bluuebunny/crossref_metadata_2025 huggingface dataset:
Anti-Inflammatory Oxysterol, Oxy210, Inhibits Atherosclerosis in Hyperlipidemic Mice and Inflammatory Responses of Vascular Cells
Authors: Stappenbeck Frank , Wang Feng , Sinha Satyesh K. , Hui Simon T. , Farahi Lia , Mukhamedova Nigora , Fleetwood Andrew , Murphy Andrew J. , Sviridov Dmitri , Lusis Aldons J. , Parhami Farhad
Published: September 2024

Background and aims: We previously reported that Oxy210, an oxysterol-based drug candidate, exhibits antifibrotic and anti-inflammatory properties. We also showed that, in mice, it ameliorates hepatic hallmarks of non-alcoholic steatohepatitis (NASH), including inflammation and fibrosis, and reduces adipose tissue inflammation. Here, we aim to investigate the effects of Oxy210 on atherosclerosis, an inflammatory disease of the large arteries that is linked to NASH in epidemiologic studies, shares many of the same risk factors, and is the major cause of mortality in people with NASH. Methods: Oxy210 was studied in vivo in APOE*3-Leiden.CETP mice, a humanized mouse model for both NASH and atherosclerosis, in which symptoms are induced by consumption of a high fat, high cholesterol “Western” diet (WD). Oxy210 was also studied in vitro using two cell types that are important in atherogenesis: human aortic endothelial cells (HAECs) and macrophages treated with atherogenic and inflammatory agents. Results: Oxy210 reduced atherosclerotic lesion formation by more than 50% in hyperlipidemic mice fed the WD for 16 weeks. This was accompanied by reduced plasma cholesterol levels and reduced macrophages in lesions. In HAECs and macrophages, Oxy210 reduced the expression of key inflammatory markers associated with atherosclerosis, including interleukin-1 beta (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), chemokine (C-C motif) ligand 2 (CCL2), vascular cell adhesion molecule-1 (VCAM-1), and E-Selectin. In addition, cholesterol efflux was significantly enhanced in macrophages treated with Oxy210. Conclusions: These findings suggest that Oxy210 could be a drug candidate for targeting both NASH and atherosclerosis, as well as chronic inflammation associated with the manifestations of metabolic syndrome.
Pulmonary and Systemic Immune Profiles Following Lung Volume Reduction Surgery and Allogeneic Mesenchymal Stromal Cell Treatment in Emphysema
Authors: Jia Li , Li Na , van Unen Vincent , Zwaginga Jaap-Jan , Braun Jerry , Hiemstra Pieter , Koning Frits , Khedoe P. , Stolk Jan
Published: September 2024

Emphysema in patients with chronic obstructive pulmonary disease (COPD) is characterized by progressive inflammation. Preclinical studies suggest that lung volume reduction surgery (LVRS) and mesenchymal stromal cell (MSC) treatment dampen inflammation. We investigated the effects of bone marrow-derived MSC (BM-MSC) and LVRS on circulating and pulmonary immune cell profiles in emphysema patients using mass cytometry. Blood and resected lung tissue were collected at the first LVRS (L1). Following 6–10 weeks of recovery, patients received a placebo or intravenous administration of 2 × 106 cells/kg bodyweight BM-MSC (n = 5 and n = 9, resp.) in week 3 and 4 before the second LVRS (L2), where blood and lung tissue were collected. Irrespective of BM-MSC or placebo treatment, proportions of circulating lymphocytes including central memory CD4 regulatory, effector memory CD8 and γδ T cells were higher, whereas myeloid cell percentages were lower in L2 compared to L1. In resected lung tissue, proportions of Treg (p = 0.0067) and anti-inflammatory CD163− macrophages (p = 0.0001) were increased in L2 compared to L1, while proportions of pro-inflammatory CD163+ macrophages were decreased (p = 0.0004). There were no effects of BM-MSC treatment on immune profiles in emphysema patients. However, we observed alterations in the circulating and pulmonary immune cells upon LVRS, suggesting the induction of anti-inflammatory responses potentially needed for repair processes.
Recent Advances in High-Entropy Ceramics: Synthesis Methods, Properties, and Emerging Applications
Authors: Verma Piyush Chandra , Tiwari Sunil Kumar , Saurabh Ashish , Manoj Abhinav
Published: September 2024

