bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Influence of Low Doses of Sucrose on Memory Deficits in Fish Exposed to Common Insecticide Based on Fipronil and Pyriproxyfen
Authors: Rarinca Viorica , Hritcu Luminita Diana , Burducea Marian , Plavan Gabriel , Lefter Radu , Burlui Vasile , Romila Laura , Ciobică Alin , Todirascu-Ciornea Elena , Barbacariu Cristian-Alin
Published: December 2024

Although pesticides have been a constant concern for decades, in the last ten years, public discussions and scientific research have emphasized their impact on human health and the environment, drawing increased attention to the problems associated with their use. The association of environmental stressors such as pesticides with a sugar-rich diet can contribute to the growing global metabolic disease epidemic through overlapping mechanisms of insulin resistance, inflammation, and metabolic dysregulation. The main aim of this study was to evaluate the behavioral effects of the exposure of Silver crucian carp (Carassius auratus gibelio) to a commercial insecticide formulation containing fipronil, pyriproxyfen, and other additives, as well as sucrose and their mixtures. The behavioral responses in the T-test showed significant abnormalities in the exploratory activity evocative of memory deficits and an increased degree of anxiety in the groups of fish treated with the insecticide formulation and the mixture of the insecticide with sucrose. Aggression, quantified in the mirror-biting test, as biting and the frequency of approaches to the mirror contact zone, was significantly decreased only in the insecticide and sucrose group. All three groups showed behavioral changes reflective of toxicity, but only the combination of the two stress factors, environmental (insecticide) and metabolic (sucrose intake), resulted in pronounced memory alterations.
The Role of Formononetin in Osteoblast Function and Mineralization Potential with Deproteinized Bovine Bone Material
Authors: Aldogan Ebru Haciosmanoglu , Başaran Deniz , Öner Bilgin , Günçer Başak
Published: December 2024

Objectives: Dental bone formation involves various cellular and molecular mechanisms, and phytoestrogens such as formononetin (FORM) are promising because of their estrogenic, anti-inflammatory, and antioxidant effects. This study investigated the effect of FORM on osteoblast proliferation, differentiation, and mineralization in combination with spongiosa granulates (BO) in vitro. Materials and Methods: Human fetal osteoblast cells (hFOB1.19) were treated with increasing concentrations of FORM (1, 10, and 100 µg/mL), BO, or their combination. Cell proliferation was assessed using a MTT assay. Alkaline phosphatase (ALP) activity, intracellular Ca2+, and Pi levels were measured using ELISA. Vascular endothelial growth factor (VEGF) and osteocalcin expression levels were analyzed by western blotting. Results: Cell proliferation increased with FORM, with or without BO, after 6 days (p < 0.001). FORM and BO had a synergistic effect on ALP activity (p < 0.001). Intracellular Ca2+ and Pi levels were highest in the BO-FORM group, suggesting superior mineralization (p < 0.05). VEGF and osteocalcin expression was significantly upregulated with FORM, alone and with BO (p < 0.05), indicating improved angiogenesis and bone maturation over 9 days. Conclusions: FORM enhances osteoblast proliferation, differentiation, and mineralization potential, particularly in BO spongiosa granulates. These data support the in vitro potential of formononetin-phytoestrogen in promoting osteoblast differentiation and mineralization potential with BO. These findings suggest that FORM, combined with BO, could improve bone augmentation in clinical applications such as maxillofacial surgery. FORM shows valuable potential for clinical applications, such as maxillofacial surgery, by promoting faster and more effective healing.
Development and Characterization of Biocompatible Cellulose—Tetraphenylethylene Hydrazone Self-Assembling Nanomicelles with Acidity-Triggered Release of Doxorubicin for Cancer Therapy
Authors: Rupel Katia , Fanfoni Lidia , Dus Jacopo , Tommasini Martina , Porrelli Davide , Medagli Barbara , Canfora Federica , Adamo Daniela , Di Lenarda Roberto , Ottaviani Giulia , Biasotto Matteo
Published: December 2024

