bluuebunny/crossref_metadata_2025 huggingface dataset:
Modern Pollen–Vegetation Relationships: A View from the Larch Forests of Central Siberia
Authors: Novenko Elena , Mazei Natalia , Shatunov Anton , Chepurnaya Anna , Borodina Ksenia , Korets Mikhail , Prokushkin Anatoly , Kirdyanov Alexander V.
Published: November 2024

Understanding how pollen assemblages represent the local and regional vegetation composition is crucial for palaeoecological research. Here, we analyze 102 surface moss/soil pollen samples collected from four study regions located in various boreal forest vegetation types in Central Siberia. Despite Larix being the most prevalent tree generus in the study area, the proportion of Larix pollen can be as low as 0.6–1.5% (0.4–4.7% on average) even in localities with a high canopy density of the species. No relationship between the quantity of Larix pollen in the spectra and the abundance of Larix in the local vegetation was found. The dominant components of the pollen assemblages are Betula and Alnus fruticosa. The pollen value of Picea is low (2.6–8.2% on average), with higher proportions at sample plots where spruce is abundant in forests. Pinus is a highly prevalent pollen species within its geographical range, comprising up to 40% of pollen assemblages. Outside of the range, the ratio of Pinus pollen was higher in habitats with low canopy density and in treeless ecosystems. The composition of herbaceous pollen and spores is significantly affected by the local plant community, offering more comprehensive insights into past vegetation patterns.
Integrating Mixed Livestock Systems to Optimize Forage Utilization and Modify Woody Species Composition in Semi-Arid Communal Rangelands
Authors: Slayi Mhlangabezi , Jaja Ishmael Festus
Published: November 2024

Communally owned rangelands serve as critical grazing areas for mixed livestock species such as cattle and goats, particularly in the arid and semi-arid regions of Southern Africa. This study aimed to evaluate the nutritional composition and woody species composition of communal rangelands where cattle and goat flocks graze together and to investigate the influence of grazing intensity on vegetation dynamics. Vegetation surveys were conducted across varying grazing intensities to assess species richness, biomass, and dietary preferences, while soil properties were analyzed to determine their interaction with vegetation attributes. Stepwise regression and path analyses were used to explore the relationships between soil characteristics, vegetation structure, and livestock dietary choices. The results revealed that high grazing pressure significantly reduced grass biomass (p = 0.003) and woody species density (p = 0.007) while increasing shrub cover (p = 0.018). Nutritional analysis indicated that goats preferred woody shrubs, which contributed 42.1% of their diet compared to 27.8% for cattle (p = 0.008). Regression analysis further showed that soil organic carbon (p = 0.002) and tree height (p = 0.041) were strong predictors of shrub cover. Seasonal variation significantly affected forage availability and nutritional content, with higher crude protein levels recorded during the wet season (p = 0.007). These findings suggest that grazing management strategies should be tailored to the distinct forage needs of cattle and goats to maintain the productivity and ecological stability of communal rangelands. A holistic approach that considers livestock dietary preferences, vegetation composition, and soil health is essential for sustainable rangeland management in mixed-species grazing systems.
Association of Shift Work, Sociodemographic Variables and Healthy Habits with Obesity Scales
Authors: Tosoratto Javier , Tárraga López Pedro Juan , López-González Ángel Arturo , Vallejos Daniela , Martínez-Almoyna Rifá Emilio , Ramirez-Manent José Ignacio
Published: November 2024

Background: Shift work has been associated with unhealthy lifestyle habits and a higher prevalence of obesity, which negatively impacts the health of shift workers. The objective of our study was to examine the influence of shift work on obesity, as well as on sociodemographic variables, anthropometric measurements, and lifestyle habits in individuals working this type of schedule. Methods: An observational, cross-sectional, descriptive study involving 53,053 workers from various labour sectors across several Spanish autonomous communities was conducted. It included 31,753 men (17,527 of them working shifts) and 21,300 women (11,281 of them working shifts). The relationship between shift work and obesity was examined, as well as its association with sex, age, social class, education level, smoking, alcohol consumption, sedentary behaviour, and unhealthy diet. Results: Obesity showed higher prevalence and mean values among shift workers across all four formulas used (BMI obesity, WtHR high, CUN BAE obesity, METS-VF high). All variables related to unhealthy lifestyle habits revealed a significantly greater prevalence among shift workers, with high statistical significance (p < 0.001). Age, sex, and social class affected the risk of obesity, with a greater prevalence observed in shift workers compared to non-shift workers (p < 0.001). Men had a higher risk than women, with an OR ranging from 1.17 (1.12–1.21) for BMI obesity to 7.45 (6.71–8.20) for METS-VF high. Conclusions: Shift workers exhibit a higher prevalence of obesity and unhealthy lifestyle habits, with men at greater risk. The variables that most significantly increase the risk of obesity include age, physical inactivity, low adherence to the Mediterranean diet, and alcohol consumption.
Real-World Data Simulation Comparing GHG Emissions and Operational Performance of Two Sweeping Systems
Authors: Ben Daya Bechir , Audy Jean-François , Lamghari Amina
Published: November 2024

