bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of The Textile Reinforcement on The Mechanical
Behavior of Polymer Composite Materials
Authors: Risteska Svetlana , Srebrenkoska Vineta , Zhezhova Silvana , Srebrenkoska Sara , Risteski Sanja , Jordeva Sonja , Golomeova Longurova Saska
Published: November 2024

Investigating the impact of textile preforms on the mechanical characteristics of composite plates made of glass textile structures and epoxy resin using compression molding technique was the goal of this work. An epoxy resin system served as the matrix, and various woven (plain, twill, basket), nonwoven (mat), and unidirectional (UD) textile structures made from E-glass fibers were employed as reinforcement elements. Compression molding of pre-impregnated semi-finished textile materials (prepregs) was used to create the composites. Fiber pull-out and delamination are the main challenges in the manufacturing of polymer textile composites. As a result, the most crucial aspects of producing polymer textile composites were making ensuring that the textile structures were well-impregnated with resin into the prepreg and that the layers in the composites had a good interface. The generated composites were then subjected to micro/macro examinations using a SEM microscope and DSC thermal analysis. Flexural strength and modulus of elasticity were measured using a three-point flexural method to assess the mechanical behavior of all manufactured textile composites. The performance of polymer textile composite materials is directly influenced by the type of textile structures used as a reinforcing composite constituent, according to the results obtained.
Intraoperative Monitoring of Sensory Evoked Potentials in Neurosurgery: A Personalized Approach
Authors: Levin Evgeny A.
Published: November 2024

Sensory evoked potentials (EPs), namely somatosensory, visual and brainstem acoustic EPs, are used in neurosurgery to monitor the corresponding functions with the aim of preventing iatrogenic neurological complications. Functional deficiency usually precede structural defect, being initially reversible, and prompt alarm may help surgeons to achieve this aim. However, sensory EPs registration requires presenting multiple stimuli and averaging of responses, which signifi-cantly lengthen this procedure. As delays can make intraoperative neuromonitoring (IONM) in-effective, it is important to reduce EP recording time.
The possibility of speeding up EP recording relies on differences between IONM and clinical neurophysiology (CN; all sensory EPs were borrowed to IONM from CN). Namely, in IONM, the patient is her/his own control and neurophysiologist is less constrained by norms and standards when in CN. Therefore, neurophysiologist can perform personalized selection of op-timal locations of recording electrodes, frequency filters passbands and stimulation rates. Vary-ing some or all of these parameters, it is often possible to significantly improve signal-to-noise ratio (SNR) for EPs, and accelerate EP recording by up to several times.
The aim of this paper is to review how this personalized approach is or may be applied during IONM for recording sensory EPs of each of abovementioned modalities. Also, the problems hindering implementation and dissemination of this approach and options for overcoming them are discussed here, as well as possible future developments.
Creating Urban Landscape Typology That Facilitates the Integration of Urban Ecological Knowledge into Practice
Authors: Masoudi Mahyar , Richards Daniel R. , McDonnell Mark J. , Yin Tiangang , Tan Puay Yok
Published: November 2024

Urban landscapes are highly heterogeneous but possess recurrent, distinguishable features known as urban landscape types, which exhibit distinct social and environmental performance, and collectively form an urban landscape typology (ULT). ULT is useful as they can aid decision-making in urban planning, predicting the impact of various development proposals. Past attempts to develop ULT overlooked important land features including land use. We developed a conceptual framework based upon the recent literature, and operationalized it using Singapore, an archetypical compact city. Fourteen geospatial variables representing five main components of urban landscapes (land cover composition and configuration, land use, building density, and elevation) were used in a heuristic process to construct a series of models using varying clustering algorithms, coupled with adjustments utilizing contextual knowledge. Models were then tested for their performance regarding practical and environmental relevance. Our results identify ten distinguishable landscape types in Singapore. The six variables that were used in characterizing the UTL were: percent impervious surface, land cover diversity, land use diversity, building density, elevation, and percent water. Initial inspection against Google Earth, followed by a systematic validation exercise by urban planning and design students and professionals confirmed that the ten landscape types adequately correspond to distinguishable urban forms. The ULT were also examined for their ability to estimate land surface temperature (LST) as an environmental performance example; our results demonstrated that all landscape types had significantly different LSTs. We believe that our ULT and the methodological approach we adopted can assist in integrating urban ecological knowledge into practice.
Thoughtseeds: A Hierarchical Model of Embodied Cognition in the Global Workspace
Authors: Kavi Prakash Chandra , Zamora-López Gorka , Friedman Daniel Ari
Published: November 2024