High-entropy ceramics (HECs) represent an emerging class of materials composed of at least five different cations or anions in near-equiatomic proportions, garnering significant attention due to their extraordinary functional and structural properties. While multi-component ceramics have played a crucial role for many years, the concept of high-entropy materials was first introduced eighteen years ago with the synthesis of high-entropy alloys, and the first high-entropy nitride films were reported in 2014. These newly developed materials exhibit superior properties over traditional ceramics, such as enhanced thermal stability, hardness, and chemical resistance, making them suitable for a wide range of applications. High-entropy carbides, borides, oxides, oxi-carbides, oxi-borides, and other systems fall within the HEC category, typically occupying unique positions within phase diagrams that lead to novel properties. HECs are particularly well suited for high-temperature coatings, for tribological applications where low thermal conductivity and similar heat coefficients are critical, as well as for energy storage and dielectric uses. Computational tools like CALPHAD streamline the element selection process for designing HECs, while innovative, energy-efficient synthesis methods are being explored for producing dense specimens. This paper provides an in-depth analysis of the current state of the compositional design, the fabrication techniques, and the diverse applications of HECs, emphasizing their transformative potential in various industrial domains.
Teaching Parents via Online Asynchronous Training to Use Speech-Generating Devices with Their Autistic Children: A Pilot Study
Authors: Fischbacher Lauren , Dodds Robin L. , Tien Ingrid Shiyin
Published: September 2024

Background/Objectives: Telepractice interventions have been found to alleviate barriers families face when seeking communication interventions. This study is a multiple-baseline single-subject design that measures parent communication opportunities and parent responsiveness to determine if parent training through online modules created for parents of children with communication support needs can be effective for training parents of autistic children with communication support needs. Methods: This study replicates work by utilizing online training used as well as the same variables and definitions. This study expands the original study by providing the children with speech-generating devices (SGDs). SGDs are an assistive technology tool to increase language production and give access to language to minimally verbal autistic people. A central difference between this study and study is that the only training parents received was the online modules and written instructions to set up the SGD. Results: Overall, the POWR modules appear to positively impact the communication opportunities provided by the parent during play and activities, increase child communication, and improve parent proficiency in implementing the POWR strategy. Conclusions: There is a need for a larger single-case study or a randomized control trial to replicate these findings. Additional instruction may be needed for parents of children with autism around responsive interactions. This study adds to innovative ways of providing family-centered training and access to AAC for those with barriers to service.
Whole-Exome Sequencing Improves Understanding of Inherited Retinal Dystrophies in Korean Patients
Authors: Park Youngchan , Kim Youngjin , Koh Insong , Lee Jong-Young
Published: September 2024

Retinitis pigmentosa (RP) encompasses a diverse range of hereditary, degenerative retinal ailments, presenting notable obstacles to molecular genetic diagnoses due to the intricate array of variants in different genes involved. This study enrolled 21 probands and their families who have been diagnosed with nonsyndromic RP but without a previous molecular diagnosis. We employed whole-exome sequencing (WES) to detect possible harmful gene variations in individuals with unknown-cause RP at the molecular level. WES allowed the identification of ten potential disease-causing variants in eight different genes. In 8 out of the total 21 patients, this method successfully identified the underlying molecular causes, such as putative pathogenic variants in genes including CRB1, KLHL7, PDE6B, RDH12, RP1, RPE65, USH2A, and RHO. A novel variant was identified in one of these genes, specifically PDE6B, providing valuable information on prospective targets for future enhanced gene therapeutic approaches.
Research on the Rheological Performance of Fast-Melting SBS-Modified Asphalt under Complex Environmental Factors
Authors: Li Ruixia , Wang Yihan , Zhu Wei , Chen Yijun , Yue Jinchao
Published: September 2024

Currently, fast-melting SBS (Styrene-Butadiene-Styrene)-modified asphalt is widely used in pavements. However, in practical applications, complex environmental factors accelerate the deterioration of asphalt material properties, significantly affecting the service life of roads during their operational period. This study aims to examine the effects of complex environmental factors, including thermal oxidation, ultraviolet radiation, and various concentrations of salt solutions, on the high and low-temperature rheological properties of fast-melting SBS-modified asphalt (abbreviated as SBS-T-modified asphalt). Pressure aging–ultraviolet aging coupling and pressure aging–ultraviolet aging different concentration salt solution coupling were selected as the aging groups to simulate complex environmental conditions. Additionally, base asphalt and pressure-aged asphalt were used as control groups. The rheological properties of SBS-T-modified asphalt were evaluated using a dynamic shear rheometer (DSR) and bending beam rheometer (BBR). The results indicate that multiple-factor coupling aging reduces both the high-temperature and low-temperature performance of SBS-T-modified asphalt compared to single-factor aging, although the impact on rheological properties is not consistent across all conditions. After the combined effects of UV aging and pressure aging, the rutting resistance and high-temperature performance of SBS-T-modified asphalt are most severely impacted. However, when coupled with salt-solution aging, the rutting resistance of SBS-T-modified asphalt improves, with the complex modulus increasing by approximately 30%. This indicates that the presence of the salt solution enhances the high-temperature performance of the asphalt. An analysis of the low-temperature rheological properties of SBS-T-modified asphalt based on Burgers model shows that the low-temperature rheological performance of SBS-T-modified asphalt worsens under three-factor coupling aging compared to two-factor or single-factor aging, leading to poorer crack resistance. Notably, after adding salt solutions, the thermal sensitivity of SBS-T-modified asphalt increases significantly, with the ΔTc value decreasing approximately sixfold for every 2% increase in salt concentration.
Bulk RNA Barcode Sequencing Reveals Role of RNA Splicing in Aging Dermal Stem Cell Modulation by a Botanical Extract
Authors: Baumann Julia , Vocat Valentine , Nowak Kathrin , Zülli Fred , Cuddapah Chennakesava , Wandrey Franziska
Published: September 2024

