Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the ...
Antimicrobial resistance (AMR) is a serious threat to global public health, necessitating rapid and precise diagnostic tools. The prevalence of novel antibiotic resistance genes (ARGs) has increased due to microbial s...
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Antimicrobial resistance (AMR) is a serious threat to global public health, necessitating rapid and precise diagnostic tools. The prevalence of novel antibiotic resistance genes (ARGs) has increased due to microbial sequencing, resulting in the need to extract vital information from vast amounts of data. Although many AMR prediction tools exist, only a few are accurate and scalable. We examined 20 widely used AMR prediction tools and chose 4 web-based tools for antimicrobial resistance surveillance over standalone software due to their easy accessibility, portability, and centralized data management, eliminating the need for complex installation and maintenance. CGE (center for Genomic Epidemiology) provides bioinformatics tools and promotes open data sharing. At the same time, CARD (Comprehensive Antibiotic Resistance database) is a valuable resource for antibiotic resistance gene information, collectively contributing to our understanding and management of antibiotic resistance. We highlighted web-based AMR prediction tools and performed a case study using the Pseudomonas aeruginosa complete plasmid sequence (CPS) to identify strengths and weaknesses in the system. Our study explored four web-based antibiotic resistance gene prediction tools: ResFinder, KmerResistance, ResFinderFG, and RGI. ResFinder excelled at finding acquired antimicrobial resistance genes as well as maintaining a database up to date. KmerResistance identified resistance genes using k-mer analysis. esFinderFG offered a unique perspective, excelling in detecting a broad range of resistant phenotypes, due to its inclusion of sequences discovered through functional metagenomics. RGI was versatile in detecting a wide range of resistance genes and provided extensive resistance mechanism information. researchers must understand the capabilities and trade-offs of these tools to make well-informed choices for efficient resistance gene identification and surveillance as the antibiotic resistance landsca
As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness th...
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We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as th...
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We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.
To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI ...
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Federated learning (FL), as a promising machine learning paradigm for large-scale distributed data, faces two security challenges of privacy and robustness: the transmitted model updates potentially leak sensitive use...
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This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsi...
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Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce pl...
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Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with...
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Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non...
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