bluuebunny/crossref_metadata_2025 huggingface dataset:
Modified Taylor Impact Tests with Profiled Copper Cylinders: Experiment and Optimization of Dislocation Plasticity Model
Authors: Rodionov Egor S. , Pogorelko Victor V. , Lupanov Victor G. , Mayer Polina N. , Mayer Alexander E.
Published: August 2023

Current progress in numerical simulations and machine learning allows one to apply complex loading conditions for the identification of parameters in plasticity models. This possibility expands the spectrum of examined deformed states and makes the identified model more consistent with engineering practice. A combined experimental-numerical approach to identify the model parameters and study the dynamic plasticity of metals is developed and applied to the case of cold-rolled OFHC copper. In the experimental part, profiled projectiles (reduced cylinders or cones in the head part) are proposed for the Taylor impact problem for the first time for material characterization. These projectiles allow us to reach large plastic deformations with true strains up to 1.3 at strain rates up to 105 s−1 at impact velocities below 130 m/s. The experimental results are used for the optimization of parameters of the dislocation plasticity model implemented in 3D with the numerical scheme of smoothed particle hydrodynamics (SPH). A Bayesian statistical method in combination with a trained artificial neural network as an SPH emulator is applied to optimize the parameters of the dislocation plasticity model. It is shown that classical Taylor cylinders are not enough for a univocal selection of the model parameters, while the profiled cylinders provide better optimization even if used separately. The combination of different shapes and an increase in the number of experiments increase the quality of optimization. The optimized numerical model is successfully validated by the experimental data about the shock wave profiles in flyer plate experiments from the literature. In total, a cheap, simple, but efficient route for optimizing a dynamic plasticity model is proposed. The dislocation plasticity model is extended to estimate grain refinement and volume fractions of weakened areas in comparison with experimental observations.
A Simplified Model for Shear Behavior of Mortar Using Biomimetic Carbonate Precipitation
Authors: Diao Yu , Bai Jitao , Sun Changyou , Huang Jianyou , Yang Chao , Hu Qingsong
Published: August 2023

As a common molecule in biomineralization, L-aspartic acid (L-Asp) has been proven to be able to induce in vitro CaCO3 precipitation, but its application in sand reinforcement has never been studied. In this study, L-Asp was employed in sand reinforcement for the first time through the newly developed biomimetic carbonate precipitation (BCP) technique. Specimens with different number of BCP spray cycles were prepared, and a series of direct shear tests were conducted to investigate the impact of spray number on shear strength, critical displacement, and residual strength. Then a simplified power model for shear stress–displacement behavior was established and calibrated with the measured data. The results show that BCP can significantly improve the shear strength of sand. As the number of spray cycles increases, both the shear strength and residual strength increase, while the critical displacement decreases. Such variations can be described with two sigmoid models and a linear model, respectively. The simplified power model performs well in most cases, especially at higher spray numbers. This study is expected to provide a practical model for the shear behavior of BCP-treated mortar.
Mitigating the Trade-Off between Non-Radiative Recombination and Charge Transport to Enable Efficient Ternary Organic Solar Cells
Authors: Zhang Yexin , Yuan Shuai , Zhang Congyang , Ding Chenfeng , Zhang Congcong , Xu Hai
Published: August 2023

Ternary organic solar cells (OSCs) have attracted intensive studies due to their promising potential for attaining high-performing photovoltaics, whereas there has been an opening challenge in minimizing the open circuit voltage (Voc) loss while retaining the optimal carrier extraction in the multiple mixture absorbers. Here, we systemically investigate a ternary absorber comprised of two acceptors and a donor, in which the resultant Voc and fill factor are varied and determined by the ratios of acceptor components as a result of the unbalance of non-radiative recombination rates and charge transport. The transient absorption spectroscopy and electroluminescence techniques verify two distinguishable charge-transfer (CT) states in the ternary absorber, and the mismatch of non-radiative recombination rates of those two CT states is demonstrated to be associated with the Voc deficit, whilst the high-emissive acceptor molecule delivers inferior electron mobility, resulting in poor charge transport and a subpar fill factor. These findings enable us to optimize the mixture configuration for attaining the maximal-performing devices. Our results not only provide insight into maximizing the photovoltage of organic solar cells but can also motivate researchers to further unravel the photophysical mechanisms underlying the intermolecular electronic states of organic semiconductors.
On the P3-Coloring of Bipartite Graphs
Authors: Dai Zemiao , Naeem Muhammad , Shafaqat Zainab , Zahid Manzoor Ahmad , Qaisar Shahid
Published: August 2023