The development of anticancer diagnostic and therapeutic strategies is of crucial importance to improve efficacy and therapeutic specificity. Here, we describe the synthesis and characterization of fluorescent self-assembling nanomicelles (NMs) based on a biocompatible polysaccharide (cellulose, CE) functionalized with a tetraphenyl ethylene derivative (TPEHy) and loaded with Doxorubicin (DOX) with aggregation-induced emission (AIE) properties and pH-dependent drug release. We obtained CE-TPEHy-NMs with an average diameter of 60 ± 17 nm for unloaded NMs and 86 ± 25 nm for NMs loaded with DOX, respectively. Upon testing different conditions, we obtained an encapsulation efficiency of 86% and a loading capacity of 90%. A controlled dialysis experiment showed that the release of DOX after 48 h is minimal at pH 7.4 (11%), increasing at pH 6.5 (50%) and at its maximum at pH 4.5 (80%). The cytotoxicity of blank and loaded CE-TPEHy-NMs at increasing concentrations and different pH conditions was tested on a MG-63 human osteosarcoma cell line. Based on viability assays at pH 7.4, neither unloaded nor loaded CE-TPEHy-NMs exerted any inhibition on cell proliferation. At pH 6.5, proliferation inhibition significantly increased, confirming the pH-dependent release. We characterized and studied the performance of CE-based amphiphilic, biocompatible NMs for controlled drug release in acidic conditions, such as tumor microenvironments. Further studies are required to optimize their synthesis process and to validate their antitumoral properties in vivo.
Thermal and Mechanical Performances Optimization of Plaster–Polystyrene Bio-Composites for Building Applications
Authors: Rabhi Aicha , Ennawaoui Amine , Radoine Hassan , Hammoumi Ismail , Assif Safaa , Chhiti Younes , Laasri Said , Ennawaoui Chouaib
Published: December 2024

Polystyrene is renowned for its excellent thermal insulation due to its closed-cell structure that traps air and reduces heat conduction. This study aims to develop sustainable, energy-efficient building materials by enhancing the thermal and mechanical properties of plaster–polystyrene bio-composites. By incorporating varying amounts of polystyrene (5% to 25%) into plaster, our research investigates changes in thermal conductivity, thermal resistance, and mechanical properties such as Young’s modulus and maximum stress. Meticulous preparation of composite samples ensures consistency, with thermal and mechanical properties assessed using a thermal chamber and four-point bending and tensile tests. The results show that increasing the polystyrene content significantly improved thermal insulation and stiffness, though maximum stress decreased, indicating a trade-off between insulation and mechanical strength.
Solid Surfaces, Defects and Detection
Authors: Song Kechen
Published: December 2024

In the modern industrial field, particularly in steel and automobile manufacturing, detecting defects in steel surfaces is crucial to product quality and safety [...]
Exploring the Impact of Surface Thermal Treatments on WC-Co Blocks Subjected to Linear Scratching: A Molecular Dynamics Simulation Study
Authors: Wang Duck Hyun , Lee Sehan , Yi Taeil
Published: December 2024

High-energy thermal treatments, such as electron beam irradiation, are crucial for enhancing the performance of tungsten carbide–cobalt (WC-Co) composites in cutting tools and wear-resistant coatings. This study utilizes molecular dynamics simulations to analyze the nanoscale effects of such treatments on WC-Co surfaces, focusing on cobalt evaporation and linear scratching phenomena. The results demonstrate that electron beam irradiation significantly accelerates cobalt evaporation, with rates depending on energy flux and local atomic environments. Embedded cobalt atoms within WC grains exhibit higher resistance to evaporation due to stronger bonding, while pure cobalt surfaces show greater susceptibility to material loss. Under high energy flux, WC-Co surfaces experience an interplay of thermal expansion, density reduction, and evaporation, resulting in accelerated degradation. Linear scratching simulations reveal that thermally treated WC-Co surfaces exhibit increased structural instability, as indicated by broader distributions of local entropy and von Mises stress, reflecting heightened susceptibility to deformation and failure. Stress concentrations from indentation and scratching are more pronounced in thermally treated samples, highlighting the influence of thermal history on mechanical behavior. Molecular dynamics simulations enable detailed insights into atomic-scale phenomena, allowing precise quantification of the effects of energy flux, material composition, and thermal treatment on structural and mechanical responses. These findings emphasize the need to optimize thermal treatment protocols to enhance the durability and performance of WC-Co composites, providing valuable guidance for the development of robust materials for industrial applications.
Effect of PLA-Nanochitosan Films on the Shelf-Life Extension of Fresh Rainbow Trout Fillets
Authors: Teimourifard Reza , Khanjari Ali , Basti Afshin Akhondzadeh , Misaghi Ali , Karabagias Ioannis Konstantinos , Ghadami Fereshteh , Gholami Fatemeh , Noori Negin , Khorrami Ramin
Published: December 2024

