bluuebunny/crossref_metadata_2025 huggingface dataset:
Fine-structure changing collisions in 87Rb upon D2 excitation in the hyperfine Paschen-Back regime
Authors: Higgins Clare R , Pizzey Danielle , Hughes Ifan G
Published: October 2024

We investigate fine structure changing collisions in 87Rb vapour upon D2 excitation in a thermal vapour at 75 ∘C; the atoms are placed in a 0.6 T axial magnetic field in order to gain access to the hyperfine Pashen-Back regime. Following optical excitation on the D2 line, the exothermic transfer 5P

3
/
2

→

5P

1
/
2

occurs as a consequence of buffer-gas collisions; the 87Rb subsequently emits a photon on the D1 transition. We employ single-photon counting apparatus to monitor the D1 fluorescence, with an etalon filter to provide high spectral resolution. By studying the D1 fluorescence when the D2 excitation laser is scanned, we see that during the collisional transfer process the

m

J

′

quantum number of the atom changes, but the nuclear spin projection quantum number,

m

I

′

, is conserved. A simple kinematic model incorporating a coefficient of restitution in the collision accounted for the change in velocity distribution of atoms undergoing collisions, and the resulting fluorescence lineshape. The experiment is conducted with a nominally ‘buffer-gas free’ vapour cell; our results show that fine structure changing collisions are important with such media, and point out possible implications for quantum-optics experiments in thermal vapours producing entangled photon pairs with the double ladder configuration, and solar physics magneto-optical filters.
Miniaturized and high-efficiency end-reflective coaxial cerenkov oscillator
Authors: Fan Zhiqiang , Zhang Zeyi , Cao Yibing , Xu He , Sun Jun
Published: October 2024

High-power microwaves have extensive applications, but their practical implementation necessitates miniaturized and efficient devices. This paper introduces a novel miniaturized and highly efficient end-reflective coaxial Cerenkov device for high-power microwave generation. The design incorporates a coaxial collector structure that achieves total reflection of forward waves, significantly enhancing the end-feedback effect without increasing the structural length. Additionally, an integrated coaxial diffractive structure works synergistically with the strong end-feedback structure. This configuration not only enables highly efficient microwave output but also establishes a strong modulation field in the diode region, thereby facilitating effective electron beam modulation without extending the device length. Simulations demonstrate an output microwave power of 2.82 GW at 9.1 GHz, with a power conversion efficiency of 54%. Notably, the axial length of the beam-wave interaction region is only 2.6 λ, substantially shorter than that of conventional high-efficiency Cerenkov oscillators. The unique combination of the strong end-feedback coaxial structure and the coaxial diffractive output contributes to achieving both high efficiency and miniaturization in the generator.
GPF-Plus: a three-stage MLS point cloud ground segmentation method based on ground plane fitting
Authors: Xiao Dexu , Guo Liang , Liu Ruijun , Liu Jun
Published: October 2024

Point cloud ground segmentation is a key preprocessing task in mobile laser scanning (MLS)-based measurement and sensing. However, ground segmentation currently faces major challenges such as diverse ground morphology, sparse point cloud data, and interference from reflection noise. Meanwhile, since the existing principal component analysis-based ground plane fitting methods lack the judgment of iterative convergence and automatic correction of non-ground plane fitting results, this not only leads to unnecessary computational overhead, but also affects the accuracy of ground segmentation. To address these issues, this paper proposes a three-stage MLS point cloud ground segmentation method based on ground plane fitting, called GPF-Plus. This method adopts a three-stage strategy based on ground plane fitting to achieve ground segmentation, which is able to effectively deal with the challenges of various terrains. Firstly, the initial ground segmentation of the original point cloud is performed to quickly produce a coarse segmentation result. Secondly, the false negative points extraction is performed to improve the recall. Finally, the false positive points extraction is performed to improve the precision. At the same time, the infinite polar grid model is used to divide the point cloud, which reduces the number of grids and effectively alleviates the problem caused by point cloud sparsity. The reflection noise removal mechanism is introduced to enhance the robustness to reflection noise. In addition, the improved ground plane fitting improves the accuracy and speed of ground plane fitting. In this paper, experimental validation is carried out using the SemanticKITTI dataset, the SimKITTI32 dataset, and the collected point clouds of the mine environment. Compared with the state-of-the-art methods, GPF-Plus has excellent accuracy, real-time performance and robustness, and has high application potential in the field of measurement and sensing.
An affine formulation of eigenstrain-based homogenization method and its application to polycrystal plasticity
Authors: Nasirov Aslan , Oskay Caglar
Published: October 2024

