Engineering Activities Research Articles

Summary of the results of field and laboratory studies of hydraulic resistance during channel bending

The reliability of channel forecasts, performed using mathematical modeling methods in the design of engineering activities on shipping rivers, largely depends on the accuracy of the assessment of hydraulic resistance and sediment transport parameters. Therefore, the hydraulic resistance of natural channels is one of the largest problems in the dynamics of channel flows and is the object of close attention of scientists. In the resistance of natural channels, roughness appears in three forms: the first of them is the roughness of the granular surface of the bottom; the second is the roughness created by boulders and the third type is the roughness of microforms: ripples and ridges. Channel mesoforms also contribute to the resistance to water movement: spits, side streams, middle streams, islands, bends of the channel and such complex formations as riffles. This type of resistance is usually called the resistance of the channel form. The resistance of the natural channel shape due to the movement and deformation of mesoforms can change significantly over time. The complexity and insufficient study of this issue significantly limit the possibilities of a theoretical approach to its solution. Therefore, the obtained results are mainly empirical or semiempirical in nature. The paper presents the results of experimental and field studies that allowed us to identify the main features of flow movement in winding sections of rivers. One of the features of flow movement at channel turns is the possibility of additional energy losses. The total resistance of a curved section of a channel is made up of three main parts: the resistance of the granular surface of the bottom, the resistance of the bottom ridges, and the resistance of the channel shape. The additional resistance created by the bend depends on the curvature of the channel, the rate of change in depth along the length of the bend, and also reflects the impact of side streams formed near the convex bank on the flow.

A Social-Network-Based Crowd Selection Approach for Crowdsourcing Mobile Apps Requirements Engineering Tasks

Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. Therefore, it is of paramount importance to hear from the interested audience about their desires and requirements in mobile apps. This has stressed the need to employ crowdsourcing in requirements engineering (RE) activities to harness the scattered talent in the crowd. RE tasks require certain software domain knowledge, hence, selecting a suitable subset of the crowd is crucial to obtain high-quality contributions. For that, we propose a crowd selection approach for crowdsourcing mobile app requirements engineering tasks which leverages the untapped crowd available on the social network Twitter (recently changed to X). This article is an extension of our previous work, where we present the proposed social-network-based crowd selection approach design, continue to work on the remaining component of the approach and evaluate the approach through a controlled experiment. For evaluation, the approach was utilized to select a real crowd that were invited to contribute to crowdsourcing requirements elicitation tasks for a fitness mobile app. The quality of the crowdsourced requirements was assessed by experts and the results have provided encouraging and compelling insights about the effectiveness of the proposed approach. The obtained assessment scores for the five quality factors clarity, creativity, relatedness, feasibility and diversity were respectively 4.36, 4.01, 4.29, 4.45 and 4.43 out of 5. Overall, we believe that the proposed social-network-based crowd selection approach could help in eliciting mobile app requirements and features that could cater to the needs of a large audience.

Engineering indigenous dispossession and plantation slavery in the Southeast Gulf coast

In this paper we examine the activities of US Army topographers and engineers in the Apalachicola-Chattahoochee-Flint (ACF) watershed during the violent transformation of the region from the heartlands of the Creek confederacy to US territorial control. A vital waterway for the Creek in the late eighteenth century, the rivers would become an important transportation network in the US plantation economy by the early nineteenth century. We emphasize that the army made its initial infrastructural improvements in the region to provide security for the plantation system. Army engineering in the ACF watershed began in the struggle for the Gulf borderlands, as white American settlers, British forces, Indigenous peoples, and Black maroons fought for control over the contested terrain. US engineers and topographers produced territorial knowledge and physical infrastructure to facilitate the occupation of Indigenous territory and elimination of potential spaces of Black freedom. Topographic knowledge would later serve as the foundation for restructuring the land as property, naturalizing its possession by white plantation owners. Similarly, roads and waterway improvements, created to facilitate troop movements, would later serve as vital transportation infrastructure for settlement and expanding plantation slavery. This paper demonstrates how military engineering techniques designed to secure the Nation in the context of race war subsequently provided the coordinates for reorganizing the land within an emergent plantation economy inside its territorial borders.