The emergence of cognition requires a framework that bridges evolutionary principles with neuro-computational mechanisms. This paper introduces the novel “thoughtseed” framework, proposing that cognition arises from dynamic interactions of self-organizing units of embodied knowledge called “thoughtseeds” within the Global Workspace of consciousness. By leveraging evolutionary theory, neuronal packets, and the Free Energy Principle, we propose a hierarchical model of cognitive states comprising Neuronal Packet Domains (NPDs), Knowledge Domains (KDs), thoughtseed networks, and meta-cognition. This hierarchical interplay, mediated by nested Markov blankets and reciprocal message passing, facilitates the emergence of thoughtseeds as coherent neural activity patterns that guide perception, action, and learning. Thoughtseeds, posited as fundamental units of thought, compete for dominance within the Global Workspace, with the dominant thoughtseed shaping conscious experiences and guiding behavior. We present an illustrative mathematical framework grounded in active inference and dynamical systems theory to model thoughtseed dynamics and their contribution to the unitary nature of consciousness. This biologically grounded model offers a novel approach to understanding the organizing principles and emergence of embodied cognition, with potential applications in consciousness, attention, and decision making.
Communication on Methodological Considerations Regarding the Quantification of DNA Impurities in the COVID-19 mRNA Vaccine Comirnaty®. Methods Protoc. 2024, 7, 41
Authors: König Brigitte , Kirchner Jürgen O.
Published: November 2024

The purpose of this communication is to clarify criticisms that have arisen in the context of our publication "Methodological Considerations Regarding the Quantification of DNA Impurities in the COVID-19 mRNA Vaccine Comirnaty®" (Methods Protoc. 2024, 7, 41 [1]). In the meantime, a preprint has appeared entitled "Quantification of objective concentrations of DNA impurities in mRNA vaccines" (Kaiser et al. [2]), which attempts to refute our findings. However, it does not succeed in doing so with the necessary persuasiveness. First of all, it is particularly important that Kaiser et al [2] have confirmed that our results are reproducible when the quantification is carried out using the Qubit® technology in accordance with the manufacturer's instructions, which is exactly what we did. However, it is then claimed by Kaiser et al [2] that the magnitude of the DNA impurities we have shown would be an effect of high amounts of RNA present in the samples. As a proof they quantified a defined concentration of DNA in the presence of very high concentrations of RNA with the Qubit® methodology. However, even the presence of 250 ng/µL RNA resulted only in a comparatively small increase in the DNA value of 0.655 ng/µL and this is far from explaining the DNA concentrations of 12 to 17.8 ng/µL that we have measured in several batches of Comirnaty®. Further, the preprint [2] mentioned that experiments with DNA extracted from the vaccine would show that the very low legal limits for DNA in Comirnaty® are met. However, the authors of this critique have failed to demonstrate that the extractions they performed are indeed quantitative, i.e. reflect the actual DNA contamination. However, based on the related published literature, this must be denied. With this in mind, we can finally confirm that both our methodology and our data, as published in our above-mentioned article [1], imply that DNA impurities as measured with Qubit® by us in Comirnaty® are reliable according to the manufacturer's premises for this DNA quantification technology. In this sense, the DNA values presented in the Kaiser et al. preprint [2] after extraction procedures are obviously artificial effects of the extractions performed and therefore do not represent the true DNA contamination of the concerned Comirnaty® batches.
Sağlık Çalışanlarının Kalite Algısı: Bir Özel Hastane Uygulaması
Authors: Arslanoğlu Ali , Yılmaz Öznur , Cetin Minel
Published: November 2024

Bu çalışmada uluslararası akreditasyon kuruluşları (JCI, TEMOS) tarafından akreditasyon belgesine sahip bir sağlık kuruluşundaki sağlık çalışanlarına yapılan anket ile çalışanların kalite algı seviyelerini ölçmek ve bunu etkileyen faktörlerin belirlenmesi amaçlanmıştır. Çalışma kesitsel ve tanımlayıcı tiptedir. Araştırmada anket yöntemi kullanılmıştır. Araştırma gönüllü katılan 295 sağlık personeli ile gerçekleştirilmiştir. Anket, katılımcılara dijital olarak gönderilmiştir. Verilerin analizi SPSS programı kullanılarak yapılmıştır. Verilerin değerlendirilmesinde tanımlayıcı istatistikler frekans, yüzde, ortalama, standart sapma, t testi, varyans analizi kullanılmıştır. Katılımcıların %52,9’u kadın, %47,1’inin erkek, %52,2’si evli %47,8’i ise bekârdır. Kurumda çalışma süreleri incelendiğinde %53,6’sı 1 yıl altı, %46,4’ünün 1 yıl ve üzeri olduğu görüldü. Sağlık çalışanlarının farklılık analizleri sonucu ortalama puanlar açısından cinsiyet değişkeni, kurumda çalışma yılı değişkeni, medeni durum değişkenlerinde alt boyutlarda anlamlı farklılıklar tespit edilmiştir. Araştırma sonucunda en yüksek ortalamanın “Kalite Uygulamalarının Yararı” (3,66±0,95); en düşük ortalamaya ise “İnsan Kaynakları Kullanımı” (3,42±0,91) alt boyutunun sahip olduğu tespit edilmiştir. Erkeklerin kadınlara oranla kalite algılarının yüksek olduğu, idari ve tıbbi kadro arasında anlamlı fark olduğu belirlenmiştir.
Well Interference Test in Naturally Fractured Gas Reservoir: Numerical Simulation and Impact on Reservoir Performance Forecasting
Authors: Gavrilov A. V. , Togaev S. E. , Limanskii E. N. , Abidov K. A.
Published: November 2024

The paper presents the evolution of the simulation approach for a green gas field in the Republic of Uzbekistan. The field is currently being appraised as part of the pilot development design process. The reservoir is composed of intensely fractured carbonate rock.

