bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive Utility of the Multi-Process Action Control Framework for Self-Reported and Device-Measured Physical Activity Behavior of Adolescents
Authors: Brown Denver M. Y. , Porter Carah D. , Huong Christopher , Groves Claire I. , Kwan Matthew Y. W.
Published: September 2024

Understanding the correlates of physical activity behavior is imperative for informing the development of interventions to address the low rates of physical activity guideline adherence among adolescents living in the United States. This cross-sectional study examined the predictive utility of the Multi-Process Action Control (M-PAC) framework for explaining self-reported and device-measured physical activity behavior among a Hispanic-majority sample of adolescents. A total of 1849 high school students (mean age = 16.0 ± 1.22 SD years; 52.3% women; 87.8% Hispanic) enrolled in one school district in south-central Texas completed a survey including instruments to assess M-PAC framework constructs (instrumental and affective attitudes, perceived capability and opportunity, behavioral regulation, habit, identity) and moderate-to-vigorous physical activity (MVPA) behavior. A subsample (n = 435) wore accelerometers for seven days. The results from robust linear regression models revealed role identity and habit were significant predictors of self-reported MVPA. Role identity was a significant predictor of accelerometer-derived daily MVPA and raw acceleration in the most active hour but not daily raw acceleration. The findings indicated reflexive processes are robust predictors of adolescent physical activity and should be the focus of interventions designed to promote adoption and maintenance of physical activity during this developmental life stage.
A Comparative Study of Machine Learning and Conventional Techniques in Predicting Compressive Strength of Concrete with Eggshell and Glass Powder Additives
Authors: Gao Yan , Ma Ruihan
Published: September 2024

Recent research has focused on assessing the effectiveness of response surface methodology (RSM), a non-machine learning technique, and artificial neural networks (ANN), a machine learning approach, for predicting concrete performance. This research aims to predict and simulate the compressive strength of concrete that replaces cement and fine aggregate with waste materials such as eggshell powder (ESP) and waste glass powder (WGP) for sustainable construction materials. In order to ensure concrete’s durability and structural integrity, a compressive strength evaluation is essential. Precise predictions maximize efficiency and advance sustainability, particularly when dealing with waste materials like ESP and WGP. The response surface methodology (RSM) and artificial neural network (ANN) techniques are used to accomplish this for practical applications in the built environment. A dataset comprising previously published research was used to assess ANN and RSM’s predictive and generalization abilities. To model and improve the model, ANN used seven independent variables, while three variables, cement, waste glass powder, and eggshell powder, improved the RSM. Both the ANN and RSM techniques are effective instruments for predicting compressive strength, according to the statistical results, which include mean squared error (MSE), determination coefficient (R2), and adjusted coefficient (R2 adj). RSM was able to achieve the R2 by 0.8729 and 0.7532 for compressive strength, while the accuracy of the results for ANN was 0.907 and 0.956 for compressive strength. Moreover, the correlation between ANN and RSM models and experimental data is high. The artificial neural network model, however, exhibits superior accuracy.
Numerical and Experimental Validation for Connecting Nature with Architecture by Mimicking Cranium into a Shell Roof
Authors: Gunasekaran Pennarasi , Rajkumar P. R. Kannan
Published: September 2024

This study focuses on a structural element bio-mimicked from the human cranium (HC) into a shell element. As the HC is effective in resisting intracranial pressure developed by the brain, a water tank was considered to use a bio-mimicked shape of a shell as a roof. An optimized numerical model was validated experimentally and compared with a conventional specimen. The structural behavior of the bio-mimicked specimen is similar and performs more efficiently than the conventional specimen in capacity ratio, crack formation, and load-carrying capacity. Methodology followed: A Computed Tomography (CT) scan of the HC was obtained in Digital Imaging and Communications in Medicine (DICOM) format for finite element analysis (FEA). From the geometric parameters of the HC, the radius of the curvature-to-thickness ratio was derived for the shell. The span and thickness of the shell under two criteria were considered. The spherical and circular shell behaviors were found to be similar to those of the HC, whereas the elliptical shell behavior was not. We studied the shape effect of the HC with the conventional slab and found that the HC shape has an impact on the behavior and is the most efficient. A bio-mimicked mono column was considered as a supporting column for the water tank and analyzed. Overall, adopting this bio-mimicking of the HC into the shell roof connects nature with sustainable architecture.
Predicting Main Characteristics of Reinforced Concrete Buildings Using Machine Learning
Authors: Alhalil Izzettin , Gullu Muhammet Fethi
Published: September 2024