Comprehensive Review of Security Requirements for Mitigating Threats and Attacks on IoT Assets

Machine learning and artificial intelligence are increasingly being utilized to automate identifying and defining security requirements (SR) and addressing diverse IoT security issues. Despite its extensive environment, IoT-focused cyberattacks had the largest attack surface. IoT security requirements include data confidentiality, integrity, authentication, access control, and privacy. Inadequate emphasis on assessing security requirements leads to attacks and threats. To address the security issues that threaten the IoT environment, additional security measures are required to protect IoT-based applications from threats and other vulnerabilities. However, the absence of the security requirement assessment in IoT systems architecture jeopardizes security, exposing the system to vulnerabilities and risking organizational assets and reputation while also escalating the cost and time required to address security issues. In this study major threats and attacks are identified relevant to the assets of IoT security requirements. To systematically identify, analyze, and address potential security threats and attacks related to IoT assets, this research proposes a three-step methodology: (1) analysis of the IoT security requirements, (2) Identification of threats and attacks in IoT, and (3) IoT assets centric security threats and attacks. An illustrative example of IoT asset security is provided to highlight potential attacks and threats relevant to IoT assets. This approach offers a practical and clear foundation for the early identification of IoT security requirements and their seamless integration into requirements engineering (RE) activities, contributing to a more secure and resilient IoT system architecture.

Artificial neural network model for convectively heated Casson fluid with the appliance of solar energy

This study is to illustrate the thermometric exchange of Casson fluid’s double diffusive nonlinear radiative heat flux over vertical plate associated with convective boundary conditions, incorporating artificial intelligence (AI)-based neural network computation technique. The AI analysis provides enhanced and optimized results with predictive modeling. The analysis utilizes a dataset generated through Mathematica environment and then embedded the filtered matrix dataset in MATLAB employs AI analysis using the Neural Auto Regressive Exogenous (NARX) method for optimized solutions. Levenberg–Marquard algorithm is used to train neural network. The model’s importance and applications span nuclear reactors, aerodynamics, hydrodynamics, transportation and radiating processes of energy transfer. The physical problem is governed by PDEs that is transformed into a set of ODEs by using similarity transformation. The flow is analyzed against the variation in significant influencing parameter like Prandtl number (Pr), velocity ratio ([Formula: see text]), convective coefficient ([Formula: see text]), Rayleigh number (Ra), buoyancy ratio parameter ([Formula: see text]), radiation parameter ([Formula: see text]) and Casson fluid parameter ([Formula: see text]). Findings of this paper are significant for various industrial, engineering and research-based activities.

Biomimetic Mineralized Collagen Scaffolds for Bone Tissue Engineering: Strategies on Elaborate Fabrication for Bioactivity Improvement.

Biomimetic mineralized collagen (BMC) scaffolds represent an innovative class of bone-repair biomaterials inspired by the natural biomineralization process in bone tissue. Owing to their favorable biocompatibility and mechanical properties, BMC scaffolds have garnered significant attention in bone tissue engineering. However, most studies have overlooked the importance of bioactivity, resulting in collagen scaffolds with suboptimal osteogenic potential. In this review, the composition of the mineralized extracellular matrix (ECM) in bone tissue is discussed to provide guidance for biomimetic collagen mineralization. Subsequently, according to the detailed fabrication procedure of BMC scaffolds, the substances that can regulate both the fabrication process and biological activities is summarized. Furthermore, a potential strategy for developing BMC scaffolds with superior mechanical properties and biological activities for bone tissue engineering is proposed.

Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment

It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good enough or impede subsequent activities. We aim to contribute empirical evidence to the effect that requirements quality defects have on a software engineering activity that depends on this requirement. We conduct a controlled experiment in which 25 participants from industry and university generate domain models from four natural language requirements containing different quality defects. We evaluate the resulting models using both frequentist and Bayesian data analysis. Contrary to our expectations, our results show that the use of passive voice only has a minor impact on the resulting domain models. The use of ambiguous pronouns, however, shows a strong effect on various properties of the resulting domain models. Most notably, ambiguous pronouns lead to incorrect associations in domain models. Despite being equally advised against by literature and frequentist methods, the Bayesian data analysis shows that the two investigated quality defects have vastly different impacts on software engineering activities and, hence, deserve different levels of attention. Our employed method can be further utilized by researchers to improve reliable, detailed empirical evidence on requirements quality.

Research on response characteristics of loess slope and disaster mechanism caused by structural plane extension under excavation

Geological disasters occur frequently in the Loess Plateau due to the joint fissures in the strata and human engineering activities. Against this background, the deformation and failure mode of the loess slope with the structural plane under excavation and the extension mechanism of the structural plane are analyzed and summarized. The results showed that: (1) Through the physical model test, the deformation failure mode of the slope is summarized as the tension-splitting, pressure-sliding shallow failure. The collapse failure process is defined as four stages: Compression deformation, creep deformation, slip deformation and slip failure. (2) Slope displacement is concentrated beneath the pressure plate, increasing linearly under load conditions but becoming nonlinear after excavation conditions. As the excavation angle rises, the displacement range along the structural plane gradually extends toward the slope toe. The displacement time-history curve shows three stages: The lifting load stage, the cumulating deformation stage, and the sliding failure stage. (3) The stress redistribution caused by excavation, prompting deformation and potential failure. As internal stress nears the soil strength limit, human-induced disturbances exacerbate stress redistribution, leading to accumulated stress. Finally released through deformation and cracking. Each excavation condition modifies the original loading transfer path, driving stress redistribution at the slope surface and at the structural plane’s tip. (4) The sudden drop in stress level and sudden rise of accumulated settlement are the characteristics of slope sliding failure. The position of the structural plane determines the position of the slope sliding surface. (5) According to the external characterization of the structural plane, the extension process of the structural plane can be defined as four stages: Initiation of crack extension, classification deformation, subsection extension and compression sealing. According to the extension of the structural plane, the spreading cracks of the slope’s internal structural plane are defined as two types: Fractured cracks and shear cracks.

Natural Hazard Assessment in the Southeastern Margin of the Ría de Arosa (Pontevedra, Spain) Using GIS Techniques

The characterization of natural hazards in coastal environments is of great necessity, especially in the current context of global climate change and increasing population concentrations. This research focuses on a multi-hazard analysis of the main geotechnical, geomorphological, hydrological, and lithological risks in the southeastern margin of the Ría de Arosa using Geographic Information System techniques. The integration of geotechnical characterization maps and natural hazard maps has allowed for the identification of areas with a high susceptibility to natural disasters, which is crucial for territorial planning and management in the context of growing urban pressure and global climate change. The results indicate that poorly consolidated surface formations, especially in transitional areas such as dunes and marshes, are particularly vulnerable. Additionally, areas with higher lithological competence have been identified, where slope changes contribute to ground instability. This analysis provides valuable tools for decision-making and the implementation of risk management policies, promoting sustainable development, the protection of coastal ecosystems, and the prevention of risks from urban planning and civil engineering activities in the Ría de Arosa.

Synthesis and application of phenol-grafted rhamnan sulfate for 3D bioprinting

Rhamnan sulfate (RS) is a sulfated polysaccharide extracted from the cell wall of the green alga Monostroma nitidum. Owing to its negative charge, RS interacts with a variety of proteins, enabling various biological activities, such as antiviral, anticoagulant, and antitumor effects. However, RS does not form a stable hydrogel under physiological conditions, which is required for its beneficial biological activities in tissue engineering. To address this limitation, we developed phenol-grafted rhamnan sulfate (RS-Ph), which allows hydrogelation via horseradish peroxidase (HRP)-mediated cross-linking reactions and can be used for 3D bioprinting. Specifically, we synthesized RS-Ph with three different -Ph content: RS-LPh (16.4 mmol/g), RS-MPh (21.3 mmol/g), and RS-HPh (31.7 mmol/g). Surface plasmon resonance measurements revealed that RS-Ph exhibited a maximum binding capacity of more than 8.3 times higher than that of sodium alginate as a negative control. Additionally, a 10% w/v RS-HPh solution formed a hydrogel within 8.2 ± 0.7 s in the presence of 10 U/mL HRP. Furthermore, high-fidelity 3D bioprinting was achieved using an RS-Ph/cellulose nanofiber composite bioink. Our results demonstrate the potential use of bioactive RS-Ph hydrogels in a wide range of tissue engineering fields, including not only bioprinting but also drug delivery systems and wound dressings.

