bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associative white matter tracts selectively predict sensorimotor learning
Authors: Vinci-Booher S. , McDonald D. J. , Berquist E. , Pestilli F. 
Published: June 2024

AbstractHuman learning varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing microstructure of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in learning a sensorimotor task, and further, if the mapping between tract microstructure and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then practiced drawing a set of 40 unfamiliar symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and measured visual recognition learning for the symbols using an old/new 2-AFC task. Results demonstrated that tract microstructure selectively predicted learning outcomes, with left hemisphere pArc and SLF3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated using repeat, held-out data and supported with complementary analyses. Results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes.
Neural connectivity patterns explain why adolescents perceive the world as moving slow
Authors: Ghorbani Foroogh , Zhou Xianzhen , Talebi Nasibeh , Roessner Veit , Hommel Bernhard , Prochnow Astrid , Beste Christian 
Published: June 2024

AbstractThat younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we studied neural activity while adolescent and adult participants segmented a movie. We show when participants were instructed to segment a movie into meaningful units, adolescents partitioned incoming information into fewer encapsulated segments or episodes of longer duration than adults. Importantly, directed communication between medial frontal and lower-level perceptual areas and between occipito-temporal regions in specific neural oscillation spectrums explained behavioral differences between groups. Overall, the study reveals that a different organization of directed communication between brain regions and inefficient transmission of information between brain regions are key to understand why younger people perceive the world as moving slow.
Characterization of a methyltransferase for iterative N-methylation at the leucinostatin termini in Purpureocillium lilacinum
Authors: Li Zixin , Jiao Yang , Ling Jian , Zhao Jianlong , Yang Yuhong , Mao Zhenchuan , Zhou Kaixiang , Wang Wenzhao , Xie Bingyan , Li Yan 
Published: June 2024

AbstractN-methyltransferase (NMT)-catalyzed methylation at the termini of nonribosomal peptides (NRPs) has rarely been reported. Here, we discover a fungal NMT LcsG for the iterative terminal N-methylation of a family of NRPs, leucinostatins. Gene deletion results suggest that LcsG is essential for leucinostatins methylation. Results from in vitro assays and HRESI-MS-MS analysis reveal the methylation sites as NH2, NHCH3 and N(CH3)2 in the C-terminus of various leucinostatins. LcsG catalysis yields new lipopeptides, some of which demonstrate effective antibiotic properties against the human pathogen Cryptococcus neoformans and the plant pathogen Phytophthora infestans. Multiple sequence alignments and site-directed mutagenesis of LcsG indicate the presence of a highly conserved SAM-binding pocket, along with two possible active site residues (D368 and D395). Molecular dynamics simulations show that the targeted N can dock between these two residues. Thus, this study suggests a method for increasing the variety of natural bioactivity of NPRs and a possible catalytic mechanism underlying the N-methylation of NRPs.
Deviatoric stress-induced metallization, layer reconstruction and collapse of van der Waals bonded zirconium disulfide
Authors: Yang Linfei , Li Junwei , Zhang Dongzhou , Liu Yuegao , Hu Qingyang 
Published: June 2024

AbstractIn contrast to two-dimensional (2D) monolayer materials, van der Waals layered transition metal dichalcogenides exhibit rich polymorphism, making them promising candidates for novel superconductor, topological insulators and electrochemical catalysts. Here, we highlight the role of hydrostatic pressure on the evolution of electronic and crystal structures of layered ZrS2. Under deviatoric stress, our electrical experiments demonstrate a semiconductor-to-metal transition above 30.2 GPa, while quasi-hydrostatic compression postponed the metallization to 38.9 GPa. Both X-ray diffraction and Raman results reveal structural phase transitions different from those under hydrostatic pressure. Under deviatoric stress, ZrS2 rearranges the original ZrS6 octahedra into ZrS8 cuboids at 5.5 GPa, in which the unique cuboids coordination of Zr atoms is thermodynamically metastable. The structure collapses to a partially disordered phase at 17.4 GPa. These complex phase transitions present the importance of deviatoric stress on the highly tunable electronic properties of ZrS2 with possible implications for optoelectronic devices.
Degassing of CO2 triggers large-scale loss of helium from magma oceans
Authors: Davis Anne H. , Caracas Razvan 
Published: June 2024

