bluuebunny/crossref_metadata_2025 huggingface dataset:
European lobbies: a comparative study of the professional profiles of lobbyists in Spain and France
Authors: Guerra-Heredia Sergio , Lobillo-Mora Gema , Martín-Martín Penélope , Espino-González Jesús
Published: December 2024

This research consists of carrying out a comparison of professional lobbyists in Spain and France, using three broad areas of data: socio-geographical, academic, and professional. The objectives set are aimed at a general and specific comparison to obtain a typical profile of the professionals of both countries. To this end, an exploratory methodology has been used, with a multivariate statistical analysis, completed with a descriptive analysis of an inductive nature, through a sample of 1,000 professional lobbyists in Spain and France in 2024. The results obtained provide a profile of lobbyists in Spain and France based on their social, academic, and professional nature. The main conclusion is that in these two countries these professionals classify their work as being in the field of “Public Affairs,” their academic background is mainly in Law and Political Science, and they live in large urban areas.
Internalized stigma level, family self-stigma, and family burden of patients receiving community mental health center services: a comparative, longitudinal study
Authors: Tozoğlu Elif Özcan , Gürbüzer Nilifer
Published: December 2024

Introduction

The study aimed to evaluate, both comparatively and longitudinally, the effects of receiving services from community mental health centers on the stigma levels of patients and relatives and the burden of care for patients with severe mental illness.

Methods

The study was planned to be conducted on patients with severe mental illness [schizophrenia spectrum disorders (SSDs) and bipolar disorder (BD)] and their relatives, followed by the community mental health center (CMHC group) and the outpatient clinic (outpatient group). It was planned to provide psychoeducation to relatives once a month for 2 h; meetings with the case manager at least once every 2 weeks; and psychosocial interventions (social inclusion, daily life activities studies, etc.) and psychoeducation for 2 h once a week for the patients. The Internalized Stigma of Mental Illness Scale (ISMI) was applied to the patients; the Zarit Caregiver Burden Scale (ZCBS) and the Self-Stigma Inventory for Families (SSI-F) were applied to the relatives at the beginning of the study, at the 6th and 12th month.

Results

The study was completed with 53 patients from the CMHC group (number of patients with SSDs = 39, number of patients with BD = 14) and 60 patients from the outpatient group (number of patients with SSDs = 45, number of patients with BD = 15). In the CMHC group, in patients with SSD, there was a statistically significant decrease in ISMI (p < 0.001), ZCBS (p < 0.001), and SSI-F (p < 0.001) scores at the end of the 12th month. In the outpatient group, in patients with SSD, there was no statistically significant decrease in ISMI (p = 0.948), ZCBS (p = 1.000), and SSI-F (p = 1.000) scores at the end of the 12th month. In the CMHC group, in patients with BD, there was a statistically significant decrease in ISMI (p = 0.002), ZCBS (p < 0.001), and SSI-F (p < 0.001) scores at the end of the 12th month. In the outpatient group, in patients with BD, there was no statistically significant decrease in ISMI (p = 0.645), ZCBS (p = 0.166), and SSI-F (p = 0.142) scores at the end of the 12th month.

Discussion

The results of our study suggest that multidimensional assessments of patients and their families, efforts to promote social participation, support for self-management in daily life, and psychoeducation may be helpful in reducing stigma and burden.
Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition
Authors: Saravia Cristina J. , Pütz Peter , Wurzbacher Christian , Uchaikina Anna , Drewes Jörg E. , Braun Ulrike , Bannick Claus Gerhard , Obermaier Nathan
Published: December 2024

Introduction

Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex. Multiple effects such as precipitation events or industrial discharges might affect the quantification of SARS-CoV-2 concentrations. Hence, analysing data from more than 150 wastewater treatment plants (WWTP) in Germany necessitates an automated and reliable method to evaluate data validity, identify potential extreme events, and, if possible, improve overall data quality.

Methods

We developed a method that first categorises the data quality of WWTPs and corresponding laboratories based on the number of outliers in the reproduction rate as well as the number of implausible inflection points within the SARS-CoV-2 time series. Subsequently, we scrutinised statistical outliers in several standard quality control parameters (QCP) that are routinely collected during the analysis process such as the flow rate, the electrical conductivity, or surrogate viruses like the pepper mild mottle virus. Furthermore, we investigated outliers in the ratio of the analysed gene segments that might indicate laboratory errors. To evaluate the success of our method, we measure the degree of accordance between identified QCP outliers and outliers in the SARS-CoV-2 concentration curves.

Results and discussion

Our analysis reveals that the flow and gene segment ratios are typically best at identifying outliers in the SARS-CoV-2 concentration curve albeit variations across WWTPs and laboratories. The exclusion of datapoints based on QCP plausibility checks predominantly improves data quality. Our derived data quality categories are in good accordance with visual assessments.

