bluuebunny/crossref_metadata_2025 huggingface dataset:
Cow Dung Biostabilized Earth Mortars: Reusability and Influence of Cow Dung Processing and Cow Diet
Authors: Pachamama Raphael N. , Faria Paulina , Rezende Marco A. P. , Moraes Patrícia M.
Published: October 2024

Historically, cow dung has been widely used as a biostabilizer in earth building, although the scientific research on this subject is still limited. The available research provides evidence of the positive effects of this bioaddition on earthen blocks and plasters, as it improves their physical and mechanical properties and durability in water contact. The present research does not aim to characterize biostabilized earthen mortars or to explain the interaction mechanisms between the earth and cow dung components, because this topic has already been investigated. Instead, it aims to investigate strategies to optimize the collection and processing of cow dung so as to optimize their effects when used in earth-plastering mortars, as well as considering the effects of using them fresh whole, dry whole, and dry ground (as a powder); the effects of two different volumetric proportions of cow dung addition, 20% and 40% (of the earth + added sand); the effects of 72 h (fermentation–humid curing) before molding the biostabilized mortar; the influence of the cow diet; and the potential of reusing cow dung stabilized mortars. The results show that as the freshness of the cow dung increases, the mortar’s durability increases under water immersion, as well as the mechanical and adhesive strength. Collecting cow dung fresh and drying (composting) it in a plastic container is more efficient than collecting cow dung that is already dry on the pasture. The cow diet and the use of dry (composted) cow dung, whole or ground into a powder, does not result in a significant difference. A 72 h period of humid curing fermentation increases the adhesive strength and durability under water. The proportion of 40% promotes better durability under water, but 20% offers greater mechanical and adhesive strength. Finally, cow dung addition does not reduce the reusability of the earth mortar. The new mortar obtained by remixing the mortar with water presents increased properties in comparison to the original reference mortar with no cow dung addition. Therefore, the contributions of this research are innovative and important, offering technical support in the area of biostabilized earth-plastering mortars. Furthermore, it is emphasized that cow dung addition can be optimized as an efficient traditional solution to increase the mechanical resistance, but especially to increase the durability of earth mortars when in contact with water. This effect is particularly important for communities lacking financial resources, but also reveals the possibility of using eco-efficient waste instead of binders obtained at high firing temperatures.
Study on the Application of Determinant-MTMD(TMDD) Vibration Reduction in Cable-Supported Pedestrian Suspension Bridge
Authors: Li Zhen , Li Dejian , Li Jincheng , Zhao Chenxi , Cheng Xuezhi , Lu Yao
Published: October 2024

In this study, multiple tuned mass dampers (MTMDs) were studied to understand their impact on the human-induced vibration response and comfort level of a pedestrian cable-supported suspension bridge. A spatial finite element model based on a specific engineering case was established. The dynamic characteristics of the bridge under human-induced loads were investigated, and its comfort level under human-induced vibrations was analyzed using the time-history method. Then, this study adjusted the design parameters of the dampers based on various optimal damper parameter expressions. Furthermore, the damping effectiveness of MTMD under different mass ratios (μ) was evaluated, and it was found that increasing the mass ratio significantly impacts damping performance. Finally, determinant-TMD (TMDD) was introduced, and a comparison between the damping effect, robustness, and performance of TMDD and MTMD was conducted. The results indicate that while increasing the mass ratio does not linearly affect maximum vibration acceleration, the damping effect increases initially and then stabilizes, with a damping rate converging at approximately 55%. However, with the TMDD approach, the maximum damping rate can reach approximately 70%, enhancing comfort levels from the “minimum CL3” achieved with MTMD to the “medium CL2” level. Additionally, while TMDD’s robustness is slightly inferior to MTMD at lower mass ratios, it demonstrates superior robustness at higher mass ratios.
Research Development and Key Issues of Pervious Concrete: A Review
Authors: Cui Bo , Luo Aizhong , Zhang Xiaohu , Huang Ping
Published: October 2024

In recent years, various aspects of research related to pervious concrete (PC) have progressed rapidly, and it is necessary to summarise and generalise the latest research results. This paper reviews and compares the raw materials of pervious concrete, examining elements such as porosity, permeability, mechanical properties, and durability. According to comparisons, we put forward an ideal aggregate model with Uneven Surface, which may reinforce the mechanical properties. By summarising the important issues of aggregate, particle size, water–cement ratio, additives and admixtures, mixing ratio design, mixing and moulding, and other factors that affect porosity, new design methods are proposed. A new effective stress model of pervious concrete based on continuous porosity and Terzaghi effective stress is developed which may fit the effective stress principle better. Finally, by summarising the research frontiers of pervious concrete, key issues that need to be addressed in future scientific research on pervious concrete are raised.
Boosting Hydrogen Evolution Behaviors of Porous Nickel Phosphate by Phosphorization Engineering
Authors: He Tao , Xu Yuan , Zhang Liqiu , Peng Lishan , Wang Hongdan , Liu Lichun , Liu Ping
Published: October 2024

