bluuebunny/crossref_metadata_2025_split huggingface dataset:
Show me your ID: NLR immune receptors with integrated domains in plants
Authors: Marchal Clemence , Michalopoulou Vassiliki A. , Zou Zhou , Cevik Volkan , Sarris Panagiotis F. 
Published: June 2022

Abstract
Nucleotide-binding and leucine-rich repeat receptors (NLRs) are intracellular plant immune receptors that recognize pathogen effectors secreted into the plant cell. Canonical NLRs typically contain three conserved domains including a central nucleotide binding (NB-ARC) domain, C-terminal leucine-rich repeats (LRRs) and an N-terminal domain. A subfamily of plant NLRs contain additional noncanonical domain(s) that have potentially evolved from the integration of the effector targets in the canonical NLR structure. These NLRs with extra domains are thus referred to as NLRs with integrated domains (NLR-IDs). Here, we first summarize our current understanding of NLR-ID activation upon effector binding, focusing on the NLR pairs Pik-1/Pik-2, RGA4/RGA5, and RRS1/RPS4. We speculate on their potential oligomerization into resistosomes as it was recently shown for certain canonical plant NLRs. Furthermore, we discuss how our growing understanding of the mode of action of NLR-ID continuously informs engineering approaches to design new resistance specificities in the context of rapidly evolving pathogens.
Towards a Systematic Review on Industry 4.0: Big data & Internet of things
Authors: Bilad Assia , Zaim Mounia , Zaim Faical 
Published: June 2022

Digital technologies are occupying more and more a very important place in the industry, and more precisely with the 4th industrial revolution or what is called industry 4.0. In addition, digital transformation requires the implementation of two tools: Big data and the Internet of Things as the two starting tools, which continue to evolve gradually.
Intending to explore on this area, this paper studies the literature to get a detailed understanding of Industry 4.0, as well as an overview of the two digitization tools namely big data and the Internet of Things used to improve the quality of processes in different areas. Through a systematic literature review (SLR), the study is an effort to provide an overview of existing big data and the Internet of Things in the literature and to study the existing studies to classify them by application domain and according to a developed architectural framework. The search identified 81 relevant articles. Analyses of the distribution of articles by publication year, domain, country, type, tool, and source are presented and discussed. A research agenda for future research are provided.
A Rare Case Report of an Intradural Left Cerebellopontine Angle Chordoma
Authors: Hazra Darpanarayan , Balasubramanian Chandramouli , Das Soutrik , Tiwari Mona , Ghosh Amit 
Published: June 2022

AbstractIntracranial intradural chordomas are rare entities constituting 1 to 3% of primary bone tumors. The mainstay of treatment remains aggressive resection of the lesion followed by adjuvant radiation therapy. We hereby report a case of a 70-year-old gentleman with intracranial, intradural chordoma arising from the left cerebellopontine angle. We hope to add to the existing minimal literature on this subject by highlighting this case, the first reported one from Asia.
Alternative splicing encodes functional intracellular CD59 isoforms that mediate insulin secretion and are down-regulated in diabetic islets
Authors: Golec Ewelina , Ekström Alexander , Noga Maciej , Omar-Hmeadi Muhmmad , Lund Per-Eric , Villoutreix Bruno O. , Krus Ulrika , Wozniak Katarzyna , Korsgren Olle , Renström Erik , Barg Sebastian , King Ben C. , Blom Anna M. 
Published: June 2022

Significance
This project describes the existence of previously unknown non–GPI-anchored CD59 isoforms required for insulin secretion, named CD59–IRIS-1 and CD59–IRIS-2, and finds reduced expression of CD59-IRIS isoforms in human diabetic islets, showing a link between dysregulation of IRIS isoforms and defects in insulin secretion in diabetic patients. These data open a path for future studies into CD59-IRIS expression and function in additional cell types capable of regulated secretion. Identification of additional specific CD59-IRIS binding partners within the cell could provide therapeutic targets for enhancement of insulin secretion in T2D.
Recurrent ancient geomagnetic field anomalies shed light on future evolution of the South Atlantic Anomaly
Authors: Nilsson Andreas , Suttie Neil , Stoner Joseph S. , Muscheler Raimund 
Published: June 2022