Reduced order models (ROMs) are typically incorporated into concurrent multiscale approaches to allow for efficient nonlinear multiscale simulations and to alleviate high cost of direct nonlinear computational homogenization schemes. ROMs based on the ideas of transformation field analysis are among the most popular in the literature since they only require linear elastic simulations for model construction and typically have low number of degrees of freedom. However, these models have been shown to deliver overly stiff response in simulating wide range of materials. The present study focuses on mitigating this problem in the context of eigenstrain-based homogenization method (EHM) using instantaneous moduli information for polycrystal elastoviscoplasticity. For this purpose, a new EHM model is developed with the intention of using affine moduli for recomputation of the instantaneous localization tensors. The accuracy of the method is compared to the original EHM and direct crystal plasticity finite element simulations for several synthetic polycrystal microstructures, loading conditions and varying phase contrast. We show that the affine model delivers consistently softer response compared to the original EHM model. In particular, the affine model delivers notably more accurate response in the presence of high phase contrast. The affine EHM is able to capture local load redistribution through recomputation of the localization tensors.
Comparing growth of titania and carbonaceous dusty nanoparticles in weakly magnetised capacitively coupled plasmas
Authors: Ramkorun Bhavesh , Chandrasekhar Gautam , Rangari Vijaya , Thakur Saikat C , Comes Ryan B , Thomas Jr Edward
Published: October 2024

This study compares the growth cycles and spatial distribution of dust cloud for titania and carbonaceous dusty nanoparticles in capacitively coupled radiofrequency plasmas, with and without the presence of a weak magnetic field of approximately 500 Gauss. Findings on cycle time, growth rate, and spatial distribution of dust cloud are discussed. The growth of nanoparticles in these plasmas is cyclic, with particles reaching their maximum size and subsequently moving out of the plasma, followed by the generation of a new particle growth cycle. The presence of the magnetic field speeds up the growth cycle in both plasma. The magnetic field also makes the spatial distribution of the two dust cloud different from each other. Langmuir probe measurement of the background plasma parameters such as electron temperature and floating potential reveal radial variations in floating potential but not electron temperature. Furthermore, the magnetic field changes the radial variation of floating potential. These measurements, however, are not sufficient to explain why the two dust clouds appear differently. It is possible that the differences occur due to a gradient in the radial distribution of the magnetic field.
Modeling optimal control strategies for HIV and gonorrhea co-infection: incorporating screening along with treatment
Authors: Arunkumar M , Murugesan K
Published: October 2024

Human immunodeficiency virus (HIV) and gonorrhea are significant infectious diseases that pose considerable public health challenges worldwide. In this study, a control-induced model is developed to explore the dynamics of HIV and gonorrhea co-infection in the presence of treatment, incorporating screening strategies as control variables. We establish qualitative behavior, such as nonnegativity and boundedness of the solutions, and compute the basic reproduction number by utilizing the next generation matrix method. Subsequently, the stability of the disease-free equilibrium is analyzed, and then sensitivity analysis is undertaken to pinpoint the most critical parameters. Furthermore, an optimal control problem is formulated to diminish the total count of infected individuals and associated costs. The existence of an optimal control is shown, and Pontryagin’s principle is employed to derive the necessary conditions for an optimality system. Additionally, numerical simulations reveal that without control measures, the model predicts a continual rise in the number of infections. However, the graphical results demonstrate that the simultaneous implementation of screening for both HIV and gonorrhea as control measures significantly reduces single infections as well as co-infections. These insights are vital for researchers and policymakers to develop effective intervention strategies for eradicating co-infections of HIV and gonorrhea.
DFT molecular modeling investigation and optical properties characterization of CR-39 films
Authors: Abdallah Asmaa S , Rashad A M , El-Lateef A M Abd , Zaki M F , Elkalashy Sh I
Published: October 2024

In this study, the structural and optical characteristics of CR-39 films were investigated both theoretically and experimentally. A number of parameters for the CR-39 structure were theoretically computed by the density functional theory (DFT) method at B3LYP/6–311 G (p, d) level of theory. FTIR and UV/Vis spectroscopy were used to determine the structure compositions and the optical parameters of the CR39 film, respectively. The computed and experimental findings show good concordance. It was found that the CR-39 compound’s total energy, dipole moment, and energy difference between the LUMO and HOMO were, respectively, −994.575 a.u., 2.772 Debye, and 7.045 eV. The MEP showed a progressive change in color, with blue signifying a low electron density and red denoting a high electron density linked to nucleophilic reactivity and electrophilic reactivity. Further, the positive charges on all hydrogen atoms, which range from 0.16506 to 0.22032 a.u., imply that they are acceptors. The positive H34 is strongly produced when electrons from the negatively charged C17 are taken up. Because they are donor atoms, part of the carbon atoms in the structure is negative, while the other atoms are positive. The highest electronegative atoms H23 and H24 were substituted, resulting in the extremely negative carbon atom C7 (−0.32436). According to the experimentally determined absorption coefficient and energy gap values, CR-39 films may find application in optoelectronic devices.
Summary of presentations reported in EXperiment confinement (EX-C) session at the 29th IAEA fusion energy conference
Authors: Xu M. , Meng C. , Hao G.Z.
Published: October 2024

