bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep neural network-based robotic visual servoing for satellite target tracking
Authors: Ghiasvand Shayan , Xie Wen-Fang , Mohebbi Abolfazl
Published: October 2024

In response to the costly and error-prone manual satellite tracking on the International Space Station (ISS), this paper presents a deep neural network (DNN)-based robotic visual servoing solution to the automated tracking operation. This innovative approach directly addresses the critical issue of motion decoupling, which poses a significant challenge in current image moment-based visual servoing. The proposed method uses DNNs to estimate the manipulator’s pose, resulting in a significant reduction of coupling effects, which enhances control performance and increases tracking precision. Real-time experimental tests are carried out using a 6-DOF Denso manipulator equipped with an RGB camera and an object, mimicking the targeting pin. The test results demonstrate a 32.04% reduction in pose error and a 21.67% improvement in velocity precision compared to conventional methods. These findings demonstrate that the method has the potential to improve efficiency and accuracy significantly in satellite target tracking and capturing.
Ergot alkaloid control in biotechnological processes and pharmaceuticals (a mini review)
Authors: Volnin A. , Parshikov A. , Tsybulko N. , Mizina P. , Sidelnikov N.
Published: October 2024

The control of ergot alkaloids in biotechnological processes is important in the context of obtaining new strain producers and studying the mechanisms of the biosynthesis, accumulation and secretion of alkaloids and the manufacturing of alkaloids. In pharmaceuticals, it is important to analyze the purity of raw materials, especially those capable of racemization, quality control of dosage forms and bulk drugs, stability during storage, etc. This review describes the methods used for qualitative and quantitative chemical analysis of ergot alkaloids in tablets and pharmaceutic forms, liquid cultural media and mycelia from submerged cultures of ergot and other organisms producing ergoalkaloid, sclerotias of industrial Claviceps spp. parasitic strains. We reviewed analytical approaches for the determination of ergopeptines (including their dihydro- and bromine derivatives) and semisynthetic ergot-derived medicines such as cabergoline, necergoline and pergolide, including precursors for their synthesis. Over the last few decades, strategies and approaches for the analysis of ergoalkaloids for medical use have changed, but the general principles and objectives have remained the same as before. These changes are related to the development of new genetically improved strains producing ergoalkaloids and the development of technologies for the online control of biotechnological processes and pharmaceutical manufacturing (“process analytical technologies,” PAT). Overall, the industry is moving toward “smart manufacturing.” The development of approaches to production cost estimation and product quality management, manufacturing management, increasing profitability and reducing the negative impact on personnel and the environment are integral components of sustainable development. Analytical approaches for the analysis of ergot alkaloids in pharmaceutical raw materials should have high enough specificity for the separation of dihydro derivatives, enantiomers and R-S epimers of alkaloids, but low values of the quantitative detection limit are less frequently needed. In terms of methodology, detection methods based on mass spectrometry have become more developed and widespread, but NMR analysis remains in demand because of its high accuracy and specificity. Both rapid methods and liquid chromatography remain in demand in routine practice, with rapid analysis evolving toward higher accuracy owing to improved analytical performance and new equipment. New composite electrochemical sensors (including disposable sensors) have demonstrated potential for real-time process control.
Anti-Müllerian hormone as a diagnostic marker for testicular degeneration in dogs: insights from cryptorchid models
Authors: Posastiuc Florin Petrișor , Rizzoto Guilherme , Constantin Nicolae Tiberiu , Nicolae George , Chiers Koen , Diaconescu Alexandru Ilie , Șerban Andreea Iren , Van Soom Ann , Codreanu Mario Darius
Published: October 2024

Introduction

The increasing prevalence of infertility in male dogs in clinical practice mirrors current trends seen in human medicine. Acquired infertility is notably more common in dogs compared to congenital causes, with conditions such as testicular degeneration leading to irreversible loss of fertility. Current diagnostic methods for testicular degeneration, such as histopathological and cytological examinations, rely on testicular biopsy or fine needle aspiration, making them less feasible for routine use. Anti-Müllerian hormone (AMH), produced by Sertoli cells, has emerged as a potential alternative biomarker for testicular health, which can be measured in serum. This study evaluates AMH as a potential marker for testicular degeneration, using cryptorchid dogs as models for impaired fertility and altered testicular histology.

