bluuebunny/crossref_metadata_2025 huggingface dataset:
Perceived Critical Success Factors for Implementing Building Information Modelling in Construction Small- and Medium-Sized Enterprises
Authors: Gheni Hussien Ihab , Saeed Rasheed Zahraa , Asaadsamani Parsa , Sarvari Hadi
Published: January 2025

Building information modelling (BIM) is an emerging technology in the building sector. As with any emerging technology, the identification of critical success factors (CSFs) for BIM is essential. On the other hand, small- and medium-sized enterprises (SMEs) consistently play a vital role in the construction industry. Therefore, it is essential to determine the critical success elements for the effective implementation of BIM in these companies. Hence, this study aims to determine the CSFs for implementing BIM in SMEs in the developing country of Iran. To accomplish this, three rounds of the Delphi technique were carried out with the participation of fifteen BIM professionals from SMEs based in Iran. According to the Delphi survey findings, a total of 27 CSFs were identified for the effective utilisation of BIM in SMEs. Subsequently, to assess the CSFs, a questionnaire utilising a five-point Likert scale measurement was designed. Then, it was distributed among specialists in construction SMEs in Iran. The questionnaire included twenty-seven factors categorised into four primary groups: technical, managerial, financial, and legal. A total of 56 questionnaires were gathered and examined. The findings indicate that the CSFs highlighted for implementing BIM in SMEs are above the average level. Furthermore, the CSFs with a high impact on successful BIM implementation in construction SMEs in Iran were determined. Four high-impact CSFs are (1) the employer’s demand; (2) understanding the advantages and practicality of implementing BIM; (3) awareness of and ensuring a return on investment; and (4) efficient and suitable legislation. The findings of this study can serve as a valuable resource for stakeholders, providing them with a useful tool to enhance decision-making about the implementation of BIM in SMEs, especially in developing countries.
Temperature Influence on the Deposition of Nitrogen-Doped Silicon Carbide Polycrystalline Films
Authors: Gavalas Michail , Greenhorn Scott , Mercier Frédéric , Zekentes Konstantinos
Published: January 2025

Polycrystalline nitrogen-doped cubic silicon carbide (3C-SiC) thin films are grown on 2″ Si wafers by a low-pressure chemical vapor deposition (LPCVD) technique with the aim for them to be used as support and active materials in microelectronic devices for neural interfaces. The effect of deposition temperature on the structural, mechanical, and electrical properties is investigated. The growth rate is varying, from 1 μm/h to 14 μm/h, along with the deposition temperature. We show that the structural and electrical properties of polycrystalline SiC are modified when changing the deposition temperature. Films with resistivity as low as (10.0 ± 0.5) mΩ·cm, a low residual stress of (−397 ± 158) MPa, and a low root mean square surface roughness of (53 ± 19) nm are achieved. Accelerated aging tests in heated phosphate buffer solution (PBS) show an etching rate of less than 1 nm/day and a steady low electrical resistivity for 77 days, indicating that the nitrogen-doped polycrystalline SiC is a chemically stable material, capable of chronic stability in a saline electrolyte.
Enhancing Thermoelectric Performance: The Impact of Carbon Incorporation in Spin-Coated Al-Doped ZnO Thin Films
Authors: Giribaldi Alberto , Giordani Cristiano , Latronico Giovanna , Bourgès Cédric , Baba Takahiro , Piscino Cecilia , Marinova Maya , Mori Takao , Artini Cristina , Rijckaert Hannes , Mele Paolo
Published: January 2025

In the present study, for the first time, aluminum-doped zinc oxide (AZO) thin films with nanoinclusions of amorphous carbon have been synthesized via spin coating, and the thermoelectric performances were investigated varying the aging period of the solution, the procedure of carbon nanoparticles’ addition, and the annealing atmosphere. The addition of nanoparticles has been pursued to introduce phonon scattering centers to reduce thermal conductivity. All the samples showed a strong orientation along the [002] crystallographic direction, even though the substrate is amorphous silica, with an intensity of the diffraction peaks reaching its maximum in samples annealed in the presence of hydrogen, and generally decreasing by the addition of carbon nanoparticles. Absolute values of the Seebeck coefficient improve when nanoparticles are added. At the same time, electric conductivity is higher for the sample with 1 wt.% of carbon and annealed in Ar with 1% of H2, both increasing in absolute value with the temperature rise. Among all the samples, the lowest thermal conductivity value of 1.25 W/(m∙K) was found at room temperature, and the highest power factor was 111 μW/(m∙K2) at 325 °C. Thus, the introduction of carbon effectively reduced thermal conductivity, while also increasing the power factor, giving promising results for the further development of AZO-based materials for thermoelectric applications.
Electrochemical Corrosion and Wear Behavior of Hot-Dip Galvanized Steel in Soils of Northern China
Authors: Jiao Xiaoyu , Jia Junhong , Chen Wei , Yang Wenlong
Published: January 2025

