bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Corruption on SMEs’ Trade Credit Management Effectiveness
Authors: Otto Werner Henk
Published: November 2024

The continued rise in SMEs' corruption-related activities results in uncertainty around their ability to sustainably contribute to economic growth, leaving SMEs financially fragile and exposed to problems associated with trade credit management resulting in business exits. Given that little research has been conducted on how corruption affects smaller businesses while corruption's impact on SMEs' trade credit management effectiveness remains largely unexamined, the study aims to determine the impact of corruption on SMEs' trade credit management effectiveness. By addressing this unanswered research gap, SMEs could be better equipped to understand how corruption affects their trade credit management in support of their overall finances. The study employed a quantitative research design with purposive sampling using a survey by administrating 10 450 online questionnaires tested by a sample of 450 SMEs across South Africa. The result aligns with expectations around corruption being detrimental to SMEs' trade credit management effectiveness while also indicating, unexpectedly, SMEs’ willingness to partake in corruption, given that SMEs benefit from increased effectiveness in managing trade credit. The study adds to exisiting literature on corruption and SMEs' trade credit management while also providing anti-corruption recommendations to SMEs that are dependent on trade credit. In so doing, SMEs could be better equipped to understand how corruption affects their trade credit management to support their overall finances contributing to improved SME creation rates and fostering entrepreneurship as a pivotal mechanism for improving South Africa’s sustainable development goals.
Impact of Telemonitoring and Telehealth Coaching on the General Nutrition Knowledge in Overweight and Obese Individuals: A Pilot Study
Authors: Eid Noura MS , Al-ofi Ebtisam A , Enani Sumia , Mosli Rana H , Saqr Raneem R. , Qutah Karimah M , Eid Sara MS
Published: October 2024

(1) Background: General nutrition knowledge is a fundamental pillar of well-being and healthy lifestyles. The aim of this study was to investigate the effect of a weight loss intervention sup-ported with telemonitoring and telehealth coaching, on improving the general nutrition in over-weight and obese individuals. (2) Methods: A total of 30 participants have completed the trial at the 3-month visit, and a total of 25 have completed the trial at the 6-month visit. All participants have enrolled in a randomized controlled trial (RCT) and received a hypocaloric tailored diet and 3 online nutrition education sessions over a 6-month period. Participants were randomly di-vided into two groups, where the intervention group were supported with weekly telemonitoring and monthly telehealth coaching vs. a control group. The Arabic validated General Nutrition Knowledge Questionnaire (GNKQ) was used, which covered 4 sections; dietary recommenda-tions; food groups and nutrient sources; healthy food choices; associations between diet–disease and weight (3) Results: Findings showed that both intervention and control groups have im-provements in GNKQ scores over time, with the intervention group demonstrating significant in-creases in overall nutrition knowledge and specific areas such as the diet-disease relationship and weight management at 3 months. In addition, changes in GNKQ scores had a significant negative association with BMI and visceral fat percentage. The findings underline the benefits of supporting nutrition interventions with telemonitoring and telehealth coaching suggesting that increased nutrition knowledge may relate to lower body fat metrics. Still, small sample size and high attrition rate of participants were considered a significant limitation in the current study, where large populations is required to confirm reliability of the obtained findings.
Preparation and Performance Study of Composite Aramid Paper for High Frequency Working Conditions
Authors: Li Xiao-Nan , Qin Tong , Zhang Wen-Xu , Wang Hong , Chen Yan-Hong , Li Kang-Le , Wang Qing , Wang Yi-Bo
Published: November 2024

When the power converter connected to the high-frequency transformer breaks through the bottleneck and reaches a frequency of 100kHz or even higher, the high-frequency transformer's inter-turn insulation faces more serious high-frequency discharge and high temperature problems. In order to improve the service performance of oil-immersed high-frequency transformer insulation paper, composite K-BNNSs particles are prepared by ultrasonic stripping, heat treatment and thermomagnetic stirring. Then, K-BNNSs particles are mixed with PMIA slurry to produce composite aramid paper. And the effects of K-BNNSs particles with different contents on the thermal conductivity, dielectric properties, partial discharge properties and mechanical properties of aramid paper are explored. It can be found that when the addition of composite particles (K-BNNSs) is 10%, the comprehensive performance of composite aramid paper is the best. Compared with Nomex paper, the in-plane and through-plane thermal conductivity of composite insulating paper F-10 is increased by 668.33% and 760.66% respectively. Besides, the high-frequency breakdown voltage is increased by 48.73% and the tensile strength is increased by 2.49%. The main reason is that the composite particles form a complete thermal conductive network in the aramid paper matrix and a large number of hydrogen bonds with the matrix, which enhances the internal interface bonding force of the material and changes the charge transport mechanism.
GRUvader ‐ Sentiment Informed Stock Market Prediction
Authors: Mamillapalli Akhila , Ogunleye Bayode , Timoteo Inacio Sonia , Shobayo Olamilekan
Published: November 2024

