bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermoplastic-Based Ballistic Helmets: Processing, Ballistic Resistance and Damage Characterization
Authors: Dias Rafael R. , Meliande Natalin M. , Kotik Hector G. , Camerini César G. , Pereira Iaci M.
Published: September 2024

Ballistic helmets are individual pieces of armor equipment designed to protect a soldier’s head from projectiles and fragments. Although very common, these helmets are responsible for several casualties due to their significant back face deformation and low ballistic resistance to projectiles. Therefore, to enhance helmet performance, studies have focused on the development of new materials and new ballistic protection solutions. The purpose of this study was to develop and evaluate a new ballistic solution using thermoplastic-based matrices. The first matrix was based on high-density polyethylene (HDPE). The second matrix was based on HDPE modified with exfoliated montmorillonite (MMT). The main manufacturing processes of a thermoplastic-based ballistic helmet are presented, along with its ballistic performance, according to the National Institute of Justice (NIJ) standard 0106.01 and an investigation of its failure mechanisms via a non-destructive technique. All the helmets resulted in level III-A ballistic protection. The postimpact helmets were scanned to evaluate the back face deformation dimensions, which revealed that the global cone deformation was deeper in the HDPE than in the HDPE/MMT helmet. The failure analysis revealed an overall larger deformation area in the HDPE and HDPE/MMT helmet delamination zones in the regions with a large radius of curvature than in the zones with the lowest radius, which is in accordance with previous simulations reported in the literature.
Core–Shell Microspheres with Encapsulated Gold Nanoparticle Carriers for Controlled Release of Anti-Cancer Drugs
Authors: Guo Lin , Zhao Qilong , Wang Min
Published: September 2024

Cancer is one of the major threats to human health and lives. However, effective cancer treatments remain a great challenge in clinical medicine. As a common approach for cancer treatment, chemotherapy has saved the life of millions of people; however, patients who have gone through chemotherapy often suffer from severe side effects owing to the inherent cytotoxicity of anti-cancer drugs. Stabilizing the blood concentration of an anti-cancer drug will reduce the occurrence or severity of side effects, and relies on using an appropriate drug delivery system (DDS) for achieving sustained or even on-demand drug delivery. However, this is still an unmet clinical challenge since the mainstay of anti-cancer drugs is small molecules, which tend to be diffused rapidly in the body, and conventional DDSs exhibit the burst release phenomenon. Here, we establish a class of DDSs based on biodegradable core–shell microspheres with encapsulated doxorubicin hydrochloride-loaded gold nanoparticles (DOX@Au@MSs), with the core–shell microspheres being made of poly(lactic-co-glycolic acid) in the current study. By harnessing the physical barrier of the biodegradable shell of core–shell microspheres, DOX@Au@MSs can provide a sustained release of the anti-cancer drug in the test duration (which is 21 days in the current study). Thanks to the photothermal properties of the encapsulated gold nanoparticle carriers, the core–shell biodegradable microspheres can be ruptured through remotely controlled near-infrared (NIR) light, thereby achieving an NIR-controlled triggered release of the anti-cancer drug. Furthermore, the route of the DOX-Au@MS-enabled controlled release of the anti-cancer drug can provide durable cancer cell ablation for the long period of 72 h.
Selection Results of Solid Material for Horizontal and Highly-Deviated Well Completion Gravel-Packing: Experiments, Numerical Simulation and Proposal
Authors: Shi Haoxian , Dong Changyin , Zhan Xinjie , Liu Chenfeng , Li Lixia , Ji Jianrong , Yu Yanjiang , Li Zhendong
Published: September 2024

Lightweight and ultra-lightweight solid materials are being used in gravel packing for horizontal wells instead of traditional quartz and ceramsite to decrease the risk of premature plugging and improve packing efficiency. Physical and numerical simulation experiments of gravel packing were conducted to assess the effectiveness of reducing solid material density and investigate its impact on packing and sand control. Packed gravel destabilization experiments highlighted the importance of high-compaction degree packing for effective sand control. Further gravel packing experiments examined the packing performance of different solid materials, revealing that lightweight solids have minimal gravitational deposition effect because their density is similar to the gravel slurry, relying primarily on fluid flow for compaction. The numerical simulation indicated that lightweight ceramsite is unsuitable for horizontal and highly-deviated wells because of its poor compaction degree and sand control, especially with high-viscosity slurry. High-density particles enhance gravitational deposition, improving packing compaction and sand control. Lightweight materials are recommended only when advanced plugging of α wave packing cannot be avoided. In highly-deviated wells, high-density materials significantly improve packing stability and sand control. This study provides clear technical guidelines for selecting solid materials for gravel packing in horizontal and highly-deviated wells.
Study on the Estimation Method of Wind Resistance Considering Self-Induced Wind by Ship Advance Speed
Authors: Park Hyounggil , Lee Pyungkuk , Kim Jinkyu , Kim Heejung , Lee Heedong , Lee Youngchul
Published: September 2024

