bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model
Authors: Jin Yongchao , Wang Renfang , Zhuang Xiaodie , Wang Kenan , Wang Honglian , Wang Chenxi , Wang Xiyin
Published: October 2022

The purpose of this study is to study the spread of COVID-19, establish a predictive model, and provide guidance for its prevention and control. Considering the high complexity of epidemic data, we adopted an ARIMA-LSTM combined model to describe and predict future transmission. A new method of the ARIMA-LSTM model paralleling by weight of regression coefficient was proposed. Then, we used the ARIMA-LSTM model paralleling by weight of regression coefficient, ARIMA model, and ARIMA-LSTM series model to predict the epidemic data in China, and we found that the ARIMA-LSTM model paralleling by weight of regression coefficient had the best prediction accuracy. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 4049.913, RMSE = 63.639, MAPE = 0.205, R2 = 0.837, MAE = 44.320. In order to verify the effectiveness of the ARIMA-LSTM model paralleling by weight of regression coefficient, we compared the ARIMA-LSTM model paralleling by weight of regression coefficient with the SVR model and found that ARIMA-LSTM model paralleling by weight of regression coefficient has better prediction accuracy. It was further verified with the epidemic data of India and found that the prediction accuracy of the ARIMA-LSTM model paralleling by weight of regression coefficient was still higher than that of the SVR model. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 744,904.6, RMSE = 863.079, MAPE = 0.107, R2 = 0.983, MAE = 580.348. Finally, we used the ARIMA-LSTM model paralleling by weight of regression coefficient to predict the future epidemic situation in China. We found that in the next 60 days, the epidemic situation in China will become a steady downward trend.
Point Processes in a Metric Space and Their Applications
Authors: Zhao Yuwei
Published: October 2022

Point processes are important in extreme value theory due to their equivalent formulations of two popular models in various applications: the block maxima models and the peak-over-threshold model. Point processes in a metric space provide tools to analyze heavy-tailed phenomena that appear in the research of extremal behaviors of functional data. To facilitate these applications of point processes, the equivalence between the weak convergence of point processes and the MO-convergence is established in the paper.
A Multi-Criteria Decision-Making Framework for Prioritizing and Overcoming Sectoral Barriers in Converting Agricultural Residues to a Building Material
Authors: Pamučar Dragan , Behzad Masoud , Janosevic Miljojko , Aburto Araneda Claudia Andrea
Published: October 2022

Biological products utilization are increasingly encouraged in different sectors such as building construction to facilitate moving towards a circular economy. However, this task is facing several barriers in supply chain and construction sectors. This study identified common barriers in converting agricultural residues to building materials and products in the agriculture sector, transportation, and manufacturing, as well as construction and operation phases in the building sector. The feasibility level to overcome the barriers has been scored. In addition, the barriers and sectors have been prioritized through ordinal priority approach. The results ranked the priority of the barriers as technology (0.3083), policy (0.2211), knowledge (0.1972), cost (0.1500), social and cultural (0.0739), and infrastructure (0.0494). Sectors were ranked in feasibility level to overcome the barriers from lowest to highest as operating, construction, manufacturing, transport, and agriculture. It is recommended to local communities to give priority to the building sector rather than supply chain and work under an integrated framework to enhance the feasibility level, which should include localization, prevention, collaboration, and digitalization. In particular, Chile should promote converting agricultural residues to building products as the project aligns with several initiatives existing in its circular economy roadmap.
Mathematics Teachers’ Professional Competence Component Model and Practices in Teaching the Linear Functional Concept—An Experimental Study
Authors: Nam Pham Sy , Tuong Hoa Anh , Weinhandl Robert , Lavicza Zsolt
Published: October 2022

Teachers’ professional development is now widely regarded as a key determinant of school effectiveness and student achievement. However, Mathematics teachers in Vietnamese secondary schools face numerous challenges in their teaching practices, such as a lack of recourse assistance, a lack of innovational teaching policy, and difficulty in using real-life examples in teaching. This study aims to identify Mathematics teachers’ professional competence components and organize a workshop to support Vietnamese teachers to enhance professional development through experiment research. A total of 40 postgraduate students on the master’s course of Mathematics Education at Saigon University are involved in this study. To reach the study objectives, we conducted three research cycles of: (1) Reviews to identify the model of Mathematics teachers’ professional competence for suitability in the Vietnamese context; (2) Developing materials and organizing the workshop on designing lessons that teach linear functional concepts in Grade 8 (13-year-old students); (3) Feedback analysis through a qualitative study with a group focus interview of 40 workshop participants. Data collection was recorded, and typed transcripts and theme development were employed in data analysis. The findings outline four criteria that serve as the cornerstone of our concept of professional competences for Mathematics teachers: I. Mathematical Knowledge; II. Interpretation of the Official Mathematics Curriculum’s Intentions; III. Understanding of Students’ Thoughts; and IV. Design of Teaching. This model was applied in materials design for the workshop and was highly appreciated and got a high level of satisfaction from participants. These findings are expected to bring benefits for stakeholders who are Mathematics teachers in schools or involved in Mathematics teacher education in higher education.
Mathematical Connections and the Mathematics Teacher’s Specialised Knowledge
Authors: De Gamboa Genaro , Caviedes Sofía , Badillo Edelmira
Published: October 2022

