bluuebunny/crossref_metadata_2025_split huggingface dataset:
A DFT/TD-DFT study on the π-spacer effect of coumarin 102-Based dyes for dye-sensitized solar cells applications
Authors: Ebied Mostafa Saad 
Published: December 2024

Abstract
Developing new organic dyes with optimized electronic structures and superior light-harvesting capabilities is crucial for enhancing the efficiency of dye-sensitized solar cells (DSSCs). In the current study, four novel D-π-A organic dye molecules (MC1-MC4), with various π spacers, were designed based on Coumarin-102 dye to investigate their prospective uses in dye-sensitized solar cells (DSSCs). Therefore, DFT and TD-DFT calculations were utilized to explore the electronic structure and photophysical and photovoltaic properties of the designed dyes. Specifically, the optimized structures, electronic parameters, Frontier molecular orbitals (FMOs), Natural Bond Orbital (NBO) analysis, Density of States (DOS) analysis, and Global Reactivity Parameters (GRPs) were evaluated. The calculated values of the energy difference between HOMO and LUMO ranged from 2.63 to 2.97 eV, with the MC2 dye having the smallest band gap (2.63 eV). Furthermore, the influence of π spacers on the performance of DSSCs was studied by calculating the Non-Linear optical (NLO) characteristics, UV–vis spectra, and other important parameters determining the photovoltaic performance of DSSCs. The results show that MC2 exhibits the highest value of light-harvesting efficiency (0.96) and a more negative ΔGinject (−1.323 eV), indicating that MC2 would demonstrate a higher JSC and could theoretically be a better sensitizer of DSSC device.
In situ electrosynthesis, optimization and stability of AgNPs within chitosan: pullulan polymeric network
Authors: Zidan Mostafa M , Reicha Fikry M , Mansour-Gabr Mohamed M , Elmezayyen Ayman S 
Published: December 2024

Abstract
In this study, a systematic investigation to achieve a deeper understanding of the synergistic effect of chitosan (CS) and pullulan (PL) as a polymeric matrix on the in situ electrosynthesis AgNPs under applied voltage 1.5 V for 2 h. The process was applied using CS: PL blend with different ratio as electrolytic solution, reducing, and stabilizing agent avoiding using toxic reducing agent, byproduct of the reaction, controlling AgNPs size and stability. Investigation of the samples using UV–visible spectroscopy (UV–vis), Inductively coupled plasma optical emission spectroscopy (ICP), transmission electron microscope (TEM), Fourier transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS) and Thermogravimetric analysis (TG) revealed that AgNPs successfully formed under controlling the size and stability. Across all ratios except using only PL as an electrolyte, the results revealed the occurrence [chitosan/pullulan] Ag+ complex band at around 250 nm and a localized peak at 420 nm attributed to the SPR of AgNPs. TEM confirmed AgNPs formation with decreasing in size and increasing in concentration as a function of increasing CS content. Findings obtained by quantifying the concentration of silver using ICP whereas the concentration of CS is raised, the concentration of silver liberated increases. Zeta potential revealed that AgNPs have a positive surface charge maximized with increasing CS content. Furthermore, stability against aging for 2 months at 4 °C revealed that the addition of PL to CS matrix works on increasing the stability of AgNPs against aging.
Green-mediated sol-gel fabrication of CuO/ZnO nanoparticles from orange peel extract for environmental remediation: insights from x-ray diffraction analysis using various models
Authors: Ali Dilawar , Muneer Iqra , Pirzada Muntaha , Ejaz Tanzeela , Yasmeen Farhat , Bashir Farooq , Hanif Muhammad , Aziz Muhammad Hammad , Wahab Rizwan 
Published: December 2024