The advancement in coloring schemes of graphs is expanding over time to solve emerging problems. Recently, a new form of coloring, namely P3-coloring, was introduced. A simple graph is called a P3-colorable graph if its vertices can be colored so that all the vertices in each P3 path of the graph have different colors; this is called the P3-coloring of the graph. The minimum number of colors required to form a P3-coloring of a graph is called the P3-chromatic number of the graph. The aim of this article is to determine the P3-chromatic number of different well-known classes of bipartite graphs such as complete bipartite graphs, tree graphs, grid graphs, and some special types of bipartite graphs. Moreover, we have also presented some algorithms to produce a P3-coloring of these classes with a minimum number of colors required.
Motion-Tracking Control of Mobile Manipulation Robotic Systems Using Artificial Neural Networks for Manufacturing Applications
Authors: Galvan-Perez Daniel , Beltran-Carbajal Francisco , Rivas-Cambero Ivan , Yañez-Badillo Hugo , Favela-Contreras Antonio , Tapia-Olvera Ruben
Published: August 2023

Robotic systems have experienced exponential growth in their utilization for manufacturing applications over recent decades. Control systems responsible for executing desired robot motion planning face increasingly stringent performance requirements. These demands encompass high precision, efficiency, stability, robustness, ease of use, and simplicity of the user interface. Furthermore, diverse modern manufacturing applications primarily employ robotic systems within disturbed operating scenarios. This paper presents a novel neural motion-tracking control scheme for mobile manipulation robotic systems. Dynamic position output error feedback and B–Spline artificial neural networks are integrated in the design process of the introduced adaptive robust control strategy to perform efficient and robust tracking of motion-planning trajectories in robotic systems. Integration of artificial neural networks demonstrates performance improvements in the control scheme while effectively addressing common issues encountered in manufacturing environments. Parametric uncertainty, unmodeled dynamics, and unknown disturbance torque terms represent some adverse influences to be compensated for by the robust control scheme. Several case studies prove the robustness of the adaptive neural control scheme in highly coupled nonlinear six-degree-of-freedom mobile manipulation robotic systems. Case studies provide valuable insights and validate the efficacy of the proposed adaptive multivariable control scheme in manufacturing applications.
Modeling Software Reliability with Learning and Fatigue
Authors: Yaghoobi Tahere , Leung Man-Fai
Published: August 2023

Software reliability growth models (SRGMs) based on the non-homogeneous Poisson process have played a significant role in predicting the number of remaining errors in software, enhancing software reliability. Software errors are commonly attributed to the mental errors of software developers, which necessitate timely detection and resolution. However, it has been observed that the human error-making mechanism is influenced by factors such as learning and fatigue. In this paper, we address the issue of integrating the fatigue factor of software testers into the learning process during debugging, leading to the development of more realistic SRGMs. The first model represents the software tester’s learning phenomenon using the tangent hyperbolic function, while the second model utilizes an exponential function. An exponential decay function models fatigue. We investigate the behavior of our proposed models by comparing them with similar SRGMs, including two corresponding models in which the fatigue factor is removed. Through analysis, we assess our models’ quality of fit, predictive power, and accuracy. The experimental results demonstrate that the model of tangent hyperbolic learning with fatigue outperforms the existing ones regarding fit, predictive power, or accuracy. By incorporating the fatigue factor, the models provide a more comprehensive and realistic depiction of software reliability.
Fractal Continuum Mapping Applied to Timoshenko Beams
Authors: Samayoa Didier , Alcántara Alexandro , Mollinedo Helvio , Barrera-Lao Francisco Javier , Torres-SanMiguel Christopher René
Published: August 2023

In this work, a generalization of the Timoshenko beam theory is introduced, which is based on fractal continuum calculus. The mapping of the bending problem onto a non-differentiable self-similar beam into a corresponding problem for a fractal continuum is derived using local fractional differential operators. Consequently, the functions defined in the fractal continua beam are differentiable in the ordinary calculus sense. Therefore, the non-conventional local derivatives defined in the fractal continua beam can be expressed in terms of the ordinary derivatives, which are solved theoretically and numerically. Lastly, examples of classical beams with different boundary conditions are shown in order to check some details of the physical phenomenon under study.
Combined SGC-Ball Interpolation Curves: Construction and IGEO-Based Shape Optimization
Authors: Zheng Jiaoyue , Hu Gang , Chen Liuxin , Ji Xiaomin
Published: August 2023

With the swift advancement of the geometric modeling industry and computer technology, traditional generalized Ball curves and surfaces are challenging to achieve the geometric modeling of various complex curves and surfaces. Constructing an interpolation curve for the given discrete data points and optimizing its shape have important research value in engineering applications. This article uses an improved golden eagle optimizer to design the shape-adjustable combined generalized cubic Ball interpolation curves with ideal shape. Firstly, the combined generalized cubic Ball interpolation curves are constructed, which have global and local shape parameters. Secondly, an improved golden eagle optimizer is presented by integrating Lévy flight, sine cosine algorithm, and differential evolution into the original golden eagle optimizer; the three mechanisms work together to increase the precision and convergence rate of the original golden eagle optimizer. Finally, in view of the criterion of minimizing curve energy, the shape optimization models of combined generalized cubic Ball interpolation curves that meet the C1 and C2 smooth continuity are instituted. The improved golden eagle optimizer is employed to deal with the shape optimization models, and the combined generalized cubic Ball interpolation curves with minimum energy are attained. The superiority and competitiveness of improved golden eagle optimizer in solving the optimization models are verified through three representative numerical experiments.
HLEGF: An Effective Hypernetwork Community Detection Algorithm Based on Local Expansion and Global Fusion
Authors: Wang Feng , Hu Feng , Chen Rumeng , Xiong Naixue
Published: August 2023