Conclusion

Good data quality is crucial for trend recognition, both on the WWTP level and when aggregating data from several WWTPs to regional or national trends. Our model can help to improve data quality in the context of health-related monitoring and can be optimised for each individual WWTP to account for the large diversity among WWTPs.
Empirical and thermal resistance approaches for debris thickness estimation on the Hoksar Glacier, Kashmir Himalaya
Authors: Ali Iram , Shukla Aparna , Romshoo Shakil A. , Lone F. A. , Garg Purushotum K. , Yousuf Bisma
Published: December 2024

Supraglacial debris modulates the thermal regime and alters glacial melt rates depending on its thickness. Thus, the estimation of debris thickness becomes imperative for predicting the hydrological response and dynamics of such glaciers. This study tests the performance of empirical and thermal resistance-based debris thickness approaches against field measurements on the Hoksar Glacier, Kashmir Himalaya. The aim of this study was accomplished using thermal imageries (Landsat 8 Operational Land Imager [Landsat-OLI], 2017 and Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER] Surface Kinetic Temperature Product [AST08], 2017) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) datasets. First, the spatially resolved estimates of debris thickness for the entire debris-covered zone were achieved by establishing an empirical relationship between debris thickness and debris surface temperature (both field and satellite thermal imageries). Second, debris thickness for every pixel of thermal imagery was executed by calculating thermal resistance from the energy balance model incorporating primary inputs from (ERA-5), debris temperature (AST08, Landsat OLI), and thermal conductivity. On comparison with field temperature and thickness measurements with satellite temperature, homogenous debris thickness pixels showed an excellent coherence (r = 0.9; p < 0.001 for TAST08 and r = 0.88; p < 0.001 for TLandsat OLI for temperature) and (r = 0.9; p < 0.001 for TAST08 and r = 0.87; p < 0.002 for TLandsat OLI for debris thickness). Both approaches effectively captured the spatial pattern of debris thickness using Landsat OLI and AST08 datasets. However, results specify an average debris thickness of 18.9 ± 7.9 cm from the field, which the empirical approach underestimated by 12% for AST08 and 28% for Landsat OLI, and the thermal resistance approach overestimated by 6.2% for AST08 and 5.1% for Landsat OLI, respectively. Debris thickness estimates from the thermal resistance approach (deviation 11.2% for AST08 and 11.6% for Landsat OLI) closely mirror the field measurements compared to the empirical approach (deviation 26.9% for AST08 and 35% for Landsat OLI). Thus, the thermal resistance approach can solve spatial variability in debris thickness on different heavily debris-covered glaciers globally without adequate knowledge of field measurements.
Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes
Authors: Du Sheng , Huang Zixin , Jin Li , Wan Xiongbo
Published: December 2024

This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. Contributions to this Special Issue highlight innovative research in advancements in work-sampling data analysis, data-driven process choreography discovery, intelligent ship scheduling for maritime rescue, process variability monitoring, hybrid optimization algorithms for economic emission dispatches, and intelligent controlled oscillations in smart structures. These studies collectively contribute to the body of knowledge on data-driven intelligent modeling and optimization, offering practical solutions and theoretical frameworks to address complex industrial challenges.
The Molecular Mechanism of Mycelial Incubation Time Effects on Primordium Formation of Pleurotus tuoliensis Through Transcriptome and Lipidomic Analyses
Authors: He Qi , Huang Chenyang , Zhang Lijiao , Gao Wei , Zhao Mengran
Published: December 2024

Pleurotus tuoliensis is a precious edible mushroom with a long cultivation cycle. Despite being cultivated in China for nearly 30 years, research on the molecular mechanisms underlying its primordium formation remains limited. In this study, the molecular mechanisms by which incubation time affects the primordium formation of P. tuoliensis were investigated using RNA-seq technology and lipid content detection. Our research revealed that the transcription of genes involved in lipid metabolism and lipid levels changed significantly during different incubation periods. Distinct differences were observed in gene transcription associated with signaling pathways, sphingolipid metabolism, fatty acid metabolism, and steroid biosynthesis in mycelia cultured for varying days and then stimulated by low temperature and light. These findings indicate that lipid accumulation and alterations in mycelial cell membrane components during incubation may affect the mycelial response to environmental signals, subsequently regulating primordium formation. This study revealed the crucial role of lipid metabolism during incubation in the primordium formation of P. tuoliensis, providing a novel perspective for investigating the molecular mechanism underlying fruiting body development.
Simulation and Experiment of Optimal Conditions for Apple Harvesting with High Fruit Stalk Retention Rate
Authors: Bao Muze , Xu Zhipeng , Hui Boxu , Zhou Qiaojun
Published: December 2024

Apples are widely cultivated primarily for fresh consumption. During mechanized harvesting, the extraction of fruit stalks can significantly impact the storage duration of fresh apples. The tensile force applied to the abscission layers is a critical factor in retaining the stalks; yet, few researchers have focused on preventing stalk pull-out during picking. In this research, we studied the phenomenon of missing stalks during mechanical picking by analyzing the tensile force exerted on the abscission layer during picking and optimizing the attitude of the end effector to achieve the highest stalk retention rate. Firstly, the tangential and normal energy release rates of the abscission layer were used as key parameters to model the cohesive zone of the abscission layer, a finite element model of the fruit–stalk–branch system was developed, based on which the actual fruit picking process using direct-pulling and twisting was simulated. Subsequently, the data obtained from the simulation were analyzed using response surface analysis, and the maximum tensile force at the time of fracture of the delamination and the time of its fracture were used as optimization parameters to find the optimal solution of the angle, direct-pulling speed, and twisting speed d to achieve the highest stalk retention rate. Finally, through field experiments, it was demonstrated that the optimal picking conditions could effectively improve the picking success rate and stalk retention rate. The results show that, when the end effector picks close to the fruit at about 58°, the stalk retention rate can reach 94.0%.
Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models
Authors: Bello Rotimi-Williams , Owolawi Pius A. , van Wyk Etienne A. , Tu Chunling
Published: December 2024

