bluuebunny/crossref_metadata_2025 huggingface dataset:
Sociopolitical Oppression and Suppression in Soyinka’s A Play of Giant
Authors: Sabr Basim Khudhair
Published: October 2024

The concept of oppression is one of the most vital concepts in contemporary political discourses in the literary works. This concept has some interdisciplinary characteristics which are associated with culture, religion, sociology and politics. Therefore, this paper critically explores various sociopolitical oppression and suppression of Africans by their leaders (fellow Africans) in most parts of the continent as portrayed by Wole Soyinka. The paper reveals how African leaders misuse power, serve their personal interest instead of the citizens who voted them to power. In the Play of Giant, Soyinka uses satire to depict the attitude of many African leaders towards their citizens. It shows that the leaders have less sympathy towards the masses. It also shows inferiority complex, dictatorship, abuse of power, intolerance, immorality, and insensitiveness of African leaders which are all against the African cultures but inherited from colonialism.
The Critical Role of Psychological Capital: Managing Influence of Stress and Burnout on Business Performance
Authors: Rana Muhammad Talha , Younas Muhammad Waqas , Hussain Mubbara , Abdin Zain Ul , Rasheed Khurram , Bashir Saqib , Ali Akbar
Published: October 2024

This research examines the moderating influence of psychological capital on the relationship among stress, burnout, and company performance. A quantitative methodology utilising a closed-end questionnaire was employed to evaluate stress, burnout, psychological capital, and business performance among 400 employees in business organisations in Islamabad. The results indicate a substantial positive association between stress and burnout, both of which adversely affect business performance. Psychological capital has emerged as a significant moderating variable, mitigating the detrimental impacts of stress on burnout and improving overall corporate performance. The findings indicate that psychological capital, which includes self-efficacy, optimism, resilience, and hope, serves as a significant asset in alleviating the adverse effects of stress and burnout in professional settings. This study underscores the necessity of creating interventions to augment psychological capital in commercial organisations to boost employee well-being and organisational performance.
Improving Medical Emergency Team Response Time for Urgent Care Patients in Primary Health Care Centers, Riyadh, Saudi Arabia
Authors: Alamri Salman Abdullah , Alanazi Abdullah Mohammed , Alanazi Abdulaziz Awadh , Alotaibi Azzam Fahad , Maher Medhat , Kofi Mostafa
Published: October 2024

Background: Emergency healthcare systems are of increasing concern in international healthcare developments and the global fight against the burdens of disease. Concerns about focusing on ambulance response time as a single indicator are addressed by showing a case-sensitive approach for developing emergency health care systems that in return can identify ambulance response as the main indicator for a particular system. Problem description: Data collected by monitoring the time of receiving the call at the ambulance division until transport time.Methods: The fishbone diagram is used as a tool for understanding the process variation, investigate and eliminating unusual occurrences by the following methodology: M.M.M.Main outcomes: Responding time in emergency cases not exceeding 5 minutes; To improve responding time; To improve types of communication; To improve compliance with the logbook of the emergency referred cases. The intervention plan was Continuous Training for the staff and Clear Guidelines for emergency calls. Review and assess the actual implementation of the project. Data will be collected by an Emergency Medical specialist. A copy of the data should be sent to CQI for a continuous monitoring process. Check the data for process improvement (incident report analysis). Document the results of the change. Modify the change, if necessary and possible. The Emergency Medical Technicians quality coordinator will check the Data. Continue improvement by close monitoring of the compliance.Results: the mean response time dropped from 15 minutes in January 2023 to less than 5 minutes in December 2023.Conclusion: this initiative positively impacts the emergency team's response time and improves patient satisfaction. Response time reduction was evident, and the quality of the health care service was improved. Reduction of response time also improved emergency preparedness
Dyeing of Cotton Woven Fabric Using Sustainable Natural Dye (Date Leaves) and Bio-Mordants
Authors: Akter Sumi , Chowdhury Mahmuda , Akhter Tahmina , Reza Md. Alimur , Rahman Shohanur
Published: October 2024

This study on natural dyes has been chosen for a sustainable dyeing method on textile through the using of 99% cotton 1% spandex woven fabric. The dye was produced from the Date Palm Leaf (Phoenix dactylifera L) using Soxhlet machine through continuous extraction method and dyed using natural bio-mordant eucalyptus, guava leaf. Fourier transform infrared spectroscopy (FTIR) test was carried for nanoparticle characterization along with color measurements with specular and UV include mode. The dyed fabrics were also assessed for wash fastness, fastness against perspiration, dry rubbing and wet rubbing property. In most of the cases the fastness property of sample without mordant is better than with mordant. Cotton fabric is used for dyeing to make it sustainable and to reduce the dependency on synthetic dyes; the natural material can also reduce the pressure on synthetic material.
Next.js, SWC, and citeproc-js
Authors: Willighagen Lars
Published: October 2024

