bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Salicylic Acid in Salinity Stress Mitigation in Dizygostemon riparius: A Medicinal Species Native to South America
Authors: Albuquerque Irislene Cutrim , Silva-Moraes Vitória Karla de Oliveira , Alves Givago Lopes , Pinheiro Jordanya Ferreira , Henschel Juliane Maciel , Lima Aldilene da Silva , Rivas Priscila Marlys Sá , Andrade Jailma Ribeiro de , Batista Diego Silva , Reis Fabrício de Oliveira , Ferraz Tiago Massi , Figueiredo Fábio Afonso Mazzei Moura de Assis , Catunda Paulo Henrique Aragão , Corrêa Thais Roseli , Felipe Sérgio Heitor Sousa
Published: November 2024

Salicylic acid (SA) is a bioregulator well-known for mitigating salinity damage in plants. However, no studies have examined the interaction between SA and salinity in Dizygostemon riparius, a species rich in bioactive molecules. Therefore, we aimed to evaluate the effect of SA application on Dizygostemon riparius under different salinity levels. A completely randomized experiment was conducted in a 2 × 3 factorial design (two SA concentrations of 0 and 100 µM and three salinity concentrations of 0, 200, and 400 mM NaCl) with five replicates. At 400 mM NaCl, leaf temperature increased by 11%, while relative water content and total soluble carbohydrates decreased by 30% and 35%, respectively, leading to reduced biomass accumulation. Notably, the SA application mitigated these effects by restoring relative water content under 400 mM NaCl and improving carboxylation efficiency and intrinsic water-use efficiency under 200 mM NaCl. Additionally, dry biomass was maintained under both 200 and 400 mM NaCl with SA treatment. These findings suggest that SA has a promising potential to alleviate salt stress in Dizygostemon riparius. Our results could inform cultivation practices, opening new perspectives on the use of SA as an attenuator of salinity stress.
Correction: El-Hefnawy et al. Fabrication of Nanofibers Based on Hydroxypropyl Starch/Polyurethane Loaded with the Biosynthesized Silver Nanoparticles for the Treatment of Pathogenic Microbes in Wounds. Polymers 2022, 14, 318
Authors: El-Hefnawy Mohamed E. , Alhayyani Sultan , El-Sherbiny Mohsen M. , Sakran Mohamed I. , El-Newehy Mohamed H.
Published: November 2024

There was an error in the original publication [...]
Polypiperazine-Based Micelles of Mixed Composition for Gene Delivery
Authors: Stancheva Rumena , Haladjova Emi , Petrova Maria , Ugrinova Iva , Dimitrov Ivaylo , Rangelov Stanislav
Published: November 2024

We introduce a novel concept in nucleic acid delivery based on the use of mixed polymeric micelles (MPMs) as platforms for the preparation of micelleplexes with DNA. MPMs were prepared by the co-assembly of a cationic copolymer, poly(1-(4-methylpiperazin-1-yl)-propenone)-b-poly(d,l-lactide), and nonionic poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide) block copolymers. We hypothesize that by introducing nonionic entities incorporated into the mixed co-assembled structures, the mode and strength of DNA binding and DNA accessibility and release could be modulated. The systems were characterized in terms of size, surface potential, buffering capacity, and binding ability to investigate the influence of composition, in particular, the poly(ethylene oxide) chain length on the properties and structure of the MPMs. Endo–lysosomal conditions were simulated to follow the changes in fundamental parameters and behavior of the micelleplexes. The results were interpreted as reflecting the specific structure and composition of the corona and localization of DNA in the corona, predetermined by the poly(ethylene oxide) chain length. A favorable effect of the introduction of the nonionic block copolymer component in the MPMs and micelleplexes thereof was the enhancement of biocompatibility. The slight reduction of the transfection efficiency of the MPM-based micelleplexes compared to that of the single-component polymer micelles was attributed to the premature release of DNA from the MPM-based micelleplexes in the endo–lysosomal compartments.
A Multi-Scale and Multi-Stage Human Pose Recognition Method Based on Convolutional Neural Networks for Non-Wearable Ergonomic Evaluation
Authors: Zhao Wei , Wang Lei , Li Yuanzhe , Liu Xin , Zhang Yiwen , Yan Bingchen , Li Hanze
Published: November 2024