The study examined the corrosion and wear characteristics of hot-dip galvanized steel in complex soil environments. The results showed that hot-dip galvanized steel exhibited improved corrosion resistance characteristics. Additionally, the sliding speed was observed to influence both the coefficient of friction (COF) and the state of the worn surface. Moreover, the corrosion resistance of hot-dip galvanized steel declined as the immersion period increased. Following the incorporation of friction behavior, the galvanized layer is prone to accelerated degradation. The wear of the galvanized layer resulted in the failure of its electrochemical protection, creating a pathway for corrosion to occur on the substrate as a result of the coupling effect of corrosion and wear. The use of hot-dip galvanized steel presents challenges when exposed to a tribocorrosion environment for a prolonged period. This study lays the groundwork for future research on the maintenance cycle of industrial structures constructed primarily with hot-dip galvanized steel.
Computational Methods for Information Processing from Natural Language Complaint Processes—A Systematic Review
Authors: Blandón Andrade J. C. , Castaño Toro A. , Morales Ríos A. , Orozco Ospina D.
Published: January 2025

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law.
Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis
Authors: Alghareb Faris S. , Hasan Balqees Talal
Published: January 2025

Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom expandable hardware, i.e., graphics processing units (GPUs). Interestingly, leveraging the synergy of parallelism and edge computing can significantly improve CPU-based hardware platforms. Therefore, this manuscript explores levels of parallelism techniques along with edge computation offloading to develop an innovative hardware platform that improves the efficacy of deep learning computing architectures. Furthermore, the multitask learning (MTL) approach is employed to construct a parallel multi-task classification network. These tasks include face detection and recognition, age estimation, gender recognition, smile detection, and hair color and style classification. Additionally, both pipeline and parallel processing techniques are utilized to expedite complicated computations, boosting the overall performance of the presented deep face analysis architecture. A computation offloading approach, on the other hand, is leveraged to distribute computation-intensive tasks to the server edge, whereas lightweight computations are offloaded to edge devices, i.e., Raspberry Pi 4. To train the proposed deep face analysis network architecture, two custom datasets (HDDB and FRAED) were created for head detection and face-age recognition. Extensive experimental results demonstrate the efficacy of the proposed pipeline-parallel architecture in terms of execution time. It requires 8.2 s to provide detailed face detection and analysis for an individual and 23.59 s for an inference containing 10 individuals. Moreover, a speedup of 62.48% is achieved compared to the sequential-based edge computing architecture. Meanwhile, 25.96% speed performance acceleration is realized when implementing the proposed pipeline-parallel architecture only on the server edge compared to the sever sequential implementation. Considering classification efficiency, the proposed classification modules achieve an accuracy of 88.55% for hair color and style classification and a remarkable prediction outcome of 100% for face recognition and age estimation. To summarize, the proposed approach can assist in reducing the required execution time and memory capacity by processing all facial tasks simultaneously on a single deep neural network rather than building a CNN model for each task. Therefore, the presented pipeline-parallel architecture can be a cost-effective framework for real-time computer vision applications implemented on resource-limited devices.
Set-Word Embeddings and Semantic Indices: A New Contextual Model for Empirical Language Analysis
Authors: Fernández de Córdoba Pedro , Reyes Pérez Carlos A. , Sánchez Arnau Claudia , Sánchez Pérez Enrique A.
Published: January 2025

We present a new word embedding technique in a (non-linear) metric space based on the shared membership of terms in a corpus of textual documents, where the metric is naturally defined by the Boolean algebra of all subsets of the corpus and a measure μ defined on it. Once the metric space is constructed, a new term (a noun, an adjective, a classification term) can be introduced into the model and analyzed by means of semantic projections, which in turn are defined as indexes using the measure μ and the word embedding tools. We formally define all necessary elements and prove the main results about the model, including a compatibility theorem for estimating the representability of semantically meaningful external terms in the model (which are written as real Lipschitz functions in the metric space), proving the relation between the semantic index and the metric of the space (Theorem 1). Our main result proves the universality of our word-set embedding, proving mathematically that every word embedding based on linear space can be written as a word-set embedding (Theorem 2). Since we adopt an empirical point of view for the semantic issues, we also provide the keys for the interpretation of the results using probabilistic arguments (to facilitate the subsequent integration of the model into Bayesian frameworks for the construction of inductive tools), as well as in fuzzy set-theoretic terms. We also show some illustrative examples, including a complete computational case using big-data-based computations. Thus, the main advantages of the proposed model are that the results on distances between terms are interpretable in semantic terms once the semantic index used is fixed and, although the calculations could be costly, it is possible to calculate the value of the distance between two terms without the need to calculate the whole distance matrix. “Wovon man nicht sprechen kann, darüber muss man schweigen”. Tractatus Logico-Philosophicus. L. Wittgenstein.
Evaluation of Diversity of Newly Bred Czech Sweet Cherry Cultivars in Extensive Plantations
Authors: Sedlák Jiří , Krška Boris , Pavliuk Liliia , Čmejlová Jana , Paprštein František , Blažková Jitka
Published: January 2025

