bluuebunny/crossref_metadata_2025 huggingface dataset:
A Three-Dimensional Spatial Interpolation Method and Its Application to the Analysis of Oxygen Deficit in the Bohai Sea in Summer
Authors: Zhao Zihan , Guo Junting , Xiao Rushui , Zheng Wei , Wang Yongzhi , Lv Xianqing , Shi Honghua
Published: February 2024

Dissolved oxygen (DO) plays a pivotal role in sustaining marine ecosystems. The Bohai Sea in China is a semi-enclosed sea, and oxygen-deficit events occur from time to time due to human activities. At present, there is a notable absence of any convenient and precise method for obtaining three-dimensional spatial data on DO, and the exploration of the physical mechanisms influencing oxygen deficit remains incomplete. This investigation uses the linear radial basis function (RBF-Linear) fitting method to conduct three-dimensional spatial interpolation for DO, which demonstrates minimal inaccuracy. Then, the RBF-Linear fitting method is utilized to collect a comprehensive three-dimensional spatial dataset encompassing temperature, salinity, and DO in the Bohai Sea in August from 2016 to 2018. The results indicate discernible interannual variations in the extent, area, and distribution of oxygen deficiency during summer in the Bohai Sea. Mechanism analysis reveals that intense precipitation episodes and an increase in wind stress curl exacerbate oxygen depletion. Additionally, the degree, location, and area of the two oxygen-deficit cores (off the Yellow River Estuary and off the Qinhuangdao) in the Bohai Sea are influenced by several factors, including current velocity, direction, local circulation position, and net horizontal transport rate. Furthermore, the study suggests that oxygen deficiency in the Bohai Sea region is currently in its early stages, with a limited degree of injury and a restricted range of influence. The use of a three-dimensional spatial interpolation method to create a complete DO field in three-dimensional space simplifies the research challenges associated with marine oxygen deficit. Moreover, this study holds particular significance for guiding the development of marine fisheries.
A Review of Underwater Robot Localization in Confined Spaces
Authors: Wu Haoyu , Chen Yinglong , Yang Qiming , Yan Bo , Yang Xinyu
Published: February 2024

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.
Hydrogen Fuel Cell as an Electric Generator: A Case Study for a General Cargo Ship
Authors: Inal Omer Berkehan , Zincir Burak , Dere Caglar , Charpentier Jean-Frédéric
Published: February 2024

In this study, real voyage data and ship specifications of a general cargo ship are employed, and it is assumed that diesel generators are replaced with hydrogen proton exchange membrane fuel cells. The effect of the replacement on CO2, NOX, SOX, and PM emissions and the CII value is calculated. Emission calculations show that there is a significant reduction in emissions when hydrogen fuel cells are used instead of diesel generators on the case ship. By using hydrogen fuel cells, there is a 37.4% reduction in CO2 emissions, 32.5% in NOX emissions, 37.3% in SOX emissions, and 37.4% in PM emissions. If hydrogen fuel cells are not used instead of diesel generators, the ship will receive an A rating between 2023 and 2026, a B rating in 2027, a C rating in 2028–2029, and an E rating in 2030. On the other hand, if hydrogen fuel cells are used, the ship will always remain at an A rating between 2023 and 2030. The capital expenditure (CAPEX) and operational expenditure (OPEX) of the fuel cell system are USD 1,305,720 and USD 2,470,320, respectively, for a 15-year lifetime, and the hydrogen fuel expenses are competitive at USD 260,981, while marine diesel oil (MDO) fuel expenses are USD 206,435.
Genome Analysis of a Newly Discovered Yeast Species, Hanseniaspora menglaensis
Authors: Ryan Adam P. , Groenewald Marizeth , Smith Maudy Th. , Holohan Cian , Boekhout Teun , Wolfe Kenneth H. , Butler Geraldine
Published: February 2024