This study seeks to explore the relationship between recent findings on mathematical connections and the Mathematics Teacher’s Specialised knowledge model. From a qualitative approach, we seek to explore the specialised knowledge mobilised during the establishment of mathematical connections in different contexts. The study takes into consideration the results from previous studies that were conducted with both prospective and in service teachers. The results suggest the need to include the notion of complexity in the mathematical connections defined by the Mathematics Teacher’s Specialised knowledge model. Similarly, they suggest that it is possible to find some ambiguities in the use of the model, so it would be enriching to consider the current classification of mathematical connections in order to improve the model as an analytical tool. Finally, the results suggest the need to include mathematical connections as part of the pedagogical content knowledge domain, since the knowledge of this domain could be mobilised during the establishment of mathematical connections.
A Zero-and-One Inflated Cosine Geometric Distribution and Its Application
Authors: Junnumtuam Sunisa , Niwitpong Sa-Aat , Niwitpong Suparat
Published: October 2022

Count data containing both excess zeros and ones occur in many fields, and the zero-and-one inflated distribution is suitable for analyzing them. Herein, we construct confidence intervals (CIs) for the parameters of the zero-and-one inflated cosine geometric (ZOICG) distribution constructed by using five methods: a Wald CI based on the maximum likelihood estimate, equal-tailed Bayesian CIs based on the uniform or Jeffreys prior, and the highest posterior density intervals based on the uniform or Jeffreys prior. Their efficiencies were compared in terms of their coverage probabilities and average lengths via a simulation study. The results show that the highest posterior density intervals based on the uniform prior performed the best in most cases. The number of new daily COVID-19-related deaths in Luxembourg in 2020 involving data with a high proportion of zeros and ones were analyzed. It was found that the ZOICG model was appropriate for this scenario.
Economic Performance and Stock Market Integration in BRICS and G7 Countries: An Application with Quantile Panel Data and Random Coefficients Modeling
Authors: Ferreira José Clemente Jacinto , Gama Ana Paula Matias , Fávero Luiz Paulo , Serra Ricardo Goulart , Belfiore Patrícia , Costa Igor Pinheiro de Araújo , Santos Marcos dos
Published: October 2022

The interest in studies aimed at understanding the integration of the stock market with the economic performance of countries has been growing in recent years, perhaps driven by the recent economic crises faced by the world. Although several studies on the topic have been carried out, the results are still far from a meaningful conclusion. In this sense, this paper considered the dual objective of investigating whether there is significant variance in the economic performance of developed and emerging markets’ countries and whether the global risk factors are statistically significant in explaining the variations in their future economic performance over time. From a sample of (i) gross domestic products from BRICS and G7 countries (total of twelve countries), and (ii) returns of the risk factors of developed and emerging stock markets for the period 1993 to 2019, we applied longitudinal regression modeling for five distinct percentiles, and random coefficients modeling (RCM) with repeated measures. We found that risk factors explain the future economic performance, there is significant variation in economic performance over time among countries, and the temporal variation in the random effects of intercepts can be explained by RCM. The results of this study confirm that stock markets follow an integration process and that moderately integrated markets may have the same risk factors. Furthermore, considering that risk factors are related to future GDP growth, they act as proxies for unidentified state variables.
Fourier Neural Solver for Large Sparse Linear Algebraic Systems
Authors: Cui Chen , Jiang Kai , Liu Yun , Shu Shi
Published: October 2022

Large sparse linear algebraic systems can be found in a variety of scientific and engineering fields and many scientists strive to solve them in an efficient and robust manner. In this paper, we propose an interpretable neural solver, the Fourier neural solver (FNS), to address them. FNS is based on deep learning and a fast Fourier transform. Because the error between the iterative solution and the ground truth involves a wide range of frequency modes, the FNS combines a stationary iterative method and frequency space correction to eliminate different components of the error. Local Fourier analysis shows that the FNS can pick up on the error components in frequency space that are challenging to eliminate with stationary methods. Numerical experiments on the anisotropic diffusion equation, convection–diffusion equation, and Helmholtz equation show that the FNS is more efficient and more robust than the state-of-the-art neural solver.
Dynamical Analysis of Discrete-Time Two-Predators One-Prey Lotka–Volterra Model
Authors: Khaliq Abdul , Ibrahim Tarek F. , Alotaibi Abeer M. , Shoaib Muhammad , El-Moneam Mohammed Abd
Published: October 2022