Abstract
This study introduces an eco-friendly and simple approach for synthesizing zinc oxide (ZnO), copper oxide (CuO), and ZnO-CuO composite nanoparticles using orange peel extract, minimizing the use of harmful chemicals while enhancing their antibacterial and photocatalytic properties. The nanoparticles were annealed at 400 °C for 2 h in the air. Characterization was conducted using x-ray Diffraction (XRD), Dynamic Light Scattering, Fourier Transform Infrared Spectroscopy (FTIR), and UV-visible Spectroscopy. XRD revealed that ZnO and CuO nanoparticles crystallized in hexagonal wurtzite and monoclinic structures, respectively, with crystallite size and lattice strain analyzed through Williamson–Hall, Size-Strain Plot, Halder-Wagner, and Wagner-Aqua methods. FTIR spectra confirmed the presence of Zn-O and CuO bonds, verifying successful synthesis. The optical bandgaps measured were 3.07 eV for ZnO, 2.702 eV for CuO, and 1.842 eV for the ZnO-CuO nanocomposite. Antimicrobial efficacy, assessed via the disc diffusion method, showed the ZnO-CuO composite exhibited enhanced antibacterial activity against both gram-positive and gram-negative strains compared to individual ZnO and CuO. Photocatalytic experiments demonstrated that under sunlight, the ZnO-CuO nanocomposite achieved 78% degradation of 10 ppm methylene blue within 90 min, outperforming individual ZnO (55%) and CuO (38%). These results highlight the ZnO-CuO composite’s potential for effective dye degradation and environmental remediation applications.
Electromagnetic shielding enhancement in cement composites: a comparative study of mechanically and thermally recycled carbon fibers
Authors: Öztürk Murat 
Published: December 2024

Abstract
The recycling of carbon fibers that have reached the end of their service life and their reintegration into new applications hold significant importance from an environmental and sustainable perspective. In this study, the aim is to enhance the electromagnetic wave shielding properties of cement-based composites by incorporating mechanically and thermally recycled and ball-milled carbon fibers at volumetric ratios of 1%, 2%, and 3%. Electromagnetic tests are conducted in the X-band frequency range (8–12.5 GHz), commonly preferred for microwave applications. The obtained results demonstrate an improvement in the SE
A
(absorption loss )and SE
T
(total power loss) values of the composite with the use of recycled fibers. This improvement is particularly more pronounced in composites containing thermally recycled fibers. The enhanced performance is attributed to the electrical conductivity imparted by carbon fibers. At a frequency of 11.9 GHz, the SE
T
value increased from 8.5 dB for the control sample to 9.2 dB for composites with 3% mechanically recycled fibers and up to 19 dB for composites with 3% thermally recycled fibers. These findings indicate that using recycled carbon fibers enhances the electromagnetic wave shielding properties, with thermal recycling contributing more significantly to this enhancement.
Exploring Milky Way rotation curves with Gaia DR3: a comparison between ΛCDM, MOND, and general relativistic approaches
Authors: Beordo William , Crosta Mariateresa , Lattanzi Mario Gilberto 
Published: December 2024

Abstract
With the release of Gaia DR3, we extend the comparison between dynamical models for the Milky Way rotation curve initiated in the previous work. Utilising astrometric and spectro-photometric data for 719143 young disc stars within |z| < 1 kpc and up to R ≃ 19 kpc, we investigate the accuracy of MOND and ΛCDM frameworks in addition to previously studied models, such as the classical one with a Navarro-Frenk-White dark matter halo and a general relativistic model. We find that all models, including MOND and ΛCDM, are statistically equivalent in representing the observed rotational velocities. However, ΛCDM, characterized by an Einasto density profile and cosmological constraints on its parameters, assigns more dark matter than the model featuring a Navarro-Frenk-White profile, with the virial mass estimated at 1.5–2.5 × 1012 M⊙ — a value significantly higher than recent literature estimates. Beyond 10–15 kpc, non-Newtonian/non-baryonic contributions to the rotation curve are found to become dominant for all models consistently. Our results suggest the need for further exploration into the role of General Relativity, dark matter, and alternative theories of gravitational dynamics in shaping Milky Way's rotation curve.
Research on high-power MRS power supply based on ZVS
Authors: Zhao Y , Sun KZ , Shang XL 
Published: December 2024