Community structure is crucial for understanding network characteristics, and the local expansion method has performed well in detecting community structures. However, there are two problems with this method. Firstly, it can only add nodes or edges on the basis of existing clusters, and secondly, it can produce a large number of small communities. In this paper, we extend the local expansion method based on ordinary graph to hypergraph, and propose an effective hypernetwork community detection algorithm based on local expansion (LE) and global fusion (GF), which is referred to as HLEGF. The LE process obtains multiple small sub-hypergraphs by deleting and adding hyperedges, while the GF process optimizes the sub-hypergraphs generated by the local expansion process. To solve the first problem, the HLEGF algorithm introduces the concepts of community neighborhood and community boundary to delete some nodes and hyperedges in hypergraphs. To solve the second problem, the HLEGF algorithm establishes correlations between adjacent sub-hypergraphs through global fusion. We evaluated the performance of the HLEGF algorithm in the real hypernetwork and six synthetic random hypernetworks with different probabilities. Because the HLEGF algorithm introduces the concepts of community boundary and neighborhood, and the concept of a series of similarities, the algorithm has superiority. In the real hypernetwork, the HLEGF algorithm is consistent with the classical Spectral algorithm, while in the random hypernetwork, when the probability is not less than 0.95, the NMI value of the HLEGF algorithm is always greater than 0.92, and the RI value is always greater than 0.97. When the probability is 0.95, the HLEGF algorithm achieves a 2.3% improvement in the NMI value, compared to the Spectral algorithm. Finally, we applied the HLEGF algorithm to the drug–target hypernetwork to partition drugs with similar functions into communities.
Fractal Divergences of Generalized Jacobi Polynomials
Authors: Sfetcu Răzvan-Cornel , Preda Vasile
Published: August 2023

The notion of entropy (including macro state entropy and information entropy) is used, among others, to define the fractal dimension. Rényi entropy constitutes the basis for the generalized correlation dimension of multifractals. A motivation for the study of the information measures of orthogonal polynomials is because these polynomials appear in the densities of many quantum mechanical systems with shape-invariant potentials (e.g., the harmonic oscillator and the hydrogenic systems). With the help of a sequence of some generalized Jacobi polynomials, we define a sequence of discrete probability distributions. We introduce fractal Kullback–Leibler divergence, fractal Tsallis divergence, and fractal Rényi divergence between every element of the sequence of probability distributions introduced above and the element of the equiprobability distribution corresponding to the same index. Practically, we obtain three sequences of fractal divergences and show that the first two are convergent and the last is divergent.
Effect of Nanoparticle Diameter in Maxwell Nanofluid Flow with Thermophoretic Particle Deposition
Authors: Srilatha Pudhari , Abu-Zinadah Hanaa , Kumar Ravikumar Shashikala Varun , Alsulami M. D. , Kumar Rangaswamy Naveen , Abdulrahman Amal , Punith Gowda Ramanahalli Jayadevamurthy
Published: August 2023

The time-dependent Maxwell nanofluid flow with thermophoretic particle deposition is examined in this study by considering the solid–liquid interfacial layer and nanoparticle diameter. The governing partial differential equations are reduced to ordinary differential equations using suitable similarity transformations. Later, these reduced equations are solved using Runge–Kutta–Fehlberg’s fourth and fifth-order method via a shooting approach. An artificial neural network serves as a surrogate model, making quick and precise predictions about the behaviour of nanofluid flow for various input parameters. The impact of dimensionless parameters on flow, heat, and mass transport is determined via graphs. The results reveal that the velocity profile drops with an upsurge in unsteadiness parameter values and Deborah number values. The rise in space and temperature-dependent heat source/sink parameters value increases the temperature. The concentration profile decreases as the thermophoretic parameter upsurges. Finally, the method’s correctness and stability are confirmed by the fact that the maximum number of values is near the zero-line error. The zero error is attained near the values 2.68×10−6, 2.14×10−9, and 8.5×10−7 for the velocity, thermal, and concentration profiles, respectively.
Deep-Learning-Based Classification of Bangladeshi Medicinal Plants Using Neural Ensemble Models
Authors: Uddin A. Hasib , Chen Yen-Lin , Borkatullah Bijly , Khatun Mst. Sathi , Ferdous Jannatul , Mahmud Prince , Yang Jing , Ku Chin Soon , Por Lip Yee
Published: August 2023