This research manifesto has a comprehensive discussion of the global dynamics of an achievable discrete-time two predators and one prey Lotka–Volterra model in three dimensions, i.e., in the space R3. In some assertive parametric circumstances, the discrete-time model has eight equilibrium points among which one is a special or unique positive equilibrium point. We have also investigated the local and global behavior of equilibrium points of an achievable three-dimensional discrete-time two predators and one prey Lotka–Volterra model. The conversion of a continuous-type model into its discrete counterpart model has been completed by adopting a dynamically consistent nonstandard difference scheme with the end goal that the equilibrium points are conserved in twin cases. The difficulty lies in how to find all fixed points O,P,Q,R,S,T,U,V and the Jacobian matrix and its characteristic polynomial at the unique positive fixed point. For that purpose, we use Mathematica software to find the equilibrium points and all of the Jacobian matrices at those equilibrium points. Moreover, we discuss boundedness conditions for every solution and prove the existence of a unique positive equilibrium point. We discuss the local stability of the obtained system about all of its equilibrium points. The discrete Lotka–Volterra model in three dimensions is given by system (3), where parameters α,β,γ,δ,ζ,η,μ,ε,υ,ρ,σ,ω∈R+ and initial conditions x0,y0,z0 are positive real numbers. Additionally, the rate of convergence of a solution that converges to a unique positive equilibrium point is discussed. To represent theoretical perceptions, some numerical debates are introduced, including phase portraits.
Application of Generalized Logistic Function to Travelling Wave Solutions for a Class of Nonlinear Evolution Equations
Authors: Li Lingxiao , Wang Mingliang , Zhang Jinliang
Published: October 2022

The generalized Logistic function that solves a first-order nonlinear ODE with an arbitrary positive power term of the dependent variable is introduced in this paper, by means of which the traveling wave solutions of a class of nonlinear evolution equations, including the generalized Fisher equation, the generalized Nagumo equation, the generalized Burgers-Fisher equation, the generalized Gardner equation, the generalized KdV-Burgers equation, and the generalized Benney equation, are obtained successfully. In these particular cases, traveling wave solutions of several important model PDEs in mathematical physics are also discovered.
Simulation-Based Optimization Approaches for Dealing with Dual-Command Crane Scheduling Problem in Unit-Load Double-Deep AS/RS Considering Energy Consumption
Authors: Hsu Hsien-Pin , Wang Chia-Nan , Dang Thanh-Tuan
Published: October 2022

Automated storage/retrieval systems (AS/RS) have been increasingly used to support operations in manufacturing firms, warehouses, and distribution centers. Usually, AS/RSs are expensive. To achieve a good return on investment (ROI), an AS/RS must operate optimally. This research focuses on solving the crane scheduling problem, which has a great and immediate impact on the performance of an AS/RS. To optimize the design and operations of an AS/RS, many past studies have applied the simulation approach. However, the simulation and optimization have been often loosely coupled, resulting in a rigorous and labor-intensive optimization procedure. Using population- and evolution-based metaheuristics to deal with the crane scheduling problem of an AS/RS is one of the research trends. However, the whale optimization algorithm (WOA) and its variants have not been used for this purpose. To address the said gaps, this research first proposes a framework for coupling the simulation and optimization closely, in which various heuristics/metaheuristics, including first-come first-serve (FCFS), RANDOM, WOA, genetic algorithms (GAs), particle swarm optimization (PSO), and especially an improved WOA (IWOA), together with dynamic programming (DP), have been used as alternative sequencing methods. Based on this framework, different simulation-based optimization approaches have been developed for solving the dual-command crane scheduling problem in a unit-load double-deep AS/RS. The experimental results show that IWOA+DP outperforms the others in terms of energy consumption.
Modelling, Simulation and Controlling of a Multi-Pump System with Water Storage Powered by a Fluctuating and Intermittent Power Source
Authors: Bordeașu Dorin , Proștean Octavian , Filip Ioan , Drăgan Florin , Vașar Cristian
Published: October 2022

In recent years, many pumping systems have begun to be powered by renewable energy generators, including mostly photovoltaic generators and, less frequently, electrical wind generators. Because of the technology’s complexity and novelty (it has not yet reached its maturation), most of those systems consist of single pumps powered by photovoltaic generators or electrical wind generators. For this reason, the current paper proposes a strategy for driving a multi-pump system with water storage powered by a fluctuating and intermittent power source, such as power grids, which are limited by price variation over different periods during the day, or photovoltaic generators and/or electrical wind generators. The current work begins by proposing a model of a multi-pump system with water storage, followed by the design of a control strategy for operating such a system powered by a fluctuating and intermittent power source in an energy-efficient manner, without sacrificing the reliability, robustness and lifetime of the plant. Finally, an analysis of two concrete situations encountered in practice is made: in one, the considered multi-pump system is powered only by a power grid limited by price variation over three periods; in the other, it is powered by a photovoltaic generator.
Atherosclerotic Plaque Segmentation Based on Strain Gradients: A Theoretical Framework
Authors: Latorre Álvaro T. , Martínez Miguel A. , Cilla Myriam , Ohayon Jacques , Peña Estefanía
Published: October 2022