A numerical analysis of the wind load for the purpose of evaluating the wind resistance acting on a ship and the validity of the wind profile applied to determine the wind load coefficient were conducted. Through the evaluation of estimation results by a wind tunnel test, CFD analysis, and present semi-empirical formulae, it was recognized that the difference in estimation of ship resistance due to wind could not be ignored. In order to identify the main causes of the difference, extensive analyses were performed for a container, tanker, and LNG carrier. In particular, the estimation results for a container ship with two islands showed unreliable results. The main reason for the difference is that each method reflects the wind speed in the vertical direction differently, and the wind profile applied when considering the self-induced wind effect is not a uniform wind profile. In the calculation of wind resistance by self-induced wind, wind resistance estimation results differed by about 1.5% to 3.4% depending on the application of uniform or non-uniform wind profile. The total wind resistance acting on the vessel shall be divided into wind resistance from a stationary vessel without speed and wind resistance caused by the forward speed of the vessel in no wind conditions. Therefore, it is reasonable to apply a uniform wind profile to estimate wind resistance caused by the ship’s forward speed, while a wind profile that reflects the effect of changes in the ship’s vertical speed should be applied to estimate the wind resistance caused by the ship’s forward speed.
Vessel Trajectory Prediction Based on Automatic Identification System Data: Multi-Gated Attention Encoder Decoder Network
Authors: Yang Fan , He Chunlin , Liu Yi , Zeng Anping , Hu Longhe
Published: September 2024

Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction.
Does ICT Investment Affect Market Share and Customer Acquisition Cost? A Comparative Analysis of Domestic and Foreign Banks Operating in India
Authors: Ansari Gulam Goush , Sen Gupta Rajorshi
Published: September 2024

Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of banks operating in India. The analysis uses a longitudinal dataset of 84 domestic and 70 foreign banks from 2000 to 2020, employing a two-step system Generalized Method of Moment (GMM). It is found that ICT investment adversely affects the market share of domestic banks, indicating a need for these banks to strategically invest more in CAC. Conversely, foreign banks are able to increase their market share through ICT investment and reduced CAC, thereby demonstrating greater efficiency in utilizing ICT. The study underscores the strategic importance of cumulative stock of ICT investment for banks. Nonetheless, it is emphasized that ICT investment must be complemented with innovative marketing strategies to enhance customer experience, reduce CAC, and increase market share. Overall, while foreign banks are able to leverage ICT to boost efficiency, domestic banks must leverage ICT to implement targeted marketing strategies and strive to enhance their customer service.
Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling
Authors: Abdul-Kareem Ahmed Amer , Fayed Zaki T. , Rady Sherine , El-Regaily Salsabil Amin , Nema Bashar M.
Published: September 2024

In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm’s potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and compared: Principal Component Analysis, correlation coefficient, and Particle Swarm Optimization. Through the fusion of financial ratios with machine learning, our model exhibits improved forecast accuracy and timeliness in predicting firms’ insolvency. The highest accuracy model is the integrated MLP + PCA model, at 98.7%. The other models, MLP + PSO and MLP + CC, also exhibit strong performance, with 0.3% and 1.1% less accuracy, respectively, compared to the first model, indicating that the first model serves as a powerful predictive approach.
Risk Analysis of Conglomerates with Debt and Equity Links
Authors: Cifuentes Arturo , Roman Rodrigo
Published: September 2024

Conglomerates play an important role in the functioning of capital markets. Therefore, assessing their response to external shocks is a significant risk management challenge not only for conglomerate executives but also for investors and regulators alike. In this context, a conglomerate refers to a group of companies typically operating across different industries and interconnected through both equity and debt relationships. Essentially, a conglomerate functions as a financial network whose nodes are linked by two layers of reciprocal connections. This paper introduces an algorithm to evaluate a conglomerate’s response to external shocks. Additionally, it proposes a protocol based on five key metrics that collectively summarize the conglomerate’s overall resilience. These metrics offer two major advantages: they facilitate comparisons between the strengths of different conglomerates and help assess the effectiveness of various strategies, such as internal capital reallocations, aimed at enhancing a conglomerate’s resilience. The algorithm’s usefulness, including its ability to detect cascades or “second-wave” defaults, is demonstrated through two illustrative examples.
Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry
Authors: Wu Fan , Liu Anqi , Chen Jing , Li Yuhua
Published: September 2024