Stock price prediction is challenging due to global economic instability, high volatility and complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examine the influence of sentiment analysis indicator in predicting stock price. Our results are in two-fold. Firstly, we present a lexicon-based sentiment analysis approach for identifying sentiment features and thus, evidence the correlation between the sentiment indicator and stock price movement. Secondly, we propose the use of GRUVader, an optimal gated recurrent unit networks for stock market prediction. Our findings suggest stand-alone models struggle when compared with AI-enhanced models. Thus, our paper made further recommendations on latter systems.
Research on Heterogeneous Network Data Fusion based on Deep Learning
Authors: HU ZHUOHUAN , Liu Minghao , LIU Lipeng , Fu Lei , Mao Yu , Tang Xirui
Published: October 2024

The advent of the era of big data has led to the emergence of heterogeneous network data fusion as a prominent area of research. Heterogeneous network data is characterised by multi-modality, multi-source, and high dimensionality, which presents significant challenges for traditional data fusion methods. These methods often encounter difficulties in processing such data, including issues such as information redundancy, data inconsistency, and high computational complexity. This paper proposes a heterogeneous network data fusion model based on a deep neural network. The model employs the Multi-Layer Perceptron (MLP) as its fundamental framework, utilising the deep neural network to facilitate joint feature representation learning on data from disparate modalities. The Adaptive Feature Reconstruction Module enables the model to learn the interrelationships between different modalities and to balance the importance of different modal features in the fusion process in a dynamic manner. Furthermore, we introduce an innovative cross-modal attention mechanism, which is capable of effectively capturing the coupling relationship between deep features in heterogeneous data, thereby enhancing the expressiveness and data fusion efficacy of the model. The experimental results demonstrate that the proposed model markedly enhances the accuracy of classification and regression tasks in comparison to traditional methodologies.
Shape Transformations of Two-Component Vesicles, Induced by the Adhesion to Flat Surfaces
Authors: Raval Jeel , Iglič Aleš , Li Bo , Góźdź Wojciech
Published: November 2024

The adhesion of two-component lipid vesicles to a flat substrate is investigated. The components are characterized by different spontaneous curvatures. The shape of the vesicles is coupled to the lateral distribution of membrane components. The influence of the adhesion on the shape transformations and the lateral distribution of membrane components is investigated. The evolution of the vesicle’s shape and the lateral distribution of membrane components in the process of vesicle adhesion is studied. It has been shown that vesicle adhesion may induce mixing or demixing of membrane components. The change of the vesicle shape due to adhesion facilitates the curvature driven segregation of the membrane components and results in the formation of large membrane domains with various mean curvatures.
Insight into Mechanical Properties and Microstructure of Recycled Aggregate Concrete Containing Carbon Fibers and Nano-SiO2
Authors: Xing Tong , Zhang Feng shao , Guan Lei
Published: November 2024

The mechanical properties and microstructure of recycled aggregate concrete (RAC) with carbon fibers (CF) and nano-SiO2 (NS) were investigated in this paper. The mechanical properties of RAC were enhanced by both single and hybrid addition of CF and NS, and the hybrid addition had a better strengthening effect. From the experimental results, it was found that the addition of CF could increase the 28d compressive strength and splitting strength of RAC by 9.05% and 22.36% respectively. Hybrid CF and NS were more conducive to improve the mechanical properties of RAC, and the enhancement effect increased first and then decreased with the increase of NS content. The optimal content of NS was 0.8wt%, which increased the 28d compressive strength and splitting strength of RAC by 20.51% and 14.53% respectively. The microstructure results indicated that the addition of CF had little effect on optimization pore structure of RAC, but the crack-inhibition action of the CF could improve the mechanical properties of RAC. The addition of NS reduced the content of CH and facilitated the formation of more (C–S–H) gel. The hydrated calcium silicate (C–S–H) gel significantly decreased the porosity and transformed harmful capillaries pores and harmful pores into harmless capillaries pores and gel pores, thus improving the mechanical properties of RAC. Therefore, the hybrid CF and NS was more conducive to enhance the performance of RAC for building materials.
Effects of Attitudes toward Remembering on Metamemory and Memory Performance in College Students
Authors: Provost Jacob , Otani Hajime , Franks Andrew
Published: November 2024

As modern technology enables instant access to virtually limitless information, students may perceive memorization of information as lacking in practical importance. The current study investigated the relationship between attitudes toward remembering and metamemory as well as objective memory performance. University students completed the Importance of Remembering questionnaire (IORQ) as a measure of attitudes toward remembering. Subjective components of memory were measured by immediate and delayed judgements of learning (JOLs), global judgements of learning (global JOLs), retroactive confidence judgements (RCJs), as well as subjective mental workload. Objective memory performance was measured using a cued recall test. The IORQ was only significantly correlated with absolute accuracy of delayed judgements of learning for words and pictures such that higher IORQ ratings were associated with less accurate judgments about how well they learned the items. No other correlations were significant. This suggests that a student’s lack of belief in the importance of remembering, at least as conceptualized on the IORQ, may not affect most aspects of memory performance, including those related to academic outcomes.
Multi-Sensor Fusion Autonomous Driving Localization System in Mining Environments
Authors: Wang Yi , Own Chung Ming , Zhang Hai Tao , Luo Ming Zhou
Published: November 2024