Background: Atherosclerotic plaque detection is a clinical and technological problem that has been approached by different studies. Nowadays, intravascular ultrasound (IVUS) is the standard used to capture images of the coronary walls and to detect plaques. However, IVUS images are difficult to segment, which complicates obtaining geometric measurements of the plaque. Objective: IVUS, in combination with new techniques, allows estimation of strains in the coronary section. In this study, we have proposed the use of estimated strains to develop a methodology for plaque segmentation. Methods: The process is based on the representation of strain gradients and the combination of the Watershed and Gradient Vector Flow algorithms. Since it is a theoretical framework, the methodology was tested with idealized and real IVUS geometries. Results: We achieved measurements of the lipid area and fibrous cap thickness, which are essential clinical information, with promising results. The success of the segmentation depends on the plaque geometry and the strain gradient variable (SGV) that was selected. However, there are some SGV combinations that yield good results regardless of plaque geometry such as ▽εvMises+▽εrθ, ▽εyy+▽εrr or ▽εmin+▽εTresca. These combinations of SGVs achieve good segmentations, with an accuracy between 97.10% and 94.39% in the best pairs. Conclusions: The new methodology provides fast segmentation from different strain variables, without an optimization step.
Application of Graph Structures in Computer Vision Tasks
Authors: Andriyanov Nikita
Published: October 2022

On the one hand, the solution of computer vision tasks is associated with the development of various kinds of images or random fields mathematical models, i.e., algorithms, that are called traditional image processing. On the other hand, nowadays, deep learning methods play an important role in image recognition tasks. Such methods are based on convolutional neural networks that perform many matrix multiplication operations with model parameters and local convolutions and pooling operations. However, the modern artificial neural network architectures, such as transformers, came to the field of machine vision from natural language processing. Image transformers operate with embeddings, in the form of mosaic blocks of picture and the links between them. However, the use of graph methods in the design of neural networks can also increase efficiency. In this case, the search for hyperparameters will also include an architectural solution, such as the number of hidden layers and the number of neurons for each layer. The article proposes to use graph structures to develop simple recognition networks on different datasets, including small unbalanced X-ray image datasets, widely known the CIFAR-10 dataset and the Kaggle competition Dogs vs Cats dataset. Graph methods are compared with various known architectures and with networks trained from scratch. In addition, an algorithm for representing an image in the form of graph lattice segments is implemented, for which an appropriate description is created, based on graph data structures. This description provides quite good accuracy and performance of recognition. The effectiveness of this approach based, on the descriptors of the resulting segments, is shown, as well as the graph methods for the architecture search.
Trigonometric Polynomial Solutions of Bernoulli Trigonometric Polynomial Differential Equations
Authors: Valls Claudia
Published: October 2022

We consider real trigonometric polynomial Bernoulli equations of the form A(θ)y′=B1(θ)+Bn(θ)yn where n≥2, with A,B1,Bn being trigonometric polynomials of degree at most μ≥1 in variables θ and Bn(θ)≢0. We also consider trigonometric polynomials of the form A(θ)yn−1y′=B0(θ)+Bn(θ)yn where n≥2, being A,B0,Bn trigonometric polynomials of degree at most μ≥1 in the variable θ and Bn(θ)≢0. For the first equation, we show that when n≥4, it has at most 3 real trigonometric polynomial solutions when n is even and 5 real trigonometric polynomial solutions when n is odd. For the second equation, we show that when n≥3, it has at most 3 real trigonometric polynomial solutions when n is odd and 5 real trigonometric polynomial solutions when n is even. We also provide trigonometric polynomial equations of the two types mentioned above where the maximum number of trigonometric polynomial solutions is achieved. The proof method will be to apply extended Fermat problems to polynomial equations.
Convergence of Neural Networks with a Class of Real Memristors with Rectifying Characteristics
Authors: Di Marco Mauro , Forti Mauro , Moretti Riccardo , Pancioni Luca , Tesi Alberto
Published: October 2022

The paper considers a neural network with a class of real extended memristors obtained via the parallel connection of an ideal memristor and a nonlinear resistor. The resistor has the same rectifying characteristic for the current as that used in relevant models in the literature to account for diode-like effects at the interface between the memristor metal and insulating material. The paper proves some fundamental results on the trajectory convergence of this class of real memristor neural networks under the assumption that the interconnection matrix satisfies some symmetry conditions. First of all, the paper shows that, while in the case of neural networks with ideal memristors, it is possible to explicitly find functions of the state variables that are invariants of motions, the same functions can be used as Lyapunov functions that decrease along the trajectories in the case of real memristors with rectifying characteristics. This fundamental property is then used to study convergence by means of a reduction-of-order technique in combination with a Lyapunov approach. The theoretical predictions are verified via numerical simulations, and the convergence results are illustrated via the applications of real memristor neural networks to the solution of some image processing tasks in real time.
Notes on Convergence Results for Parabolic Equations with Riemann–Liouville Derivatives
Authors: Le Dinh Long , Donal O’regan
Published: October 2022

