bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement in Diagnosis and Management of Nosocomial Pneumonias in a Cardiovascular Surgery Intensive Care Unit: A Multidisciplinary Approach
Authors: Kooda Kirstin J. , Zambrano Alejandra A. , Kosaski Dylan L. , Higbe Leah , Beam William Brian B. , Bohman J. Kyle K. , Wittwer Erica D. , Brady Steven D. , LeMahieu Allison M. , Fida Madiha , Shah Aditya
Published: June 2024

Background: While criteria for the diagnosis of nosocomial pneumonias exist, objective definitions are a challenge and there is no gold standard for diagnosis. We analyzed the impact of the implementation of a logical, consensus-based diagnostic and treatment protocol for managing nosocomial pneumonias in the cardiovascular surgery intensive care unit (CVS-ICU). Methods: We conducted a quasi-experimental, interrupted time series analysis to evaluate the impact of a diagnostic and treatment protocol for nosocomial pneumonias in the CVS-ICU. Impacts were measured relative to patient outcomes, diagnostic processes, and antimicrobial stewardship improvement. Descriptive statistics were used to analyze results. Results: Overall, 35 pre-protocol and 39 post-protocol patients were included. Primary clinical variables suggesting pneumonia in pre- and post-protocol patients were new lung consolidation (50% vs. 71%), new leukocytosis (59% vs. 64%), and positive culture (32% vs. 55%). Appropriate diagnostic testing improved (23% vs. 54%, p = 0.008) after protocol implementation. The proportion of patients meeting the criteria for nosocomial pneumonia (77% vs. 87%) was not statistically significant, though more patients in the post-protocol group met probable diagnostic criteria (51% vs. 77%). Duration of therapy was not significantly different (6 days [IQR = 5.0, 10.0] vs. 7 days [IQR = 6.0, 9.0]). Conclusions: The implementation of a diagnostic and treatment protocol for management of nosocomial pneumonias in the CVS-ICU resulted in improved diagnostic accuracy, advanced antimicrobial and diagnostic stewardship efforts, and laboratory cost savings without an adverse impact on patient-centered outcomes.
Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
Authors: Chen Kai , Zhang Pengtao , You Liang , Sun Jian
Published: June 2024

In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.
Federated Learning-Based Prediction of Energy Consumption from Blockchain-Based Black Box Data for Electric Vehicles
Authors: Park Jong-Hyuk , Joe In-Whee
Published: June 2024

In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations.
Biophotons: A Hard Problem
Authors: Paolis Luca De , Francini Roberto , Davoli Ivan , De Matteis Fabio , Scordo Alessandro , Clozza Alberto , Grandi Maurizio , Pace Elisabetta , Curceanu Catalina , Grigolini Paolo , Benfatto Maurizio
Published: June 2024

About a hundred years ago, the Russian biologist A. Gurwitsch, based on experiments with onion plants by measuring their growth rate, hypothesized that plants emit a weak electromagnetic field that somehow influences cell growth. This interesting observation remained fundamentally ignored by the scientific community; only in the 1950s the electromagnetic emission from some plants was measured using a photomultiplier used in single counting mode. Later, in the 1980s, several groups around the world started extensive work to understand the origin and role of this ultraweak emission, now called biophotons, coming from living organisms. Biophotons are an endogenous very small production of photons in the visible energy range in and from cells and organisms, and this emission is characteristic of living organisms. Today, there is no doubt that biophotons exist, this emission has been measured by many groups and for many different living organisms, from humans to bacteria. However, the origin of biophotons and whether organisms use them to exchange information is not yet well understood; no model proposed to date is capable of reproducing and interpreting the great variety of experimental data coming from the many different living systems measured so far. In this brief review, we present our experimental work on the biophotons coming from germinating seeds, the main experimental results, and some new methods we are using to analyze the data to open the door for interpretative models of this phenomenon clarifying its function in the regulation and communication between cells and living organisms. We also discuss ideas on how to increase the signal-to-noise ratio of the measured signal to open up new experimental possibilities that allow the measurement and the characterization of currently unmeasurable quantities.
A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
Authors: Wan Chenxia , Zhang Qinghui
Published: June 2024

