bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of CNC Working Time Depending on the Positioning of the Tools in the Magazine
Authors: Pajaziti Arbnor , Tafilaj Orlat , Hasanaj Enise , Gjelaj Afrim
Published: July 2024

This paper addresses the optimization of CNC (Computer Numerical Control) working time through the strategic positioning and grouping of cutting tools. Our analysis primarily focused on evaluating the previous arrangement of tools in the magazine and exploring the potential for repositioning them to enhance CNC operational efficiency. The work methodology is based on the collection of direct data from CNC as well as through CAD/CAM simulations. The collected data are analyzed with the Python programming language, which is one of the most used and efficient languages now. By grouping the tools according to the same function, tools were repositioned next to each other, which we obtained. After collecting the data and processing them in the Python programming language, we made a visualization of the data. Working time is reduced from 27.44 s to 26.46 s and results are analyzed in the Python Programming language. The working time is reduced by 0.98 s, which is approximately a 3.57% reduction in the working time. This indicates a significant improvement in efficiency due to the optimization of the cutting tool positions in the magazine.
Computational Fluid Dynamics-Based Optimisation of High-Speed and High-Performance Bearingless Cross-Flow Fan Designs
Authors: Bagaric Ivana , Steinert Daniel , Nussbaumer Thomas , Kolar Johann Walter
Published: July 2024

To enhance the fluid dynamic performance of bearingless cross-flow fans (CFFs), this paper presents a CFD-based optimisation of both rotor and static casing wall modifications. High-performance CFFs are essential in industrial applications such as highly specialised laser modules in the semiconductor industry. The goal for the investigated rotor modifications is to enhance the CFF’s mechanical stiffness by integrating reinforcing shafts, which is expected to increase the limiting bending resonance frequency, thereby permitting higher rotational speeds. Additionally, the effects of these rotor modifications on the fluid dynamic performance are evaluated. For the casing wall modifications, the goal is to optimise design parameters to reduce losses. Optimised bearingless CFFs benefit semiconductor manufacturing by improving the gas circulation system within the laser module. Higher CFF performance is a key enabler for enhancing laser performance, increasing the scanning speed of lithography machines, and ultimately improving chip throughput. Several numerical simulations are conducted and validated using various commissioned prototypes, each measuring 600mm in length and 60mm in outer diameter. The results reveal that integrating a central shaft increases the rotational speed by up to 42%, from 5000rpm to 7100rpm, due to enhanced CFF stiffness. However, the loss in fluid flow amounts to 61% and outweighs the gain in rotational speed. Optimising the casing walls results in a 22% increase in maximum fluid flow reaching 1800m3/h at 5000rpm. It is demonstrated that the performance of bearingless CFFs can be enhanced by modifying the geometry of the casing walls, without requiring changes to the CFF rotor or bearingless motors.
Application of Extended Normal Distribution in Option Price Sensitivities
Authors: Nayak Gangadhar , Tripathy Subhranshu Sekhar , Imoize Agbotiname Lucky , Li Chun-Ta
Published: July 2024

Empirical evidence indicates that asset returns adhere to an extended normal distribution characterized by excessive kurtosis and non-zero skewness. Consequently, option prices derived from this distribution diverge from those predicted by the Black–Scholes model. Despite the significance of option price sensitivities for risk management in investment portfolios, the existing literature lacks a thorough exploration of these sensitivities within the context of the extended normal distribution. This article addresses this research gap by deriving the Greeks for options based on the extended normal distribution. The Greeks under consideration include Vega, Delta, Theta, Gamma, Rho, Vanna, Charm, and Vera, all of which are crucial for informed financial decision-making. Furthermore, this study provides a detailed analysis of how these option price sensitivities vary with different levels of kurtosis, offering valuable insights for various market applications. This contribution not only enhances the theoretical understanding of option pricing under non-standard distributions but also presents practical implications for portfolio risk management.
Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach
Authors: Flores-Huamán Kenny-Jesús , Escudero-Santana Alejandro , Muñoz-Díaz María-Luisa , Cortés Pablo
Published: July 2024

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.
Clustering Validation Inference
Authors: Figuera Pau , Cuzzocrea Alfredo , García Bringas Pablo
Published: July 2024

Clustering validation is applied to evaluate the quality of classifications. This step is crucial for unsupervised machine learning. A plethora of methods exist for this purpose; however, a common drawback is that statistical inference is not possible. In this study, we construct a density function for the cluster number. For this purpose, we use smooth techniques. Then, we apply non-negative matrix factorization using the Kullback–Leibler divergence. Employing a unique linearly independent uncorrelated observational variable hypothesis, we construct a sequence by varying the dimension of the span space of the factorization only using analytical techniques. The expectation of the limit of this sequence follows a gamma probability density function. Then, identifying the dimension of the factorization of the space span with clusters, we transform the estimation of the suitable dimension of the factorization into a probabilistic estimate of the number of clusters. This approach is an internal validation method that is suitable for numerical and categorical multivariate data and independent of the clustering technique. Our main achievement is a predictive clustering validation model with graphical abilities. It provides results in terms of credibility, thus making it possible to compare results such as expert judgment on a quantitative basis.
Isogeometric Topology Optimization of Multi-Material Structures under Thermal-Mechanical Loadings Using Neural Networks
Authors: Qiu Yi , Xu Cheng , Peng Jiangpeng , Song Yanjie
Published: July 2024