Significance
Earth’s magnetic field, which is generated through convection in the liquid iron–rich outer core, acts as a shield against harmful particle radiation. During the past two centuries, the field strength has decreased rapidly as an area of weak field has expanded over the South Atlantic Ocean. The decreased shielding effect of the magnetic field in this area has important implications for low Earth-orbiting satellites and space travel. Using archeological and geological records, we have reconstructed the variations of Earth’s magnetic field over the past 9,000 y. We find evidence for recurrent magnetic field anomalies, similar to the present-day South Atlantic Anomaly, which provides insights into underlying geodynamo processes that may help predict the future development of Earth’s magnetic field.
Fluctuations in the collected charge in integrating photoconductive detectors under small and large signals: the variance problem
Authors: Ramaswami Kieran O , Curry Richard J , Hinder Ian , Johanson Robert E , Kasap Safa O 
Published: June 2022

Abstract
Charge collection efficiency (CE) η
0 under small signal conditions, corresponding to a uniform field in the detector medium, has been widely used in evaluating the performance of photoconductive detectors. The present paper answers the question, ‘What is the variance of the collected charge in an integrating detector as a function of photoinjection level and what are the errors if we continue to use the small signal equations?’ The variance

σ
0
2

in η
0 under small signals has been theoretically derived in the literature and has been a key factor in the detective quantum efficiency modeling of integrating detectors based on various semiconductors.

σ
0
2

is a noise source and can degrade the detector performance under incomplete charge collection. The statistical variance

σ
0
2

in the CE η
0, under small signals and the variance

σ
r
2

in the CE ηr
under an arbitrary injection level r (injected charge divided by charge on the electrodes) have been studied using the Monte Carlo simulation model developed in this work to evaluate the difference between

σ
r
2

and

σ
0
2

from small to large signals. Initial injection of electron and hole pairs and their subsequent transport and trapping in the presence of an electric field, which is calculated from the Poisson equation, is used to calculate the photocurrent. Each injected carrier is tracked as it moves in the semiconductor until it is either trapped or reaches the collection electrode. Trapped carriers do not contribute to the photocurrent but continue to contribute to the field through the Poisson equation. The instantaneous photocurrent i
ph(t) is calculated from the drift of the free carriers through the Shockley–Ramo theorem. i
ph(t) is integrated over the duration of the photocurrent to calculate the total collected charge and hence the CE ηr
. The variance

σ
r
2

in ηr
is found from multiple simulations of ηr
. The ηr
and

σ
r
2

have been generated over varying charge injection ratios r, the electron and hole ranges μτ, mean photoinjection depths δ and drift mobility ratios b. At full injection, the deviation

Δ

σ
r
2

of the CE variance

σ
r
2

from the uniform field case

σ
0
2

(

i

.

e

.
Δ

σ
r
2

=

σ
r
2

−

σ
0
2

)

may be as much as 40% larger or 20% lower than the small signal model prediction. This study provides the extent of errors involved in the variance of the CE in non-uniform fields and quantifies the increase in errors that can occur in high injection cases. In practice, typical injection ratios are less than 0.2, which means that the magnitude of percentage error

Δ

σ
r
2

/

σ
0
2

is less than 5%.
Performing broadband and tunable mathematical operations based on acoustic reconfigurable metasurfaces
Authors: Zuo Shuyu , Cai Chengxin , Li Xiaojun , Tian Ye , Liang Erjun 
Published: June 2022