Dual-component radar-signal modulation recognition is a challenging yet significant technique for electronic reconnaissance systems. To improve the lower recognition performance and the higher computational costs of the conventional methods, this paper presents a randomly overlapping dual-component radar-signal modulation recognition method based on a convolutional neural network–swin transformer (CNN-ST) under different signal-to-noise ratios (SNRs). To enhance the feature representation ability and decrease the loss of the detailed features of dual-component radar signals under different SNRs, the swin transformer is adopted and integrated into the designed CNN model. An inverted residual structure and lightweight depthwise convolutions are used to maintain the powerful representational ability. The results show that the dual-component radar-signal recognition accuracy of the proposed CNN-ST is up to 82.58% at −8 dB, which shows the better recognition performance of the CNN-ST over others. The dual-component radar-signal recognition accuracies under different SNRs are all more than 88%, which verified the fact that the CNN-ST achieves better recognition accuracy under different SNRs. This work offers essential guidance in enhancing dual-component radar signal recognition under different SNRs and in promoting actual applications.
Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework
Authors: Hossain Sk Tahsin , Yigitcanlar Tan , Nguyen Kien , Xu Yue
Published: June 2024

Local governments face critical challenges in the era of digital transformation, balancing the responsibility of safeguarding resident information and administrative documents while maintaining data integrity and public trust. These responsibilities become even more critical as they transition into smart cities adopting advanced technological innovations to revolutionize governance, enhance service delivery, and foster sustainable and resilient urban environments. Technological advancements like Internet-of-Things devices and artificial intelligence-driven approaches can provide better services to residents, but they also expose local governments to cyberthreats. There has been, nonetheless, very little study on cybersecurity issues from the local government perspective, and information on the multifaceted nature of cybersecurity in local government settings is scattered and fragmented, highlighting the need for a conceptual understanding and adequate action. Against this backdrop, this study aims to identify key components of cybersecurity in a local governmental context through a systematic literature review. This review further extends to the development of a conceptual framework providing a comprehensive understanding of the local government’s cybersecurity landscape. This study makes a significant contribution to the academic and professional domains of cybersecurity issues and policies within the local governmental context, offering valuable insights to local decision-makers, practitioners, and academics. This study also helps identify vulnerabilities, enabling stakeholders to recognize shortcomings in their cybersecurity and implement effective countermeasures to safeguard confidential information and documents. Thus, the findings inform local government policy to become more cybersecurity-aware and prepared.
Light and Nutrient Conditions Influence Fucoxanthin Production of the Microalgae Cyclotella meneghiniana
Authors: Chinnappan Santhoshkumar , Cai Jingting , Li Yanfei , Yang Zhenxiong , Sheng Yangjie , Cheng Keying , Du Hong , Liu Wenhua , Li Ping
Published: June 2024

Fucoxanthin has attracted the attention of scholars because of its health benefits in terms of anticancer, weight loss, antidiabetic, hypolipidemic, and antioxidant functions. Researchers have found that the fucoxanthin content of microalgae was higher than that of macroalgae. Therefore, the microalgae Cyclotella meneghiniana was isolated and maintained under varying light and modified nutrient conditions. The results of this study showed that Cyclotella meneghiniana had better photosynthetic activity and higher biomass under low light. Both high trace elements and high nitrogen promoted the accumulation of fucoxanthin in Cyclotella meneghiniana. Low light levels and high trace metal contents enhanced the fucoxanthin production (7.76 ± 0.30 mg g−1 DW). The results of the current study will help to enhance fucoxanthin production for commercialization.
Occupational Inhalation Health Risk Assessment of TCE Exposure in the Korean Manufacturing Industry
Authors: Seo Sueji , Shin Saemi , Byeon Sanghoon
Published: June 2024

This study aimed to assess the health risks to workers in the Korean manufacturing industries using trichloroethylene (TCE). In Republic of Korea, TCE has permissible exposure limits. In 2014, the permissible exposure limits were exceeded in two cases, necessitating a health risk assessment. The exposure value of TCE in the workplace was determined using Korea’s Workplace Environmental Monitoring Program (WEMP), and risk assessment was performed by applying a probabilistic distribution using a Monte Carlo simulation. When evaluating non-carcinogenic risks at the 50th percentile, all 20 industries had acceptable risk, and at the 95th percentile, 12 of the 20 industries had unacceptable risk. Following the cancer risk assessment, 17 out of 20 industries had unacceptable risks, and the 95th percentile of all industries had unacceptable risks. The non-carcinogenic and carcinogenic risks of TCE were highest during metal fabrication. Based on these results, metal fabrication was chosen as an industry that should receive management priority in Korea. The findings of this study serve as a foundation for managing TCE among manufacturing workers.
RockDNet: Deep Learning Approach for Lithology Classification
Authors: Abdullah Mohammed A. M. , Mohammed Ahmed A. , Awad Sohaib R.
Published: June 2024

Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.
Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
Authors: Sampaio Tatiana , Oliveira João P. , Marinho Daniel A. , Neiva Henrique P. , Morais Jorge E.
Published: June 2024