A stable and efficient porous nickel phosphate (p-NiPO/Ti) electrocatalyst on titanium sheets was developed via electrochemical deposition and low-temperature phosphatization. For obtaining the optimal performance of the p-NiPO/Ti electrocatalyst, the optimized experimental parameters of deposition and phosphatization were determined by parallel experiments. After the preparation, XPS and XRD were used to validate the chemical and amorphous structure, with SEM and TEM simultaneously validating a distinct nanosheet/nanocluster crosslinked microstructure. In particular, with phosphatization conditions maintained at 300 °C for 10 min, the p-NiPO/Ti produced demonstrated excellent charge transfer and catalytic characteristics in 1.0 M KOH. The electrocatalytic results revealed that the optimal p-NiPO/Ti with excellent catalytic performance and excellent stability (~24 h) needs lower HER overpotentials (128 mV at 10 mA cm−2 and 242 mV at 100 mA cm−2) as inputs. This research provides a promising strategy with which to use transition metal materials as catalysts in alkaline electrocatalytic hydrogen production.
Mitochondrial Dynamics Drive Muscle Stem Cell Progression from Quiescence to Myogenic Differentiation
Authors: Sommers Olivia , Tomsine Rholls A. , Khacho Mireille
Published: October 2024

From quiescence to activation and myogenic differentiation, muscle stem cells (MuSCs) experience drastic alterations in their signaling activity and metabolism. Through balanced cycles of fission and fusion, mitochondria alter their morphology and metabolism, allowing them to affect their decisive role in modulating MuSC activity and fate decisions. This tightly regulated process contributes to MuSC regulation by mediating changes in redox signaling pathways, cell cycle progression, and cell fate decisions. In this review, we discuss the role of mitochondrial dynamics as an integral modulator of MuSC activity, fate, and maintenance. Understanding the influence of mitochondrial dynamics in MuSCs in health and disease will further the development of therapeutics that support MuSC integrity and thus may aid in restoring the regenerative capacity of skeletal muscle.
Myeloid-Derived Suppressor Cells in Bladder Cancer: An Emerging Target
Authors: Klein Clément , Mebroukine Samy , Madéry Mathilde , Moisand Alexandra , Boyer Thomas , Larmonier Nicolas , Robert Grégoire , Domblides Charlotte
Published: October 2024

Bladder cancer remains a prevalent and challenging malignancy. Myeloid-derived suppressor cells (MDSCs) have emerged as key contributors to the immunosuppressive tumor microenvironment, facilitating tumor progression, immune evasion, and resistance to therapies. This review explores the role of MDSC in bladder cancer, highlighting their involvement in immune regulation; tumor progression; and resistance to therapies such as bacillus Calmette–Guérin (BCG) therapy, chemotherapy, and immune checkpoint inhibitors (ICIs). We also discuss their potential as biomarkers and therapeutic targets, with current evidence suggesting that targeting MDSCs, either alone or in combination with existing treatments such as BCG and ICIs, may enhance anti-tumor immunity and improve clinical outcomes. However,, challenges remain, particularly regarding the identification and therapeutic modulation of MDSC subpopulations. Further research is warranted to fully elucidate their role in bladder cancer and to optimize MDSC-targeted therapies.
The Fabrication of Lithium Niobate Nanostructures by Solvothermal Method for Photocatalysis Applications: A Comparative Study of the Effects of Solvents on Nanoparticle Properties
Authors: Alothoum Mohd Al Saleh , Awada Chawki
Published: October 2024

In this work, we report, for the first time, a comparative study on the effects of different solvents on the properties of LiNbO3 (LN) nanostructures. The solvothermal synthesis method was successfully used with three different solvents: 1—water, 2—methanol, and 3—benzyl. The structural and optical properties of the as-prepared nanoparticles were studied using transmission electron microscopy (TEM), X-ray diffraction (XRD), UV-Vis absorbance, Raman spectroscopy, and photoluminescence (PL). Nanoparticles of a very small size, with an average size between 3 and 10 nm, were obtained for the first time. The photocatalytic activities of the three synthesized LiNbO3 nanoparticles were studied in relation to the photodegradation of a complex and heavy reactive black 5 dye for a wastewater treatment application. The LiNbO3 synthesized with deionized water showed a higher photocatalytic activity than those synthesized using other solvents, such as methanol or benzyl.
A Reaction-Based Approach to Colorimetric Detection of Organic Analytes in Water Using a Chlorine-Containing Carbocyanine Dye and Hypochlorite
Authors: Shik Anna V. , Skorobogatov Evgenii V. , Akhmetov Ramil M. , Doroshenko Irina A. , Podrugina Tatyana A. , Sugakov Gleb K. , Beklemishev Mikhail K.
Published: October 2024