Background: In northern countries, spring requires the removal of large volumes of abrasive materials used in winter road maintenance. This sweeping process, crucial for safety and environmental protection, has traditionally relied on conventional mechanical brooms. Recent technological innovations, however, have introduced more efficient and environmentally friendly sweeping solutions; Methods: This study provides a comprehensive comparative analysis of the environmental and operational performance of these innovative sweeping systems versus conventional methods. Using simulation models based on real-world data and integrating fuel consumption models, the analysis replicates sweeping behaviors to assess both operational and environmental performance. A sensitivity analysis was conducted using these models, focusing on key parameters such as the collection rate, the number of trucks, the payload capacity, and the truck unloading duration; Results: The results show that the innovative sweeping system achieves an average 45% reduction in GHG emissions per kilometer compared to the conventional system, consistently demonstrating superior environmental efficiency across all resources configurations; Conclusions: These insights offer valuable guidance for service providers by identifying effective resource configurations that align with both environmental and operational objectives. The approach adopted in this study demonstrates the potential to develop decision-making support tools that balance operational and environmental pillars of sustainability, encouraging policy decision-makers to adopt greener procurement policies. Future research should explore the integration of advanced technologies such as IoT, AI-driven analytics, and digital twin systems, along with life cycle assessments, to further support sustainable logistics in road maintenance.
A System of Tensor Equations over the Dual Split Quaternion Algebra with an Application
Authors: Yang Liuqing , Wang Qing-Wen , Kou Zuliang
Published: November 2024

In this paper, we propose a definition of block tensors and the real representation of tensors. Equipped with the simplification method, i.e., the real representation along with the M-P inverse, we demonstrate the conditions that are necessary and sufficient for the system of dual split quaternion tensor equations (A∗NX,X∗SC)=(B,D), when its solution exists. Furthermore, the general expression of the solution is also provided when the solution of the system exists, and we use a numerical example to validate it in the last section. To the best of our knowledge, this is the first time that the aforementioned tensor system has been examined on dual split quaternion algebra. Additionally, we provide its equivalent conditions when its Hermitian solution X=X∗ and η-Hermitian solutions X=Xη∗ exist. Subsequently, we discuss two special dual split quaternion tensor equations. Last but not least, we propose an application for encrypting and decrypting two color videos, and we validate this algorithm through a specific example.
Analysis of a Mathematical Model of Zoonotic Visceral Leishmaniasis (ZVL) Disease
Authors: Modu Goni Umar , Asawasamrit Suphawat , Momoh Abdulfatai Atte , Odekunle Mathew Remilekun , Idris Ahmed , Tariboon Jessada
Published: November 2024

This research paper attempts to describe the transmission dynamic of zoonotic visceral leishmaniasis with the aid of a mathematical model by considering the asymptomatic stages in humans and animals. The disease is endemic in several countries. Data used in the research are obtained from the literature while some are assumed based on the disease dynamic. The consideration of both asymptomatic and the symptomatic infected individuals is incorporated in both humans and animals (reservoir), as well as lines of treatment for the human population. It is found that the model has two fixed points; the VL-free fixed point and the VL-endemic fixed point. Stability analysis of the fixed points shows that the VL-free fixed point is globally asymptotically stable whenever the basic reproduction number is less than one and the VL-endemic fixed point is globally asymptotically stable whenever the basic reproduction number is greater than one. Sensitivity analysis is conducted for the parameters in the basic reproduction number, and the profile of each state variable is also depicted using the data obtained from the literature and those assumed. The transmission probability from infected sandflies to animals, transmission probability from infected animals to sandflies, per capita biting rate of sandflies of animals, and rate of transfer from symptomatic infected animals to the recovered class are among the most sensitive parameters that have the greatest influence on the basic reproduction number. Moreover, the value of the basic reproduction number is obtained to be 0.98951, which may require further study, as the margin between potential disease control and outbreak is thin.
TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification
Authors: Sánchez-Antonio Carlos , Valdez-Rodríguez José E. , Calvo Hiram
Published: November 2024

Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especially when working with small or imbalanced datasets. This paper introduces TTG-Text, a novel framework that strengthens graph-based text representation by integrating typical testors—a symbolic feature selection technique that refines feature importance while reducing dimensionality. Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. By combining symbolic algorithms with graph-based models, this hybrid approach offers a more interpretable, efficient, and high-performing solution for complex NLP tasks.
Error Analysis for Semilinear Stochastic Subdiffusion with Integrated Fractional Gaussian Noise
Authors: Wu Xiaolei , Yan Yubin
Published: November 2024