Among the emerging applications of artificial intelligence is animal instance segmentation, which has provided a practical means for various researchers to accomplish some aim or execute some order. Though video and image processing are two of the several complex tasks in artificial intelligence, these tasks have become more complex due to the large data and resources needed for training deep learning models. However, these challenges are beginning to be overcome by the transfer learning method of deep learning. In furtherance of the application of the transfer learning method, a system is proposed in this study that applies transfer learning to the detection and recognition of animal activity in a typical farm environment using deep learning models. Among the deep learning models compared, Enhanced Mask R-CNN obtained a significant computing time of 0.2 s and 97% mAP results, which are better than the results obtained by Mask R-CNN, Faster R-CNN, SSD, and YOLOv3, respectively. The findings from the results obtained in this study validate the innovative use of transfer learning to address challenges in cattle segmentation by optimizing the segmentation accuracy and processing time (0.2 s) of the proposed Enhanced Mask R-CNN.
Trichoderma longibrachiatum (T6) Peptaibols Inhibiting the Monilia yunnanensis Growth and Inducing Pear Fruit Resistance in Its Infection
Authors: Lv Hang , Zhang Shuwu , Ma Nan , Boamah Solomon , Xu Bingliang
Published: December 2024

Pear fruit brown rot, caused by Monilia yunnanensis, affects pear fruit yields and quality. The present study determined Trichoderma longibrachiatum T6 (T6) peptaibols as a biological control alternative to synthetic fungicides and assessed its efficacy against M. yunnanensis through dual plate culture and surface spraying at different concentrations. T6 peptaibols effectively inhibited M. yunnanensis growth, achieving an 85.99% inhibitory rate at 1250 µg/mL after inoculation on PDA medium for 5 days, and 84.57% control efficacy on pear fruit with the same concentration at 6 days. Treatment with T6 peptaibols significantly decreased the average contents of malondialdehyde (MDA) and hydrogen peroxide (H2O2), as well as electrolyte leakage, by 31.99%, 27.93%, and 21.00% from days 1 to 9 post-inoculation, respectively, in comparison to the negative control. Additionally, the average antioxidant enzyme activities of catalase (CAT), superoxide dismutase (SOD), peroxidase (POD) and polyphenol oxidase (PPO) increased by 86.27%, 56.76%, 25.94%, and 47.88%, respectively; the average defense enzyme activities of phenylalanine ammonia-lyase (PAL), lipoxygenase (LOX), chitinase (CHI), and β-1,3-glucanase (β-Glu) increased by 63.00%, 55.70%, 26.19%, and 16.34%, respectively. Moreover, the expression levels of the antioxidant and defense-related genes (CAT, SOD, POD, PPO, CHI, LOX, PAL, β-Glu) were significantly upregulated by 2.80, 2.81, 3.03, 2.79, 3.37, 2.49, 2.73, and 1.83-folds at 3 days after inoculation compared to the negative control. Thus, T6 peptaibols effectively reduced the pathogen infection through growth inhibition and antioxidant defenses, thereby boosting fruit immunity.
Formulation, Characterization, and Antioxidant Properties of Chitosan Nanoparticles Containing Phenolic Compounds from Olive Pomace
Authors: Fierri Ilaria , Chignola Roberto , Stranieri Chiara , Di Leo Edoardo Giuseppe , Bellumori Maria , Roncoletta Sara , Romeo Alessandro , Benetti Federico , Fratta Pasini Anna Maria , Zoccatelli Gianni
Published: December 2024

Olive phenolic compounds like hydroxytyrosol (OH-Tyr), tyrosol (Tyr), and their precursors have different health-promoting properties, mainly based on their strong antioxidant capacity. However, their presence in extra-virgin olive oil (EVOO) is scarce since they are primarily contained in the by-products of oil production, such as olive pomace (OP). The aim of this work was to extract and encapsulate OP phenolic compounds into chitosan–tripolyphosphate nanoparticles (NPs) using an ionotropic gelation lyophilization approach to increase their resistance to environmental and chemical stress. NPs resulted in a monodisperse (PDI: 0.21) population of cationic NPs (ζ-potential: 33 mV, size: 229 nm) with an encapsulation efficiency (EE%), expressed as total phenolic content (TPC) and total OH-Tyr + Tyr content, of 64–65%. Mannitol and maltodextrin DE 19 (MD-19) were evaluated as lyoprotectants to counteract irreversible NP aggregation during lyophilization. The NP powder freeze dried using 0.7% of MD-19 showed the best performance, returning a monodispersed population of particles after rehydration. The antioxidant capacity of the obtained NPs was confirmed both in cell-free assays and in a THP-1 cell model of oxidative stress. This method represents a promising way to deliver health-promoting olive phenols for nutraceutical purposes and, hence, to valorize otherwise wasted by-products.
Non-Invasive Glucose Measurement Technique Based on Time-of-Flight
Authors: Asimakopoulos Konstantinos , Hristoforou Evangelos
Published: December 2024

