bluuebunny/crossref_metadata_2025 huggingface dataset:
Acrylamide in coffee products from Serbian market
Authors: Dodevska Margarita , Redžepović-Đorđević Azra , Jovetić Milica , Ivanović Nevena , Vuković Gorica
Published: October 2024

Background: Acrylamide is a compound that is mainly formed in food rich in carbohydrates, during the Maillard reaction between the amino acid asparagine and reducing sugars, at temperatures above 120 °C and at low humidity. Methods and Objectives: The aim of this study was to examine the amount of acrylamide in coffee products, the use of which has a long tradition in these regions, and to determine whether it corresponds to the reference values (roasted coffee: max 400 µg/kg, instant coffee: 850 µg/kg). The content of acrylamide was determined in 30 samples of roasted coffee and 9 samples of instant coffee, purchased in markets in the territory of the city of Belgrade. An LC-MS/MS system was used, on a column: Thermo, Part. No. 35005-052130, Dim. 50 x 2.1 mm particle size 5. Results: The concentration of acrylamide in instant coffee ranged from 314 to 830 µg/kg, and only in one sample was it lower than half of the maximum level of the reference value. In the mixture of ground roasted coffee, the obtained value for acrylamide was 194-464 µg/kg. In 93% of the samples of the ground roasted coffee mixture, the results obtained were below the maximum level of the reference value, and only in two samples the experimentally determined value for acrylamide was lower than half of the defined maximum level of the reference value. Conclusions: The amount of acrylamide in the examined samples of instant coffee and the mixture of ground roasted coffee, although it met the conditions of defined reference values in 95% of the examined samples, is very high. Knowing the tradition of coffee consumption in these areas, it is necessary to improve existing and develop new technological formulations, so that these products contain a smaller amount of acrylamide.
Experience of the tobacco quit line in Serbia
Authors: Zdravkovski Maja , Kilibarda Biljana , Ivanović Milica , Vuković Marija , Grubor Ivana , Gudelj-Rakić Jelena
Published: October 2024

Background: In Serbia, more than 19,000 people die every year due to tobacco-related illness and despite many health and socioeconomic consequences, prevalence of smoking is still high both among adults and adolescents. Implementation of effective tobacco cessation programs, including quitlines, is among most important tobacco demand reduction measures. To support people who use tobacco, Institute of Public Health of Serbia "Dr Milan Jovanovic Batut" (IPHS) established free tobacco quit line in June 2022. Methods and Objectives: The aim is to present sociodemographic characteristics, smoking history and motivation to quit, expected benefits and assessed level of nicotine dependence of users of the IPHS tobacco Quitline. We performed descriptive statistics of data obtained from 52 Quitline users collected in the period June 2022 - August 2023. Results: Majority (67%) of 52 tobacco quit line users were female and 33% were male. Quitline users were Serbian residents of all ages (15-82 years old). For most of the quitline users, health related reasons was the main motive for quitting smoking. Smoking councilors support was sought not only by smokers, but also by those who are concerned about the health of close people who smoke. As the main perceived benefit from quitting cigarettes, 77% respondents indicated health, while 9.6% indicated financial gain. According to the results of the Fagerström test, 38% had a high nicotine addiction, 33% had moderated dependence and 12% low nicotine dependence. Conclusions: The experience show that smokers as well as non-smokers from all age groups used Quitline and health was main motivation to quit. Low number of tobacco quit line users call for media campaign and other means of promotion to increase demand for smoking cessation support.
The public health significance of the modern quantum-holographic view of consciousness
Authors: Grbatinić Ivan , Medarević Aleksandar , Raković Dejan
Published: October 2024

Individual and Collective Consciousness and their Global Significance: Consciousness has been the central theme from the very beginning of philosophy. The first attempts to explain its nature appeared in psychology in the 19th century through personality theory, and then parapsychology, which contributed to the modern holistic drift of Integrative Medicine. This problem is further explored by Quantum Mechanics and Artificial Intelligence. Within Quantum-Holographic/Quantum-Gravitational Framework of Consciousness, Transpersonal Phenomena are of psychosomatic nature, with the central role of Acupuncture System which can be in a blocked state. This leads to personal disintegration as is in some forms of addictions. The unraveling of the Transpersonal Energy Blockade (Ayurvedic/Qigong/Reiki) initiates the process of permanent healing even at the level of Collective Consciousness. As a net-effect, such interventions can permanently remove the mutual conflicts at the level of the entire world. Global Conflicts and Public Health Significance: From the holistic point of view, the System of Universal Laws of Implicit Order (God) created the world of natural laws and Boundary Conditions. This system evolved with the creation of Man. The social evolution of Man, resulting from the complex organization of billions of interpersonal relationships, has led to conflicts of global proportions that create the Load Network at the level of Collective Consciousness. Man has the power to change the Boundary Conditions by praying, through the introduction of new portions of Vital Energy into the Global Interactive System. The practical implications lead to redefining the global political scene and goals of Civilization. Man's ultimate task is relieving these accumulated burdens. Conclusion: If the Holographic Paradigm (Talbot' concept of the Universe as a huge Hologram) is integrated with the Cartesian Paradigm, it would not only lead to the global relief of existing international conflicts/wars, but the achieved global social prosperity would lead to far higher level of Public Health. The Collective Consciousness Morphogenetic Network, as a communication mediator with Universal Consciousness, would have a very favorable psychosomatic global, public health effect. Therefore, work on the integrative Cartesian/Holographic Paradigm is of the utmost importance with not only individual but also global Public Health Significance.
The role of social media in the communication of public health information: A comparative study from pandemic to post-pandemic
Authors: Ninković Željka , Vujković Branko , Srećković Marijana
Published: October 2024