We analyze the error estimates of a fully discrete scheme for solving a semilinear stochastic subdiffusion problem driven by integrated fractional Gaussian noise with a Hurst parameter H∈(0,1). The covariance operator Q of the stochastic fractional Wiener process satisfies ∥A−ρQ1/2∥HS < ∞ for some ρ∈[0,1), where ∥·∥HS denotes the Hilbert–Schmidt norm. The Caputo fractional derivative and Riemann–Liouville fractional integral are approximated using Lubich’s convolution quadrature formulas, while the noise is discretized via the Euler method. For the spatial derivative, we use the spectral Galerkin method. The approximate solution of the fully discrete scheme is represented as a convolution between a piecewise constant function and the inverse Laplace transform of a resolvent-related function. By using this convolution-based representation and applying the Burkholder–Davis–Gundy inequality for fractional Gaussian noise, we derive the optimal convergence rates for the proposed fully discrete scheme. Numerical experiments confirm that the computed results are consistent with the theoretical findings.
Inverse Coefficient Problem for Epidemiological Mean-Field Formulation
Authors: Petrakova Viktoriya
Published: November 2024

The paper proposes an approach to solving the inverse epidemiological problem, written in terms of the “mean-field” theory. Finding the coefficients of an epidemiological SIR mean-field model is reduced to solving an optimization problem, for the solution of which only zero-order methods can be used. An algorithm for the solution of the inverse coefficient problem is proposed. Computational experiments were carried out to compare the obtained solutions with respect to synthetic and real data. The results of computational experiments have shown the efficiency of this approach. Ways to further improve the approach have also been determined.
Review of the Natural Time Analysis Method and Its Applications
Authors: Varotsos Panayiotis A. , Skordas Efthimios S. , Sarlis Nicholas V. , Christopoulos Stavros-Richard G.
Published: November 2024

A new concept of time, termed natural time, was introduced in 2001. This new concept reveals unique dynamic features hidden behind time-series originating from complex systems. In particular, it was shown that the analysis of natural time enables the study of the dynamical evolution of a complex system and identifies when the system enters a critical stage. Hence, natural time plays a key role in predicting impending catastrophic events in general. Several such examples were published in a monograph in 2011, while more recent applications were compiled in the chapters of a new monograph that appeared in 2023. Here, we summarize the application of natural time analysis in various complex systems, and we review the most recent findings of natural time analysis that were not included in the previously published monographs. Specifically, we present examples of data analysis in this new time domain across diverse fields, including condensed-matter physics, geophysics, earthquakes, volcanology, atmospheric sciences, cardiology, engineering, and economics.
A Hybrid Deep Learning Framework for OFDM with Index Modulation Under Uncertain Channel Conditions
Authors: Aziz Md Abdul , Rahman Md Habibur , Tabassum Rana , Sejan Mohammad Abrar Shakil , Baek Myung-Sun , Song Hyoung-Kyu
Published: November 2024

Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions.
A Numerical Stability Analysis in the Inclusion of an Inverse Term in the Design of Experiments for Mixtures
Authors: Cruz-Salgado Javier , Alonso-Romero Sergio , Ruelas-Santoyo Edgar Augusto , Miguel-Andrés Israel , Bautista-López Roxana Zaricell , Nobil Amir Hossein
Published: November 2024

A mixture experiment is one where the response depends only on the relative proportions of the ingredients present in the mixture. Different regression models are used to analyze mixture experiments, such as the Scheffé model, the Slack Variable model, and models with inverse terms. Models with inverse terms are worthy of consideration in certain applications. These models have been analyzed considering their fit quality, but not their numerical stability. This article analyzes the numerical stability of the model with inverse terms and the use of pseudo components. Likewise, a criterion is defined for the selection of the regression model with inverse terms, based on the quality of fit and numerical stability.
Industry 5.0 Drivers Analysis Using Grey-DEMATEL: A Logistics Case in Emerging Economies
Authors: Hsu Chih-Hung , Chen Shu-Jin , Huang Ming-Qiang , Le Qi
Published: November 2024