Abstract
Acoustic metasurfaces can manipulate acoustic waves at subwavelength scales, thus proved to have advantages in constructing novel compact analog computing (CAC) systems. Here, we design an acoustic CAC system based on a nondispersive focusing metasurface (FM) and a reconfigurable reflective metasurface (RRM), which can perform broadband and tunable mathematical operations. The nondispersive FM consists of the sandwich-like-structured units, featuring the non-dispersive effective refractive indexes and high transmission efficiency. The RRM is formed by the amplitude modulator (AM) and π/2-phase modulator (π/2-PM), where the moving regions in the AM and π/2-PM can control the reflection amplitude and phase distribution of RRM, respectively. The numerical results show that the proposed CAC system can perform spatial differentiation and integration on the incident acoustic wave in the frequency range from 3.0 kHz to 3.9 kHz. This work promises the broadband and tunability that are critical for practical computing devices, paving the way towards acoustic computing applications, wave processing and manipulations.
Oxidation of tantalum disulfide (TaS2) films for gate dielectric and process design of two-dimensional field-effect device
Authors: Takeuchi Hayate , Urakami Noriyuki , Hashimoto Yoshio 
Published: June 2022

Abstract
Ta-based high-κ dielectrics can be synthesized via the oxidation of TaS2 films. In this study, we investigated the wet and dry oxidation of TaS2 films via thermal annealing and plasma irradiation, respectively. The specific vibration observed via Raman spectroscopy, the bonding states observed via x-ray photoelectron spectroscopy, and capacitance measurements confirmed the oxidation of TaS2 films with a dielectric constant of ∼14.9. Moreover, the electrical transport of the TaS2 films along the in-plane direction indicated a change in conductivity before and after the oxidation. The thickness of the oxidized film was estimated. Accordingly, the layer-by-layer oxidation was limited to approximately 50 nm via plasma irradiation, whereas the TaS2 films within 150 nm were fully oxidized via thermal annealing in ambient air. Therefore, a Ta-oxide/TaS2 structure was fabricated as a stack material of insulator and metal when the thickness of the pristine film was greater than 50 nm. In addition, Ta-oxide films were integrated into bottom-gated two-dimensional (2D) field-effect transistors (FETs) using the dry transfer method. 2D FETs with multilayer MoTe2 and MoS2 films as p-type and n-type channels, respectively, were successfully fabricated. In particular, the Ta-oxide film synthesized via dry oxidation was used as a gate dielectric, and the device process could be simplified because the Ta-oxide/TaS2 heterostructure can function as a stack material for gate insulators and gate electrodes. An anti-ambipolar transistor consisting of an MoTe2/MoS2 heterojunction was also fabricated. For the transfer characteristics, a relatively sharp on-state bias range below 10 V and sufficiently high peak-to-valley ratio of 106 at V
DS = 3 V were obtained using the high-κ gate dielectric of Ta-oxide despite the presence of the multilayer channels (∼20 nm).
Compositional analysis of oxide-embedded III–V nanostructures
Authors: Ek Martin , Petersson C Leon M , Wallentin Jesper , Wahlqvist David , Ahadi Aylin , Borgström Magnus , Wallenberg Reine 
Published: June 2022

Abstract
Nanowire growth enables creation of embedded heterostructures, where one material is completely surrounded by another. Through materials-selective post-growth oxidation it is also possible to combine amorphous oxides and crystalline, e.g. III–V materials. Such oxide-embedded structures pose a challenge for compositional characterization through transmission electron microscopy since the materials will overlap in projection. Furthermore, materials electrically isolated by an embedding oxide are more sensitive to electron beam-induced alterations. Methods that can directly isolate the embedded material, preferably at reduced electron doses, will be required in this situation. Here, we analyse the performance of two such techniques—local lattice parameter measurements from high resolution micrographs and bulk plasmon energy measurements from electron energy loss spectra—by applying them to analyse InP-AlInP segments embedded in amorphous aluminium oxide. We demonstrate the complementarity of the two methods, which show an overall excellent agreement. However, in regions with residual strain, which we analyse through molecular dynamics simulations, the two techniques diverge from the true value in opposite directions.
Resistive electrode array (REA) for radiotherapy beam monitoring and quality assurance
Authors: Zygmanski Piotr , Lima Jason , Liles Arianna , Zhang Victoria , Gineitaite Indre , Sajo Erno , Brivio Davide 
Published: June 2022

