (MP). A Glassy carbon electrode (GCE) was functionalized with zinc oxide (ZnO) hollow spheres (ZnOHS) and a molecularly imprinted polymer (MIP) to form the developed sensor. Cyclic voltammetry (CV) was performed to synthesize a molecularly imprinted polymeric film on the ZnOHS modified GCE (GCE/ZnOHS) by electropolymerization of functional monomer, l-arginine (L-Arg), and template molecule, MP. The differential pulse voltammetry (DPV) was utilized to evaluate the efficiency of the electrochemical detection of MP under optimal conditions by the proposed sensor. The developed sensor recorded a good performance for detecting MP in the linear range of 5 × 10−9 to 0.1 × 10−4 mol L−1 (R2=0.985) with a detection limit (S/N = 3) of 0.5 × 10−9 mol L−1 and sensitivity of 571 nA/μmolL −1 cm −2. This electrochemical sensing system effectively detects MP in real samples with satisfactory recoveries of 90.4%, 91.9%, 118%, and 96.3% for fresh green beans, strawberry, tomato, and cabbage, respectively. © 2021 Elsevier B.V.1

Application of Stabilized Cefixime-AgNPs-GO Thin Films as Corrosion Inhibitors for Mild Steel Alloy

In this work, the corrosion inhibition of mild steel at ambient conditions by an antibiotic in a solution that contains silver nanoparticles (AgNPs) and graphene oxide (GO) was studied. GO and AGNPs were prepared by one-step simple and ecofriendly method and characterized by different techniques. Different concentrations of the inhibitor were prepared and their inhibition efficiency in acidic media was investigated. The adsorption characteristics of the inhibitor were studied and it was found that the antibiotic (Cefixime) alone and with GO combined with AgNPs inhibit the corrosion of mild steel by being adsorbed on the surface of mild steel by a physical adsorption mechanism. The adsorption of Cefixime and GO with AgNPs on the mild steel surface was found to be spontaneous. Incorporating AgNPs and GO with Cefixime showed an additional inhibition efficiency when compared with using only Cefixime. This indicates the strong inhibition efficiency offered by incorporating the antibiotic with AgNPs and GO