Appraisal results that explicitly required an immediate transition from single porosity (SP) simulation to the dual porosity (DP) approach are described along with the resulting impact on production forecasting and reservoir development design.

During an appraisal campaign, a well interference test intended for a dual media study has been conducted. The SP simulation model, which was used as a main RE tool for designing the field development, failed to simulate it. This required the implementation of the DP approach.

The DP model predicts a shorter period of gas production plateau and earlier water breakthrough compared to the SP model. The uncertainty most impacting on cumulative production is revealed to be the aquifer volume. In the DP model case, the larger the aquifer, the shorter the production plateau and the sooner water breakthrough occurs. Conversely, the SP model case establishes the positive impact of the aquifer volume on cumulative production. Thus, the SP approach conceals significant RE risks, leading to suboptimal field development decisions. The appraisal program and the pilot development project for the field have been updated to ensure they remain optimal, considering the new simulation approach.

Therefore, for the fractured reservoir – where reservoir permeability is provided by fractures and porosity is attributed to matrix – it is crucial to conduct fracture studies and implement the appropriate simulation approach from the very first steps of field exploration.

Matching a DP model to an interference test data results in determining storage and flow capacity parameters: porosity, permeability and compressibility for the matrix and fractures separately, whereas the analytical model's interpretation provides only integrated parameters of dual media – interporosity flow coefficient and storativity ratio.

Furthermore, at the early stages of a field development the fracture compressibility, which critically impacts reservoir performance, can only be evaluated using the approach presented.
Deformation Monitoring of Northern Caspian Sea Kashagan Oilfield Using Radar Satellite Remote Sensing
Authors: Bayramov E. , Tessari G. , Kada M. , Aliyeva S. , Buchroithner M. , Neafie J. , Duisenbiev A.
Published: November 2024

This study focused on the assessment of marine Kazakhstan Kashagan oil field located in the Northern Caspian Sea. High-resolution COSMO-SkyMed (CSK) and medium-resolution Sentinel-1 (SNT1) satellite images acquired during 2018-2022 were used for persistent scatterer interferometric processing (PS-InSAR). Based on the measurements from ascending and descending satellite tracks, decomposition of line-of-sight measurements were applied to achieve vertical and horizontal deformations. The practical value and novelty of the present studies were to optimize and simplify monitoring and inspection activities for geohazard risk assessment of offshore petroleum and gas infrastructure in the Caspian Sea.
Molecular Dynamic Prognosis for Ti-C10H16N2O8 Filter Cake Decomposition
Authors: Irawan S. , Wayo D. D. K. , Bayramov E. , Fathaddin M.T. , Goliatt L.
Published: November 2024

Chelating agents such as Ethylene-Diamine-Tetra-Acetic (EDTA) had been suggested to be a newly designed biodegradable filter cake breakers that could enhance efficiency in filter cake removal. This chelate-based fluid had been proven to effectively dissolved the bridging agent in the filter cake which was calcium carbonate causing the removal of the filter cake to be successful. Besides that, the ability of the chelating agents to provide low corrosion potential made chelating agent as a great alternative for live mineral acids. This provided less aquatic toxicity, more human and environment friendly and readily. However, traditional EDTA cannot performance well in the high-pressure, high-temperature (HPHT) wellbore environments. Therefore, in this study, we explore the use of Titanium-enhanced ethylenediaminetetraacetic acid (Ti-EDTA) as a novel chemical breaker for the decomposition of filter cakes formed in high-pressure, high-temperature (HPHT) wellbore environments. Using ab initio molecular dynamics (AIMD) simulations within the Quantum Espresso framework version 7.2, we examine the molecular interactions, atomic displacements, and thermodynamic behavior of Ti-EDTA in contact with synthetic-based mud (SBM) residues. The simulations track the structural evolution of Ti-EDTA during filter cake decomposition, revealing significant molecular rearrangements and enhanced solubility of the filter cakes. Compared to traditional EDTA-based and silica-based breakers, Ti-EDTA shows improved potential energy and particle mobility, indicative of greater decomposition efficiency. Our results indicate that EDTA resulted in a more negative potential energy of −1035 Ry signifying a stable system, whereas Ti-EDTA simulation yielded a less stable system with a value of −935 Ry energy. The atomic interactions at both levels indicated that Ti-EDTA possesses higher temperature by 16000 K and with a coupled higher potential energy makes an effective chemical to enhance decomposition reactions. For a higher rate of potential energy, temperature and volume, Ti-EDTA molecule best beats EDTA in terms AIMD simulated efficiency. The integration of titanium ions into the EDTA structure significantly boosted the simulation performance, making it a promising candidate for environmentally friendly wellbore cleanup applications. The study provides insights into the molecular mechanisms driving filter cake degradation and sets the stage for future experimental validation of Ti-EDTA's efficacy in field operations.
Cloud-Based Digital Solution: A Modern Approach to Casing Design for Tengizchevroil Complex Wells
Authors: Rakhmetov Z. , Morrison A. , Kamzayev N. , Mulyukova Y. , Majumdar C. , Temple P.
Published: November 2024