Abstract
We have developed a new type of detector array for monitoring of radiation beams in radiotherapy. The detector has parallel-plane architecture with multiple large-area uniform thin-film electrodes. At least one of the electrodes is resistive and has multiple signal readouts spread out along its perimeter. The integral dose deposited in the detector gives rise to multiple signals that depend on the distribution of radiation with respect to resistive electrode array (REA) geometry. The purpose of the present study was to experimentally determine basic detector response to MLC collimated x-ray fields. Two detector arrays have been characterized: circular and rectangular. The current and electrostatic potential distribution within the resistive electrode are governed by the Laplace and continuity equations with boundary conditions at the border with the readouts. Measurements for pencil beams showed that signal strength depends primarily on the distances between the location of the pencil beam and the readouts. Measurements for larger irregular MLC showed that signals as a function of time are quasi-linear with respect to MLC position and are proportional to the MLC area. Derivation of clinically relevant radiation beam parameters from REA signals, such as MLC position, MLC gap size and monitor unit per MLC segment relies on the detector response model with empirical model parameters. An approximate analytical detector response model was proposed and used to fit experiment data.
Development of ripple filter composed of metal mesh for charged-particle therapy
Authors: Tanaka Sodai , Inaniwa Taku , Matsuba Shunya 
Published: June 2022

Abstract

Objective. In charged-particle therapy, a ripple filter (RiFi) is used for broadening the Bragg peak in the beam direction. A conventional RiFi consists of plates with a fine ridge and groove structure. The construction of the RiFi has been a time-consuming and costly task. In this study, we developed a simple RiFi made of multi-layered metal mesh (mRiFi), with which the Bragg peak is broadened due to structural randomness, similar to what occurs for the already proposed RiFi with porous material. Approach. The mRiFi was constructed by stacking commercially available metal meshes at random positions and angles. The mRiFi was inexpensive to fabricate due to its high availability and low machining accuracy. The Bragg peak width modulated by the mRiFi can be uniquely determined by the wire material, wire diameter, wire-to-wire spacing of the metal mesh, and the number of mesh sheets. We fabricated four mRiFis consisting of 10, 20, 30, and 40 layers of stainless steel meshes with a wire diameter of 0.1 mm and a wire-to-wire spacing of 0.508 mm. Main results. Using the mRiFis consisting of 10, 20, 30, and 40 mesh sheets, we succeeded in broadening the Bragg peak following the normal distribution with the respective standard deviation σ values of 0.83, 1.15, 1.41, and 1.56 mm in water in experimental planar-integrated depth dose measurements with 140.3 MeV u−1 carbon-ion beams. The effect of range broadening with the mRiFi was independent of its lateral position, and the measurement of the surface dose using radiochromic films showed no severe inhomogeneity with a homogeneity index greater than 0.3 caused by the mRiFis. Significance. The developed mRiFi can be used as a RiFi in charged-particle therapy. The mRiFi has three advantages: high supply stability of the material for manufacturing it, easy fabrication, and low cost.
A Review of Various Controller Techniques Designed for the Operational Control of DC and Servo Motors
Authors: Harshitha S , Shamanth S , Chari Aditya Kumar 
Published: June 2022

Abstract
Motors stand out as critical components in every element of modern life that requires motion. Control systems that govern the operation of these devices in industrial or commercial applications become crucial as a result. Improved performance results in increased efficiency, which in the long term translates to significant cost and energy savings and improved output quality. Numerous control systems have been developed specifically for this purpose over the last few decades in response to a persistent requirement for performance improvements. With that in mind, this paper is aimed at classifying and analysing a variety of different types of motor controllers in relation to previous work done with them. The shift from conventional to more intelligent adaptive controllers is highlighted along with their respective principles. Conventional controllers are those designed for linear systems without taking into account the possibility of parametric variation occurrence. Adaptive controllers have the ability to automatically learn and adapt with the state of the plant. Using transient attributes of closed loop control systems, their performance impact and feasibility are studied in detail. Subsequent conclusions are also drawn out to provide clear and concise reference.
Application of machine learning in understanding bioactivity of beta-lactamase AmpC
Authors: Anant Prem Singh , Gupta Pratima 
Published: June 2022