This research addresses the lack of publicly available datasets for Bangladeshi medicinal plants by presenting a comprehensive dataset comprising 5000 images of ten species collected under controlled conditions. To improve performance, several preprocessing techniques were employed, such as image selection, background removal, unsharp masking, contrast-limited adaptive histogram equalization, and morphological gradient. Then, we applied five state-of-the-art deep learning models to achieve benchmark performance on the dataset: VGG16, ResNet50, DenseNet201, InceptionV3, and Xception. Among these models, DenseNet201 demonstrated the highest accuracy of 85.28%. In addition to benchmarking the deep learning models, three novel neural network architectures were developed: dense-residual–dense (DRD), dense-residual–ConvLSTM-dense (DRCD), and inception-residual–ConvLSTM-dense (IRCD). The DRCD model achieved the highest accuracy of 97%, surpassing the benchmark performances of individual models. This highlights the effectiveness of the proposed architectures in capturing complex patterns and dependencies within the data. To further enhance classification accuracy, an ensemble approach was adopted, employing both hard ensemble and soft ensemble techniques. The hard ensemble achieved an accuracy of 98%, while the soft ensemble achieved the highest accuracy of 99%. These results demonstrate the effectiveness of ensembling techniques in boosting overall classification performance. The outcomes of this study have significant implications for the accurate identification and classification of Bangladeshi medicinal plants. This research provides valuable resources for traditional medicine, drug discovery, and biodiversity conservation efforts. The developed models and ensemble techniques can aid researchers, botanists, and practitioners in accurately identifying medicinal plant species, thereby facilitating the utilization of their therapeutic potential and contributing to the preservation of biodiversity.
On the Dynamical Behavior of Solitary Waves for Coupled Stochastic Korteweg–De Vries Equations
Authors: Mohammed Wael W. , Al-Askar Farah M. , Cesarano Clemente
Published: August 2023

In this paper, we take into account the coupled stochastic Korteweg–De Vries (CSKdV) equations in the Itô sense. Using the mapping method, new trigonometric, rational, hyperbolic, and elliptic stochastic solutions are obtained. These obtained solutions can be applied to the analysis of a wide variety of crucial physical phenomena because the coupled KdV equations have important applications in various fields of physics and engineering. Also, it is used in the design of optical fiber communication systems, which transmit information using soliton-like waves. The dynamic performance of the various obtained solutions are depicted using 3D and 2D curves in order to interpret the effects of multiplicative noise. We conclude that multiplicative noise influences the behavior of the solutions of CSKdV equations and stabilizes them.
Reserve Fund Optimization Model for Digital Banking Transaction Risk with Extreme Value-at-Risk Constraints
Authors: Saputra Moch Panji Agung , Chaerani Diah , Sukono  , Md. Yusuf Mazlynda
Published: August 2023

The digitalization of bank data and financial operations creates a large risk of loss. Losses due to the risk of errors in the bank’s digital system need to be mitigated through the readiness of reserve funds. The determination of reserve funds needs to be optimized so that there is no large excess of reserve funds. Then the rest of the reserve fund allocation can be used as an investment fund by the bank to obtain additional returns or profits. This study aims to optimize the reserve fund allocation for digital banking transactions. In this case, the decision variable is value reserved based on potential loss of each digital banking, and the objective function is defined as minimizing reserve fund allocation. Furthermore, some conditions that become limitation are rules of Basel II, Basel III, and Article 71 paragraph 1 of the Limited Liability Company Law. Since the objective function can be expressed as a linear function, in this paper, linear programming optimization approach is thus employed considering Extreme Value-at-Risk (EVaR) constraints. In the use of EVaR approach in the digital banking problem, it is found that the loss meets the criteria of extreme data based on the Generalized Pareto Distribution (GPD). The strength of reserve funds using linear programming optimization with EVaR constraints is the consideration of potential losses from digital banking risks that are minimized so that the allocation of company funds becomes optimum. While the determination of reserve funds with a standard approach only considers historical profit data, this can result in excessive reserve funds because they are not considered potential risks in the future period. For the numerical experiment, the following risk data are used in the modeling, i.e., the result of a sample simulation of digital banking losses due to the risk of system downtime, system timeout, external failure, and operational user failure. Therefore, the optimization model with EVaR constraints produces an optimal reserve fund value, so that the allocation of bank reserve funds becomes efficient. This provides a view for banking companies to avoid the worst risk, namely collapse due to unbalanced mandatory reserve funds.
Toward Effective Uncertainty Management in Decision-Making Models Based on Type-2 Fuzzy TOPSIS
Authors: Madi Elissa Nadia , Zakaria Zahrahtul Amani , Sambas Aceng , Sukono
Published: August 2023