Sweet cherry (Prunus avium L.) is an economically important and profitable stone fruit species grown in the temperate and subtropical climates, bearing fruits with unique flavour and nutritional benefits. There is also a long tradition of sweet cherry breeding in the Czech Republic. We report here the evaluation of economically important fruit and vegetative characters in 27 sweet cherry cultivars grown in the Research and Breeding Institute of Pomology (RBIP) in the genebank plantation. The cultivars were evaluated phenotypically using standard descriptors for sweet cherries. Established cultivars ‘Burlat’ (early ripening) and ‘Kordia’ (late ripening) were included in the study as commercial standards. The cultivar ‘Früheste der Mark’ was used as the earliest cultivar of the sweet cherry season. The results presented in this study demonstrate the high diversity of the analyzed sweet cherry germplasm. The cultivars ‘Horka’, ‘Justyna’, and ‘Sandra’ stood out in terms of fruit size and taste quality. The evaluated newly bred Czech sweet cherry cultivars are broadly described at the end of the presented paper.
Prediction of Climate Change Impacts on the Distribution of an Umbrella Species in Western Sichuan Province, China: Insights from the MaxEnt Model and Circuit Theory
Authors: Deng Xiaoyun , Sun Qiaoyun
Published: January 2025

Climate change poses a significant threat to biodiversity. Predicting the impacts of climate change on species distribution and dispersal through computational models and big data analysis can provide valuable insights. These predictions are crucial for developing effective strategies to mitigate the threats that climate change poses to biodiversity. Our study investigated the potential impact of climate change on an umbrella species (Ursus arctos pruinosus) in Western Sichuan Province, China. We employed the MaxEnt and Circuit Theory to assess both the current and potential future shifts in the distribution and migration corridors. The results indicated that climate and environmental factors had the greatest influence on species distribution, with bioclimatic variables bio12, bio3, and elevation contributing 22.1%, 21.5%, and 19.3%, respectively. Under current climatic conditions, the total suitable habitat area for the species was 70,969.78 km2, with the largest suitable habitats located in Shiqu and Litang, accounting for 24.39% and 15.86% of the total area, respectively. However, under future climate scenarios, predictions for RCP 2.6, RCP 4.5, and RCP 8.5 showed a significant reduction in suitable habitat area, ranging from 7789.26 km2 to 16,678.85 km2. The Yajiang and Xinlong counties experienced the most severe habitat reductions, with declines exceeding 50%. Additionally, the altitudinal distribution of suitable habitats shifted, with suitable habitats gradually moving to higher elevations under future climate scenarios. Our study also analyzed the species’ dispersal paths. Under current climatic conditions, the dispersal paths predominantly followed a northwest-to-southeast orientation. However, by the 2070s, under all three RCPs, dispersal resistance is projected to significantly increase, the density of dispersal paths will decrease, and the connectivity of these paths will be reduced. In the most extreme RCP 8.5 scenario, southern dispersal paths nearly disappeared, and the dispersal paths contracted towards the northwest. These findings highlight potential threats posed by climate change to the species’ habitats and dispersal corridors, emphasizing the importance of considering both current and future climate change in conservation strategies to protect this vulnerable species and its ecosystem.
DNA Barcoding Reveals Widespread Co-Occurrence of Other Chub Species in the Range of the Endemic Catalan Chub, Squalius laietanus
Authors: Pérez-Bielsa Núria , Heras Sandra , Abras Alba , García-Marín Jose-Luis
Published: January 2025

