Developed Model Research Articles

A novel robust MPC scheme established on LMI formulation for surge instability of uncertain compressor system with actuator constraint and piping acoustic

This paper presents a new control method for surge instability of the compressor system. Due to the importance of the active control approach in improving the efficiency of the compressor system, a new scheme based on the model predictive control (MPC) technique has been considered to control surge which gives the benefits of control signal optimization by considering the constraints on states and input. Because of designing a novel MPC by using of linear matrix inequality scheme, the optimization problem is solved in less time and with less complexity. The proposed method is able to consider the limitation of the close coupled valve actuator and also covers the uncertainty on the model. In addition, the developed model describes the nonlinear behaviour of the compressor system and handles the effects of the pipe on surge instability. Using Lyapunov theory, the stability of the closed-loop system under the proposed robust active controller is shown. The simulation results show the high pressure and high efficiency operation of the system under study in different operating conditions.

Application of an offline grey box method for predicting the manoeuvring performance

The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship.

Exploring the Biochemical Mechanisms of Fluoroquinolone Compounds against Tuberculosis by Utilizing Molecular Docking and Quantitative Structure-amino Acid Relationship

Background: Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb). It is one of the leading causes of death of 1.5 million people each year. TB can be treated by directly observed treatment short course (DOTS), but due to multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis, consequences can be devastating if the single DOTS dose is missed by the patient. MDR and XDR-TB require much more attention and time to control the infection. The longer period of tuberculosis treatment has side effects and it is expensive. Objectives: This alarming condition demands the development of novel processes to diagnose the disease in its early stage as well as to produce more promising antimicrobial chemotherapeutics. The current study aimed to explore molecular mechanisms involving docking simulation-based quantitative structureamino acid relationship (QSAAR) in order to have a better understanding of the interactions between the fluoroquinolones and Mtb DNA gyrase. Methods: In this study, 24 fluoroquinolone (FQ) compounds present in the literature were selected and docked against the Mtb DNA gyrase. Further, the relationship between the minimum inhibitory concentration of the compounds and interacting amino acids was assessed using QSAAR. Results: The study has established a novel method of formulating a quantitative structure-amino acid relationship. A significant correlation (R-value=0.829) between biological activity and the docked amino acid residues responsible for producing anti-tubercular activities has been obtained. Conclusion: The predicted residues captured in the developed model have been explored to report the Mtb virulence.

Beyond Traditional Methods: Utilizing a Hybrid SIRe Model for Optimal Counselling Skill Development in Practicum Courses

Beyond Traditional Methods: Utilizing a Hybrid SIRe Model for Optimal Counselling Skill Development in Practicum Courses

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.

MHD-FiT: MHD-based dynamic reconstruction of tokamak plasma configuration

This paper introduces an innovative method for reconstructing 2D magnetic flux contours and plasma parameters of dynamically moving tokamak plasmas. While conventional methods like EFIT, based on the Grad–Shafranov equation, are suitable for plasma equilibria with a single magnetic axis, our approach utilizes the MHD equations and shows promise for tokamak plasmas in motion or containing multiple magnetic axes, which may not strictly adhere to plasma equilibria. By utilizing limited edge magnetic probe measurements, our developed model successfully reconstructs the time evolution of two merging plasma toroids in the TS-6 experiment. A comparison with direct 2D magnetic probe measurements in a low β regime reveals a reconstruction error of approximately 3%.

Pengembangan Media Pembelajaran Herbarium Barcode pada Materi Pteridophyta

Generation Z students like things related to smartphones as well as practical and interesting teaching materials. This research aims to determine the validity value and students' responses to barcode herbarium teaching material products on pteridophyta material. Researchers use the R&D method with the Borg and Gall development model, including stages: 1) research and data collection; 2) planning; 3) product development; 4) initial field trials; 5) trial revision; 6) field trials; and 7) improvement of the resulting product. The tools used for data collection were validation questionnaires and student responses. The assessment results show that the media that has been developed is very valid in the media aspect (95%) and material (90.47%), and valid in the language aspect (80%). Students gave very good responses to the herbarium barcode learning media in small-scale trials (83.97%), as well as large-scale trials (84.56%), it was concluded that the media developed was effective and received very good responses from students.