Methods

The relationship between serum AMH levels and AMH tissue expression with impaired spermatogenesis and altered histology was investigated. Serum AMH levels were determined in intact, cryptorchid, and castrated individuals using an immuno-enzymatic ELISA kit and compared between subgroups based on testicular location. Tissue AMH immuno-expression was differentially quantified in two regions of interest (ROIs), the interstitial space and the seminiferous tubule, in both descended and retained gonads. Furthermore, testicles were analyzed using histomorphometric analysis in seminiferous tubules, while spermatogenesis was evaluated using the Johnsen score.

Results

Serum AMH levels were positively correlated with AMH expression assessed in both interstitial space (ρ = 0.494, p ≤ 0.01) and seminiferous tubules (ρ = 0.610, p ≤ 0.001). Conversely, serum AMH levels showed a negative correlation with the seminiferous tubule area (ρ = −0.435, p ≤ 0.05). Smaller seminiferous tubule areas were linked to increased AMH reactivity in both seminiferous tubules (ρ = −0.774, p ≤ 0.001) and interstitial space (ρ = −0.725, p ≤ 0.001). Additionally, lower Johnsen scores were associated with higher serum AMH levels (ρ = −0.537, p ≤ 0.01) and elevated AMH expression in both seminiferous tubules (ρ = −0.756, p ≤ 0.001) and interstitial space (ρ = −0.679, p ≤ 0.001).

Discussion

Our results suggest that higher serum levels and tissue expression of AMH are linked to smaller seminiferous tubules and poorer Johnsen scores, reflecting degenerative changes and Sertoli cell dysfunction in retained testicles. Given the similarities in the mechanisms that increase AMH levels in both cryptorchid and non-cryptorchid testicles affected by testicular degeneration, this study recommends using AMH as a marker for diagnosing testicular degeneration in dogs.
Association Between Ultra-Processed Food Consumption and Cognitive Performance Among Adolescent Students From Underdeveloped Cities in Brazil: A Cross-Sectional Study
Authors: dos Santos João Victor Laurindo , de Melo Ingrid Sofia Vieira , Costa Clara Andrezza Crisóstomo Bezerra , de Almeida Layanne Cabral , Silva Dafiny Rodrigues , Ferro Débora Cavalcante , Paula Déborah Tenório Costa , Macena Mateus de Lima , Bueno Nassib Bezerra
Published: October 2024

Objectives

The association between ultra-processed foods (UPF) consumption and cognitive performance needs to be better characterized in adolescents, especially in low-income settings, where the cost of human capital is high. This study investigated the association between cognitive performance and UPF in adolescents from the countryside of the Brazilian Northeast.

Methods

Adolescents (15–18 years old) from three public high schools were included. Food intake was assessed using three 24-hour dietary recalls. The classification of foods as UPF was determined according to the Nova classification. Cognitive performance was evaluated using the Non-Verbal General Intelligence Test.

Results

116 adolescents were included, of which 50 (43.1%) showed low cognitive performance. The average energy intake was 1973.5 kcal, with 24.2% coming from UPF. Participants with low cognitive performance consumed 26.5% (95% CI: [22.2; 30.7]%) of daily energy intake from UPF compared to 22.5% ([18.8; 26.2]%) of those with medium-high cognitive performance (P = 0.17), without differences in energy and macronutrient intake.

Conclusion

Despite similar UPF consumption compared to the Brazilian average, no association was found between UPF consumption and cognitive performance in this low-income adolescent sample.
A Novel Trajectory Prediction Method Based on CNN, BiLSTM, and Multi-Head Attention Mechanism
Authors: Xu Yue , Pan Quan , Wang Zengfu , Hu Baoquan
Published: October 2024

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy.
Does the European Union Start-Up Aid Help Young Farmers to Innovate and to Join Networks?
Authors: Carbone Anna , Carillo Felicetta , Ciaian Pavel , Sardone Roberta , Antonioli Federico , Cardona Juan Tur
Published: October 2024