We propose a multi-sensor fusion localization framework for autonomous heavy-duty trucks suitable for mining scenarios, which enables high-precision, real-time trajectory generation and map construction. The motion estimated through pre-integration of the Inertial Measurement Unit (IMU) can eliminate distortions in the point cloud and provide an initial guess for LiDAR odometry optimization. The point cloud information obtained from LiDAR can assist in recovering depth information from image features extracted by the monocular camera. To ensure real-time performance, we introduce an iKD-Tree to organize the point cloud data. To address issues arising from bumpy road segments and long-distance driving in practical mining scenarios, we can incorporate a large number of relative and absolute measurements from different sources, such as GPS information and AprilTag-assisted localization data, including loop closure as factors in the system. The proposed method has been extensively evaluated on public datasets and self-collected datasets from mining sites.Our implementation is available at https://github.com/wangyicxy/iLM-slam.
A Discrete Optimal Transport Based Melding Defect Detection Method for PCB in Gas Meter
Authors: Shao Hanshu
Published: November 2024

Ensuring the quality of printed circuit boards (PCBs) in gas meters is essential to the reliable operation and safety of these devices. Traditional methods for detecting welding defects on PCBs, such as machine vision and deep learning, present significant limitations. Machine vision-based approaches lack the precision required in complex environments, while deep learning methods demand large, labeled datasets that are costly and impractical in industrial contexts. This paper introduces a novel melding defect detection method based on discrete optimal transport (DOT) theory, focused on identifying discrepancies in welding areas between sample and reference images. By limiting analysis to the welding regions, the proposed approach reduces computational costs associated with high-resolution image processing while maintaining detection precision and independence from large datasets. The DOT model calculates the optimal mass flow between images, pinpointing specific defects and providing actionable insights for defect tracing and repair, thereby overcoming the limitations of existing methods. Experimental results demonstrate that this approach is both effective and efficient, offering a robust solution for quality assurance in PCB manufacturing for gas meters.
Fisheries Sustainability Eroded by the Loss of Vulnerable Dominant Taxa and Insufficient Compensatory Production in a Coral Reef Seascape
Authors: McClanahan Tim , Kiprono Kosgei Jesse , Tom Humphries Austin
Published: November 2024

The status of tropical fisheries is poorly known because the existing methodological choices have variable outcomes and recommendations. Therefore, multiple approaches to estimating sustainability were compared, namely fisheries independent stock biomass and recovery rates, fisheries dependent landed catches, balanced harvest and gear use metrics, and fish length measurements. Unfished biomass recovery time series was established by a 45-year stock recovery relationship from 7 fisheries reserves and compared to catch and length-based estimates of sustainability. The logistic production rates (r = 0.10 ± 0.06 95% CI) and maximum equilibrium total biomass (150 ± 30 tons/km2) indicated a broad range of potential maximum sustainable yields but likely ranging from 1.1 to 3.9 (95% CI; mean = 3.8) tons/km2/year. In contrast, mean annual biomass growth rates in reserves was lower but less variable than surplus production estimates, ranging from 2.1 to 3.5 (mean=2.8 tons/km2/year). Realized catches at landing sites was lower still, ranging from 1.43 to 1.52 (mean=1.48± 0.2 tons/km2/y). Differences were largely attributable to changes in taxonomic composition and an imbalance in the proportionality of production potential versus actual capture rates. Differences in the vulnerability of taxa to fishing and a lack of compensatory increased production among fishing resistant taxa could largely account for the lost potential production. Large proportional losses of catch were measured among snappers, unicorn fish, sweetlips, goatfish, and soldierfish, while smaller proportional gains in the catch were found among resident herbivorous rabbitfish, parrotfish, and groupers. Many of these declining taxa had vulnerable schooling life histories likely to require special habitat and reserve characteristics. Evaluations of sustainability from length measurements found 17 or 7% of total and 12% of caught species had a minimum sufficient sample sizes (>30 individuals, 413 catches, 2299 individuals, and 144 species) to evaluate length and spawning metrics of sustainability. Seven of these species met length-based and 3 meet spawning potential ratio thresholds for sustainability. Consequently, length-based evaluations were unable to evaluate the sustainability of the larger community. Recommendations include a management focus on the recovery of “ghost taxa”, species abundant in the oldest reserve but not contributing to fisheries yields.
Geomechanically Model for Determining Safe Mud Weight in the South Sumatera Basin
Authors: Rasyid Aly , - Hendarmawan , Haryanto Agus Didit , Endyana Cipta
Published: November 2024