An isogeometric topology optimization (ITO) model for multi-material structures under thermal-mechanical loadings using neural networks is proposed. In the proposed model, a non-uniform rational B-spline (NURBS) function is employed for geometric description and analytical calculation, which realizes the unification of the geometry and computational models. Neural networks replace the optimization algorithms of traditional topology optimization to update the relative densities of multi-material structures. The weights and biases of neural networks are taken as design variables and updated by automatic differentiation without derivation of the sensitivity formula. In addition, the grid elements can be refined directly by increasing the number of refinement nodes, resulting in high-resolution optimal topology without extra computational costs. To obtain comprehensive performance from ITO for multi-material structures, a weighting coefficient is introduced to regulate the proportion between thermal compliance and compliance in the loss function. Some numerical examples are given and the validity is verified by performance analysis. The optimal topological structures obtained based on the proposed model exhibit both excellent heat dissipation and stiffness performance under thermal-mechanical loadings.
Radially Symmetric Positive Solutions of the Dirichlet Problem for the p-Laplace Equation
Authors: Yang Bo
Published: July 2024

We consider the p-Laplace boundary value problem with the Dirichlet boundary condition. A new lower estimate for positive solutions of the problem is obtained. As an application of this new lower estimate, some sufficient conditions for the existence and nonexistence of positive solutions for the p-Laplace problem are obtained.
Assessing the Impact of Environmental Technology on CO2 Emissions in Saudi Arabia: A Quantile-Based NARDL Approach
Authors: Islam Md. Saiful , Rehman Anis ur , Khan Imran
Published: July 2024

Climatic change and environmental degradation have become a worldwide discourse. Green innovation is commonly viewed as a means of lowering environmental pollution in the era of climate change. Considering this, the primary purpose of this study is to investigate the effects of environmental technology (ET) on CO2 emissions by controlling Saudi Arabia’s ICT use, energy use, energy intensity, and financial development. It uses a quantile-based multiple-threshold “nonlinear autoregressive distributed lag (NARDL)” estimation utilizing data from 1990 to 2020. It also conducts the ARDL and NARDL estimation techniques simultaneously for comparative outcomes. The Toda–Yamamoto (T-Y) causality assessment also crosschecks the primary multiple-threshold NARDL estimates. The outcomes reveal that ET promotes environmental pollution due to its low scale compared to the Kingdom’s technological base. ICT improves environmental quality, and energy consumption deteriorates it. All three estimation techniques confirm these findings. The multiple-threshold NARDL estimation appears robust and reveals damaging impacts of energy intensity and financial development on emissions. The T-Y causality assessment also authenticates the primary estimation outcomes. The outcomes have important implications for policymakers to focus on enhancing patents for ET, raising ICT diffusion, reducing energy intensity through generating more renewable energies, expanding financial support for ICT and green investments, and ensuring a sustainable environment.
A Multimodal Graph Recommendation Method Based on Cross-Attention Fusion
Authors: Li Kai , Xu Long , Zhu Cheng , Zhang Kunlun
Published: July 2024

Research on recommendation methods using multimodal graph information presents a significant challenge within the realm of information services. Prior studies in this area have lacked precision in the purification and denoising of multimodal information and have insufficiently explored fusion methods. We introduce a multimodal graph recommendation approach leveraging cross-attention fusion. This model enhances and purifies multimodal information by embedding the IDs of items and their corresponding interactive users, thereby optimizing the utilization of such information. To facilitate better integration, we propose a cross-attention mechanism-based multimodal information fusion method, which effectively processes and merges related and differential information across modalities. Experimental results on three public datasets indicated that our model performed exceptionally well, demonstrating its efficacy in leveraging multimodal information.
An Interpretable Breast Ultrasound Image Classification Algorithm Based on Convolutional Neural Network and Transformer
Authors: Meng Xiangjia , Ma Jun , Liu Feng , Chen Zhihua , Zhang Tingting
Published: July 2024

Breast cancer is one of the most common causes of death in women. Early signs of breast cancer can be an abnormality depicted on breast images like breast ultrasonography. Unfortunately, ultrasound images contain a lot of noise, which greatly increases the difficulty for doctors to interpret them. In recent years, computer-aided diagnosis (CAD) has been widely used in medical images, reducing the workload of doctors and the probability of misdiagnosis. However, it still faces the following challenges in clinical practice: one is the lack of interpretability, and another is that the accuracy is not high enough. In this paper, we propose a classification model of breast ultrasound images that leverages tumor boundaries as prior knowledge and strengthens the model to guide classification. Furthermore, we employ the advantages of convolutional neural network (CNN) to extract local features and Transformer to extract global features to achieve information balance and complementarity between the two neural network models which increase the recognition performance of the model. Additionally, an explanation method is used to generate visual results, thereby improving the poor interpretability of deep learning models. Finally, we evaluate the model on the BUSI dataset and compare it with other CNN and Transformer models. Experimental results show that the proposed model obtains an accuracy of 0.9870 and an F1 score of 0.9872, achieving state-of-the-art performance.
CPSGD: A Novel Optimization Algorithm and Its Application in Side-Channel Analysis
Authors: Zhang Yifan , Zhao Di , Li Hongyi , Pan Chengwei
Published: July 2024

