Industrial Fields Research Articles

Controlling Directional Liquid Transfer over a Ratchet-like Surface with Oriented Open-Wedges

Directed transfer of liquids is widely used as an important mass transfer strategy in the nature biology, industry, agriculture, and medical fields. Based on the bionic concept, researchers have constructed various forms of surface structures to realize controlled transport of liquids. However, current methods for preparing surfaces with directional liquid transport often suffer from cumbersome preparation processes, complexity of the prepared structures, and high costs. In this paper, we prepared a topological structure surface capable of precisely controlling liquid transport by a facile method. By utilizing 3D printing technology, we designed and fabricated a wedge-shaped surface with a periodic tilting scale arrangement. We accomplished the precise regulation of liquid transport on the surface of the structure, which not only realized the bi-directional transport of liquid anisotropically to the uni-directional transport on the surface but also achieved the precise regulation of the distance and speed of liquid transport through adjusting the opening angle formed by the open-wedges. We expect that the as-prepared wedge-shaped surface will provide a facile and efficient strategy for directional liquid transport.

Enhanced Deoxygenation of Solvents via an Improved Inert Gas Bubbling Method with a Ventilation Pathway.

We introduce an improved inert gas bubbling method for solvent deoxygenation, featuring a ventilation path alongside the inert gas inlet to enhance the efficiency and reproducibility. While essential for life, oxygen's reactivity can disrupt scientific and industrial processes by forming unwanted intermediates and deactivating catalysts, necessitating efficient deoxygenation methods. Traditional methods like freeze-pump-thaw (FPT) are effective but time-consuming, require stringent safety measures, and have potential limitations for use with aqueous and biological samples. Our enhanced inert gas bubbling method retains the simplicity and safety of conventional bubbling while achieving FPT-like deoxygenation efficiency, demonstrated by photoluminescence intensity and lifetime measurements in acetonitrile (ACN) and toluene (TOL). Simulations using a simplified kinetic model and the Stern-Volmer equation reveal that the added ventilation pathway reduces oxygen contamination in Ar gas bubbles, improving the deoxygenation efficiency. This method is widely applicable in academic and industrial fields, requiring consistent and efficient solvent deoxygenation.

Numerical simulation of an unsteady ternary hybrid nanofluid along a convectively heated curved stretching sheet: A Tiwari-Das model

Nanofluids are smart fluids, which are very useful for heat and mass transfer enhancement. They are very much used in industrial, transportation, biomedical, and electronics fields. In the present research, we examine the nanofluid flow across a curved stretching sheet (CSS). Also, this work’s noteworthy originality is its discussion of the impacts of the physical parameters on the ternary hybrid nanofluid flow and heat transfer. Furthermore, the simulation considers nanofluids with water as the base fluid. The Koo–Kleinstreuer–Li (KKL) model examines the viscosity and effective thermal conductivity of fluid flow with suspended nanoparticles. The governing equations of the momentum and the temperature are transformed into ordinary differential equations (ODEs) by similarity transformations. These equations are then solved using a Secant iteration method combined with a Runge–Kutta–Fehlberg (RKF) integration scheme with a shooting technique. Through graphs and tables, the influences of the relevant non-dimensional parameters are presented and analyzed. The findings show that an increase in the curvature parameter and the Biot number is to increase the temperature gradient. The Streamline profiles for various values of the magnetic parameter are also analyzed through figures. We believe that this research has significant implications in biomedical devices and pharmaceutical fluid preparation in biomedical sciences. Some of them are high temperature and cooling processes, paints, space technology, medicines, cosmetics, conductive coatings, bio-sensors, and to name a few.

Open-loop temperature rise history control method with scanning electron beam as a programmable heat source

Temperature-rise history control technology is widely applied in rapid thermal process and material thermal assessment. We present an open-loop temperature rise history control method. The fundamental concept involves loading a heat flux history onto the sample’s surface, inversed from the target temperature rise history, to regulate the sample’s temperature through programming the energy flux history of the scanning electron beam. We reconstructed the temperature-rise history of the leading edge of an aircraft during the HIFIRE-5b flight test with a relative deviation of 1%. Additionally, we reconstructed a typical temperature rise curve of a silicon wafer during rapid thermal processing with a relative deviation of 1%. In theory, this temperature control method can be extended to two-dimensional and three-dimensional heat transfer processes to achieve transient control of sample surface/cross-section temperature field with one- or two-dimensional distribution characteristics. This research can provide a high-performance temperature control technology for a wide range of industrial fields, and lay a theoretical foundation for the development of related technologies based on temperature history analysis.

