bluuebunny/crossref_metadata_2025 huggingface dataset:
Recycling Pomelo Peel Waste in the Form of Hydrochar Obtained by Microwave-Assisted Hydrothermal Carbonization
Authors: Wang Yu-Jie , Li Nan , Ni Guo-Rong , Zhou Chun-Huo , Yin Xin , Huang Hua-Jun
Published: December 2022

Pomelo peel (PP) is a kind of solid waste that is produced in the processing industry of honey pomelo. This study deeply explored the feasibility of recycling PP in the form of hydrochar (HC) by microwave-assisted hydrothermal carbonization (HTC) technology. Under the non-catalytic reaction conditions, the yield of hydrochar initially increased with the rise of reaction temperature (150–210 °C) until it remained relatively stable after 210 °C. Under the CaO-catalytical reaction condition, the yield of hydrochar did not change much at first (150–190 °C) but decreased significantly when the reaction temperature exceeded 190 °C. After the microwave-assisted HTC treatment, the PP-derived HC presented higher aromaticity, carbonization degree, porosity, and caloric value. Compared with raw PP, the nutrients in HC were more stable (conducive to being used as slow-release fertilizer). The application of CaO increased the pH value of HC and effectively promoted the accumulation of phosphorus in HC. The HC produced at 210 °C without any catalyst possessing a high devolatilization ability. Additionally, the HC obtained at 190 °C with CaO as the catalyst presented a high combustion property. In general, PP-derived HC showed great application potential in the field of soil remediation/improvement and solid fuels. This preliminary study would undoubtedly provide some important fundamental understanding of the microwave-assisted HTC of PP.
Optimizing the Infrared Photoelectric Detection Performance of Pbs Quantum Dots through Solid-State Ligand Exchange
Authors: Yang Mei , Liu Huan , Wen Shuai , Du Yuxuan , Gao Fei
Published: December 2022

Lead sulfide (PbS) quantum dots (QDs) have attracted a great deal of attention in recent decades, due to their value for applications in optoelectronic devices. However, optimizing the performance of optoelectronic devices through ligand engineering has become a major challenge, as the surfactants that surround quantum dots impede the transport of electrons. In this paper, we prepared PbS QD films and photoconductive devices with four different ligands: 1,2-ethylenedithiol (EDT), tetrabutylammonium iodide (TBAI), hexadecyl trimethyl ammonium bromide (CTAB), and sodium sulfide (Na2S). A series of characterization studies confirmed that using the appropriate ligands in the solid-state ligand exchange step for thin film fabrication can significantly improve the responsivity. The devices treated with sodium sulfide showed the best sensitivity and a wider detection from 400 nm to 2300 nm, compared to the other ligand-treated devices. The responsivity of the champion device reached 95.6 mA/W under laser illumination at 980 nm, with an intensity of 50 mW/cm2.
Remarkable Reduction in IG with an Explicit Investigation of the Leakage Conduction Mechanisms in a Dual Surface-Modified Al2O3/SiO2 Stack Layer AlGaN/GaN MOS-HEMT
Authors: Mazumder Soumen , Pal Parthasarathi , Lee Kuan-Wei , Wang Yeong-Her
Published: December 2022

We demonstrated the performance of an Al2O3/SiO2 stack layer AlGaN/GaN metal–oxide semiconductor (MOS) high-electron-mobility transistor (HEMT) combined with a dual surface treatment that used tetramethylammonium hydroxide (TMAH) and hydrochloric acid (HCl) with post-gate annealing (PGA) modulation at 400 °C for 10 min. A remarkable reduction in the reverse gate leakage current (IG) up to 1.5×10−12 A/mm (@ VG = −12 V) was observed in the stack layer MOS-HEMT due to the combined treatment. The performance of the dual surface-treated MOS–HEMT was significantly improved, particularly in terms of hysteresis, gate leakage, and subthreshold characteristics, with optimized gate annealing treatment. In addition, an organized gate leakage conduction mechanism in the AlGaN/GaN MOS–HEMT with the Al2O3/SiO2 stack gate dielectric layer was investigated before and after gate annealing treatment and compared with the conventional Schottky gate. The conduction mechanism in the reverse gate bias was Poole–Frankel emission for the Schottky-gate HEMT and the MOS–HEMT before annealing. The dominant conduction mechanism was ohmic/Poole-Frankel at low/medium forward bias. Meanwhile, gate leakage was governed by the hopping conduction mechanism in the MOS–HEMT without gate annealing modulation at a higher forward bias. After post-gate annealing (PGA) treatment, however, the leakage conduction mechanism was dominated by trap-assisted tunneling at the low to medium forward bias region and by Fowler–Nordheim tunneling at the higher forward bias region. Moreover, a decent product of maximum oscillation frequency and gate length (fmax × LG) was found to reach 27.16 GHz∙µm for the stack layer MOS–HEMT with PGA modulation. The dual surface-treated Al2O3/SiO2 stack layer MOS–HEMT with PGA modulation exhibited decent performance with an IDMAX of 720 mA/mm, a peak extrinsic transconductance (GMMAX) of 120 mS/mm, a threshold voltage (VTH) of −4.8 V, a higher ION/IOFF ratio of approximately 1.2×109, a subthreshold swing of 82 mV/dec, and a cutoff frequency(ft)/maximum frequency of (fmax) of 7.5/13.58 GHz.
Transparent Wood-Based Materials: Current State-of-the-Art and Future Perspectives
Authors: Mariani Alberto , Malucelli Giulio
Published: December 2022