Last year I got a bug report that Citation.js was not working when built in a Next.js production environment for unclear reasons. Next.js is a popular server framework to make web applications with React, and by default transforms all JavaScript files and their dependencies into “chunks” to improve page load times. In production environments, Next.js uses the Rust-based “Speedy Web Compiler” SWC to optimize and minify JavaScript code.
A Scrappy Complement to FROs: Building More BBNs
Authors: Gilliam Eric
Published: October 2024

ARPA's early decades of success have made the ARPA model iconic.
06 - De la física a la programación en Julia
Authors: Guzmán Miguel Raz , Formoso Irina , Rojas Rubén , Montilla Luis
Published: October 2024

Reutilización CC BY 4.0
A journey through time: Exploring Afro-futurism and Igbo cultural heritage in elementary education
Authors: Okafor Amaka Yvonne
Published: October 2024

This study explored the significance of including the study of some unrecognized Igbo cultural heritage into the primary education curriculum through the lens of Afrofuturism/African futurism. Drawing from data sourced from interviews conducted among individuals aged 75 years and above, this study illuminates the cultural, nutritional, and educational dimensions of integrating these elements into early education. From an Afrofuturist perspective, education serves as a vehicle for cultural preservation, empowerment, and envisioning alternative futures rooted in African heritage. The inclusion of this yet to be recognized aspect of Igbo cultural heritage in the curriculum not only imparts knowledge of traditional culinary practices and medicinal uses but also fosters a sense of cultural pride and connection among younger generations. By engaging these Igbo cultural heritage with students at a young age, they will develop a deeper understanding of their cultural identity and heritage, which will contribute to a more inclusive and diverse educational experience. More so, the study highlights the potential benefits of integrating indigenous knowledge systems into formal education, promoting sustainability, environmental stewardship, and community resilience. Through hands-on learning experiences related to agriculture, nutrition, and cultural studies, students are equipped with practical skills and critical thinking abilities essential for navigating future challenges like how to improve scientifically the use of the seeds and vegetables mentioned for the betterment of humans’ health as well as checkmate the abuse of them through unconscious overdose usage by people which leads to the damages of the organs of the body. In conclusion, integrating the study of indigenous vegetables and seeds into the primary education curriculum offers a transformative opportunity to cultivate cultural identity, foster intergenerational knowledge transfer, and advance Afrofuturist/African futurist visions of inclusive, empowered futures for African societies.
MobileNet and Streamlit Applications for Classification and Prediction of MRI-Based Types of Brain Tumor
Authors: G Kharmega Sundararaj
Published: October 2024

The rapid proliferation of abnormal cells within the cerebrum and its surrounding areas leads to the formation of tumor cells with intricate and challenging patterns. Consequently, relying solely on magnetic resonance imaging (MRI) for brain tumor detection presents complexities due to the need for precise identification amidst normal anatomical structures and potentially anomalous tissues. Recognizing the urgency of early and precise diagnosis in combating brain cancers, this paper proposes a methodology for classifying and predicting MRI based brain tumor types using MobileNet through a Streamlit application. Initially, the input image dataset undergoes preprocessing to enhance image quality, facilitated by a Gaussian filter. Subsequently, the FCM segmentation technique is employed to accurately delineate tumors’ size, location, and morphology. Finally, feature extraction and classification are performed utilizing the MobileNet framework, a deep learning method known for its accuracy and efficiency, particularly in medical image segmentation. Performance evaluation metrics such as precision, recall, and accuracy are computed, culminating in the calculation of the F score to assess the model’s effectiveness. This work leverages the Streamlit environment, a Python platform for developing web based interactive applications, to facilitate seamless user interaction. Ultimately, the predicted output images depicting various brain tumor types glioma, meningioma, pituitary, and absence of tumor are displayed within the Streamlit software interface. By offering a swift and precise means of disease classification, this approach aids healthcare professionals in promptly identifying tumor afflicted patients while streamlining computational overhead. The proposed classifier shows the highest specificity of 94.5 % and sensitivity of 97.6%.
Critical Thinking and Problem-Solving Skills in the 21st Century
Authors: Rusmin Lisnawati , Misrahayu Yuni , Pongpalilu Fien , Radiansyah Radiansyah , Dwiyanto Dwiyanto
Published: October 2024

