bluuebunny/crossref_metadata_2025 huggingface dataset:
OSBA: An Open Neonatal Neuroimaging Atlas and Template for Spina Bifida Aperta
Authors: Speckert Anna , Ji Hui , Payette Kelly , Grehten Patrice , Kottke Raimund , Ackermann Samuel , Padden Beth , Mazzone Luca , Moehrlen Ueli ,   Spina Bifida Study Group Zurich, Jakab Andras
Published: September 2024

We present the Open Spina Bifida Aperta (OSBA) atlas, an open atlas and set of neuroimaging templates for spina bifida aperta (SBA). Traditional brain atlases may not adequately capture anatomical variations present in pediatric or disease-specific cohorts. The OSBA atlas fills this gap by representing the computationally averaged anatomy of the neonatal brain with SBA after fetal surgical repair. The OSBA atlas was constructed using structural T2-weighted and diffusion tensor MRIs of 28 newborns with SBA who underwent prenatal surgical correction. The corrected gestational age at MRI was 38.1 ± 1.1 weeks (mean ± SD). The OSBA atlas consists of T2-weighted and fractional anisotropy templates, along with nine tissue prior maps and region of interest (ROI) delineations. The OSBA atlas offers a standardized reference space for spatial normalization and anatomical ROI definition. Our image segmentation and cortical ribbon definition are based on a human-in-the-loop approach, which includes manual segmentation. The precise alignment of the ROIs was achieved by a combination of manual image alignment and automated, non-linear image registration. From the clinical and neuroimaging perspective, the OSBA atlas enables more accurate spatial standardization and ROI-based analyses and supports advanced analyses such as diffusion tractography and connectomic studies in newborns affected by this condition.
The Benefits of Positive Energy Districts: Introducing Additionality Assessment in Évora, Amsterdam and Espoo
Authors: Ntafalias Aristotelis , Papadopoulos Panagiotis , van Wees Mark , Šijačić Danijela , Shafqat Omar , Hukkalainen Mari , Kantorovitch Julia , Lage Magui
Published: September 2024

Positive Energy Districts (PEDs) are a promising approach to urban energy transformation, aiming to optimize local energy systems and deliver environmental, social and economic benefits. However, their effectiveness and justification for investment rely on understanding the additional value they provide (additionality) in comparison to current policies and planning methods. The additionality perspective is not used yet in current evaluations of PED demonstrations and pilots. Therefore, this paper introduces the concept of additionality in the evaluation of PEDs, focusing on the additional benefits they bring and the circumstances under which they are most effective. We discuss the additionality of PEDs in addressing the challenges of climate neutrality and energy system transformation in three European cities that are funded by the European Commission’s H2020 Programme. It should be noted that given the ongoing status of these projects, the assessment is mainly based on preliminary results, as monitoring is still ongoing and quantitative results are not yet available. The paper discusses the drivers and barriers specific to PEDs, and highlights the challenges posed by technical complexities, financing aspects and social and legal restrictions. Conclusions are drawn regarding the concept of additionality and its implications for the wider development of PEDs as a response to the challenges of climate neutrality and energy system transformation in cities. We conclude that the additionality perspective provides valuable insights into the impact and potential of PEDs for societal goals and recommend this approach for use in the final evaluation of R&I projects involving PEDs using actual monitored data on PEDs.
A Unique Case of Unilateral Pseudogynecomastia
Authors: Kountouri Ismini , Katsarelas Ioannis , Kokkali Eftychia , Giotas Amyntas , Gkogkos Christos , Chatzinas Dimitrios , Nachopoulos Panagiotis , Faseki Afroditi , Panagiotou Alexandra , Polychronidis Athanasios , Chandolias Miltiadis , Gkiatas Nikolaos , Manolakaki Dimitra , Dimasis Periklis
Published: September 2024

