bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effectiveness of Ozone Infiltration on Patient-Reported Outcomes in Low Back Pain: A Systematic Review and Meta-Analysis
Authors: Llombart-Blanco Rafael , Mariscal Gonzalo , Cordón Violeta , Barrios Carlos , Benlloch María , Llombart-Ais Rafael
Published: October 2024

Background/Objective: The objective of this systematic review and meta-analysis was to evaluate the current evidence on the relative efficacy of ozone injections for improving patient-reported outcomes (PROMs). Methods: A literature search was conducted in four databases, and PROMs were analyzed. Odds ratios (ORs), mean differences, and standard mean differences with 95% confidence intervals (CI) were calculated. Meta-analysis was performed using Review Manager. Results: Nine studies (total number of participants = 1711) were included. Overall, the ODI (Oswestry Disability Index) showed favorable results for ozone (SMD −0.28, 95% CI −0.51 to −0.06). However, subgroup analysis by follow-up time found no significant differences at 2 weeks or 6 months but significant differences at 1 month. The VAS also favored ozone use overall (SMD −0.12, 95% CI −0.24 −0.01). Subgroup and sensitivity analyses revealed no significant differences between groups. There were also no significant differences in excellent outcomes (OR 0.95, 95% CI 0.54 to 1.67) or poor outcomes (OR 1.25, 95% CI 0.66 to 2.37). Conclusions: Ozone therapy has demonstrated limited benefits over the usual care for lower back pain. While a small advantage emerged for function at one month, the results were inconsistent, and no differences were seen in pain or clinical outcomes.
Recent Advances in Biomimetic Related Lubrication
Authors: Shao Jinqiang , Lan Guiyao , Song Haoxin , Dong Xiaoxiao , Li Ming
Published: October 2024

Friction is ubiquitous in industry and daily life, which not only leads to the wear and tear of equipment and machinery, but also causes a lot of energy waste. Friction is one of the significant factors leading to energy loss in mechanical systems. Therefore, it is essential to minimize friction losses. Creatures in nature have evolved various surfaces with different tribological characteristics to adapt to the environment. By studying, understanding, and summarizing the friction and lubrication regulation phenomena of typical surfaces in nature, various bionic friction regulation theories and methods are obtained to guide the development of new lubrication materials and lubrication systems. This article primarily discusses the study of lubrication mechanisms through biomimetic design, which is mainly divided into chemical approaches, structural strategies, and chemical–structural coupling approaches. From the chemical point of view, this paper mainly summarizes joint lubrication and engineering lubrication in biomedicine, with inspiration from lotus leaves, fish skin, and snake skin, each with unique antifriction structures which are famous for their super hydrophobicity in nature. Finally, chemical–structural coupling simulates the lubrication mechanism of natural organisms from the joint action of biological structures and chemical substances, and is applied to coating design, so as to reduce the friction and wear on coating surfaces, improve the durability and anti-pollution ability of coatings, significantly improve the tribological performance of mechanical systems, promote scientific innovation, and promote energy conservation, emission reduction, and sustainable development.
Influence of Engine Oil Degradation on Sliding Bearings, with Special Focus on Different Degrees of Nitration
Authors: Besser Charlotte , Agocs Adam , Tomastik Christian , Jankes Erik , Burda Jaromír , Kanaya Ryuji , Ando Akira , Haneda Yuma , McAleese Colin
Published: October 2024

Bismuth (Bi) can be considered for use as a green substitute for lead in bearing applications. However, accelerated Bi oxidation can occur during operation, creating a brittle surface and resulting in premature seizure failure. Thus, the aim of this study was to evaluate the influence of engine oil degradation, especially nitration processes, on the oxidation of Bi. Tailor-made artificially aged oils with different degrees of nitration were produced and utilized in static bearing oxidation tests. By means of X-ray photoelectron spectroscopy (XPS), the Bi surfaces were analyzed regarding their chemical compositions after the tests. The results were correlated with the respective oil conditions determined via conventional parameters as well as high-resolution mass spectrometry. The findings obtained revealed a direct correlation between the amount of Bi-oxide and the nitration values of the oil, proving there was a positive impact of nitration products on the oxidation of the Bi surfaces. A comparison with the Bi content in the oils demonstrated a protective effect of the oxide layer as the Bi content declined with an increase in nitration. Overall, valuable insight into understanding the impact of oil condition on engine parts is given, and the importance of testing engine parts with aged lubricants is emphasized.
Measurement and Dynamic Analysis of the Centroid Trajectories of Angular-Contact Ball Bearing Cages
Authors: You Jinyuan , Ma Xiqiang , Liu Chunyang , Zuo Mengjie , Jin Dongliang , Zhang Hao
Published: October 2024