Water quality control employs techniques mostly targeting individual analytes; group detection is also practiced, but the choice of group methods is limited, which supports interest in developing such methods. We have examined the interaction of hypochlorite with a chlorine-containing heptamethine carbocyanine dye in the presence of 30 organic and inorganic model analytes that were found to induce diverse color changes in the system. The main supposed mechanisms are retardation of the dye oxidation with hypochlorite (presumably by scavenging chlorine radicals) and substitution of chlorine atom in the dye by the most nucleophilic analytes (amines, amino acids, proteins, DNA, phenol). The grass-green substitution product is more contrastingly visible against the dark-purple hypochlorite oxidation product of the dye than against the original emerald-green dye. The indicator reaction is monitored photographically for 10–40 min and the images are processed using principal component analysis (PCA) or linear discriminant analysis (LDA), allowing for data convolution for the complex color transitions. Nitrogen compounds are discriminated from the others, and more reactive analytes (tryptophan, cysteine, bovine serum albumin, and DNA) are detected in the presence of less reactive ones in natural water. The system is promising for the development of group assays for dissolved organic matter and the discrimination of water samples.
The Impact of Parental Monitoring on Exposure to Multiple Substances and Bullying in Croatian Students
Authors: Valentic Maja , Simetin Luka , Mayer Dijana , Simetin Filip , Pavic Ivana
Published: October 2024

Background/Objectives: Adolescence is a critical period for experimenting with a wide range of risky behaviors, which are often influenced by family dynamics, including parental monitoring. This study aims to analyze the patterns of exposure to multiple substances and bullying among Croatian students by age and gender in 2022 and examine the association of exposure to multiple substances and bullying with maternal and paternal monitoring. Methods: The data were obtained from the 2022 Health Behaviour in School-aged Children (HBSC) study conducted in Croatia among students aged 11, 13, and 15. Two multinomial logistic regressions were performed separately by age and gender. Results: Exposure to multiple instances of bullying is more common among boys than girls in all three age groups, while exposure to multiple substances varies depending on age. The analysis revealed the strong protective effect of maternal monitoring against both substance use and bullying. Paternal monitoring showed less consistent effects but still indicated that lower paternal monitoring was associated with higher exposure to multiple substances and bullying, particularly at age 13. Conclusions: The patterns of multiple substance use and bullying vary by gender and age, emphasizing the need for tailored intervention strategies. Programs that strengthen parental monitoring, particularly maternal monitoring, should be prioritized.
Exploring the Longitudinal Links Popularity Goals and Adolescent Cyberbullying Perpetration: The Moderating Effects of Gender and Cultural Context
Authors: Wright Michelle F.
Published: October 2024

Background/Objectives: This longitudinal study aimed to examine how gender influences the relationship between popularity goals and cyberbullying perpetration among adolescents in China and the United States, controlling for peer-nominated popularity. Additionally, the study sought to identify potential cross-cultural differences in these dynamics. Methods: The research involved 1063 eighth and ninth graders (ages 12–16; 48.7% girls) who completed self-reported surveys in the fall of 2022 (Time 1) regarding cyberbullying behaviors and popularity goals. Peer nominations of popularity were also collected. Follow-up data on cyberbullying perpetration were obtained one year later in the fall of 2023 (Time 2). Results: Popularity goals were found to positively predict cyberbullying perpetration at Time 2 across all participants even after accounting for peer-nominated popularity. In China, this association was more pronounced among boys, whereas in the United States, the relationship was stronger for girls. Conclusions: Although cultural differences in the overall patterns were minimal, gender emerged as a significant moderating factor, revealing distinct cross-cultural variations. These findings underscore the need for culturally tailored approaches in interventions targeting adolescent cyberbullying.
Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis
Authors: Chen Jingying , Chen Chang , Xu Ruyi , Liu Leyuan
Published: October 2024