Fractional diffusion equations have applications in various fields and in this paper we consider a fractional diffusion equation with a Riemann–Liouville derivative. The main objective is to investigate the convergence of solutions of the problem when the fractional order tends to 1−. Under some suitable conditions on the Cauchy data, we prove the convergence results in a reasonable sense.
Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management
Authors: Yu Kai , Zhou Lujie , Liu Pingping , Chen Jing , Miao Dejun , Wang Jiansheng
Published: October 2022

The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises.
Robust Stabilization of Impulsive Boolean Control Networks with Function Perturbation
Authors: Sun Qilong , Li Haitao
Published: October 2022

This paper studies the robust stabilization of impulsive Boolean control networks (IBCNs) with function perturbation. A Boolean control network (BCN) with a state-dependent impulsive sequence is converted to an equivalent BCN by the semi-tensor product method. Based on the equivalence of stabilization between the IBCN and the corresponding BCN, several criteria are proposed for the robust stabilization of IBCNs. Furthermore, when the IBCN is not robustly stabilizable after the function perturbation, an algorithm is presented to modify the control or the impulse-triggered set. Finally, an example is given to verify the obtained results.
Shadowed Type-2 Fuzzy Sets in Dynamic Parameter Adaption in Cuckoo Search and Flower Pollination Algorithms for Optimal Design of Fuzzy Fault-Tolerant Controllers
Authors: Patel Himanshukumar R. , Shah Vipul A.
Published: October 2022

In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed using human knowledge, allowing for intelligent dynamic adaptations of metaheuristic parameters. To accomplish these tasks, we proposed shadowed type-2 fuzzy inference systems (ST2FISs) for two metaheuristic algorithms, namely cuckoo search (CS) and flower pollination (FP). Furthermore, with the advent of shadowed type-2 fuzzy logic, the abilities of uncertainty handling offer an appealing improved performance for dynamic parameter adaptation in metaheuristic methods; moreover, the use of ST2FISs has been shown in recent works to provide better results than type-1 fuzzy inference systems (T1FISs). As a result, ST2FISs are proposed for adjusting the Lèvy flight (P) and switching probability (P′) parameters in the original cuckoo search (CS) and flower pollination (FP) algorithms, respectively. Our approach investigated trapezoidal types of membership functions (MFs), such as ST2FSs. The proposed method was used to optimize the precursors and implications of a two-tank non-interacting conical frustum tank level (TTNCFTL) process using an interval type-2 fuzzy controller (IT2FLC). To ensure that the implementation is efficient compared with the original CS and FP algorithms, simulation results were obtained without and then with uncertainty in the main actuator (CV1) and system component (leak) at the bottom of frustum tank two of the TTNCFLT process. In addition, the statistical z-test and non-parametric Friedman test are performed to analyze and deliver the findings for the best metaheuristic algorithm. The reported findings highlight the benefits of employing this approach over traditional general type-2 fuzzy inference systems since we get superior performance in the majority of cases while using minimal computational resources.
A Review of the Chemical Extraction of Chitosan from Shrimp Wastes and Prediction of Factors Affecting Chitosan Yield by Using an Artificial Neural Network
Authors: Hosney Ahmed , Ullah Sana , Barčauskaitė Karolina
Published: October 2022

There are two viable options to produce shrimp shells as by-product waste, either within the shrimp production phases or when the shrimp are peeled before cooking by the end user. This waste is considered a double-edged sword, as it is possible to be either a source of environmental pollution, through dumping and burning, or a promising source from which to produce chitosan as a biodegradable, biocompatible biopolymer which has a variety of agricultural, industrial, and biomedical applications. Chitosan is a deacetylated form of chitin that can be chemically recovered from shrimp shells through the three sequential stages of demineralization, deproteinization, and deacetylation. The main aim of this review paper is to summarize the recent literature on the chemical extraction of chitosan from shrimp shells and to represent the physicochemical properties of chitosan extracted from shrimp shells in different articles, such as chitosan yield, moisture content, solubility, ash content, and degree of deacetylation. Another aim is to analyze the influence of the main predictors of the chemical extraction stages (demineralization, deproteinization, and deacetylation) on the chitosan yield percentage by using a multilayer perceptron artificial neural network. This study showed that the deacetylation alkali concentration is the most crucial parameter, followed by the concentrations of acid and alkali of demineralization and deproteinization, respectively. The current review was conducted to be used in prospective studies for optimizing the chemical extraction of chitosan from shrimp wastes.
Effects of Dermatan Sulfate from Marine Invertebrate Styela plicata in the Wound Healing Pathway: A Natural Resource Applied to Regenerative Therapy
Authors: Rizzo-Valente Vanessa S. , Fusco Maria A. , Cruz Renata M. M. L. , Santos Rachel A. , Silva Lucas S. , Escaleira Roberta C. , Schulz Daniel F. , Barroso Shana P. C. , Miranda Bruno L. , Santos Daniela Z. , Gregório Marcelo L. , Guerra Rodrigo J. A. , Pavão Mauro S. G.
Published: October 2022

