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\"Design and build scalable web applications quickly.This is an invaluable roadmap for meeting the rapid demand to deliver scalable applications in a startup environment. With a focus on core concepts and best practices rather than on individual languages, platforms, or technologies, Web Scalability for Startup Engineers describes how infrastructure and software architecture work together to support a scalable environment.You'll learn, step by step, how scalable systems work and how to solve common challenges. Helpful diagrams are included throughout, and real-world examples illustrate the concepts presented. Even if you have limited time and resources, you can successfully develop and deliver robust, scalable web applications with help from this practical guide. Learn the key principles of good software design required for scalable systems. Build the front-end layer to sustain the highest levels of concurrency and request rates. Design and develop web services, including REST-ful APIs. Enable a horizontally scalable data layer. Implement caching best practices. Leverage asynchronous processing, messaging, and event-driven architecture. Structure, index, and store data for optimized search. Explore other aspects of scalability, such as automation, project management, and agile teams\"-- Provided by publisher.

Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. \"Data science with Python and Dask\" teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the nYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.

The demand for a new generation of high-temperature dielectric materials toward capacitive energy storage has been driven by the rise of high-power applications such as electric vehicles, aircraft, and pulsed power systems where the power electronics are exposed to elevated temperatures. Polymer dielectrics are characterized by being lightweight, and their scalability, mechanical flexibility, high dielectric strength, and great reliability, but they are limited to relatively low operating temperatures. The existing polymer nanocomposite-based dielectrics with a limited energy density at high temperatures also present a major barrier to achieving significant reductions in size and weight of energy devices. Here we report the sandwich structures as an efficient route to high-temperature dielectric polymer nanocomposites that simultaneously possess high dielectric constant and low dielectric loss. In contrast to the conventional single-layer configuration, the rationally designed sandwich-structured polymer nanocomposites are capable of integrating the complementary properties of spatially organized multicomponents in a synergistic fashion to raise dielectric constant, and subsequently greatly improve discharged energy densities while retaining low loss and high charge–discharge efficiency at elevated temperatures. At 150 °C and 200 MV m−1, an operating condition toward electric vehicle applications, the sandwich-structured polymer nanocomposites outperform the state-of-the-art polymer-based dielectrics in terms of energy density, power density, charge–discharge efficiency, and cyclability. The excellent dielectric and capacitive properties of the polymer nanocomposites may pave a way for widespread applications in modern electronics and power modules where harsh operating conditions are present.

Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.

The implementation of continuous flow processing as a key enabling technology has transformed the way we conduct chemistry and has expanded our synthetic capabilities. As a result many new preparative routes have been designed towards commercially relevant drug compounds achieving more efficient and reproducible manufacture. This review article aims to illustrate the holistic systems approach and diverse applications of flow chemistry to the preparation of pharmaceutically active molecules, demonstrating the value of this strategy towards every aspect ranging from synthesis, in-line analysis and purification to final formulation and tableting. Although this review will primarily concentrate on large scale continuous processing, additional selected syntheses using micro or meso-scaled flow reactors will be exemplified for key transformations and process control. It is hoped that the reader will gain an appreciation of the innovative technology and transformational nature that flow chemistry can leverage to an overall process.

The scalability of microservices architectures is crucial for modern software systems, yet it presents significant challenges due to their inherent complexities. This study aims to systematically review existing literature on the scalability of microservices, identifying key strategies, challenges, and emerging trends. We conducted a systematic literature review following the PRISMA guidelines, analyzing 44 scholarly articles that specifically address the scalability of microservices. The review focused on various scaling approaches, metrics, and the effectiveness of autoscaling mechanisms. Our findings reveal a diverse body of literature with a predominant focus on autoscaling strategies, particularly those utilizing machine learning. Key challenges identified include accurate metrics collection, dynamic scaling decision-making, and balancing performance with cost and security. While progress has been made in addressing scalability challenges, significant gaps remain, particularly in standardizing autoscaling metrics. Future research should focus on developing robust, adaptive autoscaling systems that can effectively manage real-world complexities and dynamic workloads, ensuring both performance and cost optimization in microservices architectures.

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy , we present AnnData , a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).