bluuebunny/crossref_metadata_2025 huggingface dataset:
TSS Removal Efficiency and Permeability Degradation of Sand Filters in Permeable Pavement
Authors: Nguyen Phuong T.-H. , Kim Jongyeong , Ahn Jaehun
Published: May 2023

Permeable pavement is a highly effective technology in Low-Impact Development (LID) for managing stormwater runoff, which helps mitigate environmental impacts. Filters are essential components of permeable pavement systems as they prevent permeability reduction, remove pollutants, and enhance the system’s overall efficiency. This research paper focuses on exploring the influence of three factors, including total suspended solids (TSS) particle size, TSS concentration, and hydraulic gradient, on the permeability degradation and TSS removal efficiency of sand filters. A series of tests were conducted using different values of these factors. The results demonstrate that these factors have an influence on permeability degradation and TSS removal efficiency (TRE). A larger TSS particle size results in higher permeability degradation and TRE than a smaller particle size. Higher TSS concentrations lead to higher permeability degradation and lower TRE. Additionally, smaller hydraulic gradients are associated with higher permeability degradation and TRE. However, the influence of TSS concentration and hydraulic gradient seems less significant than that of TSS particle size for the values of the factors considered in the tests. In summary, this study provides valuable insights into the effectiveness of sand filters in permeable pavement and identifies the main factors that influence permeability degradation and TRE.
Copper-Based Electrocatalysts for Nitrate Reduction to Ammonia
Authors: Fang Jia-Yi , Fan Jin-Long , Liu Sheng-Bo , Sun Sheng-Peng , Lou Yao-Yin
Published: May 2023

Ammonia (NH3) is a highly important industrial chemical used as fuel and fertilizer. The industrial synthesis of NH3 relies heavily on the Haber–Bosch route, which accounts for roughly 1.2% of global annual CO2 emissions. As an alternative route, the electrosynthesis of NH3 from nitrate anion (NO3−) reduction (NO3−RR) has drawn increasing attention, since NO3−RR from wastewater to produce NH3 can not only recycle waste into treasure but also alleviate the adverse effects of excessive NO3− contamination in the environment. This review presents contemporary views on the state of the art in electrocatalytic NO3− reduction over Cu-based nanostructured materials, discusses the merits of electrocatalytic performance, and summarizes current advances in the exploration of this technology using different strategies for nanostructured-material modification. The electrocatalytic mechanism of nitrate reduction is also reviewed here, especially with regard to copper-based catalysts.
N-Doped Graphene-like Film/Silicon Structures as Micro-Capacitor Electrodes
Authors: Sedlovets Daria M.
Published: May 2023

Currently, the miniaturization of portable and autonomous devices is challenging for modern electronics. Graphene-based materials have recently emerged as one of the ideal candidates for supercapacitor electrodes, while Si is a common platform for direct component-on-chip integration. We have proposed the direct liquid-based CVD of N-doped graphene-like films (N-GLFs) on Si as a promising way to achieve solid-state on-chip micro-capacitor performance. Synthesis temperatures in the range from 800 °C to 1000 °C are investigated. Capacitances and electrochemical stability of the films are evaluated using cyclic voltammetry, as well as galvanostatic measurements and electrochemical impedance spectroscopy in 0.5 M Na2SO4. We have shown that N-doping is an efficient way to improve the N-GLF capacitance. 900 °C is the optimal temperature for the N-GLF synthesis with the best electrochemical properties. The capacitance rises with increasing film thickness which also has an optimum (about 50 nm). The transfer-free acetonitrile-based CVD on Si yields a perfect material for microcapacitor electrodes. Our best value of the area-normalized capacitance (960 mF/cm2) exceeds the world’s achievements among thin graphene-based films. The main advantages of the proposed approach are the direct on-chip performance of the energy storage component and high cyclic stability.
Experimental Research on Surface Quality of Titanium Rod Turned by Wire Electrical Discharge Turning Process
Authors: Chaubey Sujeet Kumar , Gupta Kapil
Published: May 2023

This paper reports the surface quality of a miniature cylindrical titanium rod/bar (MCTB) turned by the wire electrical discharge turning (WEDT) process using a zinc-coated wire of 250 µm diameter. The surface quality was mainly evaluated by considering the very important surface roughness parameters, i.e., the mean roughness depth. A Box–Behnken design (BBD) of the response surface methodology (RSM) based on 17 experimental runs was conducted, where the spark duration “Ton” was found as the most influential parameter affecting the mean roughness depth “RZ” of the miniature titanium bar. Further, using the grey relational analysis (GRA) technique of optimization, we obtained the least value of “RZ” 7.42 µm after machining a miniature cylindrical titanium bar with the optimum combination of WEDT’s variable parameters: Ton—0.9 µs, SV—30 V, and DOC—0.35 mm. This optimization led to a 37% reduction in the surface roughness Rz of the MCTB. The tribological characteristics of this MCTB were also found favorable after conducting a wear test. After completing a comparative study, we can claim that our results are better than those of the past research conducted in this area. The findings of this study are beneficial for the micro-turning of cylindrical bars made from a variety of difficult-to-machine materials.
The Impact of 3D Prism Cavity for Enhanced Oil Recovery Using Different Nanomaterials
Authors: Zafar Mudasar , Sakidin Hamzah , Dzulkarnain Iskandar , Hussain Abida , Sheremet Mikhail , Nazar Roslinda , Al-Yaari Abdullah , Asri Nur Asyatulmaila Mohamad , Bashir Shazia
Published: May 2023