This is a summary paper of the research activities presented in the magnetic fusion EXperiment Confinement (EX-C) session at the 29th International Atomic Energy Agency (IAEA) Fusion Energy Conference (FEC 2023), London, United Kingdom, 16–21 October 2023. This session covers a wide range of topics related to confinement and transport of magnetic fusion plasmas. The key aspects addressed in the presentations include: (i) scenario development, (ii) turbulent transport and L-H transition physics, (iii) isotope effects on confinement, (iv) impurity transport and impact of impurities on confinement, (v) influence of fueling and energetic particles on confinement, and (vi) improvements in experimental capabilities. Highlighted results presented in those manuscripts are summarized in this paper.
Effects of plasma nonuniformity on zero frequency zonal structure generation by drift Alfvén wave instabilities in toroidal plasmas
Authors: Qiu Zhiyong , Wei Guangyu , Chen Liu , Ma Ruirui
Published: October 2024

The effects of plasma nonuniformity on zero frequency zonal structure (ZFZS) excitation by drift Alfvén wave (DAW) instabilities in toroidal plasmas are investigated using nonlinear gyrokinetic theory. The governing equations describing nonlinear interactions among ZFZSs and DAWs are derived, with the contribution of DAW self-beating and radial modulation accounted for on the same footing, and the physics picture contributing to both channels clarified. The obtained equations are then used to derive the nonlinear dispersion relation, which is then applied to investigate ZFZS generation in several scenarios. In particular, it is found that the condition for zonal flow excitation by the kinetic ballooning mode (KBM) could be sensitive to plasma parameters and a more detailed investigation is needed to understand KBM nonlinear saturation, crucial for bulk plasma transport in future reactors.
A ferry route in the Skagerrak optimised via VISIR-2
Authors: Mannarini Gianandrea , Leonardo Salinas Mario
Published: October 2024

Urgent measures are needed for the shipping industry to contribute proportionately to climate change mitigation. To date, the potential of weather routing has not been reproducibly assessed, especially for short sea shipping. We employ the open-source VISIR-2 model on a ferry passage in the Skagerrak (the strait among Norway, Sweden, and Denmark), considering both sea currents and waves. By computing 5,840 least-CO2 optimal routes for 2023, we find promising results, particularly for voyages from Denmark. The savings follow a bi-exponential distribution, with over 10% CO2 emissions savings achievable on about ten days per year. Specifically, the Norwegian coastal current can be harnessed to enhance speed over ground and reduce overall emissions. The vessel performance curve needs to be tailored to specific ships in service, and the optimisation algorithm should be developed to enable just-in-time arrivals.
Manoeuvring in shallow and confined water with model predictive track controllers
Authors: Eloot Katrien , Mansuy Marc
Published: October 2024

Autonomous and computer-assisted navigation, for example through portable pilot units and data-driven decision supportive systems, is in full development. Remote-controlled vessels (manned but without a captain steering continuously on board) are used in Belgium (by Seafar in inland shipping) but also in other countries. A model predictive controller has been used to investigate environmental and operational parameters for the passage of the river Seine in Paris by different ship types. These fast-time simulations have been compared with simulations in real time executed by skippers on a ship manoeuvring simulator or measured real life tracks. A good predictability of the real situation requires accurate manoeuvring models. But what if this accuracy cannot be guaranteed? An illustration is made by comparing the tracks if, in the prescience prediction phase for the controller, wind or bank effects are neglected or doubled. The development of model predictive deep reinforcement learning control algorithms for navigation will be one of the challenges of the Data-driven Smart Shipping project that started May 2024.
Risk Analysis on Autonomous Vessels based on Systems Theory – Application of NET-HARMS method
Authors: Koimtzoglou Alexandros , Ventikos Nikolaos P. , Routsis Dimitrios , Louzis Konstantinos
Published: October 2024

The shift towards Maritime Autonomous Surface Ships is a significant development in the maritime logistics industry, with the potential to enhance efficiency, safety, and environmental sustainability. However, the integration of autonomous systems also presents new challenges and risks, particularly in the absence of empirical data for traditional risk assessment methodologies. This research tackles this problem by utilizing the Net-worked Hazard Analysis and Risk Management System (Net-HARMS) method, a systems thinking method that hasn’t been previously employed in examining MASS. The method analyses the risks associated with the EC-funded, H2020, MOSES Project, which included a concept for automating the manoeuvring and docking processes with autonomous tugboats. The Net-HARMS method offers a comprehensive and holistic approach to risk assessment, overcoming the limitations of conventional probabilistic models. By constructing a Hierarchical Task Analysis and a task network, the research maps the system’s operational framework and explores task interdependencies. The use of a risk mode taxonomy allows for the identification of task-specific and emergent risks, which are then assessed by utilising the risk matrix of the Risk-Based Assessment Tool developed by DNV, to assess the final risk as a function of the effectiveness of each risk mitigation layer and the severity of the identified task consequences. The findings provide valuable insights into critical tasks requiring enhanced risk control measures and contribute to the development of safety constraints necessary for the successful implementation of autonomous shipping technologies. By applying Net-HARMS method to the realm of autonomous ships, this research not only fills a significant gap in maritime risk analysis but also sets a precedent for future studies in this rapidly evolving field.
Data-driven construction of maritime traffic networks for AI-based route prediction
Authors: Hoffmann Volker , Webert Jan-Hendrik , Murray Brian , Graf Robert
Published: October 2024