(1) Background: Tennis has changed toward power-driven gameplay, demanding a nuanced understanding of performance factors. This review explores the role of machine learning in enhancing tennis performance. (2) Methods: A systematic search identified articles utilizing machine learning in tennis performance analysis. (3) Results: Machine learning applications show promise in psychological state monitoring, talent identification, match outcome prediction, spatial and tactical analysis, and injury prevention. Coaches can leverage wearable technologies for personalized psychological state monitoring, data-driven talent identification, and tactical insights for informed decision-making. (4) Conclusions: Machine learning offers coaches insights to refine coaching methodologies and optimize player performance in tennis. By integrating these insights, coaches can adapt to the demands of the sport by improving the players’ outcomes. As technology progresses, continued exploration of machine learning’s potential in tennis is warranted for further advancements in performance optimization.
Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
Authors: Qing Jiajun , Deng Xiaoling , Lan Yubin , Xian Jidong
Published: June 2024

The utilization of the large pretrained model (LPM) based on Transformer has emerged as a prominent research area in various fields, owing to its robust computational capabilities. However, there remains a need to explore how LPM can be effectively employed in the agricultural domain. This research aims to enhance agricultural pest detection with limited samples by leveraging the strong generalization performance of the LPM. Through extensive research, this study has revealed that in tasks involving the counting of a small number of samples, complex agricultural scenes with varying lighting and environmental conditions can significantly impede the accuracy of pest counting. Consequently, accurately counting pests in diverse lighting and environmental conditions with limited samples remains a challenging task. To address this issue, the present research suggests a unique approach that integrates the outstanding performance of the segment anything model in class-agnostic segmentation with the counting network. Moreover, by intelligently utilizing a straightforward TopK matching algorithm to propagate accurate labels, and drawing inspiration from the GPT model while incorporating the forgetting mechanism, a more robust model can be achieved. This approach transforms the problem of matching instances in different scenarios into a problem of matching similar instances within a single image. Experimental results demonstrate that our method enhances the accuracy of the FamNet baseline model by 69.17% on this dataset. Exploring the synergy between large models and agricultural scenes warrants further discussion and consideration.
Analysis of Beam Walk in Inter-Satellite Laser Link: Implications for Differential Wavefront Sensing in Gravitational Wave Detection
Authors: Qian Xing-Guang , Cui Zhao , Shi Hao-Qi , Wang Xue , Yao Wei-Lai , Gao Rui-Hong , Wang Yi-Kun
Published: June 2024

Achieving space-based gravitational wave detection requires the establishment of an interferometer constellation. It is necessary to establish and maintain stable laser interferometric links using the differential wavefront sensing (DWS) technnique. When the distant measurement beam experiences pointing jitter, it causes beam walk on the surface of the local detector. The reduced overlap between the local reference spot and the distant spot increases the nonlinear errors in the DWS technique, which need to be suppressed. Numerical analysis was conducted on the spatial beam interference signals of the DWS technique when the distant measurement beam experienced pointing jitter. An experimental measurement system was designed, and the beam walk was suppressed using a conjugate imaging system. The results show that within a range of 300 μrad, the optical path with the imaging system can reduce measurement errors by at least 83%. This way also helps to reduce pointing jitter noise in inter-satellite links, thereby improving laser pointing control accuracy.This method would provide a valuable reference for future DWS measurement systems.
Hierarchical Indexing and Compression Method with AI-Enhanced Restoration for Scientific Data Service
Authors: Song Biao , Fang Yuyang , Guan Runda , Zhu Rongjie , Pan Xiaokang , Tian Yuan
Published: June 2024

In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this paper proposes a lossy compression technique based on error control. Instead of imposing precision constraints during compression, this method first uses the JPEG compression algorithm for multi-level compression and then manages data through a tree-based index structure to achieve error control. This approach satisfies error control requirements while effectively avoiding tight coupling. Additionally, this paper enhances data restoration effects using a deep learning network and provides a range query processing algorithm for the tree-based index to improve query efficiency. We evaluated our solution using ocean data. Experimental results show that, while maintaining data precision requirements (PSNR of at least 39 dB), our compression ratio can reach 64, which is twice that of the SZ compression algorithm.
Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape
Authors: Baselga Sergio , Montbarbon Eva
Published: June 2024

Neutron/gamma pulse shape discrimination (PSD) is essential in applications such as radiation source analysis, nuclear material detection, detection of pollutants in the soil and cultural heritage. Neutrons are accompanied by gamma-ray photons due to the interaction with the environment so neutron detectors require some techniques to differentiate them. There are several methods enabling such differentiation. In the current submission, a robust estimation of the decay shape is proposed as a new alternative. To do so, a robust estimator computed by a global optimization method is used. After presenting the theoretical background and explaining the required computations to be realized, the proposed method is tested in a publicly available large dataset. Evaluations of the figure of merit and the positive discrimination rate values are used to assess the degree of improvement attained. A computing code for the method, which is easily adaptable by users to their own datasets, is also provided.
The Influence of Large Variations in Fluid Density and Viscosity on the Resonance Characteristics of Tuning Forks Simulated by Finite Element Method
Authors: Jiang Feng , Chen Dehua , He Xiao , Dai Yuyu , Tang Man , Zhou Yinqiu , Zhang Mi
Published: June 2024