Over the past century, there has been a dramatic increasing interest in the multi-criteria group decision-making (MCGDM) technique, with a considerable amount of studies published regarding it. One of the well-known approaches in the MCGDM paradigm is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The integration of the TOPSIS method with fuzzy set theory has proven to be successful in various applications. Recently, a wide array of publications has proposed implementing a type-2 fuzzy set with TOPSIS. However, the additional degree of uncertainty represented by type 2 has largely been ignored, especially in a few specific mathematical operations in the model. We propose constructing interval type-2 fuzzy membership functions (IT2 MFs) using interval-based data gathered from a survey, where this is used to generate a new scale to represent ratings for each alternative. This procedure utilized all information gathered from decision makers. In addition, we present a complete algorithm for TOPSIS based on IT2 fuzzy sets (IT2 FSs) which preserve the interval-based form output. The output in the form of intervals offers decision makers (DMs) with more detailed information, enabling them to make more nuanced decisions. This can include cautious decisions when intervals are wider and overlapping. Although understanding the exact meaning of these intervals and their widths in a decision-making context is challenging, this paper introduces a systematic method for connecting input uncertainty to output uncertainty in the TOPSIS technique. This approach establishes a solid foundation for future research. Thus far, no other researchers have suggested a data-driven method that combines TOPSIS with fuzzification and provides intervals as the final output.
Hand-Guiding Gesture-Based Telemanipulation with the Gesture Mode Classification and State Estimation Using Wearable IMU Sensors
Authors: Choi Haegyeom , Jeon Haneul , Noh Donghyeon , Kim Taeho , Lee Donghun
Published: August 2023

This study proposes a telemanipulation framework with two wearable IMU sensors without human skeletal kinematics. First, the states (intensity and direction) of spatial hand-guiding gestures are separately estimated through the proposed state estimator, and the states are also combined with the gesture’s mode (linear, angular, and via) obtained with the bi-directional LSTM-based mode classifier. The spatial pose of the 6-DOF manipulator’s end-effector (EEF) can be controlled by combining the spatial linear and angular motions based on integrating the gesture’s mode and state. To validate the significance of the proposed method, the teleoperation of the EEF to the designated target poses was conducted in the motion-capture space. As a result, it was confirmed that the mode could be classified with 84.5% accuracy in real time, even during the operator’s dynamic movement; the direction could be estimated with an error of less than 1 degree; and the intensity could be successfully estimated with the gesture speed estimator and finely tuned with the scaling factor. Finally, it was confirmed that a subject could place the EEF within the average range of 83 mm and 2.56 degrees in the target pose with only less than ten consecutive hand-guiding gestures and visual inspection in the first trial.
Modeling the Spatial Effects of Digital Data Economy on Regional Economic Growth: SAR, SEM and SAC Models
Authors: Varlamova Julia , Kadochnikova Ekaterina
Published: August 2023

The potential for the development of digital data and their infrastructure creates new opportunities for economic growth. The purpose of this study was to develop an approach to identify a set of indicators to quantify the data economy and model its impact on economic growth. The cumulative index and Gini coefficient indicated differentiation and disparity in the digital data infrastructure of 85 regions for 2016–2021. In the presence of a positive spatial correlation, digital development does not indicate clear spatial clubs. Selected according to the calculation of Lagrange multipliers and likelihood ratios, panel econometric models with spatial lags, using SAR, SEM and SAC, showed a short-term negative effect and a long-term positive effect of the digital data economy on economic growth, confirmed by the calculation of marginal effects. During the pandemic, the data economy had a positive impact on regional economic growth. The positive spatial effect of interactions between regions detected by the models in the framework of economic growth indicates the synergistic nature of digitalization. The main conclusions of this study provide evidence-based support for the digital transformation of regions and can help create information infrastructure and accumulate human capital to eliminate disparities in the digital development of regions.
Progress on Improved Fouling Resistance-Nanofibrous Membrane for Membrane Distillation: A Mini-Review
Authors: Tan Yong Zen , Alias Nur Hashimah , Aziz Mohd Haiqal Abd , Jaafar Juhana , Othman Faten Ermala Che , Chew Jia Wei
Published: August 2023

Nanofibrous membranes for membrane distillation (MD) have demonstrated promising results in treating various water and wastewater streams. Significant progress has been made in recent decades because of the development of sophisticated membrane materials, such as superhydrophobic, omniphobic and Janus membranes. However, fouling and wetting remain crucial issues for long-term operation. This mini-review summarizes ideas as well as their limitations in understanding the fouling in membrane distillation, comprising organic, inorganic and biofouling. This review also provides progress in developing antifouling nanofibrous membranes for membrane distillation and ongoing modifications on nanofiber membranes for improved membrane distillation performance. Lastly, challenges and future ways to develop antifouling nanofiber membranes for MD application have been systematically elaborated. The present mini-review will interest scientists and engineers searching for the progress in MD development and its solutions to the MD fouling issues.
Bipolar Membranes for Direct Borohydride Fuel Cells—A Review
Authors: Belhaj Ines , Faria Mónica , Šljukić Biljana , Geraldes Vitor , Santos Diogo M. F.
Published: August 2023