Background: Social media play a key role in modern health promotion strategies, enabling more effective communication, education and engagement of the public.The role of social media in providing accurate and timely information from health experts during the COVID-19 pandemic was crucial for public health protection, highlighting the need for effective communication strategies in crisis situations and their long-term sustainability. Methods and Objectives: This study evaluates the effectiveness of the Institute of Public Health of Šabac in using social media for public health communication during the COVID-19 pandemic and compares it with post-pandemic usage in 2023 to assess the sustainability of social media as a public health tool. Results: During the observed period, there was a notable decline in social media interactions across various platforms. On Facebook, the number of interactions dropped by approximately 70% from 2021 to 2023 (from 2,335 to 705), and by about 48% compared to 2022 (from 1,352 to 705). Twitter saw a decrease of around 67% in interactions from 2021 to 2023 (from 1,104 to 366), and a decline of approximately 44% compared to 2022 (from 654 to 366). Similarly, Instagram interactions fell by about 60% from 2021 to 2023 (from 896 to 354), and by roughly 47% compared to 2022 (from 667 to 354). Conclusions: Despite the observed decline in social media usage for public health communication post-pandemic, social media remains a vital channel for informing the population about public health issues by health institutions. The significant engagement during the pandemic underscores its potential as an effective tool for health communication.
Bank profitability before and during the COVID-19 crisis: The role of intellectual capital
Authors: Milašinović Marko , Ognjanović Jasmina , Mitrović Aleksandra
Published: October 2024

The banking sector, recognized as a knowledge-intensive sector, largely relies on the use of intellectual capital as a key factor for sustainable and profitable growth. The aim of the paper is to determine the contribution of intellectual capital and its components to the profitability of banks in stable and crisis situations, influenced by the COVID-19 pandemic crisis. The sample includes 21 banks that operated in Serbia in the period before the pandemic crisis (2017-2019) and during the crisis (2020-2022). The MVAIC method was applied in the paper to measure the value of intellectual capital and its components. The results of the regression analysis indicate that intellectual capital contributes to profitability both in the period before the crisis and in the period of the crisis. Structural capital had a dominant influence on profitability before the pandemic crisis, while during the crisis period, capital employed efficiency had a decisive influence.
Facial recognition systems from the perspective of European data protection standards in Poland
Authors: Góral Urszula
Published: October 2024

In Poland, facial recognition for public security purposes is a matter subject to various legal acts. The aim of the paper is to indicate the legal acts that regulate these issues, the way in which Poland approaches data protection in the context of the use of recognition systems, as well as the way of harmonizing with European guidelines. The paper offers a continuation of the discussion on the need to ensure the security of the state in accordance with the basic rights and freedoms of citizens using this technology.
The role of consumer perception and purchasing behaviour in the process of designing sleepwear
Authors: Celcar Damjana , Gruden Bole
Published: October 2024

Consumer perception and purchasing behaviour play a pivotal role in the design process, as modern consumers demand products that align with and satisfy their preferences. This paper presents a study of Slovenian consumers' perceptions and purchasing behaviours of sleepwear, which are often overlooked. The main purpose of the survey is to identify consumer habits regarding the wearing and purchasing of sleepwear in Slovenia, providing a fundamental basis for the design process. Namely, the survey that has been conducted covers various aspects, including wearing and purchasing habits, visual details, textiles, and financial preferences. The results indicate that the majority of Slovenian consumers wear sleepwear during both summer and winter, but they do not use the same type of clothing for each season. In winter, consumers primarily wear two-piece sleepwear, such as pyjamas or a combination of a long-sleeved T-shirt with long pants or leggings. In summer, they primarily wear short-sleeved T-shirts, shorts, nightdresses and two-piece nightwear. An important consideration is the material of the sleepwear, with comfort being the most crucial factor. Most often, sleepwear is made from natural materials such as cotton. Sustainability is a significant factor for Slovenian consumers, as more than half of the respondents wear eco-sleepwear. Consumers typically purchase sleepwear once a year or every two years, preferably in stores. They also like to buy sleepwear as a gift. A smaller percentage of consumers wear additional pieces like socks, bathrobes or eye masks.
Security crises as a factor of migration to the European Union
Authors: Matijašević Martin , Todorović Darko
Published: October 2024