When a high-speed rolling bearing cage comes into contact with rollers, it experiences random movement due to friction and collision, which significantly impacts the overall performance of the bearing. To further investigate the motion law of cages, a test bench for a 7010C angular-contact bearing cage was constructed. This setup utilized laser sensors to obtain changes in attitude and displacement during operation. After analyzing how cage deflection errors influenced trajectory measurements, corrections were applied to the measurement results. Additionally, an investigation was conducted into the effects of varying rotational speeds on the dynamic performance of the cage. Simulations were performed using ADAMS software, which verified both the effectiveness of the measuring method and the testing results. The findings indicated that within the tested range of rotational speeds, the centroid trajectory stability of the cage gradually improved as rotational speed increased and then began to show a tendency to deteriorate. Furthermore, there existed a negative correlation between the deflection error of the cage and the centroid trajectory stability.
Synthesis and Electrochemical Properties of Oleylamine as a Sour Saline Corrosion Inhibitor Under Laminar Flow at 40 °C
Authors: Alvarez-Malpica Jorge , Carrera-Gutiérrez Karime , Chinchillas-Chinchillas Manuel , Herrera Zaldivar Manuel , Martinez-Garcia Alfredo , Orozco-Carmona Victor M.
Published: October 2024

In this study, the synthesis of a long-chain aliphatic amino compound and its sour corrosion inhibition properties were reported. Oleylamine was obtained through the reaction of 4-(Aminomethyl) pyridine with 1-chloro-octadecane. The identification and characterization of reaction products were carried out through Fourier transform infrared spectroscopy (FTIR) and gas chromatography/mass spectroscopy (GC-MS). Oleylamine was tested as a sour corrosion inhibitor for steels. Different concentrations of oleylamine (0, 5, 10, 25, and 100 ppm) in a sour saline electrolyte were analyzed. The dynamic anticorrosive behavior of oleylamine on carbon mild steel (AISI 1018) surfaces was evaluated using a laminar flow of 100 rpm and tested with potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) measurements. After electrochemical testing, the surface of the steel specimens that were used was characterized with Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The electrochemical results of the anticorrosive efficiency of oleylamine for steel showed an exponential behavior as a function of inhibitor concentration. At a concentration of 20 ppm of the inhibitor, the anticorrosive efficiency did not show any significant changes. However, at 100 ppm of the inhibitor, an efficiency of over 95% was achieved. After the electrochemical tests, the surface of the steel samples with the inhibitor revealed the formation of an inhibitor layer that prevented the corrosion of the steel.
The Influence of Er and Zr on the Microstructure and Durability of the Mechanical Properties of an Al-Mg Alloy Containing 7 wt.% of Mg
Authors: Limanówka Kamila , Boczkal Sonia , Mitka Monika , Szymańska Elżbieta , Hrabia-Wiśnios Joanna , Kapinos Dawid , Augustyn Bogusław , Skrzyńska Renata , Grzebinoga Łukasz , Czaja Paweł , Góral Anna , Czeppe Tomasz
Published: October 2024

Al-Mg alloys are characterized by permanent solid solution hardening and can additionally be work-hardened. The high mechanical properties of Al-Mg alloys with above-standard Mg content obtained after plastic deformation processes decrease over time. The addition of minor alloying elements like Er or Zr is an alternative method to improve the durability of mechanical properties and increase the strength of Al-Mg alloys due to densely and evenly distributed dispersoids being formed. In this paper, Al-Mg alloys with above-standard Mg content (7 wt.%) and Zr and Er micro-alloying elements and their influence on the microstructure and durability of the mechanical properties were examined. The cast ingots of AlMg7 alloys were characterized by a smooth surface without cracks. The plastic deformation process in a static compression test resulted in an about 60 HBW increase in the Brinell hardness of all the deformed alloys relative to casting. It was revealed that the addition of Er and Zr significantly improved the mechanical properties and durability of the mechanical properties of the Al-Mg after annealing. The addition of Er or Zr slightly restrained the decrease in the Brinell hardness after annealing but did not inhibit it.
Facile Formation of Metallic Surface with Microroughness via Spray-Coating of Copper Nanoparticles for Enhanced Liquid Metal Wetting
Authors: Kim Ji-Hye , So Ju-Hee , Koo Hyung-Jun
Published: October 2024