Annual surveys of Irish soil samples identified three isolates, CBS 16921 (UCD88), CBS 18246 (UCD443), and CBS 18247 (UCD483), of an apiculate yeast species within the Hanseniaspora genus. The internal transcribed spacer (ITS) and D1/D2 region of the large subunit (LSU) rRNA sequences showed that these are isolates of the recently described species Hanseniaspora menglaensis, first isolated from Southwest China. No genome sequence for H. menglaensis is currently available. The genome sequences of the three Irish isolates were determined using short-read (Illumina) sequencing, and the sequence of one isolate (CBS 16921) was assembled to chromosome level using long-read sequencing (Oxford Nanopore Technologies). Phylogenomic analysis shows that H. menglaensis belongs to the fast-evolving lineage (FEL) of Hanseniaspora. Only one MAT idiomorph (encoding MATα1) was identified in all three sequenced H. menglaensis isolates, consistent with one mating type of a heterothallic species. Genome comparisons showed that there has been a rearrangement near MATα of FEL species compared to isolates from the slowly evolving lineage (SEL).
Forecasting of Airborne Conidia Quantities and Potential Insect Associations of Cryphonectria parasitica, the Causal Agent of Chestnut Blight, in England
Authors: Romon-Ochoa Pedro , Samal Pankajini , Pace Tom , Newman Tim , Oram Mark , Baxter Nicholas , Manning John A. S. , Biddle Mick , Barnard Kerry , Inward Daegan , Taylor Paul , Hendry Steven , Pérez-Sierra Ana , Ward Lisa
Published: February 2024

Sweet chestnut, an Asiatic tree introduced in many parts of Europe including the United Kingdom, is planted for nut production, timber, and amenity. Its major threat is the disease called blight, caused by the fungus Cryphonectria parasitica, which infects through wounds by airborne spores. Field trapping using sticky rods rotating traps was performed in an infected area in Devon (between May 2021 and April 2023). An improved dual hydrolysis Taqman probes real-time PCR was used. The number of spores was calculated by comparing the cycle threshold to the Ct of standards with known amounts of conidia or known target fragment copies cloned into a plasmid. Weekly spore counts were in the range of around 60 to approximately 8.5 × 103, with fluctuations of peaks (mainly in late summer–autumn 2021) and troughs. The effects of weather parameters were modelled, finding correlations between spore numbers and temperature, humidity, dewpoint, rainfall, wind speed, and wind duration. Additionally, an insect trapping was performed to confirm the presence/absence and quantity of C. parasitica conidia potentially phoretic on some insects by using the same molecular approach. None of the ten collected insect species harboured spores of this fungus.
From Nature to Design: Tailoring Pure Mycelial Materials for the Needs of Tomorrow
Authors: Whabi Viraj , Yu Bosco , Xu Jianping
Published: February 2024

Modern efforts to influence materials science with principles of biology have allowed fungal mycelial materials to take a foothold and develop novel solutions for the circular bioeconomy of tomorrow. However, recent studies have shown that the value of tomorrow’s green materials is not determined simply by their environmental viability, but rather by their ability to make the polluting materials of today obsolete. With an inherently strong structure of chitin and β-glucan, the ever-adaptable mycelia of fungi can compete at the highest levels with a litany of materials from leather to polyurethane foam to paper to wood. There are significant efforts to optimize pure mycelial materials (PMMs) through the entire process of species and strain selection, mycelial growth, and fabrication. Indeed, the promising investigations of novel species demonstrate how the diversity of fungi can be leveraged to create uniquely specialized materials. This review aims to highlight PMMs’ current trajectory, evaluate the successes in technology, and explore how these new materials can help shape a better tomorrow.
Virulence Spectra of Hungarian Pyrenophora teres f. teres Isolates Collected from Experimental Fields Show Continuous Variation without Specific Isolate × Barley Differential Interactions
Authors: Bakonyi József , Seress Diána , Nagy Zoltán Á. , Csorba Ildikó , Cséplő Mónika , Marton Tibor A. , Martin Anke , Mészáros Klára
Published: February 2024