Human history is largely characterized by the massive use of wood, the most well-known natural composite material, possessing unique thermal, mechanical, and environmental features that make it suitable for several applications, ranging from civil engineering, art, and household uses, to business uses (including furniture, stationery, shipbuilding, and fuel). Further, as a renewable and recyclable biomass, wood perfectly matches the current circular economy concept. However, because of its structure and composition, wood is not transparent: therefore, the possibility of removing the embedded lignin, hence limiting the light-scattering phenomena, has been investigated over the last ten to fifteen years, hence obtaining the so-called “transparent wood (TW)”. This latter represents an up-to-date key material, as it can be utilized as obtained or further functionalized, combining the transparency with other features (such as flame retardance, energy storage ability, and environmental protection, among others), which widen the potential (and practical) applications of wood. The present manuscript aims at summarizing first the current methods employed for obtaining transparent wood, and then the latest achievements concerning the properties of transparent wood, providing the reader with some perspectives about its novel functionalizations and applications.
Biochar Obtained from Caryocar brasiliense Endocarp for Removal of Dyes from the Aqueous Medium
Authors: Melo André L. F. C. , Carneiro Marcelo T. , Nascimento Ariane M. S. S. , Morais Alan I. S. , Bezerra Roosevelt D. S. , Viana Bartolomeu C. , Osajima Josy A. , Silva-Filho Edson C.
Published: December 2022

Given the increase in environmental pollution, especially of water, the emergence of studies that seek to develop strategies to mitigate/treat such effects have gained prominence in the world scientific community. Among the numerous adsorption processes, those made from biochar production stand out. This study analyzed the adsorption properties of the blue methylene model dye in the aqueous solution of biochar and activated biochar developed from pequi (Caryocar brasiliense) endocarp. The biochar was characterized, before and after adsorption, by infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffractometry (XRD), and thermogravimetric analysis (TG). The surface load of the materials was performed by the point of zero charge (pHPZC) method. The study also included analyses of contact time parameters and adsorbed concentration in the adsorption process. Morphological analysis showed that a more significant and profound number of fissures and pores appeared in the activated biochar compared to the biochar. Residual mass analysis evidenced that biochar lost about 15% more mass than the activated biochar, indicating that activation occurred satisfactorily. The adsorption process was well adjusted by pseudo-second-order kinetics and Langmuir’s isothermal model. The activated biochar achieved an excellent adsorption capacity of 476.19 mg.g−1, thus demonstrating to be a sound system for removing dyes from an aqueous medium.
Achieving Persistent Luminescence Performance Based on the Cation-Tunable Trap Distribution
Authors: Wang Tao , Li Rui , Zhang Mengya , Li Panlai , Wang Zhijun
Published: December 2022

Deep-red persistent luminescence (PersL) materials have promising applications in fluorescence labeling and tracking. PersL spectral range and PersL duration are considered to be the key factors driving the development of high-performance deep-red PersL materials. To address these two key issues, the performance of PersL materials was continually optimized by doping with cations (Si4+ and Al3+ ions), relying on the material of Li2ZnGe3O8:Cr3+ from the previous work of our group, and a 4.8-fold increase in PersL radiation spectrum intensity and more than twice the PersL duration was achieved (PersL duration up to 47 h). Ultimately, the obtained PersL materials are used to demonstrate their potential use in multi-level anti-counterfeiting, tracking and localization, respectively. This study provides a unique and novel entry point for achieving high-performance PersL materials by optimizing the PersL material host to modulate the electronic structure.
Nano-Confined Tin Oxide in Carbon Nanotube Electrodes via Electrostatic Spray Deposition for Lithium-Ion Batteries
Authors: Henriques Alexandra , Rabiei Baboukani Amin , Jafarizadeh Borzooye , Chowdhury Azmal Huda , Wang Chunlei
Published: December 2022

The development of novel materials is essential for the next generation of electric vehicles and portable devices. Tin oxide (SnO2), with its relatively high theoretical capacity, has been considered as a promising anode material for applications in energy storage devices. However, the SnO2 anode material suffers from poor conductivity and huge volume expansion during charge/discharge cycles. In this study, we evaluated an approach to control the conductivity and volume change of SnO2 through a controllable and effective method by confining different percentages of SnO2 nanoparticles into carbon nanotubes (CNTs). The binder-free confined SnO2 in CNT composite was deposited via an electrostatic spray deposition technique. The morphology of the synthesized and deposited composite was evaluated by scanning electron microscopy and high-resolution transmission electron spectroscopy. The binder-free 20% confined SnO2 in CNT anode delivered a high reversible capacity of 770.6 mAh g−1. The specific capacity of the anode increased to 1069.7 mAh g−1 after 200 cycles, owing to the electrochemical milling effect. The delivered specific capacity after 200 cycles shows that developed novel anode material is suitable for lithium-ion batteries (LIBs).
Machine Learning in CNC Machining: Best Practices
Authors: von Hahn Tim , Mechefske Chris K.
Published: December 2022

Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work is to practically illustrate several best practices, and challenges, discovered while building an ML system to detect tool wear in metal CNC machining. Namely, one should focus on the data infrastructure first; begin modeling with simple models; be cognizant of data leakage; use open-source software; and leverage advances in computational power. The ML system developed in this work is built upon classical ML algorithms and is applied to a real-world manufacturing CNC dataset. The best-performing random forest model on the CNC dataset achieves a true positive rate (sensitivity) of 90.3% and a true negative rate (specificity) of 98.3%. The results are suitable for deployment in a production environment and demonstrate the practicality of the classical ML algorithms and techniques used. The system is also tested on the publicly available UC Berkeley milling dataset. All the code is available online so others can reproduce and learn from the results.
A General Pose Recognition Method and Its Accuracy Analysis for 6-Axis External Fixation Mechanism Using Image Markers
Authors: Liu Sida , Song Yimin , Lian Binbin , Sun Tao
Published: December 2022

The 6-axis external fixation mechanism with Gough-Stewart configuration has been widely applied to the correction of long bone deformities in orthopedics. Pose recognition of the mechanism is essential for trajectory planning of bone correction, but is usually implemented by the surgeons’ experience, resulting in a relatively low level of correction accuracy. This paper proposes a pose recognition method based on novel image markers, and implements accuracy analysis. Firstly, a pose description of the mechanism is established with several freely installed markers, and the layout of the markers is also parametrically described. Then, a pose recognition method is presented by identifying the orientation and position parameters using the markers. The recognition method is general in that it encompasses all possible marker layouts, and the recognition accuracy is investigated by analyzing variations in the marker layout. On this basis, layout principles for markers that achieve a desired recognition accuracy are established, and an error compensation strategy for precision improvement is provided. Finally, experiments were conducted. The results show that volume errors of pose recognition were 0.368 ± 0.130 mm and 0.151 ± 0.045°, and the correction accuracy of the fracture model after taking compensation was 0.214 ± 0.573 mm and −0.031 ± 0.161°, validating the feasibility and accuracy of the proposed methods.
Coordinated Control Strategy of Electro-Hydraulic Composite Braking Torque for the Distributed Electric Vehicles
Authors: Zhou Zhigang , Yin Xiaofei , Zhang Jie
Published: December 2022

The difference in response to electric and hydraulic braking causes sudden changes in braking torque during braking mode switching. An electro-hydraulic composite braking system’s dynamic torque coordination control strategy is proposed under braking mode switching conditions. By establishing the dynamic response model of the electro-hydraulic braking system (EHB), the key factors affecting the response speed of the EHB are analyzed, and the dynamic fuzzy controller for the pressure regulation of the brake wheel cylinder is designed. At the same time, the nonlinearity and hysteresis in the hydraulic braking process are considered, as well as electrical brake response overshoots. The electric brake response model is established, and the PID controller with feedforward feedback is designed to control the motor to adjust the inertia overpressure or lag pressure deficiency in the hydraulic braking process. Finally, the simulation verification is carried out; the results show that the proposed strategy can increase the hydraulic brake response speed by 25.4%, the impact degree of the vehicle is not more than 6.25 GB, and the hydraulic steady state error does not exceed 2.3%, which improves the vehicle ride comfort under braking mode switching.
Nonlinear Analysis of Rotor-Bearing-Seal System with Varying Parameters Muszynska Model Based on CFD and RBF
Authors: Wang Rui , Wang Yuefang , Cao Xiaojian , Yang Shuhua , Guo Xinglin
Published: December 2022

The computational fluid dynamics (CFD) combined with radial basis function (RBF) method were adopted to obtain the response surface of the Muszynska nonlinear seal force model coefficient with two variables: eccentricity and rotation speed. During the implementation of the simulation, three coefficients of the seal force model were calculated in each sub-step according to the current state of the rotor-bearing seal system; following which the rotor dynamics analysis with varying parameters was realized. As with the traditional constant coefficient method, the first-order critical speed of the system was obtained, and the bifurcation point and oil film whirl of the system response were identified. The difference is that the coefficients of the traditional method ordinarily do not change with the state of the system. Comparing the results of the varying parameter method with those of the traditional method, it can be seen that the speeds of the system corresponding to the bifurcation and oil film whirl are different. The varying parameter rotor dynamics simulation method proposed in this paper provides a new way of analyzing the nonlinear characteristics of rotor-bearing-seal systems.
Combination of Thermal and Mechanical Strategies to Compensate for Distortion Effects during Profile Grinding
Authors: Schieber Christian , Hettig Matthias , Zaeh Michael Friedrich , Heinzel Carsten
Published: December 2022