Enhanced oil recovery (EOR) has been offered as an alternative to declining crude oil production. EOR using nanotechnology is one of the most innovative trends in the petroleum industry. In order to determine the maximum oil recovery, the effect of a 3D rectangular prism shape is numerically investigated in this study. Using ANSYS Fluent software(2022R1), we develop a two-phase mathematical model based on 3D geometry. This research examines the following parameters: flow rate Q = 0.01–0.05 mL/min, volume fractions = 0.01–0.04%, and the effect of nanomaterials on relative permeability. The result of the model is verified with published studies. In this study, the finite volume method is used to simulate the problem, and we run simulations at different flow rates while keeping other variables constant. The findings show that the nanomaterials have an important effect on water and oil permeability, increasing oil mobility and lowering IFT, which increases the recovery process. Additionally, it has been noted that a reduction in the flow rate improves oil recovery. Maximum oil recovery was attained at a 0.05 mL/min flow rate. Based on the findings, it is also demonstrated that SiO2 provides better oil recovery compared to Al2O3. When the volume fraction concentration increases, oil recovery ultimately increases.
Improved Yield and Electrical Properties of Poly(Lactic Acid)/Carbon Nanotube Composites by Shear and Anneal
Authors: Mi Dashan , Zhao Zhongguo , Bai Haiqing
Published: May 2023

Shear and thermal processing can greatly influence nanoparticles’ orientation and dispersion, affecting the nanocomposites’ conductivity and mechanical properties. The synergistic effects of shear flow and Carbon nanotubes (CNTs) nucleating ability on the crystallization mechanisms have been proven. In this study, Polylactic acid/Carbon nanotubes (PLA/CNTs) nanocomposites were produced by three different molding methods: compression molding (CM), conventional injection molding (IM), and interval injection molding (IntM). Solid annealing at 80 °C for 4 h and pre-melt annealing at 120 °C for 3 h was applied to research the CNTs’ nucleation effect and the crystallized volume exclusion effect on the electrical conductivity and mechanical properties. The volume exclusion effect only significantly impacts the oriented CNTs, causing the conductivity along the transverse direction to rise by about seven orders of magnitude. In addition, the tensile modulus of the nanocomposites decreases with the increased crystallinity, while the tensile strength and modulus decrease.
Influence of PBF-LB Process Atmosphere on the Fatigue Strength of Hot Isostatically Post-Densified Duplex Steel Parts Produced via the Shell Core Approach
Authors: Kaletsch Anke , Sondermann Markus , Mirz Markus , Radtke Felix , Broeckmann Christoph
Published: May 2023

Laser-based additive manufacturing is a great manufacturing technology for producing parts of any geometry. To also increase the strength and reliability of parts produced via powder bed fusion with laser beam (PBF-LB), hot isostatic pressing (HIP) is often used to densify residual porosity or lack-of-fusion defects. When components are post-densified via HIP, they do not require a high density beforehand, only a closed porosity or a dense shell. By building up samples with increased porosity, the PBF-LB process can be accelerated and productivity increased. HIP post-treatment gives the material its full density and good mechanical properties. However, with this approach, the influence of the process gases becomes important. Either argon or nitrogen is used in the PBF-LB process. It is assumed that these process gases are trapped in the pores and thus have an influence on the HIP process and also the mechanical properties after HIP. In this study, the influence of argon and nitrogen as process gases on the properties of duplex AISI 318LN steel after powder bed fusion with laser beam and hot isostatic pressing is investigated for the case of very high initial porosities.
Three-Dimensional Flower-like MoS2 Nanosheets Grown on Graphite as High-Performance Anode Materials for Fast-Charging Lithium-Ion Batteries
Authors: Lee Yeong A. , Jang Kyu Yeon , Yoo Jaeseop , Yim Kanghoon , Jung Wonzee , Jung Kyu-Nam , Yoo Chung-Yul , Cho Younghyun , Lee Jinhong , Ryu Myung Hyun , Shin Hyeyoung , Lee Kyubock , Yoon Hana
Published: May 2023

