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In previous and existing hadron storage rings, the horizontal and vertical emittances are normally the same or very close. For the Hadron Storage Ring (HSR) of the Electron-Ion Collider (EIC), the design proton transverse emittance ratio is 10:1. To maintain this large emittance ratio, we need to have an online fine decoupling system to prevent transverse emittance exchange. For this purpose, we carried out fine decoupling experiments in the Relativistic Heavy Ion Collider (RHIC) and reviewed its previous operational data. Analytical prediction and numerical simulation are preformed to estimate how small the global coupling coefficient should be to maintain a 10:1 emittance ratio.

A high-current high-brightness electron accelerator for low-energy RHIC electron cooling (LEReC) was successfully commissioned at Brookhaven National Laboratory. The LEReC accelerator includes a dc photoemission gun, a laser system, a photocathode delivery system, magnets, beam diagnostics, a superconducting rf booster cavity, and a set of normal conducting rf cavities to provide enough flexibility to tune the beam in the longitudinal phase space. Cooling with nonmagnetized rf accelerated electron beams requires longitudinal corrections to obtain a small momentum spread while preserving the transverse emittances. Electron beams with kinetic energies of 1.6 and 2.0 MeV with a beam quality suitable for cooling were successfully propagated through 100 m of beam lines, including dispersion sections, maintained through both cooling sections in RHIC and used for cooling ions in both RHIC rings simultaneously. The beam quality suitable for cooling RHIC beams was achieved in 2018, which led to the first experimental demonstration of bunched beam electron cooling of hadron beams in 2019.

Two electron lenses (e -lenses) have been in operation during the 2015 RHIC physics run as part of a head-on beam-beam compensation scheme. While the RHIC lattice was chosen to reduce the beam-beam-induced resonance-driving terms, the electron lenses reduced the beam-beam-induced tune spread. This has been demonstrated for the first time. The beam-beam compensation scheme allows for higher beam-beam parameters and therefore higher intensities and luminosity. In this paper, we detail the design considerations and verification of the electron beam parameters of the RHIC e -lenses. Longitudinal and transverse alignments with ion beams and the transverse beam transfer function measurement with head-on electron-proton beam are presented.

The Beam Energy Scan phase II (BES-II), performed in the Relativistic Heavy Ion Collider (RHIC) from 2019 to 2021, explored the phase transition between quark-gluon plasma and hadronic gas. BES-II exceeded the goal of a fourfold increase in the average luminosity over that achieved during Beam Energy Scan phase I (BES-I), at five gold beam energies: 9.8, 7.3, 5.75, 4.59, and3.85GeV/nucleon. This was accomplished by addressing several beam dynamics effects, including intrabeam scattering, beam-beam, space charge, beam instability, and field errors induced by superconducting magnet persistent currents. Some of these effects are especially detrimental at low energies. BES-II achievements are presented, and the measures taken to improve RHIC performance are described. These measures span the whole RHIC complex, including ion beam sources, injectors, beam lifetime improvements in RHIC, and operation with the world’s first bunched beam Low Energy RHIC electron Cooler (LEReC).

In this note we will generalize the results deduced in Figalli and Glaudo (Arch Ration Mech Anal 237(1):201–258, 2020) and Deng et al. (Sharp quantitative estimates of Struwe’s Decomposition. Preprint http://arxiv.org/abs/2103.15360 , 2021) to fractional Sobolev spaces. In particular we will show that for s ∈ ( 0 , 1 ) , n > 2 s and ν ∈ N there exists constants δ = δ ( n , s , ν ) > 0 and C = C ( n , s , ν ) > 0 such that for any function u ∈ H ˙ s ( R n ) satisfying, u - ∑ i = 1 ν U ~ i H ˙ s ≤ δ where U ~ 1 , U ~ 2 , … U ~ ν is a δ -interacting family of Talenti bubbles, there exists a family of Talenti bubbles U 1 , U 2 , … U ν such that u - ∑ i = 1 ν U i H ˙ s ≤ C Γ if 2 s < n < 6 s , Γ | log Γ | 1 2 if n = 6 s , Γ p 2 if n > 6 s for Γ = Δ u + u | u | p - 1 H - s and p = 2 ∗ - 1 = n + 2 s n - 2 s .

Embark on a journey through the transformative landscape of IoT with this comprehensive guide, \"Mastering IoT For Industrial Environments\". From its inception in the Industrial Revolution to its pivotal role in Industry 4.0, each chapter provides a deep dive into essential concepts. It will explore IoT architecture, microcontrollers, communication protocols, and interfacing protocols. Delve into MQTT, the protocol for IoT, and machine-to-machine communication. Discover the transition to ESP-IDF and the future of IoT in Industry 4.0. This book provides readers with practical insights into implementing IoT solutions within industrial contexts. Through a meticulously curated array of case studies and real-world applications, readers gain invaluable perspectives on the prevailing IoT trends shaping industrial landscapes. Spanning from intelligent factories and predictive maintenance to supply chain optimization and energy management, the book addresses a spectrum of topics reflective of contemporary industrial challenges and opportunities.

A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

Biopolymers are an emerging class of novel materials with diverse applications and properties such as superior sustainability and tunability. Here, applications of biopolymers are described in the context of energy storage devices, namely lithium-based batteries, zinc-based batteries, and capacitors. Current demand for energy storage technologies calls for improved energy density, preserved performance overtime, and more sustainable end-of-life behavior. Lithium-based and zinc-based batteries often face anode corrosion from processes such as dendrite formation. Capacitors typically struggle with achieving functional energy density caused by an inability to efficiently charge and discharge. Both classes of energy storage need to be packaged with sustainable materials due to their potential leakages of toxic metals. In this review paper, recent progress in energy applications is described for biocompatible polymers such as silk, keratin, collagen, chitosan, cellulose, and agarose. Fabrication techniques are described for various components of the battery/capacitors including the electrode, electrolyte, and separators with biopolymers. Of these methods, incorporating the porosity found within various biopolymers is commonly used to maximize ion transport in the electrolyte and prevent dendrite formations in lithium-based, zinc-based batteries, and capacitors. Overall, integrating biopolymers in energy storage solutions poses a promising alternative that can theoretically match traditional energy sources while eliminating harmful consequences to the environment.

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning–based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning–based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.

Apache Spark is a flexible in-memory framework that allows processing of both batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to quickly get started with Apache Spark 2.0 and write efficient big data applications for a variety of use cases.