Fish-packaging technology is essential in maintaining the quality and shelf life of fish as a valuable and perishable food. This research was performed to evaluate the influence of polylactic acid-nanochitosan (PLA-NC) films containing Bunium persicum essential oil (BPEO) (0, 0.3, 0.6, and 0.9%) on microbial, chemical, and organoleptic characteristics of rainbow trout fillets during storage under refrigeration for 13 days. On the last day of the storage period, in the rainbow trout fillets wrapped with PLA-NC films containing BPEO, the microbial population declined by at least 1 log CFU/g in comparison with control samples. The initial value of the total volatile base nitrogen (TVB-N) of fillets was 10.28 mg/100 g and it reached 31.75 mg/100 g in the control group on day 4, while in rainbow trout fillets wrapped with PLA-NC films and complemented with BPEO, TVB-N contents were below 25 mg/100 g, until the 10th day of storage. Regarding the results of the current study, the peroxide value (PV) on day 0 was 1.7 meq/kg and reached 13.66 meq/kg in the control samples on the 13th day, while this parameter was lower than 10 meq/kg in samples wrapped with PLA-NC films complemented with BPEO on the final day of the study. The findings of this study illustrated that wrapping rainbow trout fillets in PLA-nanochitosan films complemented with BPEO prolonged the shelf life of rainbow trout fillets without any undesirable organoleptic attributes during storage under refrigeration for 10 days. The topography and surface composition of the prepared films was further confirmed using scanning electron microscope (SEM) and field scanning electron microscope (FESEM) analyses.
Hybrid Vision Transformer and Convolutional Neural Network for Multi-Class and Multi-Label Classification of Tuberculosis Anomalies on Chest X-Ray
Authors: Yulvina Rizka , Putra Stefanus Andika , Rizkinia Mia , Pujitresnani Arierta , Tenda Eric Daniel , Yunus Reyhan Eddy , Djumaryo Dean Handimulya , Yusuf Prasandhya Astagiri , Valindria Vanya
Published: December 2024

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global mortality. While TB detection can be performed through chest X-ray (CXR) analysis, numerous studies have leveraged AI to automate and enhance the diagnostic process. However, existing approaches often focus on partial or incomplete lesion detection, lacking comprehensive multi-class and multi-label solutions for the full range of TB-related anomalies. To address this, we present a hybrid AI model combining vision transformer (ViT) and convolutional neural network (CNN) architectures for efficient multi-class and multi-label classification of 14 TB-related anomalies. Using 133 CXR images from Dr. Cipto Mangunkusumo National Central General Hospital and 214 images from the NIH datasets, we tackled data imbalance with augmentation, class weighting, and focal loss. The model achieved an accuracy of 0.911, a loss of 0.285, and an AUC of 0.510. Given the complexity of handling not only multi-class but also multi-label data with imbalanced and limited samples, the AUC score reflects the challenging nature of the task rather than any shortcoming of the model itself. By classifying the most distinct TB-related labels in a single AI study, this research highlights the potential of AI to enhance both the accuracy and efficiency of detecting TB-related anomalies, offering valuable advancements in combating this global health burden.
Features of the Reinforcement–Soil Interfacial Effect in Fiber-Reinforced Soil Based on Pullout Tests
Authors: Xiao Dingkan , Zhang Jun , Xu Wei , Wang Jiang , Qiu Nianyuan , Wu Juan , Ling Huiyan , Qu Bo , Yao Zhihua
Published: December 2024

To investigate the reinforcement–soil interfacial effects in fiber-reinforced soil, this study developed a novel horizontal pullout tester and conducted pullout tests on coarse polypropylene fibers in plain soil, cemented soil, and fine fiber-reinforced cemented soil. Three soil types were analyzed: low liquid limit clay, high liquid limit clay, and clay sand. The pullout tester proved to be both scientifically robust and efficient. Depending on the soil properties, coarse polypropylene fibers were pulled out intact or fractured. The pullout curves displayed distinct multi-peak patterns, with wavelengths closely linked to the fiber’s intrinsic characteristics. The pullout curve wavelength for plain soil matched the fiber’s intrinsic wavelength, while it was slightly greater in cemented soils. The peak pullout force increased with extended curing periods, higher cement content, more excellent compaction, and the addition of fine polypropylene fibers. Among these factors, compaction had the most significant impact on enhancing the soil–fiber interfacial effect. Friction, cohesion, and fiber interweaving created interlocking effects, inhibiting fiber sliding. Cement hydration processes further deformed the fiber, increasing its friction coefficient and sliding resistance. Hydration products also fill soil voids, improving soil compactness, enlarging the fiber–soil contact area, and enhancing frictional and occlusal forces at the interface.
Habitat Suitability in the Eyes of the Beholder: Using Random Forest Models to Predict Land Cover Type and Scale of Selection Through Avian Functional Traits
Authors: Julien Adisa , Melles Stephanie
Published: December 2024