The collapse of Silicon Valley Bank in 2023 was historically significant, and based on past experiences with similar banking sector shocks, it is widely expected to trigger domino effects among tech giants and startups. However, based on the analysis of risk spillover networks established by VARs estimation, we find little evidence of such a spread of risk contagion. We observe a clear downward trend in the total connectedness index of large-cap tech companies right after the the SVB collapse. Moreover, the market quickly responded in a way that isolated the financial services subcategory within the tech sector, forming a distinct community in the network. This explains how the risk contagion paths were cut off. We also provide visualised comparisons of contagion paths within the tech network before and after the SVB’s collapse.
Task Scheduling Algorithm for Power Minimization in Low-Cost Disaster Monitoring System: A Heuristic Approach
Authors: Jandaeng  Chanankorn , Kongsen  Jongsuk , Koad Peeravit , Thu May , Somchuea Sirirat
Published: September 2024

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments.
Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China
Authors: Wu Yuxin , Peng Xiuwen , Jia Guodong , Yu Xinxiao , Rao Honghong
Published: September 2024

The alteration of landscape spatial patterns (LSPs) and ecosystem services (ESs) in watersheds can have detrimental effects on the local environment and community. However, a comprehensive understanding of the current state of LSPs and ESs in watersheds around Winter Olympic venues in China is limited. Here, we assessed current LSPs and ESs and developed optimization strategies for the Xigou watershed around Winter Olympic venues in the northern agro-pastoral ecotone of China. The results indicated that the main land use type was grassland in the Xigou watershed, and landscape types were relatively homogenous. All three ESs (water yield, sediment retention, and carbon storage) generally improved from 2004 to 2020. For ESs, there was the lowest total volume of water yield in 2004 (637.44 × 104 m3). But sediment retention (10.54 × 106 t, 18.13 × 106 t, 13.28 × 106 t, and 16.85 × 106 t) had an upward, then downward, then upward trend before and after ERP. Carbon storage grew steadily. Correlation analysis suggested that the three ESs were closely related to the landscape spatial indices of average patch area (AREA_MN), contagion index (CONTAG), and Shannon’s evenness index (SHEI). AREA_MN, CONTAG, and SHEI in the eastern part of the study area promoted sediment retention and carbon storage, while in the southwestern part of the study area, they inhibited water yield and sediment retention. The results suggest that improving sediment retention by optimizing land use and cover change (LUCC) and LSPs is the main approach to further enhance ESs in the study area. Our study suggests that the inclusion of multiple landscape pattern indices can provide a more comprehensive representation of regional ecosystem service.
Non-Propagating Cracks at the Fatigue Limit of Notches: An Analysis of Notch Sensitivity and Size Effect
Authors: Chapetti Mirco Daniel
Published: September 2024

The issues of the high-cycle fatigue resistance of notches and the role of non-propagating short cracks in defining the fatigue notch sensitivity and fatigue limit of the configuration are addressed. A fracture mechanics approach is employed to determine the threshold configuration that defines the associated fatigue limit. The influence of notch sharpness, notch size, intrinsic fatigue limit, microstructural dimensions, and the threshold for crack propagation is examined. A simple expression is proposed to estimate the maximum fatigue notch factor, kfMax, which incorporates the influence of these non-propagating cracks. The fatigue limits for both blunt and sharp elliptical notches are analyzed and predicted based on experimental results reported in the literature. Additionally, shallow notches or small defects are analyzed, where it is found that the same hypothesis may not be applicable.
The Application of Machine Learning Algorithms to Bond Strength between Steel Rebars and Concrete Using Bayesian Optimization
Authors: Yan Huajun , Xie Nan , Shen Dandan
Published: September 2024

The purpose of this study is to estimate the bond strength between steel rebars and concrete using machine learning (ML) algorithms with Bayesian optimization (BO). It is important to conduct beam tests to determine the bond strength since it is affected by stress fields. A machine learning approach for bond strength based on 401 beam tests with six impact factors is presented in this paper. The model is composed of three standard algorithms, including random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), combined with the BO technique. Compared to empirical models, BO-XGB`oost was found to be the most accurate method, with values of R2, MAE, and RMSE of 0.87, 0.897 MPa, and 1.516 MPa for the test set. The development of a simplified model that contains three input variables (diameter of the rebar, yield strength of reinforcement, concrete compressive strength) has been proposed to make it more convenient to apply. According to this prediction, the Shapley additive explanation (SHAP) can help explain why the ML-based model predicts the particular outcome it does. By utilizing machine learning algorithms to predict complex interfacial mechanical behavior, it is possible to improve the accuracy of the model.
Microstructure Refinement via Nucleation of Intragranular Acicular Ferrite Stimulated by Ti-Containing Core–Shell Structured Particles in Low-Carbon Steel
Authors: Yan Zhu , Wang Chao , Duan Hua , Hao Junjie , Yuan Guo
Published: September 2024