Background/Objectives: Gynecomastia is a common condition characterized by the benign enlargement of male breast tissue, often resulting from hormonal imbalances. A rare variant, unilateral pseudogynecomastia, involves enlargement due to adipose tissue accumulation without glandular proliferation and can be associated with occupational factors. Methods: We report the case of a 45-year-old male mechanic presenting with unilateral enlargement of the left breast. The patient reported daily microtrauma on his left axilla and chest wall. The clinical evaluation and imaging revealed lipomatosis with pronounced fibrous tissue and no glandular tissue involvement. The hormonal assays were within the normal limits. The patient underwent surgical excision of excess adipose tissue using the Kornstein technique, preserving the nipple–areola complex. Results: The histopathological examination confirmed the absence of malignancy. The postoperative recovery was uneventful, and the follow-up examination at 12 months demonstrated a symmetrical breast appearance with no recurrence. This case underscores the importance of differentiating pseudogynecomastia from true gynecomastia and recognizing potential occupational risks. Surgical management using techniques that preserve the nipple–areola complex can achieve excellent cosmetic outcomes.
Evaluation of Incisor Position in a Sample of Orthodontic Patients
Authors: Rongo Roberto , Importuna Margherita Maria Eleonora , Pango Madariaga Ada Carolina , Bucci Rosaria , D’Antò Vincenzo , Valletta Rosa
Published: September 2024

Background: To achieve a successful result, the orthodontist must use a systematic approach to plan the orthodontic treatment. Defining the correct position of the upper and lower incisors and evaluating their relationship with intermaxillary discrepancy and facial divergence have been recognized as the starting point for the diagnostic decision regarding extractions and anchorage requirements. The aim of our study was to analyze the relationship between intermaxillary discrepancy (ANPg^), mandibular inclination (SN^GoGn), lip incompetence, and the positioning of the upper and lower incisors (UIPs and LIPs) in a group of orthodontic patients. This retrospective study included 290 lateral cephalograms in 122 males (42.1%) and 168 females (57.9%) aged 8 to 53 years (median 14; interquartile range IQR 12–17). Data were analyzed by means of one-way Analysis of Variance (ANOVA) and linear regression analysis. Results: This study showed a statistically significant increase in LIP values in patients with lip incompetence (p < 0.001). Moreover, the distribution of LIPs in groups with various SN^GoGn and ANPg^ angles was significantly different (p < 0.001). The regression analysis also showed a positive association between the LIP and SN^GoGn and between the LIP and ANPg^. Conclusion: The LIP presented a statistically significant difference in patients with and without lip incompetence, which varied significantly in subjects with different sagittal malocclusions and vertical face patterns.
The Development of a Quantitative Disability Assessment Tool in Patients with Idiopathic Parkinson’s Disease
Authors: Seo Han Gil , Yun Seo Jung , Song Yoojin , Lee Ho Seok , Kim Dae Hyun , Chang Won Hyuk
Published: September 2024

Background/Objectives: The objective of this study was to develop a novel quantitative disability assessment tool for patients with idiopathic Parkinson’s disease (IPD). Methods: A total of 47 patients with IPD were recruited from two hospitals. A specialist in Rehabilitation Medicine utilized the modified Schwab and England Activities of Daily Living Scale (mSEADL) as a reference, conducting a comprehensive medical chart review and an in-depth interview. The novel-developed disability measurement was calculated as ((mSEADL during the on-state) × (time of on-state)) + ((mSEADL during the off-state) × (time of off-state))/(waking time). Additionally, the degree of disability was assessed using the Korean version of the Modified Barthel Index during the on-state. Results: Twenty-four participants (51.1%) exhibited the off-state during waking hours. In patients exhibiting an off-state, the mSEADL score was significantly lower during the off-state than during the on-state (p < 0.05). The novel-developed disability measurement demonstrated a higher correlation coefficient with the reference mSEADL (0.960) than with the K-MBI (0.808), with statistical significance (p < 0.05). Conclusions: The results demonstrated that larger IPD patients exhibited an on–off phenomenon with greater dependency during the off-state. Therefore, the on–off phenomenon should be considered when evaluating disability in patients with IPD, with methods such as the novel-developed disability measurement tool in this study.
Bridging the Digital Disability Divide: Determinants of Internet Use among Visually Impaired Individuals in Thailand
Authors: Phochai Thitiphat , Setthasuravich Prasongchai , Pukdeewut Aphisit , Wetchakama Suthiwat
Published: September 2024