This paper describes the investigations and the results of an analysis of distortion compensation processes for profile grinding. Steel workpieces often change their residual stress state due to machining in a seemingly uncontrolled matter. Furthermore, in research as well as in the industry, the accurate representation of shape deviations during the cutting of slim profiled workpieces and their deformation handling is a major challenge. In this paper, a valid predictive model, developed for the compensation of distortions resulting from the effect of a laser-based treatment and a deep rolling, was calibrated by experimental data. The numerical design of these strategies provided a model for predicting compensation parameters to minimize profile grinding distortions.
Modelling of Determinants of Logistics 4.0 Adoption: Insights from Developing Countries
Authors: Khan Shahbaz , Singh Rubee , Sá José Carlos , Santos Gilberto , Ferreira Luís Pinto
Published: December 2022

With the emergence of industry 4.0, several elements of the supply chain are transforming through the adoption of smart technologies such as blockchain, the internet of things and cyber-physical systems. Logistics is considered one of the important elements of supply chain management and its digital transformation is crucial to the success of industry 4.0. In this circumstance, the existing logistics system needs to be upgraded with industry 4.0 technologies and emerge as logistics 4.0. However, the adoption/transformation of logistics 4.0 is dependent on several determinants that need to be explored. Therefore, this study has the prime objective of investigating the determinants of logistics 4.0 adoption in the context of a developing country, specifically, India. Initially, ten determinants of logistics 4.0 are established after a survey of the relevant literature and the input of industry experts. Further, a four-level structural model is developed among these determinants using the Interpretive Structural Modelling (ISM) approach. In addition, a fuzzy Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis is also conducted for the categorization of these determinants as per their driving and dependence power. The findings show that top management supports, information technology infrastructure and financial investment are the most significant determinants towards logistics 4.0 adoption. This study facilitates the supply chain partners to focus on these high-level determinants for the effective adoption of logistics 4.0. Moreover, the findings lead to a more in-depth insight into the determinants that influence logistics 4.0 and their significance in logistics 4.0 adoption in emerging economies.
Chaotic Model of Brownian Motion in Relation to Drug Delivery Systems Using Ferromagnetic Particles
Authors: Nježić Saša , Radulović Jasna , Živić Fatima , Mirić Ana , Jovanović Pešić Živana , Vasković Jovanović Mina , Grujović Nenad
Published: December 2022

Deterministic and stochastic models of Brownian motion in ferrofluids are of interest to researchers, especially those related to drug delivery systems. The Brownian motion of nanoparticles in a ferrofluid environment was theoretically analyzed in this research. The state of the art in clinical drug delivery systems using ferromagnetic particles is briefly presented. The motion of the nanoparticles in an external field and as a random variable is elaborated by presenting a theoretical model. We analyzed the theoretical model and performed computer simulation by using Maple software. We used simple low-dimensional deterministic systems that can exhibit diffusive behavior. The ferrofluid in the gravitational field without the presence of an external magnetic field in the xy plane was observed. Control parameter p was mapped as related to the fluid viscosity. Computer simulation showed that nanoparticles can exhibit deterministic patterns in a chaotic model for certain values of the control parameter p. Linear motion of the particles was observed for certain values of the parameter p, and for other values of p, the particles move randomly without any rule. Based on our numerical simulation, it can be concluded that the motion of nanoparticles could be controlled by inherent material properties and properties of the surrounding media, meaning that the delivery of drugs could possibly be executed by a ferrofluid without an exogenous power propulsion strategy. However, further studies are still needed.
Employing the Panel Quantile Regression Approach to Examine the Role of Natural Resources in Achieving Environmental Sustainability: Does Globalization Create Some Difference?
Authors: Damrah Sadeq , Satrovic Elma , Atyeh Mohamad , Shawtari Fekri Ali
Published: December 2022

In the modern era of globalization, natural resources have become an important factor in shaping a sustainable future; however, the evidence on the role of globalization in reducing the adverse environmental impacts of natural resources is relatively scarce. The current study explores the dynamic interaction between energy consumption, economic development proxied through the human development index, population, natural resources, globalization, and ecological footprint under the core idea of the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT). This research applies panel data for the period from 1999 to 2018 in nine countries with the highest oil production (Brazil, Canada, China, Iran, Kuwait, Russia, Saudi Arabia, United Arab Emirates, and the United States). The results of this study are based on the panel Method of Moments Quantile Regression (MMQR). Empirical findings foundthat economic development, energy consumption, population, and natural resources contribute to increased environmental degradation, while globalization seems the main source of environmental sustainability. Concerning the indirect impacts of globalization, expanded interaction and integration among oil-producing countries helped to inhibit ecological footprint; nevertheless, natural resources complicate the design of a sustainable future by promoting environmental degradation. Additionally, a bidirectional causality relation was discovered between population, energy consumption, globalization, and ecological footprint; however, the panel Dumitrescu and Hurlin causality test results revealed a unidirectional causality association from economic development to ecological footprint and from natural resources to ecological footprint. Our findings shed new light on the criticality of globalization in achieving environmental sustainability by providing cleaner practices that will prevent rent-seeking.
Self-Writer: Clusterable Embedding Based Self-Supervised Writer Recognition from Unlabeled Data
Authors: Mohammad Zabir , Kabir Muhammad Mohsin , Monowar Muhammad Mostafa , Hamid Md Abdul , Mridha Muhammad Firoz
Published: December 2022