The demand for fast-charging lithium-ion batteries (LIBs) with long cycle life is growing rapidly due to the increasing use of electric vehicles (EVs) and energy storage systems (ESSs). Meeting this demand requires the development of advanced anode materials with improved rate capabilities and cycling stability. Graphite is a widely used anode material for LIBs due to its stable cycling performance and high reversibility. However, the sluggish kinetics and lithium plating on the graphite anode during high-rate charging conditions hinder the development of fast-charging LIBs. In this work, we report on a facile hydrothermal method to achieve three-dimensional (3D) flower-like MoS2 nanosheets grown on the surface of graphite as anode materials with high capacity and high power for LIBs. The composite of artificial graphite decorated with varying amounts of MoS2 nanosheets, denoted as MoS2@AG composites, deliver excellent rate performance and cycling stability. The 20−MoS2@AG composite exhibits high reversible cycle stability (~463 mAh g−1 at 200 mA g−1 after 100 cycles), excellent rate capability, and a stable cycle life at the high current density of 1200 mA g−1 over 300 cycles. We demonstrate that the MoS2-nanosheets-decorated graphite composites synthesized via a simple method have significant potential for the development of fast-charging LIBs with improved rate capabilities and interfacial kinetics.
Calcium Phosphate Cements as Carriers of Functional Substances for the Treatment of Bone Tissue
Authors: Lukina Yulia , Safronova Tatiana , Smolentsev Dmitriiy , Toshev Otabek
Published: May 2023

Interest in calcium phosphate cements as materials for the restoration and treatment of bone tissue defects is still high. Despite commercialization and use in the clinic, the calcium phosphate cements have great potential for development. Existing approaches to the production of calcium phosphate cements as drugs are analyzed. A description of the pathogenesis of the main diseases of bone tissue (trauma, osteomyelitis, osteoporosis and tumor) and effective common treatment strategies are presented in the review. An analysis of the modern understanding of the complex action of the cement matrix and the additives and drugs distributed in it in relation to the successful treatment of bone defects is given. The mechanisms of biological action of functional substances determine the effectiveness of use in certain clinical cases. An important direction of using calcium phosphate cements as a carrier of functional substances is the volumetric incorporation of anti-inflammatory, antitumor, antiresorptive and osteogenic functional substances. The main functionalization requirement for carrier materials is prolonged elution. Various release factors related to the matrix, functional substances and elution conditions are considered in the work. It is shown that cements are a complex system. Changing one of the many initial parameters in a wide range changes the final characteristics of the matrix and, accordingly, the kinetics. The main approaches to the effective functionalization of calcium phosphate cements are considered in the review.
Optical Properties of Carbon Dots Synthesized by the Hydrothermal Method
Authors: Egorova Marfa , Tomskaya Aleksandra , Smagulova Svetlana Afanasyevna
Published: May 2023

In this study, the optical and structural properties of carbon dots (CDs) synthesized using a hydrothermal method were investigated. CDs were prepared from various precursors such as citric acid (CA), glucose, and birch bark soot. The SEM and AFM results show that the CDs are disc-shaped nanoparticles with dimensions of ~7 nm × 2 nm for CDs from CA, ~11 nm × 4 nm for CDs from glucose, and ~16 nm × 6 nm for CDs from soot. The TEM images of CDs from CA showed stripes with a distance of 0.34 nm between them. We assumed that the CDs synthesized from CA and glucose consisted of graphene nanoplates located perpendicular to the disc plane. The synthesized CDs contain oxygen (hydroxyl, carboxyl, carbonyl) and nitrogen (amino, nitro) functional groups. CDs have strong absorption in the ultraviolet region in the range of 200–300 nm. All CDs synthesized from different precursors displayed bright luminescence in the blue-green region of the spectrum (420–565 nm). We found that the luminescence of CDs depended on the synthesis time and type of precursors. The results show that the radiative transitions of electrons occur from two levels with energies ~3.0 eV and ~2.6 eV, which are due to the presence of functional groups.
Effect of Near-Liquidus Squeeze Casting Pressure on Microstructure and Mechanical Property of AZ91D Alloy Differential Support
Authors: Zhao Chunfang , Ma Guangquan , Liu Tong , Hu Maoliang , Ji Zesheng
Published: May 2023