A multi-rate sensor fusion and multi-task learning network for concurrent fault diagnosis of hydraulic systems

Hydraulic systems are widely used in key modern industrial fields such as mechanical manufacturing, aerospace, and heavy machinery, and their efficient and reliable operation is crucial to ensuring production safety and efficiency. However, hydraulic systems often experience concurrent faults, such as pump failures, valve blockages, pipeline leaks, and fluid contamination, which pose significant challenges to the fault diagnosis in hydraulic systems. This paper introduces a multi-task learning network that deconstructs the challenge of concurrent fault diagnosis into specific sub-tasks, enabling the simultaneous identification and classification of multiple hydraulic components' faults. Automatic channel filtering is designed to screen out sensitive channels of each component from multi-rate sensors. A dual-flow model is used to feature extraction, which can simultaneously extract the local spatial features and global semantic information. Then, four classification models are designed to identify the extracted shared features. An uncertainty weight loss is also proposed to balance the loss of different tasks. The experimental results show that our model significantly outperforms traditional methods and other popular multi-output methods in diagnosing concurrent faults.

The state of the hypothalamic-pituitary-adrenal system in workers exposed to electric and magnetic fields of industrial frequenc

Introduction. Electric and magnetic fields of industrial frequency are an integral part of production processes, and their effect on the human body is of increasing interest both among scientists and among specialists in the field of medicine and occupational safety. In recent decades, there has been growing concern about the potential impact of these areas on the health of workers, especially those who are exposed to prolonged exposure in the course of their professional activities. In this context, we pay special attention to the neuroendocrine system, which plays a key role in regulating many physiological processes, including metabolism, stress reactions and maintaining homeostasis. The study of the fundamentals of the neuroendocrine system and the mechanisms of the effect of electromagnetic fields on the body not only contributes to the deepening of scientific knowledge in the field of biomedical research, but also has practical significance in ensuring occupational safety and improving working conditions in industrial enterprises, allowing to identify possible health consequences and develop effective measures to protect workers from negative bioeffects. The study aims to research the effects of electric and magnetic fields of industrial frequency on the functional state of the hypothalamic-pituitary-adrenal system of employees of electric grid facilities. Materials and methods. Specialists examined 144 employees of the main group engaged in the repair and operation of electric grid facilities and 40 employees of the control group who were not exposed to electromagnetic fields of industrial frequency. We determined the concentration of adrenocorticotropic hormone, cortisol and dehydroepiandrosterone sulfate in blood serum samples and calculated the ratio of dehydroepiandrosterone sulfate to cortisol. Results. Workers exposed to the combined effects of electric and magnetic fields of industrial frequency had a lower (p<0.05) level of adrenocorticotropic hormone (1.9 [0.5; 3.9] pg/ml) and dehydroepiandrosterone sulfate (5.8 [3.7; 8.4] mmol/l) in the blood compared with the group of workers exposed to magnetic fields industrial frequency fields (4.4 [2.1; 12.9] pg/ml; 9.8 [6.3; 14.5] mmol/l) and to the control group (6.4 [4.0; 19.8] pg/ml; 7.8 [4.7; 13.3] mmol/l), respectively. The scientists also revealed in the group of workers exposed to combined exposure in the field, the association of a low ratio of dehydroepiandrosterone sulfate and cortisol with the development of cardiovascular diseases and feedback from work experience (p<0.05). Limitations. The study is limited by the number of people surveyed (144 employees). Conclusion. The results of the study indicate the hypoactivity of the hypothalamic-pituitary-adrenal system induced by exposure to electric and magnetic fields of industrial frequency, manifested by a decrease in the level of adrenocorticotropic hormone, dehydroepiandrosterone sulfate and its relationship to cortisol in workers exposed to combined effects of electric and magnetic fields of industrial frequency. Ethics. The work complies with ethical standards developed in accordance with the Helsinki Declaration of the World Medical Association "Ethical Principles of conducting Scientific medical research with human participation" as amended in 2000 and the "Rules of Clinical Practice in the Russian Federation" approved by Order of the Ministry of Health of the Russian Federation dated 04/01/2016 No. 200n. We received informed consent from each examined person to participate in the study, approved in accordance with the established procedure by the local Ethics Committee of Izmerov Research Institute of Occupational Health (minutes of the meeting of the Ethics Committee No. 17 dated 12/16/2015).

Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.

