bluuebunny/crossref_metadata_2025_split huggingface dataset:
A resilience‐oriented restoration framework for multi‐area active distribution network following a disaster
Authors: Beiranvandi Hekmat , Samanfar Amin , Doostizadeh Meysam , Saki Reza 
Published: July 2024

AbstractA disaster disrupts energy trading between the distribution network (DN) and autonomous areas in the multi‐area active distribution network (MA‐ADN). Since these areas are self‐interested agents, the energy transaction cost between them should be considered when determining the grid restoration scheme. In this regard, a new three‐level resilience‐oriented restoration (TLROR) framework is proposed to optimally schedule available tie‐lines, and distributed energy resources (DERs) in the MA‐ADN, considering the autonomy and privacy of their ownership. In the first level of the proposed TLROR, an electrical price vector (EPV) is created with the contribution of different areas. In the second level, each area should calculate the amount of imported active and reactive power from the DN through its tie branches and save it in the transactive power list (TPL). Finally, at the third level, the transactive energy market is cleared by the DN operator considering economic issues and operating limits. The proposed method provides energy transactions between independent agents to enhance MA‐ADN resiliency, with the least information sharing, and privacy preserving. The effectiveness of the proposed method is verified using the modified IEEE 33‐bus MA‐ADN and a part of the DN in the Pol‐e Dokhtar in the Lorestan province of Iran.
Array strategy enhances low-frequency radiation intensity and low-frequency magnetic field sensing SNR of magnetoelectric antenna
Authors: Wang Yinan , Zi Guohao , Song Enzhong , Wang Yuanhang , Zhao Shanlin , Ma Zhibo 
Published: July 2024

The magnetoelectric (ME) coupling effect is a more effective approach for reducing antenna size. Nevertheless, at low frequencies, a single ME structure generates a weak signal strength and a low signal-to-noise ratio (SNR). This study utilized an array strategy to improve the radiation and induced electromagnetic field performance of ME antennas. In this study, the array ME coupling structure, composed of Metglas and Pb (Zr1−xTix) O3 (PZT) bilayers, was operated through ME coupling resonance modes. The relationship between the sensitivity and SNR of the serially connected ME antenna array and the number of series connections was determined. On the transmission side, the impact of the multi-source power supply modes on the radiation intensity and directivity of the ME antennas was analyzed. The sensitivity, SNR, magnetic detection limit, directivity, and radiation range of the single ME and array ME antennas were tested for the key parameters. Finally, it was demonstrated that at a frequency of 31.75 kHz, the array strategy achieved a low-frequency signal transmission distance of up to 48.1 m, which is 1.7 times that of a single ME antenna. This array strategy significantly enhances the radiation intensity and magnetic field SNR of ME antennas in the low-frequency range, demonstrating its application prospects in the field of low-frequency communication.
Eyes do not lie but words do
Authors: Arslan Seçkin , Tunalı Elif Tutku , Çetin Yağmur , Aydın Özgür 
Published: July 2024

Abstract
Evidentiality encodes how a speaker has access to the information contained in his/her proposition. It has been
shown that some ‘evidential language’ speakers make a deliberate choice of evidentials while telling lies (Aikhenvald 2004). In this study, we recruited 40 native speakers of Turkish, an ‘evidential language’, to
judge statements with evidentials using an eye-movement-monitoring-during-reading study with an end-of-sentence deception
detection task. The participants read sentences with four conditions, containing a direct or indirect evidential form either
compatible or incompatible with the given information source. Our results show that the indirect evidential condition was detected
as a lie more often than the direct evidential condition. Readers had the tendency to judge stimulus material with
source-evidentiality mismatch to be untruthful. These findings were mirrored in the eye-movement data, as we found gaze duration
to be longer at the critical verb region for indirect evidential and mismatch conditions.
Tipler naked singularities in N dimensions
Authors: Solanki Kharanshu N , Mosani Karim , Deshpande Omkar , Joshi Pankaj S 
Published: July 2024

