bluuebunny/crossref_metadata_2025 huggingface dataset:
Electric Vehicle Sentiment Analysis Using Large Language Models
Authors: Sharma Hemlata , Ud Din Faiz , Ogunleye Bayode
Published: November 2024

Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle (EV) brands. This provides companies with valuable insight into the public’s opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performance compared to traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming significant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimised BERT (RoBERTa), and a generalised autoregressive pre-training method (XLNet) using Lucid Motors and Tesla Motors YouTube datasets. Results evidenced that LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically when fine-tuned. Furthermore, our findings present the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with an F1 score of at least 92%.
Examining the Relationship Between Social Vulnerability and Animal Shelter Intakes and Outcomes: Patterns and Implications
Authors: Neal Sue M. , Kremer Tom
Published: November 2024

(1) Background: This study investigates the impact of social vulnerability, as quantified by the Social Vulnerability Index (SVI), on animal shelter intakes and adoptions, and the flow of animals between SVI groups at seven shelters within the United States. Understanding how social vulnerability influences these factors is crucial for developing targeted interventions to improve shelter outcomes and community support programs. (2) Methods: The research analyzed intake and adoption data from multiple animal shelters to assess the relationship between the social vulnerability at intake and adoption locations based on Census Tract obtained from addresses, focusing on comparison across strays, seized animals, and owner surrenders. Effect sizes were calculated to determine the magnitude and practical implications of these relationships. The study also examined the distribution of outcomes and reasons for animal surrender in relation to SVI levels. (3) Results: The analysis found that animals tended to enter shelters from areas of higher vulnerability while being adopted out to homes from all levels of vulnerability. Stray animals and those seized by authorities were more likely to come from high-SVI areas and showed a stronger disparity between intake and adoption SVI levels than owner surrenders. Racial/ethnic minority status and socioeconomic status were the components of the SVI that produced the strongest differences between intake and adoption vulnerability levels. Adoptions, however, were evenly distributed across different SVI levels, suggesting equitable access to adoption services. The study also identified that animals from high-SVI areas were not euthanized at higher rates compared to those from lower-SVI areas. (4) Conclusions: The findings highlight the need for further investigation into the high numbers of stray adult dogs in areas of communities that are most socially vulnerable as well as programs to address the volume of young animals originating from these areas. The equitable distribution of adoptions across SVI levels suggests that shelters are effectively engaging with socioeconomically and racially/ethnically diverse communities. Future research should focus on exploring the dynamics of stray animal intakes and enforcement patterns to enhance community and animal welfare initiatives.
Molecular Characterization of Multidrug-Resistant and Hypervirulent New Delhi Metallo-Beta-Lactamase Klebsiella pneumoniae in Lazio, Italy: A Five-Year Retrospective Study
Authors: Rotondo Claudia , Venditti Carolina , Butera Ornella , Dimartino Valentina , Messina Francesco , Properzi Michele , Caparrelli Claudia , Antonelli Valentina , D’Arezzo Silvia , Selleri Marina , Nisii Carla , Fontana Carla ,   on behalf of the Lazio Region Laboratory Study Group
Published: November 2024

Background/Objectives: Antimicrobial resistance represents a challenge to public health systems because of the array of resistance and virulence mechanisms that lead to treatment failure and increased mortality rates. Although for years the main driver of carbapenem resistance in Italy has been the Klebsiella pneumoniae KPC carbapenemase, recent years have seen an increase in VIM and NDM metallo-beta-lactamases (MBLs). We conducted a five-year survey of New Delhi Metallo-beta-Lactamase (NDM)-producing Klebsiella pneumoniae (NDM-Kpn) clinical isolates from the Lazio region, Italy; the study aimed to elucidate the molecular mechanisms underpinning their resistant and virulent phenotype. Methods: Antimicrobial susceptibility was evaluated by automated systems and broth microdilution. In silico analysis of acquired resistance and virulence genes was performed using whole-genome sequencing (WGS), molecular typing through MLST, and core genome multi-locus sequence typing (cgMLST). Conclusions: A total of 126 clinical NDM-Kpn isolates were collected from 19 distinct hospitals in the Lazio region. Molecular analysis highlighted the existence of NDM-1 (108/126) and NDM-5 (18/126) variants, 18 Sequence Types (STs), and 15 Cluster Types (CTs). Notably, 31/126 isolates displayed a virulence score of 4, carrying ybt, ICEKp, iuc, and rmp genes. This study identified a variety of NDM-Kpn STs, mainly carrying the blaNDM-1 gene, with a significant number linked to high-risk clones. Of these isolates, 24.6% showed high-level resistance and virulence, emphasizing the risk of the spread of strains that combine multi-drug-resistance (MDR) and virulence. Proactive surveillance and international collaborations are needed to prevent the spread of high-risk clones, as well as further research into new antimicrobial agents to fight antibiotic resistance.
Antimicrobial Prescribing Patterns in GP Practices in Northern Ireland
Authors: Coleman Heather M. , Clifford Eimear , Rajiah Kingston , Ali Nermeen , Courtenay Aaron , Lowry Deborah , Jack Iain G. , Abuelhana Ahmed
Published: November 2024