Pyrenophora teres f. teres (Ptt), the causal agent of net form net blotch (NFNB) disease, is an important and widespread pathogen of barley. This study aimed to quantify and characterize the virulence of Ptt isolates collected from experimental fields of barley in Hungary. Infection responses across 20 barley differentials were obtained from seedling assays of 34 Ptt isolates collected from three Hungarian breeding stations between 2008 and 2018. Twenty-eight Ptt pathotypes were identified. Correspondence analysis followed by hierarchical clustering on the principal components and host-by-pathogen GGE biplots suggested a continuous range of virulence and an absence of specific isolate × barley differential interactions. The isolates were classified into four isolate groups (IG) using agglomerative hierarchical clustering. One IG could be distinguished from other IGs based on avirulence/virulence on one to five barley differentials. Several barley differentials expressed strong resistance against multiple Ptt isolates and may be useful in the development of NFNB-resistant barley cultivars in Hungary. Our results emphasize that the previously developed international barley differential set needs to be improved and adapted to the Hungarian Ptt population. This is the first report on the pathogenic variations of Ptt in Hungary.
Double Asymmetric Impacts, Dynamic Correlations, and Risk Management Amidst Market Risks: A Comparative Study between the US and China
Authors: Yu Poshan , Xu Haoran , Chen Jianing
Published: February 2024

Extreme shocks, including climate change, economic sanctions, geopolitical conflicts, etc., are significant and complex issues currently confronting the global world. From the US–China perspective, this paper employs the DCC-DAGM model to investigate how diverse market risks asymmetrically affect return volatility, and extract correlations between stock indices and hedging assets. Then, diversified and hedging portfolios, constructed by optimal weight and hedge ratio, are investigated using multiple risk reduction measures. The empirical results highlight that, first, diverse risks exhibit an asymmetric effect on the return volatility in the long term, while in the short term, the US stock market is more sensitive to negative return shocks than the Chinese market. Second, risks impact correlations differently across time horizons and countries. Short-term correlations are stronger than long-term ones for the US market, with the Chinese stock market displaying more stable correlations. Third, the hedging strategy is more effective in reducing volatility and risk for US stocks, while the diversification strategy proves more effective for Chinese stocks. These findings have implications for market participants striving to make their portfolios robust during turbulent times.
Measurement of Agricultural Eco-Efficiency and Analysis of Its Influencing Factors: Insights from 44 Agricultural Counties in Liaoning Province
Authors: Zhang Zhengyu , Jin Gui
Published: February 2024

Agricultural eco-efficiency (AEE) considers economic and environmental benefits and is a key indicator of green agricultural development. To achieve the multiple goals of improving agricultural production efficiency, reducing agricultural environmental damage, and reducing the input of agricultural resources, this study enriches the case study of agricultural production performance evaluation at the county level by measuring the AEE of 44 agricultural counties in Liaoning Province based on panel data and a super-efficient slacks-based measure model including undesired outputs. A two-way fixed-effects model was used to analyze the impact of agricultural development, macro-environment, and policy support on AEE. We found that the average AEE of the counties in Liaoning Province in 2014, 2016, 2018, and 2020 was 0.716, 0.735, 0.749, and 0.813, respectively, indicating a cumulative improvement rate of 13.55%. The average AEE levels gradually improved during the study period. Notably, the development of AEE among the counties was uneven. AEE was distributed in a “block-like” manner, and its local correlation presents a phenomenon of “small agglomeration and large dispersion”. In addition, the level of the agricultural economy, industrialization, and urbanization significantly promoted the improvement of AEE, and the promoting effects varied between different income levels and regions. Therefore, Liaoning Province needs to improve the AEE of each county according to local conditions and narrow the differences in AEE between counties. To continuously improve the level of rural economic development, lead the development of agricultural modernization with new urbanization, and comprehensively improve the overall AEE of counties. The research results are of guiding significance for deepening the study of AEE and can provide decision-making support for optimizing the mode of agricultural production and promoting the green development of regional agriculture.
Exploring the Ecological Effects of Rural Land Use Changes: A Bibliometric Overview
Authors: Xie Haojun , Sun Quan , Song Wei
Published: February 2024