In recent years, side-channel analysis based on deep learning has garnered significant attention from researchers. A pivotal reason for this lies in the fact that deep learning-based side-channel analysis requires minimal preprocessing of side-channel data. The automatic feature extraction property of deep learning methods drastically reduces the workload for researchers, enabling them to focus more on the core issues of side-channel analysis, namely, extracting sensitive information by attacking devices. However, in prior studies, most scholars have concentrated more on the model construction process, with little research focusing on the choice of optimizers.This paper explores a novel deep learning-based optimization algorithm—CPSGD (combined projection stochastic gradient descent). The algorithm comprises two variants, designed, respectively, for unprotected side-channel analysis (CPSGD1) and desynchronized side-channel analysis (CPSGD2), and their convergence has been theoretically proven. Experimental results demonstrate that, while maintaining the neural network structure unchanged, CPSGD1 exhibits the best performance on unprotected datasets compared to other publicly available optimizers, whereas CPSGD2 performs optimally on desynchronized datasets.
The Role of the Table of Games in the Discrete Thermostatted Kinetic Theory
Authors: Bianca Carlo
Published: July 2024

This paper is concerned with the mathematical modeling of complex living systems whose element microscopic state contains variables which can attain discrete values. Specifically, the main mathematical frameworks of the discrete thermostatted kinetic theory for active particles are reviewed and generalized. In the generalized thermostatted frameworks, which are based on nonlinear ordinary or partial differential equations, the elements of the system are viewed as active particles that are able to perform certain strategies modeled by introducing a functional-state variable called activity. Interactions, which are responsible of the evolution of the system, are modeled using the fundamentals of stochastic game theory and may be influenced by the action of an external force field coupled to a Gaussian-type thermostat. In particular, the interaction domain is modeled by introducing a weighted function and different non-homogeneous discrete frameworks are proposed and coupled with a specific thermostat. Two recent models derived within this approach are reviewed and refer to vehicular and pedestrian dynamics. Future research perspectives are discussed in the whole paper from theoretical and modeling viewpoints.
A High-Order Numerical Scheme for Efficiently Solving Nonlinear Vectorial Problems in Engineering Applications
Authors: Shams Mudassir , Carpentieri Bruno
Published: July 2024

In scientific and engineering disciplines, vectorial problems involving systems of equations or functions with multiple variables frequently arise, often defying analytical solutions and necessitating numerical techniques. This research introduces an efficient numerical scheme capable of simultaneously approximating all roots of nonlinear equations with a convergence order of ten, specifically designed for vectorial problems. Random initial vectors are employed to assess the global convergence behavior of the proposed scheme. The newly developed method surpasses methods in the existing literature in terms of accuracy, consistency, computational CPU time, residual error, and stability. This superiority is demonstrated through numerical experiments tackling engineering problems and solving heat equations under various diffusibility parameters and boundary conditions. The findings underscore the efficacy of the proposed approach in addressing complex nonlinear systems encountered in diverse applied scenarios.
Addressing Demographic Bias in Age Estimation Models through Optimized Dataset Composition
Authors: Panić Nenad , Marjanović Marina , Bezdan Timea
Published: July 2024

Bias in facial recognition systems often results in unequal performance across demographic groups. This study addresses this by investigating how dataset composition affects the performance and bias of age estimation models across ethnicities. We fine-tuned pre-trained Convolutional Neural Networks (CNNs) like VGG19 on the diverse UTKFace dataset (23,705 samples: 10,078 White, 4526 Black, 3434 Asian) and APPA-REAL (7691 samples: 6686 White, 231 Black, 674 Asian). Our approach involved adjusting dataset compositions by oversampling minority groups or reducing samples from overrepresented groups to mitigate bias. We conducted experiments to identify the optimal dataset composition that minimizes performance disparities among ethnic groups. The primary performance metric was Mean Absolute Error (MAE), measuring the average magnitude of prediction errors. We also analyzed the standard deviation of MAE across ethnic groups to assess performance consistency and equity. Our findings reveal that simple oversampling of minority groups does not ensure equitable performance. Instead, systematic adjustments, including reducing samples from overrepresented groups, led to more balanced performance and lower MAE standard deviations across ethnicities. These insights highlight the importance of tailored dataset adjustments and suggest exploring advanced data processing methods and algorithmic tweaks to enhance fairness and accuracy in facial recognition technologies.
On the Asymptotic Network Indices of Weighted Three-Layered Structures with Multi-Fan Composed Subgraphs
Authors: Bian Jicheng , Huang Da , Zhu Jian , Chen Xing
Published: July 2024