Direct liquid fuel cells (DLFCs) operate directly on liquid fuel instead of hydrogen, as in proton-exchange membrane fuel cells. DLFCs have the advantages of higher energy densities and fewer issues with the transportation and storage of their fuels compared with compressed hydrogen and are adapted to mobile applications. Among DLFCs, the direct borohydride–hydrogen peroxide fuel cell (DBPFC) is one of the most promising liquid fuel cell technologies. DBPFCs are fed sodium borohydride (NaBH4) as the fuel and hydrogen peroxide (H2O2) as the oxidant. Introducing H2O2 as the oxidant brings further advantages to DBPFC regarding higher theoretical cell voltage (3.01 V) than typical direct borohydride fuel cells operating on oxygen (1.64 V). The present review examines different membrane types for use in borohydride fuel cells, particularly emphasizing the importance of using bipolar membranes (BPMs). The combination of a cation-exchange membrane (CEM) and anion-exchange membrane (AEM) in the structure of BPMs makes them ideal for DBPFCs. BPMs maintain the required pH gradient between the alkaline NaBH4 anolyte and the acidic H2O2 catholyte, efficiently preventing the crossover of the involved species. This review highlights the vast potential application of BPMs and the need for ongoing research and development in DBPFCs. This will allow for fully realizing the significance of BPMs and their potential application, as there is still not enough published research in the field.
Effect of Thermo-Mechanical Processing on Initiation and Propagation of Stress Corrosion Cracking in 304L Austenitic Stainless Steel
Authors: Shin Ji Ho
Published: August 2023

Despite the high corrosion resistance of austenitic stainless steels (SSs), a significant reduction of stress corrosion cracking (SCC) resistance has been reported in cases of high residual stress and metastable microstructural features. In this study, the effect of thermo-mechanical processing (TMP) on the initiation and propagation of SCC in 304L SS was studied. To better understand the SCC mechanisms, three TMPs conditions—welded, solution annealed at 1050 °C for tens of seconds, and straightened—were used. The research focused on analyzing the initial microstructure, residual stress, and hardness along the depth direction to assess SCC resistance and establish correlations with the observed SCC modes. Experimental results demonstrated that transgranular SCC was observed in regions exhibiting elevated residual stress induced by welding and straightening processes. Furthermore, the presence of strain-induced martensite transformation and slip bands formed during plastic deformation were identified as additional factors contributing to the susceptibility of SCC. The study findings highlighted that the magnitude and distribution of residual stresses, in conjunction with microstructural evolution, could be varied depending on the specific TMP condition, leading to different SCC susceptibilities, cracking modes, and directions.
Enhancing the Tribological Performance of Tool Steels for Wood-Processing Applications: A Comprehensive Review
Authors: Muhammed Musa , Javidani Mousa , Heidari Majid , Jahazi Mohammad
Published: August 2023

The stochastic nature of tool wear during wood machining, owing to the dynamic properties of the biological material and its dependence on various factors, has raised significant industrial and research concerns in recent years. Explicitly, the tool wear is a product of the interaction between wood properties (such as hardness, density, and contamination level) and machining parameters (such as cutting speed, feed rate, and rake angle) alongside ambient conditions (such as temperature and humidity). The objective of this review paper is to provide an overview of recent advancements in the field of wood machining. To begin with, it highlights the important role of wood properties and ambient conditions influencing tool wear. Furthermore, the paper examines the various mechanisms involved in the wood-machining process and discusses their cost implications from an industrial perspective. It also covers technological advancements in the characterization of tool wear and explores the relationship between this parameter and other machining variables. It provides critical and analytical discussions on various methods for enhancing tool wear, including heat treatment, cryogenic treatment, thermochemical treatment, coating deposition, and hybrid treatments. Additionally, the paper incorporates statistical analysis to achieve two objectives. Firstly, it aims to identify the most significant wood property that affects tool wear and establish the correlation between this parameter and wood properties. Secondly, it investigates the effect of heat treatment parameters and carbide characteristics on tool wear as well as their correlation. Lastly, the review provides recommendations based on relevant literature for prospective researchers and industrial counterparts in the field. These recommendations aim to guide further exploration and practical applications in the subject matter.
SMetaS: A Sample Metadata Standardizer for Metabolomics
Authors: Bremer Parker Ladd , Fiehn Oliver
Published: August 2023

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.
Effects of Process Parameters on Bond Properties of Ag-2.35Au-0.7Pd-0.2Pt-0.1Cu Alloy Wire
Authors: Zhou Hongliang , Chang Andong , Fan Junling , Cao Jun , Zhang Yingchong , An Bin , Xia Jie
Published: August 2023

Bond properties were performed on Ag-2.35Au-0.7Pd-0.2Pt-0.1Cu alloy wire with a diameter of 25 µm under different process parameters. The effects of electrical flaming off (EFO) current and EFO time on the deformability of the free air ball (FAB) were investigated using scanning electron microscopy (SEM), as well as the effects of ultrasonic power and bonding force on the bond characteristic. The experimental results show that FAB grows from a preheated tip to a small ball, a regular ball, and finally to a golf ball with increasing either the EFO current or the EFO time, and the FAB presents an optimal shape at 25 mA and 650 μs. Moreover, a nonlinear relationship between FAB diameter and EFO time is obtained at an EFO current of 25 mA, which could be expressed by a cubic equation. Further, at a constant bonding force, as the ultrasonic power increased, the mashed ball diameter grew larger and larger, the capillary hole imprint became more and more obvious, and the tail width also increased, and vice versa. The optimal ultrasonic power and bonding force are 70 mW and 45 gf for ball bonding and 90 mW and 75 gf for wedge bonding, respectively. Finally, for all the bonded wire samples prepared under optimal process parameters, no ball and wedge bond lifts happened after the destructive pull test, and full intermetallic compound coverage with perfect morphology occurred on the bond pad after the ball shear test, which meant that the bonded wire samples had high bond strength and hence improved the reliability of microelectronic products. It provided technical support for the reliability research of Pt-containing Ag-based bonding alloy wires.
Biocompatible High-Resolution 3D-Printed Microfluidic Devices: Integrated Cell Chemotaxis Demonstration
Authors: Boaks Mawla , Roper Connor , Viglione Matthew , Hooper Kent , Woolley Adam T. , Christensen Kenneth A. , Nordin Gregory P.
Published: August 2023