Ecologists have long sought to identify the scales at which avian species select habitats from their surroundings. However, this is a challenging undertaking given the complex hierarchical nature of the processes involved in avian habitat selection and also given the selection of data scales (resolution and extents) available in satellite-derived land cover. Past research has largely neglected to consider how grain size limitations are related to species’ functional traits. Fortunately, with the increased ubiquity of available land cover maps and open-access datasets detailing avian functional traits, tackling these questions is becoming more feasible. Using data from the Ontario Land Cover Compilation v2, the Ontario Breeding Bird Atlas (2001–2005), and functional trait data from the AVONET dataset, we trained Random Forest models to predict scale-dependent land cover preferences based on avian functional traits. To capture changing scales, we used increasing pixel sizes from the land cover map of our study area which sought to replicate the different perceptual ranges of avian species. Our Random Forest models showcase the ability to accurately predict between natural and human-modified land cover with varying predictive accuracies. Notably, we observed heightened accuracy at smaller pixel sizes, with a subtle decline as grain size increased. By revealing the relationship between avian traits and habitat selection across multiple scales, our study advances our understanding of species–environment interactions, offering valuable insights for conservation strategies and a deeper understanding of avian habitat selection.
Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study
Authors: Soong Tse Kiat , Kim Guo Wei , Chia Daryl Kai Ann , So Jimmy Bok Yan , Lee Jonathan Wei Jie , Shabbbir Asim , Lum Jeffrey Huey Yew , Soon Gwyneth Shook Ting , Ho Khek Yu
Published: December 2024

Background: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations. Objective: To compare the performance of the AI-enabled Raman spectroscopy with that of high-definition white light endoscopy (HD-WLE) for the risk classification of gastric lesions. Methods: This was a randomized double-arm feasibility proof-of-concept trial in which participants with suspected gastric neoplasia underwent endoscopic assessment using either the Raman spectroscopy-based AI (SPECTRA IMDx™) or HD-WLE performed by expert endoscopists. Identified lesions were classified in real time as having either low or high risk for neoplasia. Diagnostic outcomes were compared between the two groups using histopathology as the reference. Results: A total of 20 patients with 25 lesions were included in the study. SPECTRA, in real-time, performed at a statistically similar level to that of HD-WLE performed by expert endoscopists, achieving an overall sensitivity, specificity, and accuracy of 100%, 80%, and 89.0%, respectively, by patient; and 100%, 80%, and 92%, respectively, by lesion, while expert endoscopists using HD-WLE attained a sensitivity, specificity, and accuracy of 100%, 80%, and 90%, respectively, by patient; and 100%, 83.3%, and 91.7%, respectively, by lesion, in differentiating high-risk from low-risk gastric lesions. Conclusions: The SPECTRA’s comparable performance with that of HD-WLE suggests that it can potentially be a valuable adjunct for less experienced endoscopists to attain accurate and real-time diagnoses of gastric lesions. Larger-scale prospective randomized trials are recommended to validate these promising results further.
An Overview of Gaucher Disease
Authors: Méndez-Cobián Daniela Anahí , Guzmán-Silahua Sandra , García-Hernández Diana , Conde-Sánchez Julian , Castañeda-Borrayo Yaocihuatl , Duey Kylee Louise , Zavala-Cerna Maria G. , Rubio-Jurado Benjamín , Nava-Zavala Arnulfo Hernán
Published: December 2024

Background: Gaucher disease (GD) is a rare autosomal recessive disorder caused by mutations in the GBA1 gene that lead to a deficiency in the glucocerebrosidase gene. This deficiency results in the accumulation of glucocerebrosides in macrophages, primarily affecting the liver, spleen, and bone marrow. Focusing on the Mexican population, this study aims to review GD’s epidemiology, clinical manifestations, and treatment options to enhance early diagnosis and optimize treatment outcomes. Methods: This study is a comprehensive literature review analyzing epidemiological data, clinical presentations, and current therapeutic approaches for Gaucher disease, including enzyme replacement therapy (ERT) and substrate reduction therapy (SRT). Conclusions: Early diagnosis and individualized treatment, primarily through enzyme replacement therapy, significantly improve the prognosis of patients with Gaucher disease, particularly type 1. Continued research is required to enhance therapeutic approaches for the neuropathic types and better understand the disease’s epidemiology in Mexico. These efforts will contribute to improved clinical outcomes and quality of life for patients.
Virtual Monoenergetic Imaging of Thoracoabdominal Computed Tomography Angiography on Photon-Counting Detector Computertomography: Assessment of Image Quality and Leveraging Low-keV Series for Salvaging Suboptimal Contrast Acquisitions
Authors: Rippel Katharina , Decker Josua A. , Luitjens Jan , Habeeballah Osama , Bette Stefanie , Braun Franziska , Kroencke Thomas J. , Scheurig-Muenkler Christian
Published: December 2024