The terms “Industry 5.0” and “smart logistics” have recently emerged as key concepts within the field of logistics. Nevertheless, the interconnection between these two concepts has been less extensively examined in academic literature, particularly in the context of emerging economies. In the contemporary business context, the logistics industry is seeking to advance sustainable development through the implementation of Industry 5.0. However, the industry is still in its nascent stages of realizing the transformation of smart logistics. Accordingly, the objective of this study is to identify the key drivers of Industry 5.0 in relation to the advancement of smart logistics in the logistics industry in emerging economies. In this study, the initial screening and identification of 15 core drivers was conducted using the fuzzy Delphi method. This involved the collation of the relevant literature and the collection of opinions from experts in the field. The identified drivers were then classified into three groups: sustainability, people-centricity, and resilience. Subsequently, the study adopted the Grey-DEMATEL method, which combines grey system theory with the decision making trial and evaluation laboratory (DEMATEL) technology. This approach enables the effective resolution of complex system issues characterized by uncertainty and incomplete information, facilitating the identification of causal relationships between the drivers and the construction of a centrality–causality outcome diagram. The study identified two key drivers: “government support policies” and “logistics standardization and infrastructure development”. This study represents a preliminary investigation into the ways managers, practitioners and policy makers can leverage Industry 5.0 to advance the field of smart logistics within the logistics industry.
Bus Schedule Time Prediction Based on LSTM-SVR Model
Authors: Ge Zhili , Yang Linbo , Li Jiayao , Chen Yuan , Xu Yingying
Published: November 2024

With the acceleration of urbanization, urban bus scheduling systems are facing unprecedented challenges. Traditional bus scheduling provides the original schedule time and the planned time of arrival at the destination, where the schedule time is the departure time of the bus. However, various factors encountered during the drive result in significant differences in the driving time of the bus. To ensure timely arrivals, the bus scheduling system has to rely on manual adjustments to optimize the schedule time to determine the actual departure time. In order to reduce the scheduling cost and align the schedule time closer to the actual departure time, this paper proposes a dynamic scheduling model, LSTM-SVR, which leverages the advantages of LSTM in capturing the time series features and the ability of SVR in dealing with nonlinear problems, especially its generalization ability in small datasets. Firstly, LSTM is used to efficiently capture features of multidimensional time series data and convert them into one-dimensional effective feature outputs. Secondly, SVR is used to train the nonlinear relationship between these one-dimensional features and the target variables. Thirdly, the one-dimensional time series features extracted from the test set are put into the generated nonlinear model for prediction to obtain the predicted schedule time. Finally, we validate the model using real data from an urban bus scheduling system. The experimental results show that the proposed hybrid LSTM-SVR model outperforms LSTM-BOA, SVR-BOA, and BiLSTM-SOA models in the accuracy of predicting bus schedule time, thus confirming the effectiveness and superior prediction performance of the model.
Modeling and Nonlinear Dynamic Characteristics Analysis of Fault Bearing Time-Varying Stiffness-Flexible Rotor Coupling System
Authors: Chen Renzhen , Lv Jingyi , Tian Jing , Ai Yanting , Zhang Fengling , Yao Yudong
Published: November 2024

There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study, the interaction between the time-varying stiffness of the rolling bearing and the transient response of the flexible rotor is considered. The Newmark-β integral method is used to solve the dynamic equation, and the relationship between the time-varying characteristics of bearing stiffness and load and the dynamic characteristics of the rotor is studied. The relationship between bearing stiffness and vibration strength is analyzed, and the influence of damage size on the time domain signal energy of the disc is analyzed. The results show that the model established in this paper can accurately reflect the dynamic interaction between the bearing and the rotor. With the extension of the bearing damage, the dynamic stiffness of the bearing attenuates, the intensity of the excitation force increases, and the vibration is transmitted to the disc, which affects the motion stability and vibration response of the disc.
Tsallis Entropy in MV-Algebras
Authors: Barbieri Giuseppina Gerarda , Lenzi Giacomo
Published: November 2024

We deal with Tsallis entropy in MV-algebraic dynamical systems. We prove that Tsallis entropy is a submeasure and that it is invariant under isomorphisms. We also provide two examples which show that Tsallis entropy allows one to distinguish some non-isomorphic MV-dynamical systems.
Solving the Control Synthesis Problem Through Supervised Machine Learning of Symbolic Regression
Authors: Diveev Askhat , Sofronova Elena , Konyrbaev Nurbek
Published: November 2024

This paper considers the control synthesis problem and its solution using symbolic regression. Symbolic regression methods, which were previously called genetic programming methods, allow one to use a computer to find not only the parameters of a given regression function but also its structure. Unlike other works on solving the control synthesis problem using symbolic regression, the novelty of this paper is that for the first time this work employs a training dataset to address the problem of general control synthesis. Initially, the optimal control problem is solved from each point in a given set of initial states, resulting in a collection of control functions expressed as functions of time. A reference model is then integrated into the control object model, which generates optimal motion trajectories using the derived optimal control functions. The control synthesis problem is framed as an approximation task for all optimal trajectories, where the control function is sought as a function of the deviation of the object from the specified terminal state. The optimization criterion for solving the synthesis problem is the accuracy of the object’s movement along the optimal trajectory. The paper includes an example of solving the control synthesis problem for a mobile robot using a supervised machine learning method. A relatively new method of symbolic regression, the method of variational complete binary genetic programming, is studied and proposed for the solution of the control synthesis problem.
Pspatreg: R Package for Semiparametric Spatial Autoregressive Models
Authors: Mínguez Román , Basile Roberto , Durbán María
Published: November 2024