This study investigated the microstructure, mechanical properties, and nucleation mechanism of acicular ferrite (AF) present in hot-rolled Ti deoxidized steel. In our experiments, the impact toughness of Ti deoxidized steel is significantly increased to 144 J at −20 °C, while those Mn and Al deoxidized steels are only 9 J and 18 J, respectively. Interlocked AF is the primary microstructure of Ti deoxidized steel. The second-phase particles of the core–shell-type structure, in which Ti2O3 is the nucleus and TiO is the outermost shell, act as effective nucleating agents to stimulate AF nucleation. The low lattice disregistry between TiO and AF is the main factor contributing to the production of AF. It is also revealed that Ti2O3 and MnS fulfill the particular orientation relationship, contributing to the formation of an Mn-depleted zone (MDZ) adjacent to MnS, proposed to be one of the possible mechanisms for promoting AF nucleation.
Effect of Current Waveform on Microstructure Evolution and Mechanical Properties of GH4169 High-Temperature Alloy Tungsten Inert Gas Additive Manufacturing
Authors: Zhang Xinlong , Zhang Jiaao , Xie Xiaodong , Jiang Zhaosong , Chen Chao , Wu Zhe , Zhang Yang
Published: September 2024

Direct current (DC) and pulsed DC tungsten inert gas (TIG) additive manufacturing processes were employed to fabricate GH4169 high-temperature alloy specimens. Upon comparing and analysing the two additive manufacturing methods, the evolution of microstructure and mechanical properties of the additively manufactured specimens were discussed. It provided a useful reference for the engineering application of pulsed DC TIG technology. The results showed that the overall forming process of the specimen was relatively stable under the DC TIG additive manufacturing and pulsed DC TIG additive manufacturing processes. The aspect ratio of the deposited layer of the pulsed DC-deposited specimen was relatively low, and the deposited layer of the pulsed DC specimen became flatter, which was conducive to maintaining the stability of the molten pool during the deposition process and improving forming accuracy. The microstructure distribution of the deposited layer from bottom to top was relatively uneven, with columnar dendrites in the bottom layer, cellular crystals in the middle layer, and equiaxed crystals in the top layer. Compared with the DC TIG additive manufacturing of GH4169 high-temperature alloy specimens, the Laves phase of the pulsed DC specimens was significantly reduced, which improved the plasticity and brittleness of the material.
A 3D Printing Platform for Design and Manufacturing of Multi-Functional Cementitious Construction Components and Its Validation for a Post-Tensioned Beam
Authors: Asaf Ofer , Bentur Arnon , Amir Oded , Larianovsky Pavel , Meyuhas Ohad Yaacov , Michli Eliad , Sprecher Aaron
Published: September 2024

Three-dimensional printing of cementitious materials for construction has been extensively investigated in recent years, with several demonstration projects successfully carried out. These efforts aim to leverage the printing process to achieve more efficient production of components compared to conventional concrete technologies. This includes both the process itself (eliminating the formwork stage) and the flexibility in producing complexly shaped elements. To maximize the potential of 3D printing in the construction industry, additional steps must be taken, grounded in a holistic view of the entire process. This involves integration of the production chain, including design, materials, and manufacturing of components, to create elements with optimal performance, encompassing structural, environmental, and architectural aspects. Such multi-functionality requires the viewing of 3D printing not just as a production technology but as a platform enabling the integration of all these components. To advance this approach, quantitative tools are developed to optimize the following three key components: material composition; manufacturing parameters to ensure buildability; and design tools to optimize multiple performance criteria, particularly structural and architectural shape. A demonstration component, namely a post-tensioned beam, featuring two multi-functional characteristics—structural and architectural—is designed, produced, and evaluated. The scientific concepts and research tools used to develop these quantitative design tools are multidisciplinary, including rheological characterization, control of the internal structure and composition of granular materials, simulation of the mechanical behavior of green material during printing, and the hardened properties of the components, all utilizing structural optimization to enhance performance.
Review and Analysis of Modern Laser Beam Welding Processes
Authors: Klimpel Andrzej
Published: September 2024

Laser beam welding is the most modern and promising process for the automatic or robotized welding of structures of the highest Execution Class, EXC3-4, which are made of a variety of weldable structural materials, mainly steel, titanium, and nickel alloys, but also a limited range of aluminum, magnesium, and copper alloys, reactive materials, and even thermoplastics. This paper presents a systematic review and analysis of the author’s research results, research articles, industrial catalogs, technical notes, etc., regarding laser beam welding (LBW) and laser hybrid welding (LHW) processes. Examples of industrial applications of the melt-in-mode and keyhole-mode laser welding techniques for low-alloy and high-alloy steel joints are analyzed. The influence of basic LBW and LHW parameters on the quality of welded joints proves that the laser beam power, welding speed, and Gas Metal Arc (GMA) welding current firmly decide the quality of welded joints. A brief review of the artificial intelligence (AI)-supported online quality-monitoring systems for LBW and LHW processes indicates the decisive influence on the quality control of welded joints.
Characteristics of Open-Graded Friction Course Macrotexture and Macrostructure and Its Effect on Skid Resistance under Rainfall
Authors: Song Liang , Yun Di , Ye Wei , Gao Jie
Published: September 2024