Skin aging is a complex, multifaceted process influenced by both intrinsic and extrinsic factors. Understanding the molecular mechanisms underlying skin aging is crucial for developing effective anti-aging strategies. Dermal stem cells play a pivotal role in maintaining skin homeostasis, but their functionality is compromised with aging. This study investigated the impact of aging on dermal stem cells and explored the potential of natural extracts in modulating their biological characteristics. Using bulk RNA barcoding and sequencing (BRB-seq), we identified differentially expressed genes (DEGs) between young and aged dermal stem cells, revealing alterations in cellular processes, including cell proliferation, ECM synthesis, and RNA splicing. We also demonstrated that a natural extract, comprising callus cells and Alpine rose leaf extracts, influenced RNA splicing in aged dermal stem cells, leading to improved dermal structure and integrity in vitro. Our findings suggest that natural extracts may exert their effects through senolytic activity and the modulation of RNA splicing, a process crucial to gene expression and cellular function. This study underscores the potential of integrating high-throughput transcriptomics in understanding skin aging, presenting new avenues for the development of innovative, sustainable, and effective anti-aging strategies.
Development of an ME/CFS Online Screener
Authors: Cathey Paul , Jason Leonard A.
Published: September 2024

Several websites have offered patients opportunities to find out whether they meet the case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The current study describes a new online screener that can be completed by individuals who might like to determine if they meet the current ME/CFS criteria. The website is available for anyone to use, and the feedback is more comprehensive than other site, particularly in providing data on how the participants’ data compares with a large ME/CFS patient population, as well as whether the current ME and ME/CFS case definitions are met.
Evaluating the Impact of Climate and Early Pandemic Policies on COVID-19 Transmission: A Case Study Approach
Authors: Meregan Mohammad , Jafari Frazad , Lotfi Ghahroud Majid , Ghassemi Nejad Jalil , Janghorban Esfahani Iman
Published: September 2024

The COVID-19 pandemic has had profound impact, necessitating a deeper understanding of factors influencing virus transmission. The negative impacts have weakened the economy and changed billions of lives around the world. COVID-19 is a new virus, and a lot of studies have tried to investigate its effect on, for example, the economy or environment. This research reveals new approaches to recognizing and stopping the spread of this virus with its connection to weather conditions and relevant parameters. By analyzing how temperature and humidity affect COVID-19 spread, alongside evaluating the effectiveness of initial public policies, this study addresses the critical gap in research by investigating the interplay between climate conditions and government regulations during the early stages of the pandemic in South Korea. This dual approach provides a comprehensive framework for understanding how environmental and policy factors jointly influence pandemic dynamics, offering valuable lessons for future global health crises. Although it focuses only on the first phase of South Korea COVID-19 regulations, outcomes show that these regulations were notably effective against the COVID-19 pandemic. The outcomes prove that higher temperature and higher relative humidity lead to lower transmission. Hence, based on the results during winter, the number of infections would be expected to speed up again.
COVID-19 Stress Is Associated with Increased Smoking among People with HIV in Western Washington: A Cross-Sectional Survey
Authors: Nguyen Anh Tuyet , Slaughter Francis , Smith Sarah , Katz David A. , Prabhu Sandeep , Wang Liying , Simoni Jane M. , Tsui Judith I. , Graham Susan M.
Published: September 2024