AbstractA spacetime singularity, identified by the existence of incomplete causal geodesics in the spacetime, is called a (Tipler) strong curvature singularity if the volume form acting on independent Jacobi fields along causal geodesics vanishes in the approach of the singularity. It is called naked if at least one of these causal geodesics is past incomplete. Here, we study the formation of strong curvature naked singularities arising from spherically symmetric gravitational collapse of general type-I matter fields in an arbitrarily finite number of dimensions. In the spirit of Joshi and Dwivedi (1993Phys. Rev.D475357), and Goswami and Joshi (2007Phys. Rev.D76084026), beginning with regular initial data, we derive two distinct (but not mutually exclusive) conditions, which we call thepositive root condition(PRC) and thesimple positive root condition(SPRC), that serve as necessary and sufficient conditions, respectively, for the existence of naked singularities. In doing so, we generalize the results of both the aforementioned works. We further restrict the PRC and the SPRC by imposing the curvature growth condition (CGC) of Clarke and Krolak (1985J. Geom. Phys.2127) on all causal curves that satisfy the causal convergence condition. The CGC then gives a sufficient condition ensuring that the naked singularities implying the PRC and implied by the SPRC, are of strong curvature type and hence correspond to the inextendibility of the spacetime. Using the CGC, we extend the results of Mosaniet al(2020Phys. Rev.D101044052) (that hold for dimensionN = 4) to the caseN = 5, showing that strong curvature naked singularities can occur in this case. However, for the caseN⩾6, we show that past-incomplete causal curves that identify naked singularities do not satisfy the CGC. These results shed light on the validity of the cosmic censorship conjectures in arbitrary dimensions.
Scalar fields around a rotating loop quantum gravity black hole: waveform, quasi-normal modes and superradiance
Authors: Xia Zhong-Wu , Yang Hao , Miao Yan-Gang 
Published: July 2024

Abstract
The rotating loop quantum gravity black hole is a newly proposed non-singular black hole, which eliminates spacetime singularities when a regularization parameter is introduced through loop quantum corrections. This parameter is expected to give rise to observable effects. In this paper, the dynamical behavior of a scalar field near a rotating loop quantum gravity black hole is investigated. Given a small initial perturbation, we obtain the waveform of massless scalar fields evolving over time. By analyzing the waveform, we find that the regularization parameter only affects the damping oscillation of waveform, but not the initial outburst and late-time tail stages. This behavior is characterized by quasi-normal modes (QNMs). Under scalar field perturbations, the loop quantum black holes remain stable. Moreover, we calculate the QNMs of massive scalar fields by three numerical methods, which are the Prony, WKB, and shooting methods, respectively. Our results indicate that the real part of QNMs increases when the regularization parameter and angular momentum grow, while the imaginary part depends on the two parameters with a more complex relationship. Finally, we study the amplification effect of rotating black holes, i.e. the superradiance. Our analyses indicate the existence of stronger superradiance around loop quantum gravity black holes compared to Kerr ones.
Inferring dynamical models from time-series biological data using an interpretable machine learning method based on weighted expression trees
Authors: Zhou Yu , Zou Xiufen 
Published: July 2024

Abstract
The growing time-series data make it possible to glimpse the hidden dynamics in various fields. However, developing a computational toolbox with high interpretability to unveil the interaction dynamics from data remains a crucial challenge. Here, we propose a new computational approach called automated dynamical model inference based on expression trees (ADMIET), in which the machine learning algorithm, the numerical integration of ordinary differential equations and the interpretability from prior knowledge are embedded into the symbolic learning scheme to establish a general framework for revealing the hidden dynamics in time-series data. ADMIET takes full advantage of both machine learning algorithm and expression tree. Firstly, we translate the prior knowledge into constraints on the structure of expression tree, reducing the search space and enhancing the interpretability. Secondly, we utilize the proposed adaptive penalty function to ensure the convergence of gradient descent algorithm and the selection of the symbols. Compared to gene expression programming, ADMIET exhibits its remarkable capability in function fitting with higher accuracy and broader applicability. Moreover, ADMIET can better fit parameters in nonlinear forms compared to regression methods. Furthermore, we apply ADMIET to two typical biological systems and one real data with different prior knowledge to infer the dynamical equations. The results indicate that ADMIET can not only discover the interaction relationships but also provide accurate estimates of the parameters in the equations. These results demonstrate ADMIET’s superiority in revealing interpretable dynamics from time-series biological data.
Design strategies and applications of atomically dispersed aerogel catalysts prepared from metal hydrogel formation
Authors: Li Wenyu , Chen Yang , Liu Chuntai , Zhang Yi 
Published: July 2024

Abstract
The preparation of metal hydrogel precursors toward atomically dispersed aerogel catalysts is a captivating subject within the catalysis research domain, exhibiting promising applications in various fields. This minireview intend to summarize those novel design principles and practical applications of metal hydrogel precursors in the creation of atomically dispersed aerogel catalysts. The manuscript focuses on crucial aspects such as the meticulous selection of metal sources and initiators, the intricate process of gel preparation, a comparative analysis of different drying methods, and strategic optimization techniques to enhance the performance of metal hydrogel precursors. Moreover, this article delves into the exploration of atomically dispersed aerogel catalysts derived from metal hydrogel precursors in the realms of photocatalysis and electrocatalysis. Lastly, existing challenges are addressed, and prospective future directions for development are outlined.
A comparative analysis between deep neural network-based 1D-CNN and LSTM models to harness the self-sensing property of the shape memory alloy wire actuator for position estimation
Authors: Singh Samarth , Bhargaw Hari N , Jadhav Mahendra , John Preetesh 
Published: July 2024