Security crises around the world as generators of migration are not a phenomenon of modern times, but the issue of migrant integration is becoming increasingly urgent due to the large number of migrants who have entered Europe over the past two decades. The recent surge has been driven by the growing number of regional conflicts, leading to migration of Palestinians, Syrians, Iraqis, Libyans, Afghans, and Eritreans who are fleeing war, ethnic conflict, or economic hardship, risking their lives in attempts to reach Europe. In recent times, transit countries like Hungary and Croatia have recorded a significant influx, while Germany has seen the largest absolute increase in first time asylum applicants due to its favorable policies towards migration and asylum seekers, as well as its relatively stable economy and high standard of living. In 2023, the political debate in the European Union focused on the major challenges facing the EU and its member states, particularly the development of the security situation in Ukraine and the management of migration, promoting integration policies that could have social, economic, and political impacts on the European Union. This paper aims to analyze the reports of international organizations to highlight the key security crises as factors driving migration to the European Union.
Review of AI-Powered Food Processing: Enhancing Safety and Sustainability
Authors:   Food Science and Nutrition, Food Engineering, Deber Zeit Agricultural Research Centre, Bishoftu, Ethiopia, Mengistu Deribe , Ashe Girma ,   Agricultural breeding, Breeder, Deber Zeit Agricultural Research Centre, Bishoftu, Ethiopia
Published: October 2024

As the global population is projected to reach 9.7 billion by 2050, ensuring food security has become increasingly challenging due to factors such as population growth, climate change, resource depletion, and environmental pollution. To address these challenges, artificial intelligence (AI) is as emerging technology that plays a crucial role in the food processing industry. This review highlights AI's essential role in food processing to enhancing the quality and safety of food products sustainably while addressing industry challenges. Artificial Intelligence significantly impacts food safety, quality assurance, and waste reduction through various applications. Its integration into food processing has the potential to revolutionize industry standards, covering tasks from product categorization to personalized nutrition. However, challenges such as job displacement, technical limitations, and ethical considerations require careful attention. Investment in research, development, training, and maintenance is crucial for successful AI integration. Despite these hurdles, AI's transformative potential to advance sustainability, food safety, and consumer needs remains significant. Artificial Intelligence is poised to enhance efficiency, foster innovation, and improve sustainability in the food processing sector, effectively addressing the evolving needs of the growing global population. Artificial Intelligence promotes a hopeful outlook for the future of food processing, confidently tackling 21st-century challenges with adaptability and resourcefulness.
Sintering of Novel Bioresorbable Mg-Sr-Ca Alloys for Biomedical Applications
Authors: Azadi Ava , O’Cearbhaill Eoin D. , Celikin Mert
Published: October 2024

Bioresorbable magnesium (Mg) alloys have been receiving increased attention as an emerging class of biomedical metallic materials due to their high biocompatibility. The potential use of additive manufacturing (AM) for biomedical Mg-based alloys will allow the processing of bioresorbable patient-specific implants. However, the low sinterability of Mg-based alloys is a key issue limiting the efficiency of post-processing required for the low-temperature AM techniques (i.e., extrusion-based techniques). This study aims to fundamentally evaluate the sinterability of the alloys based on Mg-Sr-Ca system designed via thermodynamic calculations. Powder metallurgical route with in-house powder processing was applied and final porosity levels were determined to assess sinterability where thermodynamic calculations along with the differential scanning calorimetry (DSC) were used to select the sintering parameters.
Electron Beam Powder Bed Fusion of Refractory Metals
Authors: Kirchner A. , Gaitzsch U. , Dorow-Gerspach D. , Distl B. , Franke-Jurisch M. , Zhong C. , Weißgärber T.
Published: October 2024

For refractory metals additive manufacturing of near net shape parts represents an attractive opportunity, in particular for complex geometries. The combination of high melting point, thermal conductivity and brittleness represents a challenge for fusion processes. Electron beam powder bed fusion (PBF-EB) facilitates preheat temperatures above 1000°C and vacuum processing with negligible oxygen contamination. Elemental tungsten and molybdenum were PBF-EB processed to high density from spherical and non-spherical powders. The resulting microstructure is characterized by large grains elongated in build direction and texture. Accordingly, the potential for the mechanical strength of defect-free PBF-EB refractory metals corresponds to conventionally fabricated material in recrystallized condition. The cracking behavior of tungsten tiles under extreme thermal shock was analyzed. Generated test geometries include thin-walled components and lattice structures. The Mo9Si8B alloy required 1100°C preheat temperature for crack-free processing. The microstructure is fine-grained with coarsening in the lower part. The bending strength exceeds 1200 MPa at 1000°C.
New Ultrahard Ceramics And Ceramic-metal Composites Based On Tungsten Tetraboride
Authors: Navarrete-Cuadrado Jazmina , Soria-Biurrun Tomas , Lozada-Cabezas Lorena , Moseley Steven , Alveen Patricia , Sanchez-Moreno Jose M.
Published: October 2024