The endurance and vitality of European agriculture are threatened by the aging of farmers, together with the lack of generational change. The small share of young farmers also impacts on the innovative capacity and competitiveness of the sector. The Common Agricultural Policy of the European Union started addressing the issue long ago by providing financial aid to help young farmers to start up. Over time, the aid increased and packages of measures were set to reinforce the aid and to promote investments, innovations, and networks at the farm level. While the literature focuses on analyzing the effectiveness of the start-up aid in fostering new entries, this paper assumes an original perspective as it seeks to assess whether this aid has actually promoted innovations and networks in the beneficiary farms. The analysis relies on sample data collected in 2021 in Italy and Poland via interviews of 500 farmers under 40 who had entered the sector both with and without the aid. A probit model estimates whether the beneficiaries have a higher probability to innovate and network. Then, the contribution of the aid to the intensity of the adoption of innovations and networks is estimated via an Average Treatment Effect on the Treated model (ATT). Results suggest that the start-up aid increased the adoption of innovations and networks. The ATT indicates that this positive effect holds even after correcting for self-selection bias. By adopting an original perspective, our analysis suggests that the start-up aid for young farmers goes beyond rejuvenating agriculture by fostering innovation at the farm level and by promoting networking, thus enhancing agricultural change. However, farmer behaviors in the two countries are different, suggesting quite complex patterns for the impact of this measure.
Preliminary Studies on the Effect of Soil Conditioner (AMP) Application on the Chemical and Microbiological Properties of Soil under Winter Oilseed Rape Cultivation
Authors: Szulc Piotr , Selwet Marek , Kaczmarek Tomasz , Ambroży-Deręgowska Katarzyna , Neumann Małgorzata
Published: October 2024

This study analyzed the effect of the application of a soil conditioner under the trade name of the Agro Mineral Product (AMP) in the winter rapeseed cultivation on the bacterial and fungal abundance, ion concentrations, and electrolytic conductivity of the soil solution. It was demonstrated that the AMP influenced changes in the total abundance of the culturable fractions of the soil bacteria and fungi at each of the tested time points. A stimulatory effect of the preparation on the growth of the soil bacteria and an inhibitory effect on the development of the fungi was observed, particularly at doses of 4 and 8 t·ha−1. A dose of 12 t·ha−1 proved to be the least effective in relation to the development of the soil microbiome. Increasing the AMP fertilization dose above 4 t·ha−1 caused changes in the chemistry of the soil solution (pH, EC, HCO3−, K+, and PO4-P). It is worth noting that this primarily resulted in decreases in the amounts of mobile forms of potassium (from 40.4 mg·dm−3 in the control to 26.7 mg·dm−3 at the 8 t·ha−1 dose) and orthophosphate as phosphorus (from −6.00 mg·dm−3 in the control to 3.75 mg·dm−3 at the 8 t·ha−1 dose) in the soil solution, which resulted in a reduction in the yield of the winter rapeseed (from 4.76 t·ha−1 in the control to 4.61 t·ha−1 at the 8 t·ha−1 and 4.43 t·ha−1 at the 12 t·ha−1 AMP dose).
Experimental Designs and Statistical Analyses for Rootstock Trials
Authors: Marini Richard P.
Published: October 2024

Modern agricultural research, including fruit tree rootstock evaluations, began in England. In the mid-1800s, field plots were established at the Rothamsted Research Station to evaluate cultivars and fertilizer treatments for annual crops. By the early 1900s, farmers questioned the value of field experimentation because the results were not always valid due to inadequate randomization and replication and poor data summarization. During the first half of the 20th century, Rothamsted statisticians transformed field plot experimentation. Field trials were tremendously improved by incorporating new experimental concepts, such as randomization rather than systematically arranging treatments, the factorial arrangement of treatments to simultaneously test multiple hypotheses, and consideration of experimental error. Following the classification of clonal apple rootstocks at the East Malling Research Station in the 1920s, the first rootstock trials were established to compare rootstocks and evaluate rootstock performance on different soil types and with different scion cultivars. Although most of the statistical methods were developed for annual crops and perennial crops are more variable and difficult to work with, rootstock researchers were early adopters of these concepts because the East Malling staff included both pomologists and statisticians. Many of the new statistical concepts were incorporated into on-farm demonstration plots to promote early farmer adoption of new practices. Recent enhancements in computing power have led to the rapid expansion of statistical theory, the development of new statistical methods, and new statistical programming environments, such as R. Over the past century, in many regions of the world, the adoption of new statistical methods has lagged their development. This review is intended to summarize the adoption of error-controlling experimental designs by rootstock researchers, to describe statistical methods used to summarize the resulting data, and to provide suggestions for designing and analyzing future trials.
Exploring the Relationship between Canine Paraoxonase-1 (PON-1) Serum Activity and Liver Disease Classified by Clinico-Pathological Evaluation
Authors: Meazzi Sara , Bristi Sabiha Zarin Tasnim , Bianchini Virginia , Scarpa Paola , Giordano Alessia
Published: October 2024