This article introduces the R package pspatreg, which is publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatial econometric penalized spline (P-Spline) models. These models can incorporate a nonparametric spatiotemporal trend, a spatial lag of the dependent variable, independent variables, noise, and time-series autoregressive noise. The primary functions in this package cover the estimation of P-Spline spatial econometric models using either Restricted Maximum Likelihood (REML) or Maximum Likelihood (ML) methods, as well as the computation of marginal impacts for both parametric and nonparametric terms. Additionally, the package offers methods for the graphical display of estimated nonlinear functions and spatial or spatiotemporal trend maps. Applications to cross-sectional and panel spatial data are provided to illustrate the package’s functionality.
Polarization Division Multiplexing CV-QKD with Pilot-Aided Polarization-State Sensing
Authors: Tan Zicong , Wang Tao , Xu Yuehan , Liu Xu , Li Lang , Zhang Beibei , Liu Yuchao , Huang Peng , Zeng Guihua
Published: November 2024

Continuous-variable quantum key distribution (CV-QKD) with local local oscillator (LLO) is well-studied for its security and simplicity, but enhancing performance and interference resistance remains challenging. In this paper, we utilize polarization division multiplexing (PDM) to enhance spectral efficiency and significantly increase the key rate of the CV-QKD system. To address dynamic changes in the state of polarization (SOP) in Gaussian modulated coherent states (GMCS) signals due to polarization impairment effects, we designed a time-division multiplexing pilot scheme to sense and recover changes in SOP in GMCS signals, along with other digital signal processing methods. Experiments over 20 km show that our scheme maintains low excess noise levels (0.062 and 0.043 in shot noise units) and achieves secret key rates of 4.65 Mbps and 5.66 Mbps for the two polarization orientations, totaling 10.31 Mbps. This work confirms the effectiveness of PDM GMCS-CV-QKD and offers technical guidance for high-rate QKD within metropolitan areas.
Graphs with a Fixed Maximum Degree and Order Attaining the Upper Bound on Minimum Status
Authors: Tsai Wei-Han , Shang Jen-Ling , Lin Chiang
Published: November 2024

The status (or transmission) of a vertex in a connected graph is the sum of distances between the vertex and all other vertices. The minimum status (or minimum transmission) of a connected graph is the minimum of the statuses of all vertices in the graph. Previously, sharp lower and upper bounds have been obtained on the minimum status of connected graphs with a fixed maximum degree k and order n. Moreover, for 2≤k≤n2, the following theorem about graphs attaining the maximum on the minimum status has also been proposed without proof. The theorem is as follows: Let G be a connected graph of order n with ▵(G)=k, where 2≤k≤n2. Then, the minimum status of G attains the maximum if and only if one of the following holds. (1) G is a path or a cycle, where k=2; (2) Gk,n is a spanning subgraph of G and G is a spanning subgraph of Hk,n, where 3≤k<n2; and (3) either Gn2,n is a spanning subgraph of G and G is a spanning subgraph of Hn2,n or Gn2,n is a spanning subgraph of G and G is a spanning subgraph of Hn, where k=n2 for even n≥6. For the integers n,k with 2≤k≤n−1, the graph Gk,n has the vertex set V(Gk,n)={x1,x2,⋯,xn} and the edge set E(Gk,n)={xixi+1:i=1,2,⋯,n−k}∪{xn−k+1xj:j=n−k+2,n−k+3,⋯,n}; the graph Hk,n is obtained from Gk,n by adding all the edges xixj, where n−k+2≤i<j≤n; and for even n≥6 the graph Hn is obtained from Gn2,n by adding the edge xn2−1xn2+2 and all the edges xixj, where n2+3≤i<j≤n. This study provides the proof to complete the above theorem.
The Minimal Molecular Tree for the Exponential Randić Index
Authors: Bera Jayanta , Das Kinkar Chandra
Published: November 2024

Topological indices are numerical parameters that provide a way to quantify the structural features of molecules using their graph representations. In chemical graph theory, these indices have been effectively employed to predict various physico-chemical properties of molecules. Among these, the Randić index stands out as a classical and widely used molecular descriptor in chemistry and pharmacology. The Randić index R(G) for a given graph G is defined as R(G)=∑vivj∈E(G)1d(vi)d(vj), where d(vi) represents the degree of vertex vi and E(G) is the set of edges in the graph G. Given the Randić index’s strong discrimination ability in describing molecular structures, a variant known as the exponential Randić index was recently introduced. The exponential Randić index ER(G) for a graph G is defined as ER(G)=∑vivj∈E(G)e1d(vi)d(vj). This paper further explores and fully characterizes the minimal molecular trees in relation to the exponential Randić index. Moreover, the chemical relevance of the exponential Randić index is also investigated.
State-Space Solution to Spectral Entropy Analysis and Optimal State-Feedback Control for Continuous-Time Linear Systems
Authors: Boichenko Victor A. , Belov Alexey A. , Andrianova Olga G.
Published: November 2024