WB4-B-TaB2 based materials with hardness values over 42 GPa have been obtained by glass encapsulated HIPing. Sintering of WB4-B-TaB2 powders is significantly activated by Ni additions. Porosity removal is achieved at 1100ºC-150 MPa- 1hour, that is, 250ºC below the temperature needed without nickel. However, there is strong chemical reactivity between Ni and WB4-B-TaB2 powders leading to the formation of W2B5, NiB, Ni4B3 and M2B5 phases. Since no metallic nickel remains after sintering, these ceramic composites are very brittle. Strength and toughness of WB4-B-TaB2-Ni alloys are notably improved by TiAl3 and Zr additions. Although Ni containing borides are still present in these materials, there are also Ni-rich regions free of boron after sintering, which provide a significant strengthening effect, reaching fracture strength values near 1 GPa. However, when Zr is added to the mixtures, WB4 grains are fully decomposed into a combination of mixed borides.
Recycled Ti-6Al-4V powder processed by Fusion Granulate Fabrication (FGF) and MIM in comparison to commercial plasma atomized powder
Authors: Limberg W. , Rackel M. W. , Ebel T. , Willumeit-Römer R. , Pyczak F.
Published: October 2024

An economic drawback of powder based additive manufacturing methods is the high cost of powder compared to cast materials. However, the use of recycled materials, which do not originate from the primary powder production cycle, offers potential for cost reduction. In the present contribution, the use of Ti-6Al-4V powder generated via recycling is combined with Fused Granulate Fabrication FGF and MIM processing. The starting chip material comes from aviation production leftovers. The cleaned chips were formed into cylindrical electrodes and spherical powder was produced using the EIGA process. The tensile test properties of the parts made from the recycled Ti-6Al-4V powder were validated against parts produced from commercial plasma atomized Ti-6Al-4V powder. The parts produced from recycled powder show a higher oxygen content and therefore a higher tensile strength, but only a slightly lower plastic elongation to fracture, compared to parts made from plasma atomized powder.
GÜNCEL İŞ VE SOSYAL GÜVENLİK HUKUKU
Authors: KOYUNCU Serdar Başar , YILMAZ Emre
Published: October 2024

Değerli öğrenciler İş Sağlığı ve Güvenliği müfredatında yer alan İSG Mevzuatı ve İş ve Sosyal Güvenlik Hukuku dersleri birbirlerine çok yakın konular olup kaynaklarını T.C. Resmî Gazetede yer alan Kanunlar, Yönetmelikler, Tebliğler, Yönergeler ve diğer tamamlayıcı normlardan almaktadır. İş ve Sosyal Güvenlik Hukuku bir bölüm dersi olarak bütün üniversitelerin iş sağlığı ve güvenliği programlarında ve farklı bazı bölümlerde okutulmaktadır. Çalışma hayatında karşılaşacağınız bir takım hukuksal problemlerin çözüm yolları ve başvuru kaynaklarını içeren kitabımız yedi bölüm olarak hazırlanmıştır. Kitabın sade ve anlaşılır olmasına özen gösterilmiş, önemli yerlerin altı çizilmiş, rakamsal veriler siyah punto ile vurgulanmış ve kritik konular çerçeve içerisinde gösterilmiştir. Her bölüm sonunda özet ve bölüm sonu soruları mevcuttur.
İŞ SAĞLIĞI VE GÜVENLİĞİ
Authors: KOYUNCU Serdar Başar , VURAL Dilşat
Published: October 2024

İş Sağlığı ve Güvenliği dersi, hem bölüm hem de üniversitelerde ortak ders olarak okutulmaktadır. Her geçen gün farklı dallarda zorunlu veya seçmeli bir ders olarak yaygınlaşan ve ülkemizde güvenlik kültürüne katkı sağlayan dersimize ait kitabın içeriği, bir yarı dönem boyunca hem ders notu hem de bir kaynak olarak değerlendirilmek amacıyla hazırlanmıştır.
KURUMSAL SÜRDÜRÜLEBİLİRLİK VE RAPORLAMA -Modeller, İlkeler, Stratejiler ve Standartlar -
Authors: TUTAR Salih
Published: October 2024

Bu kitap, sürdürülebilirlik kavramını tüm boyutlarıyla ele alarak, sürdürülebilir kalkınma süreçlerinin tarihsel gelişiminden başlayarak, çeşitli modeller ve uygulamalarla işletmelere rehberlik edecek bir çerçeve sunmayı hedeflemektedir. Sürdürülebilirlik raporlaması, emisyon kontrolü, yeşil finansman gibi konulara da derinlemesine yer verilen bu çalışma, teorik bilgi ile uygulama arasındaki boşluğu doldurmayı amaçlamaktadır. Ancak, derinlerde yatan asıl motivasyonum, kızıma ve onun gibi tüm çocuklara daha iyi bir gelecek bırakma ve hep birlikte daha sürdürülebilir bir gelecek inşa etme umududur
Development Of Contemporary Textile Art Inspired By The Arial View Of Historical Sites And Population Transitions Of Bahawalpur
Authors: Khalid Mian Talha Aziz
Published: October 2024