The Catalan chub, Squalius laietanus, was described in 2007 and has recently reached Vulnerable status on the IUCN Red List of Threatened Species. The assigned range for this species includes the lower course of the Ebro River in Spain and northwards to the Tech, Têt, Agly, and Massane Rivers in Southeastern France. In this study, 246 chub specimens caught at 15 sites in the nine main river basins of the Spanish range were barcoded by sequencing a 624 bp fragment of the mitochondrial COI gene. Squalius laietanus haplotypes were found in 68% of the fish caught. In a tributary of the Ebro River, all specimens (6%) were identified as the Tagus chub, S. pyrenaicus, another endemic Iberian chub species. The remaining fish (26%) had a haplotype typical of the European chub, S. cephalus, populations outside Spain. This haplotype was present in six sites in four basins, being particularly abundant in the Ebro River. Considering that Squalius species can hybridize with other native and non-native cyprinid species, the potential co-occurrence of several species in the same site or basin raises concerns about the real extent of an exclusive range for the Catalan chub and the integrity of its native gene pools throughout its range.
Alternative Treatment Option for Actinic Keratosis: 5% Potassium Hydroxide (KOH)
Authors: Dufflart Ocampo Juan David , Sánchez Cárdenas Carlos , Hernández Aguilar Elizabeth , Fonte Ávalos Verónica , Vega Memije María Elisa , Moreno-Coutiño Gabriela
Published: January 2025

Background: Actinic keratoses (AKs) are epidermal neoplasms with a risk of progression to squamous cell carcinoma. Multiple therapeutic options targeting single or multiple lesions, with varying degrees of clearance, have been described. Objective: Evaluate the number of cycles of 5% potassium hydroxide (KOH) solution required for the resolution of actinic keratosis. Methods: This study was conducted on 261 AK lesions in 29 patients. A cycle of application of KOH was indicated in 28 doses with a subsequent monthly clinical and dermoscopic review for a maximum of three cycles of application. Resolution was determined dermoscopically. Results: Dermoscopic resolution was observed in 203 (77.7%) lesions, obtaining a mean number of cycles for a complete resolution of 2.4. Better results were observed in patients who received three cycles than in those who received one or two cycles (p = 0.003 and p = 0.000, respectively). The most common adverse effect was burning, followed by xerosis (69 and 21%, respectively). Conclusions: The 5% KOH solution represents a promising therapeutic option for the treatment of single lesions of actinic keratosis, with an adequate response and tolerable use profile for patients. Based on our results, we suggest the use of 5% KOH solution in AKs with Olsen clinical classification I or II.
Diagnostic Accuracy of Shear Wave Elastography in Predicting Malignant Origins of Pleural Effusions in Emergency Departments
Authors: Nielsen Rune Wiig , Falster Casper , Posth Stefan , Jacobsen Niels , Licht August Emil , Bhatnagar Rahul , Laursen Christian Borbjerg
Published: January 2025

Objective: Ultrasound is a valuable diagnostic tool in the diagnostic work-up of dyspnea and can identify even small pleural effusions. The incorporation of shear wave elastography (SWE) represents a possible tool in stratifying pleural effusions by the risk of underlying malignancy. No previous studies on ultrasound with the incorporation of SWE have been conducted in an emergency department (ED), where such stratification might have a clinical impact by hastening referrals for the diagnostic work-up of underlying malignancy. The objective of this study was to appraise the diagnostic accuracy of ultrasonographic findings associated with thoracic malignancy as well as to calculate the optimal cutoff values for SWE in this regard. Methods: Patients with a unilateral pleural effusion of unknown origin were included in the ED and subjected to a thoracic ultrasound (TUS) scan during their first 48 h after admittance. Two index tests were applied: (i) traditional B-mode TUS examination registering the presence of diaphragmatic nodules, pleural thickenings and other findings associated with malignancy and (ii) an SWE examination of different regions of interest. The reference test was defined as the subsequent diagnosis of malignant pleural effusion (MPE) in the three months following inclusion. Results: In total, 39 patients were included. The B-mode TUS index test yielded a sensitivity of 28.57% (95%CI 3.67–70.96%) and a specificity of 90.62% (95%CI 74.98–98.02%). The SWE max of the intercostal space yielded a sensitivity of 100% (95%CI 47.82–100%) and a specificity of 59.09% (95%CI 36.35–79.29%). Conclusions: A TUS with integrated SWE may aid in identifying MPEs and improving referrals for the diagnostic work-up of underlying malignancy. Larger, adequately powered studies are warranted.
Diagnosis of Peritonsillar Abscess—A Prospective Study Comparing Clinical with CT Findings in 133 Consecutive Patients
Authors: Voruz François , Revol Rebecca , Combescure Christophe , Monnier Yan , Becker Minerva , Dulguerov Nicolas
Published: January 2025