A novel, low-cost sensor for the measurement of physiological levels of glucose in samples has been developed using Time-of-Flight (ToF) technology. The sensor, built with off-the-shelf components, leverages the scattering of light by glucose molecules to determine concentration changes by measuring the phase shift of light signals propagating through the sample. Experimental validation demonstrates the feasibility of this approach, correlating glucose concentration with optical scattering effects at a wavelength of 850 nm. This technique offers the potential for a cost-effective, non-invasive alternative to traditional methods of glucose monitoring, with applications in continuous glucose monitoring systems to improve diabetes management by reducing patient discomfort and enhancing monitoring accuracy. Further optimization of the sensor parameters and expanded testing are still needed.
Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models
Authors: Tang Bingbing , Hu Jie , Yang Mei , Zhang Chenglong , Bai Qiang
Published: December 2024

Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key challenge. To address this issue, this paper introduces a hybrid load forecasting model that integrates the Long Short-Term Memory Network (LSTM) with the Stochastic Configuration Network (SCN). We first verify the Universal Approximation Property of SCN through experiments on two regression datasets. Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. These extracted feature vectors are then used as inputs for SCN-based STLF. Finally, we evaluate the performance of the LSTM-SCN model against other baseline models using the Australian Electricity Load dataset. We also select five high-volatility regions in the test set to validate the LSTM-SCN model’s advantages in such scenarios. The results show that the LSTM-SCN model achieved an RMSE of 56.970, MAE of 43.033, and MAPE of 0.492% on the test set. Compared to the next best model, the LSTM-SCN model reduced errors by 6.016, 8.846, and 0.053% for RMSE, MAE, and MAPE, respectively. Additionally, the model consistently outperformed across all five high-volatility regions analyzed. These findings highlight its contribution to improved power system management, particularly in challenging high-volatility scenarios.
Long-Term Dynamics of Water Droplet Impact on Rotating Hydrophilic Disk
Authors: Yang Wen , Zhang Yunbo , Deng Tian , Liu Chuanyang
Published: December 2024

Ice accretion from the impingement of supercooled water droplets on the rotating components of aero-engines reduces engine efficiency and poses significant in-flight safety risks. In the present study, we experimentally investigate the impact of water droplets on the center of a rotating disk to gain insights into the icing mechanisms on these components. The effects of impact velocity and disk rotation speed on dynamic behaviors are systematically explored by visualizing the phenomena and quantitatively analyzing the evolution of droplet diameters during long time durations. Three distinct regimes of impact dynamics are identified based on the final states: stable rotation, stable ring, and ring ejection. The experimental results reveal that the spreading phase is primarily governed by inertial effects, with minimal influence from disk rotation, while the latter significantly affects the retraction phase. The maximum spreading factor increases with the impact velocity and shows little dependence on rotation, and the spreading time remains nearly unchanged. Scaling laws for the maximum and equilibrium spreading factors as functions of the Weber number and rotational Bond number are established. While the maximum spreading factor increases with impact velocity on static disks, the retraction time decreases as both the impact velocity and rotation speed increase.
AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
Authors: Miller Tymoteusz , Durlik Irmina , Łobodzińska Adrianna , Dorobczyński Lech , Jasionowski Robert
Published: December 2024

This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends, predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation, ensuring ethical practices, and aligning technological advancements with human values and real-world complexities.
Seed Biotechnologies in Practicing Sustainable Agriculture: Insights and Achievements in the Decade 2014–2024
Authors: Tiwari Pragya , Park Kyeung-Il
Published: December 2024

While agricultural crops remain at the forefront of addressing global food demands and malnutrition, depleting resources, fluctuating climatic conditions, and the adverse impact of biotic/abiotic stresses define a major challenge. Plant seeds comprise an important starting material for plant propagation, in vitro generation, and conservation, and are crucial factors in determining the quality and yield of the desired crops. The expanding horizon of precision agriculture suggests that high-quality seeds could promote crop productivity up to 15–20 percent, attributed to emerging biotechnological innovations in seed science and research. In addition, seed science comprises an integral aspect of sustainable development goals (SDGs), and plays a crucial role in Climate Action (SDG 13) and Zero hunger (SDG 2). While synthetic seed technologies highlight prospects in the propagation and conservation of key plant species, seed biopriming to address environmental stresses is innovative in climate-smart agriculture. The article discusses key developments in advanced seed biotechnologies, ranging from nano-enabled seed treatments to the non-coding RNA-mediated determination of seed traits and genetic manipulation of seeds for quality improvement. Research employing multi-omics, bioinformatics, and seed biopharming for the enhanced production of high-value metabolites is opening new avenues in seed biology and biotechnology research.
Research on the Influence of Size Effect on the Uplift and Bearing Capacity of Spiral Anchors for Deep Sea Floating Foundations
Authors: Chen Bingsan , Zhang Zhenqing , Jiang Pai , Huang Yunhan
Published: December 2024