Writer recognition based on a small amount of handwritten text is one of the most challenging deep learning problems because of the implicit characteristics of handwriting styles. In a deep convolutional neural network, writer recognition based on supervised learning has shown great success. These supervised methods typically require a lot of annotated data. However, collecting annotated data is expensive. Although unsupervised writer recognition methods may address data annotation issues significantly, they often fail to capture sufficient feature relationships and usually perform less efficiently than supervised learning methods. Self-supervised learning may solve the unlabeled dataset issue and train the unsupervised datasets in a supervised manner. This paper introduces Self-Writer, a self-supervised writer recognition approach dealing with unlabeled data. The proposed scheme generates clusterable embeddings from a small fixed-length image frame such as a text block. The training strategy presumes that a small image frame of handwritten text should include the writer’s handwriting characteristics. We construct pairwise constraints and nongenerative augmentation to train Siamese architecture to generate embeddings depending on such an assumption. Self-Writer is evaluated on the two most widely used datasets, IAM and CVL, on pairwise and triplet architecture. We find Self-Writer to be convincing in achieving satisfactory performance using pairwise architectures.
The Possibilities of Using Scoring to Determine the Relevance of Software Development Tenders
Authors: Tikshaev Ivan , Kulshin Roman , Volokitin Gennadii , Senchenko Pavel , Sidorov Anatoly
Published: December 2022

The issue of searching for tender proposals satisfying the conditions of selection on the basis of the relevance assessment algorithm is considered. The algorithm is based on a mathematical scoring model. The approbation of the model based on the historical data of the software company is presented. The possibility of using such a method to determine relevance is proved. The assumption is made about the possibility of using scoring to evaluate tenders not only for the development of software products but also in other market segments.
Polynomial Sequences and Their Applications
Authors: Costabile Francesco Aldo , Gualtieri Maria Italia , Napoli Anna
Published: December 2022

The purpose of this Special Issue is to present, albeit partially, the state of the art on the theory and application of polynomial sequences [...]
Identifying the Shortest Path of a Semidirected Graph and Its Application
Authors: Das Rakhi , Sahoo Laxminarayan , Samanta Sovan , Simic Vladimir , Senapati Tapan
Published: December 2022

The basic goal of this research is to find the shortest path of a semidirected graph and apply it to the road network system. In the field of graph theory, networks are described as directed graphs, undirected graphs, or a combination of both. However, in the modern era of computing, several networks, such as social media networks, granular networks, and road transport networks, are not linked to any of the aforementioned network categories and in reality are a hybrid of networks with both directed and undirected interconnections. To better understand the notion of these types of networks, semidirected graphs have been developed to represent such networks. In a semidirect graph, both the directed and undirected edges concepts have been introduced together in a graph. In reality, it has been noted that for every node (source/destination) which is connected to another, some links/connections are directed—i.e., one-way—and some links/connections are undirected—i.e., two-way. Considering that there is no specific direction provided and that two nodes are connected, we have established the concept of an undirected edge as two-way connectivity since this provides for nodes in both ways. In this study, the road network system has been modelled using the concept of a semidirected graph, and the shortest path through it has been determined. For the purposes of illustration, we have used a real transportation road network in this case, and the computed results have been displayed.
Edge Computing Offloading Method Based on Deep Reinforcement Learning for Gas Pipeline Leak Detection
Authors: Wei Dong , Wang Renjun , Xia Changqing , Xia Tianhao , Jin Xi , Xu Chi
Published: December 2022

Traditional gas pipeline leak detection methods require task offload decisions in the cloud, which has low real time performance. The emergence of edge computing provides a solution by enabling offload decisions directly at the edge server, improving real-time performance; however, energy is the new bottleneck. Therefore, focusing on the gas transmission pipeline leakage detection scenario in real time, a novel detection algorithm that combines the benefits of both the heuristic algorithm and the advantage actor critic (AAC) algorithm is proposed in this paper. It aims at optimization with the goal of real-time guarantee of pipeline mapping analysis tasks and maximizing the survival time of portable gas leak detectors. Since the computing power of portable detection devices is limited, as they are powered by batteries, the main problem to be solved in this study is how to take into account the node energy overhead while guaranteeing the system performance requirements. By introducing the idea of edge computing and taking the mapping relationship between resource occupation and energy consumption as the starting point, the optimization model is established, with the goal to optimize the total system cost (TSC). This is composed of the node’s transmission energy consumption, local computing energy consumption, and residual electricity weight. In order to minimize TSC, the algorithm uses the AAC network to make task scheduling decisions and judge whether tasks need to be offloaded, and uses heuristic strategies and the Cauchy–Buniakowsky–Schwarz inequality to determine the allocation of communication resources. The experiments show that the proposed algorithm in this paper can meet the real-time requirements of the detector, and achieve lower energy consumption. The proposed algorithm saves approximately 56% of the system energy compared to the Deep Q Network (DQN) algorithm. Compared with the artificial gorilla troops Optimizer (GTO), the black widow optimization algorithm (BWOA), the exploration-enhanced grey wolf optimizer (EEGWO), the African vultures optimization algorithm (AVOA), and the driving training-based optimization (DTBO), it saves 21%, 38%, 30%, 31%, and 44% of energy consumption, respectively. Compared to the fully local computing and fully offloading algorithms, it saves 50% and 30%, respectively. Meanwhile, the task completion rate of this algorithm reaches 96.3%, which is the best real-time performance among these algorithms.
A New Text-Mining–Bayesian Network Approach for Identifying Chemical Safety Risk Factors
Authors: Zhou Zhiyong , Huang Jianhui , Lu Yao , Ma Hongcai , Li Wenwen , Chen Jianhong
Published: December 2022