This paper presents a simple, fast, and cost-effective method for creating metallic microstructured surfaces by spray-coating a dispersion of copper nanoparticles (CuNPs) onto polymethyl methacrylate (PMMA) substrates, enabling the imbibition-induced wetting of liquid metal. The formation of these microstructured patterns is crucial for the spontaneous wetting of gallium-based liquid metals. Traditional techniques for producing such microstructures often involve complex and costly lithography and vacuum deposition methods. In contrast, this study demonstrates that liquid metal wetting can occur with metal microstructures formed through a straightforward spray-coating process. To immobilize the CuNPs on the polymer substrate, an organic solvent that dissolves the polymer surface was employed as the dispersion medium. The effects of various spray-coating parameters, including distance and time, on the uniformity and immobilization of CuNP films were systematically investigated. Under optimal conditions (120 s of spray time and 10 cm spray distance), CuNPs dispersed in dichloromethane (DCM) yielded uniform and stable microstructured surfaces. The spontaneous wetting of gallium-based liquid metal was observed on the fabricated CuNP film. Additionally, liquid metal selectively wet the CuNP patterns formed by stencil techniques, establishing electrical connections between electrodes. These findings underscore the potential of spray-coating for fabricating metallic surfaces to drive the formation of liquid metal patterns in flexible electronics applications.
Enhancement of 5-Fluorouracil Drug Delivery in a Graphene Oxide Containing Electrospun Chitosan/Polyvinylpyrrolidone Construct
Authors: Grant Jamie J. , Pillai Suresh C. , Perova Tatiana S. , Brennan Barry , Hinder Steven J. , McAfee Marion , Hehir Sarah , Breen Ailish
Published: October 2024

Electrospun nanofibrous mats, consisting of chitosan (CS) and polyvinylpyrrolidone (PVP), were constructed with the addition of graphene oxide (GO) for enhancement of delivery of the 5-Fluorouracil (5-Fu) chemotherapy drug. Upon studying the range of GO concentrations in CS/PVP, the concentration of 0.2% w/v GO was chosen for inclusion in the drug delivery model. SEM showed bead-free, homogenous fibres within this construct. This construct also proved to be non-toxic to CaCo-2 cells over 24 and 48 h exposure. The construction of a drug delivery vehicle whereby 5-Fu was loaded with and without GO in various concentrations showed several interesting findings. The presence of CS/PVP was revealed through XPS, FTIR and Raman spectroscopies. FTIR was also imperative for the analysis of 5-Fu while Raman exclusively highlighted the presence of GO in the samples. In particular, a detailed analysis of the IR spectra recorded using two FTIR spectrometers, several options for determining the concentration of 5-Fu in composite fibre systems CS/PVP/5-Fu and GO/CS/PVP/5-Fu were demonstrated. By analysis of Raman spectra in the region of D and G bands, a linear dependence of ratios of integrated intensities of AD and AG on the intensity of host polymer band at 1425 cm−1 vs. GO content was found. Both methods, therefore, can be used for monitoring of GO content and 5-Fu release in studied complex systems. After incorporating the chemotherapy drug 5-Fu into the constructs, cell viability studies were also performed. This study demonstrated that GO/CS/PVP/5-Fu constructs have potential in chemotherapy drug delivery systems.
Deformation Performance of Longitudinal Non-Uniformly Corroded Reinforced Concrete Columns
Authors: Sun Guoyao , Jiang Huanjun
Published: October 2024

Due to the complexity of the marine corrosive environment, the rebar corrosion in reinforced concrete (RC) bridge piers is usually longitudinal non-uniform. However, the study on the mechanical behavior of longitudinal non-uniformly corroded RC structural members is very limited. To systematically study the deformation performance of the longitudinal non-uniformly corroded RC columns, the finite element models of 106 RC columns with different parameters were established using the commercial software ABAQUS 2016. The effects of the height of the bottom section (represented in the text by the variable “position”), the length, and the rebar corrosion ratio of the corroded segment on the deformation performance of the longitudinal non-uniformly corroded RC columns were analyzed. It is found that the change in the position of the corroded segment on the column may change the most unfavorable section of the column and the failure mode. The length of the corroded segment significantly affects the yield deformation. The ultimate plastic deformation increases with the increase of position or length of the corroded segment. With the increase of rebar corrosion ratio of the corroded segment, the ultimate plastic deformation decreases.
Machine Vision to Provide Quantitative Analysis of Meltpool Stability for a Coaxial Wire Directed Energy Deposition Process
Authors: McLain Braden , Mathenia Remy , Sparks Todd , Liou Frank
Published: October 2024

