Design Procedure Research Articles

Discrete-Time Sliding Mode Control Strategies—State of the Art

Variable structure control systems are known to provide a high level of robustness to external disturbances and modeling uncertainties with comparably low computational complexity. Thanks to these features, they have found applications in various fields, such as power engineering, electronics, robotics, and aviation. In recent decades, the field of sliding mode control has developed significantly. Therefore, this study aims to discuss the basic concepts and design methodology of such strategies. Although in the 20th century, continuous-time sliding mode control has been the center of the control engineering society’s attention, it has certain major shortcomings. In particular, such control schemes result in undesirable high-frequency oscillations when applied digitally. Therefore, the more recent discrete-time approach to sliding mode control has gained recognition in the 21st century. Since the introduction of discrete-time sliding mode control strategies, the reaching law-based controller design method has been designed, within which two main paradigms may be named: the switching type and the nonswitching type quasi-sliding mode. This paper presents a broad review of the discrete-time sliding mode control strategies, starting from the definition of sliding mode through the controller design procedures and up to potential applications. The aim of this study is to provide an up-to-date state of the art and introduce readers to the newest trends and achievements in the field of sliding mode control.

Evaluation of Health Education During Covid 19 in theCity of Masvingo

This study made an evaluation of health education during COVID 19 in the City of Masvingo.The literature of the study included the background to the study, statement of the problem, major research question, sub-questions, objectives, and significance of the study, delimitations,limitations, and definition of terms. The literature reviewed in this study was drawn based on the research sub-questions which focused on the evaluation of health education during COVID 19 in the City of Masvingo. The study discusses the research design, the population, the sample and sampling procedures which were used, the research instruments and explains data presentation and analysis procedures. The research design, the instruments and the samples adopted were justified. These include the selection of research design in which the researcher considered different views of other authorities which guided the researcher to select and use both quantitative and qualitative research. The sample population consisted of fifty (50) purposive selected participants chosen in Masvingo Urban. Data collection procedures highlighted how the researcher collected data using different instruments which included a questionnaire and interviews also the advantages and disadvantages were indicated. The presentation of data was guided by the research sub questions. Data was analysed and discussed in relation to literature. The findings of the study revealed that COVID-19 is not only an individual health issue but also engages caregivers, families, extended social networks, neighbourhoods, communities, health systems and organizations and governing/political systems. Structural changes in health promotion are requisite to sustain health during the COVID-19 and future pandemics. However, the researcher recommended that campaigns should elicit the help of religious and civil society leaders for maximum effect. In addition, governments should suspend all international travel to or from the most-affected countries, and quarantine citizens who have travelled to or through those areas for at least two weeks.

Preparation, Characterization and Quantification of Mangiferin in Mangiferin-loaded Microspheres

Introduction: Mangiferin shows great promise as a potent drug for a wide variety of diseases. Its low bioavailability and poor water solubility, however, restrict its therapeutic use. Methods: The aim of the study goal was to systematically design a UV-spectroscopy method for mangiferin quantification in analytical samples that is quick, easy, and very sensitive. In order to validate the method, UV spectroscopy was used to check for specificity, accuracy, precision, and linearity. The models were constructed with Design ExpertⓇ V.10 and optimised using Box- Behnken Design (BBD), a three-factor and three-level procedure. Results: The devised method demonstrated good levels of sensitivity, specificity, accuracy, and precision, according to the results. The absorbance and concentration showed a strong linear relationship in the given 5-25 μg/ml range for several wavelengths, with correlation coefficients of 0.989, 0.982, 0.905, and 0.896 at 364.5, 370, 378, and 265nm, respectively. The intraday precision was 0.788 at 364.5 nm, 0.801 at 370 nm, 0.739 at 378 nm, and 0.721 at 256 nm for the concentration of 20 μg/ml. Particle size and entrapment efficiency, two dependent variables in the microspheres formulation, were best suited by the models derived when the observed responses were fitted to the design. Microspheres had a high entrapment efficiency and were microsized. Interestingly, drug-encapsulated microspheres containing mangiferin maintained the spherical shape. Conclusion: The existence of well-resolved peaks and good recovery of mangiferin in the analytical method for analysis makes it ideal and acceptable for further use.

Census Bureau Household Panel

The Census Bureau Household Panel is a nationally-representative, address-based, probability-based internet panel (including non-internet households) designed to improve representativeness, significantly reduce burden on sampled households, and promote high-frequency data meeting the immediate needs of the government and public. The Panel facilitates near real-time analysis of national events that may impact social, economic, or demographic characteristics of the population. It supports research to improve surveys, including testing content changes, alternative methods for enhancing data with administrative and other data sources, and adaptive survey design procedures.