Background. People living with HIV (PWH) frequently have co-morbid substance use disorders that may have been impacted by the COVID-19 pandemic. This study examined associations between COVID-related stress and increased substance use among PWH in Washington State. Methods. Between August 2020 and March 2021, we conducted an online survey of 397 PWH in western Washington. Logistic regression was used to analyze associations between a COVID-19 stress score and four self-reported outcomes: increased alcohol use, increased cigarette smoking, increased marijuana use, and increased use of illicit substances. Results. Thirty-five (38.0%) of 92 participants who smoked, 61 (23.4%) of 261 participants who used alcohol, 15 (14.6%) of 103 participants who used marijuana, and 35 (33.0%) of 102 participants who used illicit substances reported increased use of these substances. Higher COVID-19 stress scores were associated with higher odds of increased cigarette smoking (adjusted odds ratio [aOR] = 1.15, 95% confidence interval [CI]: 1.04–1.27), even after adjustment for anxiety and depressive symptoms (aOR 1.14, 95%CI: 1.03–1.27). COVID-19 stress was not associated with an increased use of alcohol, marijuana, or illicit substances. Conclusions. COVID-19-related stress was associated with self-reported increased cigarette smoking among PWH in western Washington during the pandemic.
Machine Learning-Assisted Prediction of Stress Corrosion Crack Growth Rate in Stainless Steel
Authors: Wang Peng , Wu Huanchun , Liu Xiangbing , Xu Chaoliang
Published: September 2024

Stainless-steel is extensively utilized in the key structural components of the main equipment in the nuclear island of pressurized water reactor nuclear power plants. The operational experience of nuclear power plants demonstrates that stress corrosion is one of the significant factors influencing the long-term safe operation of stainless steel in the high-temperature water of pressurized water reactor nuclear power plants. This study is based on the stress corrosion crack growth rate data of 316SS and 304SS stainless steel in the simulated primary water environment of pressurized water reactor nuclear power plants. Data mining and modeling were conducted using multiple machine learning algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), and the Sharpley Additive explanation (SHAP) method was employed to analyze the interpretability of the model. The results indicate that the stress corrosion crack growth rate prediction model based on XGBoost outperforms other models in all assessment indicators. Compared with empirical equations, XGBoost exhibits high flexibility and excellent data-driven learning capabilities. In the test set, 90% of the prediction errors are within the range of experimental values, with the maximum error multiple being 2.5, which significantly improves the prediction accuracy. Moreover, the distribution of SHAP values is consistent with the theoretical study of the stress corrosion behavior of stainless-steel, effectively reflecting the impact of cold working, temperature, and stress intensity factor on the stress corrosion crack growth rate, thereby proving the reliability of the model’s prediction results. The achievements of this study hold significant reference value and application prospects for the prediction of the stress corrosion behavior of stainless-steel in a high-temperature and high-pressure water environment of pressurized water reactor nuclear power plants.
FT-IR Spectroscopy Analysis of Kidney Stone Variability in Styria
Authors: Wregg Christoph , Rosenlechner Dominik , Zach Verena , Eigenfeld Marco , Stabentheiner Edith , Ahyai Sascha , Schwaminger Sebastian P.
Published: September 2024

Urolithiasis is a medically significant crystallization process affecting the urinary tract. Kidney stones exhibit considerable diversity as they form in complex environments containing various molecules and pathogens. Understanding the formation and occurrence of kidney stones is essential for developing strategies to prevent or reduce the invasiveness of treatments. In this study, we investigated over 300 cases of urolithiasis in Styria, Austria. The composition of the stones was evaluated using infrared spectroscopy and scanning electron microscopy. Most stones were found to be heterogeneous, predominantly composed of calcium oxalate, followed by calcium phosphate. Other components such as struvite, uric acid, and cystine were present to a lesser extent and were more likely to form homogeneous crystals. Analyzing the occurrence of these different stone types provides valuable insights into the formation processes of kidney stones and can aid in the prevention of urolithiasis in the future.
Modification of the Spectral Absorption Characteristics of ZnGeP2 in the THz and IR Wavelength Ranges Due to Diffusion Doping with Impurity Atoms of Mg, Se, Sn, and Pb
Authors: Yudin Nikolay , Dyomin Victor , Podzyvalov Sergey , Lysenko Alexey , Baalbaki Houssain , Zinovev Mikhail , Kuznetsov Vladimir , Slyunko Elena , Gabdrakhmanov Akmal , Kalsin Andrey , Voevodin Vladimir , Kulesh Maxim , Vlasov Denis
Published: September 2024