In the context of industrial robot maintenance and assembly, workers often suffer from work-related musculoskeletal disorders (WRMSDs). This paper proposes a multi-scale, multi-stage pose recognition method (MMARM-CNN) based on convolutional neural networks to provide ergonomic intervention. The method leverages computer vision technology to enable non-contact data acquisition, reducing the interference of physiological and psychological factors on assessment results. Built upon the baseline yolov8-pose framework, the method addresses complex maintenance environments, which are prone to occlusion, by introducing the Lightweight Shared Convolutional Detection Head-pose (LSCD-pose) module, Multi-Scale Channel Attention (MSCA) mechanism, and Efficient Multi-Scale Patch Convolution (EMSPC) module, enhancing the model’s feature extraction capabilities. The MMARM-CNN model was validated using the MS COCO 2017 dataset and robot assembly data collected under laboratory conditions. The experimental results show that the MMARM-CNN achieved an accuracy improvement, reaching 0.875 in the mAP@0.5 evaluation. Overall, this method demonstrates significant potential in advancing the automation and intelligence of ergonomic interventions.
Towards Achieving Circular Economy in the Production of Silica from Rice Husk as a Sustainable Adsorbent
Authors: Rodriguez-Otero Alba , Galarneau Anne , Drané Méghane , Vargas Vicmary , Sebastian Victor , Wilson Andrew , Grégoire David , Radji Sadia , Marias Frederic , Christensen Jan H. , Bouyssiere Brice
Published: November 2024

The growing concern over water pollution and waste management requires innovative solutions that promote resource efficiency within a circular economy. This study aims to utilize rice husk (RH) as a sustainable feedstock to develop highly porous silica particles and generate valuable by-products, addressing the dual challenges of waste reduction and water contamination. We hypothesize that optimizing the production of amorphous silica from acid-washed RH will enhance its adsorptive properties and facilitate the concurrent generation of bio-oil and syngas. Amorphous silica particles were extracted from acid-washed RH with a yield of 15 wt% using a combination of acid washing at 100 °C, pyrolysis at 500 °C, and calcination at 700 °C with controlled heating at 2 °C/min. The optimized material (RH2-SiO2), composed of small (60–200 nm) and large (50–200 µm) particles, had a specific surface area of 320 m2/g, with funnel-shaped pores with diameters from 17 nm to 4 nm and showed a maximum cadmium adsorption capacity of 407 mg Cd/g SiO2. Additionally, the pyrolysis process yielded CO-rich syngas and bio-oil with an elevated phenolic content, demonstrating a higher bio-oil yield and reduced gas production compared to untreated RH. Some limitations were identified, including the need for bio-oil upgrading, further research into the application of RH2-SiO2 for wastewater treatment, and the scaling-up of adsorbent production. Despite the challenges, these results contribute to the development of a promising adsorbent for water pollution control while enhancing the value of agricultural waste and moving closer to a circular economy model.
Research Progress and Prospects of Oil Saturation Evaluation Methods in Shale Oil Reservoirs
Authors: Zhao Haibo , Zheng Jiandong , Zhang Zhaoqian , Qin Hao , Wang Chunyan , Zhu Jianhua , Chen Anqi , Chen Longchuan , Liu Peng , Yang Wangwang , Hu Xuan
Published: November 2024

The core of the exploration and development of unconventional oil and gas resources, such as shale oil, lies in the effective identification and scale utilization of the sweet spot. Oil saturation is an important parameter in evaluating the sweet spot. Aiming to solve the many current problems of oil saturation logging evaluations of shale oil reservoirs, this study outlines the research progress of oil saturation logging evaluations of shale oil from the three aspects, namely, the electrical method, non-electrical method, and machine learning, through researching the literature and practical applications. At the same time, several typical saturation models are applied to shale oil reservoirs in the Qingshankou Formation of the Gulong Depression, and applicability analyses are conducted. Lastly, the advantages and disadvantages of each oil saturation calculation model are summarized, and suggestions are given for conducting research using each type of method. This study has certain significance in the selection of oil saturation logging evaluation methods for shale oil reservoirs and provides directions for improvement.
Optimizing Fermentation Strategies for Enhanced Tryptophan Production in Escherichia coli: Integrating Genetic and Environmental Controls for Industrial Applications
Authors: Ramos-Valdovinos Miguel Angel , Martínez-Antonio Agustino
Published: November 2024