Abstract
Magnetic resonance sounding is currently the only type of trenchless detection method for groundwater exploration, offering advantages such as comprehensive information acquisition, unique interpretation, and quantifiable results. The main challenge faced by magnetic resonance sounding in actual detection processes is the complex electromagnetic noise interference in the environment, making it difficult to ensure a high signal-to-noise ratio for water detection. The signal detected by the instrument can be stacked multiple times, which increases the signal-to-noise ratio by √ n times. Therefore, the collected signal can be stacked multiple times through repeated measurements. However, the power supply system of previous nuclear magnetic resonance water detectors used the LCC resonance mode, which presents issues such as low power, fewer signal acquisition times per unit time, and difficult heat dissipation. This article proposes a nuclear magnetic resonance power supply system based on Zero Voltage Switching (ZVS), which adopts constant resonance technology to reduce switching loss and shorten the time required for the power supply to charge the energy storage capacitor. This approach increases the number of measurements per unit time, thereby improving the signal-to-noise ratio.
Landslide microseismic observation with nodal seismometers and Distributed Acoustic Sensing
Authors: Liao Xiaolong , Wang Baoshan 
Published: December 2024

Abstract
On February 22, 2023, a large-scale landslide involving 10 million cubic meters of material occurred at the Xinjing Coal Mine in Inner Mongolia, China, resulting in significant casualties and property damage. To investigate the secondary effects of this landslide, microseismic activity was monitored one week after the event using Distributed Acoustic Sensing (DAS) and nodal seismometers. The instruments successfully recorded microearthquakes, which were then located using double-difference earthquake location techniques. The analysis revealed that the microseismic activity was concentrated near the underground goaf, correlating with the primary cause of the landslide — an excessively steep slope in the surface coal mine. This suggests that the landslide remains in an unstable state.
Research on precise detection of landfill based on high density resistivity method
Authors: Yuankun  , Hongchao Lu , Yinsheng Yao , Chenxing  
Published: December 2024

Abstract
Aiming at the technical problems of accurate detection of landfill sites, this paper first establishes two-dimensional and three-dimensional anomaly identification models based on the resistivity of surrounding rock at the actual boundary of landfill sites, analyzes the abnormal response characteristics of landfill sites, and obtains the electrical distribution law. After that, the 3D high density resistivity method was used to measure the landfill site, the electrical distribution law of the rock in the area was used to interpret the data, and the drilling verification was used to successfully realize the accurate detection of the landfill site.
Design and Offshore Testing of the Nodal Deep Sea Seismic–Electromagnetic Joint Acquisition System
Authors: Yao Yongchao , He Zhanxiang , Ying Zhiqi , Zhao Yunsheng 
Published: December 2024

Abstract
This paper presents the design, development, and offshore validation of OBSEM, a pioneering marine seismic-electromagnetic joint acquisition system aimed at enhancing comprehensive underwater observation capabilities. The system integrates the functionalities of ocean bottom seismometers (OBS) and ocean bottom electro-magnetometers (OBEM), enabling synchronous collection of seismic and electromagnetic signals. Detailed are the structural components, including sensors, data acquisition unit, power supply, clock synchronization system, and signal conditioning techniques employed to augment the signal-to-noise ratio of low-frequency electromagnetic signals. Noteworthy is the clock synchronization methodology employing a hybrid GPS and atomic clock configuration for precise temporal alignment. Furthermore, an automatic leveling system is engineered to maintain the seismometer’s horizontal orientation, crucial for ensuring data fidelity. The integration of seismic and electromagnetic data offers a significant advancement in marine geophysical surveys, promising heightened exploration efficacy and inversion accuracy. Offshore trials conducted in the South China Sea affirm the efficacy of OBSEM, with future endeavors aimed at deep-sea validation.
Analysis of the performance of frequency-Bessel transformation surface wave tomography method in geotechnical engineering investigation
Authors: Chai Haibin , Han Sixu , Luo Qiang , Shao Kui , Wen Yonghui , Yang Zhentao , Gao Yuqiu 
Published: December 2024