This research presents a comprehensive study of 1D geomechanics modeling and wellbore stability analysis for directional wells in the S field, South Sumatra Basin, Indonesia. The primary goals include pre-drilling analysis and determining safe mud weight requirements about well inclinations. The study integrates data from nearby drilling experiences, incorporating wireline logs, FIT/LOT data, pressure readings, rock samples, drilling records, gamma ray profiles, and mud logs. Petrophysical analyses, shear wave velocity modeling, and the Mogi-Coulomb failure criterion were employed to define a safe mud weight range for distinct formations. The geomechanics model was calibrated using caliper data and drilling records to validate the breakout model. Additionally, the study transferred the earth mechanical model from wells S-3 and S-4 to S-8 and S-9, performing sensitivity analyses to refine mud weight recommendations and prevent shear failure. The study concludes that a maximum inclination of 30 degrees should be maintained for future wells to ensure wellbore stability. These findings provide critical insights for optimizing drilling operations in the South Sumatra Basin.
Numerical Analysis of the Structural Alternatives of a Double Bottom Floor of a Panamax Class Container Ship
Authors: Fuentetaja-Merino Mario Fuentetaja , Silva-Campillo Arturo , Herreros-Sierra M. A. , Pérez-Arribas Francisco
Published: November 2024

To reduce weight and simplify maintenance, ship structures frequently include openings and cut-outs. While these features offer practical advantages, they can weaken the structural integrity of key components. This study explores the effects of these geometric discontinuities on the double-bottom floor plates of a Panamax-class container ship under axial and transverse loads. Through numerical simulations and experimental testing, we analyzed different cut-out configurations and stiffening strategies to assess their impact on stress distribution, plate thickness and fatigue behavior. The results reveal that side cut-outs significantly increase stress, particularly under transverse loads, while central openings have less impact. Additionally, increasing plate thickness consistently reduces stress levels across all models, improving structural durability. Fatigue analysis shows that certain stiffening configurations, particularly those with longitudinal stiffeners in the bottom plates, enhance fatigue life. These findings offer critical design insights for optimizing hull structures, balancing weight reduction with strength improvement. The study provides valuable recommendations for reducing stress concentrations and extending the fatigue life of ship components, contributing to more efficient and safer ship designs.
Decoding GNAO1 Mutations Using Caenorhabditis elegans Model System: Past Approaches and Future Prospectives
Authors: Yadav Shubham , Veliventi Satya Santoshi , Bhandari Somya , Gangurde Sakshi Sanjay , NAIK SHREEYA SANJAY , Bhagwat Shraddha N , Kumar Santosh
Published: November 2024

GNAO1 encephalopathies are a group of neglected genetic disorders primarily occurring due to de-novo point mutations in the Gαo protein-encoding gene in human GNAO1. This gene is reported to be highly conserved among Caenorhabditis elegans (C. elegans) and humans, with a sequence similarity of nearly 80%. Signaling pathways involved in various neurotransmitters, including GPCR pathways can be easily studied in the C. elegans model system. Therefore, using this model system to delineate downstream effectors and clinical targets to Gαo can be highly advantageous. Mutations that cause GNAO1 encephalopathy can be easily replicated in transgenic C. elegans and validated by rescuing phenotypic defects, primarily locomotion and egg-laying defects in worms. Although there are recent technical advancements in understanding the interacting proteins, there are unclear and uncertain hypotheses that explain the effect of Gαo mutations in humans. Coming to the clinical aspect of this disorder, there are no available approved diagnostic procedures to detect GNAO1 encephalopathy in the early stages of life. The present diagnostic procedures reiterate symptoms and overlap with other neurological symptoms that record neglected data of cases. Therefore, here we provide an overview of past research and a perspective of future work, with the primary objective of focusing on GNAO1 encephalopathy.
The Impact of Telemonitoring and Health Coaching on Depression, Anxiety and Stress Scales in Overweight and Obese Individuals
Authors: Eid Noura MS , Al-ofi Ebtisam A , Enani Sumia , Mosli Rana H , Saqr Raneem R. , Qutah Karimah M , Eid Sara MS
Published: November 2024

(1) Background: The literature has demonstrated several pathways which links obesity and stress. Thus, new approaches of weight management programs must also integrate health coach-ing and telemonitoring for overall health and wellbeing. The aim of the study is to measure stress, anxiety, and depression scales (DASS-21) in overweight and obese participants who joined a pilot randomized controlled trial (RCT) and the association between changes in DASS-21 scores and changes in anthropometric measures.; (2) Methods: 50 participants have been enrolled in a ran-domized controlled trial and divided in two groups; Intervention group who received a hy-pocaloric diet remotely, weekly telemonitoring and monthly health coaching; Control group who only followed a the hypocaloric diet without any support. The Arabic version of the Depression Anxiety Stress Scale (DASS-21) was used to measure depression, anxiety, and stress scales; (3) Results: Data revealed that participants from the intervention group had a significant decrease in anxiety scales after 3months when compared to the control group. In addition, correlation be-tween depression, anxiety, stress, and all anthropometric measures in the intervention group showed a moderate significant positive correlation between changes in waist circumference and depression. (4) Conclusions: Findings confirms that integrating health coaching and telemonitor-ing have a strong potential in improving wellbeing and weight loss.
Role of Long Noncoding RNAs in Chicken Viral Diseases
Authors: Sarma Anindita , Suri Parul , Justice Megan , Angamuthu Raja , Pushparaj Samuel
Published: November 2024