In this paper, three sorts of network indices for the weighted three-layered graph are studied through the methods of graph spectra theory combined with analysis methods. The concept of union of graphs are applied to design two sorts of weighted layered multi-fan composed graphs, and the accurate mathematical expressions of the network indices are obtained through the derivations of Laplacian spectra; furthermore, the asymptotic properties are also derived. We find that when the cardinalities of the vertices on a sector-edge-link tend to infinity, the indices of FONC and EMFPT are irrelevant with the number of copies of the fan-substructure based on the considered graph framework.
Research on Clothing Image Retrieval Combining Topology Features with Color Texture Features
Authors: Zhang Xu , Sun Huadong , Ma Jian
Published: July 2024

Topological data analysis (TDA) is a method of feature extraction based on data topological structure. Image feature extraction using TDA has been shown to be superior to other feature extraction techniques in some problems, so it has recently received the attention of researchers. In this paper, clothing image retrieval based on topology features and color texture features is studied. The main work is as follows: (1) Based on the analysis of image data by persistent homology, the feature construction method of a topology feature histogram is proposed, which can represent the ruler of image local topological data, and make up for the shortcomings of traditional feature extraction methods. (2) The improvement of Wasserstein distance is presented, while the similarity measure method named topology feature histogram distance is proposed. (3) Because the single feature has some problems such as the incomplete description of image information and poor robustness, the clothing image retrieval is realized by combining the topology feature with the color texture feature. The experimental results show that the proposed algorithm, namely topology feature histogram + corresponding distance, can effectively reduce the computation time while ensuring the accuracy. Compared with the method using only color texture, the retrieval rate of top5 is improved by 14.9%. Compared with the method using cubic complex + Wasserstein distance, the retrieval rate of top5 is improved by 3.8%, while saving 3.93 s computation time.
An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification
Authors: Sorour Shaymaa E. , Hassan Lamia , Abohany Amr A. , Hussien Reda M.
Published: July 2024

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML) tasks, aimed at removing uncorrelated and redundant attributes to enhance classification accuracy. This study introduces an improved binary crayfish optimization algorithm (IBCOA) designed to tackle the FS problem. The IBCOA integrates a local search strategy and a periodic mode boundary handling technique, significantly improving its ability to search and exploit the feature space. By doing so, the IBCOA effectively reduces dimensionality, while improving classification accuracy. The algorithm’s performance was evaluated using support vector machine (SVM) and k-nearest neighbor (k-NN) classifiers on eighteen multi-scale benchmark datasets. The findings showed that the IBCOA performed better than nine recent binary optimizers, attaining 100% accuracy and decreasing the feature set size by as much as 0.8. Statistical evidence supports that the proposed IBCOA is highly competitive according to the Wilcoxon rank sum test (alpha = 0.05). This study underscores the IBCOA’s potential for enhancing FS processes, providing a robust solution for high-dimensional data challenges.
On the Signless Laplacian ABC-Spectral Properties of a Graph
Authors: Rather Bilal A. , Ganie Hilal A. , Shang Yilun
Published: July 2024

In the paper, we introduce the signless Laplacian ABC-matrix Q̃(G)=D¯(G)+Ã(G), where D¯(G) is the diagonal matrix of ABC-degrees and Ã(G) is the ABC-matrix of G. The eigenvalues of the matrix Q̃(G) are the signless Laplacian ABC-eigenvalues of G. We give some basic properties of the matrix Q̃(G), which includes relating independence number and clique number with signless Laplacian ABC-eigenvalues. For bipartite graphs, we show that the signless Laplacian ABC-spectrum and the Laplacian ABC-spectrum are the same. We characterize the graphs with exactly two distinct signless Laplacian ABC-eigenvalues. Also, we consider the problem of the characterization of the graphs with exactly three distinct signless Laplacian ABC-eigenvalues and solve it for bipartite graphs and, in some cases, for non-bipartite graphs. We also introduce the concept of the trace norm of the matrix Q̃(G)−tr(Q̃(G))nI, called the signless Laplacian ABC-energy of G. We obtain some upper and lower bounds for signless Laplacian ABC-energy and characterize the extremal graphs attaining it. Further, for graphs of order at most 6, we compare the signless Laplacian energy and the ABC-energy with the signless Laplacian ABC-energy and found that the latter behaves well, as there is a single pair of graphs with the same signless Laplacian ABC-energy unlike the 26 pairs of graphs with same signless Laplacian energy and eight pairs of graphs with the same ABC-energy.
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Impact on Quality of Life (QoL) of Persons with ME/CFS
Authors: Muirhead Nina L. , Vyas Jui , Ephgrave Rachel , Singh Ravinder , Finlay Andrew Y.
Published: July 2024