This paper presents a comprehensive study of five machine learning (ML) algorithms for predicting key characteristics of Reinforced Concrete (RC) structural systems. A novel dataset, ModRes, consisting of 9723 examples derived from modal and response spectrum analyses on masonry-infilled three-dimensional RC buildings, was created for ML applications. The primary objective is to develop an ML model using five distinct algorithms from the literature, capable of concurrently predicting torsional irregularity, modal participating mass ratio (MPMR), and the fundamental period in a 3D environment, while accounting for the influence of infill walls. Additionally, the study aims to determine the applicability of pushover analysis (POA) without the need for extensive numerical modeling and analysis. This approach optimizes the preliminary design process with minimal computational effort, providing valuable insights into dynamic and torsional responses during seismic events. The Categorical Boosting algorithm demonstrated outstanding performance, achieving R2 values of 0.977 for torsional irregularity, 0.997 for the fundamental period, and 0.923 for MPMR on the test dataset. It also successfully predicted POA applicability with an error rate of only 1.36%. This study highlights the practical application of ML algorithms, underscoring their effectiveness in structural engineering.
Research on Technological Innovation Capability of Yancheng Prefabricated Construction Industry Based on Patent Information Analysis
Authors: Lu Renyan , Shi Feiting , Sun Houchao
Published: September 2024

In order to improve the innovation capabilities of Yancheng’s prefabricated construction industry, the Dawei Innojoy patent database was used to search the prefabricated construction technology patent literature data of Yancheng and other major cities in the Yangtze River Delta region from 2012 to 2022, and the prefabricated construction patents in Yancheng were analyzed from the number of patent applications. Analysis and research were conducted on trends, application type composition, applicants, technical fields, patent legal status, etc. At the same time, the prefabricated building technology innovation capability evaluation system was constructed, and the factor analysis method was used to compare and analyze the prefabricated building technology patent indicators of Yancheng and major cities in the Yangtze River Delta region. The results show that Yancheng has a small number of patent applications, a small proportion of invention patents, a low patent authorization rate, and a low patent conversion rate, and the industry-university-research chain needs to be opened up. Among the cities in the Yangtze River Delta, Yancheng’s comprehensive innovation ability in prefabricated building technology is medium to low, where it lags behind in terms of the scale and quality of technological innovation and ranks at the forefront of technological innovation operations. Based on this, the article puts forward countermeasures and suggestions for Yancheng’s prefabricated building technology patent applications from three levels, macro, meso, and micro, in order to achieve efficient innovation and promote the high-quality development of Yancheng’s new building industrialization.
Efficacy and Safety of Acupuncture and Related Techniques in the Management of Oncological Children and Adolescent Patients: A Systematic Review
Authors: Martínez García Esther , Nishishinya Aquino M. Betina , Cruz Martínez Ofelia , Ren Yiming , Xia Ruyu , Fei Yutong , Fernández-Jané Carles
Published: September 2024

Background/Objectives: Oncology acupuncture is emerging as a complementary treatment in pediatric cancer care centers. It is valued for its potential to manage symptoms associated with cancer and treatment toxicities without increasing polypharmacy. The aim of this review is to evaluate the efficacy and safety of acupuncture and related techniques in alleviating symptoms of cancer treatment in pediatric oncology patients. Methods: A comprehensive search was conducted across nine databases, including PubMed, Cochrane Library, and CNKI, up to June 2023. Inclusion criteria focused on randomized and quasi-randomized controlled trials involving pediatric oncology patients undergoing needle acupuncture or related techniques. Study selection and data extraction were independently performed by pairs of authors. Results were analyzed narratively, and meta-analysis was performed when possible. Results: Results suggest that acupuncture may help manage symptoms such as nausea and vomiting in pediatric oncology patients. However, the quality of evidence was generally low, and further research is required to substantiate these findings. Conclusions: Acupuncture shows promising results as a complementary treatment for reducing nausea and vomiting in pediatric oncology. However, current evidence is insufficient to draw conclusions for other outcomes, such as the number of vomiting episodes, reduction in antiemetic medication use, or fatigue. High-quality, rigorously designed studies are necessary to better understand the clinical relevance and safety of acupuncture in this vulnerable population.
Hydrogen-Ignited-Methanol Catalytic Co-Combustion of Aromatic Volatile Organic Compounds over PdPt/Al2O3 Bimetallic Catalyst
Authors: Munsif Sehrish , Ullah Lutf , Cao Long , Murthy Palle Ramana , Zhang Jing-Cai , Li Wei-Zhen
Published: September 2024