Introduction: Antimicrobial resistance (AMR) is a global health threat requiring immediate attention as it is set to cause ten million deaths worldwide by 2050, overtaking that of cancer. Continuation of overuse and/or misuse of these crucial medicines will prevent future generations from reaping the benefits, as the pandemic of AMR spirals out of control. Aims: The primary aim of this study was to investigate antimicrobial prescribing patterns in General Practices throughout Northern Ireland. A secondary aim was to analyse the impact of the COVID-19 pandemic on antimicrobial prescribing and consumption patterns in GP practices in Northern Ireland. Methods: A retrospective, cross-sectional quantitative study was designed to measure, analyse, and evaluate the antimicrobial prescribing patterns within GP practices in Northern Ireland, using open access Business Services Organisation (BSO) data. Results: A total of 3,168.78 kg of antibacterial drugs were prescribed in primary care throughout the duration of the study. Penicillins were the most prescribed class (59.79%), followed by tetracyclines (10.68%) and macrolides (9.53%). Access group antibiotics were the most frequently prescribed (79.35%), followed by Watch group antibiotics (20.64%), with Reserve group antibiotics equating to nearly 0% despite being prescribed. The Derry GP Federation prescribed and dispensed the greatest amount of antibiotics overall in Northern Ireland (10.90%). Despite there being no significant difference in antibiotic prescribing amongst GP federations prior to and during the COVID-19 pandemic (unpaired t-test, p > 0.05), there were differences in prescribing of individual drug classes throughout this period. Conclusions: Despite meeting World Health Organisation (WHO) targets, GP practices within Northern Ireland must achieve more to further reduce antimicrobial consumption. Although antibiotic prescribing rates here are on the decline, there was no significant difference in prescribing amongst GP federations pre- and midst-COVID-19 pandemic, thus sufficient strategies such as increased communication between colleagues and supportive measures must be implemented within GP practices to enhance antimicrobial stewardship (AMS) across Northern Ireland.
Efficacy of Omega-5 NanoPSO Treatment in the Hippocampus, Through Antioxidant Mechanisms, After an Ischemia/Reperfusion Injury, in Murine Model
Authors: Aguilar-García Irene Guadalupe , Alpirez Jonatan , Muñoz-Valle José Francisco , Trujillo-Rangel Walter Ángel , Fernández-Quezada David , Dueñas-Jiménez Sergio Horacio , Galvan-Ramírez María de la Luz , Sánchez-Parada María Guadalupe , González-Santiago Ana Elizabeth , Dueñas-Jiménez Judith Marcela , Castañeda-Arellano Rolando
Published: November 2024