Tengizchevroil (TCO) deployed a cloud-based solution in 2022, adopting engineering planning workflows to enhance collaboration among all disciplines' experts—wells drilling, geologists, geomechanics, and service providers. The key benefit was having one integrated solution that contains multiple engines and communicates with geological software, thus minimizing data re-entry and reducing manual inputs or requests to a minimum. At the time of the cloud-based deployment and adoption, the casing design workflow was considered a very complex task that needed to be verified and validated by subject matter experts. This paper outlines the casing design validation and verification process within the cloud-based solution, its results, lessons learned, and the way forward for the process.

TCO utilizes two generic casing designs for all wells, which is selected based on production reservoir targets. Design standard requires performing casing design analysis for each individual well using tailored software and then validating against design requirements.

The cloud-based solution offers both uniaxial and triaxial stress analysis for casing design, accounting for multi-axis stresses with a predefined set of load cases. This capability allowed subject matter experts to create load cases and customize workflows to align with standard TCO policies. Casing design relies on several engineering inputs, such as trajectory, wellbore schematics, pore and fracture pressure windows, drilling fluids, casing integrity tests, and other parameters. When any of these engineering inputs are altered, ensuring changes are reflected in the casing design calculation, is always a challenge. The single-entry approach of the digital platform accounts for changes and minimizes mistakes by ensuring data is entered by domain experts, thereby improving the accuracy and efficiency of the casing design process. The cloud-based solution's ability to create customized-automated load cases significantly reduces the need for manual data entry to calculate internal or external pressure profiles.

As part of the verification and validation process, TCO, collaborating with a subject matter expert, confirmed the cloud-based solution outputs provide comparable results against the previously used software, resulting in the acceptance of the new TCO Casing Design Process.

For the TCO project, the way forward involves continuing to use cloud-based solution as a tool for future planning and execution of the well construction. The uniqueness of this paper lies in its focus on the integration of digital tools that streamline complex calculations for the Casing Design analysis and foster collaborative workflows, exemplified by TCO's successful implementation of the new process.
WITHDRAWN: The Relationship of Depression, Anxiety, Stress and Post-Traumatic Stress Disorder with Professional Quality of Life Among Healthcare Workers During the COVID-19 Pandemic in Turkey
Authors: GÖRGÜN Ceren TÜRKDOĞAN , ALDOĞAN Ece UĞURLUOĞLU
Published: November 2024

The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Drivers of the 2023 record shattering marine heat extreme in the North Atlantic
Authors: Guinaldo Thibault , Cassou Christophe , Sallée Jean-Baptiste , Liné Aurélien
Published: November 2024

The year 2023 shattered numerous heat records globally and regionally. We here focus on the origins of the unprecedented and persistent warm sea surface temperature (SST) anomalies in the North Atlantic Ocean. Using a combination of multiple observational datasets and climate model large ensemble outputs, we show that the 2023 North Atlantic SST event is a manifestation of anthropogenically-forced warming, exacerbated by an extreme phase of internal climate variability and associated regional surface flux anomalies. The long-term stratification increase due to anthropogenically-driven ocean warming amplified the impacts of internal variability. At current global warming level, the 2023 event is assessed as a decadal-type occurrence for SST anomalies averaged across the entire North Atlantic basin, but it is estimated as a centennial-type event for the subtropics and the eastern basin. The distinctive horseshoe-shaped regional distribution of these anomalies is crucial to consider for correct communication and risk assessment in a warming climate.
Development of an intelligent metal forming robot and application to multi-stage Cold Forging
Authors: Tchasse Papdo , Deliktas Tahsin , Liewald Mathias
Published: November 2024

Metal forming processes often undergo different instability phases due to different factors like tool and part temperature variations, tool vibrations or frictional interactions between workpiece and tool. In the absence of an experienced process operator, these instabilities can induce a very considerable production loss. This study addresses this issue and proposes a method to develop a data-based virtual process operator equipped with the appropriate hardware and physical components that allow it to constantly monitor and if necessary regulate the process. The resulting system is introduced as the intelligent metal forming robot. The objective of this self-learning system is first to stabilize the process and ensure a certain part quality despite the noises, dynamical disturbances and user-defined changes of the part quality requirements, then, to control the process even in states that have not yet been experienced and at last to improve the control precision based on the updated process experience. This intelligent metal forming robot has been implemented and applied on a two-stage cold forging process, where the target quality feature was the part head height of a screw-like part. The results showed that, based on a qualitative process experience and on effective actuators, an intelligent self-learning system can significantly increase the robustness of a metal forming process.
Impact of the obesity paradox on 28-day mortality in elderly patients critically ill with cardiogenic shock: a retrospective cohort study
Authors: Tian Jing , Jin Ke , Qian Haohao , Xu Hongyang
Published: November 2024