Land use change is a significant contributor to global environmental change. The expansion of urban areas has increasingly impacted rural ecological environments, in particular the shift from agro-ecosystems to urban ecosystems, leading to alterations in land use patterns. Rural land use has led to economic, social, and environmental problems, including poor economic efficiency, emissions of pollutants, and increased environmental crises. The research of alterations in rural land use and their consequential environmental ramifications has garnered escalating attention, evolving into an indispensable subject of inquiry within pertinent academic disciplines. This study aims to obtain a comprehensive understanding of the ecological impacts of rural land use change. We examined 1237 literature sources through the Web of Science database and conducted a bibliometric analysis utilizing the Bibliometrix tool. Secondly, based on the results of bibliometric analysis, we conducted a review study on the impact of rural land use changes on the ecological environment, clarified the current research status in this field, and looked forward to future research directions. The study’s findings indicate that there has been a steady rise in publication volume from 1982 to 2023 and a significant potential for growth. The top three journals by publication volume are Sustainability, Land Use Policy, and Land. (2) A total of 4768 scholars from 95 countries or regions have contributed publications in this domain, notably led by researchers and institutions predominantly based in China. Developed nations, exemplified by the U.S., exhibit a notable citation frequency and robust research prowess within this field. (3) Land use, urbanization, China, ecosystem services, biodiversity, and remote sensing emerge as keywords of elevated frequency within the field, indicative of the scholarly emphasis on these subjects. (4) Studies in this domain are directed towards evaluating the effects on intrinsic components of the environment, including but not limited to soil quality, atmospheric conditions, water resources, and biodiversity. The implementation of sustainable rural land use strategies is essential for the realization of rural development and environmental protection. In future research efforts, the use of remote sensing technology holds immense potential as a robust technical tool for investigating both land use change and rural ecology, offering viable strategies for addressing environmental challenges in specific, localized regions. The results of this study can assist in comprehending the current state and direction of research in this field.
Learners’ Perceptions of Arabic Consonant Contrasts: Gender and Learning-Context Effects
Authors: Shehata Asmaa
Published: February 2024

This study investigates the effects of gender and the learning context on learners’ perceptions of Arabic consonant contrasts. To this end, 60 intermediate Arabic learners, half of which were males and half of which were females, were recruited from two different learning contexts: a study-abroad (SA) program in Egypt and an at-home (AH) setting at a western American university. Learners in the two groups were tested before and after a semester of Arabic study using a perception task that involved distinguishing four Arabic consonant contrasts: /d - dˁ/, /h - ħ/, /s - sˁ/, and /t - tˁ/). The results reveal that the advantage of females in segmental identification accuracy was not verified, as female performance did not significantly differ from male performance in any of the four target consonant contrasts. In contrast, the learning context was found to positively impact learners’ perceptions of the target Arabic consonants, as the SA group significantly outperformed their counterparts in the AH environment on the /h - ħ/ and /d - dˁ/ contrasts. However, there were no significant differences between learners in the two different contexts regarding the two other consonant contrasts (i.e., /s - sˁ/ and /t - tˁ/). The findings bring insights into the understanding of the gender-based differences in identifying problematic Arabic consonantal contrasts for English learners in two different contexts of learning, and they shed light on the implications for language pedagogy for pronunciation training.
Enhancing Language Learners’ Comprehensibility through Automated Analysis of Pause Positions and Syllable Prominence
Authors: Coulange Sylvain , Kato Tsuneo , Rossato Solange , Masperi Monica
Published: February 2024

This research paper addresses the challenge of providing effective feedback on spontaneous speech produced by second language (L2) English learners. As the position of pauses and lexical stress is often considered a determinative factor for easy comprehension by listeners, an automated pipeline is introduced to analyze the position of pauses in speech, the lexical stress patterns of polysyllabic content words, and the degree of prosodic contrast between stressed and unstressed syllables, on the basis of F0, intensity, and duration measures. The pipeline is applied to 11 h of spontaneous speech from 176 French students with B1 and B2 proficiency levels. It appeared that B1 students make more pauses within phrases and less pauses between clauses than B2 speakers, with a large diversity among speakers at both proficiency levels. Overall, lexical stress is correctly placed in only 35.4% of instances, with B2 students achieving a significantly higher score (36%) than B1 students (29.6%). However, great variation among speakers is also observed, ranging from 0% to 68% in stress position accuracy. Stress typically falls on the last syllable regardless of the prosodic expectations, with the strong influence of syllable duration. Only proficient speakers show substantial F0 and intensity contrasts.
How Parents’ Perceived Value of the Heritage Language Predicts Their Children’s Skills
Authors: Cangelosi Martina , Borghetti Claudia , Bonifacci Paola
Published: February 2024