Abstract
The article presents a performance-based comparative analysis of popular deep neural network (DNN) models such as 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) for position estimation of shape memory alloy (SMA)-based wire actuator. These DNN models utilize the self-sensing property (SSP) for position estimation of the SMA actuator. The phase-dependent electrical resistivity of SMA wire acts as SSP, where the electrical resistivity in the form of SMA wire resistance acts as inputs to the proposed models for precise estimation of the current position of the SMA actuator. For effective position control of the SMA actuator, accurate position sensor feedback is required, utilizing SSP results in the elimination of this external sensor. This will improve the overall system in terms of compactness and reduced interface complexity. Coming to DNN models, 1D-CNN has been meagerly explored in the current literature landscape for self-sensing estimation of SMA actuators. These 1D-CNN models are becoming quite popular for time series prediction for various applications and are emerging as an alternative to widely used LSTM models. In this paper, a novel implementation of a 1D-CNN model for SMA actuator position estimation has been done. A comparative analysis between 1D-CNN and LSTM has been done for prediction capability and inference speed based on performance measures such as Mean Square Error (MSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), data distribution, and average inference speed. The proposed comparative results show that 1D-CNN has a matching performance with the LSTM model with respect to prediction capability, however, 1D-CNN offers faster inference speed. The analysis of the proposed work can be useful for choosing a suitable DNN model for deployment on low computing platforms such as microcontrollers for SMA actuator-based real-time applications where time latency is a critical parameter.
Spin-polarization and Coulomb interaction dependent thermal rectification in a quantum dot system
Authors: Su Yufeng , Qi Fenghua , Jin Guojun 
Published: July 2024

Abstract
Based on the master equation approach, we investigate the thermal transport through a diode composed of a quantum dot under Coulomb interaction and tunnel-coupled to two ferromagnetic leads with antiparallel spin polarizations. We analyze the effects of spin polarizations, Coulomb interaction, mean temperature and Zeeman splitting on the thermal rectification. Firstly, we find that the thermal rectification effect is enhanced with the increase of spin polarization, because the mirror-symmetry of the system is broken by the anti-parallel spin polarization. Especially, when both leads are fully spin polarized, the asymmetry of the heat transferred by Coulomb interaction under the opposite temperature bias leads to the appearance of perfect thermal rectification and negative differential thermal conductance. Secondly, we find whether the system is in a Coulomb blockade state greatly affects the thermal rectification coefficient. As the average temperature increases or the intradot Coulomb interaction decreases, the system gradually escapes from the Coulomb blockade state, resulting in a reversal of the thermal rectification direction and ultimately leading to an increase in the rectification coefficient. Thirdly, we also find that the Zeeman splitting can be utilized to modulate the behavior of thermal rectification. Thermal rectification occurs only when Zeeman splitting and spin polarization coexist, and under different spin polarizations, the rectification coefficient exhibits different trends with the change of Zeeman splitting. These observations indicate that this structure holds potential application at a thermal rectifier as well as a thermal detector of magnetic fields.
CT-YoloTrad: fast and accurate recognition of point-distributed coded targets for UAV images incorporating CT-YOLOv7
Authors: Li Ruiguang , Wang Qiang , Wang Shun , Mao Jian , Zhang Zhenxin , Cui Ximin 
Published: July 2024

Abstract
Artificial point-distributed coded targets owns unique coded sequence numbers that can be recognized automatically. To address the issue of decreasing recognition accuracy and efficiency of existing recognition methods in complicated circumstances, an improved object detection model for coded target acquisition from unmanned aerial vehicle (UAV) images, CT-YOLOv7, is proposed. This improved model is based on the original YOLOv7 model, replacing several Conv with partial convolution (PConv), while introducing the bi-level routing attention mechanism, and designing the CBS-R structure and CBS-PR structure. In addition, the loss function is replaced with WIOU loss function to further improve the model’s performance. Based on the above, the new recognition method of point-distributed coded targets for UAV images is organized as follows. Firstly, CT-YOLOv7 is embedded into the front-end of the classical coded targets recognition process (that is, the coded targets are first extracted). Then, the extraction results are fed into the classical recognition algorithm for recognition. Lastly, the recognition results are inverse-calculated back to the original image. The new method aims to focus the processing on the region of interest to achieve fast and accurate coded targets recognition for UAV images. The experimental results show that CT-YOLOv7’s detection accuracy is 90.83%, which improves the accuracy by 8.46% and reduces the computation by 11.54% compared to the original YOLOv7. By incorporating the CT-YOLOv7 model, the time consumption for coded target recognition of a single UAV image is 150–350ms, which improves the average efficiency by 3–5 times compared with the classical method. Furthermore, the proposed method can correctly recognize regions with shadows and noise, and the recognition accuracy is improved by 15%–40%. With the method proposed in this paper, the coded targets are expected to be applied into UAV photogrammetry or remote sensing to realize accurate and quasi-real-time recognition.
Strain-tuned electronic and valley-related properties in Janus monolayers of SWSiX2 (X = N, P, As)
Authors: Qi 戚 Yunxi 云西 , Zhao 赵 Jun 俊 , Zeng 曾 Hui 晖 
Published: July 2024