Stroke is the third cause of death worldwide and a health problem, and current therapy continues to be very poor. It promotes an alteration associated with excitotoxicity, oxidative stress, and inflammatory processes, exacerbating the damage in the brain. Although cortical areas are the most affected by stroke, the hippocampus can be impacted in the long term through the pathways it connects with these areas, which are associated further with motor alterations; this encourages the search for new therapeutic approaches. Omega-5, being an antioxidant, participates in regulating oxidative stress. A recently designed nanoemulsified compound coupled with pomegranate seed oil (NanoPSO) maintains bioavailability in the body for longer. Omega-5 NanoPSO is more effective in different models of neurodegenerative diseases and metabolic disorders. Therefore, it is important to analyze the effect of omega-5 NanoPSO on ischemic damage through changes in the hippocampus, oxidative mechanisms, and behavioral outcomes. Male Wistar rats were used in five groups; three groups were subjected to an ischemic event through bilateral occlusion of the carotid arteries. An ischemia group received omega-5 NanoPSO after injury, and another group received omega-5 NanoPSO performed two weeks before the ischemic event and three weeks after the surgical process. The control and sham groups did not show changes in the hippocampus and behavior. In the ischemia group, neuronal loss, oxidative stress, and a higher expression of astrocytes were maintained in the hippocampal region, and behavior was modified. In the post and pre-treatment group with omega-5 NanoPSO, we observed reduced damage, glial proliferation, and oxidative stress. It increased neuron survival in the hippocampal region and improved the locomotion. These results highlight its promise for use in clinical settings to treat patients suffering from ischemic brain injury.
Investigation of the Fermentation Process of Moringa oleifera Leaves and Its Effects on the Growth Performance, Antioxidant Capacity, and Intestinal Microbiome of Procambarus clarkii
Authors: Li Zhengzhong , Luo Weizhu , Zhou Qunlan , Sun Cunxin , Zheng Xiaochuan , Liu Bo , Mpange Kaunda , Zhu Aimin , Wang Aimin
Published: November 2024

Moringa oleifera is renowned for its high antioxidant activity. However, few studies have been conducted on its effects on aquatic animals. The aim of this experiment was to investigate the optimal fermentation process of M. oleifera leaves and to evaluate the effects of fermented M. oleifera leaves on crayfish (9.11 ± 0.3 g) in terms of growth performance, antioxidant capacity, and gut microbiological parameters. By optimizing the fermenting material/water ratio, fermentation time, temperature, and strain, the optimal fermentation conditions of a 10% water ratio + 48 h + 30 °C + inoculation with 2% B. amyloliquefaciens (107 CFU mL−1) were obtained. These conditions resulted in notable increases in the contents of the total protein, total phenols, flavonoids, and amino acids (p < 0.05) while also leading to a notable decrease in the content of tannins in contrast to those of unfermented M. oleifera leaves (p < 0.05). The fermented M. oleifera (FMO) leaves were incorporated at five concentrations, including 0% (control (CT)), 0.25% (0.25FMO), 0.5% (0.5FMO), 1% (1FMO), and 2% (2FMO). The results showed that the 1FMO group performed better in terms of the final body weight (FBW), weight gain rate (WGR), and specific weight gain rate (SGR) compared with the CT group (p < 0.05). In addition, amylase and lipase activities were significantly higher in the 1FMO and 2FMO groups compared with the other groups (p < 0.05). The fermented M. oleifera leaves significantly increased the catalase (CAT) activity in the crayfish (p < 0.05). The superoxide dismutase (SOD) activity was significantly increased in the 0.25FMO, 1FMO, and 2FMO groups, and the malondialdehyde (MDA) content was significantly decreased while the glutathione peroxidase (GSH-Px) content was significantly increased in the 0.5FMO, 1FMO, and 2FMO groups (p < 0.05). Furthermore, the 1FMO group was observed to significantly increase the abundance of Firmicutes while simultaneously reducing the abundance of Aeromonas (p < 0.05) and adjusting the structure of the intestinal microbiome. In conclusion, this study established the optimal fermentation conditions for M. oleifera and obtained a product with high nutrient and low tannin contents. Furthermore, the incorporation of 1% FMO was demonstrated to facilitate growth, enhance the antioxidant capacity, and optimize the gut microbiology in crayfish.
Exploring Asymmetric Lens–Total Internal Reflection (AL–TIR) Optics for Uniform Ceiling Illumination in Interior Lighting
Authors: Giang Duong Thi , Duong Pham Hong , Khanh Tran Quoc
Published: November 2024

This study presents a significant advancement in LED interior lighting through the development and application of Asymmetric Lens–Total Internal Reflection (AL–TIR) optics, with a focus on enhancing lighting uniformity and indoor comfort by simulating sky-like lighting distribution. AL–TIR technology employs asymmetric lenses combined with total internal reflection to efficiently redirect and spread light, achieving a controlled and even ceiling illumination suitable for various interior applications. This research explored the establishment of ideal luminous intensity curves, devised practical AL–TIR optical designs through numerical calculations, and conducted extensive simulations to assess performance in typical indoor environments. Our findings demonstrated substantial improvements in lighting uniformity, with the AL and AL–TIR systems achieving direct illuminance uniformities of 0.78 and 0.83, respectively, compared to traditional tube LEDs at 0.25. These results, validated in several office rooms, highlight the efficacy of AL–TIR optics in revolutionizing indoor lighting design by balancing optimal lighting distribution with occupant comfort and well-being.
Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware
Authors: Joshi Sunil , Crowther Kenneth , Robinson Jarvis
Published: November 2024