AbstractThe moon-forming impact caused widespread melting and vaporization of the proto-Earth, leading to the formation of a protolunar disk1,2. Volatile elements trapped in the inner parts of the disk condensed together with heavier elements into a global magma ocean3,4. By subsequent cooling and degassing, volatiles contributed to the formation of the Earth’s secondary atmosphere5. The thermodynamic conditions at the magma ocean-atmosphere interface profoundly impacted the degree of degassing that occurred. Today, our understanding of these conditions remains unclear. Here, we simulate the degassing of carbon and helium from a magma ocean with a bulk silicate Earth composition6at presumed typical conditions for the early Earth5,7. We employ first-principles molecular dynamics calculations. We find that volatile loss is determined by pressure, temperature, and melt composition. Carbon and helium devolatilize more easily when both elements are present in the melt, leading to pronounced loss from the magma ocean. Our findings suggest that the early Earth atmosphere was carbon-rich, with a high fraction of helium and other noble gases, and thicker and hotter than previously thought. We evaluate the implications for Earth’s composition and thermal evolution, as well as other planets that may undergo similar processes with different chemistries.
Seawater oxygen isotopes as a tool for monitoring future meltwater from the Antarctic ice-sheet
Authors: Kim Hyuna , Timmermann Axel 
Published: June 2024

AbstractTo reduce uncertainties in future sea level projections, it is necessary to closely monitor the evolution of the Antarctic ice-sheet. Here, we show that seawater oxygen isotopes are an effective tool to monitor ice-sheet freshwater discharge and its contributions to sea level rise. Using the isotope-enabled Community Earth System Model with imposed estimates of future meltwater fluxes, we find that the anthropogenic ice-sheet signal in water isotopes emerges above natural variability decades earlier than salinity-based estimates. The superiority of seawater isotopes over salinity in detecting the ice-sheet melting can be attributed to the higher signal-to-noise ratio of the former and the fact that future sea ice changes only contribute little to seawater isotopes but a lot to salinity. We conclude that in particular in the Ross Sea sector, continuous seawater oxygen isotope measurements could serve as an early warning system for rapid anthropogenic Antarctic ice-sheet mass loss.
Synergistic effect by supported activated carbon between functional groups and metal oxygen vacancies: enhancing ibuprofen degradation by improving ozone mass transfer
Authors: Lai Junda , Huangfu Zizheng , Xiao Jiewen , Wang Zhenbei , Liu Yatao , Li Chen , Li Fan , Jia Yunhan , Wang Qiang , Qi Fei , Ikhlaq Amir , Kumirska Jolanta , Siedlecka Ewa Maria , Ismailova Oksana 
Published: June 2024

The fabrication and application of CuMn2O4@WAC and CuMn2O4@CSAC in heterogeneous catalytic ozonation for degrading IBP were investigated. The adsorption and decomposition mechanisms of ozone on the surfaces of AC and CuMn2O4 were explored.
A blockchain‐based resilient and secure framework for events monitoring and control in distributed renewable energy systems
Authors: Faheem Muhammad , Raza Basit , Bhutta Muhammad Shoaib , Madni Syed Hamid Hussain 
Published: June 2024

AbstractThe rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources () such as solar, hydro, wind, and geothermal into the power grid (). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green is critical to achieve the instant energy demands. Smart grid due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the . However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of . In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called for wireless sensor networks ‐based events monitoring and control in . Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for in .
Detection of autism spectrum disorder using multi‐scale enhanced graph convolutional network
Authors: Singh Uday , Shukla Shailendra , Gore Manoj Madhava 
Published: June 2024

AbstractMagnetic Resonance Imaging (MRI) based Autism Spectrum Disorder (ASD) detection approaches face various challenges due to variations in brain connectivity patterns, limited sample sizes, and heterogeneity of available data. These challenges make it hard to find consistent imaging markers. To address these issues, researchers have focused on advanced analysis methods, such as multi‐modal imaging techniques and graph‐based approaches to gain a comprehensive understanding of ASD neurobiology. However, existing graph‐based approaches for ASD detection have primarily focused on pairwise similarities between individuals, neglecting individual characteristics and features. A novel framework to detect ASD using a Multi‐Scale Enhanced Graph Convolutional Network (MSE‐GCN). The framework combines the functional connectivity of resting‐state functional MRI (rs‐fMRI) with non‐imaging phenotype data from Autism Brain Imaging Data Exchange‐I (ABIDE‐I). The framework uses MSE‐GCN to represent individuals as node in a population graph. Each node corresponds to an individual and connects to feature vectors from imaging data. Edge weights between nodes are assigned to integrate phenotypic information. Then, the multiple parallel GCN layers are designed using random walk embedding. The output of these GCN layers is then combined in the fully connected layer to detect ASD effectively. The performance of the framework is evaluated using the ABIDE‐I dataset. In addition, Recursive Feature Elimination and Multilayer Perceptron are utilised for feature selection. The outcome of this approach shows more than 10% advancement in accuracy, achieving an accuracy of 83% by incorporating phenotypic data in conjunction with MRI data within a GCN.
Toward designing a lightweight RFID authentication protocol for constrained environments
Authors: Zarbi Nasser , Zaeembashi Ali , Bagheri Nasour , Adeli Morteza 
Published: June 2024