In this study, near-liquidus squeeze casting AZ91D alloy was used to prepare differential support, and the microstructure and mechanical behavior under different applied pressure were investigated. Under the preset temperature, speed, and other process parameters, the effect of applied pressure on the microstructure and properties of formed parts was analyzed, and relevant mechanism was also discussed. The results showed that the ultimate tensile strength (UTS) and elongation (EL) of differential support can be improved by controlling real-time precision of the forming pressure. The dislocation density in the primary phase increased obviously with the pressure increasing from 80 MPa to 170 MPa, and even tangles appeared. When the applied pressure increased from 80 MPa to 140 MPa, the α-Mg grains were gradually refined, and the microstructure changed from rosette to globular shape. With increasing the applied pressure to 170 MPa, the grain could not be further refined. Similarly, its UTS and EL gradually increased with the applied pressure increasing from 80 MPa to 140 MPa. With increasing to 170 MPa, the UTS tended to be constant, but the EL gradually decreased. In other words, the UTS (229.2 MPa) and EL (3.43%) of the alloy reached the maximum when the applied pressure was 140 MPa, and the comprehensive mechanical properties were the best.
Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks
Authors: Rodríguez-Miranda Sergio , Yañez-Mendiola Javier , Calzada-Ledesma Valentin , Villanueva-Jimenez Luis Fernando , De Anda-Suarez Juan
Published: May 2023

Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.
A Review of High-Performance Computing Methods for Power Flow Analysis
Authors: Alawneh Shadi G. , Zeng Lei , Arefifar Seyed Ali
Published: May 2023

Power flow analysis is critical for power systems due to the development of multiple energy supplies. For safety, stability, and real-time response in grid operation, grid planning, and analysis of power systems, it requires designing high-performance computing methods, accelerating power flow calculation, obtaining the voltage magnitude and phase angle of buses inside the power system, and coping with the increasingly complex large-scale power system. This paper provides an overview of the available parallel methods to fix the issues. Specifically, these methods can be classified into three categories from a hardware perspective: multi-cores, hybrid CPU-GPU architecture, and FPGA. In addition, from the perspective of numerical computation, the power flow algorithm is generally classified into iterative and direct methods. This review paper introduces models of power flow and hardware computing architectures and then compares their performance in parallel power flow calculations depending on parallel numerical methods on different computing platforms. Furthermore, this paper analyzes the challenges and pros and cons of these methods and provides guidance on how to exploit the parallelism of future power flow applications.
An Advancement in Truck-Tire–Road Interaction Using the Finite Element Analysis
Authors: Fathi Haniyeh , Khosravi Mehran , El-Sayegh Zeinab , El-Gindy Moustafa
Published: May 2023

This paper aimed to investigate the cornering characteristics of a Regional Haul Steer II, RHS 315/80 R22.5 truck tire traveling on a dry, hard surface using the Finite element analysis (FEA). This research was carried out using commercial Finite Element software and Pam-Crash in an Explicit Environment. A finite element truck tire model was developed to apply the tire terrain cornering condition. The concentrated loads and boundary conditions for the rim and wheel were applied to the model. The rubber material was defined using the Mooney–Rivlin model. The truck tire cornering operating conditions, including three different speeds with respect to various positive slip angles, were investigated. Several simulations were repeated at various operating conditions, including three different inflation pressures and three different vertical loads. Subsequently, the tire lateral force was computed using the local and global frame coordinates. Additionally, the self-aligning moment was extracted from the tire cross-section at each operating condition. Finally, a comparison between the simulation results showed that the tire lateral force was highly sensitive to the variation of the slip angles at the higher domain, and also that the tire inflation pressure, regardless of the speed, was considered to be one of the main parameters directly affecting the tire-cornering properties.
Hybrid Character Generation via Shape Control Using Explicit Facial Features
Authors: Lee Jeongin , Yeom Jihyeon , Yang Heekyung , Min Kyungha
Published: May 2023

We present a hybrid approach for generating a character by independently controlling its shape and texture using an input face and a styled face. To effectively produce the shape of a character, we propose an anthropometry-based approach that defines and extracts 37 explicit facial features. The shape of a character’s face is generated by extracting these explicit facial features from both faces and matching their corresponding features, which enables the synthesis of the shape with different poses and scales. We control this shape generation process by manipulating the features of the input and styled faces. For the style of the character, we devise a warping field-based style transfer method using the features of the character’s face. This method allows an effective application of style while maintaining the character’s shape and minimizing artifacts. Our approach yields visually pleasing results from various combinations of input and styled faces.
Similarity Solution for a System of Fractional-Order Coupled Nonlinear Hirota Equations with Conservation Laws
Authors: Ali Musrrat , Gandhi Hemant , Tomar Amit , Singh Dimple
Published: May 2023

The analysis of differential equations using Lie symmetry has been proved a very robust tool. It is also a powerful technique for reducing the order and nonlinearity of differential equations. Lie symmetry of a differential equation allows a dynamic framework for the establishment of invariant solutions of initial value and boundary value problems, and for the deduction of laws of conservations. This article is aimed at applying Lie symmetry to the fractional-order coupled nonlinear complex Hirota system of partial differential equations. This system is reduced to nonlinear fractional ordinary differential equations (FODEs) by using symmetries and explicit solutions. The reduced equations are exhibited in the form of an Erdelyi–Kober fractional (E-K) operator. The series solution of the fractional-order system and its convergence is investigated. Noether’s theorem is used to devise conservation laws.
Survey of Optimization Algorithms in Modern Neural Networks
Authors: Abdulkadirov Ruslan , Lyakhov Pavel , Nagornov Nikolay
Published: May 2023