Predicting the routes of maritime traffic can improve economic efficiency, decrease ecological impact, and improve safety at sea. Over scales that are small (few hundred meters) and large (dozens to hundreds of kilometers), vessel trajectories have successfully been predicted by deep learning and (static) network-based approaches, respectively.

We present an approach for medium to large scales (few kilometers) where (a) a maritime traffic network is automatically constructed from AIS messages, and (b) vessel trajectories are predicted as most likely paths through the network. Using three regions (Stavanger, Tromsø, and Oslo), we show that the network can capture up to ∼ 90 per cent of all maritime traffic (excluding pleasure craft) with a median absolute error of ∼ 80 meters.

Vessel paths are sequences of waypoints and legs (nodes and edges) and are map-matched onto the network from vessel trajectories. Once mapped, we predict future paths for two subproblems – (i) known destination, and (ii) unknown destination. We use four algorithms (Dijkstra, Markov, MOGen, GRETEL).

For known destinations, we find that Dijkstra performs best. In Stavanger (Tromsø, Oslo), Dijkstra predicts 64 (42, 68) per cent of path segments correctly and keeps the median path error below 15 (33 and 55) meters. For unknown destinations, performance depends on the forecast horizon (the number of legs k to predict). For k ≤ 5, Markov is best and predicts 62 (48, 72) per cent of legs correctly. For k > 5, GRETEL performs best and predicts 54 (47, 63) per cent of legs correctly.

For some types of vessels, models improve by considering vessel type. For passenger vessels, models specific to them predict ∼ 10 per cent better paths with half the distance error. For tankers, paths (and distance errors) are 6 (20) per cent worse. For auxiliary vessels, path quality is unchanged, but distance error improves ∼ 36 per cent.
A Remaining Useful Life Prediction of Turbofan Engines Based on Multi-Scale Temporal Convolutional Networks with Dual Squeeze-Excitation Attention Mechanism
Authors: Wang Hairui , Li Dongjun , Li Dongwen , Li Ya , Zhu Guifu
Published: October 2024

To better handle temporal data and delve into learning the features of the data, a turbofan engine residual life prediction method is proposed, which integrates a dual-squeeze-excitation attention mechanism with a multi-scale temporal convolutional network. Firstly, utilizing a sliding window, the extracted multi-dimensional sensor features undergo overlapping sampling to enhance the model’s perception of temporal data. Secondly, a hybrid network prediction model based on DSE-MTCN is constructed, employing multi-scale convolutional kernels to expand the receptive field of convolution, assigning different weights to features, and adaptively allocating weights to hidden layer units. Lastly, the DSE-MTCN prediction model is globally optimized using the RAdam algorithm. The results demonstrate that this method effectively enhances the accuracy and generalization ability of the prediction model.
Research on Graph Multi-Attention Neural Network for Power System Transient Stability Assessment
Authors: Wang Qingwen , Yu Tenglong , Chu Hongliang , Xiong Xiaozhou , Huang Kang , Qiu Rixuan , Hu Bing , Yan Haoran , Wang Fan , Bai Yang , Wang Song , Qi Jun , Liu Siyan , Shi Danni
Published: October 2024

Diagnosing the stability of power grids based on artificial intelligence technology is a research trend. The existing artificial intelligence stability analysis methods rely on a large number of fault case data, while the graph neural network method ignores the correlation characteristics of nodes themselves and the correlation of long-distance nodes. In order to solve the problems, the graph multi-attention neural network (GMANN) was proposed. The self-attention, which characterizes the correlation between different state quantities of a single node, is proposed, firstly. Then, long-distance association attention, which represents the correlation of distant nodes in the power grid, was proposed. The long-distance correlation attention and the adjacent correlation attention in graph attention network are fused to form a global attention that reflects the global importance of the power grid. The channel attention that characterizes the importance of different pooling methods of graph convolutional network is extracted and used to obtain the importance of different convolution operations. Finally, based on the multi-source attention fusion strategy, global attention and channel attention are embedded in the graph attention neural network to form a multi-level evaluation model to achieve power grid stability evaluation efficiently guided by multiple attentions. The proposed GMANN is verified based on simulated fault cases obtained from the 10-machine 39-node system in New England. The results show that the accuracy of the GMANN can reach 97.34%, which is better than other methods. And the missed judgment rate of important indicators is significantly better than other methods.
Dynamic Prediction for Pollutant Emissions of Coal-fired Power Plant Based on CNN-LSTM-Attention
Authors: Liu Qing , Zhang Yongzhong
Published: October 2024