Background: Peritonsillar abscess (PTA) is relatively common but challenging to diagnose clinically. Several clinical signs may be used, with unknown performances. We evaluated and compared the diagnostic performance of individual and combined clinical signs (trismus, edema, pharynx immobility, uvula deviation, hot potato voice, and overall clinical impression) assessed by an otolaryngologist and of contrast-enhanced computed tomography (CT) to detect acute PTA. Methods: Prospective study in 133 consecutive adult patients (77 males, mean age = 33 years) with suspected clinical PTA and CT obtained in the emergency setting of a tertiary care hospital between November 2020 and October 2022. The standard of reference consisted of surgically proven pus within 24 h of CT or a favorable clinical evolution at 48 h without surgical intervention. Results: PTA was present in 117/133 (88%) patients, with no difference between mean age or sex distribution between the groups with and without PTA. None of the evaluated clinical signs were associated with PTA (OR = 1.26–5.43, p > 0.05), whereas the CT finding “abscess” was significantly associated with PTA (OR = 67.2, p < 0.0001). The sensitivity of individual clinical signs varied between 19.7% and 73.5%, and the sensitivity of CT was significantly higher for all clinical signs (95.7%, p < 0.0001) except for overall clinical impression (97.4%, p = 0.7266). The specificity of clinical signs varied between 12.5% and 93.8%, and the specificity of CT was significantly higher (75%, p < 0.05) for overall clinical impression and edema. All clinical signs together yielded an area under the curve (AUC) = 0.677. Conclusions: In adults, clinical assessment alone using independent clinical signs and overall clinical impression does not allow a reliable diagnosis of PTA, even when performed by an otolaryngologist. CT is reliable in diagnosing PTA and, whenever available, should be the examination method of choice for diagnosing PTA, especially by a non-specialist.
Unlocking the Potential of RNA Sequencing in COVID-19: Toward Accurate Diagnosis and Personalized Medicine
Authors: Eldien Heba M. Saad , Almaeen Abdulrahman H. , El Fath Ahmed Abo , Taha Ahmed E. , Ahmed Rehab , Elfadil Hassabelrasoul , Hetta Helal F.
Published: January 2025

COVID-19 has caused widespread morbidity and mortality, with its effects extending to multiple organ systems. Despite known risk factors for severe disease, including advanced age and underlying comorbidities, patient outcomes can vary significantly. This variability complicates efforts to predict disease progression and tailor treatment strategies. While diagnostic and therapeutic approaches are still under debate, RNA sequencing (RNAseq) has emerged as a promising tool to provide deeper insights into the pathophysiology of COVID-19 and guide personalized treatment. A comprehensive literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. We employed Medical Subject Headings (MeSH) terms and relevant keywords to identify studies that explored the role of RNAseq in COVID-19 diagnostics, prognostics, and therapeutics. RNAseq has proven instrumental in identifying molecular biomarkers associated with disease severity in patients with COVID-19. It allows for the differentiation between asymptomatic and symptomatic individuals and sheds light on the immune response mechanisms that contribute to disease progression. In critically ill patients, RNAseq has been crucial for identifying key genes that may predict patient outcomes, guiding therapeutic decisions, and assessing the long-term effects of the virus. Additionally, RNAseq has helped in understanding the persistence of viral RNA after recovery, offering new insights into the management of post-acute sequelae, including long COVID. RNA sequencing significantly improves COVID-19 management, particularly for critically ill patients, by enhancing diagnostic accuracy, personalizing treatment, and predicting therapeutic responses. It refines patient stratification, improving outcomes, and holds promise for targeted interventions in both acute and long COVID.
Exploring the Employment Experiences of People with Physical Disabilities in Vietnam: Towards Inclusive Workplaces
Authors: Nguyen Hien , Alexander June , Bellon Michelle
Published: January 2025

Despite advancements in legal protections for people with disabilities in Vietnam, significant barriers to employment persist. This qualitative phenomenological study explored the employment experiences of people with physical disabilities in Vietnam. Semi-structured interviews with 15 participants employed across different sectors were thematically analysed, with the ecological systems theory applied as a guiding framework to interpret the findings and implications. Three primary themes were revealed: accessibility, vulnerability, and discrimination and ableism. The results also emphasised the importance of positive attitudes and interactions from co-workers and supervisors in fostering inclusive workplaces. Recommendations include policy reforms for attitudinal shifts, focus on equity to improve workplace culture, and the involvement of people with disabilities in organisational decision making. These actions are essential for creating more diverse and inclusive workplaces in Vietnam, where employees with physical disabilities feel included, valued, and empowered.
Spatio-Temporal Feature Aware Vision Transformers for Real-Time Unmanned Aerial Vehicle Tracking
Authors: Zhang Hao , Ye Hengzhou , Guo Xiaoyu , Zhang Xu , Rong Yao , Li Shuiwang
Published: January 2025