Poultry farming faces significant challenges due to viral diseases, which lead to high mortality and economic losses. Long noncoding RNAs (lncRNAs) have emerged as crucial regulators in the host immune response to viral infections. This review explores the multifaceted roles of lncRNAs in chicken viral diseases, including avian leukosis, Marek’s disease, infectious bursal disease, avian influenza, infectious bronchitis, and Newcastle disease. LncRNAs influence gene expression at various levels, such as chromatin remodeling, transcriptional regulation, and post-transcriptional modifications. They interact with viral and host genes, modulating immune responses and viral replication processes. Specific lncRNAs, such as ERL lncRNA, linc-GALMD3, and loc107051710, have been identified as key players in the pathogenesis and immune response to these viral infections. Understanding the mechanisms of lncRNA action provides insights into potential diagnostic and therapeutic strategies to enhance disease resistance in poultry. This review highlights the importance of lncRNAs in the complex interplay between host and virus, paving the way for future research and applications in poultry health management.
Health Evaluation of Electromagnetic Coil Based on Mahalanobis Distance
Authors: Wang Yiyang , Li Jianan , Guo Haifeng , Shen Chao , Du Zhongbo , Li Xiang
Published: November 2024

Electromagnetic coil is the key component of rotating equipment, and its reliability is very important for the normal operation of the equipment. In order to accurately evaluate the health status of electromagnetic coil, a health evaluation and anomaly detection method based on Mahalanobis distance (MD) and Box-Cox transformations is proposed. With the help of accelerated fatigue experiment, the whole life electrical data of high, medium and low frequency spectrum of electromagnetic coil is collected, and the low and medium frequency spectrum data are constructed as the initial feature set. And then, the initial feature set is mahalanized, and Mahalanobis distance is taken as the feature to evaluate the health state of the electromagnetic coil. With the help of Box-Cox transformations, MD is transformed into normal distribution form, and the electromagnetic coil fault early warning is realized according to 3 σ criterion. The results show that MD can accurately evaluate the degradation state of the electromagnetic coil and realize early warning.
Entanglement Missed Detection of Two‐Mode Squeezed States Based on Inseparability Criterion
Authors: Cai Chunxiao , Diao Wenting , Feng Fupan , Yang Wenhai , Su Xinyu , Zhao Weigang , Ge Jinman , Ma Long , Huo Nan , Liu Yanhong , Duan Chongdi
Published: November 2024

The inseparability criterion provides a straightforward and efficient method for identifying and quantifying two-mode Gaussian quantum entanglement, making it a crucial tool in quantum optics experiments. However, it is crucial to recognize that the inseparability criterion serves only as a sufficient condition for entanglement assessment, thereby posing a risk of missed detection during evaluation. This paper investigates the use of the inseparability criterion in assessing two-mode squeezed states, with a particular focus on examining missed entanglement detection due to entanglement asymmetry. The results show that when decoherence symmetrically affects both modes, the inseparability criterion effectively detects entanglement. In contrast, when this symmetry is broken, the criterion may fail to identify entanglement, with the likelihood of missed detection increasing alongside rising asymmetry. By comparing these results with the positive partial transpose criterion, which serves as a necessary and sufficient condition, the occurrence of missed detections by the inseparability criterion is confirmed. Our research not only provide valuable insights into the application of the inseparability criterion in quantum information tasks but also deepens the understanding of its operational principles and limitations.
Preliminary Exploration on Short-Term Prediction of Local Geomagnetically Induced Currents Using Hybrid Neural Networks
Authors: Fang Yihao , Liu Jin , Jiang Haiyang , Chen Wenhao
Published: November 2024

During extreme space weather events, transient geomagnetic disturbances initiated by solar eruptive activities can induce geomagnetically induced currents (GICs), which have severe impacts on power grid systems and oil/gas pipelines. Observations indicate that GICs in power grids are characterized by large fluctuation amplitudes, broad frequency ranges, and significant randomness. Their behavior is influenced by several factors, including the sources of space weather disturbance, Earth's electrical conductivity distribution, the structural integrity and performance of power grid equipment, and so on. Consequently, predicting local grid GICs has been a highly challenging task. This paper presents a hybrid prediction approach using actual GIC data from power grids and deep learning techniques. We employ various technical methods, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Attention mechanisms, to investigate short-term prediction methods for local grid GICs. The study uses GIC monitoring samples from November 8th to November 10th, 2004, for model training and testing. The results are evaluated using the coefficient of determination R^2 , root mean square error (RMSE), and mean absolute error (MAE). Preliminary research suggests that the CEEMDAN-CNN-LSTM-Attention combined model significantly improves prediction accuracy and reduces the time delay in GIC prediction during geomagnetic storms compared to using LSTM neural networks alone.
Nonlinear Dynamic Stability Analysis of Seaplanes in Waves Using Poincaré–Lindstedt Perturbation Method
Authors: Masri Jafar , Dala Laurent , Huard Benoit
Published: November 2024