Background
Previous studies have shown that the obesity paradox exists in cardiovascular disease (CVD), giving patients a survival advantage, but controversy remains as to whether it applies to patients with cardiogenic shock (CS), especially in the elderly. We therefore aimed to determine whether obesity affects 28-day prognosis in elderly patients with CS.
Methods
We used clinical data from the Medical Information Market in Critical Care IV (MIMIC-IV) database. Critical patients with CS were categorized into two groups based on age; age < 65 and ≥ 65 years were classified as young adult patients and elderly patients, respectively. Patients were then categorized into two subgroups based on their body mass index (BMI), one with a BMI ≥ 30 kg/m² and the other with a BMI < 30 kg/m². The primary outcome was a 28-day prognosis.
Results
1827 patients from the MIMIC-IV ICU database were analyze. According to multifactorial logistic analysis, BMI > 30 kg/m2 was not a 28-day risk factor for death in elderly patients critically ill with CS (Overweight OR 1.18, P = 0.271; Obesity OR 1.05, P = 0.770; Severe obesity OR 1.06, P = 0.839; using normal weight as a reference). In contrast, underweight was a risk factor (OR 2.02, P = 0.033). Kaplan-Meier curves showed that in the older age group, 28-day survival was significantly higher in patients with BMI ≥ 30 compared to those with BMI < 30 [ 261 (66.75%) vs. 522 (60.35%), P = 0.024].
Conclusion
The obesity paradox does not seem to apply to patients with CS, whether young or elderly, but rather underweight can have an impact on the 28-day prognosis of elderly patients who are critically ill with CS.
ANN-UKF-Based Estimator for Landing Forces in Quadruped Robots
Authors: Huynh Ba-Phuc , Bae Joonbum
Published: November 2024

Estimation of landing forces is essential for legged robots to execute precise and effective movements, particularly when navigating complex and unfamiliar terrains. Traditionally, this estimation is achieved using force sensors mounted on the robot's legs. This paper introduces a novel approach that eliminates the need for force sensors by employing a force-sensorless estimator. The proposed method utilizes an artificial neural network (ANN) to forecast joint torques at the legs at specific points along the motion trajectory. This ANN model functions as a predictor within an Unscented Kalman Filter (UKF), which refines and stabilizes the force measurements. By analyzing the differences between predicted and filtered values, the method estimates both the state and magnitude of the robot’s foot impact with the terrain. The effectiveness of this approach has been validated through simulations and practical tests on a quadruped robot.
Assessment of the Robustness and Resilience of the Water System in the Bogotá River Basin: A Multi-Objective Optimization Approach
Authors: Muñoz Dairo , Rueda William J. Guerrero , Rodríguez Leonardo José González
Published: November 2024

Extreme climatic phenomena, such as the El Niño/Southern Oscillation (ENSO), have significant consequences for human life and global ecosystems. This phenomenon manifests in two forms: La Niña and El Niño. In Colombia, La Niña brings seasons of intense rainfall and flood risks, while El Niño induces warm and dry conditions, reducing river flow rates, reservoir levels, and causing water scarcity. In the Bogotá River basin, located in the Cundinamarca department, Colombia, these phenomena have resulted in severe floods and water and energy rationing in the past. Understanding and monitoring these events are fundamental for risk management and adaptation to climate change. This study focuses on evaluating the robustness and resilience of the hydrological system in the Bogotá River basin in the face of flooding and water scarcity events, analyzing strategic control points throughout the basin. A multi-objective linear programming model was implemented to represent the functioning of the hydrological system, covering 22 hydrographic sub-basins, 9 reservoirs, and 25 monitoring stations distributed along the Bogotá River. The findings of the study are crucial for risk planning and mitigation, promoting more effective management in response to extreme climatic events. However, the results indicate that the hydrological system lacks the necessary robustness and resilience to operate effectively under extreme conditions of rainfall or drought, highlighting the need to adopt strategies that improve its operational performance and meet robustness and resilience requirements, thus ensuring its functionality even under adverse climatic scenarios.
Synergistic inhibitory effect of atmospheric pressure plasma and berberine on non‑small cell lung cancer cells via inducing apoptosis
Authors: Ming Huiyun , Lu Tingting , Zhou Han , Song Wencheng , Dai Haiming
Published: November 2024

Background
Non-small cell lung cancer (NSCLC) is a type of lung cancer, the incidence and mortality rate have been high, and the use of monotherapy is easy to make patients develop tolerance. Atmospheric pressure plasma (APP) is an emerging technology for killing cancer cells in recent years, and there are fewer studies related to the combination of berberine and the mechanism of action has not been fully elucidated for NSCLC. The article primary goal is to investigate the effect of combination on NSCLC and its associated characterization.
Methods and results
Antiproliferative effects were detected by MTT assay and colony formation, and flow cytometry analysis of apoptosis and cycling showed that the combination synergistically induced apoptosis. Then, extracellular ROS levels and DCFH-DA-based kits examined intracellular ROS levels, and their effects on mitochondrial membrane potential were measured. Study reveals that co-induced apoptosis is associated with ROS accumulation. Subsequently, Western blotting (WB) detected the expression of EGFR, and the important signaling pathway proteins Ras / ERK and AKT / mTOR. Results showed that it could downregulation of EGFR protein expression and inhibit of activation of ERK/ AKT signaling pathways. Simultaneous wound healing assay and EMT marker detection were performed for the assessment of migration and EMT ability of NSCLC cells. Combination therapy inhibited migration and EMT of NSCLC cells.
Conclusion
The results of this study show that the combination can synergistically induce apoptosis of NSCLC by regulating ROS production. EGFR downregulation and AKT/ERK signaling pathway inhibition are linked to the synergistic effect.
Investigation antibacterial activity of synthesized GO-SiO 2 nano hybrid against Escherichia coli as a bacterial index in water
Authors: Mohammadzadeh Alireza , Kalankesh Laleh R. , Karbalayi Mohadeseh , Hoshanghi Zahra
Published: November 2024