In this paper, a problem of random disturbance attenuation capabilities for linear time-invariant continuous systems, affected by random disturbances with bounded σ-entropy, is studied. The σ-entropy norm defines a performance index of the system on the set of the aforementioned input signals. Two problems are considered. The first is a state-space σ-entropy analysis of linear systems, and the second is an optimal control design using the σ-entropy norm as an optimization objective. The state-space solution to the σ-entropy analysis problem is derived from the representation of the σ-entropy norm in the frequency domain. The formulae of the σ-entropy norm computation in the state space are presented in the form of coupled matrix equations: one algebraic Riccati equation, one nonlinear equation over log determinant function, and two Lyapunov equations. Optimal control law is obtained using game theory and a saddle-point condition of optimality. The optimal state-feedback control, which minimizes the σ-entropy norm of the closed-loop system, is found from the solution of two algebraic Riccati equations, one Lyapunov equation, and the log determinant equation.
Porokeratoses—A Rare Group of Dermatoses
Authors: Anderska Agnieszka , Kaczmarska-Such Agnieszka , Mazur Ewelina , Reich Adam
Published: November 2024

Porokeratoses represent a rare group of skin diseases characterized by abnormal keratinization. The condition may have a genetic background and can be triggered by environmental factors, including UV exposure and infections. Several clinical variants of porokeratosis can be distinguished, including Mibelli’s porokeratosis, disseminated superficial actinic porokeratosis, superficial disseminated porokeratosis, and porokeratosis palmaris plantaris et disseminata. Diagnosis is established based on clinical and histopathological examination, dermatoscopy, and reflectance confocal microscopy. Various treatment options are available, including topical combination therapy with cholesterol and statins, topical retinoids, cryotherapy, laser therapy, and surgical excision of lesions, but none are fully effective. The success of these treatments can vary significantly based on the specific type of porokeratosis and individual patient characteristics, with many outcomes falling short of expectations. Since the disease is considered a precancerous condition, patients with porokeratosis should remain under regular dermatological control.
Neurometabolic Profile in Obese Patients: A Cerebral Multi-Voxel Magnetic Resonance Spectroscopy Study
Authors: Vuković Miloš , Nosek Igor , Slotboom Johannes , Medić Stojanoska Milica , Kozić Duško
Published: November 2024

Background and Objectives: Obesity-related chronic inflammation may lead to neuroinflammation and neurodegeneration. This study aimed to evaluate the neurometabolic profile of obese patients using cerebral multivoxel magnetic resonance spectroscopy (mvMRS) and assess correlations between brain metabolites and obesity markers, including body mass index (BMI), waist circumference, waist-hip ratio, body fat percentage, and indicators of metabolic syndrome (e.g., triglycerides, HDL cholesterol, fasting blood glucose, insulin, and insulin resistance index (HOMA-IR)). Materials and Methods: This prospective study involved 100 participants, stratified into two groups: 50 obese individuals (BMI ≥ 30 kg/m2) and 50 controls (18.5 ≤ BMI < 25 kg/m2). Anthropometric measurements, body fat percentage, and biochemical markers were evaluated. All subjects underwent long- and short-echo mvMRS analysis of the frontal and parietal supracallosal subcortical and deep white matter, as well as the cingulate gyrus, analyzing NAA/Cr, Cho/Cr, and mI/Cr ratios, along with absolute concentrations of NAA and Cho. Results: Obese participants exhibited significantly decreased NAA/Cr and Cho/Cr ratios in the deep white matter of the right cerebral hemisphere (p < 0.001), while absolute concentrations of NAA and Cho did not differ significantly between groups (p > 0.05). NAA levels showed negative correlations with more reliable obesity parameters (waist circumference and waist-to-hip ratio) but not with BMI, particularly in the deep frontal white matter and dorsal anterior cingulate gyrus of the left cerebral hemisphere. Notably, insulin demonstrated a significant negative impact on NAA (ρ = −0.409 and ρ = −0.410; p < 0.01) and Cho levels (ρ = −0.403 and ρ = −0.392; p < 0.01) at these locations in obese individuals. Conclusions: Central obesity and hyperinsulinemia negatively affect specific brain regions associated with cognitive and emotional processing, while BMI is not a reliable parameter for assessing brain metabolism.
Sjögren’s Disease and Gastroesophageal Reflux Disease: What Is Their Evidence-Based Link?
Authors: Mieliauskaitė Diana , Kontenis Vilius
Published: November 2024