This study demonstrates that diffusion doping of ZGP single crystals with impurity atoms (Mg, Se, Sn, Pb) leads to a decrease in the specific conductivity of the samples. Consequently, this results in reduced absorption in the terahertz frequency range (150–1000 μm). It has been shown that doping ZGP samples with selenium (Se) and lead (Pb) atoms reduces absorption in the infrared region from 0.3–0.6 cm−1 to 0.06–0.09 cm−1. Doping with tin (Sn) leads to a decrease in absorption only in the wavelength region near 2.1 μm from 0.2 cm−1 to 0.05 cm−1. The proposed mechanism for the decrease in infrared absorption is a reduction in zinc vacancies due to doping with impurity atoms. This research lays the groundwork for a technology that produces ZGP crystals with minimal absorption within the 2–8 μm wavelength range, eliminating the need for fast electron beam irradiation technology. This advancement will facilitate the fabrication of ZGP crystals with arbitrary apertures.
Brief Review of Morphological Characters in the Identification of Muscomorpha (Diptera) of Sanitary and Forensic Importance
Authors: Mendonça Paloma Martins , Cortinhas Lucas Barbosa , Garção-Neto Carlos Henrique , Queiroz Margareth Maria de Carvalho
Published: September 2024

With more than 125,000 described species, Diptera are considered a megadiverse order. However, immatures display great morphological homogeneity, making few species of Diptera from the Cyclorrhaphan group easy to identify. The main species of medical, sanitary, and forensic importance are found in this group, highlighting the relevance of their description. The uniformity of the group limits sensitive techniques for identifying immatures; therefore, this study reports characters that can be used to identify these insects using scanning electron microscopy. Based on an investigation and analysis of the relevant scientific literature, we suggest combining techniques for accurate morphological identifications of flies.
Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction
Authors: Maciel Joylan Nunes , Ledesma Jorge Javier Gimenez , Ando Junior Oswaldo Hideo
Published: September 2024

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation.
Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology
Authors: Fan Ka Siu , Fan Ka Hay
Published: September 2024

Large language models (LLMs) are trained using large datasets and may be applied to language-based tasks. Studies have demonstrated their ability to perform and pass postgraduate medical examinations, and with the increasingly sophisticated deep learning algorithms and incorporation of image-analysis capabilities, they may also be applied to the Specialty Certificate Examination (SCE) in Dermatology. The Dermatology SCE sample questions were used to assess the performance of five freely available and high-performance LLMs. The LLMs’ performances were recorded by comparing their output on multiple-choice questions against the sample answers. One hundred questions, four of which included photographs, were entered into the LLMs. The responses were recorded and analysed, with the pass mark set at 77%. The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). The LLMs were generally capable of interpreting and providing reasoned responses to clinical scenarios and clinical data. This continues to demonstrate the potential of LLMs in both medical education and clinical settings.
The Effects of a Multi-Sports Program on the Physical Fitness, Quality of Life, and Well-Being of People with Intellectual and Developmental Disabilities—A Study Protocol
Authors: Diz Susana , Costa Aldo M. , Jacinto Miguel , Monteiro Diogo , Amaro Nuno , Rodrigues Filipe , Matos Rui , Antunes Raúl
Published: September 2024

We intend to develop an intervention program based on sports for people with Intellectual and Developmental Disability (IDD) to analyze its effects on physical fitness, quality of life (QoL), and well-being. The convenience sample will consist of at least 24 participants aged between 18 and 65, of both sexes, who are institutionalized with a previous diagnosis of IDD and no associated comorbidities (e.g., visual impairment or cerebral palsy). The participants will be divided into two groups: (i) the experimental group and (ii) the control group. The experimental group will have a weekly session, lasting 60 min, for 36 weeks. There will be a baseline assessment, a final assessment, and a follow-up assessment (three months after the end of the program). The results of this study are intended to contribute to the development of more assertive community interventions, based on sports, for the benefit of this population.
Linguistic and Cultural Adaptation of a Self-Determination Scale for People with Intellectual Disabilities
Authors: Castro-Durán Lilian , Sanzana-Vallejos Gloria , Vallejos-Garcías Viviana , Zapata-Lamana Rafael , Mendoza-Mardones Ana , Dávila-Opazo Loreto , Duhart-Carrión Javiera , Huaiquipan-Ortega Inti , Cigarroa Igor , Espejo-Sepúlveda Edmundo , Sandoval-Cárcamo Misael
Published: September 2024

The main objectives of this study were to adapt the AUTODDIS scale to the cultural and social dimensions of Chile and to analyze its reliability and validity. The reliability and validity were assessed through a pilot study conducted with a population of students with intellectual disabilities (ID). The linguistic adaptation was executed according to expert judgments of each indicator: adequacy, coherence, relevance, and clarity. After the scale adaptation, a scale pilot was conducted to explore its technical properties. It was performed in a public school for students with ID. The validity coefficient result was 0.998, while the complete reliability result was α = 0.978. For the reliability subscales, the results were the following: autonomy α = 0.941, self-initiation α = 0.925, self-direction α = 0.970, self-regulation α = 0.804, and self-concept α = 0.935. The results confirm the AUTODDIS scale as a valid and reliable instrument to evaluate self-determination. However, it is necessary to apply this scale to a larger number of students with ID to generalize these results.
Addressing Constraint Coupling and Autonomous Decision-Making Challenges: An Analysis of Large-Scale UAV Trajectory-Planning Techniques
Authors: Huang Gang , Hu Min , Yang Xueying , Lin Peng , Wang Yijun
Published: September 2024