We demonstrate a method to effectively 3D print microfluidic devices with high-resolution features using a biocompatible resin based on avobenzone as the UV absorber. Our method relies on spectrally shaping the 3D printer source spectrum so that it is fully overlapped by avobenzone’s absorption spectrum. Complete overlap is essential to effectively limit the optical penetration depth, which is required to achieve high out-of-plane resolution. We demonstrate the high resolution in practice by 3D printing 15 μm square pillars in a microfluidic chamber, where the pillars are separated by 7.7 μm and are printed with 5 μm layers. Furthermore, we show reliable membrane valves and pumps using the biocompatible resin. Valves are tested to 1,000,000 actuations with no observable degradation in performance. Finally, we create a concentration gradient generation (CG) component and utilize it in two device designs for cell chemotaxis studies. The first design relies on an external dual syringe pump to generate source and sink flows to supply the CG channel, while the second is a complete integrated device incorporating on-chip pumps, valves, and reservoirs. Both device types are seeded with adherent cells that are subjected to a chemoattractant CG, and both show clear evidence of chemotactic cellular migration. Moreover, the integrated device demonstrates cellular migration comparable to the external syringe pump device. This demonstration illustrates the effectiveness of our integrated chemotactic assay approach and high-resolution biocompatible resin 3D printing fabrication process. In addition, our 3D printing process has been tuned for rapid fabrication, as printing times for the two device designs are, respectively, 8 and 15 min.
Optimization of Machining Parameters to Minimize Cutting Forces and Surface Roughness in Micro-Milling of Mg13Sn Alloy
Authors: Ercetin Ali , Aslantaş Kubilay , Özgün Özgür , Perçin Mustafa , Chandrashekarappa Manjunath Patel Gowdru
Published: August 2023

This comprehensive study investigates the micro-milling of a Mg13Sn alloy, a material of considerable interest in various high-precision applications, such as biomedical implants. The main objective of the study was to explore the optimizations of variable feed per tooth (fz), cutting speed (Vc), and depth of cut (ap) parameters on the key outcomes of the micro-milling process. A unique experimental setup was employed, employing a spindle capable of achieving up to 60,000 revolutions per minute. Additionally, the study leveraged linear slides backed by micro-step motors to facilitate precise axis movements, thereby maintaining a resolution accuracy of 0.1 μm. Cutting forces were accurately captured by a mini dynamometer and subsequently evaluated based on the peak to valley values for Fx (tangential force) and Fy (feed force). The study results revealed a clear and complex interplay between the varied cutting parameters and their subsequent impacts on the cutting forces and surface roughness. An increase in feed rate and depth of cut significantly increased the cutting forces. However, the cutting forces were found to decrease noticeably with the elevation of cutting speed. Intriguingly, the tangential force (Fx) was consistently higher than the feed force (Fy). Simultaneously, the study determined that the surface roughness, denoted by Sa values, increased in direct proportion to the feed rate. It was also found that the Sa surface roughness values decreased with the increase in cutting speed. This study recommends a parameter combination of fz = 5 µm/tooth feed rate, Vc = 62.8 m/min cutting speed, and ap = 400 µm depth of cut to maintain a Sa surface roughness value of less than 1 µm while ensuring an optimal material removal rate and machining time. The results derived from this study offer vital insights into the micro-milling of Mg13Sn alloys and contribute to the current body of knowledge on the topic.
Enhancement of Terahertz Emission by Silver Nanoparticles in a Liquid Medium
Authors: Wang Haoyang , Shen Tao , Liu Jinkun , Zhu Yan , Li Hong , Wang Tianwu
Published: August 2023