In response to the issue of the size effect of helical anchors in deep-sea areas on their uplift bearing capacity, this study was proposed based on the ratio of the anchor shaft diameter to the anchor helix diameter and the ratio of the pitch to the anchor helix diameter. Eight sets of small-scale anchors were designed for laboratory model tests to study the impact of different anchor shaft diameters to anchor helix diameter ratios and the ratio of the pitch to the anchor helix diameter on uplift bearing capacity. The results indicate that smaller ratios of the anchor shaft diameter to the anchor helix diameter and the pitch to the anchor helix diameter can improve the uplift bearing capacity of helical anchors. In addition, this study found that the uplift bearing capacity of helical anchors decreased with the increase in the ratio of the anchor diameter to the anchor helix diameter and the ratio of the pitch to the anchor helix diameter. Based on this, a prediction equation for the uplift bearing capacity of helical anchors was proposed.
Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
Authors: Lan Ting , Bie Yanan , Hai Dong , Zhong Jun
Published: December 2024

In order to address the issue of heart rate susceptibility to motion artifacts (MAs) when extracting it from photoplethysmography (PPG) signals, a heart rate estimation algorithm based on the finite state machine (FSM) is proposed. The algorithm first applies band-pass filtering to the PPG and three-axis acceleration signals. The strength of MA is assessed based on the acceleration data. If a strong MA is detected, recursive least squares (RLS) filtering is applied; otherwise, it is omitted. Then, the signal is subjected to an empirical wavelet transform (EWT). Based on the EWT results, the current state is identified, and the corresponding spectral peak screening method is selected to estimate the heart rate. The mean absolute errors of the algorithm on 12 sets of public data and 8 sets of testing data are 0.93 and 1.76 beats per minute (bpm), respectively. The results of the experiment show that compared with other dominant algorithms, the proposed algorithm estimates heart rate with a smaller mean absolute error and can extract heart rate more effectively.
A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
Authors: Yu Yuhui , Xia Wenjun , Zhao Zhangyan , He Bin
Published: December 2024

Pavement cracks significantly affect road safety and longevity, making accurate crack segmentation essential for effective maintenance. Although deep learning methods have demonstrated excellent performance in this task, their large network architectures limit their applicability on resource-constrained devices. To address this challenge, this paper proposes a lightweight, fully convolutional neural network model, enhanced with spatial information. First, the backbone network structure is optimized to improve the efficiency of spatial information utilization. Second, by incorporating adaptive feature reassembly and wavelet transforms, the up-sampling and down-sampling processes are refined, enhancing the model capacity to capture spatial information. Lastly, a dynamic combined loss function is employed during training to further improve model attention on crack edge details. To validate the model performance, we trained and tested it on the Crack500 dataset and applied the trained model directly to the AsphaltCrack300 dataset. Experimental results indicate that the proposed model achieved an MIoU of 80.37% and an F1-score of 78.22% on the Crack500 dataset, representing increases of 3.08% and 5.62%, respectively, compared to EfficientNet. On the AsphaltCrack300 dataset, the model exhibited strong robustness, significantly outperforming other mainstream models. Additionally, its lightweight design provides clear advantages, making it well suited for realworld applications with limited computational resources.
Airwave Noise Identification from Seismic Data Using YOLOv5
Authors: Liang Zhenghong , Gan Lu , Zhang Zhifeng , Huang Xiuju , Shen Fengli , Chen Guo , Tang Rongjiang
Published: December 2024

Airwave interference presents a major source of noise in seismic exploration, posing significant challenges to the quality control of raw seismic data. With the increasing data volume in 3D seismic exploration, manual identification methods fall short of meeting the demands of high-density 3D seismic surveys. This study employs the YOLOv5 model, a widely used tool in object detection, to achieve rapid identification of airwave noise in seismic profiles. Initially, the model was pre-trained on the COCO dataset—a large-scale dataset designed for object detection—and subsequently fine-tuned using a training set specifically labeled for airwave noise data. The fine-tuned model achieved an accuracy and recall rate of approximately 85% on the test dataset, successfully identifying not only the presence of noise but also its location, confidence levels, and range. To evaluate the model’s effectiveness, we applied the YOLOv5 model trained on 2D data to seismic records from two regions: 2D seismic data from Ningqiang, Shanxi, and 3D seismic data from Xiushui, Sichuan. The overall prediction accuracy in both regions exceeded 90%, with the accuracy and recall rates for airwave noise surpassing 83% and 90%, respectively. The evaluation time for single-shot 3D seismic data (over 8000 traces) was less than 2 s, highlighting the model’s exceptional transferability, generalization ability, and efficiency. These results demonstrate that the YOLOv5 model is highly effective for detecting airwave noise in raw seismic data across different regions, marking the first successful attempt at computer recognition of airwaves in seismic exploration.
Robotics Applications in the Hospital Domain: A Literature Review
Authors: Cruz Elijah M. G. N. Vera , Oliveira Sancho , Correia Américo
Published: December 2024