In the 21st century, the development of critical thinking and problem-solving skills has become crucial for navigating an increasingly complex world. This study aims to explore the significance of these skills in modern education and how they can be effectively nurtured among students. The research employs a qualitative methodology, using interviews and focus group discussions with educators, students, and education experts as primary data sources. Through a thematic analysis of the data, the study identifies key strategies for integrating critical thinking and problem-solving skills into the curriculum. The results indicate that while educators recognize the importance of these skills, there are significant challenges in their implementation, including a lack of resources, teacher training, and assessment methods. Furthermore, the findings reveal that students who are exposed to curricula that emphasize critical thinking and problem-solving are better equipped to tackle real-world challenges, demonstrating enhanced creativity, adaptability, and decision-making abilities. The study concludes with recommendations for educators and policymakers to develop more comprehensive frameworks that support the integration of critical thinking and problem-solving skills in educational systems, ultimately preparing students to succeed in the 21st-century landscape.
Editorial
Authors: Joffily Mariana , Lohn Reinaldo Lindolfo , Santos Júnior João Júlio Gomes dos
Published: October 2024

Editorial: Volume 16, Número 42, Ano 2024
O insustentável peso do passado: profanações de monumentos, emoções patrimoniais e heterocronias do presente
Authors: Machado Diego Finder
Published: October 2024

Ataques a monumentos públicos, em particular a monumentos que representam passados infames que perpetuam, no presente, memórias do racismo e da violência colonial, suscitaram intensos debates em anos recentes. Com esse pano de fundo, este artigo discute o lugar dos monumentos públicos em políticas de patrimônio cultural, refletindo sobre como tais políticas mobilizam a razão e a emoção, bem como aproximam o tempo presente de seus múltiplos passados. Na primeira parte, problematiza-se a dor causada pela presença de monumentos que, em sua força simbólica, perpetuam as rememorações de violências a grupos oprimidos. Aborda, desse modo, a presença do passado e as emoções decorrentes dessa presença. Na segunda parte, volta-se à ideia de perda, porém não no sentido retórico como já abordado na história do patrimônio, mas em torno dos efeitos de comoções face ao que deixou ou pode deixar de existir. Assim, o debate desloca-se da reflexão sobre as emoções face à presença do passado para, então, problematizar as emoções causadas pelo reconhecimento de que o passado se tornou uma ausência. Por essa via, busca-se refletir a respeito da sobreposição dos tempos do patrimônio, mobilizando as noções de nostalgia e luto na interpolação entre o irreversível e o irrevogável do passado. Nas considerações finais, chama-se a atenção para o lugar das políticas de patrimônio em uma História do Tempo Presente, visando compreender como as profanações de monumentos criam fugazes espaços de aparecimento a histórias e memórias de sujeitos e grupos invisibilizados.
Brazilian Fascism: The Extreme Right and the Forever-Present Past
Authors: Pacheco Gabriela Santi
Published: October 2024

Book review: GONÇALVES, Leandro Pereira; CALDEIRA NETO, Odilon. Fascism in Brazil: From Integralism to Bolsonarism. London, and New York: Routledge, 2022.
Trama metodológica: narrativas teóricas para um “A/R/TograFormar” permanente em arte/educação
Authors: Colli Mara Rúbia de Almeida , Pereira Helena de Ornellas Sivieri
Published: October 2024

O presente artigo busca apresentar uma trama metodológica de narrativas teóricas para um “A/R/TograFormar” permanente em arte/educação, ou seja, teorias que deram sustentação ao desenvolvimento da formação permanente de arte/educadores(as) da Educação Básica de Uberlândia/MG. As ações formativas foram realizadas no “Ateliê de Formação em Arte/educação”, lócus da ação interventiva ligada à pesquisa-ação em desenvolvimento na tese de doutorado em andamento no Programa de Pós-graduação em Educação da Universidade Federal do Triângulo Mineiro – UFTM, sob o título “A/R/TograFormar: diálogos entre arte/educação, processo de criação e formação permanente docente”. Como formadora e formante do Ateliê de Formação em Arte/educação, promovemos ações práticas, leituras e estudos teóricos/imagéticos de obras de arte, ancoradas nas questões teórico-práticas da pesquisa em arte, e nas perspectivas metodológicas do meio como ponto zero, da abordagem triangular, da a/r/tografia, da pesquisa-ação interventiva e de metodologias ativas que culminaram na criação de “Narrativas Imagéticas”. A trama metodológica contribuiu para que a professora, artista, pesquisadora e formadora (propositora) encontrasse caminhos metodológicos para um “A/R/tograFormar” no processo formativo permanente para professores(as) arte/educadores(as) atuantes.
O drama como recurso educativo em John Dewey: Imagine!
Authors: Silva Tatiane , Cazula Isabela Martins , Cunha Marcus Vinicius da
Published: October 2024