Tryptophan is an essential aromatic amino acid widely used in the pharmaceutical, agricultural, and feed industries. Microbial fermentation, mainly using Escherichia coli, has become the preferred method for its production due to sustainability and lower costs. Optimizing tryptophan production requires careful control of various fermentation parameters, including nutrients, pH, temperature, and dissolved oxygen (DO) levels. Glucose, as the primary carbon source, must be fed at controlled rates to avoid metabolic overflow, which leads to by-product accumulation and reduced production efficiency. Nitrogen sources, both organic (such as yeast extract) and inorganic (like ammonium), influence biomass growth and tryptophan yield, with ammonium levels requiring careful regulation to avoid toxic accumulation. Phosphate enhances growth but can lead to by-product formation if used excessively. pH is another critical factor, with an optimal range between 6.5 and 7.2, where enzyme activity is maximized. Temperature control promotes growth and production, particularly between 30 °C and 37 °C. High DO levels increase tryptophan titers by boosting the pentose phosphate pathway and reducing by-products like acetate. Furthermore, surfactants and supplements such as betaine monohydrate and citrate help alleviate osmotic stress and enhance precursor availability, improving production efficiency. Careful manipulation of these parameters allows for high-density cell cultures and significant tryptophan accumulation, making microbial fermentation competitive for large-scale production.
Experimental Investigations of Friction Properties of Carbon Particles Derived from Sargassum Algae
Authors: Molza Audrey , Cesaire Thierry , Bercion Yves , Thomas Philippe
Published: November 2024

In Caribbean islands, the washing ashore of tons of pelagic Sargassum spp., consisting of two species Sargassum fluitans and Sargassum natans, has been regularly occurring since 2011. As green lubrication is a growing trend in the tribology industry, biochar is a promising alternative. Sargassum biochars, produced from Sargassum pelagic algae, are therefore being studied as solid lubricants. This study aims to explore their potential applications. Biochars from brown algae were pyrolyzed at 400 °C and then annealed at different temperatures (from 600 °C to 1500 °C). The Raman spectra collected on the different biochars showed that there was a structural organization of the biochars as the temperature increased. The tribologic properties of the biochars were studied and compared to a solid lubricant reference (exfoliated graphite). Raman spectroscopy analysis revealed a progressive structural reorganization with increasing temperature, leading to a 58% reduction in the coefficient of friction. The morphology and the structure of the tribofilm are investigated by profilometry, scanning electron microscopy, and Raman microspectrometry. Overall, these results can be considered as a first step for utilizing the biochar derived from brown algae Sargassum sp. as an additive in the lubricant industry, for the purpose of emission reduction.
A Simple and Green Analytical Alternative for Chloride Determination in High-Salt-Content Crude Oil: Combining Miniaturized Extraction with Portable Colorimetric Analysis
Authors: Holkem Alice P. , Agostini Giuliano , Costa Adilson B. , Barin Juliano S. , Mello Paola A.
Published: November 2024

A simple and miniaturized protocol was developed for chloride extraction from Brazilian pre-salt crude oil for further salt determination by colorimetry. In this protocol, the colorimetric analysis of chloride using digital images was carried out in an aqueous phase obtained after a simple and miniaturized extraction carefully developed for this purpose. A portable device composed of a homemade 3D-printed chamber with a USB camera was used. The PhotoMetrix app converted the images into RGB histograms, and a partial least squares (PLS) model was obtained from chemometric treatment. The sample preparation was performed by extraction after defining the best conditions for the main parameters (e.g., extraction time, temperature, type and volume of solvent, and sample mass). The PLS model was evaluated considering the coefficient of determination (R2) and the root mean square errors (RMSEs)—calibration (RMSEC), cross-validation (RMSECV), and prediction (RMSEP). Under the optimized conditions, an extraction efficiency higher than 84% was achieved, and the limit of quantification was 1.6 mg g−1. The chloride content obtained in the pre-salt crude oils ranged from 3 to 15 mg g−1, and no differences (ANOVA, 95%) were observed between the results and the reference values by direct solid sampling elemental analysis (DSS-EA) or the ASTM D 6470 standard method. The easy-to-use colorimetric analysis combined with the extraction method’s simplicity offered a high-throughput, low-cost, and environmentally friendly method, with the possibility of portability. Additionally, the decrease in energy consumption and waste generation, increasing the sample throughput and operators’ safety, makes the proposed method a greener approach. Furthermore, the cost savings make this a suitable option for routine quality control, which can be attractive in the crude oil industry.
Advanced Sliding Mode Design for Optimal Automatic Generation Control in Multi-Area Multi-Source Power System Considering HVDC Link
Authors: Tuan Dao Huy , Tran Anh-Tuan , Huynh Van Van , Duy Vo Hoang , Nhan Nguyen Huu Khanh
Published: November 2024