Background: Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability. Methods: To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provides a holistic view by computing statistical measures related to facial behavior characteristics. In contrast, the fine-grained component uncovers subtle temporal fluctuations by employing a long short-term memory (LSTM) model to capture the temporal dynamics of head pose, facial expression intensity, and expression types. To fully harness the strengths of both analyses, we implemented a feature-level attention mechanism. This not only enhances the model’s interpretability but also provides valuable insights by highlighting the most influential features through attention weights. Results: Upon evaluation using three-fold cross-validation on a self-constructed autism dataset, our integrated approach achieved an average recognition accuracy of 88.74%, surpassing the standalone coarse-grained analysis by 8.49%. Conclusions: This experimental result underscores the improved generalizability of facial behavior features and effectively mitigates the complexities stemming from the pronounced intragroup variability of those with autism, thereby contributing to more accurate and interpretable autism identification.
Augmented Reality in Dentistry: Enhancing Precision in Clinical Procedures—A Systematic Review
Authors: Puleio Francesco , Tosco Vincenzo , Pirri Rosario , Simeone Michele , Monterubbianesi Riccardo , Lo Giudice Giorgio , Lo Giudice Roberto
Published: October 2024

Background: Augmented reality (AR) enhances sensory perception by adding extra information, improving anatomical localization and simplifying treatment views. In dentistry, digital planning on bidimensional screens lacks real-time feedback, leading to potential errors. However, it is not clear if AR can improve the clinical treatment precision. The aim of this research is to evaluate if the use of AR-based instruments could improve dental procedure precision. Methods: This review covered studies from January 2018 to June 2023, focusing on AR in dentistry. The PICO question was “Does AR increase the precision of dental interventions compared to non-AR techniques?”. The systematic review was carried out on electronic databases, including Ovid MEDLINE, PubMed, and the Web of Science, with the following inclusion criteria: studies comparing the variation in the precision of interventions carried out with AR instruments and non-AR techniques. Results: Thirteen studies were included. Conclusions: The results of this systematic review demonstrate that AR enhances the precision of various dental procedures. The authors advise clinicians to use AR-based tools in order to improve the precision of their therapies.
Leveraging Social Media and Deep Learning for Sentiment Analysis for Smart Governance: A Case Study of Public Reactions to Educational Reforms in Saudi Arabia
Authors: Alotaibi Alanoud , Nadeem Farrukh
Published: October 2024

The Saudi government’s educational reforms aim to align the system with market needs and promote economic opportunities. However, a lack of credible data makes assessing public sentiment towards these reforms challenging. This research develops a sentiment analysis application to analyze public emotional reactions to educational reforms in Saudi Arabia using AraBERT, an Arabic language model. We constructed a unique Arabic dataset of 216,858 tweets related to the reforms, with 2000 manually labeled for public sentiment. To establish a robust evaluation framework, we employed random forests, support vector machines, and logistic regression as baseline models alongside AraBERT. We also compared the fine-tuned AraBERT Sentiment Classification model with CAMeLBERT, MARBERT, and LLM (GPT) models. The fine-tuned AraBERT model had an F1 score of 0.89, which was above the baseline models by 5% and demonstrated a 4% improvement compared to other pre-trained transformer models applied to this task. This highlights the advantage of transformer models specifically trained for the target language and domain (Arabic). Arabic-specific sentiment analysis models outperform multilingual models for this task. Overall, this study demonstrates the effectiveness of AraBERT in analyzing Arabic sentiment on social media. This approach has the potential to inform educational reform evaluation in Saudi Arabia and potentially other Arabic-speaking regions.
The COVID-19 Pandemic Increased the Risk of Eating Disorders and Emotional Eating Symptoms: A Review of the Current Clinical Evidence
Authors: Mentzelou Maria , Papadopoulou Sousana K. , Jacovides Constantina , Dakanalis Antonios , Alexatou Olga , Vorvolakos Theophanis , Psara Evmorfia , Ntovoli Apostolia , Chrysafi Maria , Kazis Dimitrios A. , Kosti Rena I. , Giaginis Constantinos
Published: October 2024