Graphene oxide-silicon dioxide (GO-SiO2) nanocomposites are known for their potential antibacterial activity and unique characteristics. This study was aimed to investigate antibacterial activity of GO-SiO2 nanocomposites in various ratios. Scanning electron microscope (SEM) method was used to characterize morphological structure of GO-SiO2. Disk diffusion, Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) method was used to investigation antibacterial activity in different nanoparticle ratios. The GO-SiO2 ratio of 1:1 exhibited the strongest inhibition zone against E. coli (20 mm), surpassing the individual antibacterial activity of GO and SiO2 alone. MIC and MBC tests provided valuable insights into the effectiveness of GO-SiO2 nano-hybrids against E. coli, with a MIC value of 6%, 2% and 2% for GO-SiO2 1:1 SiO2 and GO nanoparticles, respectively. MIC for GO-SiO2 in 1:2 and 2:1 ration >6% and >10% was resulted. Additionally, MBC of 12%, 4% and 4% was obtained for GO-SiO2 1:1 SiO2 and GO nanoparticles, respectively. This study successfully investigated the antibacterial properties of SiO2-GO nano-hybrids prepared through a novel imprinting route. The results demonstrate their potential as effective antibacterial agents, offering valuable insights for the development of advanced antibacterial materials.
Figure-of-eight Knot Technique combined with Subtemporal Placement for Prevention of Silicone Oil Blockade in Ahmed Glaucoma Valve：A Novel Surgical Technique for the Treatment of Refractory Glaucoma after silicone oil endotamponade
Authors: Zhang Lishuai , Chu Yanzi , Xi Huiyu , Chen Xinxia , Wei Jiajun , Zhao Jinqiao , Liu Haiyang , Chen Xiaoya
Published: November 2024

Background
In recent years, glaucoma valves have played an important role in the management of SOE-related glaucoma. However, the occurrence of tube blockage with SO has emerged as a significant factor contributing to surgical failure in these patients. One reason is that the opening of tube is too close to silicon oil, and the other is the early excessive drainage of aqueous humor. Therefore, we developed this technique to manipulate the positioning of the valve body and restrict early aqueous humor outflow and subsequently assessed its safety and efficacy during an over one-year follow-up.
Methods
After the glaucoma valve was implanted in the subtemporal region, a figure-of-eight knot was utilized to encircle the tube, forming a knot of continuous double loops. This knot gently compresses the tube, thereby constricting its lumen and restricting drainage of aqueous humor. The IOP, BCVA, and complications in all patients were observed and recorded.
Results
There are 6 eyes with secondary glaucoma received this surgical treatment. The mean of preoperative IOP was 45.5 ± 6.8mmHg, and decreased to 13.5 ± 3.0mmHg after surgery (P = 0.000). During the follow-up(17.7 ± 4.3months), we observed stable IOP, and none of the patients exhibited tube blockage caused by SO.
Conclusions
This technique can effectively prevent SO blockage in AGV.
Rediscovering the climate analogue method: insights from a data-driven approach
Authors: Chen Pei-Chun , Wang Li-Pen , Kim Dongkyun
Published: November 2024

Analogue methods are widely used in atmospheric science for weather forecasting and climate risk studies. The concept of weather/climate analogues is straightforward: it quantifies the similarity between weather conditions at target and candidate time periods by computing a distance using relevant atmospheric variables (e.g., temperatures and geopotential heights). Traditionally, Euclidean distance (ED) is used for this task, computed over the original value space of the variables. In recent years, with the rapid advance in deep learning (DL) techniques, the concept of ’features’ (or representations) has emerged, providing an alternative for analogue-related applications. This study aims to explore the potential of DL in climate- scale applications. Specifically, we propose a data-driven climate analogue approach, which involves constructing a DL-based climate dynamics model, ClimaDist. This model identifies features relevant to climate dynamics, and we use these features, instead of the original input atmospheric variables, to search for analogues. In addition to the traditional ED, we explored two other distance measures that were not widely used in the atmospheric community: the image Euclidean distance (IMED) and the structural similarity index metric (SSIM). A range of different combinations of models, distance measures and input data settings were tested using ERA5 reanalysis data, and our results suggest that the proposed ClimaDist outperforms all other model combinations in terms of the ability to reproduce both the observed distributions and variations. Finally, we employed explainable AI (XAI) techniques to analyse the effectiveness of ClimaDist in identifying climate analogues. The results indicate that the importance of climate features varies both spatially and temporally, a factor that should be accounted for in analogue identification but is often overlooked in traditional methods.
Simulation comparison of the effects of missing data imputation methods on classification performance in high dimensional data
Authors: VAROL Bugra , Omurlu Imran Kurt , Ture Mevlut
Published: November 2024