Abstract: In this study, we present an analytical tool that can be used to predict the nonlinear structural response of seaplanes advancing through sinusoidal head-sea waves. Seaplanes exhibit a unique instability phenomenon known as porpoising, which is an oscillatory motion along the heave and pitch axes that can cause serious structural damage. The heaving and pitching equations of motion are presented in the form of coupled, forced, and nonlinear Duffing-type equations with cubic nonlinearity. The analytical model developed in this study leverages the Poincaré–Lindstedt perturbation method to express the amplitude and frequency of motion in terms of all physical parameters. The accuracy and reliability of the proposed model were validated through computational fluid dynamics (CFD) simulations based on incompressible unsteady Reynolds-averaged Navier–Stokes (RANS) equations. The results show a strong agreement between the analytical tool and the CFD simulations, with minor discrepancies due to assumptions inherent in the 2D simulations, particularly the assumption that seawater only passes beneath the hull, resulting in increased buoyancy forces and reduced damping. This study offers a novel and practical method for predicting the dynamic stability of seaplanes under realistic sea conditions, potentially enhancing safety and operational efficiency by mitigating the risks associated with porpoising.
Soret Effect on the Instability of Double-Diffusive Convection in a Saturated Vertical Brinkman Porous Layer of Oldroyd-B Fluid
Authors: Ren Yuanzhen , Jian Yongjun
Published: November 2024

The instability of double-diffusive convection of an Oldroyd-B fluid in a vertical porous layer caused by temperature and solute concentration differences with the Soret effect is studied using a modified Darcy–Brinkman–Oldroyd model. Under the Oberbeck-Boussinesq approximation, the validity of Squire's theorem is demonstrated, allowing for considering only the two-dimensional linear instability. Based upon perturbation theory, an Orr-Sommerfeld eigenvalue problem is derived and numerically simulated using the Chebyshev collocation method. We examine the effects of the relevant dimensionless parameters on the neutral stability curves. It is discovered that PrD and λ1 have dual effects on the instability, respectively. When PrDc1 < PrD < PrDc2, PrD boosts flow stability. The impact of λ1 on fluid stability is influenced by PrD. Furthermore, Le and η have dual effects on the instability. When η< 0.8, it promotes flow instability. Le enhances the flow in-stability when Le < 0.7 and inhibits the instability when Le > 0.7. In addition, the magnitude of Le influences the role of Sr. There exists a critical value for Le. When Le < Lec2, Sr strengthens the in-stability of the flow, and when Le> Lec2, Sr plays an inhibiting role. Finally, the relaxation pa-rameter λ2 promotes flow stability.
Age Factor in Second Language Acquisition and Effective Instructional Methods for K-12 Learners in Vietnam
Authors: Van An
Published: November 2024

Age is an influential factor in the Second Language Acquisition (SLA). The debate on whether young learners are more successful than older learners has been going on for more than half of a century, starting from the Critical Period Hypothesis, which is based on brain structure. This study examines a range of studies revolving around the Critical Period Hypothesis in the past several decades to analyze the common SLA belief ‘Younger learners are more skillful than older learners in acquiring second language.’ The study disagrees that all younger learners are more advantageous than older learners in all areas of SLA. In fact, learners from different age categories have unique advantages and disadvantages in SLA. Moreover, the study approves a more holistic perspective on a wider range of non-biological factors that impact the success of SLA. At the end, the study puts forward a diversity of instructional methods that suit children and adolescents specifically in Vietnamese English Education context.
Multi-View 3D Reconstruction Based on FEWO-MVSNet
Authors: Yao Guobiao , Wang Ziheng , Wei Guozhong , Zhu Fengqi , Fu Qingqing , Yu Qian , Wei Min
Published: November 2024

Aiming to address the issue that the existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive patterns and weak textures in the multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on feature enhancement and weight optimization MVSNet (Abbreviated as FEWO-MVSNet). To obtain accurate and detailed global and local features, we first develop an adaptive feature enhancement approach to obtain multi-scale information from the images. Second, we introduce an attention mechanism and a spatial feature capture module to enable high-sensitivity detection for weak texture features. Third, based on the 3D convolutional neural network, the fine depth map for multi-view images can be predicted and the complete 3D model is subsequently reconstructed. Last, we evaluated the proposed FEWO-MVSNet through training and testing on the DTU, BlendedMVS, and Tanks&Temples datasets. The results demonstrate significant superiorities of our method for 3D reconstruction from multi-view images, with our method ranking first in accuracy and second in completeness when compared to the existing representative methods.
Combining CNNs and Symptom Data for Improved Monkeypox Virus Detection
Authors: Oncu Emir
Published: November 2024