This study investigates the sociodemographic and contextual determinants influencing Internet usage among individuals with visual impairments in Thailand, contributing to the literature on the digital disability divide. Data from the “Disability Survey 2022” conducted by the National Statistical Office of Thailand were used. Descriptive statistics, chi-square tests, and logistic regression analysis were performed on data from 5621 visually impaired respondents. The findings indicate that approximately 26.88% of individuals with visual impairments use the Internet. The logistic regression analysis highlights several critical disparities. Males exhibit lower odds of Internet use compared with females (adjusted odds ratio [AOR] = 0.850, p = 0.034). Younger individuals are more likely to use the Internet; a decline in use was observed with increasing age (AOR for 60+ years = 0.052, p < 0.001). Regional disparities are evident. Individuals from the northeastern (AOR = 2.044, p < 0.001), central (AOR = 1.356, p < 0.008), and southern (AOR = 1.992, p < 0.001) regions showed higher odds of Internet use compared with those from the northern region. Higher income (AOR for 5000–9999 THB = 1.798, p = 0.001), educational attainment (AOR for bachelor’s degree = 14.915, p < 0.001), and wealth index (AOR for wealthy = 5.034, p < 0.001) increase the likelihood of Internet use. Employed individuals are more likely to use the Internet (AOR = 3.159, p < 0.001) compared with unemployed individuals. Additionally, the severity of the visual impairment is crucial, with those having low vision in both eyes more likely to engage online than those who are completely blind in both eyes (AOR = 5.935, p < 0.001). These findings highlight the need for comprehensive digital inclusion initiatives that address various factors, including age-inclusive digital literacy programs, targeted regional infrastructure development, economic support to improve digital access, and advancements in assistive technologies. This study provides valuable insights for policymakers in Thailand and other developing countries, enhancing the understanding of the digital disability divide and informing strategies to foster greater digital equity.
The Simultaneous Use of Bladder Epicheck® and Urinary Cytology Can Improve the Sensitivity and Specificity of Diagnostic Follow-Up of Urothelial Lesions: Up-to-Date Data from a Multi-Institutional Cohort
Authors: Pepe Ludovica , Fiorentino Vincenzo , Pizzimenti Cristina , Riganati Giuseppe , Franchina Mariausilia , Micali Marina , Russotto Fernanda , Ieni Antonio , Tuccari Giovanni , Fadda Guido , Pierconti Francesco , Martini Maurizio
Published: September 2024

Background/Objectives: Bladder cancer is a prevalent urinary system malignancy and urinary cytology is widely used for its screening and follow-up. A novel diagnostic tool called Bladder Epicheck® (BE) is increasingly being used for monitoring the recurrence of non-muscle-invasive bladder cancer (NMIBC). The simultaneous use of BE and urinary cytology can increase the diagnostic performances in the follow-up of bladder neoplasms. Methods: In this multicenter study, we retrospectively evaluated the data of 322 patients in follow-up for a high-grade bladder carcinoma over a six-year period (from January 2018 to March 2024). The diagnostic performances of both cytology and BE and their combination were calculated using histology as gold standard. Results: Recurrences were diagnosed as high-grade urothelial carcinoma NMIBC in 18 cases, low-grade papillary NMIBC in 8 cases, and carcinoma in situ (CIS) in 4 cases. Cytological analysis correctly identified 26 out of 30 carcinomas, while 286 were correctly diagnosed as negative results. BE correctly identified 25 out of 30 carcinomas, 285 were correctly diagnosed as negative results. The combination of BE and urinary cytology correctly identified 29 out of 30 carcinomas, while 289 were correctly diagnosed as negative results. Conclusions: The combination of BE and cytology could be the most effective approach for follow-up diagnosis in patients with high-grade NMIBC, reducing unnecessary invasive procedures.
Integration of Payload Sensors to Enhance UAV-Based Spraying
Authors: Barcelos Celso O. , Fagundes-Júnior Leonardo A. , Mendes André Luis C. , Gandolfo Daniel C. , Brandão Alexandre S.
Published: September 2024