AbstractIn present times, Radio‐Frequency Identification (RFID) systems have seen a significant rise in their usage. There has been an increasing interest in developing even lighter RFID protocols suitable for resource‐constrained environments. Ensuring security and privacy remain critical challenges in RFID‐based systems. Recently proposed lightweight authentication schemes, namely LRSAS+ and LRARP+, are ideally suited for constrained devices. However, this article investigates these schemes and reveals certain vulnerabilities: LRSAS+ is susceptible to tag impersonation, desynchronization, and traceability attacks, while LRARP+ can fall prey to traceability and secret disclosure attacks. An enhanced version of these authentication systems is proposed that tackles their inherent weaknesses by leveraging the function. To verify the security of the proposed scheme, a formal analysis is conducted using Gong–Needham–Yahalom logic (GNY logic) and an automated security protocol verification tool, ProVerif. The improved scheme's effectiveness is also compared with multiple contemporary lightweight systems. The results indicate that the enhanced scheme not only meets the security requirements for lightweight authentication schemes but also achieves this with minimal computational overhead.
Hysteresis based predictive torque control without weighting factors for induction motor drives
Authors: Shahid Muhammad Bilal , Jin Weidong , Abbasi Muhammad Abbas , Husain Abdul Rashid , Bhatti Abdul Rauf , Rasool Akhtar , Galib Syed Md. 
Published: June 2024

AbstractThis work proposes a weighting‐factor‐free finite control set predictive torque control (FCS‐PTC) for induction motor drives by incorporating a hysteresis controller to eliminate stator flux error from the cost function. The tuning of the weighting factor coefficient in FCS‐PTC is critical for improved drive performance. Conventionally, the tuning is based on an intuitive understanding of the dynamical system and empirical methods, which may not yield optimal results. The proposed method regulates the flux by incorporating a separate two‐level hysteresis controller and torque by the conventional cost function‐based PTC. The cost function uses torque error only and employs a reduced number of voltage vectors. The reduction of voltage vectors is obtained through the flux hysteresis output and sector determination. The proposed work is implemented experimentally on the dSpace DS1104 controller board for a two‐level three, three‐phase voltage source inverter‐fed induction motor drive. The experimental results validate the effectiveness of the proposed work under different drive tests compared to conventional PTC and direct torque control (DTC). The obtained results show comparable dynamic performance of the drive with lower computational burden than conventional PTC. Along with computational benefit, the proposed work demonstrates improved total harmonic distortion (THD) at low‐speed regions due to the absence of a weighting factor in the cost function.
Enhanced precision in robot arm positioning: A nonlinear damping approach for flexible joint manipulators
Authors: Jafari Amir Hossein , Dhaouadi Rached , Jafari Reza 
Published: June 2024

AbstractThis article introduces an advanced nonlinear controller designed for optimizing the performance of a single‐link robot arm featuring a flexible joint. The proposed nonlinear control strategy incorporates a Proportional‐Integral (PI) controller in conjunction with a nonlinear velocity feedback component, aimed at providing effective nonlinear damping and suppressing vibrations. To validate the controller's performance, extensive simulations are conducted utilizing machine learning techniques within the Python environment. The performance of the proposed nonlinear damping controller is benchmarked against a conventional linear cascaded P‐PI control structure, with both controllers fine‐tuned using the Nelder‐Mead algorithm. Simulation results demonstrate that the nonlinear damping controller yields substantial improvements in the dynamic behavior of the robot axis arm, showcasing superior step and sinusoidal position tracking performance, along with active vibration damping capabilities. This research contributes valuable insights into the enhanced nonlinear control strategies for flexible‐joint robot arms, offering promising advancements in their overall dynamic performance.
Coherence enhancement between incoherent lasers based on passive phase compensation
Authors: Zhu Xiang , Huang Yali , He Hongxia , He Tong , Yu Xianbin 
Published: June 2024

