bluuebunny/crossref_metadata_2025_split huggingface dataset:
Grain-boundary topological superconductor
Authors: Amundsen Morten , Juričić Vladimir 
Published: August 2023

AbstractMajorana zero modes (MZMs) are of central importance for modern condensed matter physics and quantum information due to their non-Abelian nature, which thereby offers the possibility of realizing topological quantum bits. We here show that a grain boundary (GB) defect can host a topological superconductor (SC), with a pair of cohabitating MZMs at its end when immersed in a parent two-dimensional gapped topological SC with the Fermi surface enclosing a nonzero momentum. The essence of our proposal lies in the magnetic-field driven hybridization of the localized MZMs at the elementary blocks of the GB defect, the single lattice dislocations, due to the MZM spin being locked to the Burgers vector. Indeed, as we show through numerical and analytical calculations, the GB topological SC with two localized MZMs emerges in a finite range of both the angle and magnitude of the external magnetic field. Our work demonstrates the possibility of defect-based platforms for quantum information technology and opens up a route for their systematic search in future.
Reconfigurable reservoir computing in a magnetic metamaterial
Authors: Vidamour I. T. , Swindells C. , Venkat G. , Manneschi L. , Fry P. W. , Welbourne A. , Rowan-Robinson R. M. , Backes D. , Maccherozzi F. , Dhesi S. S. , Vasilaki E. , Allwood D. A. , Hayward T. J. 
Published: August 2023

AbstractIn-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear emergent dynamics, and non-volatile memory. However, to be suitable for a broad range of tasks, the material system is required to exhibit a broad range of properties, and isolating these behaviours experimentally can often prove difficult. By using an electrically accessible device consisting of an array of interconnected magnetic nanorings- a system shown to exhibit complex emergent dynamics- here we show how reconfiguring the reservoir architecture allows exploitation of different aspects the system’s dynamical behaviours. This is evidenced through state-of-the-art performance in diverse benchmark tasks with very different computational requirements, highlighting the additional computational configurability that can be obtained by altering the input/output architecture around the material system.
Asynchronous blockchain‐based federated learning for tokenized smart power contract of heterogeneous networked microgrid system
Authors: Sharma Desh Deepak 
Published: August 2023

AbstractIn a networked microgrid system (NMS), various heterogeneous microgrids are interconnected. A networked microgrid system facilitates a new kind of physical design that provides numerous advantages such as distributed economic optimization, reliability, resiliency, and focusing on distributed generations and customers. Designing the secure and privacy‐protected smart power contract between electricity suppliers and consumers, considered as agents, of different microgrids, is a challenging task in the networked‐ microgrid system. Each microgrid implements a heterogeneous or isomorphic blockchain based platform. The blockchain interoperability, inherently, presents in different blockchains implemented by various microgrids. This paper reviews the interoperability issues and smart contract designs in blockchain‐based systems and proposes new mechanisms to cater blockchain interoperability challenges to facilitate the design of secure and seamless smart contracts among different blockchains of microgrids. A network hub of heterogeneous blockchains of network microgrids has been proposed. A methodology has been developed to transfer tokens between interoperable blockchains. A distributed identity‐based microgrid (DIBM) scheme is incorporated to make the networked microgrid system secure and trustworthy. This paper suggests an effective consensus protocol for cross‐chain architecture that improves the tokenization system and smart power contract designs. Asynchronous blockchain based federated learning for peer‐to‐peer smart power exchange has been implemented in learning process of interoperable and heterogeneous blockchain based network hub of microgrid. For simulation purposes, MATLAB and python programming have been used with real‐time data of microgrids.
Minimizing the computation latency of FDMA‐based wireless powered edge computing network
Authors: Chen Xi , Jiang Guodong , Chi Kaikai , Zhang Shubin , Wei Xinchen , Chen Gang 
Published: August 2023

AbstractThe 0–1 mixed integer programming problem of binary offloading on wireless powered mobile edge computing (WP‐MEC) networks requires joint optimization of binary and continuous variables, which is computationally expensive for traditional techniques and difficult to solve within the channel coherence time under time‐varying condition. Using machine learning models to output variable values is also challenging. Hence, designing efficient and low‐complexity algorithms is crucial for optimal network performance. This paper focuses on the computation latency of the FDMA‐based WP‐MEC network and proposes a task‐offloading algorithm to minimize the total completion delay (TCD). The TCD minimization is modelled as a 0–1 MIP problem and is decomposed into a master problem of optimizing the offloading decision and the sub‐problem of optimizing other parameters under a given offloading decision. The sub‐problem is solved using optimization method, while the master problem is solved using a deep reinforcement learning algorithm. Simulation results show that the proposed algorithm can achieve almost minimal TCD with low complexity.
Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems
Authors: Fei Zhengshun , Wang Jinglong , Liu Kangling , Attahi Eric , Huang Bingqiang 
Published: August 2023