The Cholistan Desert, covering 26,933 Sq km, is a region with significant cultural assets including the ancient Rohi people's folklore, poetry, handicrafts, dances and myths. The area, known as "Deep/Greater Cholistan," is accessible only through dirt tracks and sand dunes. M. Rafique Mughal identified 410 extant sites in the desert, with 370 in the present-day district of Bahawalpur. These sites date back to the Hakra Ware period, Early Harappan, Mature Harappan, and Late Harappan periods. The Hakra civilization's remains, believed to be associated with ghosts and the dead, have special significance, more so than the Indus Valley. The current study is inspired from the architectural legacy and population transitions around historical places of princely state of Bahawalpur. The Ariel view of the[1] city serves as the basic inspiration for designing compositions. While the art and craft of this specific region provide the detail and confinement in the execution and development phases. Like rice stitch is mainly used with the combination of knots and couching where needed, to add details. Whereas, historically developed technique stippling is used to make sketches. Based on these techniques final products are shown in three different ways i.e. Installation, apparel, art piece. Fabric slabs are the integral part of the research design. Embroidered fabric slabs collectively composed of Ariel view legends made installation. For table top art piece 7 fabric slabs combine to showcase 7 decades population transition around Derawar fort. Design compositions of these seven fabric slabs depict two important facts. With passage of time, population and facilities are increasing around the fort but the fort is demolishing and losing its glory. In the category of apparel, the researchers have designed four shawls based on these compositions blending artistic creativity with practical textile applications. The researchers’ conclusion with some suggestions and recommendations was also summed up at the end of this study
Siamese network visual tracking algorithm based on second-order attention
Authors: Zhiqiang Hou , Maolin Chen , Jingyuan Ma , Fang Guo , Wangsheng Yu
Published: October 2024

In order to improve the feature expression and discrimination capabilities of the Siamese network visual tracking algorithm and obtain better tracking performance, a lightweight Siamese network visual tracking algorithm based on second-order attention is proposed. First, use the lightweight VGG-Net as the backbone of the Siamese network to obtain the deep features of the target; then, use the residual second-order pooling network and the second-order spatial attention network proposed in this article in parallel at the end of the Siamese network to obtain features with Second-order attention features with channel correlation and second-order attention features with spatial correlation; finally, using residual second-order channel attention features and second-order spatial attention features, visual tracking is achieved through a dual-branch response strategy. The proposed algorithm is trained end-to-end using the GOT-10k data set, and verified on the data sets OTB100 and VOT2018. Experimental results show that the tracking performance of the proposed algorithm has been significantly improved. Compared with the benchmark algorithm SiamFC: on the data set OTB100, the accuracy and success rate have improved by 10.0%and respectively 9.6%; on the data set VOT2018, the expected average overlap rate (EAO ) was improved 7.7% ; and tracking speed reached 48 FPS.
LPI radar waveform design based on complementary phase and discrete chaotic frequency joint coding
Authors: Shunsheng Zhang , Long Du , Wenqin Wang
Published: October 2024

In order to reduce the probability of radar radiation signals being detected by enemy passive detection systems, this paper proposes a phase and frequency joint coding low intercept Radar waveform design method. Based on the linear frequency modulation signal, this method uses complementary binary code and chaotic sequence to phase encode the intra-pulse modulation. Code and frequency coding. The numerical simulation results show that the designed waveform exhibits pseudo-random characteristics in the time-frequency domain, and the low recognition performance is improved; The signal has an extremely low peak sidelobe level after matched filtering, showing excellent low intercept performance; its three-dimensional ambiguity function diagram presents an ideal “graph”. Fishing type”, with good distance, speed resolution and anti-interference characteristics.
Tabular affiliation extraction based on graph convolutional network
Authors: Yutong Zhang , Qiyuan Li , Shujie Liu
Published: October 2024

This paper studies the problem of extracting affiliation relationships between cells in a table in the field of table recognition and analysis. The task of extracting affiliation relationships between tables is defined. Combining the similarity between table and graph structure, a graph representation method for cells in a table is given, and a graph convolutional network-based affiliation relationshipextraction model is proposed. The model aggregates features of cells in a table and their neighboring cells through a graph convolutional network, predicts whether there is an affiliation relationship between cells, and realizes relationship extraction. In order to verify the effectiveness of the model, two datasets, Rel-forms for Chinese forms and Rel-SciTSR for English forms, are annotated. Through experiments, the F1 scores on the above two datasets and the joint dataset reached , respectively 98.61%、96.55%、97.05%, verifying the effectiveness of the affiliation relationship extraction model on these two datasets, and analyzing the influence of different factors such as text content, coordinate information, cell attributes, and relative direction between cells on the experimental results of affiliation relationship extraction.
Reliability calculation and error analysis of VSV adjustment mechanism motion accuracy
Authors: Yifeng Yang , Yi Wang , Singh Harpur , Aoyu Liu
Published: October 2024