Wire-based additive manufacturing (AM) is at the forefront of complex metal fabrication because of its scalability for large components, potential for high deposition rates, and ease of use. A common goal of wire directed energy deposition (DED) is preserving a stable process throughout deposition. If too little energy is put into the deposition, the wire will stub into the substrate and begin oscillating, creating turbulence within the meltpool. If too much energy exists, the wire will overheat, causing surface tension to take over and create liquid drips as opposed to a solid bead. This paper proposes a computer vision technique to work as both a state detection and event detection system for wire stability. The model utilizes intensity variations along with frame-to-frame difference calculations to determine process stability. Because the proposed model does not rely on machine learning techniques, it is possible for an individual to interpret and adjust as they see fit. The first part of this paper describes creation and implementation of the model. The model’s capability was then evaluated using a 1D laser power experiment, which generated a wide range of stability states across varying powers. The model’s accuracy was evaluated through 3D geometry data gathered from the experimentally deposited beads. The model proved to be both capable and accurate and has potential to be used as a real-time control system with future work.
Enhancing the Mechanical Properties of Co-Cr Dental Alloys Fabricated by Laser Powder Bed Fusion: Evaluation of Quenching and Annealing as Heat Treatment Methods
Authors: Konieczny Bartlomiej , Szczesio-Wlodarczyk Agata , Andrearczyk Artur , Januszewicz Bartlomiej , Lipa Sebastian , Zieliński Rafał , Sokolowski Jerzy
Published: October 2024

Residual stresses and anisotropic structures characterize laser powder bed fusion (L-PBF) products due to rapid thermal changes during fabrication, potentially leading to microcracking and lower strength. Post-heat treatments are crucial for enhancing mechanical properties. Numerous dental technology laboratories worldwide are adopting the new technologies but must invest considerable time and resources to refine them for specific requirements. Our research can assist researchers in identifying thermal processes that enhance the mechanical properties of dental Co-Cr alloys. In this study, high cooling rates (quenching) and annealing after quenching were evaluated for L-PBF Co-Cr dental alloys. Cast samples (standard manufacturing method) were tested as a second reference material. Tensile strength, Vickers hardness, microstructure characterization, and phase identification were performed. Significant differences were found among the L-PBF groups and the cast samples. The lowest tensile strength (707 MPa) and hardness (345 HV) were observed for cast Starbond COS. The highest mechanical properties (1389 MPa, 535 HV) were observed for the samples subjected to the water quenching and reheating methods. XRD analysis revealed that the face-centered cubic (FCC) and hexagonal close-packed (HCP) phases are influenced by the composition and heat treatment. Annealing after quenching improved the microstructure homogeneity and increased the HCP content. L-PBF techniques yielded superior mechanical properties compared to traditional casting methods, offering efficiency and precision. Future research should focus on fatigue properties.
Plastic Workability and Rheological Stress Model Based on an Artificial Neural Network of SiCp/Al-7.75Fe-1.04V-1.95Si Composites
Authors: Feng Pinming , Chen Shuang , Tang Jie , Liu Haiyang , Fu Dingfa , Teng Jie , Jiang Fulin
Published: October 2024

SiCp/Al-Fe-V-Si composites exhibit complex deformation behaviors at both room and high temperatures because of the presence of SiC reinforcement particles and numerous fine dispersed Al12(Fe, V)3Si heat-resistant phases. In this work, an artificial neural network (ANN) constitutive model was established to study the deformation behavior of SiCp/Al-7.75Fe-1.04V-1.95Si composites over a wide temperature range based on uniaxial compression. Then, microstructural observation, finite element analysis, and processing maps were utilized to investigate the plastic workability. The results showed that the ANN model fit the experimental stress–strain curves with high accuracy, achieving an R2 value of 0.999. The ANN model was embedded into finite element software to study plastic deformation behaviors, which indicated that this model could accurately compute the plastic and mechanical response during the compressing process. Finally, a thermomechanical processing diagram was developed, revealing that the optimal processing parameters of the SiCp/Al-7.75Fe-1.04V-1.95Si composites were a deformation temperature of 450–500 °C and a deformation rate of 0.1–0.2 s−1.
Electrodeposition of Sn-Ru Alloys by Using Direct, Pulsed, and Pulsed Reverse Current for Decorative Applications
Authors: Verrucchi Margherita , Mazzoli Giulio , Comparini Andrea , Emanuele Roberta , Bonechi Marco , Del Pace Ivan , Giurlani Walter , Fontanesi Claudio , Kowalik Remigiusz , Innocenti Massimo
Published: October 2024