Background: There has been recent evidence to suggest that people who suffer from eating disorders (EDs) or other eating behavior disturbances, such as emotional eating (EE), are particularly vulnerable post-COVID-19. This narrative literature review aims to comprehensively analyze and scrutinize the existing clinical studies regarding the effects of the COVID-19 pandemic on people with EDs or EE. Moreover, due to the COVID-19 pandemic, it is very important to find out whether a person’s emotional state may lead them to wrongly confront their emotional behavior with food consumption. Methods: A thorough search of several databases (PubMed, Scopus, Web of Science, Public Library of Science, Google Scholar, CINAHL Complete, PsycINFO, and Academic Search Complete) was performed to identify available clinical studies on the COVID-19 pandemic published between 2020 and 2024 using relevant keywords. Results: There is substantial evidence that the COVID-19 pandemic exerted negative effects on persons suffering from EDs, including those who are symptomatic, those who are in remission, and those with an EE behavior. Characteristically, people diagnosed with anorexia nervosa (AN), bulimia nervosa (BN), or binge eating disorder (BED) showed considerable symptomatic worsening after the first wave of the COVID-19 pandemic. Clinical studies investigating the effects of the COVID-19 pandemic on persons suffering from EE have also clearly demonstrated that their mood disturbances negatively affected their eating behaviors. These studies have also shown that vulnerable people were at greater risk of EE development and progression during the COVID-19 pandemic. Conclusion: The COVID-19 pandemic seems to have exerted a significant deleterious effect on people who have a history of suffering from EDs or EE. The COVID-19 pandemic has been related to the frequency of dysfunctional eating behaviors, thus decreasing therapeutic intervention efficiency in people with EDs as well as in those with disturbed eating behaviors such as EE. In this aspect, further clinical studies are strongly recommended to investigate the exact effects of the COVID-19 pandemic as well as to explore their potential long-term deleterious complications in the post-pandemic period. Public strategies and policies should be applied to provide special healthcare for this group of patients.
Solving Ambiguity in EBSD Indexing of Long-Period Stacking Ordered (LPSO) Phase in Mg with Template Matching Approach
Authors: Liu Yushun , Yin Jian , Zhu Guo-zhen
Published: October 2024

Magnesium (Mg) alloys with long-period stacking ordered (LPSO) phases are receiving increasing interest because of their excellent mechanical performance. The close similarity in atomic stacking sequences between different LPSO polytypes and Mg lattice often leads to ambiguous indexing in electron backscatter diffraction (EBSD), a commonly used material characterization technique. Instead of the Hough transformation approach used in commercial software, an alternative indexing approach, which can catch subtle differences by matching experimental patterns with simulated ones, is explored in this study. Our results, showing ~94% of mapping data being correctly indexed as the target phase, 14H LPSO, demonstrate the capability of not only resolving the LPSO phases but also distinguishing different LPSO polytypes. This approach offers a valuable, if not unique, solution for the microscale characterization of LPSO phases, enabling precise microstructure tuning to further promote the mechanical properties of Mg alloys.
Novel Aspects in Freshwater Fauna Conservation
Authors: Marinović Zoran , Đuretanović Simona
Published: October 2024

Freshwater ecosystems, which include rivers, lakes, wetlands, springs, streams, and ponds, harbor a rich diversity of life, making them some of the most biologically productive environments on Earth [...]
Predictive and Explainable Artificial Intelligence for Neuroimaging Applications
Authors: Lee Sekwang , Lee Kwang-Sig
Published: October 2024

Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.
Delayed Diagnosis and Misdiagnosis of Lacrimal Sac Tumors in Patients Presenting with Epiphora: Diagnosis, Treatment, and Outcomes
Authors: Chu Yu-Chen , Tsai Chieh-Chih
Published: October 2024

Background/Objectives: Epiphora, or excessive tearing, is a common symptom often attributed to benign conditions such as dry eye or nasolacrimal duct obstruction. However, it can also be an early indicator of lacrimal sac tumors, which are frequently misdiagnosed or diagnosed late due to their subtle presentation. This study aims to identify the clinical features that contribute to delays and misdiagnoses of lacrimal sac tumors in patients presenting with epiphora, with the goal of improving early detection and treatment outcomes. Methods: This retrospective study reviewed medical records from Taipei Veterans General Hospital between 2007 and 2023, focusing on patients who presented with epiphora and were later diagnosed with pathologically confirmed lacrimal sac tumors. Inclusion criteria were limited to cases that were initially misdiagnosed or had a delayed diagnosis, with imaging and clinical evaluations confirming tumor-related tear drainage obstruction. Patients with non-tumor causes of epiphora were excluded. Results: Eleven cases of lacrimal sac tumors were identified, including two benign and nine malignant tumors. The average duration from symptom onset to diagnosis was 22.4 months. Common symptoms included epiphora (100%), discharge (54.5%), and hemolacria (18.2%). Subtle clinical signs, such as asymmetry in the medial canthal region and non-tender swelling, were frequently noted. Despite receiving appropriate surgical and adjuvant treatments, the impact of delayed diagnosis was significant. Two patients succumbed to tumor-related disease; one developed lung metastasis 12 years after diagnosis, and another experienced recurrence during a six-year follow-up after undergoing extensive exenteration, adjuvant chemotherapy, and radiotherapy. Conclusions: Lacrimal sac tumors can present insidiously with symptoms often mistaken for benign conditions, leading to significant diagnostic delays. Thorough history taking, meticulous physical examination, and timely imaging are crucial for early detection. Increased clinician awareness and a high index of suspicion for lacrimal sac tumors in patients with atypical epiphora are essential to improve prognosis and reduce the risk of severe outcomes.
Visualization of Aerial Droplet Distribution for Unmanned Aerial Spray Systems Based on Laser Imaging
Authors: Wang Zhichong , Qi Peng , Li Yangfan , He Xiongkui
Published: October 2024