This paper provides an overview of several secure boot architectures with a focus on key rotation. It expands on a practitioner note that the authors submitted to the 2023 IEEE Secure Development Conference. Key rotation is important due to the frequency of lost signing keys and the difficulty of managing secret keys for the long lifetimes of Industrial Internet of Things (IIOT) devices. Key rotation is not simple for IIOT due to limited resources during a secure boot process and the constraints of the firmware utilities that come from the chip vendors. This paper reviews and compares five common architectures for a secure boot that are seen across the IIOT community. For each architecture, it provides some key strengths and weaknesses associated with that architecture. The paper then provides a detailed comparison and analysis of the architectures to convince the IIOT community to move towards a strong use of certificates (instead of the traditional use of raw public keys). The intent of this paper is to provide a practitioner’s perspective on these challenges and the tradeoffs in hopes of inviting comments from chip vendors and the broader community.
Long-Distance Measurements Using a Chromatic Confocal Sensor
Authors: Kurtev Kiril Ivanov , Trujillo-Sevilla Juan Manuel , Rodríguez-Ramos José Manuel
Published: November 2024

In this work, we demonstrate the use of a chromatic confocal sensor for long-distance measurements. The sensor increases the working distance of state-of-the-art confocal sensors by a factor of 10, reaching a working distance of 620 mm. The chromatic aberration exhibited by a lens was utilized to establish the working range. The chromatic dispersion of the optics led to images of the different wavelength components at different longitudinal points along the optical axis. The sensor employs a robust algorithm to measure relative displacements of the sample’s motion. The calibration process simplifies data analysis and improves the accuracy of displacement measurements in experimental setups. To facilitate the design process, a simulator was developed specifically for this purpose. The calibration data obtained in both the experimental and the simulated data show that the simulator was able to predict the sensitivity with an error of 5%. We also describe the effect on the sensitivity of oversampling the spectrum. In addition, the superiority of low-pass filtering over Gaussian fitting over the detected spectrum is shown.
Key Frame Selection for Temporal Graph Optimization of Skeleton-Based Action Recognition
Authors: Hou Jingyi , Su Lei , Zhao Yan
Published: November 2024

Graph neural networks (GNNs) are extensively utilized to capture the spatial–temporal relationships among human body parts for skeleton-based action recognition. However, due to the inefficient information propagation caused by redundant sampling of video frames in the temporal domain, we focus on refining temporal graphs through key frame selection. To this end, we propose a multi-stage key frame selection (MSKFS) method, aiming to find the most representative frames as the graph nodes to learn compact temporal graph representations of human skeletons for action recognition. The MSKFS progressively selects key frames in two stages: (1) salient posture frame selection based on the global dynamics of body parts and (2) key frame refinement and alignment according to intra-frame correlations. The first stage captures the most salient information and aligns the corresponding information of skeleton sequences within the same category. The second stage enriches the subtle information for the integrity of the information derived by the salient frames. Moreover, variational inference is applied to differentiate the key frame refinement and alignment procedure, allowing the end-to-end optimization of arbitrary graph-based models to represent the obtained compact graph for skeleton-based action recognition. Our MSKFS method achieves state-of-the-art performances on two challenging action recognition datasets.
Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems
Authors: Ayten Kagan Koray , Dumlu Ahmet , Golcugezli Sadrettin , Tusik Emre , Kalınay Gurkan
Published: November 2024