The frequent occurrence of accidents in the chemical industry has caused serious economic loss and negative social impact. The chemical accident investigation report is of great value for analyzing the risk factors involved. However, traditional manual analysis is time-consuming and labor-intensive, while existing keyword extraction methods still need to be improved. This study aims to propose an improved text-mining method to analyze a large number of chemical accident reports. A workflow was designed for building and updating lexicons of word segmentation. An improved keyword extraction algorithm was proposed to extract the top 100 keywords from 330 incident reports. A total of 51 safety risk factors was obtained by standardizing these keywords. In all, 294 strong association rules were obtained by Apriori. Based on these rules, a Bayesian network was built to analyze safety risk factors. The mean accuracy and mean recall of the BM25 model in the comparison experiments were 10.5% and 14.38% higher than those of TF-IDF, respectively. The results of association-rule mining and Bayesian network analysis can clearly demonstrate the interrelationship between the safety risk factors. The methodology of this study can quickly and efficiently extract key information from incident reports which can provide managers with new insights and suggestions.
A Refined Jensen Inequality Connected to an Arbitrary Positive Finite Sequence
Authors: Wu Shanhe , Khan Muhammad Adil , Saeed Tareq , Sayed Zaid Mohammed Mohammed Mahdi
Published: December 2022

The prime purpose of this paper is to provide a refinement of Jensen’s inequality in connection with a positive finite sequence. We deal with the refinement for particular cases and point out the relation between the new result with earlier results of Jensen’s inequality. As results, we obtain refinements of the quasi-arithmetic and power mean inequalities. Finally, several results are obtained in information theory with the help of the main results.
The Point of No Return: Evolution of Excess Mutation Rate Is Possible Even for Simple Mutation Models
Authors: Mintz Brian , Fu Feng
Published: December 2022

Under constant selection, each trait has a fixed fitness, and small mutation rates allow populations to efficiently exploit the optimal trait. Therefore, it is reasonable to expect that mutation rates will evolve downwards. However, we find that this need not be the case, examining several models of mutation. While upwards evolution of the mutation rate has been found with frequency- or time-dependent fitness, we demonstrate its possibility in a much simpler context. This work uses adaptive dynamics to study the evolution of the mutation rate, and the replicator–mutator equation to model trait evolution. Our approach differs from previous studies by considering a wide variety of methods to represent mutation. We use a finite string approach inspired by genetics as well as a model of local mutation on a discretization of the unit intervals, handling mutation beyond the endpoints in three ways. The main contribution of this work is a demonstration that the evolution of the mutation rate can be significantly more complicated than what is usually expected in relatively simple models.
Pre-Hausdorffness and Hausdorffness in Quantale-Valued Gauge Spaces
Authors: Özkan Samed , Alsulami Samirah , Baran Tesnim Meryem , Qasim Muhammad
Published: December 2022

In this paper, we characterize each of T0, T1, Pre-Hausdorff and Hausdorff separation properties for the category L-GS of quantale-valued gauge spaces and L-gauge morphisms. Moreover, we investigate how these concepts are related to each other in this category. We show that T0, T1 and T2 are equivalent in the realm of Pre-Hausdorff quantale-valued gauge spaces. Finally, we compare our results with the ones in some other categories.
Stability and Synchronization of Fractional-Order Complex-Valued Inertial Neural Networks: A Direct Approach
Authors: Song Hualin , Hu Cheng , Yu Juan
Published: December 2022

This paper is dedicated to the asymptotic stability and synchronization for a type of fractional complex-valued inertial neural network by developing a direct analysis method. First, a new fractional differential inequality is presented for nonnegative functions, which provides an effective tool for the convergence analysis of fractional-order systems. Moreover, instead of the previous separation analysis for complex-valued neural networks, a class of Lyapunov functions composed of the complex-valued states and their fractional derivatives is constructed, and some compact stability criteria are derived. In synchronization analysis, unlike the existing control schemes for reduced-order subsystems, some feedback and adaptive control schemes, formed by the linear part and the fractional derivative part, are directly designed for the response fractional inertial neural networks, and some synchronization conditions are derived using the established fractional inequality. Finally, the theoretical analysis is supported via two numerical examples.
The E-Bayesian Methods for the Inverse Weibull Distribution Rate Parameter Based on Two Types of Error Loss Functions
Authors: Okasha Hassan M. , Basheer Abdulkareem M. , Lio Yuhlong
Published: December 2022