The zoonotic disease known as monkeypox, which is related to smallpox, can be difficult to diagnose since it resembles other illnesses that might cause the same symptoms. In this study, we present an accurate technique for monkeypox detection based on convolutional neural networks (CNNs). Our method improves diagnosis reliability by having the CNN model incorporate a dataset of nine important symptoms. For creating CNN-based monkeypox detection system, the researchers used a collection of high-definition photos and nine symptoms. The model was constructed using TensorFlow's Keras API and Python 3.10, classifying lesions based on a probabilistic score. A likelihood greater than 50% on CNN indicated that the case was really monkeypox. If not, five or more symptoms suggested that monkeypox may be present with metrics including accuracy, precision, recall, and F1-score. The study developed a CNN model to predict monkeypox from lesion images and symptom analysis. The model, built with layers including convolutional, pooling, and fully connected, achieved high accuracy and low loss, with metrics like ROC AUC (0.84), precision (0.78), and recall (0.90). The model effectively differentiates between monkeypox and other conditions. Tables illustrate the model's predictions based on various symptom combinations, highlighting its capability to assess monkeypox risk with high confidence. The study developed a CNN model integrating patient symptoms and skin lesion images for accurate monkeypox diagnosis. With a balanced accuracy of 82%, F1-Score of 84%, and sensitivity and specificity of 80% and 85%, respectively, the model effectively distinguishes monkeypox from similar illnesses. It demonstrated resilience despite limited high-resolution images, emphasizing the potential of CNNs in medical diagnosis. The model's performance can be further improved by incorporating additional data and advanced techniques, enhancing early detection in future outbreaks.
Resolution of Complete AV block to Normal Sinus Rhythm without Cardiac Pacemaker in a Patient with Severe Acidosis: A Case Report
Authors: Filiana Rari Dewinda Sudarmaji , Kusuma Taufik Ridwan Hadi , Noviar Vira Khairunisa
Published: November 2024

Background : Complete atrioventricular block (CAVB) is a life-threatening medical emergency and requires an immediate cardiac pacemaker to reduce mortality by up to 87%.

Case Illustration : A 55-year-old woman with an unknown medical history was brought to the emergency room with a Glasgow Coma Scale (GCS) of E2V1M1 (sopor comatose) and Kussmaul breathing. The patient, who was in septic shock, had Multiple Organ Dysfunction Syndrome (MODS) and severe acidosis. The patient was hemodynamically unstable, requiring vasopressor to maintain a Mean Arterial Pressure (MAP) ≥ 65 mm Hg. The analysis of the arterial blood gas revealed a pH of 7.19, pCO2 of 16 mm Hg, pO2 of 204 mm Hg, and bicarbonate of 5.9 mmol/L. Hyperkalemia (5.33mmol/L) was discovered during the electrolyte analysis. 

Conclusion : An electrocardiogram (ECG) examination showed CAVB with non-malignant ventricular extrasystoles. Without the use of a cardiac pacemaker, the ECG becomes normal sinus rhythm after corrections for severe acidosis and hyperkalemia. This case demonstrates that severe acidosis and hyperkalemia can induce reversible CAVB without the use of a pacemaker. Hence, it should be evaluated as part of the initial assessment and therapy of CAVB before a cardiac pacemaker is implanted.
Relationship Between Spinal Anesthesia Injection Speed and the Incidence of Hypotension in Patients Undergoing Cesarean Section
Authors: Mafazi Fikri Alim , Novrinta Donatila , Sasongko Himawan
Published: November 2024

Background : The incidence of post-spinal hypotension in pregnancy is very high 50-85%. There have been many studies preventing hypotension but still considered unsatisfactory. Reducing the speed of injection of spinal anesthetic drugs can prevent hypotension, but still controversial.

Objective : to determine the incidence of hypotension, onset of achieving sensory blocks, and the association of injection rate with the occurrence of post-spinal anesthetic hypotension.

Methods: A Randomised Control Trial Study, including 48 patients underwent caesarean delivery who fulfil inclusion criteria. Samples were divided into fast and slow groups. Spinal anesthesia was given using hyperbaric bupivacain 0.5% of 10mg, Fentanyl 25mcg, and 100mcg morphine total volume of 3 cc. Group A received fast injection for 10 seconds, while group B for 30 seconds. The incidence of hypotension, onset of block and incidence of side effects after spinal anesthesia were recorded.

Result : The incidence of hypotension in the fast group was 70.4%, while in slow group was 23.8%. There was a significant association between injection rate with post-spinal anesthetic hypotension (p = 0.004). Onset of Block T6 post-spinal anesthesia is faster in the fast group. Furthermore, there was no significant difference in nausea, vomiting, and chills between two groups.

Conclusion: Slow injection rate may reduce the incidence of post-spinal anesthetic hypotension. However, the onset of sensory block was faster achieved with fast rate injection, with no difference in other side effects.
Manhattan’s Street Trees: An Unfinished Public Health Story
Authors: Harris John M.
Published: October 2024

Stephen Smith launched a 40-year effort to bring trees to New York City streets in 1872, the year he founded the American Public Health Association (APHA). Smith argued that street trees would mitigate the adverse health effects of Manhattan’s summer heat and help purify the air. The young APHA endorsed Smith’s position and gave trees a prominent role in urban sanitation, but public health turned away from trees and urban reform movements as it adopted a biomedical public health model in the late 19th century. Nevertheless, Smith wrote and campaigned for a successful 1902 law requiring the New York City Parks Department to assume management of street trees in the name of public health. He then led a 1914 campaign to force the department to uphold his law. New York’s street tree program has had an erratic trajectory, but it now generally follows Smith’s vision. Public health could play a bigger role in creating greener cities and mitigating climate change with more field research and the health in all policies approach that Smith used to bring trees to Manhattan’s streets in 1914. ( Am J Public Health. 2025;115(1):66–74. https://doi.org/10.2105/AJPH.2024.307856 )
Southeast Asia: A Colonial Oil Producer Fueling Its 21st Century Industrial Revolution With Gas
Authors: Szymczak Pat Davis
Published: November 2024

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With natural gas destined to replace coal as its fuel of choice, Southeast Asia is unabashedly expanding its oil and gas industry to meet the skyrocketing energy demands of a booming population and unprecedented economic growth.