In this study, a comparative analysis of three different control methods for precise, real-time control of a complex dynamic double-tank liquid level process system was performed. Since the system in question has a time-delayed structure, feedforward proportional integral (FF-PI) control and cascaded nonlinear feedforward proportional integral delayed (CNPIR) controllers were tested on the process system. While the FF-PI controller improved the response time of the system, it showed limitations in handling external disturbances and nonlinearities. On the other hand, the CNPIR controller showed better improvements in control accuracy and lower overshoot compared to the FF-PI controller. Since the process system has a nonlinear model and is affected by external disturbances, these two controllers were inadequate in this study when compared to the fractional order adaptive proportional integral derivative sliding mode controller (FO-APIDSMC). The FO-APIDSMC controller provided fairly good performance in both tracking accuracy and disturbance rejection control for non-chattering, fast finite-time convergence, increased robustness, and uncertain dynamic processes. Experimental results reveal that the FO-APIDSMC controller achieves superior minimized tracking error and outperforms the FF-PI and CNPIR controllers by effectively handling uncertainties and external disturbances.
Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
Authors: Cheng Feng , Jia Shuchun
Published: November 2024

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.
Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm
Authors: Meng Ke , Zhang Jundong , Xu Zeming , Zhou Aobo , Wu Shuyun , Zhu Qi , Pang Jiawei
Published: November 2024

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.
New Insights in the Nanomechanical Study of Carbon-Containing Nanocomposite Materials Based on High-Density Polyethylene
Authors: Batakliev Todor , Ivanov Evgeni , Georgiev Vladimir , Angelov Verislav , Ahuir-Torres Juan Ignacio , Harvey David Mark , Kotsilkova Rumiana
Published: November 2024

The investigation of new composite materials possessing low weight but not at the expense of their mechanical performance is of great interest in terms of reducing energy consumption in many industrial applications. This study is focused on the nanomechanical characterization of high-density polyethylene (HDPE)-based composite specimens modified with equal loadings of graphene nanoplatelets (GNPs) and/or multiwall carbon nanotubes (MWCNTs). Quasi-static nanoindentation analysis revealed the impact of the carbon nanofillers on the receiving of nanocomposites with higher nanohardness and reduced modulus of elasticity, reaching values of 0.146 GPa and 3.57 GPa, respectively. The role of the indentation size effect in elastic polymer matrix was assessed by applying three distinct peak forces. Nanoscratch experiments depicted the tribological behavior of the composite samples and inferred the influence of the carbon nanofillers on the values of the coefficient of friction (COF). It seems that the incorporation of 4 wt% GNPs in the polymer structure improves the scratch resistance of the material, resulting in a higher value of the exerted lateral force and therefore leading to the detection of a higher coefficient of friction at scratch of 0.401. A considerable pile-up response of the scratched polymer specimens was observed by means of in-situ SPM imaging of the tested surface sample area. The sway of the carbon nanoparticles on the composite pile-up behavior and the effect of the pile-up on the measured friction coefficients have been explored.
Resolution Improvement in Near-Virtual-Image-Mode Light-Field Display Using Resolution-Priority Technique
Authors: Fukano Koichiro , Kudo Takaaki , Yura Toshiki , Takaki Yasuhiro
Published: November 2024

A light-field display with a near-virtual-image mode, which employs both a lens array and an aperture array, was previously proposed to provide a wide viewing zone angle and bright three-dimensional (3D) images. However, it is desirable to enhance its resolutions, which are presently equal to those of conventional displays. Thus, we proposed a technique for increasing the resolutions of 3D images generated by the light-field display with the near-virtual-image mode. The gap between the flat-panel display and lens array is reduced to decrease the magnification of the virtual images of the pixels and to enable the observation of multiple virtual pixel images through each lens. Further, we imaged the aperture array using the lens array to eliminate the gaps between the multiple pixels observed through adjacent lenses. We constructed a prototype display based on the proposed technique and verified the increase in the resolution of the prototype display compared to the original near-virtual-image light-field display.
Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design
Authors: Miebs Grzegorz , Mielniczuk Adam , Kadziński Miłosz , Bachorz Rafał A.
Published: November 2024

Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity.
High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
Authors: Peng Tang , Liu Yang , Liu Dianmi , Xie Peihong , Chen Jiawei
Published: November 2024

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.
Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
Authors: Joseph Paul , Ali Husnain , Matthew Daniel , Thomas Anvin , Jose Rejath , Mayer Jonathan , Bekbolatova Molly , Devine Timothy , Toma Milan
Published: November 2024