Dynamic response and constitutive model for coal-rock composite material subjected to impact loading

Comprehensive understanding related to the dynamic responses of the composite materials is critical to prevent natural disasters in underground engineering and mining activities. For this reason, experimental studies were undertaken using dynamic impact tests at different strain rates to explore the failure mechanism using split Hopkinson pressure bar (SHPB) system and Digital Image Correlation (DIC) on composite materials; coal-rock (C-R) and rock-coal (R-C). These results suggest a linear correlation between dissipative energy and fractal dimensions. In addition, they suggest cracks that develop predominantly on the coal side have a dominant influence on the failure process that could be attributed to the interface effects. The damage evolution was established assuming elastic conditions using a three-dimensional coupled FDM-DEM numerical simulation system. Anumerical approach is also developed for interpreting failure mechanism of the composite material based on the fabric tensor and strain energy density. Finally, a constitutiverelationshipis established considering strain rate effect and damage evolution using series element studies. There is a good agreement between the experimental and numerical results, providing justification to the proposed constitutive relationship. The numerical approach in this study is promising for assessing the performance of composite materialstaking account of dynamic loading conditions.

Ecosystem engineering at the regional scale—Beaver impact on floodplain pondscapes

Currently, global climate change has led to alterations in river discharge. In Eastern and Southern Europe, decreasing spring flood levels are observed, leading to drying and overgrowth of floodplain water bodies, especially in semiarid regions. Under such conditions, the scale of influence of the ecosystem engineer, the Eurasian beaver, which is often considered one of the tools for wetland restoration, is of interest. The aim of this study was to analyze beaver digging activity on a regional scale by estimating beaver abundance and occurrence patterns in floodplain pondscapes of the upper Khoper River and by quantifying changes in the morphometric parameters of water bodies due to the impact of this species. During the field survey and remote GIS analysis, 80 water bodies were surveyed. The results of the field study revealed that 67 % of the water bodies were inhabited by beavers, 21 % of which were inhabited by large families. Only 5.6 % of the water bodies had no traces of beaver activity. Approximately 30 % of all the water bodies under study were significantly altered, and 10–13 % were almost completely excavated by beavers. The remote sensing analyses revealed that beavers digging increased the area of the water bodies by an average of 40 %, the perimeter of the water bodies by an average of 60 %, and the shoreline development (complexity) by 60 %. In total, beaver-created structures occupied 0.05 km2 in the floodplain of the upper reaches of the river, and their total length reached 30.5 km. The main factor influencing beaver digging activity was the drying of water bodies; the values of morphometric parameters altered or created by beavers increased significantly at large differences in the maximum and minimum water levels. Our results highlight the importance of ecosystem engineering activities at the regional scale in the face of climate change and provide insight into the possibility of considering beaver digging activities as an inspiration for the development of nature-based strategies.

Spatiotemporal Evolution Analysis of Surface Deformation on the Beihei Highway Based on Multi-Source Remote Sensing Data

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately −16 mm/a over the 22 km section of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 °C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze–thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone.

Zwitterionic Polymers for Biomedical Applications: Antimicrobial and Antifouling Strategies toward Implantable Medical Devices and Drug Delivery.