Investments in regional hydrocarbon projects through 2029—particularly those in natural gas production—are forecast to exceed 4% compound annual growth rates with the spotlight on the region’s top two petroleum producers, Indonesia and Malaysia, according to the London-based Energy Council, a global network of senior energy finance executives.

Norway’s Rystad Energy predicts that final investment decisions (FIDs) will be made between now and 2028 on $100 billion in new projects in the region, twice the $45 billion in FIDs taken from 2014 to 2023.

These will include deepwater projects to develop new discoveries in Indonesia and Malaysia, and projects to create a global hub for carbon capture and storage (CCS) such as the one under study by Malaysian state-owned Petronas and Abu Dhabi’s ADNOC.

The Malay Archipelago (also called the East Indies) is the world’s largest island group, and depending on the methodology of the geographer, it encompasses the 17,508 islands of Indonesia—literally the world’s largest country comprised of islands—and the approximately 7,000 islands of the Philippines.

Indonesia and the Philippines in turn are among the 11 countries that constitute Southeast Asia after adding in Brunei, Burma (Myanmar), Cambodia, Timor-Leste, Laos, Malaysia, Singapore, Thailand, and Vietnam—all with widely diverse histories, cultures, and religions.

But all with one thing in common: a colonial past forged by adventurers from across Europe who in the 19th century arrived on their shores to extract natural resources to feed the Industrial Revolution at home.

In the case of the earliest industrial oil production in Dutch Indonesia and British Malaysia, those adventurers, over multiple generations, added Southeast Asia to the US and Azerbaijan as cornerstones of the modern oil industry while also breaking John D. Rockefeller’s monopoly of the Asian oil trade by building the first tanker to traverse the Suez Canal.

Shortly after the turn of the 20th century two of these industrialists, one Dutch and one British, merged their companies to create what is today’s energy supermajor known as Shell.

First Oil Launches Dutch East Indies Production Boom

The history of oil production in Indonesia and Malaysia dates back over centuries with tales of a cottage industry of hand-dug wells to comb petroleum seeps for medicine, to waterproof boats, and to light torches and lamps.

As the Industrial Revolution dawned, the Russian czar financed the world’s first mechanically drilled oil well in 1846 in Baku using cable-tool percussion drilling to carve out a 21-m-deep (69-ft) exploration well.

In 1859, Edwin Drake added steam-engine power to a mechanical drill to produce the US’ first industrial class oil in Titusville, Pennsylvania.
Technology Focus: EOR Operations (November 2024)
Authors: Barsotti Elizabeth
Published: November 2024

International agreements and national policies on environmental sustainability are changing the outlook for enhanced oil recovery (EOR) globally. These changes are highlighted by both monetary and intellectual commitments by oil companies around the world.

Saudi Aramco has pledged $1.5 billion toward sustainable technology. In practice, members of Aramco’s EXPEC Advanced Research Center have collaborated with King Fahd University of Petroleum and Minerals to develop sustainable surfactants for use in high-temperature, high-pressure, and high-salinity carbonate reservoirs. They used no or only green solvents to produce soluble, stable surfactants with the potential for biodegradability.

In Colombia, Ecopetrol is committed to reducing CO2 emission by 25% by 2030. As part of its efforts, it has been optimizing polymer injection in Chichimene, a heavy-oil field. By estimating life cycle greenhouse-gas emissions and energy consumption, it was able to show that polymer injection and its associated carbon intensity reduction could prevent the emission of 3,200 tons CO2 equivalent.

To comply with the UK’s North Sea Transition Deal, which mandates a 50% reduction in offshore oil and gas emissions by 2030, Ithaca Energy and Energy Research Norway is using life-cycle assessment to guide more efficient heavy-oil recovery. By using low-dose polymer injection, instead of alternatives such as thermal EOR, it was able to increase exergy while also reducing CO2 emissions by 35%.

In these studies, we already see the effect of the energy transition as companies turn away from traditionally effective but environmentally burdensome EOR methods such as steam injection and new polymers that show equal, if not greater, effectiveness with a reduced environmental cost. As we move forward with further regulation and innovation, it will be interesting to see how emerging technological developments in polymers, nanofluids, and even artificial intelligence can help producers meet both the economic and environmental criteria to maintain profitable fields.

Recommended additional reading at OnePetro: www.onepetro.org.

SPE 218653 Coinjection EOR Technology Increases Recovery and Reduces Greenhouse-Gas Emissions by G. Wasylchuk, GERI

SPE 218690 Steam Generation Using Renewables Energy—An Integrated Approach for Enhanced Oil Recovery Applications by Mustafa Al Ajmi, Petroleum Development Oman, et al.

SPE 218962 Reaching a Million Barrels in an Abu Dhabi Field by Focusing on Operations Efficiency by Masud J. Akhtar, Abu Dhabi National Oil Company, et al.