AbstractWith the rapid development of artificial intelligence technology, commercial robots have gradually entered our daily lives. In order to promote product dissemination, shopping guide robots are a new service options of commerce platforms that use tag recommendation systems to identify users' intentions. A large number of applications combine user historical tagging information with the multi‐round dialogue ability of shopping guide robots to help users efficiently search for and retrieve products of interest. Recently, tensor decomposition methods have become a common approach for modelling entity interaction relationships in tag recommendation systems. However, due to the sparsity of data, these methods only consider low‐order information of entities, making it difficult to capture the higher‐order collaborative signals among entities. Recommendation methods by autoencoders can effectively extract abstract feature representations while they only focus on the two‐dimensional relationship between users and items, ignoring the interaction relationship among users, items and tags in real complex recommendation scenarios. The authors focus on modelling the similarity relationship among entities and propose a method called deep feature fusion tag (DFFT) based on the deep feature fusion of stacked denoising autoencoders. This method can extract high‐order information with different embedding dimensions and fuse them in a unified framework. To extract robust feature representations, the authors inject random noise (mask‐out/drop‐out noise) into the tag information corresponding to users and items to generate corrupted input data, and then utilise autoencoders to encode the interaction relationship among entities. To further obtain the interaction relationship with different dimensions, different encoding layers are stacked and combined to produce a better expanded model which can reinforce each other. Finally, a decoding component is used to reconstruct the original input data. According to the experimental results on two common datasets, the proposed DFFT method outperforms other baselines in terms of the F1@N, NDCG@N and Recall@N evaluation metrics.
Multi‐round load control method considering the safe electricity consumption for industrial users based on situational awareness
Authors: Chen Zonghan , Xu Yonghai , Pan Mingming , Wang Tianze , Yuan Jindou , Chen Lin , Tao Shun 
Published: August 2023

AbstractThis paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here.
Investigation of active cell balancing performance for series connected lithium‐ion cells in electric vehicle applications
Authors: Krishnamoorthy Umapathi , Satheesh Kumar Gobichettipalyam Shanmugam , Barua Sourav , Fayek Hady Habib 
Published: August 2023

AbstractLithium‐ion batteries have a very wide application range. They can power up small electronic devices such as smart watches to larger electric vehicles. Due to its varied range of applications, they come in different packaging and in such battery packs, even when individual cell voltage exceeds by a few milli‐volts above 4.2 V, it may result in thermal runaway and explode the cell. During discharge cycle, cell imbalances hinder the use of battery to its full capacity. This in turn decreases the battery lifetime. The individual battery cells should be equalized on a regular basis to keep the imbalances to a minimum and to have a good battery life. The process of balancing the individual cell charges by measuring the cell state of charge (SoC) and its voltage in a battery pack is known as cell balancing. This paper details an active cell balancing technique that uses a buck converter for balancing a series connected battery pack of lithium‐ion cells. A buck converter along with a pair of MOSFET switches for each cell, one turned on for charging the cell and the other one turned on while discharging the cell is used in this experiment. An algorithmic model suitable for reconfigurable battery systems that measures the individual cell voltages and is developed for balancing a pack of series connected Li‐ion battery cells. The developed model is simulated using MATLAB for verifying its performance. A state of charge of 25% is maintained across the cells and when SoC value drops below this even a difference of 0.02% is sensed by the algorithm to initiate balancing function. This balancing is found to take 275 ms to balance three 3.7 V batteries and thus the model is found to respond faster. The results show that this method can self‐adaptively attain satisfactory performance within a limited equalizing period.
A comparative analysis of deep neural network models in IoT‐based smart systems for energy prediction and theft detection
Authors: Sebastian Praveen Kallukalam , Deepa K , Neelima N. , Paul Rinika , Özer Tolga 
Published: August 2023

AbstractTraditional analog and digital meters are being substantially replaced with technological advances and the Internet of Things (IoT) introduction. The smart meter is highly preferred for accessing real‐time consumption, tariff calculation, and remote system control. These smart meters also prevent the majority from bypassing theft. Despite its intelligence, it cannot be 100% secure. An error in the readings can be caused by hacking or damage to meter components making the utility companies suffer significant losses. Based on past and future energy consumption data predictions on the consumer side, various methods are used in the proposed work to identify theft or anomalies in smart meter readings. Forecast‐based detection proved to be the most effective and accurate method. The primary and secondary decision models, which employ a variety of statical analyses to identify system anomalies, serve as the foundation for the energy consumption that follows the forecasting. Past 24‐h data is needed for forecasting, which is passed through different statical calculations such as RMSE, simple moving average, and Absolute Percentage Error to conclude detecting the normal values. Long short‐term memory gives high accuracy of 97% for forecasting and detecting abnormalities.
Probability confidence correlation analysis for many‐objective optimal operation considering a set of conflict interests
Authors: Zheng J. H. , Guo Y. W. , Li Zhigang , Wu Q. H. 
Published: August 2023