The adjustable stator vane (VSV) adjustment mechanism of an aircraft engine was taken as the research object, and a kinematic model with kinematic pair clearance was established. Considering the randomness of factors such as drive error, dimensional error and assembly clearance, a VSV adjustment mechanism motion accuracy reliability model considering the failure correlation of each stator blade motion accuracy was established. The BP neural network proxy model was introduced, and the Monte Carlo method was used to calculate the reliability of the mechanism, and the variation law of the reliability with the number of stator blades and the failure threshold was given. By comparing with the system model with independent unit failure assumptions and the fully correlated system model, the rationality of the mechanism system motion accuracy reliability model considering failure correlation was verified. Finally, a method for estimating the reliability error of the complex system caused by the proxy model error was proposed, and it was proved that the reliability calculation result of the VSV adjustment mechanism is accurate and reliable.
Vehicle drivability evaluation based on PCA-SSA-XGBoost
Authors: Moore John , Gallego Guillermo , Pengcheng Wang , Kang Yang
Published: October 2024

In order to improve the efficiency and quality of vehicle drivability evaluation, a drivability evaluation model based on principal component analysis, extreme gradient boosting tree and sparrow optimization algorithm is proposed. In this paper, the power upshift of DCT vehicle is taken as a typical working condition, and 18 objective evaluation indicators under the power upshift working condition are studied and defined. The objective evaluation indicators are simplified by principal component analysis, their redundancy and blockiness are reduced, and the model input samples are optimized. The extreme gradient boosting tree model is trained to predict the subjective score of drivability, and the sparrow algorithm is used to optimize the core hyperparameters of the extreme gradient boosting tree to improve the accuracy and stability of the model. Road tests show that after the objective evaluation indicators are simplified by principal component analysis, the model evaluation accuracy reaches 97%, which is better than BPNN ( 90%), SVM ( 91%), ELM ( 92%) and SSA-XGBoost ( 95%). It is proved that the accuracy and stability of the proposed PCA-SSA-XGBoost model are better than other models, and it can complete the drivability evaluation more effectively. The evaluation model can be transferred and applied to other driving conditions, and has application value in solving the subjective and objective mapping problem in drivability evaluation.
Segmentation of multi-view video speed extraction method that can cross lane dividing lines
Authors: Hou Yue , Guo Jinsong , Lin Wei , Zhang Di , Wu Yue , Zhang Zhen
Published: October 2024

Aiming at the problem that the existing video traffic parameter extraction methods are overly dependent on manual annotation and a single perspective cannot effectively correct the dynamic driving deviation of vehicles on site, a multi-perspective video traffic parameter extraction method that can segment and cross lane dividing lines is proposed. The method consists of an automatic annotation point generation module and a multi-perspective correction module. The automatic annotation point generation module realizes the process of automatically generating annotation points by constructing a reference block based on equal-length lane dividing lines. The multi-perspective correction module proposes multiple mapping methods between vehicles and lane dividing lines and a correction speed measurement method based on the average speed probability density function to correct the two types of deviations generated by vehicles during dynamic driving. Experimental results on public data sets and measured data sets show that the vehicle speed extraction accuracy of the proposed method is better than other speed measurement methods, and it has a certain degree of universality.
Prediction of aviation safety incident risk level based on ECSDNN
Authors: Feng Xia , Sang Xiao , Tan CherMing , Zuo Haichao
Published: October 2024

Prediction of aviation safety incident risk level is an important means of active risk management. Considering the high-dimensional complexity and class imbalance of massive aviation safety incident data, a prediction method for aviation safety incident risk level based on integrated cost-sensitive deep neural network (ECSDNN) is proposed. The feature representation of aviation safety incident data is realized by using the method of embedded coding of categorical attributes and splicing of numerical attributes; the cost-sensitive matrix and cost-sensitive loss function are designed by comprehensively considering the misclassification ratio and fixed cost, and the base classifier model based on cost-sensitive deep neural network (CSDNN) is constructed; the hard voting method is used to integrate multiple base classifiers with different parameters and performances to construct the integrated prediction model ECSDNN for aviation safety incident risk level. The experimental results on the Aviation Safety Incident Reporting System (ASRS) dataset show that compared with the optimal prediction ability of the baseline algorithm, the prediction accuracy of the ECSDNN model is improved 4.51%; compared with the single CSDNN base classifier, the prediction accuracy is improved 3.17%.
Risk assessment method for civil aircraft approach and landing at high plateau based on QAR data
Authors: Chen Nongtian , Man Yongzheng , Li Junhui
Published: October 2024