Poly(ethylene glycol) (PEG) is extensively utilized in biomedical applications due to its biocompatibility; however, its thermal instability and susceptibility to oxidative degradation significantly constrain its long-term effectiveness. Zwitterionic polymers, characterized by their distinctive structure, enhanced stability, and superior biocompatibility, offer a more advantageous alternative. These polymers exhibit super hydrophilicity, resist nonspecific protein adsorption, and maintain stability in biological environments due to their charge-neutral ionic nature. Zwitterionic polymers enhance anticancer drug delivery by precisely targeting tumor cells and facilitating an efficient drug release. Their inherent antifouling properties and prolonged circulation within the bloodstream render them highly suitable for redox-sensitive drug carriers, thereby augmenting the antitumor efficacy. Moreover, zwitterionic polymers markedly mitigate biofouling in implants, biosensors, and wound dressings, thereby improving both their functionality and their therapeutic outcomes. These advantages arise from the formation of robust hydration layers, which significantly enhance the hemocompatibility and inhibit the adhesion of proteins, platelets, and bacteria. Zwitterionic polymers, including sulfobetaine (SB), phosphorylcholine (PC), and carboxybetaine (CB), are increasingly employed in blood-contacting devices and as effective coating materials for implantable devices. This mini-review paper aims to explore the recent diverse biomedical applications of zwitterionic polymers and highlight their advantageous properties compared with unmodified polymers. We will cover their use in drug delivery systems, tumor targeting nanocarriers, antibiofouling and antibacterial activities in implantable devices, tissue engineering, and diagnostic devices, demonstrating how their unique properties can translate into different applications. Through this exploration, this Perspective will display the potential of zwitterionic polymers as innovative polymer materials in the field of biomedical engineering and beyond.

Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events

Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

The complexities of permafrost changes, driven by climate warming and engineering activities, coupled with challenges in data acquisition, make it crucial and challenging to accurately predict the artificial permafrost table, particularly for subgrades in high-temperature unstable permafrost regions. To address this, this study developed a hybrid machine learning model (RF-LSTM-XGBoost) for permafrost table prediction. By analyzing climate change and ground temperature data from various positions and depths along the subgrade in the Tuotuo River section of the Qinghai-Xizang Highway, the Spearman correlation coefficient method was initially used to determine the important influencing factors. Random Forest (RF), Long Short-Term Memory Neural Network (LSTM), and Extreme Gradient Boosting (XGBoost) models were used to predict the artificial permafrost table, and grid search and cross-validation methods were employed to optimize the hyperparameters of each model. A linear weighted combination method based on the minimum cumulative absolute error was utilized to merge the models, and its performance was compared with the individual RF, LSTM, and XGBoost models. Subsequently, the feature importance of the variables in the machine learning model was analyzed. The results indicated a strong correlation between artificial permafrost table changes and factors such as daily average atmospheric temperature, subgrade surface ground temperature, and subgrade surface ground heat flux during the freezing-thawing cycle. The combined model highlighted daily atmospheric temperature as the most influential predictor, followed by ground heat flux, with the surface ground temperature being less impactful. The combined model demonstrated improved predictive accuracy, with MSE, MAPE, RMSE, MAE, and R2 values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, surpassing those of individual models. This model offers a rapid, accurate, and reliable approach for permafrost table prediction, advancing subgrade stability research in challenging permafrost environments.

Performance Evaluation of HTTP, DHCP and DNS Protocols of Data Packets for Vulnerabilities Using the Isolation Forest Algorithm

In the contemporary digital landscape, network security is paramount to safeguard data integrity and prevent unauthorized access as data have been structured in the network through protocols. In term of data structure IPv6 protocol has extended data size due to the robust addresses of 128 bits as compare to 32 bits of IPv4. Due to the improved security data structure of IPv6, the study focuses on identifying vulnerabilities in HTTP, DHCP, and DNS packets using the Isolation Forest algorithm approach, a machine-learning technique designed for anomaly detection. By analyzing packet lengths, size, payload and addressing, the study visualizes normal and anomalous behavior, providing insights into potential security threats in IPv4 network structure. The results highlight the effectiveness of Goodput, Quality of service and risk as essential factors in the network, using Little's theorem analysis and the Isolation Forest in detecting anomalies across these different network protocols, offering valuable implications for network security structures, due to IoT in recent networks. The time response determination in this paper explained details information on the time the treats entered the network, the duration of the vulnerabilities within the network, leading to a certain threshold, and traffic delay factors due to deviation of packet length and other social engineering activities. Sensitive multipurpose security devices are involved; MikroTik routers were configured and installed in the network under evaluation. Which the normal DPI technique was unable to effectively and efficiently addressed. ADPI principles and operations where the needed security measures adopted to detect those vulnerabilities which were eventually addressed and have contributed to recent measures of network security.