Polyester mooring system design and evaluation of a semi-platform in south China sea

Polyester rope offers numerous advantages over traditional steel catenary mooring systems and is considered an appealing option for deep-water mooring systems. In this paper, the design and evaluation procedure of the polyester mooring system for a semi-submersible platform located in the South China Sea is presented. A fully coupled numerical model of the semi-submersible platform, including all risers and mooring lines, has been established and calibrated through wave basin testing. To simulate the elongation behavior of polyester, a static-dynamic stiffness model is employed, and the corresponding procedure for mooring evaluation is established to simulate the mooring response under extreme environmental conditions. A comprehensive fatigue analysis is also conducted for the polyester mooring system using time domain dynamic theory. The effects of Vortex-Induced Motion (VIM) on mooring fatigue damage are also considered. The results indicate that the polyester mooring system could be safely operated at the target offshore field throughout its service life. Additionally, model test calibration is a crucial procedure during the entire mooring evaluation process, and the numerical model should be adjusted appropriately to accurately reflect the dynamic behavior of the coupled system. This study also illustrates that the stiffness of the rope plays a crucial role in polyester mooring design and global performance calculations. The proposed evaluation methodology can provide a foundation for the design of polyester mooring systems and for evaluating their safety and reliability in engineering practice.

Sufficient conditions on input-to-stabilisation of time-varying semi-linear systems with disturbances in Hilbert spaces

In this paper, we investigate the problem of input-to-state stabilisation of time-varying semi-linear systems with disturbance in Hilbert spaces. An original adaptive input-to-state stabilisation strategy to derive this exceptional problem under the nonlinearities modelling is proposed using the Lyapunov method. Since all the states are not measurable, a Luenberger observer is designed to estimate unmeasurable states for input-to-state stabilisation. Thus, in the design procedure, first, a Luenberger observer is designed, and then the adaptive output feedback controller is constructed via the estimated states and adaptation law. Moreover, we give a sufficient condition, in terms of a perturbation term, to guarantee the input-to-state stability of the time-varying semi-linear systems under a stabilising nonlinear feedback law. An example of a controlled reaction-diffusion equation is given.

Information Modeling Implementation in PAO “Rosneft” as Challenge of Digitization in Construction Industry

Key goals of construction industry modernization and quality improvement are the introduction of information modeling which is an effective tool for increasing the enterprise competitiveness through the quality of design and construction, resource optimization and efficiency of interaction between process participants throughout the entire life cycle.The article presents information modeling (3D design) in PAO “Rosneft” with respect to the tasks of ensuring adaptation to design procedure in the company and the transition to full-scale 3D design. The choice of software is justified, real examples of completed pilot projects using information modeling are described, and information modeling implementation are presented. The experience in information modeling implementation helps companies to choose one or another software, and become familiar with the process at PAO “Rosneft”. As a result of the digital transformation of design processes, a model of the life cycle management system of capital construction project is developed, the basis of which is its information model. In addition, the main problems that require solutions for the successful information modeling implementation are identified, i.e., shortcomings of domestic software, shortage or absence of highly qualified personnel, unwillingness of customers to work with technical information, efficiency decrease of the organization due to the introduction of a new approach to project implementation into existing processes, resistance from employees, customers and contractors of construction, and installation works.The information modeling introduction in the PAO “Rosneft” allow to achieve goals for im[1]port substitution of technologies and prepare a worthy response to global geopolitical challenges.

Editorial: Inaugural Issue of the International Journal of Bridge Engineering, Management and Research (IJBEMR)

It is my pleasure to share with you the inaugural issue of the newly established International Journal of Bridge Engineering, Management and Research. The International Journal of Bridge Engineering, Management and Research (BER) is a fully open access online-only journal. It serves as a forum for the publication of scientific and technical papers related to all aspects of bridge engineering and management, including structural, seismic, hydraulic and geotechnical risk analysis, structural health monitoring, static and dynamic assessments, structural retrofitting, and resiliency enhancement. The journal also publishes research papers on tunnels focusing on the areas mentioned above. Technical research papers and case studies dealing with innovative technological or computational solutions to bridges and tunnels, as well as experimental methods and novel design and theoretical analysis procedures are strong candidates for publication in the journal. This inaugural issue features five high quality papers in bridge engineering. Through this issue, I am also inviting you to register with the journal and submit your research manuscripts to the journal. We are committed to expedited and thorough peer review and publication of your research.