The prediction accuracy of pollutant emissions by using traditional modeling methods is unsatisfactory in dynamic conditions. To overcome the problem, data-driven modeling was introduced to build the dynamic model of pollutant emissions of power plants in this paper. Combining with the running data of a 300MW circulating fluidized bed (CFB) unit, the dynamic prediction models of SO2 and NO
x
emissions were established respectively by using conventional neural network-long short-term memory and attention mechanism (CNN-LSTM-Attention). Moreover, LSSVM, LSTM and CNN-LSTM were introduced for comparison to demonstrate the superiority of CNN-LSTM-Attention model respectively. Simulation results indicate that model can imitate change trend of actual data with high accuracy over a long period of time. Compared with LSSVM, LSTM and CNN-LSTM, the proposed model has better modeling performance under different load conditions. This work provides certain guidance for the application of deep learning in the industrial field.
Attention Pyramid Dilated Region-based Model for Metallurgical Defect Detection
Authors: Cheng Danpeng , Sha Wuxin , Xu Zuo , Huang Lixin , Zhang Zhendong , Du Yunpeng , Zhu Zhihua , Cao Yuan-Cheng
Published: October 2024

Defect recognition is the key to realizing automatic visual inspection and quality control in intelligent manufacturing. Deep learning (DL) has been widely applied to locate damaged areas in images, characterize impurities of materials and further analyze the quality of products. However, automatic defect detection by DL in practical industrial applications is still a challenge due to the lack of sufficient datasets and appropriate recognition methods. Here, a novel Attention Pyramid Dilated Region-based Convolutional Neural Network (ADRCNN) is proposed to realize the multi-scale defect recognition and pixel-level instance segmentation of metallographic images. We also collected a dataset containing 900 images of commercial A356 aluminum alloy with different casting defects. The ADRCNN model is evaluated on our dataset with an average detection accuracy of 87.2% and 93.3% on validation and test sets respectively. To address the overfitting problem, a pretraining fine-tuning strategy is implemented by using the pretraining weights from large-scale datasets and temporarily freezing the weights of the backbone network during the training process. The experimental results show that the proposed model can achieve satisfactory defect segmentation in metallographic images, promoting the development of intelligent manufacturing in alloy industries.
Estimation of Carbon Stock in Chinese Fir Stands Based on Stacking Ensemble Learning and LSTM Models
Authors: Wang Zhiyuan , Shen Wanling , Chen Lingyan , Xiao Jiawen , Zhang Chaofei , Zhang Shanwei , Xu Zhanghua , Liu Jinfu
Published: October 2024

Forest carbon sinks, a critical component of the global carbon cycle, constitute nearly half of the total terrestrial carbon pool. This study employed correlation analysis and factor effect analysis to quantify the influences of various factors on the volume of Chinese fir stands. A novel modeling framework was developed using the stacking ensemble learning and LSTM (LongShort-TermMemory) models. This framework incorporated diverse base learners, including XGBoost, Adaboost, KNN, and DT, which are intelligently ensemble via GBDT as a meta learner. The model was further optimized by comparing activation functions and optimizers, with ReLU selected as the activation function and Adam as the optimizer. Model accuracy was evaluated using RMSE, MAE, and R2 metrics, significantly enhancing its learning ability and generalization performance. The findings are as follows: (1) Stand stocking showed strong positive correlations with depression, age group, average diameter at breast height (ADBH), average tree height (ATH), slope, and elevation. Conversely, it exhibited significant negative correlations with origin, stand density, and slope position. In investigating Chinese fir growth on slopes, no significant growth differences were observed between downslopes and midslopes; however, both differed significantly from upper slopes. (2) The stacking ensemble learning method constructed here surpassed all existing single models in terms of estimation and assessment indices, demonstrating superior comprehensive performance. (3) Among the LSTM models, Adam-LSTM performed the best (R2=0.844), followed by Sigmoid-LSTM (R2=0.656), while the RMSprop-LSTM model performed the worst (R2=0.618). Combined with artificial intelligence methods, our optimized carbon stock estimation model can help to improve the ability of forest land management and provide a theoretical basis for the scientific management of forest areas.
VIEPred: Predicting Viral Immune Evasion with Multimodal Representation Learning
Authors: Liang Banqing , Wang Na , Huang Hao , Hou Yijie , Qiang Xiaoli
Published: October 2024