Background and Objectives: We previously reported on the impact of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) on the QoL of persons with ME/CFS and their family members. Here, we present the findings of the impact on the QoL of individuals with ME/CFS whose family members did not participate in the survey. Materials and Methods: A prospective multinational online survey was disseminated via patient charities, support groups and social media. Persons with ME/CFS completed the EuroQoL questionnaire (EQ-5D-3L). Results: Data were analysed from 876 participants from 26 countries who reported a health care professional diagnosis of ME/CFS. In total, 742 participants identified as female, 124 male and 10 preferred not to say. The mean age of the participants was 47 years (range 18–82), and the mean time to diagnosis was 14 years. The mean overall health status on a visual analogue scale for people with ME/CFS was 36.4 (100 = best health). People with ME/CFS were most often affected by inability to perform usual activities (n = 852, 97%), followed by pain (n = 809, 92%), impaired mobility (n = 724, 83%), difficulty in self-care (n = 561, 64%) and least often affected by anxiety and depression (n = 540, 62%). Conclusions: The QoL of people with ME/CFS is significantly affected globally. There was no significant difference in quality of life compared with previously published data on those with ME/CFS who did have a family member complete the family member quality of life questionnaire (FROM16). Contrary to popular misconception, anxiety and depression are the least often affected areas in persons with ME/CFS who are most impacted by their inability to perform usual activities.
The Impact on Autonomic Nervous System Activity during and Following Exercise in Adults: A Meta-Regression Study and Trial Sequential Analysis
Authors: Chiang Jui-Kun , Lin Yen-Chang , Hung Tzu-Ying , Kao Hsueh-Hsin , Kao Yee-Hsin
Published: July 2024

Background and Objectives: Exercise enhances cardiovascular health through various mechanisms, including the modulation of autonomic nervous system activity. This study aimed to systematically examine the impact of exercise on heart rate variability (HRV) in adults during and within one hour after exercise (WHAE). Materials and Methods: A comprehensive literature review was conducted using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases to identify published studies that reported the impact of exercise on autonomic nervous system activity in adults. The studies measured the absolute power of the low-frequency band (0.04–0.15 Hz) to the absolute power of the high-frequency band (0.015–0.4 Hz) (LF/HF ratio) to assess sympathetic activity and the root mean square of successive differences between normal heartbeats (RMSSD) to assess parasympathetic activity. Results: A total of 3329 studies were screened for relevance, and finally, 10 articles that utilized methods for measuring autonomic nervous system activity, such as the LF/HF ratio and RMSSD, covering 292 adult patients, were included for meta-analysis. In the current meta-analysis, we observed a significant decrease in parasympathetic activity during and after exercise, as indicated by RMSSD, compared to pre-exercise levels (mean difference [MD] = −4.96, 95% confidence interval [CI]: −8.00 to −1.91, p = 0.003). However, sympathetic activity after exercise, represented by the LF/HF ratio, showed a borderline significant increase compared to pre-exercise levels (MD = 1.06, 95% CI: −0.01 to 2.12, p = 0.052). The meta-regression model found that factors associated with RMSSD included mean age, male gender, and duration post-exercise. Additionally, the factor associated with the LF/HF ratio was the healthy condition of participants. The trial sequential analysis provided robust evidence of a decrease in RMSSD and an increase in the LF/HF ratio during and WHAE. Conclusions: Given the limitations of the current study, the findings suggest that a significant decrease in parasympathetic activity and a borderline significant increase in sympathetic activity in adults during and WHAE, as confirmed by trial sequential analysis. Meta-regression analysis indicated that parasympathetic activity was negatively associated with participant age and male gender, but positively associated with duration post-exercise. Additionally, increased sympathetic activity was linked to the healthy conditions of participants. This study suggests that exercise might differentially affect autonomic balance in individuals with chronic conditions compared to healthy individuals. This highlights the potential need for tailored exercise interventions to improve autonomic function across different populations.
Mangiferin Induces Post-Implant Osteointegration in Male Diabetic Rats
Authors: Ongan Bünyamin , Ekici Ömer , Sadi Gökhan , Aslan Esra , Pektaş Mehmet Bilgehan
Published: July 2024

Background and Objectives: Hyperglycemia is known to undermine the osteointegration process of implants. In this study, the effects of mangiferin (MF) on the post-implant osteointegration process in a type-II diabetes model were investigated molecularly and morphologically. Materials and Methods: Sprague Dawley male rats were divided into three groups: control, diabetes, and diabetes + MF. All animals were implanted in their tibia bones on day 0. At the end of the 3-month experimental period, the animals’ blood and the implant area were isolated. Biochemical measurements were performed on blood samples and micro-CT, qRT-PCR, histological, and immunohistochemical measurements were performed on tibia samples. Results: MF significantly improved the increased glucose, triglyceride-VLDL levels, and liver enzymes due to diabetes. By administering MF to diabetic rats, the osteointegration percentage and bone volume increased while porosity decreased. DKK1 and BMP-2 mRNA expressions and OPN, OCN, and OSN mRNA–protein expressions increased by MF administration in diabetic rats. Additionally, while osteoblast and osteoid surface areas increased with MF, osteoclast and eroded surface areas decreased. Conclusions: The findings of our study indicate that MF will be beneficial to the bone-repairing process and osteointegration, which are impaired by type-II diabetes.
Prevalence and Associated Factors with Frailty Using the Kihon Checklist among Community-Dwelling Older Adults in Taiwan
Authors: Chen Chien-Chih , Hsu Wei-Chien , Wu Yi-Hsuan , Lai Fang-Yu , Yang Pei-Yu , Lin I-Ching
Published: July 2024