Acute and chronic dermatological injuries need rapid tissue repair due to the susceptibility to infections. To effectively promote cutaneous wound recovery, it is essential to develop safe, low-cost, and affordable regenerative tools. Therefore, we aimed to identify the biological mechanisms involved in the wound healing properties of the glycosaminoglycan dermatan sulfate (DS), obtained from ascidian Styela plicata, a marine invertebrate, which in preliminary work from our group showed no toxicity and promoted a remarkable fibroblast proliferation and migration. In this study, 2,4-DS (50 µg/mL)-treated and control groups had the relative gene expression of 84 genes participating in the healing pathway evaluated. The results showed that 57% of the genes were overexpressed during treatment, 16% were underexpressed, and 9.52% were not detected. In silico analysis of metabolic interactions exhibited overexpression of genes related to: extracellular matrix organization, hemostasis, secretion of inflammatory mediators, and regulation of insulin-like growth factor transport and uptake. Furthermore, in C57BL/6 mice subjected to experimental wounds treated with 0.25% 2,4-DS, the histological parameters demonstrated a great capacity for vascular recovery. Additionally, this study confirmed that DS is a potent inducer of wound-healing cellular pathways and a promoter of neovascularization, being a natural ally in the tissue regeneration strategy.
A Critical Comparison of the Advanced Extraction Techniques Applied to Obtain Health-Promoting Compounds from Seaweeds
Authors: Quitério Eva , Grosso Clara , Ferraz Ricardo , Delerue-Matos Cristina , Soares Cristina
Published: October 2022

Marine macroalgae are rich in bioactive compounds that can be applied in several fields, mainly food, cosmetics, and medicine. The health-promoting effects of bioactive compounds, such as polyphenols, polysaccharides, carotenoids, proteins, and fatty acids, have been increasingly explored, especially regarding their antioxidant activity and improvement in human health. To extract these valuable compounds, advanced technologies that include Supercritical-Fluid Extraction (SFE), Pressurised-Liquid Extraction (PLE), Ultrasound-Assisted Extraction (UAE), Microwave-Assisted Extraction (MAE), Enzyme-Assisted Extraction (EAE), Ultrasound-Microwave-Assisted Extraction (UMAE) and Liquefied Gas Extraction (LGE) have been assessed due to their notable advantages over the conventional methods (Solid–Liquid and Soxhlet extraction). These advanced techniques are considerably influenced by different extraction parameters such as temperature, pressure, type of solvent, extraction time, solvent:solid material ratio, power (MAE, UAE, and UMAE), enzymes used (EAE), and factors related to the macroalgae matrix itself. Optimizing these process parameters for each method is critical to obtain better efficiency results for the targeted bioactive compounds. Macroalgae are natural sources with undeniable beneficial effects on human health. In this context, optimising the extraction techniques discussed in this review should prioritise exploiting these valuable resources’ wide range of bioactive properties.
Discovery of p-Terphenyl Metabolites as Potential Phosphodiesterase PDE4D Inhibitors from the Coral-Associated Fungus Aspergillus sp. ITBBc1
Authors: Guo Zhikai , Abulaizi Ailiman , Huang Ling , Xiong Zijun , Zhang Shiqing , Liu Tianmi , Wang Rong
Published: October 2022

Chemical investigation of the fermentation extract of the coral-associated fungus Aspergillus sp. ITBBc1 led to the discovery of five unreported p-terphenyl derivatives, sanshamycins A–E (1–5), together with five previously described analogues, terphenyllin (6), 3-hydroxyterphenyllin (7), candidusin A (8), 4,5-dimethoxycandidusin A (9), and candidusin C (10). Their structures were elucidated by HRESIMS data and NMR spectroscopic analysis. Compound 1 represents the first example of p-terphenyls with an aldehyde substitution on the benzene ring. Compounds 2–4 feature varying methoxyl and isopentenyl substitutions, while compound 5 features a five-membered lactone linked to a biphenyl. These findings expand the chemical diversity of the family of p-terphenyl natural products. Compounds 1–6 and 9 were evaluated for their inhibitory activity against type 4 phosphodiesterase (PDE4), which is a fascinating drug target for treatment of inflammatory, respiratory, and neurological diseases. Compound 3 was the most potent and exhibited PDE4D inhibitory activity with an IC50 value of 5.543 µM.
Chemosensory-Related Genes in Marine Copepods
Authors: Roncalli Vittoria , Uttieri Marco , Capua Iole Di , Lauritano Chiara , Carotenuto Ylenia
Published: October 2022