The multi-area multi-source power system (MAMSPS), which uses a variety of power sources including gas, hydro, thermal, and renewable energy, has recently been implemented to balance the growing demand for electricity and the overall capacity for power generation. In this paper, an integral sliding mode control with a single-phase technique (ISMCSP) is applied to two areas, with each area including gas–wind–thermal power systems with HVDC system. Firstly, a two-area gas–wind–thermal power system with HVDC (TAGWTPSH) is the first model in this scheme to consider the parameter uncertainties of a MAMSPS. Secondly, sliding mode design law with a single-phase technique is introduced to alleviate chattering and oscillation problems. Then, power system stability is ensured by the Lyapunov control theory based on the new LMIs technique. Thirdly, the ISMCSP’s effectiveness in a MAMSPS is also assessed under random load patterns and parameter variations regarding settling time and over-/undershoot. The ISMCSP was created to alter the fundamental sliding mode control, and therefore the suggested approach performs better than recently published approaches. This is demonstrated by the frequency overshoot deviation value in frequency deviations: 0.7 × 10−3 to 2.8 × 10−3 for the TAGWTPSH with the suggested ISMCSP. In the last case, for random changes in load from −0.4 to +0.5 p. u, the proposed ISMCSP method still stabilizes the frequency of the areas meeting the standard requirements for AGC.
Intelligent Model-Free Control for Power Line Inspection Robots: Tackling Input Time Delays with Data-Driven Solutions
Authors: Zhang Nan , Su Jingyi , Huang Jiahui , Long Xinyuan , Chen Hua
Published: November 2024

This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance.
The Processing of a Novel Health Beverage Based on Extracts from Green Tea and Chios Mastiha
Authors: Itziou Aikaterini , Ziouzios Dimitrios , Zaralis Konstantinos , Lakioti Evangelia , Karayannis Vayos , Tsanaktsidis Constantinos
Published: November 2024

In the present study, the processing, characterization, and assessment of novel non-alcoholic and sugar-free drinks based on bioactive extracts from valuable natural sources, such as green tea enriched with Chios mastiha, are considered. Currently, the transition towards the consumption of healthy and sustainable food and beverages promoting human health and well-being is strongly encouraged and biologically active compounds from natural resources have a broad range of ap-plications in this sector. In this context, three beverages (all non-alcoholic, non-carbonated, and sugar-free) were created, including extracts of green tea with Chios mastiha, matcha green tea with Chios mastiha and louisa green tea with Chios mastiha, and an evaluation of their biological potential was performed. Specifically, an analysis of water, extracts, and additives for the beverage production was carried out. Microbiological and nutritional value determination was also conducted in samples of the three products. According to the experimental results, the novel health beverage produced from green tea enriched with Chios Mastiha extracts was found to have improved organoleptic characteristics and was microbiologically stable and safe for a period of 180 days from the production date at 25 °C. It is also considered stable and safe for 3 days after production, even if it remains open at 25 °C. In view of a possible scale-up of this application, safety, and preservation control should continue for at least 540 days from the date of production. In conclusion, the current research findings support the development of a novel non-alcoholic sugar-free health drink based on bioactive extracts from green tea enriched with Chios mastiha, to contribute to maintaining human health and also to strengthen the economy.
Hydrogen in Burners: Economic and Environmental Implications
Authors: Cavalcanti Matheus Henrique Castanha , Pappalardo Juliano Rodrigues , Barbosa Luciano Tavares , Brasileiro Pedro Pinto Ferreira , Roque Bruno Augusto Cabral , da Rocha e Silva Nathália Maria Padilha , Silva Milena Fernandes da , Converti Attilio , Barbosa Celmy Maria Bezerra de Menezes , Sarubbo Leonie Asfora
Published: November 2024