Robotic systems are increasingly being used in healthcare. These systems improve patient care both by freeing healthcare professionals from repetitive tasks and by assisting them with complex procedures. This analysis examines the development and implementation of the use of robotic systems in healthcare. It also examines the application of artificial intelligence (AI), which focuses on the autonomy of robotic systems, enabling them to perform tasks autonomously. It describes the main areas of use of robots in hospitals, gives examples of the main commercial or research robots, and analyzes the main practical and safety issues associated with the use of these systems. Using the main databases, including PubMed, IEEE Xplore, MDPI, ScienceDirect, ACM Digital Library, BioMed Central, Springer, and others, an extensive search for papers related to the topic was conducted. This resulted in 59 papers being identified as eligible for this review. The article concludes with a discussion of future research areas that will ensure the effective integration of autonomous robotic systems in healthcare.
Charge Transfer Excitation of NeAr+ Ions in Collisions with Electrons
Authors: Narits Alexander , Kislov Konstantin , Lebedev Vladimir
Published: December 2024

We study the resonant processes of the excitation of weakly bound NeAr+ ions in collisions with the free electrons of Ne/Ar mixture plasma under conditions typical of the active media of gas lasers and plasma-based UV radiation sources. The transitions leading to the population of charge transfer electronic terms are considered. Using an original theoretical approach developed recently, we study the dependences of the cross-sections of several competing resonant processes on the incident electron energy and the gas temperature of the plasma. The role of the continuous states of internuclear motion is discussed. We highlight the specific features of the processes considered that stem from the low binding energy of NeAr+ ions and demonstrate that, in the weakly bound systems, the efficiencies of different charge transfer excitation channels differ greatly from those obtained for ions with moderate dissociation energies.
Correction: Wiechers et al. Development of a Process for Direct Recycling of Negative Electrode Scrap from Lithium-Ion Battery Production on a Technical Scale and Its Influence on the Material Quality. Batteries 2024, 10, 218
Authors: Wiechers Patrick , Hermann Anna , Koob Sofia , Glaum Fabian , Gleiß Marco
Published: December 2024

In the original publication [...]
Gender Differences in the Dynamics and Kinematics of Running and Their Dependence on Footwear
Authors: Scharl Tizian , Frisch Michael , Fuss Franz Konstantin
Published: December 2024

Previous studies on gender differences in running biomechanics have predominantly been limited to joint angles and have not investigated a potential influence of footwear condition. This study shall contribute to closing this gap. Lower body biomechanics of 37 recreational runners (19 f, 18 m) were analysed for eight footwear and two running speed conditions. Presenting the effect size Cliff’s Delta enabled the interpretation of gender differences across a variety of variables and conditions. Known gender differences such as a larger range of hip movement in female runners were confirmed. Further previously undiscovered gender differences in running biomechanics were identified. In women, the knee extensors are less involved in joint work. Instead, compared to men, the supinators contribute more to deceleration and the hip abductors to acceleration. In addition to differences in extent, women also show a temporal delay within certain variables. For the foot, ankle and shank, as well as for the distribution of joint work, gender differences were found to be dependent on footwear condition, while sagittal pelvis and non-sagittal hip and thigh kinematics are rather consistent. On average, smaller gender differences were found for an individual compared to a uniform running speed. Future studies on gender differences should consider the influence of footwear and running speed and should provide an accurate description of the footwear condition used. The findings of this study could be used for the development of gender-specific running shoes and sports and medical products and provide a foundation for the application of smart wearable devices in gender-specific training and rehabilitation.
Application of the Chitooligosaccharides and Fluorescence Polarization Technique for the Assay of Active Lysozyme in Hen Egg White
Authors: Mukhametova Liliya I. , Zherdev Dmitry O. , Eremin Sergei A. , Levashov Pavel A. , Siebert Hans-Christian , Tsvetkov Yury E. , Yudina Olga N. , Krylov Vadim B. , Nifantiev Nikolay E.
Published: December 2024

This study describes the applicability of the fluorescence polarization assay (FPA) based on the use of FITC-labeled oligosaccharide tracers of defined structure for the measurement of active lysozyme in hen egg white. Depending on the oligosaccharide chain length of the tracer, this method detects both the formation of the enzyme-to-tracer complex (because of lectin-like, i.e., carbohydrate-binding action of lysozyme) and tracer splitting (because of chitinase activity of lysozyme). Evaluation of the fluorescence polarization dynamics enables simultaneous measurement of the chitinase and lectin activities of lysozyme, which is crucial for its detection in complex biological systems. Hen egg white lysozyme (HEWL), unlike human lysozyme (HL), formed a stable complex with the chitotriose tracer that underwent no further transformations. This fact allows for easy measurement of the carbohydrate-binding activity of the HEWL. The results of the lysozyme activity measurement for hen egg samples obtained through the FPA correlated with the results obtained using the traditional turbidimetry method. The FPA does not have the drawbacks of turbidimetry, which are associated with the need to use bacterial cells that cannot be precisely standardized. Additionally, FPA offers advantages such as rapid analysis, the use of compact equipment, and standardized reagents. Therefore, the new express technique for measuring the lysozyme activity is applicable for evaluating the complex biomaterial, including for the purposes of food product quality control.
Assessment of Agrimonia eupatoria L. and Lipophosphonoxin (DR-6180) Combination for Wound Repair: Bridging the Gap Between Phytomedicine and Organic Chemistry
Authors: Kaňuchová Miriam , Brindza Lachová Veronika , Bogdanová Kateřina , Sabová Jana , Bonová Petra , Vasilenko Tomáš , Kováč Ivan , Novotný Martin , Mitrengová Petra , Sahatsapan Nitjawan , Čoma Matúš , Švajdlenka Emil , Kolář Milan , Bohuš Peter , Mučaji Pavel , Zajíček Robert , Rejman Dominik , Gál Peter
Published: December 2024