Adaptive spatial down-sampling method based on object occupancy distribution for video coding for machines

As the performance of machine vision continues to improve, it is being used in various industrial fields to analyze and generate massive amounts of video data. Although the demand for and consumption of video data by machines has increased significantly, video coding for machines needs to be improved. It is therefore necessary to consider a new codec that differs from conventional codecs based on the human visual system (HVS). Spatial down-sampling plays a critical role in video coding for machines because it reduces the volume of the video data to be processed while maintaining the shape of the data’s features that are important for the machine to reference when processing the video. An effective method of determining the intensity of spatial down-sampling as an efficient coding tool for machines is still in the early stages. Here, we propose a method of determining an optimal scale factor for spatial down-sampling by collecting and analyzing information on the number of objects and the ratio of the area occupied by the object within a picture. We compare the data reduction ratio to the machine accuracy error ratio (DRAER) to evaluate the performance of the proposed method. By applying the proposed method, the DRAER was found to be a maximum of 21.40 dB and a minimum of 11.94 dB. This shows that video coding gain for the machines could be achieved through the proposed method while maintaining the accuracy of machine vision tasks.

Optimizing drying time of lacquer-based MTCS-modified SiO2-TiO2 coatings for enhanced mechanical durability, superhydrophobicity and photocatalytic activity

This research successfully improved the mechanical durability of a superhydrophobic coating on wet lacquer. Methyltrichlorosilane (MTCS) modified SiO2-TiO2 nanoparticles (NPs) were applied to a wet lacquer layer with various drying times using a simple spraying technique. The optimal coating exhibited superhydrophobicity with a water contact angle of 157° and a sliding angle of 2°. Additionally, the superhydrophobic surface could withstand over 100,000 water drops, 200 g of sand, and 40 tape-peeling repetitions, indicating high mechanical stability. Moreover, the superhydrophobic surface demonstrated photocatalytic activity by degrading methylene blue (MB) solution. The coating showed excellent adaptability to different substrates, including automotive body panels, with self-cleaning properties. This simple and effective strategy has potential for large-scale fabrication for practical applications in various industrial fields.

Correction of systematic low-frequency time delay errors in THz systems by absorption line shape optimization

Time-domain terahertz systems can face challenges due to systematic delay errors introduced by the employed delay mechanism, potentially leading to poor data quality. This article introduces a procedure to address these challenges by correcting low-frequency systematic errors that distort the acquired spectra and incorrectly diminish narrow absorption features. Our procedure solves an optimization problem aiming to find the corrected time-signal pairs that maximize the depth of narrow absorption features, and we highlight how the flexibility of the procedure, in principle, allows for correcting error profiles of arbitrary shape. We demonstrate the effectiveness of this approach through experiments using both simulated and experimentally recorded THz data, and the results show significant improvements in spectral accuracy. We believe this can pave the way for more reliable and precise terahertz spectroscopy measurements, enhancing its applications in various scientific and industrial fields.

Flatness jump phenomenon in cold-rolled steel strip with high temperature: Root cause and solution

The steel strip produced in the 20-high cold rolling mill has the characteristic of high temperature in a certain factory, and a rare strip flatness phenomenon occurs in the fourth pass, different from the common flatness defects, the flatness value of each position in the strip width shows an irregular and drastic fluctuation state along with time, and it is called the flatness jump phenomenon, which seriously affects the stability of the rolling process and the production efficiency. Aiming at this problem, a method combining the rolling experiment with the numerical simulation is used to study its generation mechanism and seek for effective control technique. Through the thermal imaging measurement and the analysis of the production data, the temperature fluctuation in the fourth pass is the main reason for the flatness jump, which is related to the nucleate boiling state when the emulsion impacts on the high-temperature strip surface in the cooling process. Based on the rolling control experiments of different cooling factors, the critical temperature range for the occurrence of flatness jump is determined, which is used for the improvement of cooling technique subsequently, and the range of emulsion exit flow is optimized to 500 L/min ∼ 2050 L/min. The rolling experiment is carried out in the industrial field, it is found that the production process is stable and the flatness jump does not appear, with the small value basically within the range of ± 10 IU, and the scheme is planned for long-term application after a period of optimization, which could ultimately enhance the stability of the rolling process and the performance of finished product. Further, the paradigm and information provided in this work would be beneficial for understanding the formation mechanism of the similar complicated flatness phenomenon in cold rolling and optimizing the industrial applications of cooling technology.

Research Progress of Deep-Red to Near-Infrared Electroluminescent Materials Based on Organic Cyclometallated Platinum(II) Complexes.

