bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism
Authors: Chen Xinzhi , Cai Chengzhi , He Xinyi , Mei Duan 
Published: October 2024

Abstract
With the aim of enhancing the accuracy of current models for forecasting vegetable prices and improving market structures, this study focuses on the prices of bell peppers at the Nanhuanqiao Market in Suzhou. In this paper, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model for vegetable price forecasting based on Principal Component Analysis (PCA) and Attention Mechanism (ATT). Initially, we utilized the Pearson correlation coefficient to filter out the factors impacting prices. Then, we applied PCA to reduce dimensionality, extracting key price features. Next, we captured local sequence patterns with CNN, while handling time-series features with GRU. Finally, these outputs were integrated via ATT to generate the final prediction. Our results indicate that the hybrid CNN-GRU model, enhanced by PCA and ATT, achieved a Root Mean Square Error (RMSE) as low as 0.1642. This performance is 11.11%, 11.11%, and 15.79% better than that of the PCA-CNN, PCA-GRU, and CNN-GRU-ATT models, respectively. Furthermore, in order to prove the effectiveness of our proposed model, the proposed model is compared with the state-of-the-art models and classical machine learning algorithms under the same dataset, the results indicate that our proposed hybrid deep learning model based on PCA and ATT shows the best performance. Consequently, our model offers a valuable reference for vegetable price prediction.
Exploring the pre-inflationary dynamics in loop quantum cosmology with a DBI scalar field
Authors: Mohammadi Abolhassan 
Published: October 2024

Abstract
Loop quantum cosmology is a symmetry-reduced application of loop quantum gravity. The theory predicts a bounce for the universe at the Planck scale and resolves the singularity of standard cosmology. The dynamics is also governed by an effective Hamiltonian, which predicts a modified Friedmann equation containing the quadratic terms of the energy density. The term plays an essential role in the high energy regime, but the equations return to the standard form in the low energy regime. The evolution of the universe in the pre-inflationary period is studied in the framework of loop quantum cosmology, where the DBI scalar field is assumed to be the dominant component of the universe. Using the numerical method, we provide the evolution of the DBI field. The background evolution shows that there are three phases as: bouncing phase, transition phase and slow-roll inflationary phase. There is also a short period of super-inflation just at the beginning of the bounce phase. The field first climbs the potential and then reaches the turning point where ϕ̇ disappears and the potential energy becomes the dominant part of the energy density. This is the time when the slow roll inflation begins and the field slowly rolls down the potential. The results indicate that there are a few e-fold expansions in the bounce phase, about N = 3.5–4, and the universe experiences about N = 59 e-fold expansions in the slow-roll inflation phase.
Geometric neural network based on phase space for BCI-EEG decoding
Authors: Carrara Igor , Aristimunha Bruno , Corsi Marie-Constance , de Camargo Raphael Y , Chevallier Sylvain , Papadopoulo Théodore 
Published: October 2024

Abstract

Objective. The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for Brain–computer interface (BCI), where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes. Approach. Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the Phase-SPDNet architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark framework. Main results. The results of our Phase-SPDNet demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. Significance. This new architecture is explainable and with a low number of trainable parameters.
Identification of perceived sentences using deep neural networks in EEG
Authors: Valle Carlos , Mendez-Orellana Carolina , Herff Christian , Rodriguez-Fernandez Maria 
Published: October 2024

Abstract

Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data. Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area. Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network’s predictions, offering valuable insights into the neural processes underlying language perception and comprehension. Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.
Reducing power requirements for high-accuracy decoding in iBCIs
Authors: Karpowicz Brianna M , Bhaduri Bareesh , Nason-Tomaszewski Samuel R , Jacques Brandon G , Ali Yahia H , Flint Robert D , Bechefsky Payton H , Hochberg Leigh R , AuYong Nicholas , Slutzky Marc W , Pandarinath Chethan 
Published: October 2024

Abstract

Objective. Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings (‘spikes’) for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes. Approach. Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech. Main results. In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes. Significance. Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.
User-wise perturbations for user identity protection in EEG-based BCIs
Authors: Chen Xiaoqing , Li Siyang , Tu Yunlu , Wang Ziwei , Wu Dongrui 
Published: October 2024

Abstract

Objective. An electroencephalogram (EEG)-based brain–computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g. user identity, emotion, disorders, etc which should be protected. Approach. We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e. random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected. Main results. Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations. Significance. Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.
Modeling and Analysis of MEMS Electrostatic Energy Harvesting System
Authors: Karthik Reddy G , Singh Pulkit , Ravi Kiran Chand , Anusha B , Gowri Chaduvula , Vivek R 
Published: October 2024