This work focuses on the use of load sensors to help with spraying tasks using unmanned aerial vehicles (UAVs). The study details the construction of a prototype for load measurement to validate the proof of concept. To simulate the application of agricultural pesticides, the UAV follows a predefined route and an image processing system detects the presence of diseased plants. After detection, the UAV pauses its route momentarily and activates the spraying device. The payload sensor monitors the fertilizer application process, which determines whether the amount of pesticide has been fully applied. If the storage tank is empty or the remaining quantity is insufficient for another operation, the system will command the UAV to return to the base station for refueling. Experimental validations were carried out in an indoor controlled environment to verify the proposal and the functionality of the in-flight payload monitoring system. Additionally, the UAV’s flight controller demonstrated robust performance, maintaining stability despite the challenges posed by liquid-load oscillations and varying payloads during the spraying process. In summary, our main contribution is a real-time payload monitoring system that monitors weight during flight to avoid over- or under-spraying. In addition, this system supports automatic refueling, detecting low levels of pesticides and directing the UAV to return to base when necessary.
Mask R-CNN and Centroid Tracking Algorithm to Process UAV Based Thermal-RGB Video for Drylot Cattle Heat Stress Monitoring
Authors: Dadallage Keshawa M. , Amogi Basavaraj R. , Khot Lav R. , Leal Yepes Francisco A.
Published: September 2024

This study developed and evaluated an algorithm for processing thermal-RGB video feeds captured by an unmanned aerial vehicle (UAV) to automate heat stress monitoring in cattle housed in the drylots. The body surface temperature (BST) of individual cows was used as an indicator of heat stress. UAV data were collected using RGB and thermal infrared imagers, respectively, at 2 and 6.67 cm per pixel spatial resolution in Spring 2023 (dataset-1) and Summer 2024 (dataset-2). Study sites were two commercial drylots in Washington State. The custom algorithms were developed to: (1) detect and localize individual cows using a Mask R-CNN-based instance segmentation model combined with centroid tracking; and (2) extract BST by averaging the thermal-imagery pixels for each of the segmented cows. The algorithm showed higher detection accuracy with RGB images as input (F1 score: 0.89) compared to thermal (F1 score: 0.64). BST extraction with combined RGB and thermal imaging approach required corrections for alignment problems associated with differences in optics, imaging field of view, resolution, and lens properties. Consequently, thermal imaging-only approach was adopted for assessing real-time cow localization and BST estimation. Operating at one frame per second, algorithm successfully detected 72.4% and 81.65% of total cows in video frames from dataset-1 (38 s) and -2 (48 s), respectively. The mean absolute difference between algorithm output and ground truth (BSTGT) was 2.1 °C (dataset-1) and 3.3 °C (dataset-2), demonstrating satisfactory performance. With further refinements, this approach could be a viable tool for real-time heat stress monitoring in large-scale drylot production systems.
Multi-Robot Path Planning Algorithm for Collaborative Mapping under Communication Constraints
Authors: Zhou Chengyu , Li Junxiang , Shi Meiping , Wu Tao
Published: September 2024

In extensive outdoor collaborative exploration tasks, multiple robots require efficient path planning methods to ensure rapid and comprehensive map construction. However, current collaborative mapping algorithms often integrate poorly with path planning, especially under limited communication conditions. Such conditions can complicate data exchange, leading to inefficiencies and missed areas in real-world environments. This paper introduces a path planning approach specifically designed for distributed collaborative mapping tasks, aimed at enhancing map completeness, mapping efficiency, and communication robustness under communication constraints. We frame the entire task as a k-Chinese Postman Problem (k-CPP) and optimize it using a genetic algorithm (GA). This method fully leverages topology maps to efficiently plan subpaths for multiple robots, ensuring thorough coverage of the mapping area without the need for prior navigation maps. Additionally, we incorporate communication constraints into our path planning to ensure stable data exchange among robots in environments with only short-range communication capabilities. Field experiment results highlight the superior performance of our method in terms of stability, efficiency, and robust inter-robot communication.
Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency Cherry Orchards: A Comparative Study
Authors: Morgan Grayson R. , Stevenson Lane
Published: September 2024