Pulsed current has proven to be a promising alternative to direct current in electrochemical deposition, offering numerous advantages regarding deposit quality and properties. Concerning the electrodeposition of metal alloys, the role of pulsed current techniques may vary depending on the specific metals involved. We studied an innovative tin–ruthenium electroplating bath used as an anti-corrosive layer for decorative applications. The bath represents a more environmentally and economically viable alternative to nickel and palladium formulations. The samples obtained using both direct and pulsed currents were analyzed using various techniques to observe any differences in thickness, color, composition, and morphology of the deposits depending on the pulsed current waveform used for deposition.
Thermal Conductivity of AlSi10MnMg Alloy in Relation to Casting Technology and Heat Treatment Method
Authors: Nováková Iva , Jelínek Milan , Švec Martin
Published: October 2024

Nowadays, with the development of electromobility, the requirements not only for the mechanical properties but also for the thermal conductivity of castings are increasing. This paper investigates the influence of casting and heat treatment technology on the thermal diffusivity and thermal conductivity of an AlSi10MnMg alloy. The thermal diffusivity was monitored as a function of temperature in the range of 50–300 °C for the material cast by high-pressure die casting (HPDC) and also by gravity sand casting (GSC) and gravity die casting (GDC). This study also investigated the effect of the T5 heat treatment temperature (artificial ageing without prior solution treatment—HT200, HT300, and HT400) on the thermal conductivity of the material cast by different technologies. Experiments confirmed that the thermal diffusivity or thermal conductivity of the alloy depends on the casting technology. The slower the cooling rate of the casting, the higher the thermal conductivity value. For the alloy in the as-cast condition, the thermal conductivity at 50 °C is in the range of about 125 to 138 [W·m−1·K−1]. Regardless of the casting method, the thermal conductivity tends to increase with temperature (50–300 °C). Furthermore, a positive effect of heat treatment without prior solution treatment (HT200, HT300, and HT400) on the thermal conductivity was demonstrated. Regardless of the casting method of the samples, the thermal conductivity also increases with increasing heat treatment temperature. The results further showed that when artificial ageing is performed in industrial practice on castings to increase mechanical properties in the temperature range of 160–230 °C, this heat treatment has a positive effect on thermal conductivity.
Influence of Strain Rate on Barkhausen Noise in Trip Steel
Authors: Pitoňák Martin , Mičietová Anna , Moravec Ján , Čapek Jiří , Neslušan Miroslav , Ganev Nikolaj
Published: October 2024

This paper deals with Barkhausen noise in Trip steel RAK 40/70+Z1000MBO subjected to uniaxial plastic straining under variable strain rates. Barkhausen noise is investigated especially with respect to microstructure alterations expressed in terms of phase composition and dislocation density. The effects of sample heating and the corresponding Taylor–Quinney coefficient are considered as well. Barkhausen noise of the tensile test is measured in situ as well as after unloading of the samples. In this way, the contribution of external and residual stresses on Barkhausen noise can be distinguished in the direction of tensile loading, as well as in the transversal direction. It was found that the in situ-measured Barkhausen noise grows in both directions as a result of tensile stresses and the realignment of domain walls. The post situ-measured Barkhausen noise drops down in the direction of tensile load due to the high opposition of dislocation density at the expense of the growing transversal direction due to the prevailing effect of the realignment of domain walls. The temperature of the sample remarkably grows along with the increasing strain rate which corresponds with the increasing Taylor–Quinney coefficient. However, this effect plays only a minor role, and the density of the lattice imperfection expressed especially in terms of dislocation density prevails.
Fatigue Life Prediction of 2024-T3 Al Alloy by Integrating Particle Swarm Optimization—Extreme Gradient Boosting and Physical Model
Authors: Li Zhaoji , Yue Haitao , Zhang Ce , Dai Weibing , Guo Chenguang , Li Qiang , Zhang Jianzhuo
Published: October 2024

The multi-parameter characteristics of the physical model pose a challenge to the fatigue life prediction of 2024-T3 aluminum (Al) alloy. In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in this study. The fatigue performance and failure mechanism of the 2024-T3 Al alloy are analyzed. Furthermore, the fatigue life prediction physical model of the 2024-T3 Al alloy is established by using the energy method of fracture mechanics. The physical model incorporates critical physical parameters. Meanwhile, the PSO algorithm optimizes the hyperparameters of the XGBoost model based on fatigue data of the 2024-T3 Al alloy. Eventually, the optimized XGBoost model is used to solve the parameters of the physical model. Furthermore, the analytical equation of the fatigue life prediction model is obtained. This paper provides a new method for solving the parameters of the fatigue life prediction model, which reduces the error and cost of obtaining the model parameters in the experiment and shortens the time required.
Numerical Simulation and Experimental Analysis for Microwave Sintering Process of Lithium Hydride (LiH)
Authors: Lu Yuanjia , Shuai Maobing , Gao Jiyun , Ye Xiaolei , Guo Shenghui , Yang Li , Huang Bin , Zhang Jiajia , Hou Ming , Gao Lei , Zhou Ziqi
Published: October 2024