Electric heating is frequently employed to treat volatile organic compounds (VOCs) through catalytic combustion. However, it is associated with problems such as slow heating, high energy consumption, and low efficiency. This study explores PdPt/Al2O3 catalysts for igniting methanol (MeOH) through H2 catalytic combustion, providing internal on-site heating of catalyst active sites. It also investigates VOCs’ abatement using H2-ignited-MeOH combustion without H2 and external heating. Bimetallic catalysts enhance activity and reduce thermal aging. Hydrogen gas (H2) can initiate the MeOH combustion at room temperature with the addition of very small amounts, even below its low explosive limit of 4%. This process optimizes MeOH ignition at approximately 350 °C, even when the concentration of H2 is as low as 0.01%. This method enhances combustion kinetics, converting MeOH and VOCs into CO2 and water. Catalytic performance is independent of PdPt nanoparticle sizes in fresh and spent catalysts, represented in XRD and STEM. Using hydrogen as an igniting agent provides a clean, effective method to initiate catalytic reactions, addressing traditional challenges and enhancing VOCs’ decomposition efficiency.
Superhydrophobic Surface Modification of a Co-Ru/SiO2 Catalyst for Enhanced Fischer-Tropsch Synthesis
Authors: Bootpakdeetam Pawarat , Igboenyesi Oluchukwu Virginia , Dennis Brian H. , MacDonnell Frederick M.
Published: September 2024

Commercial silica support pellets were impregnated and calcined to contain cobalt oxide and ruthenium oxide for Fischer-Tropsch synthesis (FTS). The precatalyst pellets were split evenly into two groups, the control precatalyst (c-precat) and silylated precatalyst (s-precat), which were treated with 1H,1H, 2H, 2H-perfluorooctyltriethoxysilane (PFOS) in toluene. The samples of powderized s-precat were superhydrophobic, as determined by the water droplet contact angle (>150°) and sliding angle (<1°). Thermal analysis revealed the PFOS groups to be thermally stable up to 400 °C and temperature programmed reduction (TPR) studies showed that H2 reduction of the cobalt oxide to cobalt was enhanced at lower temperatures relative to the untreated c-precat. The two active catalysts were examined for their FTS performance in a tubular fixed-bed reactor after in situ reduction at 400 °C for 16 h in flowing H2 to give the active catalysts c-cat and s-cat. The FTS runs were performed under identical conditions (255 °C, 2.1 MPa, H2/CO = 2.0, gas hourly space velocity (GHSV) 510 h–1) for 5 days. Each catalyst was examined in three runs (n = 3) and the mean values with error data are reported. S-cat showed a higher selectivity for C5+ products (64 vs. 54%) and lower selectivity for CH4 (11 vs. 17%), CO2 (2 % vs. 4 %), and olefins (8% vs. 15%) than c-cat. S-cat also showed higher CO conversion, at 37% compared to 26%, leading to a 64% increase in the C5+ productivity measured as g C5+ products per g catalyst per hour. An analysis of the temperature differential between the catalyst bed and external furnace temperature showed that s-cat was substantially more active (DTinitial = 29 °C) and stable over the 5-day run (DTfinal = 22 °C), whereas the attenuated activity of c-cat (DTinitial = 16 °C) decayed steadily over 3 days until it was barely active (DTfinal < 5 °C). A post-run surface analysis of s-cat revealed no change in the water contact angle or sliding angle, indicating that the FTS operation did not degrade the PFOS surface treatment.
Correction: Dong et al. Smurf1 Suppression Enhances Temozolomide Chemosensitivity in Glioblastoma by Facilitating PTEN Nuclear Translocation. Cells 2022, 11, 3302
Authors: Dong Lei , Li Yang , Liu Liqun , Meng Xinyi , Li Shengzhen , Han Da , Xiao Zhenyu , Xia Qin
Published: September 2024

In the original publication [...]
Evaluating the Effectiveness of Enoxaparin in Treating Pediatric Arterial Thrombosis in Saudi Arabia
Authors: Al-Meshary Meshary , Alotaibi Abdulrahman , Alsagri Nouf S. , AlZhrani Asmaa , Ardah Husam I. , Alnuhait Mohammed A.
Published: September 2024