With the global population on the rise and arable land diminishing, the need for sustainable and precision agriculture has become increasingly important. This study explores the application of unoccupied aerial systems (UAS) in precision agriculture, specifically focusing on Montmorency cherry orchards in Payson, Utah. Despite the widespread use of UAS for various crops, there is a notable gap in research concerning cherry orchards, which present unique challenges due to their physical structure. UAS data were gathered using an RTK-enabled DJI Mavic 3M, equipped with both RGB and multispectral cameras, to capture high-resolution imagery. This research investigates two primary applications of UAS in cherry orchards: tree height mapping and crop health assessment. We also evaluate the accuracy of tree height measurements derived from three UAS data processing software packages: Pix4D, Drone2Map, and DroneDeploy. Our results indicated that DroneDeploy provided the closest relationship to ground truth data with an R2 of 0.61 and an RMSE of 31.83 cm, while Pix4D showed the lowest accuracy. Furthermore, we examined the efficacy of RGB-based vegetation indices in predicting leaf area index (LAI), a key indicator of crop health, in the absence of more expensive multispectral sensors. Twelve RGB-based indices were tested for their correlation with LAI, with the IKAW index showing the strongest correlation (R = 0.36). However, the overall explanatory power of these indices was limited, with an R2 of 0.135 in the best-fitting model. Despite the promising results for tree height estimation, the correlation between RGB-based indices and LAI was underwhelming, suggesting the need for further research.
G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8
Authors: Zhao Xiaofeng , Zhang Wenwen , Xia Yuting , Zhang Hui , Zheng Chao , Ma Junyi , Zhang Zhili
Published: September 2024

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.
Quantifying Loss to the Economy Using Interrupted Time Series Models: An Application to the Wholesale and Retail Sales Industries in South Africa
Authors: Masena Thabiso Ernest , Shongwe Sandile Charles , Yeganeh Ali
Published: September 2024

A few recent publications on interrupted time series analysis only conduct preintervention modelling and use it to illustrate postintervention deviation without quantifying the amount lost during the intervention period. Thus, this study aims to illustrate how to estimate and quantify the actual amounts (in South African Rands—ZAR) that the negative impact of the intervention effects of the COVID-19 pandemic had on the South African total monthly wholesale and retail sales using the seasonal autoregressive integrated moving average (SARIMA) with exogenous components (SARIMAX) model. In addition, the SARIMAX model is supplemented with three approaches for interrupted time series fitting (also known as a pulse function covariate vector), which are: (i) trial and error, (ii) quotient of fitted values and actual values, and (iii) a constant value of 1 throughout the intervention period. Model selection and adequacy metrics indicate that fitting a pulse function with a trial-and-error approach produces estimates with the minimum errors on both datasets, so a more accurate loss in revenue in the economy can be approximated. Consequently, using the latter method, the pandemic had an immediate, severe negative impact on wholesale trade sales, lasting for 15 months (from March 2020 to May 2021) and resulted in a loss of ZAR 302,339 million in the economy. Moreover, the retail sales were also negatively affected, but for 8 months (from March 2020 to October 2020), with a 1-month lag or delay, suggesting the series felt the negative effects of the pandemic one month into the intervention period and resulted in a loss of ZAR 87,836 million in the economy.
Online Pedagogies and the Middle Grades: A Scoping Review of the Literature
Authors: Eisenbach Brooke , Coleman Bridget
Published: September 2024

Online teaching and learning has had a presence in K12 public education since the 1990s. Following the COVID-19 global pandemic, there has been a surge in online learning and an increase in research surrounding the role of online teaching and learning within the K12 context. However, while the inclusion of online pedagogies flourishes throughout middle-grade educational settings, there is limited contemporary research that speaks specifically to effective online instruction of young adolescent learners. This scoping review examines the existing body of literature pertaining to online pedagogies within the middle grades in an effort to map the current trends, gaps, and overall state of research pertaining to national and international middle-level online pedagogy. Researchers screened research and pedagogically centered peer-reviewed articles published between 2013 and 2024. The reviewed articles were charted according to standardized details—e.g., author(s), publication year, research purpose, study location, participant demographics, methodology, and outcomes—to identify themes relevant to online pedagogical approaches, national or international contextual considerations, connection to the Association for Middle Level Education (AMLE) characteristics, and more. The findings speak to trends and gaps within middle-grade online pedagogical research with recommendations for additional examination of research and pedagogy specific to middle-grade online teaching and learning.
Updating Calculus Teaching with AI: A Classroom Experience
Authors: Torres-Peña Roberto Carlos , Peña-González Darwin , Chacuto-López Ellery , Ariza Edwan Anderson , Vergara Diego
Published: September 2024