This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Over-sampling Technique, resulting in a diverse distribution of movement types. Three models were evaluated: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Decision Tree Regressor achieved an accuracy of 0.892 and an R2 score of 1.0 on the training dataset, and 0.939 on the test dataset. The Boosting Regressor achieved an accuracy of 0.908 and an R2 score of 0.99 on the training dataset, and 0.943 on the test dataset. The Bagging Regressor was selected due to its superior performance and trade-offs such as computational cost and scalability. It achieved an accuracy of 0.950, an R2 score of 0.996 for the training data, and an R2 score of 0.959 for the test data. This study also employs K-Fold cross-validation and learning curves to validate the robustness of the Bagging Regressor model. The proposed system addresses practical implementation challenges in real-time monitoring, such as data latency and false positives/negatives, and is designed for seamless integration with hospital IT infrastructure. This research demonstrates the potential of machine learning to enhance patient safety in healthcare settings.
Bone Scintigraphy in Cardiac Transthyretin-Related Amyloidosis: A Novel Time-Saving Tool for Semiquantitative Analysis, with Good Potential for Predicting Different Etiologies
Authors: Mattoni Susanna , Morrone Maria Francesca , Della Gala Giuseppe , Prisco Sonia Elisa , Sguazzotti Maurizio , Saturi Giulia , Longhi Simone , Fanti Stefano , Bonfiglioli Rachele , Strigari Lidia
Published: November 2024

(1) Background: The visual and semiquantitative analysis of Technetium-99metastable-3,3-diphospono-1,2-propanodicarboxylic acid (99mTc-DPD) bone scintigraphy is promising for diagnosing cardiac amyloidosis but time-consuming. We validated a faster method, the geometric mean (GM) method with a semi-automated workflow, for heart–whole body (WB) ratio (H/WBr), heart retention (Hr), and WB retention (WBr) calculations compared to the classic method (CM) established in the literature. The capability of semiquantitative scintigraphy indexes to differentiate the etiology in transthyretin-related cardiac amyloidosis (cATTR) patients was investigated. (2) Methods: H/WBr, Hr, and WBr were calculated by extracting counts for WB, kidneys, bladder, and heart on early and late planar image scans and applying background, scan-time, and decay corrections, using CM and GM both on a referring workstation and on a semi-automated workflow in external software. The comparison between CM and GM was assessed with Pearson’s correlation, Lin’s Concordance Correlation Coefficient (CCC), and Bland–Altman analysis. H/WBr, Hr, and WBr and several clinical variables were used to implement LASSO, Random Forest (RF), and Neural Network (NN) models to predict mutated and wild-type ATTR etiologies. ROC curves and AUC were calculated. (3) Results: Hr, WBr, and H/WBr using CM and GM were highly correlated. Bland–Altman analysis between CM and GM showed biases of 0.12% [CI:0.04%;0.19%] for H/WBr, 0.07% [CI: 0.01%; 0.13%] for Hr, and -0.50% [CI: −1.22%; 0.22%] for WBr. LASSO and NN models had good performance in predicting etiologies with AUC values of 87.3% and 73.6%, respectively. The RF model showed a poorer AUC of 55.8%. (4) Conclusions: The GM in the assisted workflow was validated against the CM. LASSO and NN approaches allowed a good prediction performance to be obtained for patient etiology.
Potential Evaluation of Twin-Screw Air Expanders with Dual-Lead Rotors Used in PEMFC Systems
Authors: Liu Mingkun , Wang Chuang , Han Yaoxiang , Xing Ziwen
Published: November 2024

The reduction in the power cost of air supply systems has emerged as a critical challenge in the development of polymer electrolyte membrane fuel cells. This study proposes the use of dual-lead rotors to improve the performance of twin-screw expanders for the purpose of boosting expanders’ recovery power and consequently lowering the power cost of the air supply subsystem, which is hardly investigated in previous publications. For this purpose, a mathematical model is built to assess the potential of improving the expander performance by means of the dual-lead rotors. And the influence of lead and length of the high-pressure rotor segment and overall rotor length are analyzed. The results demonstrate that the smaller lead and larger length of the high-pressure rotor segment result in better geometric characteristics and thus thermodynamic performance. For example, case #4 with dual-lead rotors exhibits a larger rotating angle at the suction end and a larger suction area than those of constant-lead rotors by 43° and 100%, respectively, which further lower the suction pressure loss. Compared with constant-lead rotors, the maximum increments in the mass flowrate and indicated power are observed as 45% and 25.4%, respectively. However, the dual-lead rotors could not effectively contribute to an increase in the isentropic indicated efficiency of twin-screw expanders due to the severe leakage, and hence, it becomes crucial to address the leakage issues in twin-screw expanders.
Seasonal Characteristics of Particulate Matter by Pollution Source Type and Urban Forest Type
Authors: Lee Bobae , Sou Hong-Duck , Yeon Poungsik , Lee Hwayong , Park Chan-Ryul , Choi Sumin , Park Seoncheol
Published: November 2024