Driven by the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, the field of UAV object tracking has witnessed significant progress. This study introduces an innovative single-stream UAV tracking architecture, dubbed NT-Track, which is dedicated to enhancing the efficiency and accuracy of real-time tracking tasks. Addressing the shortcomings of existing tracking systems in capturing temporal relationships between consecutive frames, NT-Track meticulously analyzes the positional changes in targets across frames and leverages the similarity of the surrounding areas to extract feature information. Furthermore, our method integrates spatial and temporal information seamlessly into a unified framework through the introduction of a temporal feature fusion technique, thereby bolstering the overall performance of the model. NT-Track also incorporates a spatial neighborhood feature extraction module, which focuses on identifying and extracting features within the neighborhood of the target in each frame, ensuring continuous focus on the target during inter-frame processing. By employing an improved Transformer backbone network, our approach effectively integrates spatio-temporal information, enhancing the accuracy and robustness of tracking. Our experimental results on several challenging benchmark datasets demonstrate that NT-Track surpasses existing lightweight and deep learning trackers in terms of precision and success rate. It is noteworthy that, on the VisDrone2018 benchmark, NT-Track achieved a precision rate of 90% for the first time, an accomplishment that not only showcases its exceptional performance in complex environments, but also confirms its potential and effectiveness in practical applications.
A Simultaneous Control, Localization, and Mapping System for UAVs in GPS-Denied Environments
Authors: Munguia Rodrigo , Grau Antoni , Bolea Yolanda , Obregón-Pulido Guillermo
Published: January 2025

Unmanned Aerial Vehicles (UAVs) have gained significant attention due to their versatility in applications such as surveillance, reconnaissance, and search-and-rescue operations. In GPS-denied environments, where traditional navigation systems fail, the need for alternative solutions is critical. This paper presents a novel visual-based Simultaneous Control, Localization, and Mapping (SCLAM) system tailored for UAVs operating in GPS-denied environments. The proposed system integrates monocular-based SLAM and high-level control strategies, enabling autonomous navigation, real-time mapping, and robust localization. The experimental results demonstrate the system’s effectiveness in allowing UAVs to autonomously explore, return to a home position, and maintain consistent mapping in virtual GPS-denied scenarios. This work contributes a flexible architecture capable of addressing the challenges of autonomous UAV navigation and mapping, with potential for further development and real-world application.
Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method
Authors: Xu Heng , Zhu Dayong
Published: January 2025

As Unmanned Aerial Vehicle (UAV) technology advances, UAVs have attracted widespread attention across military and civilian fields due to their low cost and flexibility. In unknown environments, UAVs can significantly reduce the risk of casualties and improve the safety and covertness when performing missions. Reinforcement Learning allows agents to learn optimal policies through trials in the environment, enabling UAVs to respond autonomously according to the real-time conditions. Due to the limitation of the observation range of UAV sensors, UAV target search missions face the challenge of partial observation. Based on this, Partially Observable Deep Q-Network (PODQN), which is a DQN-based algorithm is proposed. The PODQN algorithm utilizes the Gated Recurrent Unit (GRU) to remember the past observation information. It integrates the target network and decomposes the action value for better evaluation. In addition, the artificial potential field is introduced to solve the potential collision problem. The simulation environment for UAV target search is constructed through the custom Markov Decision Process. By comparing the PODQN algorithm with random strategy, DQN, Double DQN, Dueling DQN, VDN, QMIX, it is demonstrated that the proposed PODQN algorithm has the best performance under different agent configurations.
UAV Detection with Passive Radar: Algorithms, Applications, and Challenges
Authors: Tang Zhibo , Ma He , Qu Youmin , Mao Xingpeng
Published: January 2025

The unmanned aerial vehicle (UAV) industry has developed rapidly in recent years and is being applied in a wide range of fields. However, incidents involving unauthorized UAVs that threaten public safety have occurred frequently, highlighting the need for effective and accurate methods to detect and respond to illegal UAVs. This has led to the emergence of various UAV detection technologies, among which passive radar stands out due to its unique advantages. This review aims to offer insights that can support further research and development in the field of UAV detection using passive radar. We begin by exploring the origins of passive radar and then provide a comprehensive overview of its progress from multiple angles, particularly focusing on its application in UAV detection. Finally, we provide a forward-looking discussion on the future development trends and challenges faced by passive radar in UAV detection.
The Impact of AI-Generated Instructional Videos on Problem-Based Learning in Science Teacher Education
Authors: Pellas Nikolaos
Published: January 2025