In the context of mathematics education, the integration of artificial intelligence (AI) in teaching calculus is revolutionizing instructional methodologies and enhancing learning experiences both inside and outside the classroom. This study explores the use of specific AI tools, including ChatGPT, MathGPT, Gemini, and Wolfram Alpha, to deepen students’ understanding of key mathematical concepts such as derivatives and rates of change through continuous interaction with a virtual tutor. By employing well-designed prompts, these tools facilitated problem-solving exercises that were verified and refined by AI, fostering both precision in calculations and conceptual clarity. Observations from the classroom implementation reveal that students not only improved their accuracy in performing derivative calculations but also developed a clear understanding of the distinctions between average and instantaneous rates of change. The AI tools created a dynamic, adaptive learning environment, providing immediate feedback and simulations that significantly boosted student engagement and motivation. These findings underscore the potential of AI to transform mathematics education by making learning more personalized and accessible, ultimately enhancing educational outcomes and preparing students for future academic and professional challenges. Furthermore, this study introduces an innovative approach to refining AI prompts and interactions, highlighting the importance of iterative improvement to enhance the quality of AI feedback. This approach is crucial for developing better problem-solving skills and ensuring a comprehensive understanding of mathematical concepts.
From Bottom-Up Towards a Completely Decentralized Autonomous Electric Grid Based on the Concept of a Decentralized Autonomous Substation
Authors: Aoun Alain , Kashmar Nadine , Adda Mehdi , Ibrahim Hussein
Published: September 2024

The idea of a decentralized electric grid has shifted from being a concept to a reality. The growing integration of distributed energy resources (DERs) has transformed the traditional centralized electric grid into a decentralized one. However, while most efforts to manage and optimize this decentralization focus on the electrical infrastructure layer, the operational and control layer, as well as the data management layer, have received less attention. Current electric grids rely on centralized control centers (CCCs) that serve as the electric grid’s brain, where operators monitor, control, and manage the entire grid infrastructure. Hence, any disruption caused by a cyberattack or a natural event, disconnecting the CCC, could have numerous negative effects on grid operations, including socioeconomic impacts, equipment damage, market repercussions, and blackouts. This article introduces the idea of a fully decentralized electric grid that leverages autonomous smart substations and blockchain integration for decentralized data management and control. The aim is to propose a blockchain-enabled decentralized electric grid model and its potential impact on energy markets, sustainability, and resilience. The model presented underlines the transformative potential of decentralized autonomous grids in revolutionizing energy systems for better operability, management, and flexibility.
A Novel Impedance-Based Parallel Cooperative Control Method for Front and Rear Landing Gear Hydraulic Systems of UAVs
Authors: Qiu Hua , Wang Xinyu , Shi Guozhao , Li Xinrong , Zhang Shuai , Kong Xiangdong , Ba Kaixian , Yu Bin
Published: September 2024

Cargo handling issues affect the ability of large heavy-duty Unmanned Aerial Vehicles (UAVs) to transport cargo and limit the development of large UAVs. Compared to conventional landing gear, hydraulically controlled landing gear can tilt the drone within a specified angle, facilitating smoother loading and unloading of goods. Therefore, it is important to study the hydraulic landing gear control system for a UAV to make the UAV’s tilt possible. In this paper, an impedance-based parallel cooperative control method for front and rear landing gear hydraulic systems of large heavy-duty UAVs is presented, which can achieve UAV tilting within a reasonable angle during the loading and unloading of cargoes by large, heavy-duty UAVs. This paper establishes the physical model of the UAV’s landing gear, the mathematical model of the hydraulic system, and the kinematic model of the airframe. Through kinematic analysis, the correlation between each hydraulic dive unit’s (HDU’s) extension length in the landing gear and the UAV’s tilt angle is established. This paper introduces a two-fold based-loop parallel control technique, featuring angle based-loop control for the UAV’s front and position based-loop control for its rear landing gear. It aims to enable the UAV to freely tilt for loading and unloading cargo at a predetermined angle, by measuring the UAV’s tilting angle, the HDU’s force exerted on the landing gear, and its positional parameters. Ultimately, the practicality of this technique is confirmed through simulations and experiments.
Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches
Authors: Ciravegna Gabriele , Galante Franco , Giordano Danilo , Cerquitelli Tania , Mellia Marco
Published: September 2024

Resistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant.
Dynamic Routing Using Fuzzy Logic for URLLC in 5G Networks Based on Software-Defined Networking
Authors: Wu Yan-Jing , Chen Menq-Chyun , Hwang Wen-Shyang , Cheng Ming-Hua
Published: September 2024

Software-defined networking (SDN) is an emerging networking technology with a central point, called the controller, on the control plane. This controller communicates with the application and data planes. In fifth-generation (5G) mobile wireless networks and beyond, specific levels of service quality are defined for different traffic types. Ultra-reliable low-latency communication (URLLC) is one of the key services in 5G. This paper presents a fuzzy logic (FL)-based dynamic routing (FLDR) mechanism with congestion avoidance for URLLC on SDN-based 5G networks. By periodically monitoring the network status and making forwarding decisions on the basis of fuzzy inference rules, the FLDR mechanism not only can reroute in real time, but also can cope with network status uncertainty owing to FL’s fault tolerance capabilities. Three input parameters, normalized throughput, packet delay, and link utilization, were employed as crisp inputs to the FL control system because they had a more accurate correlation with the network performance measures we studied. The crisp output of the FL control system, i.e., path weight, and a predefined threshold of packet loss ratio on a path were applied to make routing decisions. We evaluated the performance of the proposed FLDR mechanism on the Mininet simulator by installing three additional modules, topology discovery, monitoring, and rerouting with FL, on the traditional control plane of SDN. The superiority of the proposed FLDR over the other existing FL-based routing schemes was demonstrated using three performance measures, system throughput, packet loss rate, and packet delay versus traffic load in the system.
Automatic Overtaking Path Planning and Trajectory Tracking Control Based on Critical Safety Distance
Authors: Huang Juan , Sun Songlin , Long Kai , Yin Lairong , Zhang Zhiyong
Published: September 2024

The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage’s velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method’s potential to improve the performance and reliability of autonomous driving systems.
Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism
Authors: Yan Yuhan , Fu Haiyan , Wu Fan
Published: September 2024

Due to the explosive rise of multimodal content in online social communities, cross-modal learning is crucial for accurate fake news detection. However, current multimodal fake news detection techniques face challenges in extracting features from multiple modalities and fusing cross-modal information, failing to fully exploit the correlations and complementarities between different modalities. To address these issues, this paper proposes a fake news detection model based on a one-dimensional CCNet (1D-CCNet) attention mechanism, named BTCM. This method first utilizes BERT and BLIP-2 encoders to extract text and image features. Then, it employs the proposed 1D-CCNet attention mechanism module to process the input text and image sequences, enhancing the important aspects of the bimodal features. Meanwhile, this paper uses the pre-trained BLIP-2 model for object detection in images, generating image descriptions and augmenting text data to enhance the dataset. This operation aims to further strengthen the correlations between different modalities. Finally, this paper proposes a heterogeneous cross-feature fusion method (HCFFM) to integrate image and text features. Comparative experiments were conducted on three public datasets: Twitter, Weibo, and Gossipcop. The results show that the proposed model achieved excellent performance.
AUTOSAR-Compatible Level-4 Virtual ECU for the Verification of the Target Binary for Cloud-Native Development
Authors: Kim Hyeongrae , Kwak Junho , Cho Jeonghun
Published: September 2024

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts.
Optical Investigation of Sparks to Improve Ignition Simulation Models in Spark-Ignition Engines
Authors: Kottakalam Saraschandran , Alkezbari Ahmad Anas , Rottenkolber Gregor , Trapp Christian
Published: September 2024

The use of renewable fuels in place of fossil fuels in internal combustion engines is regarded as a viable method for achieving zero-impact-emission powertrains. However, to achieve the best performance with these fuels, these engines require further optimization, which is achieved through new combustion strategies and the use of advanced ignition systems such as prechambers. Since simulations greatly accelerate this development, accurate simulation models are needed to accurately predict the combustion phenomenon, which requires a deep understanding of the ignition phenomenon as it significantly affects combustion. This work presents a comprehensive experimental methodology to study sparks under engine conditions, providing quantitative data to improve and validate ignition simulation models. The goal was to determine the volume generated by sparks under engine conditions that can initiate combustion and use this information to improve simulation results to match the experimental results. The visible sparks were observed with high-speed cameras to understand their time-resolved evolution and interaction with the flow. The heat transfer from the plasma was also visualized using a modified Background-Oriented Schlieren technique. The information gained from the experimental observations was used to improve an ignition simulation model. Since the velocity of the plasma was found to be slower than the surrounding flow, a user-defined parameter was included to calibrate the velocity of the simulated plasma particles. This parameter was calibrated to match the simulated spark length to the experimental spark length. In addition, since the previous simulation model did not take the heat transfer from the plasma into account, the simulated plasma particles were coupled to have heat transfer to the surroundings. Based on a comparison of the simulation results with the experimental results, the improved approach was found to provide a better physical representation of the spark ignition phenomenon.
Initial Insights into Teleworking’s Effect on Air Quality in Madrid City
Authors: Bañuelos-Gimeno Jorge , Sobrino Natalia , Arce-Ruiz Rosa
Published: September 2024