Living organisms deeply rely on the acquisition of chemical signals in any aspect of their life, from searching for food, mating and defending themselves from stressors. Copepods, the most abundant and ubiquitous metazoans on Earth, possess diversified and highly specified chemoreceptive structures along their body. The detection of chemical stimuli activates specific pathways, although this process has so far been analyzed only on a relatively limited number of species. Here, in silico mining of 18 publicly available transcriptomes is performed to delve into the copepod chemosensory genes, improving current knowledge on the diversity of this multigene family and on possible physiological mechanisms involved in the detection and analysis of chemical cues. Our study identifies the presence of ionotropic receptors, chemosensory proteins and gustatory receptors in copepods belonging to the Calanoida, Cyclopoida and Harpacticoida orders. We also confirm the absence in these copepods of odorant receptors and odorant-binding proteins agreeing with their insect specificity. Copepods have evolved several mechanisms to survive in the harsh marine environment such as producing proteins to respond to external stimulii. Overall, the results of our study open new possibilities for the use of the chemosensory genes as biomarkers in chemical ecology studies on copepods and possibly also in other marine holozooplankters.
Complexing the Marine Sesquiterpene Euplotin C by Means of Cyclodextrin-Based Nanosponges: A Preliminary Investigation
Authors: Bertoli Alessandra , LoBue Anthea , Quattrini Luca , Sartini Stefania , Polini Beatrice , Carpi Sara , Frontini Francesco Paolo , Di Giuseppe Graziano , Guella Graziano , Nieri Paola , La Motta Concettina
Published: October 2022

Euplotin C is a sesquiterpene of marine origin endowed with significant anti-microbial and anti-tumor properties. Despite the promising functional profile, its progress as a novel drug candidate has failed so far, due to its scarce solubility and poor stability in aqueous media, such as biological fluids. Therefore, overcoming these limits is an intriguing challenge for the scientific community. In this work, we synthesized β-cyclodextrin-based nanosponges and investigated their use as colloidal carriers for stably complex euplotin C. Results obtained proved the ability of the carrier to include the natural compound, showing remarkable values of both loading efficiency and capacity. Moreover, it also allowed us to preserve the chemical structure of the loaded compound, which was recovered unaltered once extracted from the complex. Therefore, the use of β-cyclodextrin-based nanosponges represents a viable option to vehiculate euplotin C, thus opening up its possible use as pharmacologically active compound.
Regioselective Synthesis of 6-O-Acetyl Dieckol and Its Selective Cytotoxicity against Non-Small-Cell Lung Cancer Cells
Authors: Shin Hyeon-Cheol , Kim Yongkyun , Choi Jaeyeong , Kang Hyun Bae , Han Seung-Yun , Park Kwangyong , Hwang Hye Jeong
Published: October 2022

Dieckol, a phlorotannin from Ecklonia cava, has shown potential for use as an anticancer agent that selectively kills cancer cells. However, it is necessary to amplify its potency without damaging its inherent safety in order to develop it as a competitive chemotherapeutic. Here, we explored the controlled O-acylations of dieckol. Acyl groups could be consistently introduced to the 6-O position of dieckol with a high regioselectivity, which was confirmed by NOESY, HMBC and HSQC spectroscopies. In cytotoxicity studies on the newly synthesized 6-O-acetyl, 6-O-benzoyl dieckols and previously synthesized 6-O-alkyl dieckols against A549 vs. normal cells, all of the derivatives showed low cytotoxicity in normal cells with an IC50 of 481–719 μM, and highly structure-dependent cytotoxicity in A549 cells with an IC50 of 7.02 (acetyl)−842.26 (benzyl) μM. The selectivity index also showed a large structure dependency in the range of 0.67 (benzyl)–68.58 (acetyl). An analysis of the structure–activity relationship indicated that the activity was dramatically reduced in the presence of a benzene ring and was highly increased in the presence of small polar substituents. Conclusions: Controlled mono-O-modifications of dieckol could be a powerful tool to enhance the anticancer activity of dieckol, thus contributing to the development strategy for dieckol-based chemotherapeutics.
Paspalines C–D and Paxillines B–D: New Indole Diterpenoids from Penicillium brefeldianum WZW-F-69
Authors: Lin Weiwen , Li Hanpeng , Wu Zhiwen , Su Jingyi , Zhang Zehong , Yang Li , Deng Xianming , Xu Qingyan
Published: October 2022

Five new indole diterpenoids named paspaline C–D (1–2) and paxilline B–D (3–5), as well as eleven known analogues (6–16), were identified from fungus Penicillium brefeldianum strain WZW-F-69, which was isolated from an abalone aquaculture base in Fujian province, China. Their structures were elucidated mainly through 1D- and 2D-NMR spectra analysis and ECD comparison. Compound 1 has a 6/5/5/6/6/8 hexacyclic ring system bearing 2,2-dimethyl-1,3-dioxocane, which is rare in natural products. Compound 2 has an unusual open F-ring structure. The cytotoxic activities against 10 cancer cell lines and antimicrobial activities against model bacteria and fungi of all compounds were assayed. No compound showed antimicrobial activity, but at a concentration of 1 μM, compounds 1 and 6 exhibited the highest inhibition rates of 71.2% and 83.4% against JeKo-1 cells and U2OS cells, respectively.
Benefits of Virtual Reality Program and Motor Imagery Training on Balance and Fall Efficacy in Isolated Older Adults: A Randomized Controlled Trial
Authors: Kim So-Hyun , Cho Sung-Hyoun
Published: October 2022