Abstract
The ability of microorganisms like bacteria to develop mechanisms against the treatment is becoming a concern globally. This topic of concern is called Antimicrobial Resistance aka AMR. In this study, with the help of machine learning algorithms we are trying to evaluate the activity of molecules that have been tested experimentally either to bind or not bind the beta lactamases. Machine learning is a technique for analysis of data which teaches the computers what naturally comes to living organisms. Beta lactamases are diverse family of microbial enzymes that hydrolyse the cyclic amide bond of susceptible to beta-lactam antibiotics. Studying the effects and functioning of beta lactamases enzymes can provide better insights into the AMR mechanism adopted by the microorganisms. AMR is one of the top 10 global public health threats facing humanity in this era. Therefore, finding potential compounds that can combat these microorganisms is very important. Here, we have considered few plant-based flavonoids and terpenoids and checked the bioactivity against these beta lactamases containing microorganisms by using machine learning algorithms. A large dataset having more than 62,000 compounds and their pPotency values against beta lactamase AmpC was obtained from ChEMBL and employed in QSAR (quantitative structure activity relationship) model in order to understand the origin of their bioactivity. Several set of fingerprint descriptors and predictive models were constructed and results are obtained.
Gis Based Spatial Analysis and Prediction of Covid-19 Cases
Authors: Sahu Mridu , Jhariya D.C. , Singh Richa , Srivastava Ishika , Mishra Sunny Kumar 
Published: June 2022

Abstract
COVID-19 has come out to be a threat that has far-reaching repercussions in all parts of human existence; as a result, it is the most pressing concern facing countries around the world. This paper is centred on using a geographic information system to map COVID-19 instances across India, followed by COVID-19 case projections in various areas of India. A geographic information system (GIS) is a computer system that verifies, records, stores and displays data about places on the Earth’s surface, with India as the primary emphasis. Because the COVID-19 has had a distinct influence on different parts of India, the research we conducted provides a correct connection between past, current, and future instances in India employing prediction by using the SARIMA(Seasonal Autoregressive Integrated Moving Average) model to forecast time series. Python is used to implement the project. Several databases, including global databases like Natural Earth, UNEP Environmental Data Explorer, GRUMP, and national databases like Open Data Archive and ISRO’s Geo-Platform, are utilised to collect data for mapping and displaying instances across the country. These databases are combined to get the required output that is to be plotted and displayed. The prediction of coronavirus cases has also been done using the SARIMA model with an accuracy of 95.37percent.
Hyperspectral Image Classification using Hybrid Deep Convolutional Neural Network
Authors: Nayak Omprakash , Khandare Hrishikesh , Parida Nikhil Kumar , Giri Ramnivas , Janghel Rekh Ram , Govil Himanshu 
Published: June 2022

Abstract
The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.
The effect of addition of WS2 nanosheets on the performance of ZnO nanorods-based photodetector
Authors: Salsabilla Aufa , Fauzia V. , Imawan C. 
Published: June 2022

Abstract
Fabrication of the heterostructure of ZnO/transition metal dichalcogenides (TMD) has attracted much attention to improve the performance of ZnO-based photodetectors. In this work, we report the effect of the addition of WS2 nanosheets on the performance of ZnO nanorods based photodetectors. ZnO nanorods were prepared using the hydrothermal method, and the few layers of WS2 nanosheets were prepared by liquid-phase exfoliation. Exfoliated WS2 solution was deposited on the surface of ZnO nanorods by spin-coating method, and the photodetector performance was calculated via current-voltage measurement in the dark and under laser light sources with a wavelength of UV 365, cyan 505, and red 625 nm. The results show that the dark current significantly decreased after the addition of WS2 on the ZnO nanorods surface. The UV detector performances sensitivity is increased from 129% to 334%, responsivity is increased from 0.93 A/W to 1.11 A/W, and the detectivity is increased from 2.5 × 109 to 4.4 × 109 Jones. However, both samples cannot be used as visible light detectors because of the low photogenerated current.