For centuries, fossil fuels have been the primary energy source, but their unchecked use has led to significant environmental and economic challenges that now shape the global energy landscape. The combustion of these fuels releases greenhouse gases, which are critical contributors to the acceleration of climate change, resulting in severe consequences for both the environment and human health. Therefore, this article examines the potential of hydrogen as a sustainable alternative energy source capable of mitigating these climate impacts. It explores the properties of hydrogen, with particular emphasis on its application in industrial burners and furnaces, underscoring its clean combustion and high energy density in comparison to fossil fuels, and also examines hydrogen production through thermochemical and electrochemical methods, covering green, gray, blue, and turquoise pathways. It discusses storage and transportation challenges, highlighting methods like compression, liquefaction, chemical carriers (e.g., ammonia), and transport via pipelines and vehicles. Hydrogen combustion mechanisms and optimized burner and furnace designs are explored, along with the environmental benefits of lower emissions, contrasted with economic concerns like production and infrastructure costs. Additionally, industrial and energy applications, safety concerns, and the challenges of large-scale adoption are addressed, presenting hydrogen as a promising yet complex alternative to fossil fuels.
Research on Flow Field Prediction in a Multi-Swirl Combustor Using Artificial Neural Network
Authors: Qian Weijia , Yang Siheng , Liu Weijie , Xu Quanhong , Zhu Wenbin
Published: November 2024

In aero-engine combustion research, the pursuit of cost-effective and rapid methods for acquiring precise flow fields across various operating conditions remains a significant challenge. This study offers novel insights into the rapid modeling of complex multi-swirling flows, introducing flow-field-based analytical methods to evaluate flow topologies, spray dispersion, ignition dynamics, and flame propagation patterns. A data-driven model is proposed to predict the swirling velocity field inside a multi-swirl combustor, using spatial coordinates and air pressure drops as input features. Particle Image Velocimetry (PIV) experiments under different air pressure drops are performed to generate the necessary flow field dataset. A fully connected deep neural network is designed and optimized with a focus on prediction accuracy, training efficiency, and mitigation of over-fitting. The predicted flow characteristics, including swirling jets, shear layers, recirculation zones, and velocity profiles, align closely with the PIV experimental results. This demonstrates the model’s capability to effectively capture the intricate multi-swirling flow structures and the complex relationships between input parameters and the resulting flow field. Furthermore, the trained model shows excellent generalization capability, accurately predicting flow fields under previously unseen operating conditions. Finally, combustion-relevant characteristics, such as ignition and flame propagation, are successfully extracted and analyzed from the predicted flow fields using the proposed deep learning framework.
Resource Recovery from Abandoned Mine Drainage Galleries via Ion Exchange: A Case Study from Freiberg Mining Area, Germany
Authors: Abeywickrama Janith , Karimi Katayoun , Grimmer Marlies , Hoth Nils , Drebenstedt Carsten
Published: November 2024

The discharge of metal-loaded mining-influenced waters can significantly pollute downstream water bodies for many kilometers. Addressing this issue at the earliest discharge point is crucial to prevent further contamination of the natural environment. Additionally, recovering metals from these discharges and other sources of contamination can reduce the environmental impacts of mining and support the circular economy by providing secondary raw materials. This study focused on optimizing zinc recovery from mining-influenced water in the Freiberg mining region in Germany, where significant loads of zinc are released into the Elbe River. By employing pretreatment techniques, conducting 100 mL scale ion-exchange column experiments, and refining the regeneration process, we aimed to identify optimal conditions for efficient zinc removal and recovery. Initial tests showed that aminophosphonic functionalized TP 260 resin had a high affinity for aluminum, occupying 93% of the resin’s capacity, while zinc capacity was limited to 0.2 eq/L. To improve zinc recovery, selective precipitation of aluminum at pH 6.0 was introduced as a pretreatment step. This significantly increased the zinc loading capacity of the resin to 1 eq/L. Under optimal conditions, a concentrated zinc solution of 18.5 g/L was obtained with 100% recovery. Sulfuric acid proved more effective than hydrochloric acid in eluting zinc from the resin. Further analysis using SEM-EDX revealed residual acid on the resin, indicating a need for additional study on long-term resin performance and capacity variation. The research also highlighted the environmental impact of the Freiberg mining area, where three drainage galleries currently contribute nearly 85 tons of zinc annually to the Elbe River. This study underscores the feasibility of efficient zinc recovery from these point sources of pollution using advanced ion-exchange processes, contributing to circular economy efforts and environmental conservation.
Effect of Joining Mechanism on the Mechanical Recycling of Polymer–Metal Composite Parts
Authors: Boekhoff Sandra , Zetzener Harald , Kwade Arno
Published: November 2024