The use of tuning forks to measure fluid density and viscosity is widely employed in fields such as food, medicine, textiles, automobiles, petrochemicals, and deep drilling. The explicit analytical model based on the Euler–Bernoulli cantilever-beam theory for the relationship between tuning-fork resonance characteristics and the density and viscosity of fluid is only applicable to the situation where the fluid viscous effect is very small. In this paper, the finite element method is used to simulate the influence of large variations in fluid density and viscosity on the resonance characteristic parameters (resonant frequency and quality factor) of the tuning fork. The numerical simulation results are compared with the analytical analysis results and experimental measurement results. Then, the sensitivity of tuning-fork resonance characteristic parameters to fluid density and viscosity is studied. The results show that compared with the analytical results, the numerical simulation results have a higher degree of agreement with the experimental measurement results. The relative difference in resonant frequency is less than 2%, and the relative difference in quality factor is less than 4%. This indicates that the finite element method includes the influence of fluid viscosity on tuning-fork resonance parameters, which is more in line with the actual conditions than the analytical model. Simulating and analyzing the sensitivity of the tuning fork to fluid density and viscosity by the finite element method, it is possible to consider the situation where fluid density and viscosity vary over a large range. Compared with experimental measurements, this method has higher efficiency and can significantly save time and economic costs. This study can overcome the limitation of existing explicit analytical models, which are only applicable when the viscous effects of the fluid are very small. It enables a more accurate simulation of the coupling vibration between tuning forks and fluids, thereby providing theoretical references for further optimizing tuning-fork structural parameters to enhance the accuracy of measuring fluid characteristic parameters.
AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
Authors: Ramirez-Rodriguez Ana Elena , Arevalo-Ancona Rodrigo Eduardo , Perez-Meana Hector , Cedillo-Hernandez Manuel , Nakano-Miyatake Mariko
Published: June 2024

The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two identical branches with shared weights and image features to compare image blocks and identify tampered areas. Subsequently, a K-means algorithm is applied to identify similar centroids and determine the precise localization of duplicated regions in the image. The experimental results encompass various splicing attacks to assess effectiveness, demonstrating a high accuracy of 98.6% and a precision of 97.5% for splicing manipulation detection. This study presents an advanced splicing image forgery detection and localization algorithm, showcasing its efficacy through comprehensive experiments.
Enhancing Paenibacillus sp. Cold-Active Acetyl Xylan Esterase Activity through Semi-Rational Protein Engineering
Authors: Ji Keunho , Nandanwar Sondavid , Jeon So Yeon , Yang Gyu Ri , Liu Lixiao , Oh Hyun-Myung , Kim Hak Jun
Published: June 2024

Interest in protein engineering for the enzymatic production of valuable products, such as pharmaceutical compounds and biofuels, is growing rapidly. The cold-active acetyl xylan esterase from Paenibacillus sp. (PbAcE) presents unusually broad substrate specificity. Here, we engineered a hydrophobic substrate-binding pocket to enable the accommodation of relatively large alcohol substrates, such as linalyl acetate and α-terpinyl acetate. To identify candidate residues for engineering, we performed covalent docking of substrates to the Ser185 active site using the HCovDock program. Functional hotspots were analyzed using HotSpot Wizard 3.1. Lys91, His93, and Tyr182 were selected for site-saturation mutagenesis (SSM). After generating the SSM mutant library, a qualitative colorimetric assay was conducted to identify positive mutants. Three, two, and five single mutants were selected for Lys91, His93, and Tyr182, respectively. The best single mutants were then sequentially combined to generate double and triple mutants. Single mutants exhibited a 10–30% increase in activity compared to that of wild-type PbAcE, while no significant synergistic improvements were observed in the double and triple mutants. The increase in activity against both linalyl acetate and α-terpinyl acetate was similar. Mutation did not affect the acetyl binding and catalysis. Further research on the acetyl binding pocket will provide insights into substrate specificity and aid in efficient biocatalyst development for industrial applications.
Numerical Analysis of the Breakdown Process of CF3I at Low Pressure
Authors: Wu Yifan , Wang Zhijiang , Wu Hao , Jiang Wei
Published: June 2024