An Open-Graded Friction Course (OGFC) presents a rough surface and a porous structure and provides skid resistance under wet conditions, differing from that of a dense graded mixture. This study explored the distribution of surface macrotexture with depth in OGFC. Using cross-sectional images and semantic image segmentation techniques, the internal structure, porosity, and void size distribution were analyzed to assess the effectiveness of rainfall drainage. Skid resistance was evaluated with a British Pendulum Tester, focusing on the influence of surface macrotexture and internal macrostructure, particularly with regard to contact depth. Results show that finer gradations increase surface roughness peaks, which are concentrated near the top surface. In contrast, coarser mixtures exhibit a greater effective contact depth and more peaks with higher curvature. Finer gradations also result in lower porosity, greater void dispersion, and smaller average void diameters. During heavy rainfall, OGFC-13 exhibits the highest friction coefficient due to its effective contact, surface roughness, and internal voids, which facilitate water expulsion. This research provides insights into the skid resistance mechanism of OGFC in wet conditions and offers practical guidance for selecting the optimal gradation.
Enhanced Cyclically Stable Plasticity Model for Multiaxial Behaviour of Magnesium Alloy AZ31 under Low-Cycle Fatigue Conditions
Authors: Litrop Aljaž , Klemenc Jernej , Nagode Marko , Šeruga Domen
Published: September 2024

Magnesium alloys, particularly AZ31, are promising materials for the modern automotive industry, offering significant weight savings and environmental benefits. This research focuses on the challenges associated with accurate modelling of multiaxial cyclic plasticity at small strains of AZ31 under low-cycle fatigue conditions. Current modelling approaches, including crystal plasticity and phenomenological plasticity, have been extensively explored. However, the existing models reach their limits when it comes to capturing the complexity of cyclic plasticity in magnesium alloys, especially under multiaxial loading conditions. To address this gap, a cyclically stable elastoplastic model is proposed that integrates elements from existing models with an enhanced algorithm for updating stresses and hardening parameters, using the hyperbolic tangent function to describe hardening and ensure a stabilised response with closed hysteresis loops for both uniaxial and multiaxial loading. The model is based on a von Mises yield surface and includes a kinematic hardening rule that promises a stable simulation of the response of AZ31 sheets under cyclic loading. Using experimental data from previous studies on AZ31 sheets, the proposed model is optimised and validated. The model shows promising capabilities in simulating the response of AZ31 sheet metal under different loading conditions. It has significant potential to improve the accuracy of fatigue simulations, especially in the context of automotive applications.
Effects of Cooling Media on Microstructure and Mechanical Properties in Friction Stir Welded SA516 Gr.70 Cryogenic Steel Joints
Authors: Wang Xiuying , Wang Yu , Xu Jiujun , Sun Juncai , Wang Yuqian , Xie Guangming
Published: September 2024

SA516 Gr.70 steels were welded by friction stir welding (FSW) under various media of air, water, and water + CO2 cooling, and the effect of the cooling media on the microstructure and mechanical properties of joints was systematically analyzed. The nugget zone (NZ) under the air-cooling condition contained coarse bainite + martensite. Martensite was obtained by decreasing the cooling media temperature. Furthermore, tensile fracturing of the joints occurred in the basal metal (BM), and the ultimate tensile strength of the joints under various cooling media was similar to that of the BM. However, with decreasing cooling media temperature, the total elongation of the joints noticeably increased. Good strength (545 MPa) and elongation (16.8%) were obtained in the joints under the water + CO2 cooling condition since the fine martensite microstructure enhanced the plastic deformation capacity of the joints. In addition, in the NZ under water + CO2 cooling condition, good toughness of 110 J/cm2 was obtained due to a high fraction of high-angle boundaries and fine martensite.
The Effect of the Loading Frequency on the Dynamic Bending Strength of Spruce Wood
Authors: Lipovec Zupanc Enej , Humar Miha , Fajdiga Gorazd
Published: September 2024

Wood is increasingly being used in construction as an alternative to steel and concrete. As wood is an inhomogeneous material, this has a strong effect on its static and dynamic properties. When timber is used as a load-bearing component, there is a possibility that it will be exposed to unfavourable weather conditions (wind) or dynamic environments (vibrations), leading to fatigue of the material. In this article, the effects of load frequency and load magnitude on the durability of Norway spruce wood (Picea abies) were investigated. The frequencies of 5 and 10 Hz were compared at three load levels of 70%, 80% and 90% of the static breaking force. The research results show that the load magnitude has a major influence on the dynamic strength at the same fatigue frequency. Each increase in load means a lower dynamic strength of the spruce, which is reflected in the load cycles achieved. In addition, the dynamic properties of spruce wood deteriorate with an increasing loading frequency, which is more pronounced at higher loading forces. These test results are the basis for determining the Wöhler curve, which is required as input data for the material properties in numerical calculations to determine the durability of the material.
Investigation of MO Adsorption Kinetics and Photocatalytic Degradation Utilizing Hollow Fibers of Cu-CuO/TiO2 Nanocomposite
Authors: Theodorakopoulos George V. , Papageorgiou Sergios K. , Katsaros Fotios K. , Romanos George Em. , Beazi-Katsioti Margarita
Published: September 2024