Due to higher molecular density, lower ionization potential, and a better self-healing property compared with gases, liquid targets have been used for laser-induced terahertz generation for many years. In this work, a liquid target used for terahertz radiation is embedded with silver nanoparticles (Ag NPs), which makes the material have both the fluidity of liquids and conductivity of metals. Meanwhile, the experimental setup is easier to implement than that of liquid metals. Polyvinyl alcohol (PVA) is used as a stabilizing agent to avoid precipitation formation. It is observed that the power of 0.5 THz radiation from the Ag NP suspension is five times stronger than that from liquid water in identical experimental conditions. In addition, the reusability of the material is investigated using multiple excitations. UV–visible spectroscopy and TEM imaging are carried out to analyze the target material after each excitation. As a result, quasispherical Ag NP suspensions show good reusability for several excitations and only a decrease in particle concentration is observed. By contrast, the chain-like Ag NP suspension shows poor stability due to PVA damage caused by intense laser pulses, so it cannot be used in a recyclable manner.
Integrated Plastic Microfluidic Device for Heavy Metal Ion Detection
Authors: Filippidou Myrto Kyriaki , Kanaris Aris Ioannis , Aslanidis Evangelos , Rapesi Annita , Tsounidi Dimitra , Ntouskas Sotirios , Skotadis Evangelos , Tsekenis George , Tsoukalas Dimitris , Tserepi Angeliki , Chatzandroulis Stavros
Published: August 2023

The presence of heavy metal ions in soil, air and water constitutes an important global environmental threat, as these ions accumulate throughout the food chain, contributing to the rise of chronic diseases, including, amongst others, cancer and kidney failure. To date, many efforts have been made for their detection, but there is still a need for the development of sensitive, low-cost, and portable devices able to conduct on-site detection of heavy metal ions. In this work, we combine microfluidic technology and electrochemical sensing in a plastic chip for the selective detection of heavy metal ions utilizing DNAzymes immobilized in between platinum nanoparticles (PtNPs), demonstrating a reliable portable solution for water pollution monitoring. For the realization of the microfluidic-based heavy metal ion detection device, a fast and easy-to-implement fabrication method based on the photolithography of dry photosensitive layers is proposed. As a proof of concept, we demonstrate the detection of Pb2+ ions using the prototype microfluidic device.
High-Sensitivity Piezoelectric MEMS Accelerometer for Vector Hydrophones
Authors: Shi Shuzheng , Ma Liyong , Kang Kai , Zhu Jie , Hu Jinjiang , Ma Hong , Pang Yongjun , Wang Zhanying
Published: August 2023

In response to the growing demand for high-sensitivity accelerometers in vector hydrophones, a piezoelectric MEMS accelerometer (PMA) was proposed, which has a four-cantilever beam integrated inertial mass unit structure, with the advantages of being lightweight and highly sensitive. A theoretical energy harvesting model was established for the piezoelectric cantilever beam, and the geometric dimensions and structure of the microdevice were optimized to meet the vibration pickup conditions. The sol-gel and annealing technology was employed to prepare high-quality PZT thin films on silicon substrate, and accelerometer microdevices were manufactured by using MEMS technology. Furthermore, the MEMS accelerometer was packaged for testing on a vibration measuring platform. Test results show that the PMA has a resonant frequency of 2300 Hz. In addition, there is a good linear relationship between the input acceleration and the output voltage, with V = 8.412a − 0.212. The PMA not only has high sensitivity, but also has outstanding anti-interference ability. The accelerometer structure was integrated into a vector hydrophone for testing in a calibration system. The results show that the piezoelectric vector hydrophone (PVH) has a sensitivity of –178.99 dB@1000 Hz (0 dB = 1 V/μPa) and a bandwidth of 20~1100 Hz. Meanwhile, it exhibits a good “8” shape directivity and consistency of each channel. These results demonstrate that the piezoelectric MEMS accelerometer has excellent capabilities suitable for use in vector hydrophones.
Early Fluvoxamine Reduces the Risk for Clinical Deterioration in Symptomatic Outpatients with COVID-19: A Real-World, Retrospective, before–after Analysis
Authors: Tsiakalos Aristotelis , Ziakas Panayiotis D. , Polyzou Eleni , Schinas Georgios , Akinosoglou Karolina
Published: August 2023

Fluvoxamine, a selective serotonin reuptake inhibitor with anti-inflammatory properties, has gained attention as a repurposed drug to treat COVID-19. We aimed to explore the potential benefit of fluvoxamine on outpatients with early SARS-CoV-2 infection. We performed a retrospective study of fluvoxamine adult outpatients with symptomatic COVID-19 disease of early onset (<5 days), in the context of an infectious diseases private practice, between September–December 2021, in Greece. Patients with disease duration ≥5 days, dyspnea and/or hypoxemia with oxygen saturation <94% in room air and pregnancy were excluded from the analysis. In total, 103 patients, 54 males/49 females with a median age of 47 years (39–56), were included in this study. Patient characteristics were balanced before and after the introduction of fluvoxamine. Two patients in the fluvoxamine arm (3.8%; 95% CI 0.4–13) had clinical deterioration compared to 8 patients in the standard of care group (16%; 95% CI 7.2–29.1, p < 0.04). After controlling for age, sex, body mass index > 30 and vaccination status, fluvoxamine was independently associated with a lower risk of clinical deterioration (adj. OR 0.12; 95% CI 0.02–0.70, p < 0.02). Adding on fluvoxamine to treatment for early symptomatic COVID-19 patients may protect them from clinical deterioration and hospitalization, and it is an appealing low-cost, low-toxicity option in the community setting and warrants further investigation.