Dense lithium hydride (LiH) is widely used in neutron shielding applications for thermonuclear reactors and space systems due to its unique properties. However, traditional sintering methods often lead to cracking in LiH products. This study investigates the densification sintering of LiH using microwave technology. A multiphysics model was established based on the measured dielectric properties of LiH at different temperatures, allowing for a detailed analysis of the electromagnetic and thermal field distributions during the microwave heating of cylindrical LiH samples. The results indicate that the electric field distribution within the LiH is relatively uniform, with resistive losses concentrated primarily in the LiH region of the microwave cavity. LiH rapidly absorbs microwave energy, reaching the sintering temperature of 520 °C in just 415 s. Additionally, the temperature difference between the low- and high-temperature regions during the sintering process remains below 5 °C, demonstrating excellent uniform heating characteristics. The microwave sintering process enhances interface migration within the LiH samples, resulting in dense metallurgical bonding between grains. In summary, this research provides valuable insights and theoretical support for the rapid densification of LiH materials, highlighting the potential of microwave technology in improving material properties.
Measuring the Electro-Optical Kerr Effect Against the Background of Electro-Absorption Modulation in Liquids
Authors: Ledzion Rafał , Izdebski Marek , Rambo Anita
Published: October 2024

A new approach to the dynamic polarimetric method is proposed, which allows for the decoupling of electro-optical Kerr effect measurements from the electro-absorption effect in partially transparent liquids. The method is illustrated by using the results of engine oil measurements as a function of temperature and modulating field frequency. It was shown that the birefringence induced in the sample, the modulation of the ordinary wave transmission, and the modulation of the extraordinary wave transmission in the sample can be shifted in phase with respect to the square of the applied alternating modulating field. Each of these three phase shifts can depend differently on the temperature and frequency. Neglecting the influence of electro-absorption on electro-optical measurements in liquids or considering electro-absorption as an effect correlated in phase with induced birefringence may lead to significant measurement errors. This indicates that the Kerr constant and the electro-absorption coefficients for an alternating electric field should be considered as complex quantities instead of real values, as they have been traditionally. The proposed approach fills an important gap in measurement techniques described in the literature, which may provide erroneous results for measurements of the Kerr constant in partially transparent liquids including many industrially important liquids.
Innovative Electrostatic Precipitator Solutions for Efficient Removal of Fine Particulate Matter: Enhancing Performance and Energy Efficiency
Authors: Sokolovskij Edgar , Kilikevičius Artūras , Chlebnikovas Aleksandras , Matijošius Jonas , Vainorius Darius
Published: October 2024

The removal of particulate matter (PM) from air streams is essential for advancing environmental technologies and safeguarding public health. This study explores the performance of an electrostatic precipitator (ESP) in eliminating fine and ultra-fine PM under varied experimental conditions. It uniquely examines the influence of PM size and feed rate on ESP removal efficiency. The system’s use of low voltages enhances energy sustainability, while its innovative design improves corona discharge, leading to significant reductions in fine and ultra-fine PM emissions. Plants using electrical devices are increasingly being incorporated into material processing lines to reduce pollution in the surrounding work area, as well as to collect particle emissions in the atmosphere. It is also possible to recycle some raw materials in this way with low energy consumption. This cleaning technology increases the added value of industrial equipment, which affects its competitiveness and its impact on sustainable manufacturing. The experimental results indicate a steady electrostatic field voltage of 15.1 kilovolts, with an airflow maintained at 0.8 m/s through a doser at 2.5 bar, eliminating the need for a fan. The PM feed rate varied between 2 and 20 mm/h, with six trials conducted to ensure the data were consistent. Preliminary studies devoid of ESP intervention demonstrated little PM removal, since buildup on the chamber walls distorted the results. The installation of the ESF markedly enhanced the removal efficiency, achieving up to 95.5%. Further analysis revealed that ESP performance depended on PM concentration in the agglomeration chamber, achieving a clearance rate exceeding 98% under optimal conditions. Fine PM (0.35 to 8.7 µm) was more efficiently removed than ultra-fine PM (0.2 to 0.35 µm). The highest removal efficiency was observed at a feed rate of 0.962 mg/s, while the lowest occurred at 0.385 mg/s. A strong positive correlation between particle concentration and removal efficiency (Pearson value up to 0.829) was observed, particularly at feed rates of 0.128, 0.641, and 1.283 mg/s. The study’s findings confirm that the ESP is highly effective in removing particulate matter, particularly fine and ultra-fine particles, with an optimal feed rate, significantly enhancing the system’s performance.
Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine
Authors: Kolokas Alexandros , Mallioris Panagiotis , Koutsiantzis Michalis , Bialas Christos , Bechtsis Dimitrios , Diamantis Evangelos
Published: October 2024