Predicting coronaviral immune evasion is crucial for identifying and responding to new COVID-19 variants in advance, thereby optimizing vaccine development and public health strategies to prevent further outbreaks. Hence, we present VIEPred, a model designed to predict mutations in every amino acid of the RBD. However, protein representation learning is a challenge task due to the hierarchical structure of proteins. Our method uses a sequence encoder based on dilated convolutions and attention mechanisms to generate sequence embeddings and a structure encoder employing geometric vector perceptrons (GVP) and graph neural networks (GNN) to produce protein structure representations. These features are combined using a cross-attention fusion module and subsequently processed through a fully connected layer to output the probability of viral immune evasion. Extensive ablation studies led to an optimal model with ACC of 88.4% and AUC of 86.8%. Compared to existing prediction methods, VIEPred demonstrates superior predictive performance across most evaluation metrics for both SARS-CoV-2 and influenza viruses.
Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion
Authors: Li Yue , Huang Boyang , Xiao Xiaobing , Liu Anjiang , Jin Xing
Published: October 2024

Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.
Expression Recognition Based on Visual Transformers with Novel Attentional Fusion
Authors: Feng Boju , Zhang Hongling
Published: October 2024

Facial Expression Recognition (FER) encounters considerable obstacles in practical settings, which are attributed to factors such as occlusions, a range of head positions, facial shape alterations, and motion blur under specific limitations. Despite notable advancements in the automation of FER throughout the past several decades, the majority of prior studies have concentrated on FER within the confines of a laboratory environment. Nonetheless, the complexity of FER is significantly heightened by real-world challenges, including but not limited to occlusions and varied head poses, which lead to areas with insufficient information and intricate backgrounds. As an alternative to earlier CNN-based techniques, we suggest that it is not only viable but also advantageous to convert facial images into a series of visual terms and to tackle expression recognition from a holistic viewpoint. Consequently, we have developed the Visual Transformer with Feature Fusion to confront the challenges of FER in uncontrolled conditions, a process that is divided into two principal stages. Initially, we have put forth the Attention Selective Fusion (ASF) concept to capitalize on the two distinct sets of feature maps produced by a pair of CNN branches. ASF integrates global and local attention mechanisms to synthesize various pieces of discriminative information from these maps. Subsequently, these merged feature maps are transformed into a sequence of visual terms by a process of flattening and projection. Following this, taking cues from the achievements of Transformers in the field of natural language processing, we advocate for the employment of global self-attention to delineate the interrelations among the visual terms. Our proposed strategy has been rigorously tested across three public datasets known for their uncontrolled FER scenarios (RAF-DB, FERPlus, and AffectNet). The results from our comprehensive experiments indicate that our technique surpasses other existing methods, with an accuracy rate of 88.14% on RAF-DB, 88.81% on FERPlus, and 61.85% on AffectNet, thereby establishing new benchmarks in the field. Moreover, the cross-dataset evaluation conducted on the CK+ dataset affirms the robust generalizability of our proposed approach.
Finding Zero Correlation Linear Hulls Using Quantum Bernstein-vazirani Algorithm
Authors: Xie Huiqin , Zhao Zhangmei
Published: October 2024

Facing the threat of quantum computing to cryptography schemes, it is urgent to study the applications of quantum algorithms to cryptanalysis and evaluate the security of cryptographic schemes in a quantum computing environment. To do this, it is necessary to analyze the ability of cryptanalysis methods, such as integral and linear attacks, when combined with quantum algorithms. In this article, we investigate the properties of block ciphers with SPN structure and their security against a quantum adversary and apply the Bernstein-Vazirani algorithm to zero correlation linear attacks. Based on the relation between linear approximates of SPN ciphers and the dual cipher’s linear structures, we proposed a quantum algorithm that can find linear hulls with zero correlation of SPN ciphers. We show the validity of the proposed quantum algorithm and estimate the corresponding computational complexity. Implementing our quantum algorithm only costs polynomial qubits and quantum gates.
Open-set Communication Scene Recognition Based on Metric Learning
Authors: Wang Yankun , Guo Dengke , Ma Dongtang , Zhang Xiaoying , Xiong Jun
Published: October 2024

In this paper, we study the problem of recognizing communication scenes under open set protocols, i.e., making the extracted features of communication scenes sufficiently separable under a suitable metric space. To achieve it, we design a feature fusion model based on a convolutional neural network to extract the features of communication scenes, and we take the real and imaginary parts of the channel frequency response as model inputs to extract the scene features sufficiently. Furthermore, in order to optimize the distribution of scene features in angular space, a Linear-Softmax loss function is designed to restrict the inter-class angles of extracted features to be larger than the intra-class angles by two hyperparameters. Meanwhile, a cosine distance-based communication scene recognition algorithm is designed to complete the communication scene recognition by calculating the cosine distance between features. Finally, it is tested on actual measurement data sets, and the experimental results show that compared with the state-of-the-art loss functions, the proposed Linear-Softmax loss function enables the learning of the maximum angular margin. The Linear-Softmax loss function combined with the proposed communication scenes recognition algorithm can complete the recognition of both closed-set and open-set test data, with a recognition accuracy as high as 97.28% on the closed-set test set and 85.65% on the open-set test set.
Comparison of the experience of using digital games in mathematics education in Ukraine and Israel
Authors: Voievoda A L , Klochko O V , Gurevych R S , Konoshevskyi O L
Published: October 2024