Este artigo propõe a utilização do drama como recurso educativo, considerando o seu potencial para despertar entusiasmo, transmitir ideias, princípios e valores, de modo a estimular nos estudantes a crença em seu poder pessoal. O texto caracteriza a noção de drama com base nas concepções filosóficas e educacionais de John Dewey, com ênfase nos conceitos de experiência estética e imaginação oriundos das obras Democracia e educação, Como pensamos e Arte como experiência. Com o propósito de estimular os professores a utilizarem o drama, o artigo apresenta um experimento de pensamento baseado na noção deweyana de pensamento reflexivo. Trata-se da simulação de uma aula em que o professor propõe aos alunos uma situação problemática: como fazer uma viagem utilizando meios de transporte inusitados. A conclusão remete aos conceitos abordados no artigo e ao uso do experimento de pensamento como contribuições para o desenvolvimento da sensibilidade estética.
Taking Locus Standi of International Actors Seriously
Authors: Szepelak Katarzyna
Published: October 2024

On October 4th 2024 the Court of Justice issued its judgement in Front Polisario II upholding the judgement in which GC annulled Council’s decision on the conclusion of the trade agreement between European Union and Morocco. In its landmark judgement the Court acknowledged the legal standing of Front Polisario – the liberation movement representing rights of the people of Western Sahara. The Court’s findings advance the flexible and adequate approach on access to EU’s courts – and for that are worth applauding.
Bridging the CFSP Gap
Authors: Chawla Kamya
Published: October 2024

The CJEU interprets its Common Foreign and Security Policy jurisdiction in light of the objectives set by the Lisbon Treaty, thereby integrating part and parcel of the CFSP into the rest of the European Union acquis. This aligns the CFSP with the general principles and constitutional rules set in the Treaty. As the Court advances the integration of CFSP jurisdiction within the broader EU legal order, the judgements of 10 September 2024 in Neves 77 Solutions and KS and KD v Council and Others serve as landmark ruling for the future of judicial review in CFSP.
Road extraction from remote sensing images by combining attention and context fusion
Authors: Yunhong Li , Mei Wang , Xueping Su , Limin Li , Fuxing Zhang , Teji Hao
Published: October 2024

Aiming at the problem that the objects in remote sensing images are complex, and the roads are long, thin, continuously distributed and easily blocked, a road extraction model for remote sensing images combining attention and context fusion (ACFD-LinkNet) is proposed. The model is based on the D-LinkNet network. First, a strip attention module is used after the last convolutional layer of the D-LinkNet network encoder to enhance the feature extraction ability of roads of different scales, better capture the global features of the road, and capture the long-distance information of the road; secondly, a context fusion module (CFM) is proposed and added to the feature transfer part of the network codec to predict the road connection between adjacent pixels, fuse the road information between different levels of the context, and solve the problem of obstacles blocking the road connection; finally, the cross entropy loss function and Dice loss function of the improved model are set with multi-loss function hyperparameter weight distribution to solve the imbalance of positive and negative samples in the data set, and the best segmentation accuracy is obtained by adjusting the weight ratio. Experiments were conducted on DeepGlobe and CHN6-CUG datasets, and the comprehensive index F1 value reached 86.76%、92.12%, which was improved compared with the D-LinkNet model . 3.96%、1.13%In addition, compared with Unet, Deeplabv3+, A 2-FPN and other networks, it has the best performance.
Uranium solid performance test of butterfly rebar-corrugated pipe slurry hoe connection
Authors: Jinquan Zhao , Long Zhou , Yonggang Ding , Rongji Zhu
Published: October 2024

To provide a theoretical basis for the application of spiral reinforcement-corrugated tube grout-pin connection in prefabricated structures, 21 pin-fixed pull-out specimens were designed and manufactured. Through unidirectional pull-out tests, the failure mode, load-displacement curve and the influence of different influencing factors on the bond pin-fix strength of the specimens were analyzed. The bond strength calculation formula of spiral reinforcement-corrugated tube grout-pin connection was fitted, and the recommended value of the pin-fix length was proposed. The test results show that the failure modes of the specimens are mainly steel bar pull-out, crack pull-out, steel bar breakage and rib breakage; the bond strength decreases with the increase of pinning length and steel bar diameter; with the increase of corrugated pipe diameter, the bond strength changes nonlinearly with the steel bar diameter, and the pinning performance is best when the diameter is 50 mm and 60 mm; the average ultimate bond strength increases with the increase of grouting material strength and the pinning length can be further shortened to 5 d; in engineering applications, when the steel bar diameter is not greater than 16 mm, it is recommended that the pinning length be 5 d∼7 d, the aperture ratio be 2.5∼4.28, and when the grouting material strength is , the pinning length should not be less than fcu≥80MPa5 d.
Investigating Tire Ground Contact Mechanics Under Partial Hydroplaning Conditions: A Multivariate Analysis Approach
Authors: Congzhen Liu , Gao Chen , Hongzhu Liu , Qiang Ma , Chengwei Xu , Hui Meng , Guol Wang
Published: October 2024