Artificial Intelligence (AI) has gained significant prominence in science education, yet its practical applications, particularly in teacher training, remain underexplored. Specifically, there is a lack of research on AI’s potential to support personalized professional development through automated analysis of classroom interactions and tailored feedback. As science teacher education requires skill development in complex scientific concepts within problem-based learning (PBL) contexts, there is a growing need for innovative, technology-driven instructional tools. AI-generated instructional videos are increasingly recognized as powerful tools for enhancing educational experiences. This study investigates the impact of AI-generated instructional videos, designed using established instructional design principles, on self-efficacy, task performance, and learning outcomes in science teacher education. Employing a within-subjects design, the current study included pre-test, post-test, and transfer assessments to evaluate learning durability and transferability, consistent with design-based research methodology. Moreover, this study compares the effectiveness of two AI-generated instructional video formats: one with an embedded preview feature allowing learners to preview key concepts before detailed instruction (video-with-preview condition) and another without this feature (video-without-preview condition). It specifically examines the role of preview features in enhancing these outcomes during training on scientific concepts with 55 Greek pre-service science teachers (n = 55; mean age 27.3 years; range 22–35). The results demonstrated that the videos effectively supported self-efficacy, task performance, and knowledge retention. However, no significant differences were observed between videos with and without preview features across all assessed metrics and tests. These findings also indicate that AI-generated instructional videos can effectively enhance knowledge retention, transfer, and self-efficacy, positioning them as promising assets in science teacher education. The limited impact of the preview feature highlights the need for careful design and evaluation of instructional elements, such as interactivity and adaptive learning algorithms, to fully realize their potential.
Pre-Service Primary School Teachers’ Understanding of Biogeochemical Cycles of Elements
Authors: Ribič Luka , Devetak Iztok , Potočnik Robert
Published: January 2025

Understanding environmental issues such as biogeochemical cycles of substances on a local and global level is important in order to be able to act responsibly and sustainably. Inadequate teacher training has proven to be the main reason why environmental education has failed to reach its full potential. The aim of the present study is therefore to investigate students’ level of knowledge about biogeochemical cycles in relation to their secondary school achievements in chemistry, biology, and physics, their individual interest for learning these topics, and their self-esteem regarding cycles of substances on Earth. A total of 145 undergraduate pre-service primary school teachers attending their first or third year of an undergraduate teacher education program at the Faculty of Education, University of Ljubljana, in 2024 participated in this quantitative study, which uses the causal non-experimental method of pedagogical research. The data were collected using a three-tier achievement test and a paper–pencil questionnaire, which were both developed by the researchers. The results show that pre-service primary school teachers possess roughly adequate knowledge of the environmental topic of biogeochemical cycles. Their individual interest and self-esteem related to learning biogeochemical cycles were found to be significant predictors of their performance in an achievement test on biogeochemical cycles. Their final grade in biology may also be a significant predictor of their knowledge of this topic. Finally, pre-service primary school teachers’ misconceptions related to the topic of biogeochemical cycles were determined. Although the number of their misconceptions on this topic is low, teachers’ environmental education nevertheless needs to be improved in order to optimize their work in the classroom and help environmental education reach its full potential.
Introduction of Hydrosphere Environmental Problems in Lower Secondary School Chemistry Lessons
Authors: Klemen Taja , Devetak Iztok
Published: January 2025

The 2030 Agenda for Sustainable Development focuses on ensuring a healthy environment for present and future generations by avoiding risks associated with consumption, exploitation of natural resources and pollution. Maintaining an environmentally aware society to address hydrosphere environmental problems requires environmental literacy, which should be continuously improved through environmental education. The study aimed to evaluate the effectiveness of integrating hydrosphere environmental problems into chemistry lessons through an online workshop, comparing students’ achievements and their situational and individual interest before and after participating in the workshop. Altogether, 145 students from seven Slovenian lower secondary schools participated in the study. Three-tier achievement tests assessed students’ knowledge about hydrosphere environmental problems before and after the workshop, while 15-item and 10-item questionnaires measured individual and situational interest. Results showed that 42.1% of students achieved more than half of the points on the preliminary achievement test, while following the workshop, 61.5% of students achieved better results on the achievement post-test. Students struggled to understand the chemical structures of pollutants and their effects on the hydrosphere. No significant differences in results on the post-test and delayed achievement test were identified, but high individual and situational interest positively influenced students’ results on all achievement tests.
Redefining 6G Network Slicing: AI-Driven Solutions for Future Use Cases
Authors: Botez Robert , Zinca Daniel , Dobrota Virgil
Published: January 2025