To provide consistent air purification benefits from urban forests, it is crucial to identify common characteristics that allow for similar experimental setups. This study aimed to analyze PM10 concentrations in urban forests near pollution sources and understand their mitigation effects. Data from the Asian Initiative for Clean Air Networks, Korea, were used, focusing on three urban forests adjacent to road and industrial pollution sources in Korea, with PM10 concentrations collected during 2021. Considering high PM10 concentrations during winter and spring, these seasons were divided into two sub-periods, resulting in six seasonal periods for analysis. To address the right-skewed PM10 distribution and reduce outlier influence, the Kruskal–Wallis test was used. The results showed that “good” PM10 levels were lowest in early spring, increasing to a peak in summer before declining. High PM10 events were concentrated in spring, early spring, and early winter. The Kruskal–Wallis test indicated lower median PM10 concentrations in urban forests compared to pollution sources in the latter half of the year, while no significant median differences were found in the first half. Distribution visualizations further confirmed that even during high PM10 periods, all urban forests showed lower PM10 values compared to pollution sources. In conclusion, PM10 concentrations in urban forests were consistently lower than in pollution sources across all seasons, demonstrating their effectiveness in air purification at both road and industrial pollution sources. Future research should consider additional variables, such as PM2.5, to further explore differences between pollution sources.
Naturally Occurring Compounds Targeting Peroxisome Proliferator Receptors: Potential Molecular Mechanisms and Future Perspectives for Promoting Human Health
Authors: Spanoudaki Maria , Chrysafi Maria , Papadopoulou Sousana K. , Tsourouflis Gerasimos , Pritsa Agathi , Giaginis Constantinos
Published: November 2024

Background: Peroxisome-proliferator-activated receptors (PPARs) constitute nuclear transcription factors controlling gene expression associated with cell growth and proliferation, diverse proteins, lipids, and glucose metabolism, being related to several other pathophysiological states such as metabolic disorders, atherogenesis, carcinogenesis, etc. The present survey aims to analyze the natural compounds that can act as agonists for the PPAR-α, PPAR-β/δ, and PPAR-γ system targeting, highlighting how the amazing biochemical diversity of natural compounds can yield new insights into this “hotspot” of the scientific field. Methods: A narrative review was performed by searching the recent international literature for the last two decades in the most authoritative scientific databases, like PubMed, Scopus, Web of Science, and Embase, using appropriate keywords. Results: Several natural compounds and/or their synthetic derivatives can act as ligands of PPARs, stimulating their transcriptional activity and enabling their use as preventive and/or therapeutic agents for several disease states, such as inflammation, oxidative stress, metabolic disturbances, atherogenesis, and carcinogenesis. Although synthetic compounds are increasingly used as drugs to manage health problems, serious side effects have been observed, while their natural analogues exhibit only few minor side effects. Conclusions: Further clinical studies on natural compounds such as ligands of PPARs and the evaluation of the related molecular mechanisms are needed to implement an effective strategy concerning the pharmaco-technology, food chemistry, and nutrition to introduce them as part of clinical and dietary practice.
Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
Authors: Jia Wendi , Chen Quanlong
Published: November 2024