Background: Thrombosis is the abnormal formation of blood clots within blood vessels; it results from an imbalance between fibrinolytic, pro-coagulant, and anticoagulant systems. Pediatric arterial thrombosis, especially related to catheter usage, is an emerging issue with limited evidence. This study evaluates the efficacy of enoxaparin in treating arterial thrombosis in pediatric patients at a single center. Methods: A retrospective single-center study included children under 14 years old diagnosed with catheter-related arterial thrombosis (CAT) and treated with low-molecular-weight heparin (LMWH) at King Abdulaziz Medical City between 2016 and 2021. Patients without follow-up at our institution or those using other anticoagulants were excluded. Data collected included age, sex, weight, catheter type, location and degree of thrombosis, ultrasonographic results, treatment duration, hemoglobin and platelet levels, and missed refills. Radiologic confirmation of CAT was required for inclusion. Results: This study included 111 children treated with enoxaparin for non-cerebral arterial thrombosis. The median age at diagnosis was 3 months, with 58% being male patients. Most cases (87%) involved cardiac catheterization, and all were confirmed using ultrasonography. Complete thrombus resolution was achieved in 90% of patients, partial resolution in 8.1%, and 1.8% had no resolution. The median duration of enoxaparin therapy was 20 days. Multivariate analysis indicated that higher age and lower body weight were associated with a higher risk of non-resolution. Indwelling catheters also posed a greater risk of non-resolution compared to cardiac catheters. Conclusions: Enoxaparin proved effective in treating catheter-related arterial thrombosis in children, with high resolution rates and few side effects. This study helps inform treatment strategies in pediatric thrombosis management and highlights the need for further research to refine treatment durations and address patient risk factors.
A Comprehensive Analog–Mixed Signal (AMS) Simulations Environment
Authors: Castaldo Enrico , Gibilaro Marco Eugenio
Published: September 2024

The analog–mixed signal simulation environment, used for verifying non-volatile memory macrocells, is presented. It has been adopted over the last decade, providing excellent results in verification coverage, efficiency, and flexibility. This methodology ensures a smooth and effective transition from full transistor/fully analog simulations to fully digital simulations while maintaining most of the environment’s features. This allows verification designers to exchange data, stimuli, and results, thereby enhancing debugging capabilities and reducing simulation time.
Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data
Authors: Wu Zhaoyun , Jin Tao , Liu Xiaoxia , Zhang Zhongwei , Zhao Binbin , Zhang Yehao , He Xuewu
Published: September 2024

Throughout the huller shelling process, the rubber rollers progressively deteriorate. The velocity of the rubber rollers decreases as the distance between the rollers rises. These modifications significantly influence the rate at which rice hulling occurs. Hence, the implementation of real-time online detection is crucial for maintaining the operational efficiency of the huller. Currently, the prevailing inspection methods include manual inspection, 2D vision inspection, deep learning methods, and machine vision methods. Nevertheless, these conventional techniques lack the ability to provide detailed information about the faulty components, making it challenging to conduct comprehensive defect identification in three dimensions. To address this issue, point cloud technology has been incorporated into the overall detection of the working condition of the huller. Specifically, the Random Sample Consensus segmentation algorithm and the adaptive boundary extraction algorithm have been developed to identify abnormal wear on the rubber rollers by analyzing the point cloud data on their surface. A solution technique has been developed for the huller to compensate for the speed of the rubber rollers and calculate the mean values of their radii. Additionally, a numerical simulation algorithm is proposed to address the dynamic change in the roller spacing detection. The results show that point cloud data can be utilized to achieve real-time and precise correction of anomalous wear patterns on the surface of rubber rollers.
Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach
Authors: Al-Tarawneh Mutaz A. B. , Al-irr Omar , Al-Maaitah Khaled S. , Kanj Hassan , Aly Wael Hosny Fouad
Published: September 2024

The widespread dissemination of fake news on social media has necessitated the development of more sophisticated detection methods to maintain information integrity. This research systematically investigates the effectiveness of different word embedding techniques—TF-IDF, Word2Vec, and FastText—when applied to a variety of machine learning (ML) and deep learning (DL) models for fake news detection. Leveraging the TruthSeeker dataset, which includes a diverse set of labeled news articles and social media posts spanning over a decade, we evaluated the performance of classifiers such as Support Vector Machines (SVMs), Multilayer Perceptrons (MLPs), and Convolutional Neural Networks (CNNs). Our analysis demonstrates that SVMs using TF-IDF embeddings and CNNs employing TF-IDF embeddings achieve the highest overall performance in terms of accuracy, precision, recall, and F1 score. These results suggest that TF-IDF, with its capacity to highlight discriminative features in text, enhances the performance of models like SVMs, which are adept at handling sparse data representations. Additionally, CNNs benefit from TF-IDF by effectively capturing localized features and patterns within the textual data. In contrast, while Word2Vec and FastText embeddings capture semantic and syntactic nuances, they introduce complexities that may not always benefit traditional ML models like MLPs or SVMs, which could explain their relatively lower performance in some cases. This study emphasizes the importance of selecting appropriate embedding techniques based on the model architecture to maximize fake news detection performance. Future research should consider integrating contextual embeddings and exploring hybrid model architectures to further enhance detection capabilities. These findings contribute to the ongoing development of advanced computational tools for combating misinformation.
The Landscape of Breast Cancer Molecular and Histologic Subtypes in Canada
Authors: Wilkinson Anna N. , Ellison Larry F. , McGee Sharon F. , Billette Jean-Michel , Seely Jean M.
Published: September 2024

Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15–99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70–74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99–100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes.
Exploring Demographic Representation and Reporting in Lung Cancer Clinical Trials with Canadian Sites from 2013 to 2023
Authors: Land Sierra A. , Wani Rajvi J. , Inam Naila , Hewitt Hilary J. G. , Muniz Covizzi Paulo Eduardo , Rivard Tarah Sheculski
Published: September 2024

This review evaluates the reporting of demographic characteristics and the diversity of participants of phase III lung cancer clinical trials with Canadian research sites. A literature search was conducted using the ClinicalTrials.gov registry to identify clinical trials conducted between 1 January 2013, and 31 December 2023. The demographic reporting practices and the representation of sex/gender, racial, and ethnic groups were assessed. The location of Canadian research sites was also examined for trends in reporting and representation. Associated publications were reviewed for demographic data collection methods. Of the 25 clinical trials, 24 reported race and 18 also reported ethnicity. All clinical trials reported sex/gender, and the city and province of the participating Canadian sites. Most participants were White (66.1%), identified as not Hispanic or Latino (81.4%), and were male (57.8%). The provinces with the most clinical trial sites were Ontario (43.6%) and Quebec (34.2%). Lung cancer clinical trials lack adequate demographic reporting and representation of females, diverse patient groups, and geographical locations in Canada with high lung cancer incidence rates. Specifically, the Indigenous Peoples of Canada and Nunavut require better representation in lung cancer clinical trials conducted in Canada. These findings highlight the need to improve diversity and demographic representation in clinical research.
Evaluating Treatment Modalities for Reducing Recurrence in Central Giant Cell Granuloma: A Narrative Review
Authors: Aliu Flamur , Shabani Donika Bajrami , Aliu Iliriana , Qeli Etleva Droboniku , Kaçani Gerta , Fiorillo Luca , Meto Aida
Published: September 2024

Treating central giant cell granuloma (CGCG) is challenging due to high recurrence rates and variable therapy responses. This study examines the efficacy of various treatments in reducing CGCG recurrence. A literature review explored outcomes of surgical excision, curettage, intralesional corticosteroid injection, and adjuvant therapy, considering factors like lesion location, size, and histological features. Aggressive surgical techniques such as en bloc resection were found to potentially lower recurrence rates compared to conservative approaches. However, treatment should be tailored to individual patient needs. Further research is needed to confirm these findings and improve treatment strategies. A concise literature review was conducted using PubMed, MEDLINE, and Google Scholar, focusing on papers published from 1986 to 2024. Search terms included “central giant cell granuloma”, “recurrence”, “treatment modalities”, and “surgical excision”. Studies reporting recurrence rates and treatment outcomes for CGCG were analyzed. Twenty-nine studies were reviewed, including six studies on surgical excision and curettage, eight studies on intralesional corticosteroid injections, six studies on calcitonin therapy, five studies on interferon-alpha therapy, and four studies on the therapy with denosumab. Analysis indicated that aggressive surgical treatments like en bloc resection were associated with lower recurrence rates compared to conservative methods. Predictors of recurrence included lesion size (>3 cm), location (mandible), and aggressive histopathological features. Aggressive surgical excision combined with nonsurgical methods may lower recurrence rates, while conservative techniques remain viable in some cases. Further prospective research is needed to validate these findings and enhance CGCG treatment options.
Reinforcement Learning-Based Turning Control of Asymmetric Swept-Wing Drone Soaring in an Updraft
Authors: Cui Yunxiang , Yan De , Wan Zhiqiang
Published: September 2024