Abstract
Energy harvesting generators for low-power wireless electronic devices have drawn more attention in recent years. The design and analysis of a MEMS energy harvester based on electrostatic transduction are discussed in this study. Because a folded beam design has the advantage of being able to move huge dimensions, be compliant in the desired direction, and stiffer in the orthogonal direction. Large displacement of the energy harvester’s proof mass results in improved performance. The spice circuit of the harvester has been modelled and performed simulation to evaluate the output power of the harvester in LTSPICE. FEM simulations using COMSOL were used to assess the natural frequency and mode shape of the system and compare results with those of analytically determined values.
Dirac points and inverse problems of quantum graphs associated with Archimedean tilings
Authors: Jatulan Eduardo O , Law Chun-Kong 
Published: October 2024

Abstract
One interesting phenomenon of graphene is the presence of the conical singularity or Dirac points. Using the quantum graph model, we show that there exist three classes of possible Dirac points for all of the periodic quantum graphs associated with Archimedean tilings, when the potentials are identical and even. They occur at the periodic eigenvalues, anti-periodic eigenvalues, and other double eigenvalues of the dispersion relations, respectively. We also characterize their associated potentials. Moreover, we show that there are no other possible Dirac points. Our work is systematic and rigorous. Finally, we solve an inverse spectral problem for the potential, given the knowledge of the pure point and absolutely continuous spectra.
Data Models for Casting Processes – Performances, Validations and Challenges
Authors: Horr A M , Gómez Vázquez R , Blacher D 
Published: October 2024

Abstract
Data-driven models with their associated data learning and training schemes can be utilised for the light metal casting processes. This paper presents the basis of data model building processes along with data training and learning exercises for vertical direct chill casting and high pressure die casting (HPDC) applications. The concepts of efficient database building, data translations and sampling, as well as real-time model building and validations are briefly discussed. Rigorous performance studies were additionally carried out for two real-world case studies. Different combinations of data solvers and interpolators are adapted for the model building techniques, while machine learning schemes are used for data trainings.
Effect of welding gas mixtures on weld material and bead geometry
Authors: Unger Michael , Neves José L. , Schnall Martin 
Published: October 2024

Abstract
Shielding gas is an essential parameter in welding processes, serves to shield the molten material from the atmosphere and external contaminations while providing a stable electric-arc pathway during the process, thus influencing the quality of the weld bead and several other aspects of the process. Therefore, any welding-based process, such as wire-arc additive manufacturing, could benefit from shielding gas mixture optimisation to selectively improve the processing and possibly the final mechanical properties of the welded material. In this study welding of EN AW 5183 aluminium (single weld bead and wire-arc additive manufacturing build-up) was conducted using several gas mixtures and their influence on final geometry defects, and microstructure measured. The gas mixtures used were a combination of varying amounts of carbon dioxide, nitrogen, and oxygen. These were added to commercially available argon gas. It was observed that: CO2 additions led to an unfavourable bonding defect and increased mean grain size; nitrogen additions caused a decrease in mean grain size, decreased pore count while maintaining a similar pore volume fraction to the reference argon mixture samples; oxygen additions showed slight reduction of mean grain size, decreased pore count and decreased pore volume fraction. These results further showcase that varying shielding gas mixtures influence the final material properties and should be considered as an additional improvement route for conventional welding and additive manufacturing.
Overview on aluminium alloys as sinks for end-of-life vehicle scrap
Authors: Pogatscher Stefan , Samberger Sebastian 
Published: October 2024

Abstract
A fundamental principle in metallurgy is that the higher the purity of metals and alloys, the more favourable their properties will be. However, as the recycling of materials in production becomes increasingly significant, the levels of impurities are also on the rise. In the case of aluminium, the consequences can be detrimental due to the low solubility of most elements in this metal, which leads to the formation of brittle intermetallic phases (IMPs). Moreover, once impurities have entered aluminium, it is difficult to remove them. In 2017, almost 100 million cars were produced worldwide. Historically, vehicle design prioritised performance, resulting in a multi-material mix to utilise the best materials for each application. This included over 40 different wrought and cast aluminium alloys, Cu-based materials for electrics, and steels for high-strength applications. In the recycling of end-of-life vehicles (ELVs), high purity wrought Al alloys are today down-cycled to low purity cast engine blocks. However, recent advancements show that the drawback of increase IMP-fractions can be turned into benefits through the strategic design of heterostructured alloys. A first successful alloy example from this approach enables interesting forming properties, previously only found in 5xxx series wrought aluminium alloys, in combination with a matrix composition and age-hardening potential known from 6xxx series wrought aluminium alloys. A second examples reviews compositions directly resulting from ELV scrap. By manipulating IMPs it is feasible to create heterostructures with an interesting balance of strength and ductility. These approaches challenge traditional views, allowing for a greater volume fraction of intermetallic phases. Understanding the formation and role of intermetallic particles is crucial. This work gives an overview to the current problem and the state of the art and addressed the potential of upcycled aluminium alloys that tolerate high impurity levels by using intermetallic phases as impurity sinks.
A prairie dog optimization (PDO)-based MPPT technique under partial and complex partial shading conditions
Authors: M Vishnu Priya , G Anand Kumar , K Premkumar 
Published: October 2024