The breakdown of CF3I gas at low pressure is of significant importance for applications in fields such as aerospace and microelectronics. However, the DC low-pressure breakdown characteristics of CF3I remain underexplored. In this work, we utilize a one-dimensional implicit particle-in-cell/Monte Carlo collision (PIC/MCC) algorithm to investigate the complete DC breakdown process of low-pressure CF3I. Our model accounts for ion–molecule collisions, recombination reactions, and external circuit influences. The breakdown process is delineated into three stages: before breakdown, breakdown, and after breakdown. In the before-breakdown stage, both the density and energy of particles are low. In the breakdown stage, the rapid increase in electron density and energy accelerates ionization reactions, leading to successful breakdown. The circuit behavior transitions from capacitive to resistive, sharing voltage with the external resistance. In the after-breakdown stage, continued positive ion growth leads to the formation of a thin anode sheath and a negative plasma potential. Energy production, including heating power and secondary electron emission (SEE) power, balances with energy loss through collision and boundary absorption. Specifically, 62% of the total heating power comes from positive ions, 1.5% from negative ions, and approximately 85% of electron energy is lost via boundary absorption. Finally, we compare the Paschen curves of CF3I with those of SF6, providing insights that are beneficial for the application of CF3I as an SF6 alternative.
Optical Characterization of a Rotationally Asymmetric Refractive Multifocal Intraocular Lens Compared to a Standard Monofocal One
Authors: Albarrán-Diego César , García-Montero María , Garzón Nuria , Gómez-Pedrero José Antonio
Published: June 2024

This study compares a standard monofocal intraocular lens (IOL) with two rotationally asymmetric refractive multifocal IOLs, analyzing power profiles and wavefront data across three nominal powers. Tested IOLs included monofocal Acunex AN6 and multifocal AN6V (addition +1.50 D) and AN6VM (addition +3.00 D) by Teleon Surgical, at powers +10.00 D, +20.00 D, and +30.00 D. Using NIMO TR1504, power profiles, spherical aberration, and coma were analyzed. Multifocal lenses displayed an asymmetrical design, with zones for far and near vision and a central notch for far power. The multifocal AN6V and AN6VM showed neutral spherical aberration in the center, increasing to positive values, and finally achieving negative values towards the periphery. The vertical coma coefficient [Z(3:−1)] remains almost stable for the monofocal IOLs. The lower-addition multifocal IOLs induce primary vertical coma of +0.11 microns at 3 mm away from the lens center compared to +0.21 microns obtained with the high-addition lens. We can conclude that the multifocal AN6V and AN6VM lenses display an asymmetrical design. The final addition provided by these lenses depends on its nominal power. The variation of SA with optic size is more pronounced for the monofocal model, and it is dependent on the nominal power.
Impact of Prolonged Screening and COVID-19 Infection on Acquired Colour Vision Deficiencies Assessed by the Farnsworth–Munsell 100 Hue Test
Authors: Moncho Santonja María , Jordán Jorge , Micó Bárbara , López Daniel , Tortajada Montañana Ignacio , Peris-Fajarnés Guillermo
Published: June 2024

Over the past decade, global screening time has increased, a trend intensified by the COVID-19 pandemic, leading to the integration of screens into daily life. Studies have documented the adverse effects of prolonged screening on ocular health and binocular vision, such as dry eye syndrome, blurry vision, headaches, myopia, and visual fatigue. However, it remains unclear if prolonged screening affects the development of colour vision defects. Objectives: This study aimed to determine the relationship between (a) prolonged screening and acquired colour vision deficiencies and (b) COVID-19 infection and acquired colour vision deficiencies. Methods: A population of 50 individuals with normal trichromatic vision, aged 20 to 30 years, with an average daily screening time of 516.7 min, was evaluated. Participants were initially screened using the Ishihara 32-plate Test to exclude those with congenital colour vision deficiencies. The Farnsworth–Munsell 100 Hue Test (FM100H) and Square Root Total Error Score (√TES) were used to evaluate acquired colour vision deficiencies under standardized conditions. The dataset underwent dual analysis: (1) detailed statistical scrutiny and (2) comparison of √TES values with historical data from 1982, 1991, 2001, and 2002. Results: The global group had a √TES (Mean ± SD) of 5.40 ± 1.58, the COVID-19 subgroup 5.46 ± 1.62, and the non-COVID-19 subgroup 5.32 ± 1.51. No significant differences were found between the √TES values from this population and those reported in previous studies. Statistical analysis showed no significant correlation between gender and COVID-19 infection with √TES values. Conclusions: Neither screening time nor COVID-19 infection appears to significantly impact the occurrence of permanently acquired colour vision deficiencies in individuals aged 20 to 30 years.
Innovative Implementation of Computational Fluid Dynamics in Proteins Denaturation Process Prediction in Goose Breast Meat and Heat Treatment Processes Optimization
Authors: Szpicer Arkadiusz , Bińkowska Weronika , Stelmasiak Adrian , Zalewska Magdalena , Wojtasik-Kalinowska Iwona , Piwowarski Karol , Półtorak Andrzej
Published: June 2024