The growing field of family language policies (FLPs), defined as overt and explicit planning in relation to language use among family members, has garnered increasing interest. FLPs influence child–caretaker interactions and are closely linked to child language development and acquisition. This study investigates the impact of FLPs on children’s proficiency in their heritage language (HL). Employing a multi-method approach, we conducted semi-structured interviews with 53 multilingual parents to explore their beliefs, ideologies, and language management within the family context. Concurrently, their children were administered standardized tasks in their heritage language (HL) to assess receptive vocabulary skills and morphosyntactic comprehension. Our findings indicate that parents’ perceived value of the HL significantly correlates with their children’s language performance, going beyond the influence of reported domestic language usage.
Ventilation-Based Strategy to Manage Intraoperative Aerosol Viral Transmission in the Era of SARS-CoV-2
Authors: Brimmo Ayoola T. , Glia Ayoub , Barajas-Gamboa Juan S. , Abril Carlos , Rodríguez John , Kroh Matthew , Qasaimeh Mohammad A.
Published: February 2024

In operating theaters, ventilation systems are designed to protect the patient from airborne contamination for minimizing risks of surgical site infections (SSIs). Ventilation systems often produce an airflow pattern that continuously pushes air out of the area surrounding the operating table, and hence reduces the resident time of airborne pathogen-carrying particles at the patient’s location. As a result, patient-released airborne particles due to the use of powered tools, such as surgical smoke and insufflated CO2, typically circulate within the room. This circulation exposes the surgical team to airborne infection—especially when operating on a patient with infectious diseases, including COVID-19. This study examined the flow pattern of functional ventilation configurations in view of developing ventilation-based strategies to protect both the patient and the surgical team from aerosolized infections. A favorable design that minimized particle circulation was deduced using experimentally validated numerical models. The parameters adapted to quantify circulation of airborne particles were particles’ half-life and elevation. The results show that the footprint of the outlet ducts and resulting flow pattern are important parameters for minimizing particle circulation. Overall, this study presents a modular framework for optimizing the ventilation systems that permits a switch in operation configuration to suit different operating procedures.
Lateral Tarsoplasty Combined with a Full-Thickness Skin Graft for Managing Severe Lower Eyelid Ectropion Following the Subciliary Approach for Infra-Orbital Rim Fracture: A Retrospective Observational Study
Authors: Cho Wonseok , Kim Chang Gyun , Jang Eun A , Kim Kyu Nam
Published: February 2024

Subciliary incision is a common approach for facial fracture surgery; however, it has a higher incidence of lower lid ectropion, which can be particularly challenging for beginning surgeons to manage. This study reports the usage of lateral tarsoplasty combined with a full-thickness skin graft (FTSG) to correct severe ectropion following the subciliary approach for infra-orbital rim fractures. We retrospectively reviewed all facial fracture cases involving infra-orbital rim fractures through a subciliary approach treated in our department between March 2021 and May 2023. Electronic medical records and clinical digital photographs of patients who met the inclusion criteria were reviewed. After reviewing 196 cases that used the subciliary approach, we found 6 patients (3.06%; 4 males and 2 females; mean age, 68.5 ± 4.89 years) with postoperative severe ectropion managed using lateral tarsoplasty and FTSG. The mean ectropion development and correction times after facial fracture surgery were 0.78 ± 0.24 and 0.91 ± 0.37 months, respectively. At the 12-month follow-up, all patients showed favorable outcomes, and the position of their lower eyelids was well maintained without ectropion recurrence. Based on these successful outcomes, lateral tarsoplasty combined with FTSG is proposed to be an effective and straightforward method for managing lower eyelid ectropion caused by facial fracture surgery.
Optimizing Childhood Obesity Management: The Role of Edmonton Obesity Staging System in Personalized Care Pathways
Authors: De Wolf Astrid , Nauwynck Elise , Vanbesien Jesse , Staels Willem , De Schepper Jean , Gies Inge
Published: February 2024