In recent years, the near-infrared (NIR) light-emitting materials have attracted increasing attention due to the broad application prospects in the fields of military industry, aerospace, lighting, display and wearable devices. As the transition metal complexes, platinum(II) complexes have been shown to emit luminescence efficiently in NIR organic light-emitting diodes because of the unique d8 electron structure. This structure ensures that the platinum(II) complex molecules exhibit a high planarity, variety of excited states, and strong intermolecular interactions. This review summarizes the research progress of deep red to NIR organic light-emitting materials based on platinum(II) complexes in recent years and provides a certain reference for the further design and synthesis of NIR platinum(II) complex luminescent materials with superior performance.

Pose Estimation of a Cobot Implemented on a Small AI-Powered Computing System and a Stereo Camera for Precision Evaluation.

The precision of robotic manipulators in the industrial or medical field is very important, especially when it comes to repetitive or exhaustive tasks. Geometric deformations are the most common in this field. For this reason, new robotic vision techniques have been proposed, including 3D methods that made it possible to determine the geometric distances between the parts of a robotic manipulator. The aim of this work is to measure the angular position of a robotic arm with six degrees of freedom. For this purpose, a stereo camera and a convolutional neural network algorithm are used to reduce the degradation of precision caused by geometric errors. This method is not intended to replace encoders, but to enhance accuracy by compensating for degradation through an intelligent visual measurement system. The camera is tested and the accuracy is about one millimeter. The implementation of this method leads to better results than traditional and simple neural network methods.

Production of invertase from Penicillium spp. under solid state fermentation and its immobilization on magnetic nanoparticles

Invertase is an industrially significant enzyme that catalyzes the hydrolysis of sucrose into glucose and fructose. The present work aimed to produce invertase from Penicillium spp. under solid-state fermentation, utilizing banana peel waste as the substrate, and to characterize and immobilize the enzyme using chitosan-coated magnetic nanoparticles. The partially purified invertase was obtained via ammonium sulfate precipitation. The stability of crude, partially purified, and immobilized invertase was assessed for various parameters such as pH, temperature, thermal stability, metal ions, and chemical inhibitors. Invert syrup was produced via continuous bioconversion. The optimal incubation period for invertase production was observed to be four days, resulting in a maximum invertase activity of 75 U/ml. The maximum activities of the crude and partially purified invertase were observed at pH 6, temperature 50°C, and metal ion KCl, while the chemical inhibitor EDTA enhanced the activity. Partial purification of the crude invertase with 70% ammonium sulfate yielded 49 U/ml, and immobilization on chitosan-coated magnetic nanoparticles resulted in a 59.01% immobilized yield. The maximum immobilized invertase activity was observed at 70°C, and the immobilization procedure did not significantly influence pH, metal ions, or chemical inhibitor activity. Invert syrup production via continuous bioconversion resulted in an enzyme activity of 31 U/ml. This study highlights the potential applications of invertase in the food industry, pharmaceuticals, confectionaries, and related fields.

The Synopsis of Soft Computing.

The traditional method of problem-solving, known as hard computing, is limited in its ability to handle modern digital technology and real-world problems accurately. Soft computing, a newer paradigm, offers a more versatile approach by utilizing multi-valued logic and human knowledge to solve complex, nonlinear problems efficiently. Unlike hard computing, soft computing can handle imprecise data and uncertainty effectively. This methodology has been successfully applied across various sectors, including scientific, industrial, and medical fields, providing more accurate results. Soft computing is good for its contributions to revolutionizing problem-solving techniques, being tolerant of imprecision, uncertainty, and linguistic variables, and offering approximate solutions to intricate problems.

Roll force prediction using hybrid genetic algorithm with semi-supervised support vector regression

Roll force prediction plays a significant role in rolling schedule and optimization. For a specific steel grade, the roll force can be determined by the aid of several factors: rolling speed, initial thickness, ratio of thickness reduction, the starting temperature of the strip, and the friction coefficient in the contact region. Roll force prediction mathematical models are sometimes rare and inaccurate. This paper presents a new approach to predict roll separating force using semi-supervised support vector regression (SSSVR). The parameters affecting the sensitivity of the SSSVR were optimized using the genetic algorithm to maximize the r-squared accuracy score. The intelligent system is evaluated using two quality metrics: the root mean square error (RMSE) and the mean absolute error calculated between the measured force from the industrial rolling field and the predicted force using the proposed system from one side, and the measured force and the calculated force from another side. Obtained results show the improvement while using the intelligent predictive system. The reduction in RMSE was achieved by the proposed system by 66.9% and 32.1% for oval and round shape passes, respectively in comparison to the conventional calculation method.