This comprehensive study explores the kinetics of adsorption and its photocatalytic degradation of methyl orange (MO) using an advanced copper-decorated photocatalyst in the form of hollow fibers (HFs). Designed to boost both adsorption capacity and photocatalytic activity, the photocatalyst was tested in batch experiments to efficiently remove MO from aqueous solutions. Various isotherm models, including Langmuir, Freundlich, Sips, Temkin, and Dubinin–Radushkevich, along with kinetic models like pseudo-first and pseudo-second order, Elovich, Bangham, and Weber–Morris, were utilized to assess adsorption capacity and kinetics at varying initial concentrations. The results indicated a favorable MO physisorption on the nanocomposite photocatalyst under specific conditions. Further analysis of photocatalytic degradation under UV exposure revealed that the material maintained high degradation efficiency and stability across different MO concentrations. Through the facilitation of reactive oxygen species generation, oxygen played a crucial role in enhancing photocatalytic performance, while the degradation process following the Langmuir–Hinshelwood model. The study also confirmed the robustness and sustained activity of the nanocomposite photocatalyst, which could be regenerated and reused over five successive cycles, maintaining 92% of their initial performance at concentrations up to 15 mg/L. Overall, this effective nanocomposite photocatalyst structured in the form of HF shows great promise for effectively removing organic pollutants through combined adsorption and photocatalysis, offering valuable potential in wastewater treatment and environmental remediation.
The Effects of Laser-Assisted Winding Process Parameters on the Tensile Properties of Carbon Fiber/Polyphenylene Sulfide Composites
Authors: Geng Hongbo , Cao Xuewen , Zu Lei , Pan Helin , Zhang Guiming , Zhang Qian , Fu Jianhui , Zhou Lichuan , Wu Qiaoguo , Jia Xiaolong , Liu Honghao
Published: September 2024

Currently, there is limited research on the in situ forming process of thermoplastic prepreg tape winding, and the unclear impact of process parameters on mechanical properties during manufacturing is becoming increasingly prominent. The study aimed to investigate the influence of process parameters on the mechanical properties of thermoplastic composite materials (CFRP) using laser-assisted CF/PPS winding forming technology. The melting point and decomposition temperature of CF/PPS materials were determined using DSC and TGA instruments, and based on the operating parameters of the laser-assisted winding equipment, the process parameter range for this fabrication technology was designed. A numerical model for the temperature of laser-heated CF/PPS prepreg was established, and based on the filament winding process setup, the heating temperature and tensile strength were simulated and tested. The effects of process parameters on the heating temperature of the prepreg and the tensile strength of NOL rings were then analyzed. The non-dominated sorting genetic algorithm (NSGA-II) was employed to globally optimize the process parameters, aiming to maximize winding rate and tensile strength. The results indicated that core mold temperature, winding rate, laser power, and their interactions significantly affected mechanical properties. The optimal settings were 90 °C, 418.6 mm/s, and 525 W, achieving a maximum tensile strength of 2571.51 MPa. This study provides valuable insights into enhancing the forming efficiency of CF/PPS-reinforced high-performance engineering thermoplastic composites.
Enhancing the Mechanical Properties of AM60B Magnesium Alloys through Graphene Addition: Characterization and Regression Analysis
Authors: Huang Song-Jeng , Sanjaya Jeffry , Adityawardhana Yudhistira , Kannaiyan Sathiyalingam
Published: September 2024

The light weight and high strength of magnesium alloys have garnered significant attention, rendering them suitable for various applications across industries. Nevertheless, to meet industrial requirements, the mechanical properties must be improved. This investigation explores the potential of graphene addition to enhance the mechanical properties of AM60B magnesium alloy. Tests were conducted on samples with different weight percentages (wt.%) of graphene (0 wt.%, 0.1 wt.%, and 0.2 wt.%) using stir casting. The elongation and tensile strength of the composite materials were also assessed. The phase composition, particle size, and agglomeration phenomena were analyzed using characterization techniques such as X-ray diffraction, optical microscopy, and SEM-EDS. The yield strength of the magnesium alloy was enhanced by approximately 13.4% with the incorporation of 0.1 wt.% graphene compared to the alloy without graphene. Additionally, an 8.8% increase in elongation was observed. However, the alloy tensile properties were reduced by adding 0.2 wt.% graphene. The tensile fractography results indicated a higher probability of brittle fracture with 0.2 wt.% graphene. Furthermore, regression analysis employing machine learning techniques revealed the potential of predicting the stress–strain curve of composite materials.
Buckling Performance Evaluation of Double-Double Laminates with Cutouts Using Artificial Neural Network and Genetic Algorithm
Authors: Ju Ruiqing , Zhao Kai , Featherston Carol A. , Liu Xiaoyang
Published: September 2024