Agrimonia eupatoria L. (AE) has a rich tradition of use in wound healing improvement across various cultures worldwide. In previous studies, we revealed that Agrimonia eupatoria L. water extract (AE) possesses a rich polyphenolic composition, displaying remarkable antioxidant properties. Our investigations also demonstrated that lipophosphonoxin (LPPO) exhibited antibacterial efficacy in vitro while preserving the proliferation and differentiation of fibroblasts and keratinocytes. Building upon our prior findings, in this study, we intended to examine whether a combination of AE and LPPO could enhance skin wound healing while retaining antibacterial attributes. The antibacterial activity of AE/LPPO against Staphylococcus aureus was evaluated, alongside its effects on fibroblast-to-myofibroblast transition, the formation of extracellular matrix (ECM), and endothelial cells and keratinocyte proliferation/phenotype. We also investigated AE/LPPO’s impact on TGF-β1 and VEGF-A signaling in keratinocytes/fibroblasts and endothelial cells, respectively. Additionally, wound healing progression in rats was examined through macroscopic observation and histological analysis. Our results indicate that AE/LPPO promotes myofibroblast-like phenotypic changes and augments ECM deposition. Clinically relevant, the AE/LPPO did not disrupt TGF-β1 and VEGF-A signaling and accelerated wound closure in rats. Notably, while AE and LPPO individually exhibited antibacterial activity, their combination did not lead to synergism, rather decreasing antibacterial activity, warranting further examination. These findings underscore substantial wound healing improvement facilitated by AE/LPPO, requiring further exploration in animal models closer to human physiology.
From Structure to Function: Analysis of the First Monomeric Pyridoxal-5′-Phosphate-Dependent Transaminase from the Bacterium Desulfobacula toluolica
Authors: Bakunova Alina K. , Matyuta Ilya O. , Nikolaeva Alena Y. , Rakitina Tatiana V. , Boyko Konstantin M. , Popov Vladimir O. , Bezsudnova Ekaterina Yu.
Published: December 2024

The first monomeric pyridoxal-5′-phosphate (PLP)-dependent transaminase from a marine, aromatic-compound-degrading, sulfate-reducing bacterium Desulfobacula toluolica Tol2, has been studied using structural, kinetic, and spectral methods. The monomeric organization of the transaminase was confirmed by both gel filtration and crystallography. The PLP-dependent transaminase is of the fold type IV and deaminates D-alanine and (R)-phenylethylamine in half-reactions. The enzyme shows high stereoselectivity; no deamination of L-amino acids and (S)-phenylethylamine is detected. Structural analysis and subsequent mutagenesis led to the conclusion that the monomeric architecture of the enzyme is the only one possible and sufficient for stereoselectivity and PLP binding, but not for the overall double-substrate transamination reaction and the stability of the holo form with the reduced cofactor—pyridoxamine-5′-phosphate. These results extend the structural university of the PLP fold type IV enzymes and demonstrate the need for deeper analysis of the sequence–structure–function relationships in the transaminases.
Anaplastic Lymphoma Kinase (ALK) Inhibitors Enhance Phagocytosis Induced by CD47 Blockade in Sensitive and Resistant ALK-Driven Malignancies
Authors: Malighetti Federica , Villa Matteo , Mauri Mario , Piane Simone , Crippa Valentina , Crespiatico Ilaria , Cocito Federica , Bossi Elisa , Steidl Carolina , Civettini Ivan , Scollo Chiara , Ramazzotti Daniele , Gambacorti-Passerini Carlo , Piazza Rocco , Mologni Luca , Aroldi Andrea
Published: December 2024