Paraoxonase-1 (PON-1), a liver-synthesized enzyme, acts as a negative acute-phase reactant during systemic inflammation in dogs. Given the hepatic synthesis of this enzyme, the presence of liver diseases may influence PON-1, thus affecting its reliability as a biomarker for inflammatory/oxidative systemic diseases. The aim of this study is to investigate PON-1 activity variations among dogs suspected of liver injury or failure, evaluating the influence of hepatic diseases on PON-1 activity. A total of one-hundred-sixty dogs were retrospectively enrolled and categorized into three groups based on clinical presentation and laboratory results: control (C = 20), suspected liver injury (INJ = 114), and suspected liver failure (FAIL = 26). The INJ group was further divided into subgroups based on the severity of the alanine aminotransferase (ALT) increase. Both the INJ and FAIL groups were further divided based on serum macroscopic appearance. The PON-1 activity was quantified using a paraoxon-based method, which is already validated in dogs. No significant difference in PON-1 activity was observed between the C and INJ groups, despite a significant increase in the subgroups with moderate and severe elevations of ALT. The dogs with icteric serum exhibited decreased PON-1 activity, while lipemic serum was associated with an increased PON-1 activity. A significant reduction in PON-1 activity was noted in the FAIL group, compared to both C and INJ groups (p < 0.0001), regardless of serum appearance. Given the retrospective nature of this study, additional evaluations (e.g., histopathology, imaging) were not performed. The results obtained here suggest the importance of interpreting PON-1 activity cautiously in dogs with suspected liver disease.
Evaluation of Female Recipient Infertility and Donor Spermatogonial Purification for Germ Cell Transplantation in Paralichthys olivaceus
Authors: Ren Yuqin , Tao Yuehong , Sun Zhaohui , Wang Yufen , Li Weidong , He Zhongwei , Wang Guixing , Yang Yucong , Hou Jilun
Published: October 2024

Since the advent of germ cell transplantation (GCT), it has been widely used in shortening the fish breeding cycle, sex-controlled breeding and the protection of rare and endangered fish. In this study, the effectiveness of female sterile recipient preparation and donor stem cell isolation and purification were comprehensively evaluated for spermatogonial stem cell transplantation (SSCT) in Paralichthys olivaceus. The best way to prepare sterile recipients was found to be giving three-year-old fish four intraovarian injections of busulfan (20 mg/kg body weight) combined with exposure to a high temperature (28 °C) after the spawning season compared with the two other ways, which induced apoptosis of most of the endogenous germ cells, resulting in shrinkage of the spawning plate and enlargement of the ovarian lumen. Further analysis showed that both the gonadosomatic index and germ-cell-specific vasa expression were significantly lower than those of the natural-temperature group before treatment (p < 0.05). A high percentage (>60.00%) of spermatogonial stem cells (SSCs) were obtained after isolation and purification and were transplanted into the prepared recipients. After three weeks of SSCT, the numbers of PKH26-labeled SSCs were increased in the ovaries of the recipients. These findings provide a basis for the establishment of an ideal SSCT technique using P. olivaceus females as the recipients, ultimately contributing to the efficient conservation of male germplasm resources and effective breeding.
Exploring Angiotensin II and Oxidative Stress in Radiation-Induced Cataract Formation: Potential for Therapeutic Intervention
Authors: Kumar Vidya P. , Kong Yali , Dolland Riana , Brown Sandra R. , Wang Kan , Dolland Damian , Mu David , Brown Milton L.
Published: October 2024

Radiation-induced cataracts (RICs) represent a significant public health challenge, particularly impacting individuals exposed to ionizing radiation (IR) through medical treatments, occupational settings, and environmental factors. Effective therapeutic strategies require a deep understanding of the mechanisms underlying RIC formation (RICF). This study investigates the roles of angiotensin II (Ang II) and oxidative stress in RIC development, with a focus on their combined effects on lens transparency and cellular function. Key mechanisms include the generation of reactive oxygen species (ROS) and oxidative damage to lens proteins and lipids, as well as the impact of Ang II on inflammatory responses and cellular apoptosis. While the generation of ROS from water radiolysis is well established, the impact of Ang II on RICs is less understood. Ang II intensifies oxidative stress by activating type 1 receptors (AT1Rs) on lens epithelial cells, resulting in increased ROS production and inflammatory responses. This oxidative damage leads to protein aggregation, lipid peroxidation, and apoptosis, ultimately compromising lens transparency and contributing to cataract formation. Recent studies highlight Ang II’s dual role in promoting both oxidative stress and inflammation, which accelerates cataract development. RICs pose a substantial public health concern due to their widespread prevalence and impact on quality of life. Targeting Ang II signaling and oxidative stress simultaneously could represent a promising therapeutic approach. Continued research is necessary to validate these strategies and explore their efficacy in preventing or reversing RIC development.
Analyzing Supervised Machine Learning Models for Classifying Astronomical Objects Using Gaia DR3 Spectral Features
Authors: Pérez Cruz Orestes Javier , Martínez Pinto Cynthia Alejandra , Navarro Jiménez Silvana Guadalupe , Corral Escobedo Luis José , Outeiro Minia Manteiga
Published: October 2024