The study examined the opinion of school students regarding the expediency and possibilities of using digital mathematical games in the process of teaching mathematics, and also carried out a comparative analysis of the results of the research conducted in Ukraine and Israel. The issue of the distribution of digital mathematical games in Ukraine and Israel was studied, and it was found out which digital mathematical games are the most popular in the process of teaching mathematics to students in schools in these countries. It was found that the vast majority of students from these countries played digital mathematical games. In the study of students’ interest in playing digital mathematical games, the questions were whether students played digital mathematical games, whether their mathematics teachers used digital mathematical games in mathematics lessons, whether students would like to use digital mathematical games when learning mathematics at school. Also, most students responded that their teachers used digital math games in math lessons and would like their teachers to continue using them. However, despite a relatively high percentage of students who are interested in using digital math games in math lessons, there are students who consider traditional math lessons to be more appropriate. It was established that there is a statistically significant difference between the results of a survey of schoolchildren in Ukraine and Israel regarding the expediency of using mathematical digital games in teaching mathematics: with a probability of 95% more surveyed schoolchildren in Ukraine (9.6%) are supporters of traditional teaching of mathematics (without the use of digital games), compared to 2.4% of surveyed schoolchildren in Israel. The statistical significance of the differences in the answers of students of the schools of these countries was checked using the ϕ*-Fisher criterion. As a result of the research, it was found that the interest of schoolchildren of both countries in the use of digital mathematical games in the learning process is growing. Almost half of the respondents in these countries expressed the opinion that they would be interested in learning more about digital math games and trying to play them. However, there remain a number of unresolved important issues related to the methodology of this process, including didactic ones.
Measurement of ascorbic acid samples’ optical rotation via an improvised polarimeter for purity assessment
Authors: Felicidario R J M , delos Santos R M
Published: October 2024

The polarization of light finds practical application in physics and chemistry through the optical activity phenomenon, where polarimeters play a crucial role. This research builds on the improvised polarimeter designed by Kvittingen and Sjursnes [1], implemented with relevant modifications, to measure optical rotations of over-the-counter ascorbic acid samples. The study aims to assess the purity of two brands of ascorbic acid through polarimetry, comparing the calculated specific rotation with the literature values and supplementing the characterization with melting point determination. The constructed polarimeter, assembled using Lego bricks, provides an affordable alternative for educational purposes, addressing the challenges observed in the accessibility of commercial polarimeters for classroom demonstrations. The methodology encompasses pre-experiment steps involving polarimeter construction, the experiment utilizing polarimetry and complementary melting point determination, and post-experiment analysis to determine specific rotation from the measured optical rotations. Results indicate that Brand X exhibited specific rotations close to theoretical values, inferring high purity. Conversely, Brand Y shows significant deviations, suggesting potential impurities. These conclusions are supported by melting point data. The comprehensive approach combining polarimetry and melting point determination enhances the reliability of purity assessments, showcasing the effectiveness of the improvised polarimeter in practical applications.
Cognitive aspects of interaction in the “Human — Artificial Intelligence” system
Authors: Fedorets V M , Klochko O V , Tverdokhlib I A , Sharyhin O A
Published: October 2024

The article, based on empirical and theoretical research, reveals the phenomenology of transformations of the human cognitive sphere when interacting with artificial intelligence. The analysis of the indicated changes in the cognitive sphere is carried out on the basis of the “Concept of cognitive multi-channel Human-Computer interaction” developed by us. The essence of this concept is that the interaction of the cognitive sphere of human and artificial intelligence is implemented on the basis of the actualization and formation of typical cognitive phenomena. These phenomena are considered systemically and multifunctionally, namely as relatively independent cognitive: types of interactions, stages, strategies, channels, ontologies. Within the conceptual and substantive framework of this concept, we distinguish the following types of cognition (channels, strategies, etc.): I – orientational-cognitive; II – subject-cognitive; III – communicative and cognitive; IV – cognitive and analytical; V – cognitive and hermeneutic; VI-cognitive-ontological; VII – cognitive and creative. The identification of the indicated types of cognitive interactions is aimed at its representation as a complex, dynamic, multidimensional, multichannel intellectual system, the features of which are significant for educational and sociocultural practices, as well as for further development of artificial intelligence technologies, including its functional orientation and specificity, ergonomics, architecture, design and features of the interface. A study was conducted among students of higher education institutions aimed at determining the cognitive specificity (structure) of interaction in the “Human – Artificial Intelligence” system. Based on the analysis of the results of the distribution of answers for each of the test questions and the interpretation of the results of the cluster analysis (the Canopy algorithm was used), the dominance of the “I – orientational-cognitive” type of interactions was determined, which indicates a rather significant but initial interest in artificial intelligence technologies. There is also a relatively even distribution of all other types of cognitive interactions. The above reveals the novelty and innovation of artificial intelligence technology. This correlates with the respondents having developed different types of cognition, namely: orientational, analytical-synthetic, conceptual, interpretive, ontological, creative thinking, and corresponding intellectual intentions and motivation to use artificial intelligence tools in various spheres of activity.
Application of artificial intelligence in higher education institutions for developing soft skills of future specialists in the sphere of information technology
Authors: Shumeiko N V , Osadcha K P
Published: October 2024