In order to be able to recycle composite components made of polymer and metal, which are used in the automotive industry, the joints must be broken. The success of the separation is influenced by the stress and also by the joining mechanism between the polymer and the metal. Here, force-fit and form-fit connected components are produced and crushed in a rotor impact mill with two different rotors. The results show that the crushing results differ significantly for the different rotors and for the various joining processes. In short, the hammer-type rotor provides much finer and better-separated fragments and the force-fit joints enable a better separation of metal and polymers. The additional cooling of the samples also changes the result in a way, where deep cooling significantly improves the separation of the metal and the polymer. Different types of polymers also led to a different separation result with both rotors.
Introduction to the Special Issue “Focusing on the Elusive: Centering on Religious and Spiritual Influences within Contexts of Child and Young Adulthood Development”
Authors: Abo-Zena Mona M. , Rana Meenal
Published: November 2024

The religious is not elusive because it lurks behind ordinary phenomena, but because it is woven into the phenomena [...]
Measuring Muslim Lifestyle Using a Halal Scale
Authors: Riegel Ulrich , Engel Daniel , Penthin Marcus , Pirner Manfred L.
Published: November 2024

The background and motivation of the research presented in this article is the obligation of Public Theology to do justice to young Muslim refugees as a minority group in German society regarding the role of religiosity in the way they are coping with life. In the research process, the authors became increasingly aware that most instruments to measure religiosity have a Western and/or Protestant bias in that they are more interested in religious attitude than in religious practice and/or religious lifestyle, which is very important for Muslim religiosity. Therefore, this article focuses on the distinction between halal and haram as indicators of religious practice according to Muslim benchmarks. Both the concept and the operationalization of a two-dimensional instrument of living a halal life are described. The instrument distinguishes between the individual importance of halal goods (food, medicine, cosmetics) and services (doctors) and their availability in the local environment. Each of the two dimensions comprises four items. Construct validity is shown by confirmatory factor analysis (CFIrobust = 0.934, TLIrobust = 0.902, RMSEArobust = 0.114 [0.073; 0.156]) of a sample of N = 155 Muslim adolescents who have fled to Germany from Syria, Iraq and Afghanistan. External validity is tested by analyzing the correlation of the measurement instrument developed by the authors with the Centrality of Religiosity Scale. The presented halal instrument offers an approach to Muslim lifestyle that meets the orthopractic character of this religion. At the same time, it addresses the consequential dimension of religion within quantitative research.
The Influence of Regional Anesthesia on the Systemic Stress Response
Authors: Reysner Tomasz , Wieczorowska-Tobis Katarzyna , Kowalski Grzegorz , Grochowicka Monika , Pyszczorska Monika , Mularski Aleksander , Reysner Malgorzata
Published: November 2024

Background: The systemic stress response to surgery is a complex physiological process characterized by neuroendocrine, sympathetic, and inflammatory activation. While necessary for survival, this response can lead to adverse outcomes such as hyperglycemia, immune suppression, cardiovascular complications, and delayed recovery. Regional anesthesia (RA) has been shown to modulate this stress response more effectively than general anesthesia (GA) by blocking nociceptive signaling and attenuating the release of stress mediators. Objectives: This review aims to elucidate how RA influences the systemic stress response, highlighting its clinical benefits in reducing postoperative pain, improving hemodynamic stability, minimizing inflammatory responses, and preserving immune function. Additionally, this review examines evidence from clinical trials supporting using RA to improve surgical outcomes, particularly in high-risk populations. Methods: A comprehensive narrative review of the literature was conducted to explore the physiological impact of RA on the systemic stress response and its associated clinical outcomes. Studies comparing RA to GA across various surgical procedures were evaluated, focusing on neuroendocrine modulation, sympathetic inhibition, inflammatory attenuation, and the implications for pain management, cardiovascular and pulmonary function, and immune preservation. Results: RA significantly attenuates the neuroendocrine response by reducing the release of cortisol and catecholamines, thereby improving hemodynamic stability and reducing myocardial oxygen consumption. RA also inhibits the sympathetic nervous system, leading to improved cardiovascular outcomes. Furthermore, RA mitigates the inflammatory response by reducing pro-inflammatory cytokine levels, reducing the risk of systemic inflammatory response syndrome (SIRS), sepsis, and pulmonary complications. Clinical studies and meta-analyses consistently demonstrate that RA reduces postoperative pain, opioid consumption, and the incidence of cardiovascular and pulmonary complications, particularly in elderly and high-risk patients. Conclusions: RA offers a significant advantage in modulating the systemic stress response to surgery, improving postoperative outcomes by reducing pain, enhancing cardiovascular stability, and preserving immune function. Its benefits are particularly pronounced in high-risk populations such as the elderly or those with pre-existing comorbidities. Given the growing evidence supporting its efficacy, RA should be considered a critical component of multimodal perioperative care strategies aimed at minimizing the systemic stress response and improving recovery. Future research should optimize RA techniques and identify patient-specific factors to enhance therapeutic benefits.
Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers
Authors: Chang Victor , Sivakulasingam Sharuga , Wang Hai , Wong Siu Tung , Ganatra Meghana Ashok , Luo Jiabin
Published: November 2024