Abstract
The frequency-bessel transform surface wave tomography method has emerged as a novel method for surface wave imaging during the past few years. This technique facilitates imaging large-scale crust-mantle structures and enables shallow engineering exploration. Compared to conventional methods like SPAC, it markedly enhances the quality of the surface wave dispersion curve, particularly in higher mode, enhancing the reliability of inversion. Its active and passive source data processing integration is particularly noteworthy, significantly broadening surface wave methods’ application scope. Our research reviews the performance of this technique in addressing common geological issues encountered in geotechnical engineering investigation, such as bedrock surface detection, cover layer detection, karst detection, and permafrost layer detection. Moreover, it recommends this innovative technique’s widespread adoption and further advancement.
Three dimensional inversion of mise-a-la-masse-based electric data using U-Net convolutional neural network
Authors: Meng Yuhang , Li Jinghe , He Zhanxiang , Zhang Yujie 
Published: December 2024

Abstract
Nonlinear inversion algorithms are away ill-pose for the limited and special data measurement, such as mise-a-la-masse-based electric method and borehole to surface electromagnetic method. It is natural to develop new inversion algorithms without calculating the the Jacobian matrix and its inverse matrix, especially for three dimensional inversion. Basing on U-Net fully convolutional neural network, a three dimensional inversion of mise-a-la-masse-based electric data is proposed in this paper. The random rotated spheroids are used to synthesize diverse geological models. A typical U-Net learning network is constructed with incorporating the Dice coefficient and the input data feedback to the trained network. Synthetic datasets testing validates the effectiveness of this proposed algorithm for mise-a-la-masse-based electric data.
Two-dimensional deep learning magnetotelluric inversion
Authors: Liu W , Wang H , Guo T , Yan MS , Xi ZZ 
Published: December 2024

Abstract
Deep learning (DL) inversion methods have showcased promising applications in solving geophysical inverse problems. In this work, we develop a DL model (SwinTUNet) to enable accurate reconstruction of the nonlinear mapping between the network inputs and outputs and achieve two-dimensional (2-D) magnetotelluric (MT) inversion. SwinTUNet is built by employing the strong Swin Transformer as the backbone network and adopting the UNet architecture. Moreover, to create a training dataset that aligns with practical MT inverse problems and ensuring effective applications, we design and generate a set of random synthetic resistivity models with gradual-varying resistivity values. Inversion examples demonstrate that the proposed SwinTUNet inversion method holds a great promise in promoting the applicability of DL inversion methods in realistic MT prospecting scenarios.
Online evaluation method of current transformer operation error based on differential noise filtering and current carrying classification
Authors: Qi Chengfei , Wang Yachao , Zhang Yuning , Wang Yaoyu , Zhang Ru , Bi Chaoran , Yi Shuxian , Zhang Xiwei , Liu Yan 
Published: December 2024

Abstract
A current transformer is an important equipment for detection and protection in power systems. Its stability is poor in long-term operation. In this paper, an online evaluation method of current transformer operation error based on differential noise filtering and current carrying classification is proposed. The stability and classification of the current secondary side data are carried out by using the differential noise filtering-current carrying classification method. The principal component analysis (PCA) method is used to analyze and model the processed measurement data, and the evaluation standard quantity Q is obtained. By comparing the real-time evaluation statistics and standard quantities, the real-time evaluation of the current transformer measurement error is completed. Comparative experiments show that the measurement error of 0.2 level current transformer can be accurately evaluated by using the method proposed in this paper.
Power tower bolt looseness detection method based on phase-sensitive optical time domain reflection technology
Authors: Xu Qiang , Ji Hang , Si Wenrong , He Bing , Zhao Yingying 
Published: December 2024