Background: Anaplastic lymphoma kinase (ALK) plays a role in the development of lymphoma, lung cancer and neuroblastoma. While tyrosine kinase inhibitors (TKIs) have improved treatment outcomes, relapse remains a challenge due to on-target mutations and off-target resistance mechanisms. ALK-positive (ALK+) tumors can evade the immune system, partly through tumor-associated macrophages (TAMs) that facilitate immune escape. Cancer cells use “don’t eat me” signals (DEMs), such as CD47, to resist TAMs-mediated phagocytosis. TKIs may upregulate pro-phagocytic stimuli (i.e., calreticulin, CALR), suggesting a potential therapeutic benefit in combining TKIs with an anti-CD47 monoclonal antibody (mAb). However, the impact of this combination on both TKIs-sensitive and resistant ALK+ tumors requires further investigation. Methods: A panel of TKIs-sensitive and resistant ALK+ cancer subtypes was assessed for CALR and CD47 expression over time using flow cytometry. Flow cytometry co-culture and fluorescent microscopy assays were employed to evaluate phagocytosis under various treatment conditions. Results: ALK inhibitors increased CALR expression in both TKIs-sensitive and off-target resistant ALK+ cancer cells. Prolonged TKIs exposure also led to CD47 upregulation. The combination of ALK inhibitors and anti-CD47 mAb significantly enhanced phagocytosis compared to anti-CD47 alone, as confirmed by flow cytometry and fluorescent microscopy. Conclusions: Anti-CD47 mAb can quench DEMs while exposing pro-phagocytic signals, promoting tumor cell phagocytosis. ALK inhibitors induced immunogenic cell damage by upregulating CALR in both sensitive and off-target resistant tumors. Continuous TKIs exposure in off-target resistant settings also resulted in the upregulation of CD47 over time. Combining TKIs with a CD47 blockade may offer therapeutic benefits in ALK+ cancers, especially in overcoming off-target resistance where TKIs alone are less effective.
Efficacy and Safety of Pazopanib in the Treatment of Thyroid Cancer: A Systematic Review
Authors: Mederle Alexandra Laura , Stana Loredana Gabriela , Ilie Adrian Cosmin , Borza Claudia , Streian Caius Glad , Nistor Daciana , Cerbulescu Teodor , Belovan Biliana , Lascu Ana
Published: December 2024

Pazopanib, a multi-targeted tyrosine kinase inhibitor, has been explored for its efficacy in treating various subtypes of thyroid cancer, including differentiated thyroid carcinoma (DTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC). This systematic review assesses the efficacy and safety of pazopanib, focusing on the progression-free survival (PFS), overall survival (OS), and response rates and adverse events. A comprehensive search was conducted in databases including PubMed, Scopus, and Web of Science up to October 2024 to identify randomized controlled trials and phase II clinical trials that investigated the use of pazopanib in thyroid cancer. The PRISMA guidelines were followed for data extraction and quality assessment. The review included six studies encompassing 289 patients, presenting a comprehensive overview of pazopanib’s application across different thyroid cancer subtypes. The studies reported median PFS rates ranging from 2.1 to 11.7 months and median OS rates ranging from 5.7 months to not reached. The partial response rates varied from 5% to 49%. Adverse events were common, with hypertension occurring in up to 71.7% of patients, and fatigue and diarrhea were also frequently reported. Grade 3–5 adverse events led to treatment discontinuations in up to 14% of patients. Pazopanib shows variable efficacy across thyroid cancer types, offering significant benefits in MTC and refractory DTC in terms of PFS and OS but limited impact in ATC. The adverse event profile necessitates careful management, particularly regarding hypertension and other treatment-related toxicities. Further studies are required to refine the therapeutic protocols for pazopanib, exploring combination therapies that may enhance its efficacy and reduce the adverse outcomes.
Increased Rates of Infectious Diseases in Fibromyalgia Patients: A Population-Based Case-Control Study
Authors: Vinker-Shuster Michal , Magen Eli , Green Ilan , Merzon Eugene , Golan-Cohen Avivit , Israel Ariel
Published: December 2024

Introduction: Fibromyalgia (FM) patients are known to have medical comorbidities. This study characterized the rates of infectious diseases in FM patients compared to the general population. Methods: A nationwide population-based case-control study was conducted, including all patients diagnosed with FM by a rheumatologist compared to a matched 5:1 control group within a large health maintenance organization in Israel (January 2002 to December 2023). Demographic, anthropometric, and health habit data were extracted from medical records as well as the ICD-9 codes of diagnoses related to infectious diseases in 9232 FM patients and 46,160 controls. Infection rates in the FM patients were compared to the controls over a mean follow-up of 6.7 years. Results: The FM patients had a significantly higher incidence of viral, bacterial, fungal, and parasitic diseases compared to the controls. The FM patients had significantly higher odds ratios (ORs) for respiratory/sinopulmonary infections, including upper respiratory tract infections (OR = 1.49), influenza (OR = 1.80), tonsillitis (OR = 1.40), sinusitis (OR = 1.98), otitis media (OR = 1.84), otitis externa (OR = 1.48), and pneumonia (OR = 1.60), all p < 0.01. They also experienced more gastrointestinal infections, including gastroenteritis (OR = 1.40), Helicobacter pylori (OR = 2.05), candidal esophagitis (OR = 7.88), and giardiasis (OR = 3.41), all p < 0.01. They had a higher prevalence of genitourinary infections, including urinary tract infections (OR = 1.79) and pelvic inflammatory disease (OR = 3.17), p < 0.01 as well as skin infections such as abscess (OR = 1.74) and cellulitis (OR = 1.64) and systemic infections such as symptomatic COVID-19 (OR = 1.76) and Cytomegalovirus (CMV) (OR = 1.85), all p < 0.01. Conclusions: The FM patients had a significantly higher incidence of infectious diseases than the general population. Further research is needed to better understand the underlying mechanisms and develop targeted interventions to address infection risks in FM patients.