Background: The aim of this study was to assess the possibility of image improvement of ECG-gated, high-pitch computed tomography angiography (CTA) of the thoracoabdominal aorta before transaortic valve replacement (TAVR) on a novel dual-source photon-counting detector CT (PCD-CT) in the setting of suboptimal low-contrast attenuation. Methods: Continuously examined patients who underwent an ECG-gated, high-pitch CTA of the aorta on a PCD-CT with a contrast decrease of at least 50% between the ascending aorta and the common femoral arteries (CFA) were included. Patient characteristics were documented. Virtual monoenergetic imaging (VMI) reconstructions with three keV settings were generated. CT values and noise were measured for five vascular segments of the aorta and the CFA. Signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were calculated. Two independent board-certified radiologists rated the images with the focus on vascular attenuation, vessel sharpness, and image quality using a 5-point Likert scale. Results: Fifty-five patients (mean age 77.4 ± 8.5 years; 15 women) were included. The SNR was significantly higher at 40 and 45 keV VMI compared to reference 70 keV (p < 0.001 and p = 0.005, respectively). The same was shown for the CNR (p < 0.001 and p = 0.0049, respectively). Subjective image evaluation showed a significant increase in vessel attenuation in the lower keV reconstructions, while the overall image quality decreased only slightly. Furthermore, 50% (8/16) of primarily non-diagnostic scans were considered diagnostic when using low-keV reconstructions (p > 0.05). Conclusions: ECG-gated CTA of the aorta in high-pitch mode on PCD-CT with suboptimal contrast enhancement at the level of the CFA can be salvaged by using low-keV VMI. This implies the possibility of radiation dose reduction by eliminating the need for repeat scans.
The Quantitation of EPA and DHA in Fish Oil Dietary Supplements Sold in the United States
Authors: Hilton Joseph E. , Setzer William N. , Satyal Prabodh , Poudel Ambika , Ashcroft Sawyer , Satyal Rakesh
Published: December 2024

The dietary supplement industry is expanding, but it still remains far less regulated than the pharmaceutical industry, resulting in inconsistent quality control. Limited research has been performed in the U.S., particularly regarding the quantification of omega-3 fatty acids like eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). The aim of this study was to quantify the omega-3 fatty acid content in fish oil capsules and liquids purchased from major stores and a grocery chain and compare the results to their label claims. A total of 6 out of 15 were found to have EPA levels ±20% and 10 out of 15 were ±20% outside of the label claim for DHA. One product did not have a label claim for EPA and DHA, but did have a claim for total omega-3 content. These discrepancies indicate that consumers may unwittingly be consuming higher or lower quantities of omega-3 fatty acids than expected, potentially affecting their health outcomes.
Absenteeism and Productivity Loss Due to Influenza or Influenza-like Illness in Adults in Europe and North America
Authors: Fisman David , Postma Maarten , Levin Myron J. , Mould-Quevedo Joaquin
Published: December 2024

Healthy working-age adults are susceptible to illness or caregiving requirements resulting from annual seasonal influenza, leading to considerable societal and economic impacts. The objective of this targeted narrative review is to understand the societal burden of influenza in terms of absenteeism and productivity loss, based on the current literature. This review includes 48 studies on the impact of influenza and influenza-like illness (ILI) and reports on the effect of influenza vaccination, age, disease severity, caring for others, comorbidities, and antiviral prophylaxis on absenteeism and productivity loss due to influenza/ILI, focusing on publications originating from Canada, Europe, and the United States. Influenza/ILI results in substantial work time and productivity loss among working adults and students in Canada, Europe, and the United States, particularly those who are unvaccinated, are <65 years of age, or who have severe disease. Considerable work time and productivity loss is attributable to illness and caregiver burden related to influenza. Further research is required on the impact of influenza on absenteeism and productivity loss in adults with comorbidities to support the development of effective employer policies for working adults with underlying health conditions.
Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm
Authors: Han Songyue , Wang Mingyu , Duan Junhong , Zhang Jialong , Li Dongdong
Published: December 2024

In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.
A Global Coverage Path Planning Method for Multi-UAV Maritime Surveillance in Complex Obstacle Environments
Authors: Li Yiyuan , Chen Weiyi , Fu Bing , Wu Zhonghong , Hao Lingjun
Published: December 2024