Soaring drones can use updrafts to reduce flight energy consumption like soaring birds. With control surfaces that are similar to those of soaring birds, the soaring drone achieves roll control through asymmetric sweepback of the wing on one side. This will result in asymmetry of the drone. The moment of inertia and the inertial product will change with the sweepback of the wing, causing nonlinearity and coupling in its dynamics, which is difficult to solve through traditional research methods. In addition, unlike general control objectives, the objective of this study was to enable the soaring drone to follow the soaring strategy. The soaring strategy determines the horizontal direction of the drone based on the vertical wind situation without the need for active control of the vertical movement of the drone. In essence, it is a horizontal trajectory tracking task. Therefore, based on the layout and aerodynamic data of the soaring drone, reinforcement learning was adopted in this study to construct a six-degree-of-freedom dynamic model and a control flight training simulation environment for the soaring drone with asymmetric deformation control surfaces. We compared the impact of key factors such as different state spaces and reward functions on the training results. The turning control agent was obtained, and trajectory-tracking simulations were conducted.
Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques
Authors: Linero-Ramos Rafael , Parra-Rodríguez Carlos , Espinosa-Valdez Alexander , Gómez-Rojas Jorge , Gongora Mario
Published: September 2024

This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets.
An Innovative New Approach to Light Pollution Measurement by Drone
Authors: Bobkowska Katarzyna , Burdziakowski Pawel , Tysiac Pawel , Pulas Mariusz
Published: September 2024

The study of light pollution is a relatively new and specific field of measurement. The current literature is dominated by articles that describe the use of ground and satellite data as a source of information on light pollution. However, there is a need to study the phenomenon on a microscale, i.e., locally within small locations such as housing estates, parks, buildings, or even inside buildings. Therefore, there is an important need to measure light pollution at a lower level, at the low level of the skyline. In this paper, the authors present a new drone design for light pollution measurement. A completely new original design for an unmanned platform for light pollution measurement is presented, which is adapted to mount custom sensors (not originally designed to be mounted on a unmanned aerial vehicles) allowing registration in the nadir and zenith directions. The application and use of traditional photometric sensors in the new configuration, such as the spectrometer and the sky quality meter (SQM), is presented. A multispectral camera for nighttime measurements, a calibrated visible-light camera, is used. The results of the unmanned aerial vehicle (UAV) are generated products that allow the visualisation of multimodal photometric data together with the presence of a geographic coordinate system. This paper also presents the results from field experiments during which the light spectrum is measured with the installed sensors. As the results show, measurements at night, especially with multispectral cameras, allow the assessment of the spectrum emitted by street lamps, while the measurement of the sky quality depends on the flight height only up to a 10 m above ground level.
Intelligent Trajectory Prediction Algorithm for Hypersonic Vehicle Based on Sparse Associative Structure Model
Authors: Liu Furong , Lu Lina , Zhang Zhiheng , Xie Yu , Chen Jing
Published: September 2024

The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need for large data samples and poor general applicability. To address these challenges, this paper presents a novel trajectory forecasting approach based on the Sparse Association Structure Model (SASM). The SASM can uncover the relationship among known data, transfer associative relationships to unknown data, and improve the generalization of the model. Firstly, a trajectory database is established for different maneuvering modes based on the six-degree-of-freedom motion equations and models of attack and bank angles of the HGV. Subsequently, three trajectory parameters are selected as prediction variables according to the maneuvering characteristics of the HGV. A parameters prediction model based on the SASM is then constructed to predict trajectory parameters. The SASM model demonstrates high accuracy and generalization in forecasting the trajectories of three different HGV types. Experimental results show a 50.35% reduction in prediction error and a 48.7% decrease in average processing time compared to the LSTM model, highlighting the effectiveness of the proposed method for real-time trajectory forecasting.
Mentalized Affectivity, Helicopter Parenting, and Psychopathological Risk in Emerging Adults: A Network Analysis
Authors: Cruciani Gianluca , Fontana Andrea , Benzi Ilaria Maria Antonietta , Sideli Lucia , Parolin Laura Antonia Lucia , Muzi Laura , Carone Nicola
Published: September 2024

Emerging adulthood represents a critical stage characterized by heightened risks for anxiety, depression, and somatization symptoms development. Research has shown that difficulties in emotional identification, expression, and processing, as well as dysfunctional parenting styles, may exacerbate symptoms in emerging adults. The present study aimed at examining the interplay between mentalized affectivity (i.e., emotional identification, processing, and expression), helicopter parenting, and psychopathological risk in 913 Italian cisgender emerging adults (M = 24.34, SD = 2.81; 71.20% assigned female at birth), using network analysis. The results indicated moderate to strong associations between psychopathological symptoms, with emotional processing difficulties significantly associated with general anxiety, depression, and, to a lesser extent, somatization. Additionally, increased degrees of helicopter parenting from mothers were linked to increased psychopathology and higher emotional processing difficulties. These findings emphasize the importance of addressing the interconnection between symptoms and emotional processing to prevent and treat psychopathological risks in emerging adults. Moreover, interventions targeting intrusive and overprotecting parenting behaviors may promote well-being among emerging adults.
A Maneuver in the Trade-Off Space of Federated Learning Aggregation Frameworks Secured with Polymorphic Encryption: PolyFLAM and PolyFLAP Frameworks
Authors: Moshawrab Mohammad , Adda Mehdi , Bouzouane Abdenour , Ibrahim Hussein , Raad Ali
Published: September 2024