In this paper, we present an analysis of the effectiveness of various machine learning algorithms in classifying astronomical objects using data from the third release (DR3) of the Gaia space mission. The dataset used includes spectral information from the satellite’s red and blue spectrophotometers. The primary goal is to achieve reliable classification with high confidence for symbiotic stars, planetary nebulae, and red giants. Symbiotic stars are binary systems formed by a high-temperature star (a white dwarf in most cases) and an evolved star (Mira type or red giant star); their spectra varies between the typical for these objects (depending on the orbital phase of the object) and present emission lines similar to those observed in PN spectra, which is the reason for this first selection. Several classification algorithms are evaluated, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Gradient Boosting (GB), and Naive Bayes classifier. The evaluation is based on different metrics such as Precision, Recall, F1-Score, and the Kappa index. The study confirms the effectiveness of classifying the mentioned stars using only their spectral information. The models trained with Artificial Neural Networks and Random Forest demonstrated superior performance, surpassing an accuracy rate of 94.67%.
Comparison of Different Numerical Methods in Modeling of Debris Flows—Case Study in Selanac (Serbia)
Authors: Krušić Jelka , Pastor Manuel , Tayyebi Saeid M. , Đurić Dragana , Đurić Tina , Samardžić-Petrović Mileva , Marjanović Miloš , Abolmasov Biljana
Published: October 2024

Flow-type landslides are not typical in this region of the Balkans. However, after the Tamara cyclone event in 2014, numerous such occurrences have been observed in Serbia. This paper presents the initial results of a detailed investigation into debris flows in Serbia, comparing findings from two programs: RAMMS DBF and Geoflow SPH. Located in Western Serbia, the Selanac debris flow is a complex event characterized by significant depths in the initial block and entrainment zone. Previous field investigations utilized ERT surveys, supplemented by laboratory tests, to characterize material behavior. Approximately 450,000 m3 of material began to flow following an extreme precipitation period, ultimately traveling 1.2 km to the deposition zone. For validation purposes, ERT profiles from both the deposition zone and the source area were utilized, with particular attention given to areas where entrainment was substantial, as this had a significant impact on the final models. The first objective of this research is to conduct a detailed investigation of debris flow using field investigations: geophysical (ERT) and aerial photogrammetry. The second objective is to evaluate the capacity of two debris flow propagation models to simulate the reality of these phenomena. The GeoFlow-SPH code overestimated the maximum propagation thickness in comparison to the RAMMS model. The numerical results regarding final depths closely align, especially when considering the estimated average depth in the deposition zone. The results confirm the necessity of using multiple simulation codes to more accurately predict specific events.
Size-Dependent Mechanical Properties and Excavation Responses of Basalt with Hidden Cracks at Baihetan Hydropower Station through DFN–FDEM Modeling
Authors: Ding Changdong , Liu Zhenjiang , Mei Xiancheng , Ouyang Shaoming
Published: October 2024

Basalt is an important geotechnical material for engineering construction in Southwest China. However, it has complicated structural features due to its special origin, particularly the widespread occurrence of hidden cracks. Such discontinuities significantly affect the mechanical properties and engineering stability of basalt, and related research is lacking and unsystematic. In this work, taking the underground caverns in the Baihetan Hydropower Station as the engineering background, the size-dependent mechanical behaviors and excavation responses of basalt with hidden cracks were systematically explored based on a synthetic rock mass (SRM) model combining the finite-discrete element method (FDEM) and discrete fracture network (DFN) method. The results showed that: (1) The DFN–FDEM model generated based on the statistical characteristics of the geometric parameters of hidden cracks can consider the real structural characteristics of basalt, whereby the mechanical behaviors found in laboratory tests and at the engineering site could be exactly reproduced. (2) The representative elementary volume (REV) size of basalt blocks containing hidden cracks was 0.5 m, and the mechanical properties obtained at this size were considered equivalent continuum properties. With an increase in the sample dimensions, the mechanical properties reflected in the stress–strain curves changed from elastic–brittle to elastic–plastic or ductile, the strength failure criterion changed from linear to nonlinear, and the failure modes changed from fragmentation failure to local structure-controlled failure and then to splitting failure. (3) The surrounding rock mass near the excavation face of underground caverns typically showed a spalling failure mode, mainly affected by the complex structural characteristics and high in situ stresses, i.e., a tensile fracture mechanism characterized by stress–structure coupling. The research findings not only shed new light on the failure mechanisms and size-dependent mechanical behaviors of hard brittle rocks represented by basalt but also further enrich the basic theory and technical methods for multi-scale analyses in geotechnical engineering, which could provide a reference for the design optimization, construction scheme formulation, and disaster prevention of deep engineering projects.
Enhancing a You Only Look Once-Plated Detector via Auxiliary Textual Coding for Multi-Scale Rotating Remote Sensing Objects in Transportation Monitoring Applications
Authors: Bao Sarentuya , Zhang Mingwang , Xie Rui , Huang Dabhvrbayar , Kong Jianlei
Published: October 2024