The main goal of machine learning is the creation of self-learning algorithms in many areas of human activity. It allows a replacement of a person with artificial intelligence in seeking to expand production. The theory of artificial neural networks, which have already replaced humans in many problems, remains the most well-utilized branch of machine learning. Thus, one must select appropriate neural network architectures, data processing, and advanced applied mathematics tools. A common challenge for these networks is achieving the highest accuracy in a short time. This problem is solved by modifying networks and improving data pre-processing, where accuracy increases along with training time. Bt using optimization methods, one can improve the accuracy without increasing the time. In this review, we consider all existing optimization algorithms that meet in neural networks. We present modifications of optimization algorithms of the first, second, and information-geometric order, which are related to information geometry for Fisher–Rao and Bregman metrics. These optimizers have significantly influenced the development of neural networks through geometric and probabilistic tools. We present applications of all the given optimization algorithms, considering the types of neural networks. After that, we show ways to develop optimization algorithms in further research using modern neural networks. Fractional order, bilevel, and gradient-free optimizers can replace classical gradient-based optimizers. Such approaches are induced in graph, spiking, complex-valued, quantum, and wavelet neural networks. Besides pattern recognition, time series prediction, and object detection, there are many other applications in machine learning: quantum computations, partial differential, and integrodifferential equations, and stochastic processes.
A Multi-Scaling Reinforcement Learning Trading System Based on Multi-Scaling Convolutional Neural Networks
Authors: Huang Yuling , Cui Kai , Song Yunlin , Chen Zongren
Published: May 2023

Advancements in machine learning have led to an increased interest in applying deep reinforcement learning techniques to investment decision-making problems. Despite this, existing approaches often rely solely on single-scaling daily data, neglecting the importance of multi-scaling information, such as weekly or monthly data, in decision-making processes. To address this limitation, a multi-scaling convolutional neural network for reinforcement learning-based stock trading, termed multi-scaling convolutional neural network SARSA (state, action, reward, state, action), is proposed. Our method utilizes a multi-scaling convolutional neural network to obtain multi-scaling features of daily and weekly financial data automatically. This involves using a convolutional neural network with several filter sizes to perform a multi-scaling extraction of temporal features. Multiple-scaling feature mining allows agents to operate over longer time scaling, identifying low stock positions on the weekly line and avoiding daily fluctuations during continuous declines. This mimics the human approach of considering information at varying temporal and spatial scaling during stock trading. We further enhance the network’s robustness by adding an average pooling layer to the backbone convolutional neural network, reducing overfitting. State, action, reward, state, action, as an on-policy reinforcement learning method, generates dynamic trading strategies that combine multi-scaling information across different time scaling, while avoiding dangerous strategies. We evaluate the effectiveness of our proposed method on four real-world datasets (Dow Jones, NASDAQ, General Electric, and AAPLE) spanning from 1 January 2007 to 31 December 2020, and demonstrate its superior profits compared to several baseline methods. In addition, we perform various comparative and ablation tests in order to demonstrate the superiority of the proposed network architecture. Through these experiments, our proposed multi-scaling module yields better results compared to the single-scaling module.
Stability Analysis for Digital Redesign of Discrete-Time Switched Systems Using H∞ Linear Matrix Inequality
Authors: Hu Nien-Tsu
Published: May 2023

In this paper, the stability problem for the digital redesign of discrete-time switched systems using H∞ linear matrix inequality (LMI) is investigated. We propose the switching time approach for digital redesign between controller work and failure, and this switching time will limit the system output within the system capacity. When the controller fails, the overall system will be unstable. Therefore, if the digital redesign controller is not restored in a certain period of time, the system output will exceed the system capacity. To solve this problem, we propose a switching law to determine the switching time between the stable mode (controller work) and the unstable (controller failure) mode; this will limit the overall system states in the unstable mode. In addition, the digital redesign controller has the advantage of faster tracking. After we propose a discrete-time switching system with stable and unstable modes, we use H∞ linear matrix inequality (LMI) and Lyapunov functions to prove the stability in detail. Finally, the numerical example illustrates the feasibility of the proposed approach.
Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems
Authors: Casal-Guisande Manuel , Cerqueiro-Pequeño Jorge , Bouza-Rodríguez José-Benito , Comesaña-Campos Alberto
Published: May 2023

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.
Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market
Authors: Ku Chin Soon , Xiong Jiale , Chen Yen-Lin , Cheah Shing Dhee , Soong Hoong Cheng , Por Lip Yee
Published: May 2023