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain.
A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies
Authors: Lioupa Vasiliki , Karsiotis Panagiotis , Arosio Riccardo , Hasiotis Thomas , Wheeler Andrew J.
Published: November 2024

Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea (Gera Gulf, Lesvos Island, Greece). The complexity of this environment makes it an ideal area to “crash test” (test to the limit) and compare the results of the delineation methods. A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. Three semi-automated tools were also employed to map the reefs: the Benthic Terrain Modeler (BTM), Confined Morphologies Mapping (CoMMa), and eCognition Multiresolution Segmentation. BTM did not function properly with irregular reef footprints, but by modifying both the bathymetry and slope, the outcome was improved, producing accurate results that appeared to exceed the accuracy of manual mapping. CoMMa, a new GIS morphometric toolbox, was a “one-stop shop” that, besides generating satisfactory reef delineation results (i.e., detecting the same total reef area as the manual method), was also used to extract the morphometric characteristics of the polygons resulting from all the methods. Lastly, the Multiresolution Segmentation also gave satisfactory results with the highest precision. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the goodness-of-fit (GOF) with the Precision, Recall, and F1 Scores producing values higher than 0.78, suggesting a good detection accuracy for the semi-automated methods. The analysis reveals that the semi-automated methods provided more efficient results in comparison with the time-consuming manual mapping. Overall, for this case study, the modification of the bathymetry and slope enabled the results’ accuracy to be further enhanced. This study asserts that the use of semi-automated mapping is an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making.
Impact of Site Conditions on Quercus robur and Quercus petraea Growth and Distribution Under Global Climate Change
Authors: Konatowska Monika , Młynarczyk Adam , Rutkowski Paweł , Kujawa Krzysztof
Published: November 2024

Climate change has significant natural and economic implications, but its extent is particularly challenging to assess in forest management, a field which combines both of the previous aspects and requires the evaluation of the impact of climate change on tree species over a 100-year timeframe. Oaks are among the tree species of significant natural and economic value in Europe. Therefore, the aim of this study was to analyze all oak stands in Poland and verify the hypothesis regarding differences between Quercus robur and Quercus petraea stands in terms of soil type, annual total precipitation, average annual air temperature, and the length of the growing season. Additionally, this study aimed to analyze the impact of these differences on the growth rates of both oak species and test whether climate change may affect oak stands. A database containing 195,241 tree stands, including different oak species with varying shares in the stand (from 10% to 100%), was analyzed. A particular emphasis was placed on Q. robur and Q. petraea. The results show that, although both oak species have a wide common range of occurrence, there are clear differences in their habitat preferences. Based on the ordinal regression analysis of selected oak stands, it was concluded that an increase in air temperature of 1 °C could impair the growth of Q. robur and slightly improve the growth of Q. petraea. This may indicate the possibility of expanding the geographic range of sessile oaks towards the east and northeast under warming climatic conditions, provided that appropriate moisture conditions are maintained.
Euler Kernel Mapping for Hyperspectral Image Clustering via Self-Paced Learning
Authors: Zhang Fenggan , Yan Hao , Zhao Jianwei , Hu Haojie
Published: November 2024