Tire hydroplaning is one of the main causes of traffic accidents on slippery roads. In order to study the ground contact mechanical characteristics of tires under partial hydroplaning conditions, 205/55the R16 passenger car tire was taken as the research object, a tire hydroplaning CEL simulation model was established, and the test plan was created using the orthogonal experimental design method. Through range analysis, partial correlation analysis and multivariate linear regression analysis, a tire side slip UA model suitable for different working conditions on slippery roads was established. Comparative analysis shows that the model can accurately predict the tire ground contact mechanical characteristics on slippery roads, and provide a theoretical reference for studying the tire multi-condition hydroplaning performance.
Improvement of bionic surface of thrust rod ball joint and finite element analysis
Authors: Huamin Liu , Shuhan Yang , Yi Li , Ce Liang , Qigang Han
Published: October 2024

The thrust rod is an important component of heavy-duty vehicles. It can transmit force and torque during vehicle driving and play the role of flexible connection. This paper mainly applies the bionic friction-increasing principle of locust foot pads to the rubber surface of the thrust rod ball joint of commercial vehicles. A bionic ball crown-shaped flexible protrusion is constructed on the surface of the rubber ball joint to increase the stiffness of the ball joint. The height, radius and spacing of the bionic flexible protrusion are changed, and the finite element analysis is performed using Abaqus software to study the radial stiffness, deflection stiffness and torsional stiffness of the thrust rod ball joint. The results show that the best effect is achieved when the protrusion height is 1 mm, the radius is 4 mm and the spacing is 10 mm. Finally, the optimal solution is comprehensively analyzed, and the feasibility of the solution is verified through simulation and experiment.
Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification
Authors: Bin Xian , Yinxin Wang , Ling Wang
Published: October 2024

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.
A text sentiment analysis method based on BiGRU and capsule network
Authors: Baiyou Qiao , Phoon Kok Kwang , Lu Yang , Youwen Jiang
Published: October 2024

Most of the existing sentiment analysis methods based on product review texts rarely consider the aspect features of the review texts, and the relevant analysis models do not consider the long-term contextual dependency features and local text features at the same time, thus affecting the accuracy of sentiment analysis. A text sentiment analysis method based on a bidirectional gated recurrent network (BiGRU) and a capsule network is proposed. This method first uses a word frequency statistics-based method to extract the aspect features of the review text and integrates them into the word vector representation, thereby effectively improving the expressive power of the word vector. Then, BiGRU is used to extract the long-term contextual dependency features of the text, and the capsule network is used to extract the local features of the text, thereby achieving high-precision text sentiment analysis based on aspects. Experimental results on real datasets show that the proposed method is superior to existing sentiment analysis models such as bidirectional long short-term memory network (BiLSTM), CNN-LSTM, and TextCNN in terms of evaluation indicators such as accuracy, precision, recall, and score.
Mechanical properties of lander bionic buffer structure based on additive manufacturing
Authors: Zhenglei Yu , Qing Cao , Jundong Zhang , Pengwei Sha , Jingfu Jin
Published: October 2024

In order to meet the energy absorption requirements of the landing buffer, this paper starts with the buffer filling material of the buffer, draws on the Kelvin structure and spiral structure, and applies the engineering bionics principle to design and establish two structural models: a soccerball-like bionic structure and a multi-spiral bionic structure. Considering the energy absorption and reusability requirements of the lander buffer structure, the bionic structure sample is prepared by additive manufacturing technology and NiTi alloy with shape memory effect. The mechanical properties, energy absorption and recoverability of the sample are analyzed and verified by simulation and experiment. The results show that the accuracy of the numerical simulation is verified by comparing the force-displacement curves of the simulation test and the isometric static pressure test; the multi-spiral bionic structure has better mechanical properties, and its maximum energy absorption is 3096.23 J; the recovery rates of the two structures are as high as 98.02%and respectively 97.12%, and the recovery rate of the soccerball-like bionic structure is slightly higher. This study uses additive manufacturing to prepare the leg-type lander buffer bionic structure, which provides a reference for the bionic design of the lander buffer structure.
Rolling bearing fault diagnosis based on optimized A-BiLSTM
Authors: Ping Yu , Beer Michael , Wagner Laura , Jie Cao
Published: October 2024