Stock market predictions are a challenging problem due to the dynamic and complex nature of financial data. This study proposes an approach that integrates the domain knowledge of investors with a long-short-term memory (LSTM) algorithm for predicting stock prices. The proposed approach involves collecting data from investors in the form of technical indicators and using them as input for the LSTM model. The model is then trained and tested using a dataset of 100 stocks. The accuracy of the model is evaluated using various metrics, including the average prediction accuracy, average cumulative return, Sharpe ratio, and maximum drawdown. The results are compared to the performance of other strategies, including the random selection of technical indicators. The simulation results demonstrate that the proposed model outperforms the other strategies in terms of accuracy and performance in a 100-stock investment simulation, highlighting the potential of integrating investor domain knowledge with machine learning algorithms for stock price prediction.
Summation Laws in Control of Biochemical Systems
Authors: Westerhoff Hans V.
Published: May 2023

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers, such as 0, −1, 1, and −2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.
Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration
Authors: Romeh Ali El , Mirjalili Seyedali , Gul Faiza
Published: May 2023

Exploring unknown environments using multiple robots has numerous applications in various fields but remains a challenging task. This study proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME), which combines Coordinated Multi-Robot Exploration (CME) and African Vultures Optimization Algorithm (AVOA) to optimize the construction of a finite map in multi-robot exploration. We compared HVCME with four other similar algorithms using three performance measures: run time, percentage of the explored area, and the number of times the method failed to complete a run. The experimental results show that HVCME outperforms the other four methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.
A Novel “Finite Element-Meshfree” Triangular Element Based on Partition of Unity for Acoustic Propagation Problems
Authors: Dang Sina , Wang Gang , Chai Yingbin
Published: May 2023

The accuracy of the conventional finite element (FE) approximation for the analysis of acoustic propagation is always characterized by an intractable numerical dispersion error. With the aim of enhancing the performance of the FE approximation for acoustics, a coupled FE-Meshfree numerical method based on triangular elements is proposed in this work. In the proposed new triangular element, the required local numerical approximation is built using point interpolation mesh-free techniques with polynomial-radial basis functions, and the original linear shape functions from the classical FE approximation are employed to satisfy the condition of partition of unity. Consequently, this coupled FE-Meshfree numerical method possesses simultaneously the strengths of the conventional FE approximation and the meshfree numerical techniques. From a number of representative numerical experiments of acoustic propagation, it is shown that in acoustic analysis, better numerical performance can be achieved by suppressing the numerical dispersion error by the proposed FE-Meshfree approximation in comparison with the FE approximation. More importantly, it also shows better numerical features in terms of convergence rate and computational efficiency than the original FE approach; hence, it is a very good alternative numerical approach to the existing methods in computational acoustics fields.
Bioactive Polyketides and Benzene Derivatives from Two Mangrove Sediment-Derived Fungi in the Beibu Gulf
Authors: Peng Bo , Cai Jian , Xiao Zimin , Liu Manli , Li Xinlong , Yang Bin , Fang Wei , Huang Yi-You , Chen Chunmei , Zhou Xuefeng , Tao Huaming
Published: May 2023

To discover bioactive natural products from mangrove sediment-derived microbes, a chemical investigation of the two Beibu Gulf-derived fungi strains, Talaromyces sp. SCSIO 41050 and Penicillium sp. SCSIO 41411, led to the isolation of 23 natural products. Five of them were identified as new ones, including two polyketide derivatives with unusual acid anhydride moieties named cordyanhydride A ethyl ester (1) and maleicanhydridane (4), and three hydroxyphenylacetic acid derivatives named stachylines H–J (10–12). Their structures were determined by detailed nuclear magnetic resonance (NMR) and mass spectroscopic (MS) analyses, while the absolute configurations were established by theoretical electronic circular dichroism (ECD) calculation. A variety of bioactive screens revealed three polyketide derivatives (1–3) with obvious antifungal activities, and 4 displayed moderate cytotoxicity against cell lines A549 and WPMY-1. Compounds 1 and 6 at 10 μM exhibited obvious inhibition against phosphodiesterase 4 (PDE4) with inhibitory ratios of 49.7% and 39.6%, respectively, while 5, 10, and 11 showed the potential of inhibiting acetylcholinesterase (AChE) by an enzyme activity test, as well as in silico docking analysis.
Exploring Bioactive Compounds in Brown Seaweeds Using Subcritical Water: A Comprehensive Analysis
Authors: Park Jin-Seok , Han Ji-Min , Shin Yu-Na , Park Ye-Seul , Shin Ye-Ryeon , Park Sin-Won , Roy Vikash , Lee Hee-Jeong , Kumagai Yuya , Kishimura Hideki , Chun Byung-Soo
Published: May 2023