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.
Transformation of Modern Urban Park Based on User’s Spatial Perceived Preferences: A Case Study of Kowloon Walled City Park in Hong Kong
Authors: Di Shuyi , Chen Zimeng , Ren Zhipeng , Ding Tianyi , Zhao Zheng , Hou Yilei , Chen Zejin
Published: September 2024

Urban parks enhance residents’ quality of life and health by fostering a harmonious relationship between people and nature, so effective park design needs to prioritize ecological protection, sustainable landscapes, and practical spatial structures to achieve these benefits. This study takes the typical case of urban park renovation and reconstruction—Kowloon Walled City Park—as an example to conduct research and divides the interior of the park into four types of areas: contemporary built-up area; historical relic area; natural–folk custom area; and ecological conservation area. Based on 405 valid questionnaire data for respondents, this study conducts empirical research using a combination of the Semantic Differential (SD) method, Importance Performance Analysis (IPA) model, and unordered multivariate logit choice model, comprehensively describes and analyzes individual spatial perception and preferences, and further discusses the potential factors affecting individual perception preferences. The results show that there are differences in many characteristics between different areas in Kowloon Walled City Park. At the same time, people generally prefer park areas that integrate modern and traditional elements, natural and cultural environments, and pay attention to the balance between naturalness and sociality in park design. These results provide useful information for planners, developers, and others, as well as data for designing urban park construction with higher practical value and natural benefits.
Ecological Restoration Increases the Diversity of Understory Vegetation in Secondary Forests: An Evidence from 90 Years of Forest Closures
Authors: Ma Yuhua , Xu Fengyu , Wei Jingya , Wang Wei , Wu Zhen , Xu Duanyang , Haider Fasih Ullah , Li Xu , Dong Yan
Published: September 2024

Ecological restoration and biodiversity are currently hot issues of global environmental concern. However, knowledge about the specific impacts of restoration duration on understory vegetation diversity remains limited. Therefore, this study comprehensive employed a spatial approach to compare the differences in understory plant diversity and species composition among secondary forests with varying ecological restoration ages (0, 10, 30, 60, and 90-year-old stands) in Huangfu Mountain National Forest Park. This methodology allowed us to clarify the key factors affecting the composition of the understory plant community and investigate the regulatory mechanisms influencing changes in understory plant diversity. The results showed that shrub Shannon’s index value, shrub evenness, herb Shannon’s index value, herb richness, and herb evenness were significantly affected by the years of restoration, with 10 years and 90 years being the highest and 60 years being the lowest. Substrate diversity was the main factor influencing plant diversity in the shrub layer. Overstory richness, soil C/N, soil C, soil N, soil bacterial Observed OTUs, soil bacterial Chao1, soil bacterial Pielou_e, and substrate diversity were the drivers of plant diversity in the herb layer. Overstory evenness had a direct effect (0.256) and an indirect effect (0.284) on herb evenness through light availability and fungal Simpson’s index value. Light availability directly negatively affected herb evenness (−0.360). In addition, 52.6% of the factors affecting the herb evenness index were from the arboreal layer evenness, light availability, and fungal Simpson’s index value. To sum up, moderate disturbance of the understory environment of natural secondary forests can be carried out after 10 years of restoration, which is more conducive to the increase of understory plant diversity. This comprehensive study provides a theoretical basis for formulating ecological restoration measures for secondary forests, particularly in understanding the optimal timing and nature of disturbance in the restoration process, reassuring the audience about the validity and reliability of the findings.