In order to improve the efficiency of hyperparameter setting and its adaptability to the model, and to reduce the high cost and low efficiency of manually setting model parameters, a rolling bearing fault diagnosis method based on Honey Badger Algorithm (HBA) optimized attention bidirectional long short-term memory network (HBA-A-BiLSTM) is proposed. Firstly, the optimal hyperparameter combination of A-BiLSTM model is searched by HBA, and then the fault diagnosis performance of A-BiLSTM model under the optimal hyperparameter is tested. Finally, the generalization ability of the model is tested based on data sets under different working conditions. The fault diagnosis effect of the proposed method is verified by using CWRU data set, and the diagnosis accuracy and confusion matrix are used for evaluation. The experimental results show that compared with other swarm intelligence optimization algorithms, the Honey Badger algorithm has good global search performance and fast convergence speed. The fault diagnosis accuracy of the optimized final model reaches 99.5%, which has good effects. It can also achieve stable and accurate fault diagnosis performance under different working conditions and has strong generalization ability.
Road object detection method based on improved YOLOv5 algorithm
Authors: Hongzhi Wang , Mingxuan Song , Chao Cheng , Dongxuan Xie
Published: October 2024

Aiming at the existing network's poor recognition of distant targets in road traffic scenes, insufficient expression of target features, and inaccurate target positioning, a road target detection method based on the improved YOLOv5 algorithm is proposed. Firstly, the feature extraction structure of the YOLOv5 algorithm is summarized, and the shortcomings of the original network structure are analyzed; secondly, a small target detection layer is added on the basis of the original network, and the recognition ability of the network for distant targets is improved by supplementing the fusion feature layer and introducing an additional detection head; thirdly, the original detection head is decoupled, and the expression ability of the network for target features is improved by changing the bounding box regression and target classification process into two branches; then, the prior frame is re-clustered, and the height-width ratio of the prior frame is adjusted by the K-means++ algorithm to enhance the network's positioning ability for the target; finally, ablation, comparison and visualization verification experiments are carried out with AP, mAP and FPS as evaluation indicators. The results show that the detection speed of the proposed algorithm on the BDD100K dataset is 95.2 frames/second, and the average accuracy reaches 55.6%, which is higher than that of the YOLOv5 algorithm 6.7%. It can be seen that the improved YOLOv5 algorithm has good target detection accuracy while meeting the real-time requirements of detection. It is suitable for road target detection tasks in complex traffic environments and has guiding significance for improving the visual perception ability of autonomous vehicles.
Current status and challenges of lightweight design and manufacturing technology for aerospace structures
Authors: Wenhe Liao , Ning Dai
Published: October 2024

Lightweight technology refers to the technology of reducing the structural mass by optimizing materials, structures and manufacturing processes while meeting the requirements of structural performance. It has become one of the key technologies for the development of the new generation of aerospace equipment. This paper first analyzes the development history of lightweight technology. Secondly, from the perspectives of design principles, composition methods and optimization methods, three types of lightweight design methods are introduced: bionic structure design, cellular structure design and efficient topology optimization design. Then, from the perspective of lightweight manufacturing processes and modes, the application of additive manufacturing, collaborative manufacturing and composite material manufacturing in lightweight manufacturing of aerospace structures is explained. Finally, the challenges and future development of lightweight technology are discussed.
Generation of maintenance and troubleshooting strategies for complex equipment based on Bayesian network reinforcement learning
Authors: Liu Baoding , Zhang Yingzhi , Han Chenyu , Tang Diyin
Published: October 2024

To solve the problem of long decision-making time and high total cost of generated strategies in traditional heuristic maintenance and troubleshooting decision-making methods, a method for complex equipment maintenance and troubleshooting strategy generation based on Bayesian network combined with reinforcement learning is proposed . In order to better utilize the knowledge of complex equipment models, Bayesian network is used to express maintenance and troubleshooting knowledge, and in order to be closer to the actual situation of complex equipment, the failure probability analyzed according to the failure mode, impact and criticality is used as the prior probability of the Bayesian network after reasonable transformation; in order to generate maintenance and troubleshooting strategies using the decision-making process of reinforcement learning, a method for converting the maintenance and troubleshooting decision problem into a reinforcement learning problem is proposed ; in order to better solve the transformed reinforcement learning problem, the observation-repair action pair is introduced to reduce the problem scale, and the action mask is set to process the dynamic action space. The simulation verification results show that under the unified performance index , the proposed method obtains higher index values than the traditional method, which proves the effectiveness and superiority of the method.
A clothing classification algorithm based on self-attention information compensation
Authors: Shuchuang Zhu , Wenhui Li
Published: October 2024

Traditional content-based clothing classification has high requirements for image features. When there are many clothing styles, its accuracy is difficult to meet the application requirements of clothing classification. A parallel self-attention classification network based on deep learning method is proposed. The network adds a parallel self-attention compensation branch on the basis of ResNet50. This branch can improve the feature extraction quality in clothing classification tasks and gradually supplement the shallow detail information missing in the deep network. A comparative experiment was carried out on the DeepFashion dataset, and the experimental results proved the effectiveness of this method.
Staged suppression of main-lobe compound jamming using airborne distributed arrays
Authors: Liubo Hu , Jianxin Wu , Quanhua Liu , Lei Zhang
Published: October 2024