Experimental study of a novel prestressed precast concrete beam-to-column joint based on plastic hinge relocation

To avoid the difficulties and high costs associated with traditional prestressed dry joints in construction, a novel post-tensioned precast concrete frame based on plastic hinge relocation (PPFBP) beam-to-column joint is proposed in this study. The precast columns and beams were integrated solely through the application of post-tensioned (PT) strands. The combined use of the haunched beams and the vertical slots for plastic hinge relocation simplifies the connection of the joint and enhances assembly efficiency. The mechanism of the proposed joint is analyzed first, followed by the development of a complete design procedure that complies with the criteria for plastic hinge relocation and self-centering. Three full-scale interior beam-to-column joints were designed and tested under cyclic loading to investigate the seismic performance in hysteresis curves, ductility, energy dissipation capacity, and residual displacement. The results indicate that the bearing capacity and ductility of the proposed joint are similar to those of cast-in-place structures, with the advantage of minimal residual displacement, enabling rapid post-earthquake repairability. The hysteresis curve of PPFBP joint with plastic hinge relocation exhibits a flag shape, indicating significant self-centering capability and energy dissipation ability. The cumulative energy dissipation of PPFBP joint with plastic hinge relocation is much higher compared to that without it, achieving a maximum equivalent viscous damping coefficient of about 15 %, which is similar to that of hybrid dry joints. The insufficient strength of the prestressed grouting material led to bond slippage in the specimen with bonded PT strands, exhibiting a hysteresis response similar to that with partially bonded PT strands. Finally, the prediction of the bearing capacity in PPFBP joint is consistent with the experimental results, validating the effectiveness of the proposed design method.

Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Decentralized control of nonlinear complex systems

Abstract In the paper, a novel approach to decentralized controller design for nonlinear systems is introduced. The proposed method is based on the relationship between the stability of complex nonlinear systems and the stability of their subsystems. The design procedure of the decentralized controller consists of three steps. In the first step, the stability of the complex nonlinear system is calculated. In the second step, stability conditions at the subsystem level are obtained such that guarantee the stability of the complex nonlinear system. Finally, in the third step, a controller design method is used to ensure that the subsystem stability conditions obtained in the second step are met. As an example, to better understanding the proposed method two simple nonlinear models are used to demonstrate the effectiveness of the proposed method.

Simultaneous interval estimation of actuator fault and state for a class of nonlinear systems by zonotope analysis

In this paper, an actuator fault and state interval estimation method for a class of nonlinear systems is proposed by integrating observer design and zonotope analysis. For the considered systems, we present a novel unknown input observer structure with broad applications. The design procedure is based on H∞ method to decrease the influence of unknown but bounded process disturbances and measurement noise. Moreover, a novel interval estimation method is presented based on zonotope analysis to obtain tighter intervals. Numerical simulations of a quadruple-tank system are conducted to assess the performance of the proposed approach.

Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

Abstract Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

Adaptive distributed fault estimation observer design for nonlinear time-delay multi-agent systems with directed graphs

This paper focuses the development of an adaptive observer-based distributed fault estimation observer (DFEO) for multi-agent nonlinear time-delay systems under a directed communication topology. The process involves constructing a fault estimation observer for each agent based on their relative output estimation errors. An adaptive DFEO design is proposed for multi-agent systems with a directed communication topology. Furthermore, investigating the effect of faulty agents on the criteria of fault detection on healthy agents. The proposed design is represented with Linear Matrix Inequalities (LMIs) to establish a systematic design procedure. The study also includes simulation results to demonstrate the effectiveness of the proposed design techniques.

Downburst wind loading on bridge decks through an analytical model

A mathematical model is proposed for estimating the wind loads caused by the passage of a downburst through horizontal structures, such as bridge decks. The model simulates the three-dimensional flow fields of stationary and environmentally-driven downbursts. It is based on radial and vertical shaping functions of the velocity components, following a path developed for aircraft structures. The radial shaping function of the horizontal velocity is defined with attention to reproducing the local increase in velocity observed in the average field measurements. In addition, the coefficients of an existing empirical vertical shaping function of the horizontal velocity are re-calibrated to better fit the available field data. The shaping functions of the vertical components are derived by imposing mass conservation.The model allows for predicting the large angles of attack on the decks during the passage of downbursts with small to medium diameters. The occurrence of such large angles of attack also implies a change in the aerodynamic design procedure of bridge decks, for which wind tunnel tests are commonly conducted only for angles of attack in the range of about ±10°. The aerodynamic coefficients of a NACA profile and a pseudo-rectangular section are used due to the absence in the literature of aerodynamic coefficients of bridge decks for very large angles of attack. Several runs are conducted by varying the downburst properties and the deck height above the ground to provide results useful for the aerodynamic design of bridges. The results confirm the key role played by the vertical component of the velocity in developing the wind loads on the decks during the passage of a downburst. Non-negligible wind loads are obtained, which are not predictable with the existing approaches. The need for conducting dynamic analyses of flexible bridges is also emerging.