In this study, we characterized the bioactive properties of three important brown seaweed species, Sargassum thunbergii, Undaria pinnatifida, and Saccharina japonica, by subcritical water extraction (SWE), as these species are well known for their beneficial health effects. Their physiochemical properties, including potential antioxidant, antihypertensive, and α-glucosidase inhibitory activity, and the antibacterial activity of the hydroysates were also analyzed. The highest total phlorotannin, total sugar content, and reducing sugar content in the S. thunbergii hydrolysates were 38.82 ± 0.17 mg PGE/g, 116.66 ± 0.19 mg glucose/g dry sample, and 53.27 ± 1.57 mg glucose/g dry sample, respectively. The highest ABTS+ and DPPH antioxidant activities were obtained in the S. japonica hydrolysates (124.77 ± 2.47 and 46.35 ± 0.01 mg Trolox equivalent/g, respectively) and the highest FRAP activity was obtained in the S. thunbergii hydrolysates (34.47 ± 0.49 mg Trolox equivalent/g seaweed). In addition, the seaweed extracts showed antihypertensive (≤59.77 ± 0.14%) and α-glucosidase inhibitory activity (≤68.05 ± 1.15%), as well as activity against foodborne pathogens. The present findings provide evidence of the biological activity of brown seaweed extracts for potential application in the food, pharmaceutical, and cosmetic sectors.
Effect of Marine-Derived Saccharides on Human Skin Fibroblasts and Dermal Papilla Cells
Authors: Augustyniak Aleksandra , McMahon Helena
Published: May 2023

The skin is the largest organ of the human body, composed of a diverse range of cell types, non-cellular components, and an extracellular matrix. With aging, molecules that are part of the extracellular matrix undergo qualitative and quantitative changes and the effects, such as a loss of skin firmness or wrinkles, can be visible. The changes caused by the aging process do not only affect the surface of the skin, but also extend to skin appendages such as hair follicles. In the present study, the ability of marine-derived saccharides, L-fucose and chondroitin sulphate disaccharide, to support skin and hair health and minimize the effects of intrinsic and extrinsic aging was investigated. The potential of the tested samples to prevent adverse changes in the skin and hair through stimulation of natural processes, cellular proliferation, and production of extracellular matrix components collagen, elastin, or glycosaminoglycans was investigated. The tested compounds, L-fucose and chondroitin sulphate disaccharide, supported skin and hair health, especially in terms of anti-aging effects. The obtained results indicate that both ingredients support and promote the proliferation of dermal fibroblasts and dermal papilla cells, provide cells with a supply of sulphated disaccharide GAG building blocks, increase ECM molecule production (collagen and elastin) by HDFa, and support the growth phase of the hair cycle (anagen).
Oligoclonal Band Status and Features of Radiological and Clinical Findings in Patients with Multiple Sclerosis in Lithuania
Authors: Aleksandravičiūtė Emilija , Stankevičiūtė Radvilė , Balnytė Renata , Šaknys Laurynas , Ulozienė Ingrida
Published: May 2023

Background and Objectives: Multiple sclerosis (MS) is a widely spread and debilitating disease with 2.8 million people worldwide currently affected. However, the exact pathogenesis of the disease and its progression remains incompletely understood. According to the revised McDonald criteria, cerebrospinal fluid oligoclonal bands (CSF OCBs) magnetic resonance imaging (MRI) results, in conjunction with clinical presentation, remain the gold standard of MS diagnostics. Therefore, this study aims to evaluate the association between CSF OCB status and features of radiological and clinical findings in patients with multiple sclerosis in Lithuania. Materials and Methods: The selection of 200 MS patients was performed in order to find associations between CSF OCB status, MRI data and various disease features. The data were acquired from outpatient records and a retrospective analysis was performed. Results: OCB positive patients were diagnosed with MS earlier and had spinal cord lesions more frequently than OCB negative patients. Patients with lesions in the corpus callosum had a greater increase in the Expanded Disability Status Scale (EDSS) score between their first and last visit. Patients with brainstem lesions had higher EDSS scores during their first and last visit. Even so, the progression of the EDSS score was not greater. The time between the first symptoms and diagnosis was shorter for patients who had juxtacortical lesions than patients who did not. Conclusions: CSF OCBs and MRI data remain irreplaceable tools when diagnosing multiple sclerosis as well as prognosing the development of the disease and disability.
Correlation between Cumulative Methotrexate Dose, Metabolic Syndrome and Hepatic Fibrosis Detected by FibroScan in Rheumatoid Arthritis Patients
Authors: Lertnawapan Ratchaya , Chonprasertsuk Soonthorn , Siramolpiwat Sith , Jatuworapruk Kanon
Published: May 2023