The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries.
Effect of an Applied Magnetic Field on Joule Heating-Induced Thermal Convection
Authors: Hiremath Anupam M. , Yoshikawa Harunori N. , Mutabazi Innocent
Published: October 2024

Thermal convection induced by internal heating appears in different natural situations and technological applications with different internal sources of heat (e.g., radiation, electric or magnetic fields, chemical reactions). Thermal convection due to Joule heating in weak electrical conducting liquids such as molten salts with symmetric thermal boundary conditions is investigated using linear stability analysis. We show that, in the quasi-static approximation where the induced magnetic field is negligible, the effect of the external magnetic field consists of the delay in the threshold of thermal convection and the increase in the size of thermoconvective rolls for an intense magnetic field. Analysis of the budget of the perturbations’ kinetic energy reveals that the Lorentz force contributes to the dissipation of the kinetic energy.
Modeling and Performance Evaluation of a Cellular Network with OMA and NOMA Users with Batch Arrivals by Means of an M[X]/M/S/0 Model
Authors: Vásquez-Toledo Luis Alberto , González-Flores Carlos , Lopez-Guerrero Miguel , Prieto-Guerrero Alfonso , Tirado-Méndez José Alfredo , Marcelín-Jiménez Ricardo , Rodriguez-Colina Enrique , Pascoe-Chalke Michael , Castillo-Soria Francisco R.
Published: October 2024

Nowadays, efficient spectrum usage is one of the most important design principles to take into account in wireless communications due to the exponential growth of mobile devices. In that sense, solutions such as Non-Orthogonal Multiple Access (NOMA) and cognitive radio (CR) have been proposed. In essence, NOMA allows some interference level by using non-orthogonal resource allocation with a tolerable increase in receiver complexity employing successive interference cancellation (SIC). In this work, a novel mathematical model of teletraffic for users performing accessment, simultaneously, by means of Orthogonal Multiple Access (OMA) and NOMA, is developed using a Markovian process that considers bursts of arrivals to model the access schemes. This novel procedure implies a closed-form solution of the proposed system compared to other works where these parameters are estimated assuming the moment generating function obtained with approximation models. The model is validated with a discrete event simulator, considering different scenarios and simulation conditions. The simulation results are in agreement with the mathematical solution proposed.
Discrete Joint Random Variables in Fréchet-Weibull Distribution: A Comprehensive Mathematical Framework with Simulations, Goodness-of-Fit Analysis, and Informed Decision-Making
Authors: Das Diksha , Alshammari Tariq S. , Rashedi Khudhayr A. , Das Bhanita , Jyoti Hazarika Partha , Eliwa Mohamed S.
Published: October 2024

This paper introduces a novel four-parameter discrete bivariate distribution, termed the bivariate discretized Fréchet–Weibull distribution (BDFWD), with marginals derived from the discretized Fréchet–Weibull distribution. Several statistical and reliability properties are thoroughly examined, including the joint cumulative distribution function, joint probability mass function, joint survival function, bivariate hazard rate function, and bivariate reversed hazard rate function, all presented in straightforward forms. Additionally, properties such as moments and their related concepts, the stress–strength model, total positivity of order 2, positive quadrant dependence, and the median are examined. The BDFWD is capable of modeling asymmetric dispersion data across various forms of hazard rate shapes and kurtosis. Following the introduction of the mathematical and statistical frameworks of the BDFWD, the maximum likelihood estimation approach is employed to estimate the model parameters. A simulation study is also conducted to investigate the behavior of the generated estimators. To demonstrate the capability and flexibility of the BDFWD, three distinct datasets are analyzed from various fields, including football score records, recurrence times to infection for kidney dialysis patients, and student marks from two internal examination statistical papers. The study confirms that the BDFWD outperforms competitive distributions in terms of efficiency across various discrete data applications.
Mathematical Data Models and Context-Based Features for Enhancing Historical Degraded Manuscripts Using Neural Network Classification
Authors: Savino Pasquale , Tonazzini Anna
Published: October 2024