For the compound jamming scenario where noise suppression jamming and interrupted sampling repeater jamming simultaneously exist, we propose a method of staged suppression of them using distributed arrays. The basic principle is using the generalized inner product for the selection of non-uniform samples. By using the generalized inner product quadratically for the staged selection of the samples, we can obtain the statistical feature estimates of the noise suppression jamming and the interrupted sampling repeater jamming, respectively, and then complete the robust suppression of the compounded jamming using the distributed array spatial domain adaptive processing. With typical configuration of parameters, we validate and analyze the effectiveness and performance of the proposed method through numerical simulation experiments.
Robot inverse kinematics solution for center selection battle royale optimization algorithm
Authors: Yufei Zhang , Limin Wang , Jianping Zhao , Zhiyao Jia , Mingyang Li
Published: October 2024

In order to solve the problems of high time complexity and insufficient global exploration ability of the battle royale optimization algorithm, this paper proposes an improved battle royale optimization algorithm based on chaos mapping, center selection and elite adaptive strategy. Through benchmark function testing, compared to particle swarm optimization algorithm, whale optimization algorithm, and the battle royale optimization algorithm, the improved algorithm significantly reduces time complexity and notably enhances convergence precision, speed, and stability. The improved algorithm is applied to the inverse kinematics problem of robots. The accuracy and The stability of the improved algorithm is better than those of the traditional battle royale optimization algorithm, it demonstrates superior solving precision and stability, proving its practicality and potential for development in solving robot inverse kinematics problems.
A robust visual question answering approach to reduce multimodal bias
Authors: Fengshuo Zhang , Yu Li , Xiangqian Li , Jinan Xu , Yufeng Chen
Published: October 2024

Currently, many visual question answering models have bias problems. Specifically, when the question-answer relationship in the training data shows a more obvious mapping relationship, the model shows poor generalization ability. For such biased predictions, existing research work mainly considers language bias, while ignoring the bias information introduced by images. In order to enhance the robustness of visual question answering models, a bias reduction method is proposed, and on this basis, the influence of language and visual information on bias is explored. Furthermore, two bias learning branches are constructed to capture language bias and the bias caused by language and images respectively, and the bias reduction method is used to obtain more robust prediction results. Finally, according to the difference in prediction probability between the standard visual question answering and bias branches, the samples are dynamically weighted, so that the model can dynamically adjust the learning degree for samples with different bias levels. Experiments on datasets such as VQA-CP v2.0 prove the effectiveness of the proposed method and alleviate the influence of bias on the model.
Temporally enhanced abnormal behavior detection based on multi-channel coupling
Authors: Dongping Zhang , Qi Pan , Daobin Ma , Hongmei Mi , Lili Lin
Published: October 2024

Abnormal behavior detection in surveillance videos based on deep learning has becomea hot topic in current research. However, due to the complexity and variability of crowd movement and external environment, detecting abnormal behavior faces great challenges. Current abnormal behavior recognition models have limitations in feature extraction and pay insufficient attention to dynamic temporal features. To address these problems, a spatiotemporal enhancement anomaly detection method based on multi-channel coupling is proposed. Based on the SlowFast network, a multi-channel coupled temporal enhancement module and a spatial enhancement module are introduced respectively to extract more discriminative static and dynamic feature information. A large number of experiments are carried out on three benchmark datasets (Violent Flow, Hockey Fight, and Real-life Violence Situations). The results show that the prediction accuracy of the proposed abnormal behavior recognition method reaches 95.3%、97.3%and respectively 94%, thus verifying the effectiveness of the method.
A Finite-Time Path Tracking Control Scheme for Unmanned Vehicles Based on Multidimensional Taylor Network and Adaptive Filtering
Authors: Yuzhan Wu , Chenlong Li , Guanghong Gong , Junyan Lu
Published: October 2024

Aiming at the path tracking control problem of unmanned vehicles under the conditions of model uncertainty and measurement noise, a finite-time path tracking control scheme based on multidimensional Taylor network (MTN) is proposed. The MTN model is used to characterize the uncertainty of the unmanned vehicle model, and the improved back propagation (BP) algorithm is used as its learning algorithm; an adaptive MTN filter is designed to filter out the measurement noise, MTN is used as a filter, and the least mean square (LMS) algorithm is used as its learning algorithm; an MTN finite-time controller is designed to perform precise path tracking control on the unmanned vehicle, which can quickly and accurately track the reference path; according to the finite-time control theory, the convergence proof verifies the effectiveness of the proposed method through unmanned vehicle simulation experiments.
Image Restoration of Electrical Well Logging Based on Fourier Convolution
Authors: Qianxiao Su , Dexin Qiao , Yili Ren , Zhou Feng , Shenglan Lin , Ruiqi Huang
Published: October 2024