This paper aims to delineate the significant role of artificial intelligence (AI) in developing the soft skills of future information technology (IT) professionals. To achieve this goal, the authors give some relevant considerations concerning using AI in teaching humanitarian subjects and explore the use of AI to develop soft skills essential for future IT professionals. First, the analysis of the survey results confirms that students are not aware that using AI in education is very helpful. That is why many students (51.8%) do not use them at all. Therefore, we have an intention to give the examples of the use of AI tools for educational purposes to equip students, first of all, with understanding of soft skills value, and, secondly, let them complete the tasks that contribute to the development and improvement of their soft skills. Second, we defined that the soft skills (communication, negotiation, problem-solving, finding a common language with colleagues and clients, public speaking, and intercultural) are essential for conducting professional communication with colleagues in the sphere of IT to satisfy the demands of the IT industry in the modern-day world. Third, the paper contributes scientifically to investigating the integration of AI technologies in the university-level educational landscape. Integrating AI in higher education institutions offers a promising route for developing or boosting the soft skills necessary for future specialists in the field of IT. Drawing on a comprehensive review of literature focusing on the use of AI technologies in the contemporary world and considering the empirical data from the online survey, the study investigates the opportunities of AI tools to foster soft skills required to achieve the active collaboration of IT employees for companies.
2PM waveform from loop corrected soft theorems
Authors: Alessio Francesco , Di Vecchia Paolo
Published: October 2024

We introduce a classical version of the loop corrected soft graviton theorem and we use it to compute the universal part of the one-loop (2PM) waveform up to sub-subleading order in the energyωof the emitted graviton for spinless black holes scattering. In particular, we compute the action of the soft operators on the classically resummed four-point amplitude, that can be written in terms of the exponential of the eikonal phase (and is therefore non-perturbative in the Newton’s constantGN) and then we perform the usual Post-Minkowskian expansion in powers ofGN. We find perfect agreement with the existing 2PM literature at the ordersω−1,log⁡ωandωlog2⁡ω, which are universal. Furthermore, we use this method to compute the universal part of theωlog⁡ωcontribution to the 2PM waveform. Even if in the present analysis we limit ourselves to compute the soft 2PM waveform, our general formulae can be used to extract all universal PM orders of the terms connected with the infrared divergences up to non-linear memory contributions, once the impulse at the corresponding precision is known. Our approach, based on the resummed eikonal amplitude, gives a unified picture of the various computations of the classical soft graviton behaviour that are present in the literature since the seminal paper by Weinberg (1965Phys. Rev.140B516–24).
History of Land Use and Origin of Oil Palm Plantation Land Status (A Study Case in PT LPYE, North Sumatra)
Authors: Affandi R
Published: October 2024

These days, it is widely believed that the development of oil palm plantations in Indonesia is related to the problem of primary forest deforestation. To prove this problem, research was conducted to study the status and land use history of the large state oil palm plantations, as well as the development of changes in oil palm plantation land cover, focusing on PT LPYE in North Sumatra. Data was taken through interviews and literature studies related to the history of palm oil companies at PT LPYE. The development of land cover at PT LPYE were documented using visually interpreted imagery to create land cover status maps in two time periods (1987-1988). The results show that PT LPYE’s land cover status origin was the other use area (APL) (97.1%), with the remainder being forest area in the form of limited production forest (HPT) (2.3%) and permanent production forest (HP) (0.6%). Based on the 2014 forest area map, most of PT LPYE’s land has the status of other use area (99.9%), while 0.1% has the status of production forest (HP). However, this change has received permission with a Forest Release Decree through the Minister of Forestry Decree No.494/Kpts-II/1999 covering an area of 5,143.75 ha. PT LPYE’s land was the community land and has been used since 1955 as a shifting cultivation area for the community, which was then built on an oil palm plantation by PT SSL in 1979. Based on the results of the interpretation of Landsat image data, the land cover of PT LPYE’s plantation in 1988 was 4009.01 ha, an increase from 1987, which was 3838.50 ha. Apart from that, PT LPYE also has other land cover in the form of old shrubs and open land. Referring to the Indonesian definition of “deforestation”, this oil palm plantation is not the result of deforestation and not a direct cause of primary deforestation”