With the increase in UAV scale and mission diversity, trajectory planning systems faces more and more complex constraints, which are often conflicting and strongly coupled, placing higher demands on the real-time and response capabilities of the system. At the same time, conflicts and strong coupling pose challenges the autonomous decision-making capability of the system, affecting the accuracy and efficiency of the planning system in complex environments. However, recent research advances addressing these issues have not been fully summarized. An in-depth exploration of constraint handling techniques and autonomous decision-making issues will be of great significance to the development of large-scale UAV systems. Therefore, this paper aims to provide a comprehensive overview of this topic. Firstly, the functions and application scenarios of large-scale UAV trajectory planning are introduced and classified in detail according to the planning method, realization function and the presence or absence of constraints. Then, the constraint handling techniques are described in detail, focusing on the priority ranking of constraints and the principles of their fusion and transformation methods. Then, the importance of autonomous decision-making in large-scale UAV trajectory planning is described in depth, and related dynamic adjustment algorithms are introduced. Finally, the future research directions and challenges of large-scale UAV trajectory planning are outlooked, providing directions and references for future research in the fields of UAV clustering and UAV cooperative flight.
Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter
Authors: Suti Aleksander , Di Rito Gianpietro , Mattei Giuseppe
Published: September 2024

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.
A Fault-Tolerant Multi-Agent Reinforcement Learning Framework for Unmanned Aerial Vehicles–Unmanned Ground Vehicle Coverage Path Planning
Authors: Ramezani Mahya , Amiri Atashgah M. A. , Rezaee Alireza
Published: September 2024

In this paper, we introduce a fault-tolerant multi-agent reinforcement learning framework called SERT-DQN to optimize the operations of UAVs with UGV central control in coverage path planning missions. Our approach leverages dual learning systems that combine individual agent autonomy with centralized strategic planning, thus enhancing the efficiency of cooperative path planning missions. This framework is designed for high performance in environments with fault uncertainty detected and operational challenges such as interruptions in connectivity and compromised sensor reliability. With the integration of an innovative communication system between agents, our system appropriately handles both static and dynamic environments. Also, we introduce similarity-based shared experience replay to attain faster convergence and sample efficiency in the multi-agent system. The architecture is specially designed to respond adaptively to such irregularities by effectively showing enhanced resilience in scenarios where data integrity is impaired due to faults or the UAV faces disruptions. Simulation results indicate that our fault tolerance algorithms are very resilient and do indeed improve mission outcomes, especially under dynamic and highly uncertain operating conditions. This approach becomes critical for the most recent sensor-based research in autonomous systems.
String Invention, Viable 3-3-1 Model, Dark Matter Black Holes
Authors: Nielsen Holger B.
Published: September 2024

With our very limited memories, we provide a brief review of Paul Frampton’s memories of the discovery of the Veneziano model, with this indeed being string theory, with Y. Nambu, and, secondly, his 3-3-1 theory. The latter is, indeed, a non-excluded replacement for the Standard Model with triangle anomalies being cancelled, as they must in a truly viable theory. It even needs (essentially) three as the family number! Moreover, primordial black holes as dark matter is mentioned. We end with a review of my own very speculative, utterly recent idea that for the purpose of the classical approximation, we could, using the functional integral as our rudimentary assumption taken over from quantum mechanics, obtain the equations of motion without the, in our opinion, very mysterious imaginary unit i, which usually occurs as a factor in the exponent of the functional integrand, which is this i times the action. The functional integral without the mysterious i leads to the prediction of some of the strongest features in cosmology, and also seems to argue for as few black holes as possible and for the cosmological constant being zero.
Information FOMO: The Unhealthy Fear of Missing Out on Information—A Method for Removing Misleading Data for Healthier Models
Authors: Pickering Ethan , Sapsis Themistoklis P.
Published: September 2024

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are either misleading or bring unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data lead to worse performance and instabilities of the surrogate model, often termed sample-wise “double descent”. We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models.
Information Bottleneck Driven Deep Video Compression—IBOpenDVCW
Authors: Leiderman Timor , Ben Ezra Yosef
Published: September 2024