Given a sample, E-Bayesian estimates, which are the expected Bayesian estimators over the joint distributions of two hyperparameters in the prior distribution, are developed for the inverse Weibull distribution rate parameter under the scaled squared error and linear exponential error loss functions, respectively. The corresponding expected mean square errors, EMSEs, of E-Bayesian estimators based on the sample are derived. Moreover, the theoretical properties of EMSEs are established. A Monte Carlo simulation study is conducted for the performance comparison. Finally, three data sets are given for illustration.
Modeling the Operation of Signal-Controlled Intersections with Different Lane Occupancy
Authors: Morozov Viacheslav , Shepelev Vladimir , Kostyrchenko Viktor
Published: December 2022

In many cities of the world, the problem of traffic congestion on the roads remains relevant and unresolved. It is especially noticeable at signal-controlled intersections, since traffic signalization is among the most important factors that reduce the maximum possible value of the traffic flow rate at the exit of a street intersection. Therefore, the development of a methodology aimed at reducing transport losses when pedestrians move through signal-controlled intersections is a joint task for the research and engineering community and municipalities. This paper is a continuation of a study wherein the results produced a mathematical model of the influence of lane occupancy and traffic signalization on the traffic flow rate. These results were then experimentally confirmed. The purpose of this work is to develop a method for the practical application of the mathematical model thus obtained. Together with the obtained results of the previous study, as well as a systems approach, traffic flow theory, impulses, probabilities and mathematical statistics form the methodological basis of this work. This paper established possible areas for the practical application of the previously obtained mathematical model. To collect the initial experimental data, open-street video surveillance cameras were used as vehicle detectors, the image streams of which were processed in real time using neural network technologies. Based on the results of this work, a new method was developed that allows for the adjustment of the traffic signal cycle, considering the influence of lane occupancy. In addition, the technological, economic and environmental effects were calculated, which was achieved through the application of the proposed method.
Entropies Via Various Molecular Descriptors of Layer Structure of H3BO3
Authors: Ghani Muhammad Usman , Kashif Maqbool Muhammad , George Reny , Ofem Austine Efut , Cancan Murat
Published: December 2022

Entropy is essential. Entropy is a measure of a system’s molecular disorder or unpredictability, since work is produced by organized molecular motion. Entropy theory offers a profound understanding of the direction of spontaneous change for many commonplace events. A formal definition of a random graph exists. It deals with relational data’s probabilistic and structural properties. The lower-order distribution of an ensemble of attributed graphs may be used to describe the ensemble by considering it to be the results of a random graph. Shannon’s entropy metric is applied to represent a random graph’s variability. A structural or physicochemical characteristic of a molecule or component of a molecule is known as a molecular descriptor. A mathematical correlation between a chemical’s quantitative molecular descriptors and its toxicological endpoint is known as a QSAR model for predictive toxicology. Numerous physicochemical, toxicological, and pharmacological characteristics of chemical substances help to foretell their type and mode of action. Topological indices were developed some 150 years ago as an alternative to the Herculean, and arduous testing is needed to examine these features. This article uses various computational and mathematical techniques to calculate atom–bond connectivity entropy, atom–bond sum connectivity entropy, the newly defined Albertson entropy using the Albertson index, and the IRM entropy using the IRM index. We use the subdivision and line graph of the H3BO3 layer structure, which contains one boron atom and three oxygen atoms to form the chemical boric acid.
Investigation of Mixed Convection in Spinning Nanofluid over Rotating Cone Using Artificial Neural Networks and BVP-4C Technique
Authors: Hassan Ali , Haider Qusain , Alsubaie Najah , Alharbi Fahad , Alhushaybari Abdullah , Galal Ahmed
Published: December 2022

The significance of back-propagated intelligent neural networks (BINs) to investigate the transmission of heat in spinning nanofluid over a rotating system is analyzed in this study. The buoyancy effect is incorporated along with the constant thermophysical properties of nanofluids. Levenberg–Marquardt intelligent networks (ANNLMBs) are employed to study heat transmission by using a trained artificial neural network. The system of highly non-linear flow governing partial differential equations (PDEs) is transformed into ordinary differential equations (ODEs) which is taken as a system model. This achieved system model is utilized to generate data set using the “Adams” method for distinct scenarios of heat transmission investigation in a spinning nanofluid over a rotating system for the implementation of the proposed ANNLMB. Additionally, with the help of training, testing, and validation, the approximate solution of heat transmission in a spinning nanofluid in a rotating system is obtained using a BNN-based solver. The generated reference data achieved employing the proposed artificial neural network based on a Levenberg–Marquardt intelligent network is distributed in the following manner: training at 82%, testing at 9%, and validation at 9%. Furthermore, MSE, histograms, and regression analyses are performed to depict and discuss the impact of the varying influence of key parameters, such as unsteadiness “s” in spinning flow, Prandtl number effect “pr”, the rotational ratio of nanofluid and cone α1 and buoyancy effect γ1 on velocities F′G and temperature Θ profiles. The mean square error confirms the accuracy of the achieved results. Prandtl number and unsteadiness decrease the temperature profile and thermal boundary layer of the rotating nanofluid.
Polyphenolic Compounds Isolated from Marine Algae Attenuate the Replication of SARS-CoV-2 in the Host Cell through a Multi-Target Approach of 3CLpro and PLpro
Authors: Nagahawatta D. , Liyanage N. , Je Jun-Geon , Jayawardhana H. , Jayawardena Thilina , Jeong Seong-Hun , Kwon Hyung-Jun , Choi Cheol , Jeon You-Jin
Published: December 2022