Background and Objectives: for isolated older adults, alternative training indoors to maintain balance is essential; however, related studies are lacking. To improve the balance of isolated older adults and reduce their fear of falling, we aimed to examine the balance–keeping effect of a virtual reality (VR) program and motor imagery training (MIT) and propose training that could improve physical activity among older adults. Methods: a total of 34 older adults admitted to a convalescent hospital were assessed. VR (n = 12) and MIT (n = 10) groups completed their assigned intervention in six weeks, whereas the control group (CG) (n = 12) did not. The follow–up was performed after two weeks. Results: in group × time interactions, body center movement area, open and closed eyes balance scores, and fall efficacy were significantly different (p < 0.05). In contrast with the VR group, the MIT group did not show a significant difference in the open or closed eyes balance scores depending on the period. However, there was a significant difference between the MIT group and CG in the open eyes balance score post-test (d = 1.13, 95% confidence interval, 0.40–12.33). Conclusions: we propose VR and MIT as training methods to prevent physical weakness in isolated older adults.
Relationship between Precarious Employment and Unmet Dental Care Needs among Korean Workers: A Longitudinal Panel Study
Authors: Che Xianhua , Sohn Minsung , Moon Sungje , Park Hee-Jung
Published: October 2022

Background and Objectives: Precarious workers experience certain conditions, such as low income, instability in employment, and lack of social security. Precarious employment has increased barriers to the use of dental care services, resulting in more unmet dental care needs. The aim of this study was to identify unmet dental care needs among precarious workers in Korea’s labor market, using data from the Korea Health Panel Survey (2011–2017). Materials and Methods: Based on job and income security criteria, four groups were formed: Group A (individuals with job and income security), Group B (individuals reporting job security with income insecurity), Group C (individuals reporting job insecurity with income security), and Group D (individuals with job and income insecurity). We measured self-reported unmet dental need or the inability to receive necessary dental care owing to the past economic burdens. Panel logistic regression analyses were performed to determine the effect of precarious employment on unmet dental care needs for all participants. Results: Approximately 16% of the respondents reported having unmet dental care needs. Unmet dental care needs owing to economic reasons were higher among male workers in groups C and D than among male workers in Group A. In particular, male workers aged 50 years and above in Group B were 3.36 times more likely to have unmet dental care needs than those in Group A. In Group D, female workers showed a high probability of having dental care needs owing to economic reasons. Moreover, female workers aged 18–49 years witnessed an increase in unmet dental care needs. Conclusions: Korean workers with unstable employment and/or income are at a higher risk of having unmet dental care needs owing to financial factors. The findings suggest an urgent need to implement robust national health insurance policies to improve efforts aimed at reducing unmet dental care needs that potentially decreases the disparity in oral health among precariously employed workers. Furthermore, it is necessary to implement comprehensive labor market policies such as sickness benefits for those in precarious employment.
Selected Factors Influencing the Intensity of Postoperative Pain in Patients after Orthopedic and Gynecological Surgeries
Authors: Banaś Klaudia , Więch Paweł , Trojnar Patrycja , Guty Edyta , Skórka Mateusz , Soroń Małgorzata , Nowak Anna , Bazaliński Dariusz
Published: October 2022

Currently, pain is considered the fifth vital sign, and its effective relief is a priority in many surgical departments. The aim of this study was to determine the influence of selected factors on the intensity of postoperative pain after orthopedic and gynecological procedures. The study was conducted in a group of 200 patients undergoing orthopedic and gynecological procedures at the County Hospital in Nisko between August 2017 and January 2018. The method of estimation and document analysis was applied in the course of the study. A standardized tool was used—Polish adaptation of the Melzack Pain Questionnaire (MPQ), an individual documentation of patients and a scientific and research questionnaire developed by the authors. The tests were conducted for three consecutive days from the day of the operation. The strongest pain immediately after surgery was experienced by the patients after general endotracheal anesthesia, and the mildest after full intravenous short-term anesthesia (p < 0.05). Procedures lasting more than 60 min were associated with greater pain intensity on all tested days. The sex of the patient did not significantly affect the level of the perceived intensity of postoperative pain (p > 0.05). The highest intensity of pain occurred on day 0 after surgery, with a declining trend in the following days (p < 0.001). The highest intensity of postoperative pain was caused by gynecological procedures as well as laparotomy and arthroplasty (p < 0.001). The variability of the results indicates the need for an individual approach to each patient, both in pain assessment and treatment. Therefore, continuous improvement of the quality of health services provided in accordance with the guidelines for pain relief is necessary.