This study aimed to calculate the optimal thermal processing parameters for goose meat using CFD simulation. CFD provides a precise determination of heat treatment conditions by predicting protein denaturation and mass loss, leading to higher quality and improved sensory experience and, thus, acceptance of products. Accurate calculation of these conditions reduces energy losses and enhances process efficiency in the food industry. This study focused on the prediction of protein denaturation and cooking loss in goose breast meat during roasting. Specific CFD techniques, including conjugate heat transfer and phase change models, were utilized to ensure accuracy in protein denaturation prediction. These models accounted for variations in meat composition, such as fat and water content across different samples, which improved the accuracy of the predictions. Optimal conditions were determined using a mathematical model. These conditions were 164.65 °C, 63.58% humidity, and a fan rotation of 16.59 rpm for 2000 s. The myosin, collagen, and actin denaturation levels, as well as cooking loss, closely matched predicted values. The findings show that CFD is a valuable method for evaluating protein denaturation and cooking loss in goose breast meat, potentially improving product quality and consistency in gastronomy and the meat industry. This innovative optimization method enhances food production efficiency and elevates sensory characteristics, physicochemical properties, and nutritional value, contributing to consumer satisfaction and market competitiveness. The model proposed in this paper can be adapted to predict denaturation in other types of meat or food products with necessary modifications, offering broad applicability. Potential limitations of using CFD in protein denaturation prediction in complex food matrices include the need for detailed compositional data and computational resources, which can be addressed in future research.
Motion Branch Transformation of 3(Rc)PU Parallel Mechanism with Reconfigurable Joint and Kinematic Performance Index
Authors: Chai Chao , Li Ruiqin , Ning Fengping , Li Yanlong
Published: June 2024

A novel 3(Rc)PU parallel mechanism based on the reconfigurable circular groove cross joint (Rc joint) is proposed. The motion branch transformation relationship and constraint performance evaluation method for the mechanism are studied. According to constrained screw analysis, the (Rc)PU limb provides a constraint couple to the moving platform in one motion mode and no constraint to the platform in another motion mode. According to the two configurations of the limb, the 3(Rc)PU mechanism has four different motion branches, namely 3T, 1R3T, 2R3T, and 3R3T. Based on the input selection principle, the input selection scheme of the 3(Rc)PU mechanism is determined, and it is concluded that, to achieve stable motion, at least two input drive pairs need to be applied on each limb. A unified kinematic model with four motion branches is established, and the geometric constraint equations of the 3(Rc)PU parallel mechanism are derived. After selecting the input actuated joints, the local minimization transmission index (LMTI) of the reconfigurable mechanism under different motion branches is established to evaluate the motion/force transmission performance of the reconfigurable mechanism under different motion branches. The conclusion shows that the LMTI values satisfy the corresponding constraint conditions, and the 3(Rc)PU parallel mechanism has good motion/force transmission performance.
Three Existence Results in the Fixed Point Theory
Authors: Zaslavski Alexander J.
Published: June 2024

In the present paper, we obtain three results on the existence of a fixed point for nonexpansive mappings. Two of them are generalizations of the result for F-contraction, while third one is a generalization of a recent result for set-valued contractions.
From the Crossing Numbers of K5 + Pn and K5 + Cn to the Crossing Numbers of Wm + Sn and Wm + Wn
Authors: Staš Michal , Fortes Jana , Švecová Mária
Published: June 2024

The crossing number of a graph is a significant measure that indicates the complexity of the graph and the difficulty of visualizing it. In this paper, we examine the crossing numbers of join products involving the complete graph K5 with discrete graphs, paths, and cycles. We analyze optimal drawings of K5, identify all five non-isomorphic drawings, and address previously hypothesized crossing numbers for K5+Pn, and K5+Cn. Through a simplified approach, we first establish cr(K5+Dn) and then extend our method to prove the crossing numbers cr(K5+Pn) and cr(K5+Cn). These results also lead to new hypotheses for cr(Wm+Sn) and cr(Wm+Wn) involving wheels and stars. Our findings correct previous inaccuracies in the literature and provide a foundation for future research.
The Collaborative Spark That Ignited the Field of Stromal Stem Cell Biology
Authors: Triffitt James T.
Published: June 2024