Synthesis, structure, I–V characteristics, and optical properties of chromium oxide thin films for optoelectronic applications

Abstract Improving coating technology and thin film formation by optimizing the experimental parameters has become essential for various industrial and technological fields. This work aims to study the influence of the precursor materials on the physical and electro-optical properties of Cr2O3 thin films. The solutions were prepared using the sol–gel route and deposited on glass slides using the spin coating technique. The structure and morphology of the films were studied using XRD, FT-infrared (IR), and field-emission scan-electron microscope. The results indicated the formation of a high-purity Cr2O3 (Eskolaite) phase in the form of spherical nanoparticles with sizes of 17–25 nm. Three bands appear at 490, 765, and 889 cm − 1 {{\rm{cm}}}^{-1} in the FTIR spectra, which are attributed to Cr – O {\rm{Cr}}{\rm{\mbox{--}}}{\rm{O}} / Cr ═ O {\rm{Cr}}{\rm{═}}{\rm{O}} vibrations. The I–V curves showed linear behavior and good ohmic features. Ultraviolet-visible-near infrared spectra showed that the films are highly transparent, with band gaps in the range of 2.60–2.90 eV, and refractive indices in the range of 1.92–2.25. The sheet resistances, the new figure of merit, the real and fictional dielectric constants, and the optical conductivity were discussed. The Cr2O3 thin films are the best candidates for various utilizations, including solar cells, sensors in the IR region, and energy storage.

Fabrication and characterization of a new high-strength alloy via WAAM using GMAW+cold wire feeding

The demand for high-strength steels has continued to increase in various fields of industry due to the advancement in technology. The wire arc additive manufacturing (WAAM) enables the fabrication of high-strength structures from different materials by controlling the raw wire in layers. This study focuses on the fabrication of high-strength steel by WAAM using the gas metal arc welding + cold wire feeding (GMAW + CWF) technique. A new alloy was formed by simultaneously adding 316LSi stainless steel from the CWF to the low alloyed ER70S-6 steel from the GMAW torch. In this way, three different new alloyed structures were produced with varied 316LSi ratios ranging from 10 %, 20 % and 25 % respectively. Besides, their metallurgical and mechanical properties were compared with each other and with single material WAAMed structures. In the macrostructure examinations, no defects due to the mixture of both materials were found and both materials melted properly to create the new alloy. Due to the alloying elements transferred from 316LSi, the microstructure of the new material parts were completely changed compared to the single material WAAMed components and bainite and martensite phases were found in the new alloys. The hardness increased significantly compared to the single-material WAAMed structures and increased up to 139 % and 84 % compared to the single-material WAAMed ER70S-6 and 316LSi, respectively. The tensile strength showed a great improvement and reached almost 1200 MPa, increased by 142 % compared to the single-material WAAMed structure. The fabricated new alloys have the potential to be a candidate material for industries that use high-strength steel.

Regional fintech development, financing constraints, and enterprise innovation: evidence from China’s A-share non-financial listed companies

ABSTRACT This study uses a fixed effect model to examine the impact based on a sample of non-financial A-share listed companies in the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2010 to 2019. The empirical results show that regional fintech development has a significantly positive effect on enterprise innovation, while heterogeneity analysis results indicate that this effect is more significant for small and medium-sized enterprises and secondary industry fields. Further research finds that regional fintech development not only has significant spatial autocorrelation, but it also has spillover effects on enterprise innovation. Additionally, the mechanism test shows that financing constraints are part of the mediating factor between regional fintech and enterprise innovation. We conclusively present three recommendations for enriching the development of regional fintech, thus allowing for increased innovation potential and the sustainable growth of companies.

Study on thermal-assisted underwater friction stir welding for carbon fiber-reinforced polyetherimide

Friction stir welding (FSW) of carbon fiber-reinforced thermoplastic has shown attractive applications in industrial fields. However, the heat input during the FSW is hard to control, which leads to significant material loss and carbon fiber damage. In this paper, a carbon fiber-reinforced polyetherimide (CF-PEI) plate is joined via thermal-assisted underwater friction stir welding (UFSW). The evaluation of heat-assisted UFSW joints is conducted by studying the surface morphology, microstructure of the weld interface, the state and distribution of carbon fibers, and tensile analysis. The findings indicated that water can reduce heat input, and the water temperature has a significant impact on the weld properties. Specifically, at a water temperature of 60°C, there is a considerable reduction in material loss and carbon fiber damage. The study also reveals that this temperature facilitates the interweaving of carbon fibers into a stable structure at the interface and their integration with the PEI substrate. This reduces the inhibition of the material on carbon fiber elongation, ultimately enhancing the bonding interface and improving tensile shear properties. The maximum tensile strength value of the welds reached 33.6 MPa, equivalent to 79.6% of the CF-PEI sheet. Compared to FSW, joint tensile strength is increased by 45.5%.