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments.
Enhancing Teachers’ Creativity with an Innovative Training Model and Knowledge Management
Authors: Skrbinjek Vesna , Vičič Krabonja Maja , Aberšek Boris , Flogie Andrej
Published: December 2024

In the post-COVID-19 era, education requires teachers to engage learners across diverse learning environments (at school or other formal institutions, at home, outdoors, or in virtual environments) using innovative learning strategies. To meet these challenges, teachers must upskill their creativity and strengthen their pedagogical digital competencies and knowledge management skills. This study introduces the innovative teacher training and support (TTS-IPCD) model to enhance teachers’ creativity and pedagogical digital competencies. This research involved a sample of 350 teachers from 75 primary and secondary schools over a four-year period. Teachers’ creativity was measured using the Kirton Adaption–Innovation Inventory (KAI), assessing key metrics such as problem-solving flexibility, openness to change, and inclination toward novel approaches. Quantitative analysis was conducted using an independent samples t-test to evaluate teacher creativity changes. The results indicated that the TTS-IPCD model enhanced teacher creativity in the direction of a stronger propensity toward innovative behaviors, including embracing diversity and change in their work, solving problems through novel approaches, and adopting a holistic perspective rather than strictly adhering to established routines. Furthermore, the TTS-IPCD model improved teamwork and collaboration, contributing to the development of more adaptive and innovative learning environments. These findings highlight the importance of continuous professional development of teachers focused on creative pedagogy and digital competencies to equip teachers for the evolving educational landscape.
Examining Students’ (Inter-)Disciplinary Practice Beliefs in STEM Makerspaces
Authors: Andrews Madison E. , Boklage Audrey
Published: December 2024

Makerspaces have emerged as a popular supplement to formal K-16 STEM education, offering students opportunities to engage in hands-on, creative activities that integrate multiple disciplines. However, despite their potential to foster interdisciplinary learning, these spaces often reflect the techno-centric norms prevalent in STEM. As a result, makerspaces tend to be dominated by white, male, middle-class participants and focused on tech-centric practices, which may limit both who participates in these spaces and what types of activities they do there. To address calls to broaden student participation in makerspaces, we surveyed and interviewed undergraduate STEM students to understand how students’ perceptions of making and the makerspace itself influence their modes of participation. Using the lens of repertoires of practice, we identify which practices students believe to “count” in a STEM makerspace, finding that many students hold narrow, discipline-specific beliefs about making, which, for some students, were preventive of them visiting the facility. However, we also discover that students’ beliefs of making practices were malleable, indicating potential for shifting these views towards more inclusive, interdisciplinary beliefs. We conclude with recommendations for educators and makerspace administrators to broaden students’ conceptualizations of making practices and supporting such practices in STEM makerspaces.
An Experiment of AI-Based Assessment: Perspectives of Learning Preferences, Benefits, Intention, Technology Affinity, and Trust
Authors: Alamäki Ari , Khan Umair Ali , Kauttonen Janne , Schlögl Stephan
Published: December 2024

The rising integration of AI-driven assessment in education holds promise, yet it is crucial to evaluate the correlation between trust in general AI tools, AI-based scoring systems, and future behavioral intention toward using these technologies. This study explores students’ perspectives on AI-assisted assessment in higher education. We constructed a comprehensive questionnaire supported by relevant studies. Several hypotheses grounded in the literature review were formulated. In an experimental setup, the students were tasked to read a designated chapter of a paper, answer an essay question about this chapter, and then have their answers evaluated by an AI-based essay grading tool. A comprehensive data analysis using Bayesian regression was carried out to test several hypotheses. The study finds that remote learners are more inclined to use AI-based educational tools. The students who believe that AI-based essay grading is less effective than teacher feedback have less trust in AI-based essay grading, whereas those who find it more effective perceive more benefit from it. In addition, students’ affinity for technology does not significantly impact trust or perceived benefits in AI-based essay grading.
Understanding the Second Victim Phenomenon Among Healthcare Workers in an Italian Hospital
Authors: Sedile Raffaella , Zizza Antonella , Bastiani Luca , Carluccio Eugenia , Marrazzi Marinella , Bellandi Tommaso , Spagnolo Giorgio O.
Published: December 2024