AbstractCoherent light beams play an important role in the development of opto‐electronic microwave and terahertz technologies, serving as a source for generating RF carrier signals with very high quality. The authors explore in this work an approach to enhance the coherence between two incoherent lasers so as to provide coherent light beams, and proof‐of‐concept demonstrates a terahertz signal generation with low phase noise. Based on the proposed passive phase compensation technique, the phase noise between two independent lasers can be well suppressed and the carrier to noise ratio in the terahertz regime remains high. In the experiment, the beating phase noise between two independent lasers is reduced from −60.68 dBc/Hz@10kHz to −90.05 dBc/Hz@10kHz at about 280 GHz, which is promisingly applicable in the terahertz photonics community.
Normalized frequency‐domain block sign algorithm using ℓ1$\ell _{1}$‐norm minimization
Authors: Choi Jeong‐Hwan , Chang Joon‐Hyuk 
Published: June 2024

AbstractIn this letter, a normalized frequency‐domain block sign‐error algorithm (NFBSA) is proposed by minimizing the ‐norm of the posteriori error vector for filter weight. The NFBSA updates the filter weight vector by employing the sign value of the priori error vector, which can mitigate the impact of impulsive noise. Furthermore, a variable step‐size algorithm (VSS) suitable for application in the NFBSA is proposed, which minimizes the ‐norm of the posteriori error vector with respect to the step‐size. The system identification simulations are conducted using a first‐order autoregressive signal as an input, assuming that impulsive noise occurs. The simulation results demonstrate that the NFBSA exhibits superior misalignment performance compared to the conventional time‐domain sign algorithm, and the NFBSA with the proposed VSS outperformed other VSS‐based sign algorithms.
A probabilistic approach on uncertainty modelling and their effect on the optimal operation of charging stations
Authors: K. K. Nandini , N. S. Jayalakshmi , Jadoun Vinay Kumar 
Published: June 2024

AbstractUncertainty analysis deals with the fluctuations and unpredictability of the electrical power generated from renewable resources (RRs), such as solar PV and wind energy systems. This paper gives an insight into various techniques used for the uncertainty analysis and a probabilistic Monte Carlo Simulation is applied for modelling the uncertainties concerned with RRs and electric vehicle (EV) load in the MATLAB platform. The uncertainty associated with the price sensitivity of EV charging and the state of charge of EVs is taken as a prime factor for analysis in the present work. Despite the fluctuations and unpredictability of electricity generation and consumption, the considered system ensures that the total amount of electricity supplied by solar PV, wind and grid matches the total amount of electricity demanded by EV load. Rao‐1, Rao‐2 and Rao‐3 algorithms are applied in this work to optimize the operation cost of charging stations under uncertain conditions and without any uncertainties. The results obtained without uncertainties by Rao algorithms are compared with the existing particle swarm optimisation method. In the presence of uncertainties, Rao‐1 and Rao‐2 algorithms are compared with Rao‐3 and it is found that the Rao‐3 algorithm performed better.
Optimized decomposition and identification method for multiple power quality disturbances
Authors: Zhaoqing Wang , Yanzhao Chang , Jianlei Chen , Weiyu Bao 
Published: June 2024

AbstractThe complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability.
Research on transmission line path planning model based on TFN‐AHP and ACO
Authors: Wang Bin , Hua Chunliang , Luo Haijun , Suo Bao , Zu Guohua 
Published: June 2024

AbstractTransmission line planning is affected by a variety of geographic information factors, and traditional path planning methods are difficult to meet the future intelligent planning needs. Here, based on the geographic information system platform for geographic information data processing, the triangular fuzzy number analytic hierarchy process (TFN‐AHP) is utilized for weight analysis to construct the cost layer. Meanwhile, guided mechanism, dimension‐reducing mechanism, and avoidance mechanism are proposed to improve the ant colony optimization (ACO) algorithm, to shorten the planning time and improve the route planning effect. Finally, a local area in Guizhou Province is selected for case study and compared with the results of traditional ant colony optimization, and the results prove the effectiveness of the transmission line planning model proposed here.