The study aims to examine the performance of different missing data imputation methods on accurately estimating missing data in high dimensional datasets and their impact on classification using extreme learning machines (ELM). Random datasets were generated with n = 150 observations, p = 500 independent variables, and different missing data rates. Various imputation methods were used, including mean, median, random, k-nearest neighbors (KNN), missing value imputation with random forests (I-RF), multivariate imputations by chained equations with classification and regression trees (MICE-CART), as well as direct and indirect use of regularized regression (DURR and IURR) methods specifically developed for high dimensional data. The performance of the methods was evaluated based on their proximity to the reference classification scores obtained using ELM. I-RF, MICE-CART, DURR, and IURR, followed by KNN methods, exhibited better performance at low missing rates, while DURR and IURR methods stood out at high missing rates.
Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization
Authors: VELUSAMY SARAVANAN , MURUGAN PALLIKONDA RAJASEKARAN
Published: November 2024

The precise interpretation of the ECG signal can reveal the condition of the heart. ECG signal analysis can assist in identifying any abnormalities or arrhythmias in the heart. Premature Ventricular Contractions (PVCs) are irregular heartbeats that may signal the presence of a heart ailment. Long-term ECGs are commonly utilized in clinical practice to diagnose PVCs. However, analyzing these long-term ECGs is time-consuming for cardiologists and requires human involvement. This research proposes a robust approach for detecting R peaks in QRS complexes using a recurrent neural network. Our proposed methodology was applied to the well-known MIT-BIH Arrhythmia Database (MIT-DB) dataset and the China Physiological Signal Challenge (2020) database, which contains over a million beats. The hybrid linearization technique uses an adaptive filter and discrete wavelet transform (DWT) to remove noise from the ECG signal. The next step is to use principle component analysis (PCA) to extract characteristics from the ECG data. Lastly, the R peak signals are classified using long short-term memory (LSTM) to improve accuracy through optimization techniques like Grey Wolf optimization (GWO). The algorithm's performance was also evaluated using the MIT-BIH Arrhythmia database and the China Physiological Signal Challenge (2020). The suggested formal technique yields the best results for R-peak detection on CPSC-DB, with F1-score of 95.3%, recall of 96.8%, accuracy of 99.5%, and precision of 95.3%. The F1-score, recall, and precision of the algorithms on MIT-DB are all equivalent to, or better than, those of the competing methods.
Hybrid Cloud-Edge AI Framework for Real-Time Predictive Analytics in Digital Twin Healthcare Systems
Authors: Jameil Ahmed K , Al-Raweshidy Hamed
Published: November 2024

The development of Digital Twin (DT) systems for healthcare introduces new opportunities and challenges, particularly in the areas of real-time processing and predictive analytics. This research presents a hybrid cloud-edge architecture designed to improve the efficiency and responsiveness of remote healthcare monitoring. By integrating edge computing with cloud resources, the system facilitates real-time data transfer and decision-making based on live patient met-rics. A hybrid machine learning model, combining Multi-Layer Perceptron (MLP) and XGBoost, was applied to predict key health indicators, such as heart rate, blood oxygen saturation, body temperature, and diabetes mellitus. Far-edge and near-edge devices were deployed to handle data processing at various stages, reducing latency and improving overall system performance. The results demonstrate significant improvements, with a 40% reduction in latency, a 15% increase in efficiency, and 30% improvement in throughput compared to non-DT systems. The hybrid AI model exhibited robust performance, with cross-validation accuracy reaching 97.48% and real-time accuracy measured at 95.45%. Additionally, it reduced testing time by 58.4%, highlighting its efficiency for real-time healthcare applications. This framework enhances the scalability, efficiency, and reliability of remote healthcare systems, offering a practical solution for real-time patient monitoring in healthcare applications.
NDVI and vegetation volume as predictors of urban bird diversity
Authors: Fairbairn Andrew J. , Katholnigg Sophia , Leichtle Tobias , Merkens Lisa , Schroll Louis , Weisser Wolfgang W. , Meyer Sebastian T.
Published: November 2024