Second victim syndrome (SVS) refers to the psychological trauma experienced by healthcare workers (HCWs) as a result of being involved in an adverse event (AE). Research on the prevalence of SVS and the support needed for HCWs who experience it is limited. A cross-sectional study was conducted at the Health Local Unit of Lecce, in Puglia, to identify the phenomenon of SVS among HCWs and recognize the forms of support received and desired. A validated questionnaire, IT-SVEST, was administered to doctors and nurses. The survey received responses from 250 HCWs, and 41% of respondents reported being involved in an AE that could cause SVS. Among the seven dimensions measuring the effects of the SVS and two outcome variables, the highest percentage of agreement was found for psychological distress (23.5%), followed by turnover intentions (19.8%) and physical distress (9.9%); 23.8% of the interviewees declared that they did not receive institutional support, and 9.9% identified help resources mostly in non-work-related support (9.9%), followed by supervisor support (9.3%). The multivariable binary logistic regression analysis showed a positive association between the occurrence of an AE and the medical doctor profession (OR = 4.267, p ≤ 0.0001), and affiliation to intensive care departments (OR = 5.133, p ≤ 0.0001) and male gender (OR = 2.069, p = 0.033). SVS is a serious problem that affects the entire health system, systematic surveys and appropriate institutional responses including formal support programs for affected HCWs are a priority.
A Comprehensive Survey on Generative AI Solutions in IoT Security
Authors: López Delgado Juan Luis , López Ramos Juan Antonio
Published: December 2024

The influence of Artificial Intelligence in our society is becoming important due to the possibility of carrying out analysis of the large amount of data that the increasing number of interconnected devices capture and send as well as making autonomous and instant decisions from the information that machines are now able to extract, saving time and efforts in some determined tasks, specially in the cyberspace. One of the key issues concerns security of this cyberspace that is controlled by machines, so the system can run properly. A particular situation, given the heterogeneous and special nature of the environment, is the case of IoT. The limited resources of some components in such a network and the distributed nature of the topology make these types of environments vulnerable to many different attacks and information leakages. The capability of Generative Artificial Intelligence to generate contents and to autonomously learn and predict situations can be very useful for making decisions automatically and instantly, significantly enhancing the security of IoT systems. Our aim in this work is to provide an overview of Generative Artificial Intelligence-based existing solutions for the very diverse set of security issues in IoT environments and to try to anticipate future research lines in the field to delve deeper.
Multi-Objective Topology Optimization of Thin-Plate Structures Based on the Stiffener Size and Layout
Authors: Yin Qin , Guo Junsong , Kan Yingzhe , Ma Jinghua , Deng Congying
Published: December 2024

To address the limitations of existing optimization methods that focus on single objectives or neglect stiffener features, a multi-objective topology optimization (MOTO) method is proposed based on the stiffener size and layout. By constraining the initial structural performance parameters, the optimal stiffener height is determined through size optimization. Based on the stiffener height, single-objective topology optimization is used to achieve the best material distribution. The stiffener width is treated as a design variable, while MOTO is performed on the load point displacement, first natural frequency, and mass, thereby yielding an optimal stiffener width and performance. Finally, a multi-dimensional analysis of the stiffener height, width, and dynamic and static characteristics of the stiffened thin-plate structure is conducted. The results indicate that the optimized stiffener layout is considerably improved. Compared to the initial structure, the maximum and average displacements of the load point are reduced by 23.26% and 8.62%, respectively. The first natural frequency increases by 3.81%, while the maximum resonance amplitude and overall structural mass decrease by 39.97% and 1.99%, respectively. The results indicate that the optimized structure achieves a lightweight design while maintaining better stiffness and low-frequency vibration resistance. The feasibility and effectiveness of the proposed method are validated.
MaQA: A Manual Text-Based Approach for Car-Specific Question Answering
Authors: Park Cheoneum , Jeong Seohyeong , Kim Juae
Published: December 2024

In the past few years, intelligent virtual assistant technology has had a significant impact on our daily lives, enabling us to easily access information through simple voice commands. In this paper, we present MaQA (Manual Question Answering), an approach for solving domain-specific question answering in the automobile sector, aiming to broaden the impact of virtual assistants by integrating them into the automotive domain. The goal of MaQA is to accurately classify car-related questions into one of the 1526 FAQ categories. To tackle this task, we constructed the MaQA dataset in both Korean and English, each consisting of 45,641 questions, and propose an end-to-end neural FAQ model. Our approach achieved accuracy scores of 85.24% and 82.95% on the Korean and English datasets, respectively, with 12× and 13.4× faster inference, outperforming the BERT-based model in both accuracy and latency.
Peach Leaf Shrinkage Disease Recognition Algorithm Based on Attention Spatial Pyramid Pooling Enhanced with Local Attention Network
Authors: Zhang Caihong , Zhang Pingchuan , Hu Yanjun , Ma Zeze , Ding Xiaona , Yang Ying , Li Shan
Published: December 2024