With the rapid development of intelligent information technologies, remote sensing object detection has played an important role in different field applications. Particularly in recent years, it has attracted widespread attention in assisting with food safety supervision, which still faces troubling issues between oversized parameters and low performance that are challenging to solve. Hence, this article proposes a novel remote sensing detection framework for multi-scale objects with a rotating status and mutual occlusion, defined as EYMR-Net. This proposed approach is established on the YOLO-v7 architecture with a Swin Transformer backbone, which offers multi-scale receptive fields to mine massive features. Then, an enhanced attention module is added to exploit the spatial and dimensional interrelationships among different local characteristics. Subsequently, the effective rotating frame regression mechanism via circular smoothing labels is introduced to the EYMR-Net structure, addressing the problem of horizontal YOLO (You Only Look Once) frames ignoring direction changes. Extensive experiments on DOTA datasets demonstrated the outstanding performance of EYMR-Net, which achieved an impressive mAP0.5 of up to 74.3%. Further ablation experiments verified that our proposed approach obtains a balance between performance and efficiency, which is beneficial for practical remote sensing applications in transportation monitoring and supply chain management.
An Assessment of the Impact of Protective Lifeline Safety Systems on Formwork Systems
Authors: Tuz Lechosław , Gołkowski Mariusz
Published: October 2024

Work related to the installation of formwork and the pouring of concrete involves the need to move on the formwork walls. There is a high risk of falling off the wall and falling during work. It is therefore necessary to use systems that allow for safe work at heights, but also the safety of the formwork systems themselves. This article presents the results of tests under real conditions of a person falling from the formwork. Two safety systems were tested: one based on a pole–rope system with a flexible joint and one based on climbing arrester hooks. The test results showed that the additional forces occurring in the system do not exceed 6.4 kN and that the stresses reach the highest value only at the site of installation of the system and do not exceed the yield strength of the steel used in the formwork structure. The results obtained indicate that during a fall, the fall energy is absorbed so much that there is no damage to the formwork elements, and the highest effort is observed for the formwork arrester hook and reaches 87% of the structure effort. In the case of systems with flexible posts, the maximum load values do not exceed 30% of the structure. The presented results may be useful in designing and planning assembly works and using formwork systems in concrete pouring conditions.
Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
Authors: Fan Cheng , He Xiongjun , Huang Liwen , Li Haoyu , Wen Teng
Published: October 2024

In order to ensure the safety of navigation in a bridge area, and based on the theory of the safety potential field, a method of ship safety assessment and early warning in an inland river bridge area is proposed. Firstly, the risk elements associated with ship collisions in a bridge area are classified. Secondly, these risks are quantified using the potential energy field, the boundary potential field and the behavioural field, and then the ship state under the influence of wind flow, predicted by the Kalman filter, is quantified using the kinetic energy field. Then, the above four potential energy fields are merged to obtain a superposition field, and the magnitude of the instantaneous risk in the bridge area is obtained based on its magnitude. Finally, the change of field strength values under different moments is used for early warning. The results of the simulation of a ship passing through the piers of the Baijusi Bridge show that the model can effectively quantify the risk of a ship–bridge collision in the inland bridge area and provide real-time warning of the risk of a ship–bridge collision in the bridge area, which is of great significance for improving the safety of the inland bridge area.
Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
Authors: Lee Sanghoon , Park Soochang
Published: October 2024

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.
Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging
Authors: Peklar Rok , Mikac Urša , Serša Igor
Published: October 2024

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.
Hospital Web Quality Multicriteria Analysis Model (HWQ): Development and Application Test in Spanish Hospitals
Authors: Tejedor Santiago , Romero-Rodríguez Luis M.
Published: October 2024

