bluuebunny/crossref_metadata_2025_split huggingface dataset:
Carbon Management in Large-Scale Electric Vehicle Temporal and Spatial Scheduling with Automotive Electronic Forensics
Authors: Cao Yongsheng , Zeng Guangquan , Zhao Caiping 
Published: May 2024

Abstract

Electric vehicles (EVs) offer a promising and environmentally friendly solution to reduce reliance on fossil fuels. As the adoption of electric vehicles and solar power grows, both opportunities and challenges are presented for power systems. An increasing EV fleet, paired with more distributed solar generation, brings both promise and complexity to grid operations. High penetration levels of EVs and rooftop photovoltaics will complicate load forecasting and add variability that power systems were not traditionally designed to accommodate. This study focuses on the collaborative optimization scheduling of generators, EVs, and solar power to address this issue. A novel approach is proposed in this paper, involving a two-layer optimization problem for the distribution systems. This strategy aims to optimize the charging and discharging schedules of EVs in both time and space domains while considering solar power and digital evidence collection. In the upper layer problem, the coordination between EVs, thermal generators, and base load is optimized in the transmission grid, taking into account the availability of solar power. This optimization problem aims to determine the optimal periods for EV charging with digital evidence collection in the time domain. We focus on the spatial scheduling of EV load in the distribution grid in the lower layer problem. To evaluate the proposed two-layer optimization strategy, simulations were performed using a benchmark power system model. This model incorporates an 8-unit transmission network interconnected with an IEEE 33-bus distribution feeder. Through the simulations, the impacts of various factors are assessed. The analysis considers different electricity pricing scenarios and EV adoption levels. It also examines how the placement of EV charging load across the distribution grid affects results. The simulation results demonstrate that the proposed strategy effectively integrates solar power and improves the economic efficiency of grid operation, while also benefiting EV users by optimizing the charging and discharging of electric vehicles. Furthermore, the performance highlights the significance of considering the location of EV load in distribution network planning. The strategy also considers forensic data collection from electric vehicles, which can provide valuable insights for distribution network operators. The location-specific forensic data collection allows network constraints and opportunities to be assessed based on real-world EV usage at the distribution level.
Use of CPT and other Parameters for Estimating Soil Unit Weight Using Optimised Machine Learning Models
Authors: Roy Swaranjit , Abir Abrar Rahman , Ansary Mehedi A. 
Published: May 2024

Abstract

In this research, we explore the prediction of soil unit weight using five advanced machine learning algorithms: AdaBoost with Random Forest, Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Regression (SVR) and XGBoost. Random Forest serves as a weak learner within AdaBoost. We optimize the hyperparameters of these algorithms using randomized search cross-validation (RSCV) and evaluate their performance using mean average error (MAE), root mean square error (RMSE), and R2 metrics. The input features consist of soil sample depth (D), moisture content (MC), fine content (FC), cone tip resistance (QC), and cone local resistance (FS). Additionally, we employ an autoencoder-based feature augmentation technique to enhance the models' ability to capture complex patterns in the data. Before feature augmentation, AdaBoost with Random Forest achieves the highest performance (R2 = 0.896), while SVR exhibits the lowest accuracy (R2 = 0.7402) on the test dataset. Post-augmentation, both AdaBoost with RF and SVR show improvements in R2, MAE and RMSE values, indicating that augmented features capture more variability. XGBoost, Random Forest, and Multi-Layer Perceptron rank 2nd, 3rd, and 4th, respectively, in terms of R2 value. SHAP analysis reveals that QC and FS negatively impact model accuracy, while FC and MC have both positive and negative effects. D emerges as the most influential feature contributing positively to model accuracy. In conclusion, AdaBoost with Random Forest yields the highest accuracy in predicting soil unit weight, with D being the most critical feature.
Diffraction-driven parallel convolution processing with integrated photonics
Authors: Chen Hongwei , Huang Yuyao , Liu Wencan , Sun Run , Fu Tingzhao , Wang Yaode , Huang Zheng , Yang Sigang 
Published: May 2024