Background and Objectives: Frailty in older adults is associated with adverse health outcomes. This study aimed to analyze the frailty status of community-dwelling older adults in Taiwan using the Kihon Checklist (KCL) and explore associations with demographic, physiological, and functional factors. Materials and Methods: In this cross-sectional study, 278 community-dwelling older adults were classified as robust, prefrail, or frail based on their KCL scores. Participants underwent physical fitness assessments including muscle strength and endurance tests, walking speed tests, and flexibility tests. One-way ANOVA and logistic regression analyses were used to examine differences and associations between frailty status and physical fitness indicators. Results: 36% of participants were robust, 47.1% prefrail, and 16.9% frail. The robust group significantly outperformed the prefrail and frail groups in the 30 s sit-to-stand test, 2.44 m sit-to-walk test, and walking speed (p < 0.001). The 2.44 m sit-to-walk test was a significant predictor of prefrailty (OR = 1.18, 95% CI = 1.02–1.36) after adjusting for other physical fitness indicators. Conclusions: Lower limb functional capacity, particularly in the 2.44 m sit-to-walk test, was significantly associated with pre-frailty among community-dwelling older adults in Taiwan. Early screening, the classification of frailty by the Kihon Checklist, and targeted interventions focusing on lower limb strength, endurance, and mobility are crucial for preventing and delaying frailty progression in older populations.
Effects of Sevoflurane and Fullerenol C60 on the Heart and Lung in Lower-Extremity Ischemia–Reperfusion Injury in Streptozotocin-Induced Diabetes Mice
Authors: Örnek Ender , Alkan Metin , Erel Selin , Yığman Zeynep , Dursun Ali Doğan , Dağlı Aslı , Sarıkaya Badegül , Kip Gülay , Polat Yücel , Arslan Mustafa
Published: July 2024

Background and Objectives: Lower-extremity ischemia–reperfusion injury can induce distant organ ischemia, and patients with diabetes are particularly susceptible to ischemia–reperfusion injury. Sevoflurane, a widely used halogenated inhalation anesthetic, and fullerenol C60, a potent antioxidant, were investigated for their effects on heart and lung tissues in lower-extremity ischemia–reperfusion injury in streptozotocin (STZ)-induced diabetic mice. Materials and Methods: A total of 41 mice were divided into six groups: control (n = 6), diabetes–control (n = 7), diabetes–ischemia (n = 7), diabetes–ischemia–fullerenol C60 (n = 7), diabetes–ischemia–sevoflurane (n = 7), and diabetes–ischemia–fullerenol C60–sevoflurane (n = 7). Diabetes was induced in mice using a single intraperitoneal dose of 55 mg/kg STZ in all groups except for the control group. Mice in the control and diabetes–control groups underwent midline laparotomy and were sacrificed after 120 min. The DIR group underwent 120 min of lower-extremity ischemia followed by 120 min of reperfusion. In the DIR-F group, mice received 100 μg/kg fullerenol C60 intraperitoneally 30 min before IR. In the DIR-S group, sevoflurane and oxygen were administered during the IR procedure. In the DIR-FS group, fullerenol C60 and sevoflurane were administered. Biochemical and histological evaluations were performed on collected heart and lung tissues. Results: Histological examination of heart tissues showed significantly higher necrosis, polymorphonuclear leukocyte infiltration, edema, and total damage scores in the DIR group compared to controls. These effects were attenuated in fullerenol-treated groups. Lung tissue examination revealed more alveolar wall edema, hemorrhage, vascular congestion, polymorphonuclear leukocyte infiltration, and higher total damage scores in the DIR group compared to controls, with reduced injury parameters in the fullerenol-treated groups. Biochemical analyses indicated significantly higher total oxidative stress, oxidative stress index, and paraoxonase-1 levels in the DIR group compared to the control and diabetic groups. These levels were lower in the fullerenol-treated groups. Conclusions: Distant organ damage in the lung and heart tissues due to lower-extremity ischemia–reperfusion injury can be significantly reduced by fullerenol C60.
Prognostic Role of Pre- and Post-Treatment [18F]FDG PET/CT in Squamous Cell Carcinoma of the Oropharynx in Patients Treated with Chemotherapy and Radiotherapy
Authors: Dondi Francesco , Gazzilli Maria , Albano Domenico , Rizzo Alessio , Treglia Giorgio , Pisani Antonio Rosario , Palumbo Carmen , Rubini Dino , Racca Manuela , Rubini Giuseppe , Bertagna Francesco
Published: July 2024