Russia has produced many scientists of great renown in a multitude of fields from chemistry, physics, astronautics, and mathematics to biology, pathology, and medicine [...]
Pectin as a Biomaterial in Regenerative Endodontics—Assessing Biocompatibility and Antibacterial Efficacy against Common Endodontic Pathogens: An In Vitro Study
Authors: Abdelgawad Raghda Magdy , Damé-Teixeira Nailê , Gurzawska-Comis Katarzyna , Alghamdi Arwa , Mahran Abeer H. , Elbackly Rania , Do Thuy , El-Gendy Reem
Published: June 2024

Regenerative endodontics (REP) is a new clinical modality aiming to regenerate damaged soft and hard dental tissues, allowing for root completion in young adults’ teeth. Effective disinfection is crucial for REP success, but commonly used antimicrobials often harm the niche dental pulp stem cells (DPSCs). To our knowledge, this is the first study to explore the biocompatibility and antimicrobial potential of pectin as a potential natural intracanal medicament for REPs. Low methoxyl commercial citrus pectin (LM) (pectin CU701, Herbstreith&Fox.de) was used in all experiments. The pectin’s antibacterial activity against single species biofilms (E. faecalis and F. nucleatum) was assessed using growth curves. The pectin’s antimicrobial effect against mature dual-species biofilm was also evaluated using confocal laser scanning microscopy (CLSM) after 30 min and 7 days of treatment. The DPSC biocompatibility with 2% and 4% w/v of the pectin coatings was evaluated using live/dead staining, LDH, and WST-1 assays. Pectin showed a concentration-dependent inhibitory effect against single-species biofilms (E. faecalis and F. nucleatum) but failed to disrupt dual-species biofilm. Pectin at 2% w/v concentration proved to be biocompatible with the HDPSCs. However, 4% w/v pectin reduced both the viability and proliferation of the DPSCs. Low concentration (2% w/v) pectin was biocompatible with the DPSCs and showed an antimicrobial effect against single-species biofilms. This suggests the potential for using pectin as an injectable hydrogel for clinical applications in regenerative endodontics.
Cardiac Arrhythmia Risk after Anti-Cancer Drug Exposure and Related Disease Molecular Imaging Outlook: A Systematic Review, Meta-Analysis, and Network Meta-Analysis
Authors: Li Hongzheng , Yang Wenwen , Peng Yuxuan , Huang Mingyan , Liao Feifei , Lu Aimei , Yu Zikai , Zhao Xin
Published: June 2024

Background: Chemotherapy is the main first-line treatment, but there is a problem of adverse reactions to systemic drugs. Chemotherapeutic agents may cause adverse effects on the body, influencing the prognosis. Whether the clinical application of anthracyclines is associated with an increased arrhythmic risk remains controversial. To evaluate the arrhythmic risk of anthracyclines as a class, and the comparative risk for each drug, we conducted a systematic review, meta-analysis, and network meta-analysis. Methods: PubMed, Web of Science, EMBASE, and the Cochrane Library were searched, up to March 2022, for randomized controlled trials, cohort studies, and case–control studies that investigated the association between anthracyclines treatment and the risk of arrhythmia. We followed the PRISMA 2020 guidelines for data selection and extraction. Outcomes were pooled using fixed effects models in cohort studies and randomized controlled studies, and random models in single-arm studies. Direct and indirect comparisons in network meta-analysis were performed using frequentist methods. Results: In total, 4 cohort studies, 8 RCTs, and 18 single-arm studies were included in our analysis. Anthracyclines’ use was associated with a statistically significant 90% increase in the risk of arrhythmia (odds ratio [OR] 1.90; 95% confidence interval [CI] 1.62–2.24) and a 114% increase in the risk of supraventricular arrhythmia (OR 2.14; 95% CI 1.18–3.89). And the single-arm studies also indicated that the incidence of arrhythmia rate is 20% and the 95% CI is 15/100–25/100. Epirubicin ranked most likely to have the highest risk of arrhythmia compared with non-anthracycline antineoplastic drugs in the analysis (OR 43.07 [95% CI 2.80–2105.83]) by network meta-analysis. Conclusions: Our findings show a significant association between anthracyclines’ use and an increased risk of arrhythmia, especially supraventricular arrhythmia. Epirubicin ranked with the highest probability of arrhythmia. These results indicated that cardiac rhythm should be strictly monitored during the application of anthracyclines in clinical practice, and a possible therapy for anthracycline-associated arrhythmia should be explored. Molecular imaging technology is an important means to study the mechanism of drug action on cardiac electrophysiology in the future. By imaging molecular targets in cardiac cells, the effects of drugs on the electrophysiological properties of cardiac cells can be understood, which provides information for the development of safer and more effective drugs.
Excess Iodine Consumption Induces Oxidative Stress and Pancreatic Damage Independently of Chemical Form in Male Wistar Rats: Participation of PPAR-γ and C/EBP-β
Authors: Arbez-Evangelista Cristian , Arroyo-Xochihua Omar , Ortega-Ibarra Ilse Haide , Ortega-Ibarra Edú , De León-Ramírez Yeimy Mar , Cuevas-Romero Estela , Arroyo-Helguera Omar
Published: June 2024