Background and Objectives: Methotrexate (MTX) is routinely prescribed for rheumatoid arthritis (RA) patients, but high cumulative doses may lead to hepatic fibrosis. Additionally, a high proportion of RA patients suffer from metabolic syndrome, which also increases the risk of hepatic fibrosis. This cross-sectional study aimed to explore the association between a cumulative MTX dose, metabolic syndrome, and hepatic fibrosis in patients diagnosed with RA. Materials and Methods: RA patients undergoing treatment with MTX were examined using transient elastography (TE). All patients, regardless of having hepatic fibrosis, were compared to identify the risk factors. Results: Two hundred and ninety-five rheumatoid arthritis patients were examined using FibroScan. One hundred and seven patients (36.27%) were found to have hepatic fibrosis (TE > 7 kPa). After multivariate analysis, only BMI (OR = 14.73; 95% CI 2.90–74.79; p = 0.001), insulin resistance (OR = 312.07; 95% CI 6.19–15732.13; p = 0.04), and cumulative MTX dosage (OR 1.03; 95% CI 1.01–1.10; p = 0.002) were associated with hepatic fibrosis. Conclusions: While the cumulative MTX dose and metabolic syndrome are both the risk factors of hepatic fibrosis, metabolic syndrome, including a high BMI and insulin resistance, poses a greater risk. Therefore, MTX-prescribed RA patients with metabolic syndrome factors should be attentively monitored for signs of liver fibrosis.
Is Nail–Canal Diameter Discordance a Risk Factor for the Excessive Sliding of Cephalomedullary Nails in Geriatric Intertrochanteric Fracture Surgery?
Authors: Lim Eic Ju , Kim Ji Wan , Lee Jeuk , Kim Chul-Ho
Published: May 2023

Background and Objectives:: There were limited studies which investigated nail diameter as a predictor for cephalomedullary nail (CMN) failure in intertrochanteric fracture (ITF). We aimed to evaluate the surgical outcomes of CMN in fragility ITF following nail–canal (N–C) diameter discordance. Materials and Methods: From November 2010 to March 2022, we retrospectively reviewed 120 consecutive patients who underwent CMN surgeries due to fragility ITF. We included patients with acceptable reduction and a tip–apex distance ≤ 25 mm. The N–C diameter differences both in anterior–posterior (AP) and lateral-view X-rays were measured, and we compared the number of excessive sliding instances and the rate of implant failure between the N–C concordance (≤3 mm) and discordance (>3 mm) group. Simple linear regression was used to determine the strength of the relationship between the N–C difference and sliding distance. Results: The sliding distance showed no differences between the groups in the AP (3.6 vs. 3.3 mm, p = 0.75) and lateral view (3.5 vs. 3.4 mm, p = 0.91). For analyses in the AP view, the AP-concordance and AP-discordance groups had 14 (25%) and 14 patients (22%) with a sliding distance of >5 mm (p = 0.69), while treatment failure occurred in 3 (5%) and 3 (3%) patients, respectively (p = 0.66). For analyses in the lateral view, the lat-concordance and lat-discordance groups had 8 (27%) and 20 patients (22%) with a sliding distance of >5 mm (p = 0.62), while treatment failure occurred in 1 (3%) and 4 (4%) patients, respectively (p = 1.00). Linear regression analyses showed that the N–C difference in either views was not a significant predictor of sliding distance in both the AP (R2 = 0.002, p = 0.60) and lateral views (R2 = 0.007, p = 0.35). Conclusions: If appropriate fracture reduction and fixation are achieved, the N–C discordance of short CMN does not affect treatment outcomes in ITF.
Modelling and Comparative Analysis of Different Methods of Liquid Membrane Separations
Authors: Kostanyan Artak E. , Voshkin Andrey A. , Belova Vera V. , Zakhodyaeva Yulia A.
Published: May 2023

This article is devoted to a brief review of the modelling of liquid membrane separation methods, such as emulsion, supported liquid membranes, film pertraction, and three-phase and multi-phase extraction. Mathematical models and comparative analyses of liquid membrane separations with different flow modes of contacting liquid phases are presented. A comparison of the processes of conventional and liquid membrane separations is carried out under the following assumptions: mass transfer is described by the traditional mass transfer equation; the equilibrium distribution coefficients of a component passing from one of the phases to another are constant. It is shown that, from the point of view of mass transfer driving forces, emulsion and film pertraction liquid membrane methods have advantages over the conventional conjugated extraction stripping method, when the mass-transfer efficiency of the extraction stage is significantly higher than the efficiency of the stripping stage. The comparison of the supported liquid membrane with conjugated extraction stripping showed that when mass-transfer rates on the extraction and stripping sides are different, the liquid membrane method is more efficient, while when they are equal to each other, both processes demonstrate the same results. The advantages and disadvantages of liquid membrane methods are discussed. The main disadvantages of liquid membrane methods—low throughput and complexity—can be overcome by using modified solvent extraction equipment to carry out liquid membrane separations.