The Hospital Web Quality Multicriteria Analysis Model (HWQ) is constructed, designed, and validated in this research. For this purpose, we examined the web quality analysis models specialized in hospitals and health centers through a literature review and the most current taxonomies to analyze digital media. Based on the benchmarking and walkthrough methods, the analysis model was built and validated by a panel of experts (X = 3.54, CVI = 0.88, Score Σ = 45.58). To test its applicability and reliability, the model was pilot-tested on the websites of the ten public and private hospitals with the best reputation in Spain in 2022, according to the Merco Sanitario ranking. The results showed very similar web structures divided by specific proposals or sections of some centers. In this regard, this study identifies a general communication proposal in hospitals that does not adapt to the guidelines of screen-mediated communication, as well as a lack of personalization and disruptive storytelling ideation. In addition, the work concludes that Spanish hospitals, for the moment, have not opted for formats and technological developments derived from the possibilities of gamified content, 360° immersion, Virtual Reality (V.R), or Augmented Reality (A.R).
The Role of Pericytes in Inner Ear Disorders: A Comprehensive Review
Authors: Maniaci Antonino , Briglia Marilena , Allia Fabio , Montalbano Giuseppe , Romano Giovanni Luca , Zaouali Mohamed Amine , H’mida Dorra , Gagliano Caterina , Malaguarnera Roberta , Lentini Mario , Graziano Adriana Carol Eleonora , Giurdanella Giovanni
Published: October 2024

Inner ear disorders, including sensorineural hearing loss, Meniere’s disease, and vestibular neuritis, are prevalent conditions that significantly impact the quality of life. Despite their high incidence, the underlying pathophysiology of these disorders remains elusive, and current treatment options are often inadequate. Emerging evidence suggests that pericytes, a type of vascular mural cell specialized to maintain the integrity and function of the microvasculature, may play a crucial role in the development and progression of inner ear disorders. The pericytes are present in the microvasculature of both the cochlea and the vestibular system, where they regulate blood flow, maintain the blood–labyrinth barrier, facilitate angiogenesis, and provide trophic support to neurons. Understanding their role in inner ear disorders may provide valuable insights into the pathophysiology of these conditions and lead to the development of novel diagnostic and therapeutic strategies, improving the standard of living. This comprehensive review aims to provide a detailed overview of the role of pericytes in inner ear disorders, highlighting the anatomy and physiology in the microvasculature, and analyzing the mechanisms that contribute to the development of the disorders. Furthermore, we explore the potential pericyte-targeted therapies, including antioxidant, anti-inflammatory, and angiogenic approaches, as well as gene therapy strategies.
MGACL: Prediction Drug–Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning
Authors: Zhang Pinglu , Lin Peng , Li Dehai , Wang Wanchun , Qi Xin , Li Jing , Xiong Jianshe
Published: October 2024

The identification of drug–target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network’s false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named Meta Graph Association-Aware Contrastive Learning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision–recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.
The Relationship between Vitamin D and TyG Index in Prediabetes and Type 2 Diabetes Mellitus among an Indian Tribal Community: A Cross-Sectional Study
Authors: Mahat Roshan Kumar , Rathor Prasanna Kumar , Rathore Vedika , Arora Manisha , Panda Suchismita , Marndi Gujaram
Published: October 2024

Background: Vitamin D deficiency is thought to increase the likelihood of insulin resistance (IR) and diabetes onset. The objective of this study was to examine the association between the triglyceride glucose (TyG) index and vitamin D levels in individuals with prediabetes and type 2 diabetes mellitus (T2DM) in the tribal community of India. Methods: This study included 270 participants, consisting of 90 individuals with prediabetes, 90 individuals with T2DM, and 90 control patients. Anthropometric and biochemical characteristics were evaluated in all participants. 25-hydroxyvitamin D [25(OH)D] levels were measured using a chemiluminescent immunoassay. The TyG index was computed as Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)]/2. Spearman correlation analysis and linear regression analysis were performed to assess the relationship between the TyG index and 25(OH)D levels in people diagnosed with prediabetes and T2DM. The optimum cut-off value of the TyG index for detecting vitamin D deficiency was determined by receiver operating characteristic (ROC) curve analysis. Results: We observed a significant reduction in vitamin D levels in individuals with prediabetes and T2DM compared to those in the control group. However, the TyG index was significantly greater in individuals with prediabetes and T2DM than in controls. Statistical analysis revealed a significant negative correlation between the TyG index and 25(OH)D in both prediabetes and T2DM. Conclusions: The TyG index demonstrated a negative association with vitamin D levels and was identified as an independent predictor of vitamin D deficiency in individuals with prediabetes and T2DM.
Oral State and Salivary Cortisol in Multiple Sclerosis Patients
Authors: Kapel-Reguła Aleksandra , Chojdak-Łukasiewicz Justyna , Rybińska Anna , Duś-Ilnicka Irena , Radwan-Oczko Małgorzata
Published: October 2024