Although the double-double (DD) laminates proposed by Tsai provide a promising option for achieving better structural performance with lower manufacturing and maintenance costs, the buckling performance of perforated DD laminates still remains clear. In this study, optimal ply angles, rotation angles, and the corresponding maximum buckling loads are determined for DD laminates with various cutouts, which are used for comparisons to evaluate the effects of cutout size and shape on the buckling behaviour of perforated DD laminates. Apart from conventional circular and elliptical cutouts, the use of a combined-shape cutout for DD laminates is also investigated. As a large number of optimisations are required to obtain the maximum buckling loads for different cases in this study, an efficient optimisation method for perforated DD laminates is proposed based on an artificial neural network (ANN) and a genetic algorithm (GA). Unlike conventional quadaxial (QUAD) laminates, the repetition of a four-ply sublaminate in DD laminates makes their layup to be represented by only two ply angles; hence, the application of ANN models for predicting the buckling behaviour of various perforated DD laminates is studied in this paper. The superior performance of the ANN models is demonstrated by comparisons with other machine learning models. Instead of using the time-consuming FEA, the developed ANN model is utilised within a GA to obtain the maximum buckling load of perforated DD laminates. Compared to the circular cutout, the use of elliptical and combined-shape cutouts leads to more noticeable changes in the optimal ply angles as the cutout size increases. Based on the obtained results, the use of the combined-shape cutout is recommended for DD laminates.
Rapid Assessment of Sulfate Resistance in Mortar and Concrete
Authors: Mousavinezhad Seyedsaleh , Toledo William K. , Newtson Craig M. , Aguayo Federico
Published: September 2024

Extensive research has been conducted on the sulfate attack of concrete structures; however, the need to adopt the use of more sustainable materials is driving a need for a quicker test method to assess sulfate resistance. This work presents accelerated methods that can reduce the time required for assessing the sulfate resistance of mixtures by 70%. Class F fly ash has historically been used in concrete mixtures to improve sulfate resistance. However, environmental considerations and the evolving energy industry have decreased its availability, requiring the identification of economically viable and environmentally friendly alternatives to fly ash. Another challenge in addressing sulfate attack durability issues in concrete is that the standard sulfate attack test (ASTM C1012) is time-consuming and designed for only standard mortars (not concrete mixtures). To expedite the testing process, accelerated testing methods for both mortar and concrete mixtures were adopted from previous work to further the development of the accelerated tests and to assess the feasibility of testing the sulfate resistance of mortar and concrete mixtures rapidly. This study also established criteria for interpreting sulfate resistance for each of the test methods used in this work. A total of 14 mortar mixtures and four concrete mixtures using two types of Portland cement (Type I and Type I/II) and various supplementary cementitious materials (SCMs) were evaluated in this study. The accelerated testing methods significantly reduced the evaluation time from 12 months to 21 days for mortar mixtures and from 6 months to 56 days for concrete mixtures. The proposed interpretation method for mortar accelerated test results showed acceptable consistency with the ACI 318-19 interpretations for ASTM C1012 results. The interpretation methods proposed for the two concrete sulfate attack tests demonstrated excellent consistency with the ASTM C1012 results from mortar mixtures with the same cementitious materials combinations. Metakaolin was shown to improve sulfate resistance for both mortar and concrete mixtures, while silica fume and natural pozzolan had a limited impact. Using 15% metakaolin in mortar or concrete mixtures with Type I/II cement provided the best sulfate resistance.
High-Precision Instance Segmentation Detection of Micrometer-Scale Primary Carbonitrides in Nickel-Based Superalloys for Industrial Applications
Authors: Zhang Jie , Zheng Haibin , Zeng Chengwei , Gu Changlong
Published: September 2024

In industrial production, the identification and characterization of micron-sized second phases, such as carbonitrides in alloys, hold significant importance for optimizing alloy compositions and processes. However, conventional methods based on threshold segmentation suffer from drawbacks, including low accuracy, inefficiency, and subjectivity. Addressing these limitations, this study introduced a carbonitride instance segmentation model tailored for various nickel-based superalloys. The model enhanced the YOLOv8n network structure by integrating the SPDConv module and the P2 small target detection layer, thereby augmenting feature fusion capability and small target detection performance. Experimental findings demonstrated notable improvements: the mAP50 (Box) value increased from 0.676 to 0.828, and the mAP50 (Mask) value from 0.471 to 0.644 for the enhanced YOLOv8n model. The proposed model for carbonitride detection surpassed traditional threshold segmentation methods, meeting requirements for precise, rapid, and batch-automated detection in industrial settings. Furthermore, to assess the carbonitride distribution homogeneity, a method for quantifying dispersion uniformity was proposed and integrated into a data processing framework for seamless automation from prediction to analysis.
Excellent Electrochromic Properties of Ti4+-Induced Nanowires V2O5 Films
Authors: Deng Yufei , Li Hua , Liang Jian , Liao Jun , Huang Min , Chen Rui , Long Yinggui , Robichaud Jacques , Djaoued Yahia
Published: September 2024