Background: The prognostic role of imaging with [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) in oropharynx cancer (OPC) has been demonstrated in the past. The aim of this study was to assess the prognostic impact of both baseline and post-treatment PET/CT in patients with OPC and treated with chemo- and/or radiotherapy. Methods: The PET/CT parameters of scans performed before and after therapy were collected and analyzed to find significant prognosticators for progression-free survival (PFS) and overall survival (OS). Human papillomavirus (HPV) infection’s influence on the prognosis was also taken into account. Results: A total of 66 patients were included in the study. The staging volumetric parameters of PET/CT were significant prognosticators for OS, while the same parameters were affordable predictors for PFS at the restaging evaluation. No significant correlations between HPV infection and PET/CT parameters were reported. Conclusion: The prognostic role of volumetric [18F]FDG PET/CT parameters in patients with OPC was reported.
Influence of Feed Composition on the Separation Factor during Nanofiltration of Organic Acids
Authors: Tottoli Gustavo , Galier Sylvain , Roux-de Balmann Hélène
Published: July 2024

In this study, nanofiltration experiments using synthetic solutions containing acetate, butyrate, and lactate are carried out to assess the impact of the feed composition, i.e., feed concentration and feed proportions, on the separation factor of couples of solutes in binary and ternary solutions. In binary solutions, no influence of the solute proportions in the feed was pointed out, whatever the couple of solutes. The separation factor of acetate/butyrate and acetate/lactate was found to decrease with increasing feed concentration, while that of lactate/butyrate remained constant. The separation factors of acetate/lactate and lactate/butyrate were identical in ternary solutions compared to binary ones, showing no impact of the addition of the third solute. In ternary solutions, the presence of lactate decreased the separation factor of acetate/butyrate, but this decrease was not influenced by the proportion of lactate.
Metabolic Pathways in Hydrocephalus: Profiling with Proteomics and Advanced Imaging
Authors: Davis Laura May , Hwang Misun
Published: July 2024

Hemorrhagic hydrocephalus is a common pathology in neonates with high mortality and morbidity. Current imaging approaches fail to capture the mechanisms behind its pathogenesis. Here, we discuss the processes underlying this pathology, the metabolic dysfunction that occurs as a result, and the ways in which these metabolic changes inform novel methods of clinical imaging. The imaging advances described allow earlier detection of the cellular and metabolic changes, leading to better outcomes for affected neonates.
Accumulation Characteristics of Natural Ophiocordyceps sinensis Metabolites Driven by Environmental Factors
Authors: Wang Tao , Tang Chuyu , Chen Jianbo , Liang Jing , Li Yuling , Li Xiuzhang
Published: July 2024

The environment is an important factor affecting the composition and abundance of metabolites in O. sinensis, which indirectly determines its edible function and medicinal potential. This study integrated metabolomics and redundancy analysis (RDA) to analyze the metabolite profile characteristics and key environmental factors influencing O. sinensis in various production areas. A total of 700 differentially accumulated metabolites (DAMs) were identified, primarily comprising lipids, organic acids, and organoheterocyclic compounds. Results from hierarchical cluster analysis and KEGG indicated distinct accumulation patterns of these DAMs in O. sinensis from different regions, with enrichment in pathways such as tryptophan metabolism and glycerophospholipid metabolism. Environmental factors like annual mean precipitation, pH, temperature, and altitude were found to significantly influence metabolite composition, particularly lipids, organic acids, and nucleosides. Overall, this study highlights the impact of environmental factors on metabolite diversity in O. sinensis and sheds light on the evolutionary processes shaping its metabolic landscape.
Dietary Triple-Strain Bacillus-Based Probiotic Supplementation Improves Performance, Immune Function, Intestinal Morphology, and Microbial Community in Weaned Pigs
Authors: Xue Lei , Long Shenfei , Cheng Bo , Song Qian , Zhang Can , Hansen Lea Hübertz Birch , Sheng Yongshuai , Zang Jianjun , Piao Xiangshu
Published: July 2024

Probiotics provide health benefits and are used as feed supplements as an alternative prophylactic strategy to antibiotics. However, the effects of Bacillus-based probiotics containing more than two strains when supplemented to pigs are rarely elucidated. SOLVENS (SLV) is a triple-strain Bacillus-based probiotic. In this study, we investigate the effects of SLV on performance, immunity, intestinal morphology, and microbial community in piglets. A total of 480 weaned pigs [initial body weight (BW) of 8.13 ± 0.08 kg and 28 days of age] were assigned to three treatments in a randomized complete block design: P0: basal diet (CON); P200: CON + 200 mg SLV per kg feed (6.5 × 108 CFU/kg feed); and P400: CON + 400 mg SLV per kg feed (1.3 × 109 CFU/kg feed). Each treatment had 20 replicated pens with eight pigs (four male/four female) per pen. During the 31 d feeding period (Phase 1 = wean to d 14, Phase 2 = d 15 to 31 after weaning), all pigs were housed in a temperature-controlled nursery room (23 to 25 °C). Feed and water were available ad libitum. The results showed that the pigs in the P400 group increased (p < 0.05) average daily gain (ADG) in phase 2 and tended (p = 0.10) to increase ADG overall. The pigs in the P200 and P400 groups tended (p = 0.10) to show improved feed conversion ratios overall in comparison with control pigs. The pigs in the P200 and P400 groups increased (p < 0.05) serum immunoglobulin A, immunoglobulin G, and haptoglobin on d 14, and serum C-reactive protein on d 31. The pigs in the P200 group showed an increased (p < 0.01) villus height at the jejunum, decreased (p < 0.05) crypt depth at the ileum compared with other treatments, and tended (p = 0.09) to have an increased villus–crypt ratio at the jejunum compared with control pigs. The pigs in the P200 and P400 groups showed increased (p < 0.05) goblet cells in the small intestine. Moreover, the pigs in the P400 group showed down-regulated (p < 0.05) interleukin-4 and tumor necrosis factor-α gene expressions, whereas the pigs in the P400 group showed up-regulated occludin gene expression in the ileum. These findings suggest that SLV alleviates immunological reactions, improves intestinal microbiota balance, and reduces weaning stress in piglets. Therefore, SOLVENS has the potential to improve health and performance for piglets.
A Comparative Analysis of the Efficacy of Bacterial Lysate versus Antibiotic Therapy in the Treatment of Experimental Peri-Implantitis in Rats
Authors: Ancuţa Diana Larisa , Alexandru Diana Mihaela , Ţucureanu Cătălin , Coman Cristin
Published: July 2024