Abstract
Due to the vibration threat during the operation of equipment, the bolt structure of the power tower will be loose, which seriously threatens the safety of power operation and maintenance. An innovative method utilizing the principle of phase-sensitive optical time domain reflection has been designed to effectively detect the loosening of bolts on power towers in response to the aforementioned issues. Phase-sensitive optical time-domain reflection technology is introduced to capture subtle vibration signals caused by bolt loosening. The detection principle is constructed to adjust the components of the static magnetic field in different directions so that the electromagnetic ultrasonic transducer can receive transverse guided waves and longitudinal guided waves. An in-depth analysis was conducted on the operational mechanism of phase-sensitive optical time-domain reflection technology. According to this principle, the ultrasonic wave emitted by an electromagnetic ultrasonic transducer propagates along a certain path, and the guided wave velocity is calculated. Thus, the realization process of bolt looseness detection in a power tower is constructed. The experimental results show that the loosening rate of bolts is effectively reduced after the application of this method, which greatly promotes overall safety. The root mean square error of this method is smaller, and the detection effect is the best.
A knowledge graph-based method for intelligent risk assessment of power grid
Authors: Zhang Jun , Zhang Chen , Han Xinyang , Liu Xiaohan , Zhu Xiaoping , Chen Lifei , Jin Xiaoling 
Published: December 2024

Abstract
With the advancement of science and technology, the material resources that support people’s lives are becoming increasingly abundant, leading to increasingly complex accidents in the electricity system caused by factors such as Natech. Currently, a superficial security check problem exists in analyzing potential hazards in China. Moreover, the evaluation work becomes superficial, resulting in inaccurate identification results and making it difficult to ensure the long-term safety and stability of the power system. Based on these issues, this paper proposes a power system safety assessment method based on a standardized digital knowledge graph (KG). In this method, firstly, through research on standardized digital transformation theory, a triplet dataset based on event accidents is established to support knowledge graph construction; secondly, through knowledge graph construction, hazard analysis and evaluation of the power system are achieved.
Health monitoring method of power supply chain based on R-tree dynamic index algorithm
Authors: Cui Lichong , Chu Huayu , Su Lei , Fu Yanyang , Wang Qi , Li Zihan 
Published: December 2024

Abstract
In order to discover and manage the risks in the supply chain in time, the entire supply chain is clearly defined, and a health monitoring method for the power grid material supply chain based on the R-tree dynamic index algorithm is constructed. The overall internal supply chain operation structure of the power company is analyzed. The speed histogram is used to divide the speed field corresponding to the index algorithm in order to measure the node speed domain accurately. The rectangular bounding box of the root node is calculated. The internal health data of the power supply chain is identified. The fuzzy clustering operation is used. The original sample size is obtained. The algorithm data is clustered under the action of the index matrix. Bad data is identified in the supply chain. The overlap threshold and the intra-class compact threshold are initialized. The R-tree dynamic index of the optimal clustering is obtained. Quantity output results are generated, and unhealthy data in the material supply chain is classified. The identification and monitoring of unhealthy data in the supply chain are realized. Experiments show that the proposed method can efficiently monitor the risk factors existing in the material supply chain, and the monitoring of supply chain health risks is consistent with the actual occurrence value, the error is small, the method is feasible and effective, and the robustness is good.
Analysis of static-dynamic global path planning for the rosa roxburghii picking robot
Authors: Chen Zuxing , Feng Junjie , Zhu Kun , Yang Zhenyan , Wang Yanhong , Ren Mingyue 
Published: December 2024

Abstract
In the context of the Rosa Roxburghii picking robot operating in the Rosa Roxburghii park, path planning represents a critical challenge and a key area of study. To address this, a globally optimized A* algorithm has been developed, which integrates A* (A-star) with the dynamic window approach (DWA) for local optimization. The process begins by incorporating the robot’s positional information into the traditional evaluation function, along with establishing a weight function that balances both cost and heuristic factors. Following this, the conventional eight search directions are reduced to seven, enhancing the efficiency of the path search. To increase the smoothness of the path, unnecessary turning points are eliminated while retaining essential ones for node optimization. A safety domain value is also introduced to further minimize extraneous turns, ensuring comprehensive global path optimization. For effective realtime dynamic obstacle avoidance. The combination of the two algorithms enables online path planning, resulting in a smooth trajectory based on the globally optimal path. Simulation outcomes demonstrate that this approach significantly reduces path length, average turning angle, and execution time, with its effectiveness validated through additional simulation experiments.