A common cause of deterioration in historic manuscripts is ink transparency or bleeding from the opposite page. Philologists and paleographers can significantly benefit from minimizing these interferences when attempting to decipher the original text. Additionally, computer-aided text analysis can also gain from such text enhancement. In previous work, we proposed the use of neural networks (NNs) in combination with a data model that characterizes the damage when both sides of a page have been digitized. This approach offers the distinct advantage of allowing the creation of an artificial training set that teaches the NN to differentiate between clean and damaged pixels. We tested this concept using a shallow NN, which proved effective in categorizing texts with varying levels of deterioration. In this study, we adapt the NN design to tackling remaining classification uncertainties caused by areas of text overlap, inhomogeneity, and peaks of degradation. Specifically, we introduce a new output class for pixels within overlapping text areas and incorporate additional features related to the pixel context information to promote the same classification for pixels adjacent to each other. Our experiments demonstrate that these enhancements significantly improve the classification accuracy. This improvement is evident in the quality of both binarization, which aids in text analysis, and virtual restoration, aimed at recovering the manuscript’s original appearance. Tests conducted on a public dataset, using standard quality indices, reveal that the proposed method outperforms both our previous proposals and other notable methods found in the literature.
BlockLoader: A Comprehensive Evaluation Framework for Blockchain Performance Under Various Workload Patterns
Authors: Wang Gang , Zhang Yanfeng , Ying Chenhao , Zhang Qinnan , Peng Zhiyuan , Li Xiaohua , Yu Ge
Published: October 2024

Hyperledger Fabric is one of the most popular permissioned blockchain platforms widely adopted in enterprise blockchain solutions. To optimize and fully utilize the platform, it is desired to conduct a thorough performance analysis of Hyperledger Fabric. Although numerous studies have analyzed the performance of Hyperledger Fabric, three significant limitations still exist. First, existing blockchain performance evaluation frameworks rely on fixed workload rates, which fail to accurately reflect the performance of blockchain systems in real-world application scenarios. Second, the impact of extending the breadth and depth of endorsement policies on the performance of blockchain systems has yet to be adequately studied. Finally, the impact of node crashes and recoveries on blockchain system performance has yet to be comprehensively investigated. To address these limitations, we propose a framework called BlockLoader, which offers seven different distributions of load rates, including linear, single-peak, and multi-peak patterns. Next, we employ the BlockLoader framework to analyze the impact of endorsement policy breadth and depth on blockchain performance, both qualitatively and quantitatively. Additionally, we investigate the impact of dynamic node changes on performance. The experimental results demonstrate that different endorsement policies exert distinct effects on performance regarding breadth and depth scalability. In the horizontal expansion of endorsement policies, the OR endorsement policy demonstrates stable performance, fluctuating around 88 TPS, indicating that adding organizations and nodes has minimal impact. In contrast, the AND endorsement policy exhibits a declining trend in performance as the number of organizations and nodes increases, with an average decrease of 10 TPS for each additional organization. Moreover, the dynamic behaviour of nodes exerts varying impacts across these endorsement policies. Specifically, under the AND endorsement policy, dynamic changes in nodes significantly affect system performance. The TPS of the AND endorsement policy shows a notable decline, dropping from 79.6 at 100 s to 41.96 at 500 s, reflecting a reduction of approximately 47% over time. Under the OR endorsement policy, the system performance remains almost unaffected.
A Stacking Ensemble Based on Lexicon and Machine Learning Methods for the Sentiment Analysis of Tweets
Authors: Malebary Sharaf J. , Abulfaraj Anas W.
Published: October 2024

Sentiment is employed in various fields, such as collecting web-based opinions for the formulation of governmental policies, measuring employee and customer satisfaction levels in business organizations, and measuring the sentiment of the public in political and security matters. The field has recently faced new challenges since algorithms must operate with highly unstructured sentiment data from social media. In this study, the authors present a new stacking ensemble method that combines the lexicon-based approach with machine learning algorithms to improve the sentiment analysis of tweets. Due to the complexity of the text with very ill-defined syntactic and grammatical patterns, using lexicon-based techniques to extract sentiment from the content is proposed. On the same note, the contextual and nuanced aspects of sentiment are inferred through machine learning algorithms. A sophisticated bat algorithm that uses an Elman network as a meta-classifier is then employed to classify the extracted features accurately. Substantial evidence from three datasets that are readily available for public analysis re-affirms the improvements this innovative approach brings to sentiment classification.
Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors
Authors: Siddiqi Faheem Ul Rehman , Ahmad Sadiq , Akram Tallha , Ali Muhammad Umair , Zafar Amad , Lee Seung Won
Published: October 2024

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.
A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector
Authors: Liu Xiaohong , Meng Qingchun , Sun Ruiqi , Zhang Xiangwei
Published: October 2024

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.