Abstract

Traditional electronic processors face challenges such as bandwidth limitations and high power consumption when handling extensive linear operations for deep learning tasks. As an alternative, optical computing has garnered attention for its potential in parallel and energy-efficient computations. However, the exploration of high-density optical computing architectures on integrated photonic platforms has been limited, primarily due to constraints in neuron numbers and control engineering complexities. In response to these challenges, we report a diffraction-driven multi-kernel optical convolution unit (MOCU) to enable on-chip parallel convolution processing. Utilizing cascaded silica 1D metalines as pre-trained large-scale weights and employing spatial multiplexing at the output facet, MOCU enables simultaneous computing of diverse convolutions passively within one single unit. With MOCU, we build optical convolutional neural networks (OCNNs), enabling efficient processing of machine visions with concise architecture. To address fabrication errors inherent in MOCU-embedded OCNN, a lightweight electronic neural network runs concurrently with the OCNN to calibrate systematic deviations via low-rank adaptation (LoRA) algorithm with negligible overhead. The fabricated MOCU chip achieves the highest independent 8-kernel convolutions in parallel, with each kernel size of 3 × 3 and occupying a footprint of merely 0.06mm^2. The proposed architecture synergistically leverages the benefits of both photonic and electronic technologies, offering a potential design methodology for next-generation deep learning hardware aimed at achieving high compute density and prominent energy efficiency.
Effects of Liquid Urea Rates on Nitrogen Dynamics, Growth, and Yield of Grain Corn (Zea Mays L.)
Authors: Mota Ahmmed Md , Samsuri Abd. Wahid , Sukor Arina Shairah Abdul , Amaily Akter , Adibah Mohd. Amin 
Published: May 2024

Abstract

Arbitrary use of urea fertilizer reduces nitrogen use efficiency (NUE) and increases the risk of environmental pollution. An experiment was conducted at the Universiti Putra Malaysia from November 2019 to March 2020 to evaluate the application methods and rates of liquid urea(LU) on the yield performance of corn. The treatments were, U0 = control, GU100 = Granular urea(GU) 100%, LU100 = LU 100%, LU50 = LU 50% and LU33 = LU 33%, in two equal splits at 10th and 28th days after sowing (DAS) in randomized completely block design, replicates four. Results showed that plant height (206.99cm, 216.92cm, 214.61cm), ear height (88.13cm, 88.63cm, 86.00cm), days of maturity (88.75, 89.00, 86.75), number of seeds per kernel row (32.25, 34.50, 33.75), fresh cob weight (10886.60kg/ha, 10946.60kg/ha, 10927.93kg/ha) and 100-grain weight (20.51g, 22.50 g, 21.39 g) of corn were not different significantly(p ≤ 0.05) in GU100, LU100 and LU50 treatments, respectively. The highest yield of corn was found with LU100 (6249.03kg/ha) treatment whereas the yield in LU50 (5666.50kg/ha) and GU100 (5746.64kg/ha) were not different significantly. Nitrogen(%) in plants was the highest in LU100 followed by LU50 treatment which was significantly higher than GU100 treatment. The total N content was also the highest in LU100 (102.83kg/ha) though the total N content was not different significantly in LU50 (77.62kg/ha) and GU100 (83.84kg/ha) treatments. The NUE was the highest in LU50 (66.92%) treatment followed by LU100 (51.47%) treatment. The results of the study suggested that the LU100 was the best application rate while LU50 treatment was comparable to GU100 in corn cultivation.
Age-related hearing loss and dementia risk causality: two-way Mendelian randomization analysis
Authors: Tian Yu , Leng Hui , Wang Bo Shen , Dong Ye , Sun Jia Wei , Yuan Yi Lin 
Published: May 2024