Sjögren’s disease (SjD), or primary Sjögren’s syndrome (pSS), is a heterogeneous chronic autoimmune disorder with multiple clinical manifestations that can develop into non-Hodgkin’s lymphoma in mucosa-associated lymphoid tissue. SjD is one of the autoimmune diseases with the maximum delayed diagnosis due to its insidious onset, heterogeneous clinical features and varied course. It is increasingly recognized that extraglandular manifestations represent a clinical challenge for patients with SjD. The European League Against Rheumatism (EULAR) Sjögren’s Syndrome (SS) Disease Activity Index (ESSDAI) is a systemic disease activity index designed to measure disease activity in patients with primary Sjogren’s syndrome. It consists of 12 domains: cutaneous, pulmonary, renal, articular, muscular, peripheral nervous system, central nervous system, hematological, glandular, constitutional, lymphadenopathy and lymphoma, biological. More than a quarter of patients with pSS may have systemic features that are not included in the ESSDAI classification, i.e., various cardiovascular, ophthalmic, ENT, and other systemic or organ involvement that increase the magnitude of the systemic phenotype in the disease. The ESSDAI also excludes the gastrointestinal (GI) tract, and unfortunately, GI manifestations are not routinely assessed. Gastroesophageal reflux disease (GERD) is one of the most prevalent gastrointestinal disorders, impairing quality of life and consuming a large volume of medical resources. Recently carried out the Mendelian randomized trial confirmed the causal link between SjD and gastroesophageal reflux disease (GERD) and showed that GERD is a risk factor for SjD. This review aims to provide an overview of the research describing evidenced based links between Sjögren’s disease and gastroesophageal reflux disease, with the intention of ensuring that any systemic pathology in Sjögren’s disease is properly assessed and that management of the disease is directed towards the patient. A comprehensive literature search was carried out on PubMed, Web of Science, Scopus and the Cochrane Library databases. Two researchers searched for published studies indexed from inception to 1 September 2024 using the keywords ‘Sjögren’s syndrome’ OR ‘Sjögren’s disease’ AND ‘gastroesophageal reflux disease’ AND ‘microbiota’ OR microbiota dysbiosis’. We limited our search for scientific articles to human studies, and only included articles in English. Overall, there is a lack of evidence-based studies assessing the association between GERD and Sjögren’s disease and the changes in the microbiota associated with GERD in a multidisciplinary setting. Such studies are needed for the future, as this will improve the early diagnosis of Sjögren’s disease and the personalized management of the disease.
Safety Implications of Off-Label Medication Use in Athletes: A Narrative Review
Authors: Silva Vítor , Madeira Ricardo , Joaquim João , Matos Cristiano
Published: November 2024

In recent years, the off-label use of medications in sports has increased significantly, primarily driven by psychological and social factors. Athletes frequently misuse drugs without adequate medical supervision, relying on unreliable sources of information, which leads to improper usage and serious health risks. This narrative review analyzes literature from PubMed® (Medline), Scopus®, and Web of Science® databases, focusing on studies up to December 2023, to examine the safety concerns related to off-label drug use in sports. The review presents an overview of the off-label use of pharmacological substances by athletes, focusing on both hormonal and non-hormonal drugs. Hormonal substances such as anabolic steroids and growth hormones, and non-hormonal agents like diuretics and β2-agonists, are frequently abused. These practices are associated with severe side effects, including infections, cardiovascular complications, hormonal imbalances, psychological disorders, dependence, and even cases of death. The study emphasizes the need for stronger regulation, public awareness initiatives, and preventive strategies to mitigate the health risks associated with this growing trend.
An Improved Process for Solving the Sintering Problem of Al-Si Alloy Powder Metallurgy
Authors: Tian Hexin , Zhao Fachang , Ma Dongmei , Zhao Xingming , Meng Jingran , Zhang Guangan , Wu Fufa
Published: November 2024

The Al-Si alloy prepared by the traditional powder metallurgy method has the problems of difficult sintering and low mechanical properties. In this paper, rotary extrusion combined with rapid solidification/powder metallurgy technology (RS/PM) is proposed and designed to produce an Al-10Si alloy. In subsequent sintering at different temperatures, better metallurgical bonding between particles can be achieved to fulfil the aim of improving the mechanical properties of the material. Additionally, the continuous preparation of materials can be achieved. Finally, it is verified that the density of the Al-10Si alloy sintered at 525 °C is 2.61 g/cm3, reaching 97.0% of the theoretical density, and the tensile strength reaches 336 MPa, which is much higher than that of the alloy prepared by the traditional casting method and powder metallurgy method.
Multi-Metal Additive Manufacturing by Extrusion-Based 3D Printing for Structural Applications: A Review
Authors: Mazeeva Alina , Masaylo Dmitriy , Konov Gleb , Popovich Anatoliy
Published: November 2024