Peri-implantitis (PI) is a current concern whose understanding and resolution are ongoing. We aimed to evaluate in vivo a new treatment with antibacterial properties, based on bacterial lysates obtained from the strains of Aggregatibacter actinomycetemcomitans, Streptococcus oralis, and Fusobacterium nucleatum. This research was conducted on 30 rats with PI which were divided into three groups and treated with antibiotic and anti-inflammatory (AAi) drugs, bacterial lysates (BLs), and saline (C), respectively. The monitoring period included the clinical and paraclinical examination where hematological, immunological, imaging, and histopathological analysis were performed. No particular clinical signs were observed, but the radiological examination showed the loss of all implants in group C, in contrast to group BL which had the highest survival rate of devices. White cells showed a decrease from the PI period, as did the immunological analysis. Only IL-6 showed an increase in the AAi and BL groups. Histopathologically, the C group presented a high degree of bone destruction, and in the BL group, many attenuated inflammatory phenomena appeared compared to the AAi animals. Bacterial lysates have similar effects to antibiotic-based therapeutic regimens for PI, and their future use may help to improve the current therapeutic management of the disease.
Serological and Molecular Detection of Citrus Tristeza Virus: A Review
Authors: Shang Pengxiang , Xu Longfa , Cheng Tong
Published: July 2024

Citrus tristeza virus (CTV) is a globally pervasive and economically significant virus that negatively impacts citrus trees, leading to substantial reductions in fruit yield. CTV occurs within the phloem of infected plants, causing a range of disease phenotypes, such as stem pitting (SP), quick decline (QD), and other detrimental diseases. Research on CTV is challenging due to the large size of its RNA genome and the diversity of CTV populations. Comparative genomic analyses have uncovered genetic diversity in multiple regions of CTV isolates’ genomes, facilitating the classification of the virus into distinct genotypes. Despite these challenges, notable advancements have been made in identifying and controlling CTV strains through serological and molecular methods. The following review concentrates on the techniques of nucleic acid identification and serological analysis for various CTV isolates, assisting in the comparison and evaluation of various detection methods, which are crucial for the effective management of CTV diseases, and so contributes to the innovation and development of CTV detection methods.
Human Ocular Toxoplasmosis in Romania: History, Epidemiology, and Public Health: A Narrative Review
Authors: Ghenciu Laura Andreea , Hațegan Ovidiu Alin , Bolintineanu Sorin Lucian , Dănilă Alexandra-Ioana , Iacob Roxana , Stoicescu Emil Robert , Lupu Maria Alina , Olariu Tudor Rareș
Published: July 2024

Toxoplasmosis, caused by the protozoan parasite Toxoplasma gondii (T. gondii), presents a significant global health concern, particularly for immunocompromised individuals and congenitally infected newborns. Despite its widespread prevalence, there are limited data on T. gondii seroprevalence and ocular toxoplasmosis in Romania. This review aims to summarize the research accomplished on the prevalence and epidemiology of human ocular toxoplasmosis in Romania. Ocular toxoplasmosis, a leading cause of infectious posterior uveitis worldwide, involves complex interactions between host immune responses and parasite factors. Clinically, it presents as focal necrotizing retinitis, characterized by active focal retinal lesions with adjacent chorioretinal scarring, often accompanied by vitreous inflammation and anterior chamber reactions. Diagnosis relies on clinical examination supported by fundus photography, optical coherence tomography (OCT), and serological assays. The authors followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, conducting a literature review on PubMed, Google Scholar, and Scopus. Our focus was on ocular toxoplasmosis in Romania, and we used keywords and specific MeSH terms. Finally, 17 articles met all the criteria, as summarized in the PRISMA diagram. This study underscores the need for improved diagnostic methods, increased research efforts, and comprehensive public health education to mitigate the burden of toxoplasmosis and ocular toxoplasmosis in Romania.