AbstractAs the proportion of renewable energy penetrated in power systems further increases, the optimal operation faces a large obstacle to satisfying various conflict interests simultaneously. This paper first formulates a many‐objective optimal operation problem of the new power system considering a comprehensive set of economic, environmental, and reliable objectives. To figure out this problem efficiently, this paper proposes a probability confidence correlation analysis method (PCCA) utilizing a t‐distributed stochastic neighbor embedding (T‐SNE) standard to greatly preserve the distribution information and separability of the objectives. Consequently, the objectives can be aggregated and the objective dimension is reduced based on the important sorting and correlation of the objectives. Based on the outcome of the T‐SNE, the adopted optimal operation problem with low dimensional objectives is solved by a multiple producer group search optimizer (GSOMP) with less computation burden, to obtain the Pareto‐optimal solution set. Simulation studies are conducted on IEEE 30‐bus, 39‐bus and 57‐bus systems to investigate the performance of the proposed PCCA in terms of efficiency and accuracy, making comparisons of the existing many‐objective optimization algorithms.
An in vitro comparison of intra-operative isohemagglutinin and human leukocyte antigen removal techniques in pediatric heart transplantation
Authors: Hayes Emily A. , Walczak Ashley B , Goodhue Meyer Erin , Nicol Kathleen , Deitemyer Matthew , Duffy Vicky , Moore Padilla Michelle , Gajarski Robert J. , Nandi Deipanjan 
Published: August 2023

Background: Highly sensitized pediatric patients awaiting heart transplantation experience longer wait times and thus higher waitlist mortality. Similarly, children less than 2 years of age have increased waitlist times and mortality when compared to their older peers. To improve the likelihood of successful transplantation in these patients, various strategies have been utilized, including peri-operative plasmapheresis. However, limited data exists comparing plasmapheresis techniques for antibody reduction. This study’s aim was to compare the in vitro magnitude of isohemagglutinin titers (IT) and human leukocyte antigen (HLA) antibody removal and the time required between membrane-based plasmapheresis (MP) and centrifuge-based plasmapheresis (CP) incorporated into the extracorporeal (EC) circuit. Methods: Two MP (Prismaflex) and two CP (Spectra Optia, Terumo BCT) circuits were incorporated into four separate EC circuits primed with high titer, highly sensitized type O donor whole blood. Assays were performed to determine baseline IT and anti-HLA antibodies and then at 30-minute increments until completion of the run (two plasma volume exchanges) at two hours. Results: There was a decrease in anti-A and anti-B IgM and IgG titers with both MP and CP. Mean anti-A and anti-B titer reduction was by 4.625 titers (93.7% change) and 4.375 titers (93.8% change) using MP and CP, respectively. At 2 h of apheresis, CP reduced 62.5% of all ITs to ≤ 1:4, while MP reduced 50% of ITs to ≤ 1:4. Additionally, reduction of anti-HLA class II antibody to mean fluorescence intensity (MFI) <3000 was achieved with both MP and CP. At 2 h of apheresis, CP reduced MFI by 2–3.5 fold and MP reduced MFI by 1.7–2.5 fold. Both demonstrated similar hemolytic and thrombotic profiles. Conclusions: In this in vitro plasmapheresis model of IT and anti-HLA antibody reduction, both MP and CP incorporated into the EC circuit can be used quickly and effectively to reduce circulating antibodies. While CP may have some greater efficiency, further study is necessary to verify this in vivo.
Bite-Sizing Digital Literature in theTwenty-First Century
Authors: Elliott Kamilla 
Published: August 2023