Video compression remains a challenging task despite significant advancements in end-to-end optimized deep networks for video coding. This study, inspired by information bottleneck (IB) theory, introduces a novel approach that combines IB theory with wavelet transform. We perform a comprehensive analysis of information and mutual information across various mother wavelets and decomposition levels. Additionally, we replace the conventional average pooling layers with a discrete wavelet transform creating more advanced pooling methods to investigate their effects on information and mutual information. Our results demonstrate that the proposed model and training technique outperform existing state-of-the-art video compression methods, delivering competitive rate-distortion performance compared to the AVC/H.264 and HEVC/H.265 codecs.
Utilizing Investment in Fixed Assets and R&D as a Catalyst for Boosting Productivity to Stimulate Economic Growth
Authors: Atabayeva Assiya , Kurmanalina Anar , Kalkabayeva Gaukhar , Lambekova Aigerim , Myrzhykbayeva Ainur , Akbayev Yerbolsyn
Published: September 2024

Investments form the basis for high-quality economic growth by ensuring renewal of production capacities and improvement of technologies and processes, thereby increasing labor productivity. Investment research is key to understanding its impact on the country’s economic advancement and more effective government policymaking to stimulate investment activity. The purpose of this paper is to study the relationship between fixed capital and R&D investments and labor productivity growth, as well as to determine the optimal level of these investments in determining the greatest effect on labor productivity growth. We use a regression model and the least squares method to empirically analyze data for seven countries for the period between 1997 and 2022. We calculate estimated values using SPSS and Python. The results show a certain impact of fixed assets and R&D investments on labor productivity growth. However, the payoff varies across the countries under study. Furthermore, despite its relatively small volumes, investment in R&D brings a greater effect on productivity growth than investment in fixed capital. With other factors remaining constant, the calculated optimums for investments in fixed assets and R&D show maximum points of growth in labor productivity.
Design and Optimization of a Gorlov-Type Hydrokinetic Turbine Array for Energy Generation Using Response Surface Methodology
Authors: Chalaca Andrés , Velásquez Laura , Rubio-Clemente Ainhoa , Chica Edwin
Published: September 2024

Hydrokinetic arrays, or farms, offer a promising solution to the global energy crisis by enabling cost-effective and environmentally friendly energy generation in locations with water flows. This paper presents research focused on the design and optimization of a Gorlov-type vertical-axis hydrokinetic turbine array for power generation. The study involved (i) numerical simulations using computational fluid dynamics (CFD) software with the six degrees of freedom (6DoF) tool, (ii) optimization techniques such as response surface methodology, and (iii) experimental testing in natural environments. The objective was to develop an efficient system with low manufacturing and maintenance costs. A key finding was that the separation distance between rotors, both along and across the fluid flow, is a critical parameter in designing hydrokinetic arrays. For this study, a triangular array configuration, termed Triframe, was used, consisting of three Gorlov-type turbines with four blades each. The optimization process led to separation distances based on the diameter (D) of the turbines, with 15.9672D along the fluid flow (X) and 4.15719D across the flow (Y). Finally, an experimental scale model of the hydrokinetic array was successfully constructed and characterized, demonstrating the effectiveness of the optimization process described in this study.
Assessment of Breakwater as a Protection System against Aerodynamic Loads Acting on the Floating PV System
Authors: Panjwani Balram
Published: September 2024

Offshore floating photovoltaic (FPV) systems are subjected to significant aerodynamic forces, especially during extreme wind conditions. Accurate estimation of these forces is crucial for the proper design of mooring lines and connection systems. In this study, detailed CFD simulations were performed for various PV panel configurations, and using these CFD simulation correlations were developed to estimate lift and drag forces as a function of the number of panels. These correlations provide valuable tools for designing large-scale FPV systems with multiple PV modules. Additionally, this study investigates the potential of using breakwaters to reduce aerodynamic forces on FPV systems. Breakwaters, typically used to mitigate wave impacts, can also serve as wind barriers, significantly reducing wind forces before they reach the FPV array. Aerodynamic simulations with and without a breakwater were conducted using CFD to assess this effect. The results show a substantial reduction in lift and drag coefficients, especially for angles of attack up to 10 degrees, demonstrating the effectiveness of the breakwater in protecting the FPV system. However, beyond this threshold, the effectiveness of the breakwater of 2 m reduces. These findings highlight the importance of strategic breakwater placement and heights and their role in enhancing FPV system resilience. The insights gained from this study are critical for optimizing breakwater design and placement, ensuring the structural integrity and performance of FPV systems in varying environmental conditions. The data generated will also contribute to future design improvements for floating PV systems.
Acoustic Detection of Pipeline Blockages in Gas Extraction Systems: A Novel Approach
Authors: Liu Chun , Man Zhongyi , Li Wenlong
Published: September 2024