In the field of aeronautics, aircraft, as a critical aviation tool, exert a decisive influence on the structural integrity and safety of the entire system. Accurate prediction of the stress field distribution and variations within the aircraft structure is of great importance to ensuring its safety performance. To facilitate such predictions, a rapid assessment method for stress fields based on a multilayer perceptron (MLP) neural network is proposed. Compared to the traditional machine learning algorithm, the random forest algorithm, MLP demonstrates superior accuracy and computational efficiency in stress field prediction, particularly exhibiting enhanced adaptability when handling high-dimensional input data. This method is applied to predict stresses in the wing rib structure. By performing finite element meshing on the wing ribs, the angle of attack, inflow velocity, and node coordinates are utilized as input tensors for the model, enabling it to learn the stress distribution in the wing ribs. Additionally, a peak stress prediction model is separately established for regions experiencing peak stresses. The results indicate that the MAPE of the stress field prediction model is within 5%, with a coefficient of determination R2 exceeding 0.994. For the peak stress model, the MAPE is within 2%, with an R2 exceeding 0.995. This method offers faster computation and greater flexibility, presenting a novel approach for structural strength assessment.
Optimizing Nitrate Fertilizer Production Using Plasma-Activated Water (PAW) Technology: An Analysis of Dielectric Properties
Authors: Pakprom Jariya , Santalunai Samroeng , Charoensiri Weerawat , Ramjanthuk Sukdinan , Janpangngern Pisit , Thongsopa Chanchai , Thosdeekoraphat Thanaset , Santalunai Nuchanart , Santalunai Samran
Published: November 2024

This study investigates the potential of Plasma-Activated Water (PAW) technology for the production of nitrate fertilizer, focusing on the dielectric properties of plasma-treated water, such as the dielectric constant and dielectric loss factor. The research aims to elucidate the impact of plasma treatment and varying water flow rates on these properties. An experimental approach was employed wherein ion-free water was subjected to plasma treatment at different flow rates (0.5, 1.0, and 2.0 L/min) and durations (1, 2, and 3 h). The results reveal a marked enhancement in the dielectric properties of the water following plasma treatment, with the most significant improvements observed at a flow rate of 0.5 L per minute and a treatment duration of 3 h, and dielectric efficiencies of 97.82%, 97.21%, and 96.61% achieved at flow rates of 0.5, 1.0, and 2.0 L/min, respectively. These findings demonstrate that PAW technology enhances the efficiency of nitrate fertilizer production by optimizing energy storage and reducing energy losses. The study underscores the potential of PAW as a sustainable, environmentally benign alternative to conventional chemical fertilizers, contributing to more efficient and sustainable agricultural practices.
Influence of Exposure Distance on Light Irradiance of Dental Curing Lamps in Various Operating Modes
Authors: Lehmann Anna , Nijakowski Kacper , Mroczyk Marta , Podgórski Filip , Czarnecka Beata , Surdacka Anna
Published: November 2024

The efficiency of photopolymerisation significantly impacts achieving a high degree of conversion and, consequently, determines the success and strength of resin-based composite (RBC) restorations. The study aimed to measure the light irradiance of selected LED curing lamps, taking into account various exposure modes and the increased distance of the light source from the radiometer surface. The study material consisted of 21 LED polymerisation lamps of a single type (Woodpecker Medical Instrument Co., Guilin, China) with three exposure modes: standard, soft start, and pulse. During the measurement, the distance was increased from 0 mm to 8 mm, every 2 mm. Light irradiance measurements were made with a Bluephase Meter II photometer (Ivoclar Vivadent, Opfikon, Switzerland). Increasing the distance affected the soft mode the most, causing a significant drop in light irradiance on the photometer. Standard mode coped best with distance. Even at a distance of 0 mm, the soft start mode does not reach the power of the standard and pulse modes. The standard mode seems to be the most clinically effective, especially if it is planned to polymerise a material in a deep cavity. The soft start mode, as the least resistant to increasing distance, is recommended for use in front teeth or the cervical area.
Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
Authors: Haq Qazi Emad ul , Faheem Muhammad Hamza , Ahmad Iftikhar
Published: November 2024

Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. The phishing attacker spreads messages containing false URL links through emails, social media platforms, or messages, targeting people to steal sensitive data like credentials. Attackers generate phishing URLs that resemble those of legitimate websites to gain these confidential data. Hence, there is a need to prevent the siphoning of data through the duplication of trustworthy websites and raise public awareness of such practices. For this purpose, many machine learning and deep learning models have been employed to detect and prevent phishing attacks, but due to the ever-evolving nature of these attacks, many systems fail to provide accurate results. In this study, we propose a deep learning-based system using a 1D convolutional neural network to detect phishing URLs. The experimental work was performed using datasets from Phish-Tank, UNB, and Alexa, which successfully generated 200 thousand phishing URLs and 200 thousand legitimate URLs. The experimental results show that the proposed system achieved 99.7% accuracy, which was better than the traditional models proposed for URL-based phishing detection.