Abstract
Exploring novel two-dimensional (2D) valleytronic materials has an essential impact on the design of spintronic and valleytronic devices. Our first principles calculation results reveal that the Janus SWSiX
2 (X = N, P, As) monolayer has excellent dynamical and thermal stability. Owing to strong spin–orbit coupling (SOC), the SWSiX
2 monolayer exhibits a valence band spin splitting of up to 0.49 eV, making it promising 2D semiconductor for valleytronic applications. The opposite Berry curvatures and optical selection rules lead to the coexistence of valley and spin Hall effects in the SWSiX
2 monolayer. Moreover, the optical transition energies can be remarkably modulated by the in-plane strains. Large tensile (compressive) in-plane strains can achieve spin flipping in the SWSiN2 monolayer, and induce both SWSiP2 and SWSiAs2 monolayers transit from semiconductor to metal. Our research provides new 2D semiconductor candidates for designing high-performance valleytronic devices.
Detection of Increasing γ-Ray Emissions from 4FGL J1718.5+4237 with Fermi-LAT
Authors: Zheng Jiao , Zhang Pengfei , Zhang Li 
Published: July 2024

Abstract
We report a gradual brightening γ-ray source, 4FGL J1718.5+4237, in 0.1–500.0 GeV, which may be associated with a blazar NVSS J171822+423948 with a redshift of ∼2.7. We analyzed 15.25 yr of γ-ray data recorded by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope and detected significant γ-ray emissions in the direction of the blazar with a test statistic (TS) of ∼135. Based on timing analysis using a 1 yr time bin, we have observed a gradual brightening in γ-ray emissions from the target. In our analysis, we categorize them into two states: Quiet (TS ∼ 0) and Loud (TS ∼ 226) states, with the distinction occurring in 2016 August (MJD 57602.69). From the Quiet state to the brightest period (the last data point), the γ-ray flux in 0.1–500.0 GeV increased by more than 12-fold from <0.2 × 10−8 photons cm−1 s−1 to 2.6 × 10−8 photons cm−1 s−1. Additionally, we studied the spectral properties in detail for the Loud state and the overall data. While no significant variation was detected, both exhibited a spectral index Γ of ∼2.6. The origin of the brightening γ-ray emissions from the target is not yet clear. Future long-term multi-wavelength observations and studies will provide insight into the astrophysical mechanisms of the target.
Measurement of bidirectional transmittance distribution function in the visible and near-infrared spectral range
Authors: Aschan Robin , Manoocheri Farshid , Lanevski Dmitri , Ikonen Erkki 
Published: July 2024

Abstract
This work characterizes a facility for bidirectional transmittance distribution function (BTDF) measurements in the visible and near-infrared (NIR) wavelength range. The facility includes an absolute reference gonioreflectometer, with an extended capability for BTDF measurements, and a commercial instrument, Cary 7000, using a goniometer extension, Universal Measurement Accessory. The facility characterization includes measurements of two quasi-Lambertian diffusers for their BTDF. The diffuser samples include a piece of porous polytetrafluoroethylene (PTFE) and a piece of fused synthetic silica, HOD-500. The samples are measured for their spectral BTDF in the wavelength range from 450 nm to 1650 nm in 50 nm steps, and in the viewing zenith angle range from −35° to 35° in 5° steps. Measurements are performed in-plane, using an incident zenith angle of 0°, while rotating the samples around their surface normal from 0° to 180° in 90° steps. Rotation of the sample serves the dual purpose of checking the sample rotational symmetry in azimuth angles and for averaging the measurement results. The characterization results show that both samples exhibit Lambertian characteristics across the visible and NIR wavelength range. Furthermore, both samples show a smooth increase in their spectral BTDF as a function of wavelength. Notably, the PTFE sample demonstrates a steeper slope in its spectral BTDF and a higher signal above 650 nm. The facility capability for measurements of spectral BTDF is validated by a rigorous uncertainty analysis and by comparing the difference in measurement results between the absolute gonioreflectometer and the commercial instrument. Characterization results indicate that the absolute reference gonioreflectometer has a standard uncertainty ranging from 0.29% to 0.42%, dependent on the BTDF measurement wavelength. Experimental results show that the devices of the facility deviate in their BTDF within their combined expanded uncertainty, affirming the capability and reliability of the facility for BTDF measurements.