Urban expansion and densification pose a challenge to urban biodiversity. Rapid estimation of biodiversity could help urban planners balance development and conservation goals. While the Normalised Difference Vegetation Index (NDVI) has proven useful for predicting urban bird diversity, new products derived from remote sensing, such as vegetation volume, could provide more detailed descriptions of available habitat, potentially improving biodiversity predictions. We evaluated the effectiveness of NDVI and vegetation volume as predictors of urban bird diversity and local community composition for different buffers around 86 sampling points in Munich, Germany. Using linear models, we showed that a 100 m buffer best described bird diversity (highest r2) for both NDVI and vegetation volume compared to the other buffers. Contrary to expectations, NDVI was superior to vegetation volume in predicting bird diversity (mean r2 NDVI = 0.45, mean r2 vegetation volume 0.37). We found a shift in community composition from species associated with human-modified landscapes to those associated with forests along an urban greenness gradient. In contrast to diversity, we found that vegetation volume was slightly better at predicting community composition. Using NDVI to predict bird diversity across Munich, we demonstrated its potential for predicting city-wide bird diversity. We discuss how such predictive maps can be used for urban planning and conservation. As urbanisation continues to impact global biodiversity, refining ecological models for urban planning will be crucial to developing more biodiverse urban environments.
Using agro-hydrological machine-learning to spatially target investments in sustainable groundwater irrigation
Authors: Urfels Anton , McDonald Andrew , Ajay Anurag , Arenas-Calle Laura , Craufurd Peter , Deb Proloy , De Graaf Inge E.M. , Halsema Gerardo Van , Karki Saral , Kishore Avinash , Kumar Virender , Mkondiwa Maxwell , Rossiter David , Seijgers Chris , Nayak Hari , Sherpa Sonam , Srivast Amit , Laborte Alice
Published: November 2024

Groundwater irrigation supports over 40% of global crop production and stabilizes yields amidst climatic change. Yet, over-abstraction can cause water scarcity, disrupt ecosystems, and increase greenhouse gas emissions. Governments and international financial institutions have made significant investments in sustainable groundwater irrigation but require enhanced spatial targeting to increase impact. In response, this study employs an agro-hydrological machine-learning approach to analyze spatial patterns of (i) crop yield responses to increased irrigation and (ii) groundwater sustainability in South Asia – characterized by smallholder farming, increasing groundwater dependence, and post-green revolution sustainability challenges. We show that modestly increasing irrigation intensity in groundwater-rich areas with high yield responses could boost rice production by 2.22Mt annually – sufficient to feed over 33 million people with little anticipated risks of groundwater depletion. However, current investments overlook these areas. Our approach can be globally applied to catalyze sustainable irrigation through integrated use of expanding agricultural and hydrological datasets.
Hormones and science learning: New insights into adolescent motivation
Authors: Ofek-Geva Ella , Kubsch Marcus , Batushansky Albert , Mehlman Tevie , Shuster Michal Vinker , Yeshayahu Yonatan , Brandis Alexander , Fortus David
Published: November 2024

The decline in students' motivation to learn science during adolescence is a widespread phenomenon documented across many countries. While social and educational factors affecting this decline are well-studied, the role of pubertal physiological changes remains unexplored. Here we show that changes in dehydroepiandrosterone sulfate (DHEA-S) and testosterone levels correlate with shifts in students' science learning motivation, particularly during elementary school. Our study of elementary and junior high school students reveals that higher levels of these hormones associate with lower overall motivation in science during elementary years, primarily affecting personal motivation and perceived parental emphasis. This relationship becomes less pronounced in junior high school, suggesting a critical window for hormone-sensitive motivational changes in early adolescence. These findings bridge endocrinology and education research, demonstrating how biological development influences academic motivation during puberty. Our research provides a new framework for understanding the complex interplay between physiological changes and educational outcomes, suggesting the need for developmentally-informed approaches to maintaining students' engagement with science.
Геоисторические компоненты этнополитических концепций в идеологических доктринах коммунистических режимов Румынии, Болгарии и Албании (конец 60-х – 70-е годы ХХ в.)
Authors: Ар.А. Улунян
Published: November 2024

Коммунистические режимы Румынии, Болгарии и Албании активно использовали концепции появления иразвития народов этих стран в своих идейно-политических целях как для реализации внутриполитических установок, так и внешнеполитических задач. Историческая география и этногенез румын, болгар и албанцев превращались в инструмент формирования национальной идентичности при активном участии властей в этом процессе. В конце 60-х начале 70-х годов ХХ в. он активизировался в силу объективных общественно-политических причин. Научные концепции становились основой геоисторических конструктов, призванных объяснить и закрепить в общественном сознании этих стран, атакже в международном мнении вполне конкретные представления как о государствах, имеющих историческое прошлое, тесно связанное не только с Балканским регионом, но иЕвропой в целом. Несмотря на особенности выдвигавшихся концепций, все они подчеркивали автохтонный характер народов трех стран и их особый вклад в европейское историческое развитие.

The communist regimes of Romania, Bulgaria and Albania actively used the concepts of the emergence and development of the peoples of these countries for their ideological and political purposes both for the implementation of domestic political objectives and foreign policy tasks. The historical geography and ethnogenesis of the Romanians, Bulgarians, and Albanians turned into an instrument of forming a national identity with the active participation of the authorities in this process. In the late 1960s early 1970s it became active for objective socio-political reasons. Scientific concepts became the basis of geohistorical constructs designed to explain and consolidate in the public consciousness of these countries, as well as in international opinion, ideas about them as states having a historical past, closely connected not only with the Balkan region, but also with Europe as a whole. Despite the particularities of the concepts put forward, they all emphasized the autochthonous nature of the peoples of the three countries and their special contribution to European historical development.