Ti4+-doped V2O5 films with nanowires on top and a dense, long nanorod layer on the bottom were successfully fabricated using the spin-coating route. During the electrochromic cycling, charge transfer resistance (Rct) decreases while ion-diffusion ability (KΩ) rapidly drops in the first ten cycles and then levels off. Low Rct and morphology of nanowires collaboratively improved the electrochromic behavior of Ti4+-doped V2O5 films by enhancing the charge transfer speed and minimizing polarization and dissolution. The obtained Ti4+-doped V2O5 film shows better electrochromic properties than the undoped V2O5 film, with a coloration efficiency (CE) of 34.15 cm2/C, coloration time of 9.00 s, and cyclic retention of 82.6% at cycle 100. In contrast, the corresponding values for the undoped V2O5 film were 23.57 cm2/C, 13.16 s, and 43.6%.
Synergistically Boosting Li Storage Performance of MnWO4 Nanorods Anode via Carbon Coating and Additives
Authors: Wang Duo , Wang Zhaomin , Wang Chunli , Yin Dongming , Liang Yao , Wang Limin , Cheng Yong , Feng Ming
Published: September 2024

Polyanionic structures, (MO4)n−, can be beneficial to the transport of lithium ions by virtue of the open three-dimensional frame structure. However, an unstable interface suppresses the life of the (MO4)n−-based anode. In this study, MnWO4@C nanorods with dense nanocavities have been synthesized through a hydrothermal route, followed by a chemical deposition method. As a result, the MnWO4@C anode exhibits better cycle and rate performance than MnWO4 as a Li-ion battery; the capacity is maintained at 718 mAh g−1 at 1000 mA g−1 after 400 cycles because the transport of lithium ions and the contribution of pseudo-capacitance are increased. Generally, benefiting from the carbon shell and electrolyte additive (e.g., FEC), the cycle performance of the MnWO4@C electrode is also effectively improved for lithium storage.
Micromechanical and Tribological Performance of Laser-Cladded Equiatomic FeNiCr Coatings Reinforced with TiC and NbC Particles
Authors: Okulov Artem , Iusupova Olga , Liu Kun , Li Jie , Stepchenkov Alexander , Zavalishin Vladimir , Korkh Yulia , Kuznetsova Tatyana , Mugada Krishna Kishore , Moganraj Arivarasu
Published: September 2024

This paper discusses a comparative micromechanical and tribological analysis of laser-cladded equiatomic FeNiCr coatings reinforced with TiC and NbC particles. Two types of coatings, FeNiCr-TiC (3 wt.% TiC) and FeNiCr-NbC (3 wt.% NbC), were deposited onto an AISI 1040 steel substrate by means of short-pulsed laser cladding. The chemical composition, microstructure, and micromechanical and tribological characteristics of the coatings were systematically investigated via optical and scanning electron microscopy, Raman spectroscopy, and mechanical and tribological tests. The average thicknesses and compositional transition zones of the coatings were 600 ± 20 μm and 150 ± 20 μm, respectively. Raman spectroscopy revealed that both coatings are primarily composed of a single FCC γ-phase (γ-FeNiCr). The FeNiCr + 3 wt.% TiC coating exhibited an additional TiC phase dispersed within the γ-FeNiCr matrix. In contrast, the FeNiCr + 3 wt.% NbC coating displayed a more homogeneous distribution of finely dispersed NbC phase throughout the composite, leading to enhanced mechanical behavior. Micromechanical characterization showed that the FeNiCr + 3 wt.% NbC coating possessed higher average microhardness (3.8 GPa) and elastic modulus (180 GPa) compared to the FeNiCr + 3 wt.% TiC coating, which had values of ~3.2 GPa and ~156 GPa, respectively. Both coatings significantly exceeded the AISI 1040 steel substrate in tribological performance. The FeNiCr + 3 wt.% TiC and FeNiCr + 3 wt.% NbC coatings exhibited substantial reductions in both weight loss (37% and 41%, respectively) and wear rate (33% and 42%, respectively) compared to the substrate material. These findings indicate that more finely dispersed NbC particles are better suited for hardening laser-cladded equiatomic FeNiCr-NbC coatings, making them advanced candidates for industrial applications.