Childhood obesity, affecting 29% of 7–9-year-olds across 33 European countries, is a significant public health challenge. Its persistence into adulthood poses grave health risks influenced by genetic, environmental, and socio-economic factors. Belgium introduced a new care pathway in December 2023, based on the Edmonton Obesity Staging System for Pediatrics (EOSS-P), addressing four health domains and staging obesity severity. This pathway operates across three levels: primary care physicians, Paediatric Multidisciplinary Obesity Management Centres (PMOCs), and Centers of Expertise for Paediatric Obesity Management (CEPOs). Each stage of EOSS-P demands tailored interventions. Early stages involve dietary interventions, physical activity promotion, and behavior modifications. As obesity severity progresses, treatments intensify, encompassing psychological support, anti-obesity medications, and, in some cases, bariatric surgery. Throughout these stages, the involvement of multidisciplinary teams is crucial, emphasizing family-based approaches and continuous monitoring. This article provides detailed guidelines for healthcare professionals, delineating interventions and recommendations tailored to each EOSS-P stage. It emphasizes a holistic approach that extends beyond BMI-based diagnosis, promoting personalized care and prompt escalations between care levels, thereby ensuring optimal management of childhood obesity. This comprehensive framework aims to address the complexities of childhood obesity, emphasizing the importance of timely and targeted interventions for better health outcomes.
Optimizing Load Capacity Predictions in Gas Foil Thrust Bearings: A Novel Full-Ramp Model
Authors: Ying Ming , Liu Xinghua , Zhang Yue , Zhang Chongbin
Published: February 2024

Gas film thickness significantly influences the performance prediction of Gas Foil Thrust Bearings (GFTB). However, the Classical Model (CM) for GFTBs exhibits inaccuracies in describing gas film thickness. In this paper, we explore the differences in the details of gas film thickness modeling and propose a Parallel Segmentation Model (PSM), which fixes the errors of the CM in describing the gas film thickness in the ramp section, and a Full-Ramp Model (FRM), to which a more realistic description of the gas film in the flat section is also added. Comparative analysis, utilizing a publicly available test dataset based on the open-source GFTB structure, establishes that the FRM surpasses the CM and PSM in accurately predicting load capacity. In-depth analysis shows that the location of the minimum gas film thickness for determining the load capacity is located at the innermost circle of the free end of the top foil, whereas the FRM is subjected to the same load with a larger film thickness at this location, which may be due to the unique geometry of the top foil of the FRM. Subsequently, employing the FRM, a parametric study explores load capacity in GFTB, considering variables such as ramp height, top foil thickness, bump foil stiffness, ramp section extent, and top foil area. The results demonstrate that GFTB load capacity exhibits a linear increase with the expansion of the top foil area. Moreover, the load capacity increases with augmented top foil thickness and bump foil stiffness, albeit at a decreasing rate. Additionally, an increase in ramp section extent initially enhances load capacity, reaching a maximum value before declining. Similarly, an increase in ramp height initially augments load capacity, attaining a maximum before subsequent diminution.
Study on Thin Lamination of Carbon Fiber Based on Mechanical Broadening
Authors: Chen Yanlei , Hui Yanbo , Zhu Dengjie , Li Xingle , Wang Hongxiao
Published: February 2024

Carbon fiber has excellent mechanical properties and plays an important role in modern industry. However, due to the complexity of the carbon fiber widening process, the industrial application of carbon fiber is limited. By designing the carbon fiber widening equipment of automaton, the relationship between the widening width of carbon fiber and the process parameters is studied, and the optimum developing process parameters are obtained, to improve the performance of carbon fiber composites to a certain extent. In this study, the widening process of carbon fiber was studied based on the mechanical broadening method. Firstly, an automatic broadening equipment was designed, and the effects of the initial tension, the number of straight rods, the number of convex rods, and the drawing speed on the widened width during the broadening process were discussed. The widening effect was evaluated by SEM imaging and mechanical testing. At the same time, the factors affecting the broadening width and broadening defects during the broadening process were analyzed, and the optimal broadening process parameters were obtained. The results showed that within a specific range, a higher initial tension, a greater number of convex rods, and an appropriate speed resulted in relatively smaller damage to the broadening of carbon fibers. Through the design of automatic broadening, this experiment explores optimal broadening process parameters, provides a reference for the improvement of the carbon fiber broadening process and further promotes large-scale industrial applications of carbon fiber.
Design, Assembly and Control of a Differential/Omnidirectional Mobile Robot through Additive Manufacturing
Authors: Padilla-García Erick Axel , Cruz-Morales Raúl Dalí , González-Sierra Jaime , Tinoco-Varela David , Lorenzo-Gerónimo María R.
Published: February 2024