Background: MS patients experience gradual and progressive functional limitation, bulbar symptoms, cognitive dysfunction, and psychiatric disorders that can impinge on oral status. This study aimed to investigate the oral state, oral hygiene habits, and salivary cortisol levels in patients with relapsing-remitting multiple sclerosis (RRMS) compared to healthy controls. It also evaluated systemic parameters: disease duration, type of Disease Modifying Therapy (DMT), disability score, professional activity, and smoking in the study group. Methods: This study included 101 patients (71 women and 30 men, aged 16–71 years) and 51 healthy volunteers (36 women and 15 men, aged 28–82 years). The oral examination assessed the number of teeth, type and number of dental fillings and prosthetic restoration, oral hygiene state, and salivary cortisol. Results: It was found that MS patients had significantly more professional activity, swallowing problems, pronunciation issues, dry mouth, and taste disturbances than the control group. They brushed their teeth twice daily significantly less often. The API was higher, while the SBI was lower in MS patients. Disease duration positively correlated with age and number of missing teeth. The Expanded Disability Status Scale positively correlated with age, disease duration, number of missing teeth, number of composite fillings, and right and left-hand Nine Hole Peg test scores, and negatively correlated with the Sulcus Bleeding Index. Salivary cortisol levels did not differ between groups and correlated only with the disability scale. Conclusions: MS patients require ongoing dental care and preventive measures to manage both general and oral health symptoms effectively.
The Role of Curcumin in Modulating Vascular Function and Structure during Menopause: A Systematic Review
Authors: Mad Azli Amanina Athirah , Salamt Norizam , Aminuddin Amilia , Roos Nur Aishah Che , Mokhtar Mohd Helmy , Kumar Jaya , Hamid Adila A. , Ugusman Azizah
Published: October 2024

The risk of developing cardiovascular disease (CVD) escalates in women during menopause, which is associated with increased vascular endothelial dysfunction, arterial stiffness, and vascular remodeling. Meanwhile, curcumin has been demonstrated to enhance vascular function and structure in various studies. Therefore, this study systematically reviewed the recent literature regarding the potential role of curcumin in modulating vascular function and structure during menopause. The Ovid MEDLINE, PubMed, Scopus, and Web of Science electronic databases were searched to identify relevant articles. Clinical and preclinical studies involving menopausal women and postmenopausal animal models with outcomes related to vascular function or structure were included. After thorough screening, seven articles were selected for data extraction, comprising three animal studies and four clinical trials. The findings from this review suggested that curcumin has beneficial effects on vascular function and structure during menopause by addressing endothelial function, arterial compliance, hemodynamic parameters, and the formation of atherosclerotic lesions. Therefore, curcumin has the potential to be utilized as a supplement to enhance vascular health in menopausal women. However, larger-scale clinical trials employing gold-standard techniques to evaluate vascular health in menopausal women are necessary to validate the preliminary results obtained from small-scale randomized clinical trials involving curcumin supplementation (INPLASY, INPLASY202430043).
Adaptive Bi-Operator Evolution for Multitasking Optimization Problems
Authors: Wang Changlong , Wang Zijia , Kou Zheng
Published: October 2024

The field of evolutionary multitasking optimization (EMTO) has been a highly anticipated research topic in recent years. EMTO aims to utilize evolutionary algorithms to concurrently solve complex problems involving multiple tasks. Despite considerable advancements in this field, numerous evolutionary multitasking algorithms continue to use a single evolutionary search operator (ESO) throughout the evolution process. This strategy struggles to completely adapt to different tasks, consequently hindering the algorithm’s performance. To overcome this challenge, this paper proposes multitasking evolutionary algorithms via an adaptive bi-operator strategy (BOMTEA). BOMTEA adopts a bi-operator strategy and adaptively controls the selection probability of each ESO according to its performance, which can determine the most suitable ESO for various tasks. In an experiment, BOMTEA showed outstanding results on two well-known multitasking benchmark tests, CEC17 and CEC22, and significantly outperformed other comparative algorithms.