Abstract

In response to the ongoing debate in clinical studies about the relationship between age-related hearing impairment and dementia, a two-sample Mendelian randomization (MR) study using a genome-wide association study (GWAS) database was conducted to elucidate the genetic effects of age-related hearing loss on four types of dementia (dementia, vascular dementia, and Parkinson's dementia，Alzheimer's disease).MR analyzed data from two independent sources on age-related hearing loss (exposure) and four dementias (outcome). We used the inverse variance weighting (IVW) method, the MR Egger method, and the weighted median (WM) method to investigate the impact of age-related hearing loss on dementia.In addition, we analyzed heterogeneity, horizontal diversity, and stability through Cochran's Q-test, MR-Egger intercept, and leave-one-out sensitivity analysis, respectively.Age-related hearing loss was associated with dementia (OR=1.054, 95% CI:0.633-1.754, P=0.840), vascular dementia (OR=1.985, 95% CI:0.550-7.169), P=0.295), Parkinson's dementia (OR=2.263, 95% CI:0.235-21.774, P=0.480), andAlzheimer's disease (OR=1.001, 95% CI:0.999-1.004, P=0.392), but no causal effect was found.The results of our study indicate that there is no statistically significant responsible association between age-related hearing loss and any of the four dementias, in accordance to our two-sample MR analysis.
Towards Visually Interpreting Variational Autoencoders
Authors: Li Runze , Liu Wenqian , Zheng Meng , Torop Max , Rajadhyaksha Milind , Dy Jennifer , Kose Kivanc , Karanam Srikrishna , Wu Ziyan , Bhanu Bir , Radke Richard , Camps Octavia 
Published: May 2024

Abstract

Recent advances in convolutional neural network (CNN) interpretability have led to a wide-variety of gradient-based visual attention techniques for generating visual attention maps. However, most of these methods require a classification-type design architecture, and consequently concentrate on classification/categorization-type tasks. Extending these methods to generate visual attention maps for other kinds of computer vision models, e.g., variational autoencoders (VAE) is not trivial. In this paper, we present a method that helps bridge this crucial gap, proposing to compute VAE attention as a means for interpreting the latent space learned by a VAE. We first present methods to generate visual attention maps from the learned latent space, and then show how they can be used in a variety of applications: localizing anomalies in images, including medical imagery, and improved latent space disentanglement. We conduct extensive experiments on a wide-variety of benchmark datasets to demonstrate the efficacy of the proposed VAE attention.
Analytical modelling of cutting force in pulsed laser-assisted turnings of SiCp/Al composites
Authors: Mingming Lu , Qiao Zhun , Chen Shuang , Lin Jieqiong , Zhai Shuaijie , Gong Hong 
Published: May 2024

Abstract

Particle-reinforced composites (SiCp/Al) are widely used in aerospace and military industries due to their excellent properties. Heating SiCp/Al with a pulsed laser softens the material, which reduces cutting forces, decreases tool wear and improves surface quality. During pulsed laser heating-assisted cutting of SiCp/Al, the modeling of cutting force prediction is complicated by the laser's involvement in the material temperature increase and its property changes. Therefore, a cutting force prediction model was developed considering the effect of laser heating on the properties of SiCp/Al materials. First, the source of cutting force of common cutting SiCp/Al is analyzed and the cutting force model of common cutting SiCp/Al is established. Secondly, the cutting temperature of pulsed laser heating-assisted cutting of 25% SiCp/Al was measured experimentally. Finally, the temperature-susceptible parameters were correlated with the temperature of pulsed laser heating-assisted cutting of SiCp/Al and introduced into the cutting force model for ordinary cutting of SiCp/Al, so as to obtain a cutting force model that takes into account the effect of laser heating on the performance of SiCp/Al, and experimental verification was carried out. The error between the predicted and experimental values is within 20%, which proves the feasibility of the prediction model and provides a theoretical basis for the back-sequence pulsed laser heating-assisted cutting of SiCp/Al.
Research on series arc fault detection methodHousehold loads based on voltage signals
Authors: Li Bin , Shu Jiahui , Cui Feifan 
Published: May 2024