Maintaining user privacy in machine learning is a critical concern due to the implications of data collection. Federated learning (FL) has emerged as a promising solution by sharing trained models rather than user data. However, FL still faces several challenges, particularly in terms of security and privacy, such as vulnerability to inference attacks. There is an inherent trade-off between communication traffic across the network and computational costs on the server or client, which this paper aims to address by maneuvering between these performance parameters. To tackle these issues, this paper proposes two complementary frameworks: PolyFLAM (“Polymorphic Federated Learning Aggregation of Models”) and PolyFLAP (“Polymorphic Federated Learning Aggregation of Parameters”). These frameworks provide two options to suit the needs of users, depending on whether they prioritize reducing communication across the network or lowering computational costs on the server or client. PolyFLAM reduces computational costs by exchanging entire models, eliminating the need to rebuild models from parameters. In contrast, PolyFLAP reduces communication costs by transmitting only model parameters, which are smaller in size compared to entire models. Both frameworks are supported by polymorphic encryption, ensuring privacy is maintained even in cases of key leakage. Furthermore, these frameworks offer five different machine learning models, including support vector machines, logistic regression, Gaussian naïve Bayes, stochastic gradient descent, and multi-layer perceptron, to cover as many real-life problems as possible. The evaluation of these frameworks with simulated and real-life datasets demonstrated that they can effectively withstand various attacks, including inference attacks that aim to compromise user privacy by capturing exchanged models or parameters.
A Hybrid Text Summarization Technique of Student Open-Ended Responses to Online Educational Surveys
Authors: Karousos Nikos , Vorvilas George , Pantazi Despoina , Verykios Vassilios
Published: September 2024

This study introduces a hybrid text summarization technique designed to enhance the analysis of qualitative feedback from online educational surveys. The technique was implemented at the Hellenic Open University (HOU) to tackle the challenges of processing large volumes of student feedback. The TextRank and Walktrap algorithms along with GPT-4o mini were used to analyze student comments regarding positive experiences, study challenges, and suggestions for improvement. The results indicate that students are satisfied with tutor–student interactions but concerns were raised about educational content and scheduling issues. To evaluate the proposed summarization approach, the G-Eval and DeepEval summarization metrics were employed, assessing the relevance, coherence, consistency, fluency, alignment, and coverage of the summaries. This research addresses the increasing demand for effective qualitative data analysis in higher education and contributes to ongoing discussions on student feedback in distance learning environments. By effectively summarizing open-ended responses, universities can better understand student experiences and make informed decisions to improve the educational process.
Methods for the Viscous Loss Calculation and Thermal Analysis of Oil-Filled Motors: A Review
Authors: Zhang Jian , Shao Yinxun , Long Yinxin , He Xiangning , Wu Kangwen , Cai Lingfeng , Wu Jianwei , Fang Youtong
Published: September 2024

Oil-filled motors (OFMs) are widely used in deep-sea exploration and oil well extraction. During motor operation, the rotor stirs the oil in the air gap, causing viscous loss. Viscous loss affects the temperature distribution inside the motor. Accurately calculating the viscous loss and temperature rise in OFMs can provide a basis for optimizing the motor’s structural design. Motor structural parameters, including the rotor’s outer diameter, air gap, and slot opening, have a significant impact on viscous loss. The working conditions of OFMs, such as rotor speed and environmental temperature, also affect viscous loss. The viscosity of hydraulic oil is highly influenced by temperature, and changes in viscosity can lead to changes in viscous loss. These changes in viscous loss, in turn, alter the temperature distribution. Therefore, the coupling relationship between viscous loss and temperature must be considered. Additionally, when Taylor vortices occur in the fluid, the surface roughness of the rotor also has a significant influence on viscous loss. Currently, both domestic and international research on viscous loss and thermal analysis struggle to simultaneously consider the coupling of viscous loss and the temperature field, rotor surface roughness, and the effect of motor structure. This paper summarizes the methods used in recent years for studying viscous loss and thermal analysis, and puts forward some suggestions for future research on the coupling of the OFM temperature field and viscous loss.
Potentials of Green Hydrogen Production in P2G Systems Based on FPV Installations Deployed on Pit Lakes in Former Mining Sites by 2050 in Poland
Authors: Sikora Mateusz , Kochanowski Dominik
Published: September 2024