Variation-aware automated design and optimization of sub-1V bandgap voltage reference

A robust systematic gm/ID-based design procedure for a CMOS low-voltage bandgap reference is introduced. The proposed approach is technology node independent, and it eliminates invoking the simulator in the loop by using precomputed lookup tables (LUTs) generated once. The proposed methodology is capable of addressing the impact of PVT corners and random mismatch. The proposed procedure is verified against Spectre simulations and yields very accurate results. Moreover, the bandgap reference automated synthesis procedure is fully vectorized, enabling the concurrent synthesis of multiple design points in a short time. As a result, large datasets can be generated to span the whole design space, which enables global optimization. Next, local optimization algorithms can be utilized to quickly determine the degrees of freedom of an optimal design point that meets a set of specifications. The speedup of the proposed methodology is around 140x compared to simulation-based optimization in addition to accomplishing better results.

Enabling High-Power Conditioning and High-Voltage Bus Integration Using Series-Connected DC Transformers in Spacecrafts

This article proposes a photovoltaic power processor for high-voltage and high-power distribution bus, between 300 V and 900 V, to be used in future space platforms like large space stations or lunar bases. Solar arrays with voltages higher than 100 V are not available for space application, being necessary to apply power conversion techniques. The idea behind this is to use series-connected zero-voltage and zero-current unregulated and isolated DC converters to achieve high bus voltage from the existing solar arrays. Bus regulation is then achieved through low-frequency hysteretic control. Topology description, semiconductor selection, design procedure, simulation and experimental validation, including tests in vacuum and partial pressures, are presented.

Parametric design methodology for yoke-magnetization in magnetic flux leakage detection systems

Magnetic flux leakage (MFL) testing is a widely employed non-destructive testing (NDT) method, particularly for ferromagnetic materials. This paper proposes a systematic design procedure for the yoke-magnetization component in MFL systems utilizing permanent magnets. The dimensions of the yoke-magnetization are parameterized, and the design methodology is formulated in the form of analytical equations based on fundamental engineering principles and optimization techniques. The proposed design procedure outlines all the required parameters and metrics to establish a yoke-magnetization configuration tailored for a specified application, thereby facilitating scalability. The theoretical framework identifies the optimal operating point that maximizes leakage flux while minimizing the magnetic field amplitude. A genetic algorithm is employed to minimize the dimensions of the permanent magnets while maintaining a sufficient magnetic field strength for specimen magnetization. An experimental setup is constructed to validate the accuracy of the proposed design procedure. The experimental results exhibit consistency with finite element method (FEM) simulations conducted using a case study. Specifically, the findings reveal that the thickness of the magnetic bridge significantly impacts the leakage fluxes at defect sites within the specimen. When the thickness of the magnetic bridge is 10 mm, slightly smaller than the optimal thickness of approximately 11 mm, the leakage flux at the defect site leaks out towards the surrounding air insignificantly. In contrast, a thickness of 15 mm is observed to strongly improve the leakage flux at the defect site.

Application to novel smart techniques for decarbonization of commercial building heating and cooling through optimal energy management

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.

In-situ halloysite functionalization of anionic hybrid gels possessing easily implemented site-specific (alkyl)amide and carboxyl groups

Design of functional materials with tunable properties based on anionically-modified copolymer network of poly (N-isopropyl acrylamide-co-methacrylic acid) P(NIPA-co-MA) containing linear polyacrylamide and various amounts of Halloysite nanotubes (Hal) was presented using a facile route of solution casting. The organic/inorganic nanostructures based on thermo-responsive PNIPA were constructed to create hybrid materials via in situ polymerization with different Hal-loading and then used for the removal of cationic methylene blue (MB) dye from aqueous solutions. To achieve the desired mechanical property, the optimum composition of hybrids was determined following a simple and cost-effective synthesis procedure. In situ one-pot polymerization was promoted by semi-IPN formation, following a much simpler route than traditional preparation approaches for stable cross-linked hybrid formation. The swelling of hybrids decreased by 2.9-fold with loading of 5.40 % Hal, confirming the hydrogen bonding interactions between Hal and PAAm/P(NIPA-co-MA) network. The mechanical properties of Hal-doped hybrid gels showed an increase–decrease tendency with increase of Hal-content from 0.80 to 5.40 % (w/v) due to the high aspect ratio of Hal and strong secondary interactions between Hal and PAAm/P(NIPA-co-MA) chains. The effective cross-link density for Hal-doped hybrids was expressed by a cubic polynomial as a function of Hal concentration. Temperature-sensitive swelling results showed that the advantage of sustained release property of Hal can be combined with excellent controllability of PNIPA-based network. Adsorption results of MB onto Hal-doped hybrids bearing methacrylic acid moieties demonstrated their potential as efficient adsorbents for the removal of cationic dyes. A new route offered by the proposed procedure for design of hybrids containing “green” one-dimensional nanofillers creates an innovative perspective on robust hybrid structures for practical applications.