Abstract

In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces Inception with multi-scale parallel convolution operation, and combines Bidirectional Long Short-Term Memory Recurrent Network (BiLSTM) with attention mechanism. Firstly, a household experimental platform was built, and the line voltage signal obtained by the experiment was subjected to wavelet transform and Principal Component Analysis (PCA) dimensionality reduction to construct a dataset. Secondly, Inception is introduced to extract the multi-level features of the samples, and the parallel output is input into BiLSTM after global max pooling layer. Then, self-attention is used to perform reinforcement learning on the hidden state vector. Finally, the output results are classified by the fully connected layer. Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. In addition, additional experiments at different sampling frequencies show that the method has good adaptability, and the identification accuracy has better performance when the sampling frequency is 10KHZ, which has certain theoretical guiding significance for the development of the series arc fault detection device in the next step.
A usability study on the inGAIT-VSO: effects of a variable-stiffness ankle-foot orthosis on the walking performance of children with cerebral palsy
Authors: Noort Luc van , Crey Nikko Van , Rouse Elliott J. , Martínez-Caballero Ignacio , Asseldonk Edwin H.F. van , Bayón Cristina 
Published: May 2024

Abstract

Background Ankle-foot orthoses (AFOs) are commonly used by children with cerebral palsy (CP), but traditional solutions are unable to address the heterogeneity and evolving needs amongst children with CP. One key limitation lies in the inability of current passive devices to customize the torque-angle relationship, which is essential to adapt the support to the specific individual needs. Powered alternatives can provide customized behavior, but often face challenges with reliability, weight, and cost. Overall, clinicians find certain barriers that hinder their prescription.In recent work, the Variable Stiffness Orthosis (VSO) was developed, enabling stiffness customization without the need for motors or sophisticated control.
Methods This work evaluates a pediatric version of the VSO (inGAIT-VSO) by investigating its impact on the walking performance of children with CP and its potential to be used as a tool for assessing the effect of variable stiffness on pathological gait. Data was collected for three typical developing (TD) children and six pediatric participants with CP over two sessions involving walking/balance tasks and questionnaires.
Results The sensors of the inGAIT-VSO provided useful information to assess the impact of the device. Increasing the stiffness of the inGAIT-VSO significantly reduced participants' dorsiflexion and plantarflexion. Despite reduced range of motion, the peak restoring torque increased with stiffness. Overall the participants' gait pattern was altered by reducing crouch gait, preventing drop-foot and supporting body weight. Participants with CP exhibited significantly lower (p<0.05) physiological cost when walking with the inGAIT-VSO compared to normal condition (own AFO or shoes only). Generally, the device did not impair walking and balance of the participants compared to normal conditions. According to the questionnaire results, the inGAIT-VSO was easy to use and participants reported positive experiences.
Conclusion The inGAIT-VSO stiffnesses significantly affected participants' plantarflexion and dorsiflexion and yielded objective data regarding walking performance in pathological gait (e.g. ankle angle, exerted torque and restored assistive energy). These effects were captured by the sensors integrated in the device without using external equipment. The inGAIT-VSO shows promise for customizing AFO stiffness and aiding clinicians in selecting a personalized stiffness based on objective metrics.
Bioinformatics-based pan-cancer analysis of UBE2C
Authors: Zhang Yi-xuan , Hu Ming-guang , Yang Chunbaixue , Peng Jia , Wei Hui-ping , Wang Yan-bing , Li-ning Su , Xue-liang Wu 
Published: May 2024

Abstract

Ubiquitin-conjugating enzyme E2C(UBE2C) is a significant member of the ubiquitin-conjugating enzyme family, playing a crucial role in the Ubiquitin-Proteasome Pathway and being essential for cell cycle regulation and cyclin degradation. It is highly expressed in various tumor tissues, and this expression is closely associated with the occurrence and development of tumors, particularly UBE2C-related tumors. Currently, there are no studies on pan-cancer analysis of UBE2C.Therefore, the objective of this study was to present comprehensive pan-cancer analysis data on UBE2C for clinical research and to offer data-driven support for early cancer diagnosis. In this study, we performed pan-cancer analysis of UBE2C by bioinformatics, explored and collated the data of many types of tumors in TCGA-based databases, performed differential analysis, expression and prognostic value analysis, immunoassay and mutation analysis, screened the cancer species by applying different methods in the R programming language, and analyzed the genomic heterogeneity and gene expression of UBE2C in the pan-cancer. In addition, we plotted UBE2C-associated survival curves for eight cancer types. Finally, we validated the changes of UBE2C in pan-cancer by downloading the images of UBE2C immunohistochemistry results integrating normal and tumor tissues from 8 sites through The Human Protein Atlas Version: 23.0 (https://www.proteinatlas.org/) database. Through the pan-cancer analysis of UBE2C, the gene was analyzed in terms of clinical prognostic value, tumor immunotherapeutic value, and immunoassessment value, which will provide a reference for subsequent research based on UBE2C to explore the aspects of tumor diagnosis, treatment, assessment and management.
Study on Routing and Scheduling for Unmanned Electric Loaders Considering Charging
Authors: Wei Xiaoxu , Niu Chen , Zhao Lianzheng , Wang Yongsheng 
Published: May 2024