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model.
A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery
Authors: Chen Xiaoqiang , Deng Hui , Zhang Wenjiang , Zhang Houxi
Published: November 2024

The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models—Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai–Tibet Plateau, including Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation.
Efficient Phase Step Determination Approach for Four-Quadrant Wind Imaging Interferometer
Authors: Yan Tingyu , Ward William , Zhang Chunmin , Guo Shiping
Published: November 2024

A four-quadrant wind imaging interferometer is a new generation of wind imaging interferometer with the valuable features of being monolithic, compact, light, and insensitive to temporal variations in the source. Its applications include remote sensing of the wind field of the upper atmosphere and observing important dynamical processes in the mesosphere and lower thermosphere. In this paper, we describe a new phase step determination approach based on the Lissajous figure, which provides an efficient, accurate, and visual method for the characterization and calibration of this type of instrument. Using the data from wavelength or thermal fringe scanning, the phase steps, relative intensities, and instrument visibilities of four quadrants can be retrieved simultaneously. A general model for the four-quadrant wind imaging interferometer is described and the noise sensitivity of this method is analyzed. This approach was successfully implemented with four-quadrant wind imaging interferometer prototypes, and its feasibility was experimentally verified.
FireNet: A Lightweight and Efficient Multi-Scenario Fire Object Detector
Authors: He Yonghuan , Sahma Age , He Xu , Wu Rong , Zhang Rui
Published: November 2024

Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates. Additionally, they struggle with detecting small objects, limiting their effectiveness in early fire warnings and rapid responses. As real-time monitoring demands grow, traditional methods often fall short in smart city and drone applications. To address these issues, we propose FireNet, integrating a simplified Vision Transformer (RepViT) to enhance global feature learning while reducing computational overhead. Dynamic snake convolution (DSConv) captures fine boundary details of flames and smoke, especially in complex curved edges. A lightweight decoupled detection head optimizes classification and localization, ideal for high inter-class similarity and small targets. FireNet outperforms YOLOv8 on the Fire Scene dataset (FSD) with a mAP@0.5 of 80.2%, recall of 78.4%, and precision of 82.6%, with an inference time of 26.7 ms. It also excels on the FSD dataset, addressing current fire detection challenges.
Using Resistivity Structure to Study the Seismogenic Mechanism of the 2021 Luxian Ms6.0 Earthquakes
Authors: Liu Xuehua , Zhan Yan , Zhao Lingqiang , Sun Xiangyu , Lou Xiaoyu
Published: November 2024

Over the past few years, there has been a noticeable change in the occurrence of seismic disasters in Sichuan, China. The focus has shifted from Western Sichuan to the previously more stable Southeastern Sichuan. The recent Ms6.0 earthquake in Luxian, Southeastern Sichuan, on 16 September 2021, has once again captured the interest of scholars, who are closely examining the seismogenic environment and potential seismic hazards in the region. We conducted a magnetotelluric (MT) array survey in the Luxian earthquake area to explore the deep seismogenic environment of the 2021 Luxian Ms6.0 earthquake zone and understand the potential effects of industrial extraction on seismic activities. Here are the insights we obtained: Underneath the anticline in the Luxian Ms6.0 earthquake area, there is a structure that mainly exhibits high resistance. On the other hand, beneath the syncline, a structure with medium to low resistance is observed. The epicenter of the mainshock was identified near the intersection of high- and low-resistance media within the Fuji syncline area. Smaller aftershocks that followed the mainshock were mainly concentrated in the low-resistance layers at depths of 3–5 km in the Fuji syncline area. MT survey results have confirmed the existence of a detachment zone in the shallow crust near the epicenter of the Luxian Ms6.0 earthquake. It is believed that this detachment layer played a significant role in the seismogenic process of the Luxian Ms6.0 earthquake. During different stress conditions, this layer became active and caused the compression and faulting of a hidden fault below, resulting in the Luxian Ms6.0 earthquake. After the main earthquake, a series of smaller aftershocks with varying focal mechanisms occurred as the stress fields continued to release. It is important to note that the Luxian Ms6.0 earthquake highlights the ongoing high stress levels in the southern region of the Sichuan Basin. This emphasizes the need for continued monitoring and consideration of potential seismic hazards in the southern Sichuan area.
Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau
Authors: Chen Xinyu , Song Minhong , Wang Yaqi , Wu Tongwen
Published: November 2024

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.