A global health concern has emerged as a response to the recent SARS-CoV-2 pandemic. The identification and inhibition of drug targets of SARS-CoV-2 is a decisive obligation of scientists. In addition to the cell entry mechanism, SARS-CoV-2 expresses a complicated replication mechanism that provides excellent drug targets. Papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro) play a vital role in polyprotein processing, producing functional non-structural proteins essential for viral replication and survival in the host cell. Moreover, PLpro is employed by SARS-CoV-2 for reversing host immune responses. Therefore, if some particular compound has the potential to interfere with the proteolytic activities of 3CLpro and PLpro of SARS-CoV-2, it may be effective as a treatment or prophylaxis for COVID-19, reducing viral load, and reinstating innate immune responses. Thus, the present study aims to inhibit SARS-CoV-2 through 3CLpro and PLpro using marine natural products isolated from marine algae that contain numerous beneficial biological activities. Molecular docking analysis was utilized in the present study for the initial screening of selected natural products depending on their 3CLpro and PLpro structures. Based on this approach, Ishophloroglucin A (IPA), Dieckol, Eckmaxol, and Diphlorethohydroxycarmalol (DPHC) were isolated and used to perform in vitro evaluations. IPA presented remarkable inhibitory activity against interesting drug targets. Moreover, Dieckol, Eckmaxol, and DPHC also expressed significant potential as inhibitors. Finally, the results of the present study confirm the potential of IPA, Dieckol, Eckmaxol, and DPHC as inhibitors of SARS-CoV-2. To the best of our knowledge, this is the first study that assesses the use of marine natural products as a multifactorial approach against 3CLpro and PLpro of SARS-CoV-2.
Complexes of Cu–Polysaccharide of a Marine Red Microalga Produce Spikes with Antimicrobial Activity
Authors: Yehuda Nofar , Gheber Levi A. , Kushmaro Ariel , (Mails) Arad Shoshana
Published: December 2022

Metal–polysaccharides have recently raised significant interest due to their multifunctional bioactivities. The antimicrobial activity of a complex of Cu2O with the sulfated polysaccharide (PS) of the marine red microalga Porphyridium sp. was previously attributed to spikes formed on the complex surface (roughness). This hypothesis was further examined here using other Cu–PS complexes (i.e., monovalent-Cu2O, CuCl and divalent-CuO, CuCl2). The nanostructure parameters of the monovalent complexes, namely, longer spikes (1000 nm) and greater density (2000–5000 spikes/µm2) were found to be related to the superior inhibition of microbial growth and viability and biofilm formation. When Escherichia coli TV1061, used as a bioluminescent test organism, was exposed to the monovalent Cu–PS complexes, enhanced bioluminescence accumulation was observed, probably due to membrane perforation by the spikes on the surface of the complexes and consequent cytoplasmic leakage. In addition, differences were found in the surface chemistry of the monovalent and divalent Cu–PS complexes, with the monovalent Cu–PS complexes exhibiting greater stability (ζ-potential, FTIR spectra, and leaching out), which could be related to spike formation. This study thus supports our hypothesis that the spikes protruding from the monovalent Cu–PS surfaces, as characterized by their aspect ratio, are responsible for the antimicrobial and antibiofilm activities of the complexes.
Structural Characterization and Spatial Mapping of Tetrodotoxins in Australian Polyclads
Authors: McNab Justin M. , Briggs Matthew T. , Williamson Jane E. , Hoffmann Peter , Rodriguez Jorge , Karuso Peter
Published: December 2022

Tetrodotoxin (TTX) is a potent marine neurotoxin that occurs in several Australian phyla, including pufferfish, toadfish, gobies, and the blue-ringed octopus. These animals are partially immune, and TTX is known to bioaccumulate and subject to trophic transfer. As such, it could be more ubiquitously distributed in animals than is currently known. Flatworms of the order Polycladida are commonly occurring invertebrates in intertidal ecosystems and are especially diverse in Australian waters. While TTX has been identified in polyclads from Japan and New Zealand, Australian species have yet to be tested. In this study, several eastern Australian polyclad flatworm species from the suborders Cotylea and Acotylea were tested for TTX and analogs by HILIC-HRMS to understand the distribution of this toxin within these suborders. Herein, we report the detection of TTX and some known analogs in polyclad species, one of which is a pest to shellfish aquaculture. We also report, for the first time, the application of MALDI mass spectrometry imaging utilized to map TTX spatially within the intestinal system of polyclads. The identification of TTX and its analogs in Australian flatworms illustrates a broader range of toxic flatworms and highlights that analogs are important to consider when studying the distributions of toxins in animals.