Unmanned aerial spray systems (UASSs) are a commonly used spraying method for plant protection operations. However, their spraying parameters have complex effects on droplet distribution. The large-scale 3D droplet density distribution measurement method is insufficient, especially since the downwash wind is easily affected by the environment. Therefore, there is a need to develop a technique that can quickly visualize 3D droplet distribution. In this study, a laser imaging method was proposed to quickly scan moving droplets in the air, and a test method that can visualize 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution of targeted and conventional UAVs was tested, and the methods for signal processing, noise reduction, and point cloud rebuilding for laser imaging were developed. Compared with the simulation results, laser imaging showed the pattern of droplet distribution from the two UAV structures well. The results showed that the laser imaging based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly.
Adaptive Sliding Mode Control for Trajectory Tracking of Quadrotor Unmanned Aerial Vehicles Under Input Saturation and Disturbances
Authors: Kuang Jingyang , Chen Mou
Published: October 2024

This paper addresses the challenging issue of trajectory tracking for uncertain quadrotor unmanned aerial vehicles (UAVs), particularly under the constraints of input saturation and external disturbances. It introduces adaptive laws for managing uncertainties in mass and inertia moments without requiring prior knowledge, ensuring effective control even with varying system parameters. To counteract the effects of input saturation, the study incorporates an auxiliary system designed to compensate for these limitations. Additionally, a disturbance observer (DO) is utilized to manage and mitigate the impact of time-varying external disturbances. The proposed control strategy integrates a sliding mode adaptive control approach with an inner–outer loop structure, enhancing robustness and adaptability. Numerical simulations demonstrate the effectiveness of the designed control strategy.
Crowd Density Estimation via Global Crowd Collectiveness Metric
Authors: Mei Ling , Yu Mingyu , Jia Lvxiang , Fu Mingyu
Published: October 2024

Drone-captured crowd videos have become increasingly prevalent in various applications in recent years, including crowd density estimation via measuring crowd collectiveness. Traditional methods often measure local differences in motion directions among individuals and scarcely handle the challenge brought by the changing illumination of scenarios. They are limited in their generalization. The crowd density estimation needs both macroscopic and microscopic descriptions of collective motion. In this study, we introduce a Global Measuring Crowd Collectiveness (GMCC) metric that incorporates intra-crowd and inter-crowd collectiveness to assess the collective crowd motion. An energy spread process is introduced to explore the related crucial factors. This process measures the intra-crowd collectiveness of individuals within a crowded cluster by incorporating the collectiveness of motion direction and the velocity magnitude derived from the optical flow field. The global metric is adopted to keep the illumination-invariance of optical flow for intra-crowd motion. Then, we measure the motion consistency among various clusters to generate inter-crowd collectiveness, which constitutes the GMCC metric together with intra-collectiveness. Finally, the proposed energy spread process of GMCC is used to merge the inter-crowd collectiveness to estimate the global distribution of dense crowds. Experimental results validate that GMCC significantly improves the performance and efficiency of measuring crowd collectiveness and crowd density estimation on various crowd datasets, demonstrating a wide range of applications for real-time monitoring in public crowd management.
LiDAR-Based Unmanned Aerial Vehicle Offshore Wind Blade Inspection and Modeling
Authors: Oliveira Alexandre , Dias André , Santos Tiago , Rodrigues Paulo , Martins Alfredo , Almeida José
Published: October 2024

The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.
Safety-Critical Fixed-Time Formation Control of Quadrotor UAVs with Disturbance Based on Robust Control Barrier Functions
Authors: Song Zilong , Huang Haocai
Published: October 2024

This paper focuses on the safety-critical fixed-time formation control of quadrotor UAVs with disturbance and obstacle collision risk. The control scheme is organized in a distributed manner, with the leader’s position and velocity being estimated simultaneously by a fixed-time distributed observer. Meanwhile, a disturbance observer that combines fixed-time control theory and sliding mode control is designed to estimate the external disturbance. Based on these techniques, we design a nominal control law to drive UAVs to track the desired formation in a fixed time. Regarding obstacle avoidance, we first construct safety constraints using control barrier functions (CBFs). Then, obstacle avoidance can be achieved by solving an optimization problem with these safety constraints, thus minimally affecting tracking performance. The main contributions of this process are twofold. First, an exponential CBF is provided to deal with the UAV model with a high relative degree. Moreover, a robust exponential CBF is designed for UAVs with disturbance, which provides robust safety constraints to ensure obstacle avoidance despite disturbance. Finally, simulation results show the validity of the proposed method.
A Non-Stochastic Special Model of Risk Based on Radon Transform
Authors: Makowski Marcin , Piotrowski Edward W.
Published: October 2024