Civil aircraft approach and landing at high altitude is a high-risk stage of plateau flight. In order to effectively implement the risk identification and grade judgment of approach and landing at high altitude, an LSTM-DNN deep learning risk assessment method based on variable fuzzy recognition with weights removed is proposed. This method is first based on the high-altitude flight data recorded by the quick access recorder (QAR), and draws on the civil aircraft flight quality monitoring project specification (FOQA) consultation notice and industry QAR monitoring standards. Combined with the indicator importance analysis and Delphi expert survey, five key monitoring items are extracted as the risk assessment indicators of civil aircraft approach and landing at high altitude, including large heading change during landing, low track, large descent rate during 2000-1000 ft approach, vertical acceleration at touchdown, and large descent rate during 500-50ft approach. Then, in order to overcome the subjective bias of the evaluation indicator weight, the weight of the evaluation indicator is determined by the weight removal method, and the risk grade membership function is constructed based on the variable fuzzy recognition method. Finally, a risk assessment model for civil aircraft approach and landing at high altitude based on LSTM-DNN is established. Taking the Chengdu-Lhasa approach and landing segment as an example, the QAR data was extracted to train and test the risk assessment model, and the results were compared with those of Logistic multivariate regression, support vector machine (SVM) and other evaluation methods. The results show that the average recognition rate of the LSTM-DNN deep learning model based on variable fuzzy recognition with weights removed reaches 94.18%, and the highest can reach 94.79%, which verifies the objective effectiveness of the risk assessment method.
Heterogeneous multi-task allocation algorithm based on multi-round distributed auction
Authors: Lu Ye , Zhou Rui , Li Xing , Liu Zhiheng , Di Bin
Published: October 2024

Aiming at the distributed decision-making needs of different combat tasks such as multi-machine collaborative detection, attack, and evaluation, a multi-round distributed auction algorithm for heterogeneous tasks with time constraints is established. The algorithm flexibly solves the distributed collaborative task allocation problem with complex constraints between drones with different capabilities. In the distributed task allocation framework, each drone can realize distributed asynchronous computing and asynchronous communication, and can ensure good solution efficiency and accuracy. The task allocation principle is mainly based on the nearest allocation, while taking into account the target value and time constraints, realizing the priority allocation of important targets under limited resources and making the total journey as short as possible. Simulation results show that: given any number of drones and targets and the configuration of drone combat capabilities, the heterogeneous multi-round distributed auction algorithm can obtain good allocation results.
Moving target detection based on multi-satellite joint passive microwave imaging
Authors: Li Rui , Deng Li , Duan Ran
Published: October 2024

Using satellite formations to form a space-based passive interferometric imaging system can achieve high spatial resolution, but the sparse detection baseline will cause aliasing in the inversion image, affecting the detection of single-pixel point targets. Considering the slowly varying characteristics of the observation area, a target detection method based on image sequence is proposed. The background is estimated by multi-frame images, and the background of the inversion image is eliminated; energy is gathered based on sidelobe characteristics, the noise in the target area is estimated, and candidate targets are selected; the motion trajectory of the target is obtained by combining the temporal motion characteristics, and the detection of moving point targets is realized. Taking the formation system composed of 10 geostationary orbit satellites as an example, 300 simulation experiments are carried out for the detection of 50 ship point targets. The results show that the proposed method can realize point target detection in a large field of view, with an average false alarm rate 14.5%and an average missed alarm rate 19.5%.
Discrete state transfer algorithm based on deep reinforcement learning for solving flexible job shop scheduling problem
Authors: Jiazheng Zhu , Cong Wang , Xinkai Li
Published: October 2024

Flexible Job Shop Scheduling (FJSP) is a scheduling problem widely used in real life. The research on its intelligent algorithm has important academic significance and application value. In order to solve FJSP, this paper proposes a discrete state transfer algorithm based on proximal policy optimization (DSTA-PPO) with the optimization goal of minimizing the maximum completion time. DSTA-PPO has the following three characteristics: (1) Considering that FJSP requires simultaneous scheduling of process sorting and machine allocation, a state feature that can fully express the current scheduling problem is designed by combining process coding and machine coding. (2) A variety of critical path-based search operations are designed for process sorting and machine allocation. (3) Through reinforcement learning training, it can effectively guide the agent to select the correct search operation to optimize the current scheduling sequence. Through simulation experiments based on different data sets, the effectiveness of each link of the algorithm was verified. At the same time, it was compared with the existing algorithms on the same example with minimizing the maximum completion time. The comparison results show that the proposed algorithm can solve the examples in most examples with a shorter completion time, and effectively solve the flexible job shop scheduling problem.
Upper limit assessment of GIS equipment failure based on time series model and deep learning algorithm
Authors: Chattin Linda , Wilson Brian , Scott Megan , Roberts Steven
Published: October 2024

If a sudden fault of gas-insulated switchgear is not handled in time, it will delay production and cause certain economic losses to the enterprise. Therefore, this study proposes an upper limit assessment algorithm for equipment faults based on time series model and deep learning. The irregular fluctuation components in the time series model are stabilized by using empirical mode decomposition, and the long short-term memory network in the deep learning algorithm is introduced to combine the two. The equipment fault data is obtained by the joint algorithm. After empirical mode decomposition, the intrinsic mode function component of the data signal is obtained, and the sensitive component is extracted from it. The relationship value and mutual information between it and the original data signal are analyzed to complete the fault data feature extraction. In the process of equipment fault upper limit assessment, the parameter optimization and convergence are controlled, and the fault deep learning model is constructed. After the refraction coefficient and reflection coefficient are determined, the upper limit assessment of equipment faults is realized. The proposed method is experimentally tested. The results show that the proposed algorithm has an ideal upper limit assessment result of faults, and the obtained curve has a high degree of fit with the actual result curve. It can be proved that the proposed algorithm can scientifically evaluate the upper limit of equipment faults and has certain application value.
Resource optimization of multi-UAV assisted communication system based on user scheduling
Authors: Jingmin Tang , Jiaqi Huang , Bingwen Wang , Yaolian Song , Guicai Yu
Published: October 2024