Gas extraction is crucial for coal mine safety, yet pipeline blockages by solid slag and water severely hinder efficiency and pose risks. Traditional detection methods are limited by rapid signal attenuation and noise interference. In this study, an acoustic detection technology is introduced for pipeline blockages, utilizing sensors at potential blockage points to collect sound wave data. Experiments with a scaled pipeline model reveal that slag blockages produce characteristic peaks in the 1200 Hz–2000 Hz range, while water blockages show peaks in the 1 kHz–2 kHz and 3.5 kHz–4.5 kHz bands. The longitudinal blockage intensity and extraction pressure significantly affect the sound pressure levels. A reliable fitting model predicts the blockage intensity based on acoustic signals, achieving high accuracy. This novel method enhances blockage identification, offering a non-invasive, cost-effective solution that improves coal mine safety and efficiency.
Design and Testing of a Multi-Cylinder Piezopump for Hydraulic Actuation
Authors: Sell Nathan , Sciatti Francesco , Plummer Andrew , Love Tom
Published: September 2024

Hydraulic actuation systems are widely used in industries such as aerospace, the marine industry, off-highway vehicles, and manufacturing. There has been a shift from the hydraulic distribution of power from a centralized supply to electrical power distribution, to reduce the maintenance requirements and weight and improve the efficiency. However, hydraulic actuators have many advantages, such as power density, durability, and controllability, so the ability to convert electrical to hydraulic power locally to drive an actuator is important. Traditional hydraulic pumps are inefficient and unsuitable for low-power applications, making piezopumps a promising alternative for the conversion of electrical to hydraulic power in the sub-100 W range. Currently, the use of piezopumps is limited by their maximum power (typically a few watts or less) and low flows. This paper details the design, simulation, and testing of a multi-cylinder piezopump designed to push the envelope of the power output. The simulation results demonstrate that pumps with two or three cylinders show increasing benefits in terms of hydraulic and electrical performance due to the reduced flow and current ripple compared to a single-cylinder pump. The experimental results from a two-cylinder pump confirm this, and the effect of the phase relationship between the drive signals is investigated in detail. The experimental pump has fast-acting disc-style reed non-return valves, allowing piezostack drive frequencies of up to 1.4 kHz to be used. Custom power electronics tailored to the pump are developed. These features are critical in demonstrating the potential for multi-cylinder piezopumps to play an important role as a future actuation solution.
Assessing the Sustainability of Agricultural Bioenergy Potential in the European Union
Authors: Ignat Gabriela , Șargu Lilia , Prigoreanu Ioan , Șargu Nicu , Ulinici Andrian , Bordeianu Gabriela Daniela
Published: September 2024

The present study aims to assess the sustainability of bioenergy potential from agriculture in the European Union in the period 2012–2021, with a particular focus on material flow and emissions management, bioenergy and recycling impacts, while assessing the potential of bioenergy from agriculture and analyzing the degree of self-sufficiency and import dependency in the biomass economy. While biomass has significant potential in the EU energy transition, its use is accompanied by challenges related to sustainability, carbon neutrality, efficiency and economic viability. Using a quantitative approach based on official statistical data, this research tracked the evolution of biomass imports, exports, domestic extraction and consumption, providing a comprehensive picture of the stability and adaptability of the biomass economy in the European Union. The results indicate a steady increase in domestic extraction and a stability in consumption, reflecting a high capacity of the European Union to manage biomass resources; thus, the degree of self-sufficiency has been high throughout the period, with a moderate dependence on imports, showing an adaptable economy. The conclusions suggest that in order to maintain this stability, the European Union must continue to develop balanced economic and environmental policies that support the sustainable use of biomass and contribute to the energy transition and environmental objectives.
Fatty Acids as Phase Change Materials for Building Applications: Drawbacks and Future Developments
Authors: Herrera Paola , De la Hoz Siegler Hector , Clarke Matthew
Published: September 2024

The worldwide population growth and its increasing affluence have led to an increase in global building energy consumption. Therefore, developing sustainable energy storage materials to mitigate this problem has become a high priority for many researchers. Organic phase change materials (PCMs), such as fatty acids, have been extensively studied for thermal energy storage in building applications due to their excellent performance in absorbing and releasing energy within the environment temperature ranges. However, issues related to their thermal conductivity, stability, and flammability could limit the potential and require addressing. In this review, organic PCMs, with a special focus on fatty acids, are discussed. This review covers recent studies related to PCM synthesis from bio-sources, methods for PCM incorporation in building materials, methods for enhancing organic PCM thermal properties, flammability challenges, and life cycle assessment. Finally, future opportunities are summarized.