Abstract

The application of unmanned electric loader technology facilitates safe and efficient enterprise production. By framing the cargo transport from multiple bins to electric loader hoppers as an MDVRPTW-EV issue, this paper proposes the SPBO-ACO hybrid metaheuristic algorithm, which combines the local search capability of the Student Psychology Optimization algorithm with Ant Colony Optimization principles to address path planning challenges. The SPBO-ACO algorithm leverages route length classification, strong and weak perturbations, and learning operators to enhance solution exploration.Testing based on standard MDVRPTW benchmark test instances shows high scalability and stability with 25% of results exceeding or approaching optimal solutions in 20 benchmark cases while 85% have errors compared to optimal solutions that do not exceed 10%. When applied to an industrial setting, the algorithm significantly reduces the unmanned loader's driving distance during raw material feeding and filling, demonstrating its practical effectiveness.
On The Determination of the Position of the Mental Foramen Based on the Eruption of the Third Molar
Authors: Yarkan İlda Sinem , Ozdogmus Omer 
Published: May 2024

Abstract

The primary objective of this study is to ascertain whether third molars manifest a perceptible impact on the localization of the mental foramen. In this study, we conducted an extensive investigation utilizing panoramic radiographs to examine the morphology and spatial relationships of the foramen mentale in a diverse patient population. There are three prominent factors studied in the radiograph analyses: mental foramen position in both vertical and horizontal orientations, gonial angles, and the eruption status of the third molar. The statistical analyses of the data reveal that there exists a relationship between full eruption of third molar and the vertical and horizontal position of mental foramen. In addition, the findings imply that there is no statistically significant relationship between gonial angles and eruption status of third molar. This study paves also that way of future research endeavors which aim to investigate in detail both anatomical and physiological aspects to unravel the underlying mechanisms governing such complex relationships.
Highly porous carbonaceous aerogel for symmetric supercapacitor with dual-redox active electrolyte revealing high performance pseudo-capacitance
Authors: Vijayan Divyadharsini , Kumaresan Thileep Kumar , Subashchandrabose Raghu , Gnanamuthu RM , Vediappan Kumaran , Gunasekaran Sivagaami Sundari 
Published: May 2024

Abstract

Due to the increasing global energy demand, there is an urgent need for more efficient energy storage systems. Redox-active electrolytes offer a promising, long-term solution for high-energy-density supercapacitors, as they enhance energy density and pseudo-capacitance through redox reactions. This study introduces a symmetric supercapacitor utilizing a dual redox-active electrolyte, achieving the highest levels of pseudo-capacitance and energy density with a biomass-derived carbonaceous aerogel (BCA) electrode. The BCA electrode's unique flower-like cubic-sphere structure, partially graphitized edges, and high specific surface area of around 1299 m²g⁻¹ enable efficient access for electrolyte ions, enhancing charge storage. The dual-redox electrolyte, comprising KBr and methyl Viologen dichloride (MVCl2), enhances the performance of both electrodes through redox reactions, resulting in a maximum half-cell specific capacitance of about 887 Fg⁻¹ at 1 Ag⁻¹ current density. When incorporated into a supercapacitor device, this dual-redox-electrolyte system achieves a maximum specific capacitance of 560 Fg⁻¹ and an energy density of approximately 77 WhKg⁻¹ at the same 1 Ag⁻¹ current density, with outstanding capacitance retention of approximately 93% over 10,000 charge-discharge cycles. Thus, the combination of KBr and MVCl2 in the KOH electrolyte, as demonstrated in this work, enhances redox behaviour, leading to significantly more efficient energy storage solutions.