The concept of risk is fundamental in various scientific fields, including physics, biology and engineering, and is crucial for the study of complex systems, especially financial markets. In our research, we introduce a novel risk model that has a natural transactional–financial interpretation. In our approach, the risk of holding a financial instrument is related to the measure of the possibility of its loss. In this context, a financial instrument is riskier the more opportunities there are to dispose of it, i.e., to sell it. We present a model of risk understood in this way, introducing, in particular, the concept of financial time and a financial frame of reference, which allows for associating risk with the subjective perception of the observer. The presented approach does not rely on statistical assumptions and is based on the transactional interpretation of models. To measure risk, we propose using the Radon transform. The financial concept of risk is closely related to the concepts of uncertainty, entropy, information, and error in physics. Therefore, the well-established algorithmic aspects of the computed tomography method can be effectively applied to the broader field of uncertainty analysis, which is one of the foundational elements of experimental physics.
User-Perceived Capacity: Theory, Computation, and Achievable Policies
Authors: Liu Yuanrui , Zhao Xiaoyu , Chen Wei
Published: October 2024

User-perceived throughput is a novel performance metric attracting a considerable amount of recent attention because it characterizes the quality of the experience in mobile multimedia services. For instance, it gives a data rate of video streaming with which a user will not experience any lag or outage in watching video clips. However, its performance limit remains open. In this paper, we are interested in the achievable upper bound of user-perceived throughput, also referred to as the user-perceived capacity, and how to achieve it in typical wireless channels. We find that the user-perceived capacity is quite limited or even zero with channel state information at the receiver (CSIR) only. When both CSIR and channel state information at the transmitter (CSIT) are available, the user-perceived throughput can be substantially improved by power or even rate adaptation. A constrained Markov decision process (CMDP)-based approach is conceived to compute the user-perceived capacity with joint power–rate adaptation. It is rigorously shown that the optimal policy obeys a threshold-based rule with time, backlog, and channel gain thresholds. With power adaptation only, the user-perceived capacity is equal to the hard-delay-constrained capacity in our previous work and achieved by joint diversity and channel inversion.
LPI Radar Waveform Recognition Based on Hierarchical Classification Approach and Maximum Likelihood Estimation
Authors: Rhee Kiwon , Baik Jaeyoung , Song Changhoon , Shin Hyun-Chool
Published: October 2024

The importance of information gathering is emphasized to minimize casualties and economic losses in warfare. Through electronic warfare, which utilizes electromagnetic waves, it is possible to discern the enemy’s intentions and respond accordingly, thereby leading the battle advantageously. Consequently, related research is actively underway. The development of various radar signal modulation techniques has revealed limitations in the existing modulation recognition methods, necessitating the development of distinguishing features to overcome these limitations. This paper proposes and analyzes distinguishing features that can differentiate various modulation schemes. Eleven distinguishing features were employed, and twenty-two types of modulated signals, including analog, digital, and composite modulation, were classified using hierarchical classification approach and maximum likelihood estimation (MLE). The proposed method achieves a recognition performance of 99.76% at an SNR of 20 dB and 98.45% at an SNR of 8 dB.
Synergy as the Failure of Distributivity
Authors: Sevostianov Ivan , Feinerman Ofer
Published: October 2024

The concept of emergence, or synergy in its simplest form, is widely used but lacks a rigorous definition. Our work connects information and set theory to uncover the mathematical nature of synergy as the failure of distributivity. For the trivial case of discrete random variables, we explore whether and how it is possible to get more information out of lesser parts. The approach is inspired by the role of set theory as the fundamental description of part–whole relations. If taken unaltered, synergistic behavior is forbidden by the set-theoretic axioms. However, random variables are not a perfect analogy of sets: we formalize the distinction, highlighting a single broken axiom—union/intersection distributivity. Nevertheless, it remains possible to describe information using Venn-type diagrams. The proposed multivariate theory resolves the persistent self-contradiction of partial information decomposition and reinstates it as a primary route toward a rigorous definition of emergence. Our results suggest that non-distributive variants of set theory may be used to describe emergent physical systems.
Human Health Adaptation Strategies to Climate-Induced Extreme Weather Events: A Systematic Review
Authors: Amnuaylojaroen Teerachai , Parasin Nichapa
Published: October 2024