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks.
Evaluation of Velocity Signals Measured by Laser in Hydrophone Calibration Based on a Normalized Dynamic Time-Warping Algorithm
Authors: Liu Xiaowei , Zhu Haijiang , Wang Min , Yang Ping , Wang Ke , He Longbiao
Published: January 2025

Laser heterodyne interferometry plays a crucial role in measuring the velocity of water particles during the calibration of hydrophones with the optical method. The velocity of water particles acts as an indicator of acoustic-pressure variations and can be used to evaluate the stability of the acoustic field. The calibration of hydrophones requires a stable acoustic field environment; currently, though, the assessment of acoustic field stability is largely subjective. This study introduces the Normalized Dynamic Time-Warping (NDTW) algorithm, which objectively evaluates acoustic field stability. Sine-fitting is applied to the region of interest in the measured signal to obtain a reference signal. Subsequently, the NDTW algorithm is used to calculate the difference between the measured and reference signals, enabling the assessment of acoustic field stability. The NDTW algorithm effectively identifies subtle differences between signals and addresses the accumulation errors arising from varying signal lengths. The calibration results showed that for signals of high quality within the identified frequency band, the calibration outcomes obtained using the NDTW algorithm deviated from the reciprocity method by no more than 0.7 dB. For frequency bands with poor signal quality identified by the NDTW algorithm, the deviation between the calibration results and the reciprocity method exceeded 0.7 dB.
Research on Transformer Temperature Early Warning Method Based on Adaptive Sliding Window and Stacking
Authors: Zhang Pan , Zhang Qian , Hu Huan , Hu Huazhi , Peng Runze , Liu Jiaqi
Published: January 2025

This paper proposes a transformer temperature early warning method based on an adaptive sliding window and stacking ensemble learning algorithm, aiming to improve the accuracy and robustness of temperature prediction. The transformer temperature early warning system is crucial for ensuring the safe operation of the power system, and temperature prediction, as the foundation of early warning, directly affects the early warning effectiveness. This paper analyzes the characteristics of transformer temperature using support vector regression, random forest, and gradient boosting regression as base learners and ridge regression as the meta-learner to construct a stacking model. At the same time, Bayesian optimization is used to automatically adjust the sliding window size, achieving adaptive sliding window processing. The experimental results indicate that the temperature prediction method based on adaptive sliding window and stacking significantly reduces prediction errors, enhances the model’s adaptability and generalization ability, and provides more reliable technical support for transformer fault warning.
A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks
Authors: Lee Jaehyun , Cho Youngho , Lee Ryungeon , Yuk Simon , Youn Jaepil , Park Hansol , Shin Dongkyoo
Published: January 2025

Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effective detection methods accordingly. In particular, data poisoning attacks can be realistic in an adversarial situation where we retrain a DL model with a new dataset obtained from an external source during transfer learning. By this motivation, we propose a novel defense method that partitions and inspects the new dataset and then removes malicious sub-datasets. Specifically, our proposed method first divides a new dataset into n sub-datasets either evenly or randomly, inspects them by using the clean DL model as a poisoned dataset detector, and finally removes malicious sub-datasets classified by the detector. For partition and inspection, we design two dynamic defensive algorithms: the Sequential Partitioning and Inspection Algorithm (SPIA) and the Randomized Partitioning and Inspection Algorithm (RPIA). With this approach, a resulting cleaned dataset can be used reliably for retraining a DL model. In addition, we conducted two experiments in the Python and DL environment to show that our proposed methods effectively defend against two data poisoning attack models (concentrated poisoning attacks and random poisoning attacks) in terms of various evaluation metrics such as removed poison rate (RPR), attack success rate (ASR), and classification accuracy (ACC). Specifically, the SPIA completely removed all poisoned data under concentrated poisoning attacks in both Python and DL environments. In addition, the RPIA removed up to 91.1% and 99.1% of poisoned data under random poisoning attacks in Python and DL environments, respectively.
Control and Applications of Intelligent Unmanned Aerial Vehicles
Authors: Yan Yunda , Yi Dewei , Lu Hao , Gao Lan
Published: January 2025

Embedding intelligence into the control design of unmanned aerial vehicles (UAVs) has become increasingly critical as these systems are deployed in complex and uncertain environments, often requiring them to adapt dynamically to unpredictable events [...]