Although additive manufacturing is a relatively new technology, it has been widely accepted by industry and academia due to the wide variety of prototypes that can be built. Furthermore, using mobile robots to carry out different tasks allows greater flexibility than using manipulator robots. In that sense, and based on those above, this article focuses on the design and assembly of a multi-configurable mobile robot that is capable of changing from a differential to an omnidirectional configuration. For this purpose, a sequential mechatronic design/control methodology was implemented to obtain an affordable platform via additive manufacturing which is easily scalable and allows the user to change from one configuration to another. As a proof of concept, this change is made manually. Fabrication, construction, and assembly processes for both structures are presented. Then, a hierarchical control law is designed. In this sense and based on Lyapunov’s method, a low-level controller is developed to control the angular speed of the wheels to a desired angular speed, and a medium-level controller controls the robot’s attitude to follow a desired Cartesian trajectory. Finally, the control strategies are implemented in both prototype configurations, and through experimental results, the theoretical analysis and the construction of the mobile robot are validated.
Sampled-Data Cooperative Adaptive Cruise Control for String-Stable Vehicle Platooning with Communication Delays: A Linear Matrix Inequality Approach
Authors: Jang Yong Hoon , Kim Han Sol
Published: February 2024

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.
Machine Vision-Based Surface Defect Detection Study for Ceramic 3D Printing
Authors: Zhou Jing , Li Haili , Lu Lin , Cheng Ying
Published: February 2024

A set of online inspection systems for surface defects based on machine vision was designed in response to the issue that extrusion molding ceramic 3D printing is prone to pits, bubbles, bulges, and other defects during the printing process that affect the mechanical properties of the printed products. The inspection system automatically identifies and locates defects in the printing process by inspecting the upper surface of the printing blank, and then feeds back to the control system to produce a layer of adjustment or stop the printing. Due to the conflict between the position of the camera and the extrusion head of the printer, the camera is placed at an angle, and the method of identifying the points and fitting the function to the data was used to correct the camera for aberrations. The region to be detected is extracted using the Otsu method (OSTU) on the acquired image, and the defects are detected using methods such as the Canny algorithm and Fast Fourier Transform, and the three defects are distinguished using the double threshold method. The experimental results show that the new aberration correction method can effectively minimize the effect of near-large selection caused by the tilted placement of the camera, and the accuracy of this system in detecting surface defects reached more than 97.2%, with a detection accuracy of 0.051 mm, which can meet the detection requirements. Using the weighting function to distinguish between its features and defects, and using the confusion matrix with the recall rate and precision as the evaluation indexes of this system, the results show that the detection system has accurate detection capability for the defects that occur during the printing process.
Advanced Learning Technique Based on Feature Differences of Moving Intervals for Detecting DC Series Arc Failures
Authors: Dang Hoang-Long , Kwak Sangshin , Choi Seungdeog
Published: February 2024

DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems.
Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift
Authors: Alzahrani Salha M.
Published: February 2024

The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.
A New Multi-Target Three-Way Threat Assessment Method with Heterogeneous Information and Attribute Relevance
Authors: Gao Yang , Lyu Na
Published: February 2024

Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment.
State-Difference Feedback Control for Discrete-Time Takagi–Sugeno Fuzzy Descriptor Systems with Parameter Uncertainties and External Noises
Authors: Lin Zi-Yao , Chang Wen-Jer , Su Che-Lun
Published: February 2024

This research focuses on the development of state-difference feedback controllers for discrete-time (DT) nonlinear descriptor systems. Discrete-time nonlinear DA systems consist of difference and algebraic equations and play a crucial role in describing dynamic behavior and capturing the constraints or relationships within the system. However, analytical stability may pose additional challenges due to the unique characteristics of the system. Utilizing fuzzy model-based techniques, the DT nonlinear DA system discussed in this study can be effectively represented using the Takagi–Sugeno (T-S) fuzzy model. After linearizing the nonlinear system through the T-S fuzzy model, traditional linear control techniques become applicable. These techniques are then applied to T-S fuzzy systems to establish stability criteria. This article chooses the Lyapunov function as the method used to analyze system stability. Additionally, we use a free-weighting matrix to introduce additional degrees of freedom. In summary, this paper presents simulation results and discussions to verify the effectiveness of the proposed design approach.
Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments
Authors: Su Hang , Wang Wei
Published: February 2024

Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the optimal solution. Therefore, obtaining the variables that cause the target outcome through a causal inference method is a more direct and effective method. This paper presents a new approach for invariant feature learning based on causal inference (IFCI). IFCI detects causal variables unaffected by the environment through the causal inference method. IFCI focuses on partial causal relationships to work efficiently even in the face of high-dimensional data. Our proposed causal inference method can accurately infer causal effects even when the treatment variable has more complex values. Our method can be viewed as a pretreatment of data to filter out variables whose distributions change between different environments, and it can then be combined with any learning method for classification and regression. The result of empirical studies shows that IFCI can detect and filter out environmental variables affected by the environment. After filtering out environmental variables, even a model with a simple structure and common loss function can have strong OOD generalization capability. Furthermore, we provide evidence to show that classifiers utilizing IFCI achieve higher accuracy in classification compared to existing OOD generalization algorithms.
Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention Mechanisms
Authors: Sheng Jinfang , Yang Zili , Wang Bin , Chen Yu
Published: February 2024

Graph embedding plays an important role in the analysis and study of typical non-Euclidean data, such as graphs. Graph embedding aims to transform complex graph structures into vector representations for further machine learning or data mining tasks. It helps capture relationships and similarities between nodes, providing better representations for various tasks on graphs. Different orders of neighbors have different impacts on the generation of node embedding vectors. Therefore, this paper proposes a multi-order adjacency view encoder to fuse the feature information of neighbors at different orders. We generate different node views for different orders of neighbor information, consider different orders of neighbor information through different views, and then use attention mechanisms to integrate node embeddings from different views. Finally, we evaluate the effectiveness of our model through downstream tasks on the graph. Experimental results demonstrate that our model achieves improvements in attributed graph clustering and link prediction tasks compared to existing methods, indicating that the generated embedding representations have higher expressiveness.
Differential Fault and Algebraic Equation Combined Analysis on PICO
Authors: Ding Linxi , Zhang Hongxin , Xu Jun , Fang Xing , Wu Yejing
Published: February 2024

In modern information technology, research on block cipher security is imperative. Concerning the ultra lightweight block cipher PICO, there has been only one study focused on recovering its complete master key, with a large search space of 264, and no fault analysis yet. This paper proposes a new fault analysis approach, combining differential fault and algebraic equation techniques. It achieved the recovery of PICO’s entire master key with 40 faults in an average time of 0.57 h. S-box decomposition was utilized to optimize our approach, reducing the time by a remarkable 75.83% under the identical 40-fault condition. Furthermore, PICO’s complete master key could be recovered with 28 faults in an average time of 0.78 h, indicating a significant 237 reduction in its search space compared to the previous study. This marks the first fault analysis on PICO. Compared to conventional fault analysis methods DFA (differential fault analysis) and AFA (algebraic fault analysis), our approach outperforms in recovering PICO’s entire master key, highlighting the cruciality of key expansion complexity in block cipher security. Therefore, our approach could serve to recover master keys of block ciphers with comparably complicated key expansions, and production of more secure block ciphers could result.
Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction
Authors: Zhao Zixue , Cui Tianxiang , Ding Shusheng , Li Jiawei , Bellotti Anthony Graham
Published: February 2024

Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied extensively. However, despite ongoing research, there is no consensus on the most effective technique. The choice of resampling technique is closely related to the dataset size and imbalance ratio, and its effectiveness varies across different classifiers. Moreover, there is a notable gap in research concerning suitable techniques for extremely imbalanced datasets. Therefore, this study aims to compare popular resampling techniques across different datasets and classifiers while also proposing a novel hybrid sampling method tailored for extremely imbalanced datasets. Our experimental results demonstrate that this new technique significantly enhances classifier predictive performance, shedding light on effective strategies for managing the class imbalance problem in credit risk prediction.