Green hydrogen production is expected to play a major role in the context of the shift towards sustainable energy stipulated in the Fit for 55 package. Green hydrogen and its derivatives have the capacity to act as effective energy storage vectors, while fuel cell-powered vehicles will foster net-zero emission mobility. This study evaluates the potential of green hydrogen production in Power-to-Gas (P2G) systems operated in former mining sites where sand and gravel aggregate has been extracted from lakes and rivers under wet conditions (below the water table). The potential of hydrogen production was assessed for the selected administrative unit in Poland, the West Pomerania province. Attention is given to the legal and organisational aspects of operating mining companies to identify the sites suitable for the installation of floating photovoltaic facilities by 2050. The method relies on the use of GIS tools, which utilise geospatial data to identify potential sites for investments. Basing on the geospatial model and considering technical and organisational constraints, the schedule was developed, showing the potential availability of the site over time. Knowing the surface area of the water reservoir, the installed power of the floating photovoltaic plant, and the production capacity of the power generation facility and electrolysers, the capacity of hydrogen production in the P2G system can be evaluated. It appears that by 2050 it should be feasible to produce green fuel in the P2G system to support a fleet of city buses for two of the largest urban agglomerations in the West Pomerania province. Simulations revealed that with a water coverage ratio increase and the planned growth of green hydrogen generation, it should be feasible to produce fuel for net-zero emission urban mobility systems to power 200 buses by 2030, 550 buses by 2040, and 900 buses by 2050 (for the bus models Maxi (40 seats) and Mega (60 seats)). The results of the research can significantly contribute to the development of projects focused on the production of green hydrogen in a decentralised system. The disclosure of potential and available locations over time can be compared with competitive solutions in terms of spatial planning, environmental and societal impact, and the economics of the undertaking.
Potential of Wheat Straw for Biogas Production by Anaerobic Digestion in South Africa: A Review
Authors: Kamusoko Reckson , Mukumba Patrick
Published: September 2024

Wheat straw (WS) is a promising substrate for biogas production by anaerobic digestion (AD) due to its high carbohydrate content. An estimated 0.603 million t yr−1 of WS are generated from wheat production systems in South Africa. This is equivalent to an energy potential of 11 PJ. Despite this, WS is still undervalued as a bioenergy resource in South Africa due to its structural complexity and low nitrogen content. WS disposal methods, such as use in livestock bedding, burning and burying into the soil, inter alia, are not sustainable and may contribute to global warming and climate change. The commercialization of the AD of WS needs to be further developed and promoted. Pre-treatment (i.e., physical, chemical, biological and hybrid methods) and anaerobic co-digestion (AcoD) are novel strategies that can support the conversion of WS into biogas and other value-added products. Current and future research should focus on optimizing pre-treatment and AcoD conditions towards industrialization of WS into valuable products. This paper focuses on the potential use of WS for biogas production in South Africa. The aim is to create information that will promote research and development, and encourage policy makers and stakeholders to participate and invest in WS biogas technology. Were WS biogas technology fully adopted, we believe that it would alleviate energy insecurity and environmental degradation, and sustain the livelihoods of citizens in South Africa.
Low Vibration Control Scheme for Permanent Magnet Motor Based on Resonance Controllers
Authors: Ma Chi , Xu Wenzhong , Liu Mingtian , Hong Jianfeng
Published: September 2024

For an electric locomotive traction motor, it is necessary to maintain relatively low vibration and noise due to the higher design standards. By using effective motor control strategies and implementing current harmonic suppression schemes, motor efficiency and vibration and noise suppression can be effectively improved. This study investigates the current harmonic suppression strategy for permanent magnet synchronous motors by (1) constructing a mathematical model of the permanent magnet motor to explore the sources of low-order harmonics currents such as fifth and seventh harmonics, as well as high-order harmonics at switch frequencies and their multiples, and analyzing the electromagnetic force characteristics generated by the current, and (2) establishing a vector control system for the permanent magnet motor. To suppress the fifth and seventh harmonic components in the current, a resonance controller is constructed, which utilizes the parallel connection of a resonator and PI controller to achieve low-order harmonic suppression. The factors affecting the effectiveness of the resonance controller’s suppression are also analyzed. The experiments are conducted, and the current harmonic suppression scheme constructed in this study can effectively reduce the harmonics in the current, thereby reducing motor vibration and noise.