Abstract
The bite-sizing of fiction and poetry on twenty-first-century digital platforms constitutes a mode of adaptation that not only adapts literature to new technologies, economies, and modes of production and consumption but also adapts discourses of literary time and space and longstanding metaphors of literature as ‘food’. This article’s central argument is that digital literary bite-sizing, a combination of byte-sizing (digitizing) literature and bite-sizing literary portions for consumption simultaneously compresses and expands literary space and time and literary consumption. It is well documented that digitization, itself a technology of ‘byte-sizing’, has compressed literature into the tiny spaces of globally portable, hand-held devices, and reduced the time taken to access literature, paradoxically generating an exponential increase in the number of literary producers, products, and consumers. This essay considers how a concomitant bite-sizing of literary content on twenty-first-century digital platforms produces a further paradoxical compression and expansion of literary production and consumption. The adaptive dynamics are by no means homogenous, but amid a variety of producer reasons for bite-sizing literary portions, the sizes of portions nominated ‘bite-sized’, and consumer responses to them, the dynamic of compressed production to foster greater consumption remains, where ‘greater’ is not always a matter of quantity but in some cases a matter of quality. Although bite-sizing digital literary portions represent a literary adaptation not only to new technologies, platforms, and but also to digital bite-sizing more pervasively. Conversely, bite-sizing adapts to specifically literary practices and tropes that depart from discourses of bite-sizing other digital content.
VCFshiny: an R/Shiny application for interactively analyzing and visualizing genetic variants
Authors: Chen Tao , Tang Chengcheng , Zheng Wei , Qian Yanan , Chen Min , Zou Qingjian , Jin Yinge , Wang Kepin , Zhou Xiaoqing , Gou Shixue , Lai Liangxue 
Published: August 2023

AbstractSummaryNext-generation sequencing generates variants that are typically documented in variant call format (VCF) files. However, comprehensively examining variant information from VCF files can pose a significant challenge for researchers lacking bioinformatics and programming expertise. To address this issue, we introduce VCFshiny, an R package that features a user-friendly web interface enabling interactive annotation, interpretation, and visualization of variant information stored in VCF files. VCFshiny offers two annotation methods, Annovar and VariantAnnotation, to add annotations such as genes or functional impact. Annotated VCF files are deemed acceptable inputs for the purpose of summarizing and visualizing variant information. This includes the total number of variants, overlaps across sample replicates, base alterations of single nucleotides, length distributions of insertions and deletions (indels), high-frequency mutated genes, variant distribution in the genome and of genome features, variants in cancer driver genes, and cancer mutational signatures. VCFshiny serves to enhance the intelligibility of VCF files by offering an interactive web interface for analysis and visualization.Availability and implementationThe source code is available under an MIT open source license at https://github.com/123xiaochen/VCFshiny with documentation at https://123xiaochen.github.io/VCFshiny.
dRFEtools: dynamic recursive feature elimination for omics
Authors: Benjamin Kynon J M , Katipalli Tarun , Paquola Apuã C M 
Published: August 2023

Abstract

Motivation
Advances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared to observations. Moreover, biological processes are associated with networks of core and peripheral genes, while traditional feature selection approaches capture only core genes.

Results
To overcome these limitations, we present dRFEtools that implements dynamic recursive feature elimination (RFE), reducing computational time with high accuracy compared to standard RFE, expanding dynamic RFE to regression algorithms, and outputting the subsets of features that hold predictive power with and without peripheral features. dRFEtools integrates with scikit-learn (the popular Python machine learning platform) and thus provides new opportunities for dynamic RFE in large-scale omics data while enhancing its interpretability.

Availability and implementation
dRFEtools is freely available on PyPI at https://pypi.org/project/drfetools/ or on GitHub https://github.com/LieberInstitute/dRFEtools, implemented in Python 3, and supported on Linux, Windows, and Mac OS.
Phylogeography of Limia vittata (Cyprinodontiformes: Poeciliidae): geographical distribution of mitochondrial haplotypes is comparable to other Cuban poeciliids
Authors: Rodríguez-Machado Sheila , Ponce de León José L , Germon Isabelle , Casane Didier , García-Machado Erik 
Published: August 2023

Abstract
The genus Limia is endemic to the Greater Antilles with a single species in Cuba, Limia vittata, widespread in lowland ecosystems. The limited data available regarding its population structure suggest that its levels of intraspecific divergence are lower compared to other Cuban poeciliids. Furthermore, little is known about the dispersal pathways and demographic changes experienced by present-day populations. By analysing two mitochondrial markers and testing scenarios of dispersal and vicariance, we provide insights into the evolutionary history of L. vittata. Here, we show that this species is formed by three genetically differentiated and geographically delimited groups of populations. We also found moderate intraspecific divergence and a mtDNA pattern of high haplotype diversity and low nucleotide diversity which is consistent with a rapid demographic expansion. Our Bayesian analysis recovered dispersal from eastern to western Cuba as the most likely scenario explaining the current distribution of this species. Limia vittata exhibited a distinct regional distribution of haplogroups that corresponds to major core landmasses within Cuba, along with a signature of rapid population expansion during relatively recent palaeoclimatic changes that occurred during the formation of the archipelago. Similar phylogeographic patterns have been identified in other freshwater and terrestrial taxa on the island.