Imaging logging is an important technical means in logging evaluation of complex reservoirs. Through imaging logging, a two-dimensional image of the resistivity distribution around the well can be obtained, which can be used to evaluate the development of well wall fractures and caves and the formation sedimentary structure. However, due to the characteristics of resistivity imaging logging instruments, blank strips will appear on the resistivity logging image, which increases the difficulty of computer processing of electrical imaging data. The current image repair method and the existing neural network image repair method are not effective enough when the part to be filled is large. Therefore, there is an urgent need for an intelligent repair method based on deep learning. Based on a fast Fourier convolution-based imaging logging image blank strip filling network, this paper constructs the electrical imaging logging images of the southwest oil and gas field as a data set, and trains a new deep learning algorithm for intelligent filling of blank strips in imaging logging images based on fast Fourier convolution. The time of various algorithms is compared. The results show that the algorithm has a better repair effect in imaging logging images with large strip widths, and the repair efficiency is significantly improved. This method can realize the rapid, accurate and intelligent repair of blank strips in imaging logging, achieve the rapid generation of full-borehole images, and solve the difficulties in obtaining full-borehole images.
Research on key aircraft identification method based on time-effective network
Authors: Hongyong Wang , Lisu Ma , Ping Xu
Published: October 2024

For the problem of identifying key aircraft in air traffic situation, existing research has failed to fully consider the spatiotemporal effects in the actual operation of air traffic. Therefore, a key aircraft identification method based on time-effective network is proposed. The convergence relationship and complexity between aircraft are used to construct a time-effective network model through the neighbor topology overlap coefficient, and the key aircraft is determined based on the eigenvector centrality. A network attack is carried out on the key aircraft nodes to observe the changes in sector complexity, and compared with the attack based on static network indicators, an improved genetic algorithm is used to assign new entry time to the aircraft nodes deleted by the network attack, so as to verify the selection effect of key aircraft. The actual data verification shows that compared with the static network attack, this method is more efficient in reducing the average complexity of the sector when removing key aircraft. The improved genetic algorithm has higher convergence when solving the key aircraft entry time allocation problem, making the sector complexity more stable within a certain period of time. Analysis of the control effect of key aircraft shows that the time-effective network method can more accurately identify aircraft that have a greater impact on sector complexity within a period of time than the static network.
All-weather airport runway foreign object detection based on mixed attention
Authors: Jingbo Zhang , Jie Ren , Meiqi Wang
Published: October 2024

Foreign object detection on airport runways plays a vital role in the safe takeoff and landing of aircraft. However, the existing detection algorithms have the phenomenon of missed detection and false detection under different lighting and weather runway environments. Therefore, a YOLOv5 foreign object detection algorithm suitable for all-weather airport runway environment is proposed. Aiming at the problem of a certain degree of feature loss in the pooling process of the original network, a cross-stage local spatial pyramid pooling module is designed, which can adaptively extract deep feature semantic information and enhance the multi-scale representation ability of the network; a hybrid attention module is introduced in the feature fusion part, and the channel and spatial feature weights are redistributed to strengthen the feature difference between foreign objects and irrelevant background elements; in view of the fact that small foreign objects are difficult to identify and locate, resulting in missed detection, a multi-scale positioning loss function is designed, and the detection ability of small targets is improved by adding similarity measurement; the optimized training strategy is used to train the MS-FOD dataset. Experimental results show that the improved algorithm achieves 95.83%mAP and Recall, which are respectively improved 94.31% by and 15.69% compared with the original YOLOv5 3.68% , and the detection speed is 68 frames /s , which meets the needs of real-time foreign object detection. The effectiveness of the proposed algorithm for foreign object detection on airport runways is effectively verified.
Autonomous trajectory planning method for aircraft based on ripple diffusion algorithm
Authors: Yingfei Zhang , Liyan Yuan , Hang Zhou , Xiaobing Hu
Published: October 2024

Aiming at the problem that route optimization in dynamic continuous airspace (non-discrete road network) is difficult to ensure optimality and has low computational efficiency, a non-circular ripple diffusion algorithm is proposed. Firstly, considering the influence of time-varying airflow and obstacle area in dynamic airspace, the airspace wind speed model, route network and dynamic network weight model are established, and 6 airspace environments and 4 road network structures with the influence of time-varying airflow and obstacle area are constructed. Then, the effects of traditional dynamic path optimization algorithm, co-evolutionary path optimization algorithm and non-circular ripple diffusion algorithm on route optimization are compared in 1000 experiments, taking the optimized path length, flight time and calculation time as indicators. The simulation results show that the traditional dynamic path optimization algorithm has the worst effect under the three indicators, and the calculation time of non-circular ripple diffusion algorithm is significantly less than that of the other two algorithms, which effectively improves the engineering calculation efficiency.