This systematic review evaluates the health impacts of climate-induced extreme weather events and the effectiveness of various adaptation strategies. Seventeen studies were analyzed, focusing on adaptation measures such as agricultural adjustments, renewable energy, ecosystem restoration, infrastructure redesign, and public health interventions. Significant health impacts were identified, including increased morbidity and mortality due to heatwaves, floods, and vector-borne diseases. The success of adaptation strategies was found to be highly dependent on local context, implementation capacity, and sustainability. This review underscores gaps in data quality, the generalizability of findings, and the integration of adaptation measures into public health policies. An urgent need exists for interdisciplinary approaches and community engagement to ensure sustainable, equitable health outcomes in the face of climate change. Future research should focus on these areas to strengthen public health resilience.
Circular Economy: Municipal Solid Waste and Landfilling Analyses in Slovakia
Authors: Tokarčíková Emese , Ďurišová Mária , Trojáková Terézia
Published: October 2024

The pursuit of shifting Slovakia towards a circular economy is met with a multitude of obstacles, including the pervasive consumerist mindset among Slovakians. This mindset favors packaged food, leading to its improper disposal in municipal waste instead of being recycled. Furthermore, the inclination towards landfills poses a significant challenge in the management of municipal solid waste (MSW). To address this issue, a quantitative analysis was conducted using developed and validated models, incorporating various factors related to MSW management in Slovakia. Our study confirmed the significance of parameters such as MSW management costs and population size in the amount of MSW generated. Furthermore, our findings include a short-term forecast for MSW generation in Slovakia for the next two years. These results, based on quantitative data, provide valuable insights for policymakers and waste management authorities in Slovakia, emphasizing the urgent need for a transition towards a more sustainable and circular economy.
An Evaluation of Education Policy in England since 2010 and the Policy Consequences for Small Primary Schools
Authors: Ovenden-Hope Tanya
Published: October 2024

The fate of small primary schools (pupils aged five–eleven years old) in England is linked to education policy reforms. This paper presents an evaluation of selected education policy reforms in England since 2010 with suggested consequences for small primary schools. The research was undertaken using a qualitative research methodology that employed a document analysis method to create a Policy Document Analysis Frame (PDAF) for the policy evaluation. The implications of the policy reforms for small primary schools were examined through the conceptual lens of Educational Isolation. Educational Isolation identifies the limiting factors of place for a school’s access to the resources required for school improvement. Education policy reform is focused on school improvement. Small primary schools in England are typically located in rural neighbourhoods with sparse populations, resulting in an average of just over 100 pupils per school. The findings suggest that the contextual factors of place and size for small primary schools in England present disadvantages in relation to the selected national education policy reforms. The national funding formula, Multi Academy Trusts (groups of schools independently regulated but centrally funded by the state), and National Standards are the key policy reforms, driven by a neo-liberal ideology, that are suggested to have implications for the future of small schools. Recommendations are offered to the new 2024 Labour government that would support the re-instatement of small primary schools as an integral part of the education landscape in England.
Developing Doctoral Theses in Education: The Role of Systematic Reviews in the Spanish Context
Authors: Cerezo-Pizarro Mario , Presentación-Muñoz Alain , Calzada-Rodríguez José Ignacio
Published: October 2024

The production and development of doctoral theses have grown exponentially with the advent of the Internet and the democratization of access to information and education. In the field of education, this production is no stranger to this trend, so it is interesting to analyze the implications, causes and scientific–academic contributions of this increase. To this end, a systematic literature review (SLR) was carried out using the PRISMA 2020 protocol, with the Dialnet database as the documentary source, based on a previous study that justified its use and the availability of documents. Thus, a set of pre-established criteria are defined to identify doctoral theses carried out in Spanish universities and related to the education area that have been published in the last 17 years, finding a total of (n = 120) publications whose analysis answered the researchers’ questions focused on identifying patterns and strategies in the publication and methodological design of this type of document and what is the role of systematic reviews of the literature in them. In this sense, this research process aimed to analyze this kind of production and facilitate the process of designing new theses and research projects in the field of education. In this sense, this research process aimed to analyze this sort of output and facilitate the process of designing new theses and research projects in the field of education. The results make it possible to identify the increased importance of SLR in the development of doctoral theses and reveal the predominant models related to this type of production. Additionally, other aspects, such as the most common universities or research fields, the quantity and nature of subsequent studies, etc., concerning doctoral theses that incorporate an SLR were determined. Thus, conducting an SLR represents a solid and structured approach to initiate and build up the research process of doctoral theses, being essential to address students’ potential training needs in these regards.