Additive manufacturing (AM) is a rapidly developing technical field that is becoming an irreplaceable tool to fabricate unique complex-shaped parts in aerospace, the automotive industry, medicine, and so on. One of the most promising directions for AM application is the design and production of multi-material components with different types of chemical, structural, and architectural gradients that also promote a breakthrough in bio-inspired approaches. At the moment there are a lot of different AM techniques involving various types of materials. This paper represents a review of extrusion-based AM techniques using metal-polymer composites for structural metal parts fabrication. These methods are significantly cheaper than powder bed fusion (PBF) and directed energy deposition (DED) techniques, though have a lower degree of part detail. Thus, they can be used for low-scale production of the parts that are not rentable to produce with PBF and DED. Multi-material structures application in machinery, main aspects of feedstock preparation, the subsequent steps of extrusion-based 3D printing, and the following treatment for manufacturing single-metallic and multi-metallic parts are considered. Main challenges and recommendations are also discussed. Multi-metallic extrusion-based 3D printing is just a nascent trend requiring further wide investigation, though even now it shows pretty interesting results.
Laser Cutting of Non-Woven Fabric Using UV Nanosecond Pulsed Laser
Authors: Fu Jiajun , Liu Chao , Zhao Runhan , Wang Huixin , Yu Zhongjie , Wang Qinghua
Published: November 2024

The efficient cutting of non-woven fabric shows great significance to the development of the textile industry. In recent years, laser cutting technology has been widely applied in the clothing industry due to its high efficiency and cutting quality. In this work, a UV nanosecond pulsed laser with a wavelength of 355 nm and a max power of 6.5 W is used to cut non-woven fabric with a thickness of 0.15 mm. The variation of kerf width, surface morphology, and chemical contents are investigated under different laser processing parameters, and the optimal processing parameter is determined. The experimental results demonstrate that the degree of crystallization and chemical composition of the kerf on the non-woven fabric surface is significantly influenced by laser cutting parameters such as laser scanning speed (from 100 to 700 mm/s) and frequency (from 20 to 70 kHz). The scanning speed of 500 mm/s and frequency of 30 kHz are considered the best parameters for achieving abundant energy for the complete and efficient cutting of non-woven fabric. In addition, the level of carbonization and oxidation reaches a relatively low value, and the kerf width is 0.214 mm, which is considered a reasonable value under the optimal processing parameters, showing high cutting quality. Furthermore, the effect of different cutting treatments on surface morphology and chemical contents is also studied. The experimental results present that the non-woven fabric cut by laser possesses a flat kerf, showing a similar effect to that of scissor cutting. Moreover, due to the programmability of laser processing patterns, it is possible to create more intricate designs on non-woven fabric. This facilitates the application and promotion of laser-cut non-woven fabrics. These results can provide a certain reference for laser cutting in the textile industry and are expected to allow for the cutting of high-quality kerf with low carbonization and oxidation.
Analysis of Entropy Generation for Mass and Thermal Mixing Behaviors in Non-Newtonian Nano-Fluids of a Crossing Micromixer
Authors: Lakhdar Ayache , Skander Jribi , Tayeb Naas Toufik , Mostefa Telha , Hossain Shakhawat , Kim Sun Min
Published: November 2024

This work’s objective is to investigate the laminar steady flow characteristics of non-Newtonian nano-fluids in a developed chaotic microdevice known as a two-layer crossing channels micromixer (TLCCM). The continuity equation, the 3D momentum equations, and the species transport equations have been solved numerically at low Reynolds numbers with the commercial CFD software Fluent. A procedure has been verified for non-Newtonian flow in studied geometry that is continuously heated. Secondary flows and thermal mixing performance with two distinct intake temperatures of nano-shear thinning fluids is involved. For an extensive range of Reynolds numbers (0.1 to 25), the impact of fluid characteristics and various concentrations of Al2O3 nanoparticles on thermal mixing capabilities and pressure drop were investigated. The simulation for performance enhancement was run using a power-law index (n) at intervals of different nanoparticle concentrations (0.5 to 5%). At high nano-fluid concentrations, our research findings indicate that hydrodynamic and thermal performances are considerably improved for all Reynolds numbers because of the strong chaotic flow. The mass fraction visualization shows that the suggested design has a fast thermal mixing rate that approaches 0.99%. As a consequence of the thermal and hydrodynamic processes, under the effect of chaotic advection, the creation of entropy governs the second law of thermodynamics. Thus, with the least amount of friction and thermal irreversibilities compared to other studied geometries, the TLCCM arrangement confirmed a significant enhancement in the mixing performance.