Background: Human beings consume different chemical forms of iodine in their diet. These are transported by different mechanisms in the cell. The forms of iodine can be part of thyroid hormones, bind to lipids, be an antioxidant, or be an oxidant, depending on their chemical form. The excessive consumption of iodine has been associated with pancreatic damage and diabetes mellitus type 2, but the association between disease and the chemical form consumed in the diet is unknown. This research analyzes the effect of excessive iodine consumption as Lugol (molecular iodine/potassium iodide solution) and iodate on parameters of pancreatic function, thyroid and lipid profiles, antioxidant and oxidant status, the expression of IR/Akt/P-Akt/GLUT4, and transcription factors PPAR-γ and CEBP-β. Methods: Three groups of Wistar rats were treated with 300 μg/L of iodine in drinking water: (1) control, (2) KIO3, and (3) Lugol. Results: Lugol and KIO3 consumption increased total iodine levels. Only KIO3 increased TSH levels. Both induced high serum glucose levels and increased oxidative stress and pancreatic alpha-amylase activity. Insulin levels and antioxidant status decreased significantly. PPAR-γ and C/EBP-β mRNA expression increased. Conclusion: The pancreatic damage, hypertriglyceridemia, and oxidative stress were independent of the chemical form of iodine consumed. These effects depended on PPAR-γ, C/EBP-β, GLUT-4, and IR.
Bone Regeneration and Polyetheretherketone Implants in Maxillo-Facial Surgery and Neurosurgery: A Multidisciplinary Study
Authors: Todaro Mattia , Saponaro Gianmarco , Perquoti Federico , Gasparini Giulio , Signorelli Francesco , Tartaglione Tommaso , Moro Alessandro
Published: June 2024

Polyetheretherketone (PEEK) in the last few years has emerged as an exceedingly promising material for craniofacial defects due to its biocompatibility and mechanical properties. However, its utilization remains controversial due to its inertness and low osteoinductivity. This study aimed to investigate the postoperative outcomes of patients undergoing maxillo-facial and neurosurgical procedures with PEEK implants. The focus is on evaluating bone regrowth on the surface and edges of the implant, periosteal reactions, and implant positioning. A retrospective analysis of 12 maxillo-facial surgery patients and 10 neurosurgery patients who received PEEK implants was conducted. CT scans performed at least one year post operation were examined for bone regrowth, periosteal reactions, and implant positioning. In maxillo-facial cases, the analysis included mandibular angle and fronto-orbital reconstruction, while neurosurgical cases involved cranioplasty. In maxillofacial surgery, 11 out of 12 patients showed radiological evidence of bone regrowth around PEEK implants, with favorable outcomes observed in craniofacial reconstruction. In neurosurgery, 9 out of 10 patients exhibited minimal or none bone regrowth, while one case demonstrated notable bone regeneration beneath the PEEK implant interface. The study highlights the importance of implant design and patient-specific factors in achieving successful outcomes, providing valuable insights for future implant-based procedures.
Database Bias in the Detection of Interdomain Horizontal Gene Transfer Events in Pezizomycotina
Authors: Aguirre-Carvajal Kevin , Munteanu Cristian R. , Armijos-Jaramillo Vinicio
Published: June 2024

Horizontal gene transfer (HGT) is a widely acknowledged phenomenon in prokaryotes for generating genetic diversity. However, the impact of this process in eukaryotes, particularly interdomain HGT, is a topic of debate. Although there have been observed biases in interdomain HGT detection, little exploration has been conducted on the effects of imbalanced databases. In our study, we conducted experiments to assess how different databases affect the detection of interdomain HGT using proteomes from the Pezizomycotina fungal subphylum as our focus group. Our objective was to simulate the database imbalance commonly found in public biological databases, where bacterial and eukaryotic sequences are unevenly represented, and demonstrate that an increase in uploaded eukaryotic sequences leads to a decrease in predicted HGTs. For our experiments, four databases with varying proportions of eukaryotic sequences but consistent proportions of bacterial sequences were utilized. We observed a significant reduction in detected interdomain HGT candidates as the proportion of eukaryotes increased within the database. Our data suggest that the imbalance in databases bias the interdomain HGT detection and highlights challenges associated with confirming the presence of interdomain HGT among Pezizomycotina fungi and potentially other groups within Eukarya.