Parent involvement in young children's engineering learning

ABSTRACT This study examined parents' experience of involvement in their children's engineering activities. A convenient sample of 24 parents, whose children were enrolled in pre-kindergarten to second grade, participated in the interviews individually. Results indicated that parents enjoyed their time with their children when engaging in engineering activities, they enjoyed the family time and they were happy to see how capable their children were. At the same time, results also revealed some challenges that parents experienced, such as selecting a topic, giving up control, and breaking up the projects into sessions. Parents reported engineering toys at home but some of them need help recognising what constitutes an engineering toy. Suggestions for supporting parents and improving their experience of involvement in their children's engineering activities are provided. Implementations in terms of carrying out engineering activities at home and at school are also included.

Preface: 6th International Conference on Information Science and Electronic Technology (ISET 2024)

We are pleased to announce that the 2024 6th International Conference on Information Science and Electronic Technology (ISET 2024) was successfully held in Venice, Italy during September 07-08, 2024. The objective of this conference is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in information science, computer applications, mechatronics, electrical engineering, and electronic technology. This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations, and to find global partners for future collaboration. There were over 50 participants attended ISET 2024. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and Q&A discussion. Their insightful speeches had triggered heated discussion in the conference. Every participant praised this conference for disseminating useful and insightful knowledge. We would like to acknowledge all of those who have supported ISET 2024. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the organizing committee for their valuable advices in the organization and helpful peer review of the papers. The Conference Scientific Committee Venice, Italy

Method of Detecting Liquid Meniscus in Capillary Using Ultraviolet Sensor

The accuracy of determining the physical and chemical properties of a liquid, in particular, assessing the viscosity of the liquid used, is considered an important technical problem in medicine, food, oil, chemical industries, as well as in other areas that determine the quality and safety of human life and activity. For example, assessing the viscosity of the liquid used is a key stage in the design of hydraulic systems. Today, the improvement of methods for determining the viscosity of a liquid is on the path to automation (digitalization) of measurement instruments and methods. In this area, the activities of metrologists and engineers are consistent with the national program «Digital Economy of the Russian Federation» adopted in 2017. The article discusses the capillary method for determining the kinematic viscosity of a liquid as the most accurate of all possible methods at present. The method is based on determining the time of liquid flow between two marks, which are applied to the walls of a glass capillary viscometer forming a measuring tank. One of the main problems of this method is that all stages of measurement are performed by a person, even recording the moment when the liquid meniscus crosses the mark, which is a violation of one of the key principles of process automation – independence of execution, which only implies monitoring the operation of the system. Since ready-made solutions do not fit the laboratory’s measurement tasks, the management decided to develop and conduct a study of an information and measuring system that will carry out the process of detecting the liquid meniscus at the mark level, as well as recalculate the measured time interval into the kinematic viscosity value. The key decision in this development is the choice of a sensor for detecting the liquid meniscus. Based on the literature review, it was decided to use a photoelectric sensor. A study of the transmission spectra of liquids used in this method was conducted to select the wavelength at which the sensor will operate. Thus, a type of photoelectric sensor designed to operate in the UV range has been selected to automate the method of detecting the liquid meniscus when it crosses a mark applied to a capillary tube. The article is addressed to metrologists who perform verification and calibration of glass capillary viscometers, specialists in the field of experimental and theoretical viscometry. The method proposed by the authors can become the basis for further improvement of the method for measuring the kinematic viscosity of a liquid.