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.
Scheduling optimization of continuous climb and descent operations in busy terminal airspace
Authors: Bay John S. , Taylor Laura , Lewis Anthony , White Rebecca
Published: October 2024

Based on the current terminal airspace structure, a method for scheduling aircraft arrival and departure that integrates trajectory optimization, conflict detection and multi-objective optimization is proposed to help implement continuous climb and descent operations in busy terminal airspace. First, based on the multi-stage optimal control theory, the Gaussian pseudo-spectral method is used to propose a vertical profile optimization method for continuous climb and descent operations, and the trajectory optimization of continuous climb and descent operations based on the cost index is realized. Secondly, according to the wake turbulence interval and release interval used by the runway, and the horizontal and vertical intervals of air operations, the Mahalanobis distance is used to establish an aircraft conflict detection model. Then, considering the demands of air traffic control, airlines, airports and other operating units, a multi-objective scheduling model and method for aircraft arrival and departure that can achieve the optimization results are proposed. Finally, two sets of arrival and departure data at Guangzhou Baiyun Airport during busy periods are selected, multiple interval parameters are set, alternative paths are introduced, and case analysis and comparative studies are carried out. The results show that during the busy period dominated by departures, the terminal airspace of Guangzhou Baiyun Airport can achieve continuous climb and descent operations during busy periods; during the busy period dominated by arrivals, two aircraft could not be dispatched. The introduction of alternative paths can reduce the number of aircraft that cannot be dispatched.
Research on high dynamic pixel structure based on adaptive integral capacitor
Authors: Suiyang Liu , Zhongjie Guo , Ningmei Yu , Ruiming Xu
Published: October 2024

Infrared image sensing technology has attracted wide attention due to its advantages such as being unaffected by the environment, good target recognition, and strong anti-interference ability. However, with the improvement of the integration of infrared focal planes, the constraints between the dynamic range, noise, and full-shade of the optoelectronic system are particularly prominent. Therefore, in order to solve the contradiction between noise in weak light and full-shade capacity in strong light, in a 5 T infrared pixel circuit, the relationship between the capacitance value and voltage of the inverted MOS capacitor in a specific voltage range is used to make the integral capacitance of the infrared image sensor automatically change from 6.5 fF to 37.5 fF. A high dynamic pixel structure based on adaptive integral capacitance is proposed. Based on 55 nm CMOS process technology, 12288 × 12288its performance parameters are studied in a pixel-scale infrared sensor. The research results show that 5.5m × 5.5mthe small-size pixel has a large full-shade capacity of 1.31 Me- and a variable conversion gain, a noise of less than 0.43 e, and a dynamic range of more than 130 dB.
Rolling bearing fault diagnosis method based on MSDCNN in strong noise environment
Authors: Ivy Julie , Wilson Brian , Scott Megan , Roberts Steven
Published: October 2024

Aiming at the problems of poor noise resistance, high computational complexity and insufficient generalization performance of traditional bearing fault diagnosis methods based on deep learning, a rolling bearing fault diagnosis method based on multi-scale dynamic convolutional neural network (MSDCNN) is proposed. Firstly, the one-dimensional vibration signal of the rolling bearing is converted to the frequency domain by Fourier transform, and the features are further extracted by wide convolution kernel. Secondly, a multi-scale dynamic convolution structure is proposed, and the feature information extracted by convolution kernels of different sizes is given different weights by using an improved channel attention mechanism. Then, a self-calibrating spatial attention mechanism (SCSAM) is designed, and the extracted feature information is input into the spatial attention mechanism to capture the importance of different regions. Finally, the features are further extracted by small convolution kernels, and the fault category is classified by using Softmax classifier. The fault diagnosis performance of the proposed model is verified by three different data sets. The experimental results show that compared with other intelligent models, the proposed model has higher classification accuracy, better generalization ability and stronger robustness under strong noise background.
Elvek, rendszerek és a tudomány: pályakép a magyar versenyjog szülőatyjáról
Authors: Kreisz Brigitta , Vadász Vanda
Published: October 2024

A tanulmány Vörös Imre versenyjogi munkásságáról és annak hatásáról nyújt áttekintést mind a magyar jogfejlődés, mind a versenyjog jogirodalma és jogelmélete alakulásának szempontjából. Először a magyar versenyjogi jogszabályalkotást vizsgáljuk, kiemelve Vörös Imre szerepét, majd európai és nemzetközi perspektívából tekintjük át versenyjogi szemléletének sajátosságait.