Abstract
This study deals with the new algorithm known as prairie dog optimization (PDO) to improve the performance of the PV system under partial and complex partial conditions. This algorithm is based on the exploration and exploitation behavior of prairie dog. Conventional methods like ‘perturb and observe, cuckoo search optimization (CS), particle swarm optimization (PSO), flower pollination algorithm, and the Grey wolf optimization algorithm are used to compare the performance of the prairie dog optimization (PDO). These algorithms are compared based on tracking efficiency and output power. This analysis is done using MATLAB Simulink under a variety of weather conditions. PDO improves and outperforms other algorithms in terms of performance and capability. The PDO has high efficiency and has produced more output power when compared to other algorithms in this study. A 250 W prototype model is developed to validate the performance of the PDO MPPT. The PDO has outperformed in various weather conditions, the calculation time of PDO algorithm is less, and has zero oscillations at maximum power point, and has a quick convergence rate.
The Death of Darwin and After
Authors: Hewitt Martin 
Published: October 2024

Abstract
The debate over Darwinian evolution in Britain evolved in the fifteen years after Darwin’s death in 1882. It moved into a more ecclesiological phase, in which the dominant struggle seemed to be the precise nature of the ‘true’ Darwinian belief. The late-Victorian generation, born c.1845–59, whose voices became progressively more significant in evolutionary debates as the 1880s progressed, tended to divide into different evolutionary camps: ultra-Darwinians, neo-Darwinians, and Lamarckians (or Spencerians). These discussions, often as heated as earlier disagreements between those for and against Darwin, were pursued alongside the continued scepticism of older generations, and to the discomfort of the high Victorians, who saw them as unnecessarily sectarian. The sense that Darwinian ideas were triumphing encouraged greater attention to their broader implications: in philosophy where Victorian Idealism became inflected with Darwinism, in theology, and in social thought, where late Victorians were much more open to social Darwinist approaches.
Development and validation of a novel tool for identification and categorization of non-technical errors associated with surgical mortality
Authors: Ey Jesse D , Kollias Victoria , Herath Matheesha B , Lee Octavia , Bruening Martin H , Wells Adam J , Maddern Guy J 
Published: October 2024

Abstract

Background
Up to half of all surgical adverse events are due to non-technical errors, making non-technical skill assessment and improvement a priority. No specific tools are available to retrospectively identify non-technical errors that have occurred in surgical patient care. This original study aimed to develop and provide evidence of validity and inter-rater reliability for the System for Identification and Categorization of Non-technical Error in Surgical Settings (SICNESS).

Methods
A literature review, modified Delphi process, and two pilot phases were used to develop and test the SICNESS tool. For each pilot, 12 months of surgical mortality data from the Australian and New Zealand Audit of Surgical Mortality were assessed by two independent reviewers using the SICNESS tool. Main outcomes included tool validation through modified Delphi consensus, and inter-rater reliability for: non-technical error identification and non-technical error categorization using Cohen’s κ coefficient, and overall agreement using Fleiss’ κ coefficient.

Results
Version 1 of the SICNESS was used for pilot 1, including 412 mortality cases, and identified and categorized non-technical errors with strong–moderate inter-rater reliability. Non-technical error exemplars were created and validated through Delphi consensus, and a novel mental model was developed. Pilot 2 included an additional 432 mortality cases. Inter-rater reliability was near perfect for leadership (κ 0.92, 95% c.i. 0.82 to 1.00); strong for non-technical error identification (κ 0.89, 0.84 to 0.93), communication and teamwork (κ 0.89, 0.79 to 0.99), and decision-making (κ 0.85, 0.79 to 0.92); and moderate for situational awareness (κ 0.79, 0.71 to 0.87) and overall agreement (κ 0.69, 0.66 to 0.73).

Conclusion
The SICNESS is a reliable and valid tool, enabling retrospective identification and categorization of non-technical errors associated with death, occurring in real surgical patient interactions.