Aiming at many challenges in the recognition task of peach leaf shrink disease, such as the diversity of object size of diseased leaf disease, complex background interference, and inflexible adjustment of model training learning rate, we propose a peach leaf shrink disease recognition algorithm based on an attention generalized efficient layer aggregation network. Firstly, the rectified linear unit activation function is used to effectively improve the stability and performance of the model in low-precision computing environments and solve the problem of partial gradient disappearance. Secondly, the integrated squeeze-and-excitation network attention mechanism can adaptively focus on the key areas of pests and diseases in the image, which significantly enhances the recognition ability of the model to the characteristics of pests and diseases. Finally, combined with fast pyramid pooling enhanced with Local Attention Networks, the deep fusion of cross-layer features is realized to improve the ability of the model to identify complex features and optimize the operation efficiency. The experimental results on the peach leaf shrink disease recognition dataset show that the proposed algorithm achieves a significant improvement in performance compared with the original YOLOv8 algorithm. Specifically, mF1, mPrecision, mRecall, and mAP increased by 0.1075, 0.0723, 0.1224, and 0.1184, respectively, which provided strong technical support for intelligent and automatic monitoring of peach pests and diseases.
Intellectual Humility in the Workplace
Authors: Hendy Nhung T.
Published: December 2024

Intellectual humility (IH), defined as the extent to which one is aware of one’s own intellectual limitations, is an understudied construct in organizational research. As a moral virtue, IH has been studied in philosophy and religion for decades. As a psychological and behavioral tendency construct, IH has gained significant attention among psychology researchers over the past decade due to rising extremism regarding social and political issues in the US and around the world. One reason for the increased research interest in IH includes the potential benefits from IH in terms of reducing social and political polarization and reducing stress and anxiety, which may improve individual overall well-being. This article provides an overview of IH as a multi-dimensional construct, its psychometric properties and nomological network, and its potential benefits in organizations and employee well-being. The article concludes with a call for more interdisciplinary research on improving our knowledge and theories of IH as well as its construct measurement considering the recent coming-of-age adoption of artificial intelligence (AI) models to harness the power of IH in sustaining individual well-being.
Limaçon Technology in Power Generation
Authors: Belfiore Christopher , Hossain Shazzad , Phung Truong , Sultan Ibrahim
Published: December 2024

Limaçon rotary machines are the heart of power generation systems, especially small- and micro-scale ones. These machines are the prime movers that play the main role in converting the potential energy to other useful forms of work, such as mechanical and/or electrical; the generated energy can also be stored in batteries or in the form of hydrogen. The focus of this paper is on the working of this limaçon technology, and the embodiments and mechanical drives to produce the unique motion of these machines. This paper will also discuss the related power-generating cycles and control schemes.
A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring
Authors: Alqaydi Saif , Zeiada Waleed , El Wakil Ahmed , Alnaqbi Ali Juma , Azam Abdelhalim
Published: December 2024

Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance.
Mapping Soil Properties in Tropical Rainforest Regions Using Integrated UAV-Based Hyperspectral Images and LiDAR Points
Authors: Chen Yiqing , Shi Tiezhu , Li Qipei , Yang Chao , Wang Zhensheng , Chen Zongzhu , Pan Xiaoyan
Published: December 2024

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve soil management practices to replace the time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability of soil properties over linear models, their practical and automated application for predicting soil properties using remote sensing data requires further assessment. Therefore, this study aims to integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images and Light Detection and Ranging (LiDAR) points to predict the soil properties indirectly in two tropical rainforest mountains (Diaoluo and Limu) in Hainan Province, China. A total of 175 features, including texture features, vegetation indices, and forest parameters, were extracted from two study sites. Six ML models, Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), were constructed to predict soil properties, including soil acidity (pH), total nitrogen (TN), soil organic carbon (SOC), and total phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced to obtain optimal model hyperparameters. The results showed that compared with the default parameter tuning method, BOA always improved models’ performances in predicting soil properties, achieving average R2 improvements of 202.93%, 121.48%, 8.90%, and 38.41% for soil pH, SOC, TN, and TP, respectively. In general, BOA effectively determined the complex interactions between hyperparameters and prediction features, leading to an improved model performance of ML methods compared to default parameter tuning models. The GBDT model generally outperformed other ML methods in predicting the soil pH and TN, while the XGBoost model achieved the highest prediction accuracy for SOC and TP. The fusion of hyperspectral images and LiDAR data resulted in better prediction of soil properties compared to using each single data source. The models utilizing the integration of features derived from hyperspectral images and LiDAR data outperformed those relying on one single data source. In summary, this study highlights the promising combination of UAV-based hyperspectral images with LiDAR data points to advance digital soil property mapping in forested areas, achieving large-scale soil management and monitoring.