Securing China’s rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models

Ensuring the security of China’s rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil properties and agricultural statistics from 2000 to 2017. The research evaluates six artificial intelligence (AI) models including machine learning (ML), deep learning (DL) models and their hybridization to predict rice production across China, particularly focusing on the main rice cultivation areas. These models were random forest (RF), extreme gradient boosting (XGB), conventional neural network (CNN) and long short-term memory (LSTM), and the hybridization of RF with XGB and CNN with LSTM based on eleven combinations (scenarios) of input variables. The main results identify that hybrid models have performed better than single models. As well, the best scenario was recorded in scenarios 8 (soil variables and sown area) and 11 (all variables) based on the RF-XGB by decreasing the root mean square error (RMSE) by 38% and 31% respectively. Further, in both scenarios, RF-XGB generated a high correlation coefficient (R2) of 0.97 in comparison with other developed models. Moreover, the soil properties contribute as the predominant factors influencing rice production, exerting an 87% and 53% impact in east and southeast China, respectively. Additionally, it observes a yearly increase of 0.16 °C and 0.19 °C in maximum and minimum temperatures (Tmax and Tmin), coupled with a 20 mm/year decrease in precipitation decline a 2.23% reduction in rice production as average during the study period in southeast China region. This research provides valuable insights into the dynamic interplay of environmental factors affecting China’s rice production, informing strategic measures to enhance food security in the face of evolving climatic conditions.

Modeling the economic performance of small ruminant pastoralist flocks and financial impact of changes in reproductive performance and mortalities in Kajiado county, Kenya

This study investigated the economic performance of pastoralist small ruminant flocks in Kajiado county, Kenya, and the financial impact of mortalities and changes in performance indicators. A survey of 129 pastoralist small ruminant flocks captured production and economic data for a 12-month period via interview. Simulation models were then developed for flocks with different performance levels and trading activities. Their gross margins, and the financial impact from mortalities, were estimated. Sensitivity analyses were conducted for select performance indicators to assess their relative economic importance. On average, pastoralists who were not purchasing supplementary feed (79% of flocks) had gross margins of KSh 3,016 – KSh 3,123 per reproductive female. Goat production generated 2.43–2.51 times greater returns than sheep production. High efficiency mixed flocks achieved 2.04–2.06 times greater returns than medium efficiency flocks, and 12.19–14.25 times greater returns than low efficiency flocks. Pastoralist-only flocks had 3.82–4.01 times greater returns than pastoralist-traders. Financial losses due to mortality were on average 17.81% of the starting flock value. Fecundity and abortion rates in sheep, and parturition rate in goats, had the greatest economic impact. The developed models could support decision-making toward improving the economic performance of pastoralist flocks and the evaluation of interventions and external shocks.

Minor Loss Coefficient for Abrupt Section Changes in a Cylindrical Pipe Using a Numerical Approach

Abrupt section changes are a classic problem in the study of flow in cylindrical ducts or pipes. For its analysis, there are a wide set of exiting data from previous studies, among which some authors stand out and will be mentioned. Those previous works have been used to obtain reliable results for the resolution of section changes along a pipe, either due to cross area increases or reductions on a 1D basis. It is also known that a numerical 2D axisymmetric simulation (CFD) could find a consistent result compared to experimental data in almost all fluid flow fields. The main novelty of the present study is the development of a simple numerical approach used to solve the minor loss calculation. Firstly, a theoretical analysis is developed, and then the results of the numerical simulations carried out on the behavior that affects the water and air flow rate in an abrupt section change, for both contraction and expansion problems, are presented. In both cases, the results are analyzed with different meshes (discretizations) and turbulence models. Finally, the obtained numerical results are compared with those in the technical literature. Also, a theoretical approach is shown in order to show a whole frame of the discussion. The core results are the loss coefficient evolution as a function of the section change both for the sudden contraction and the expansion of a pipe flow. As the results follow the existing experimental values, it is concluded that the developed model provides a feasible and quick design tool to analyze possible geometrical changes without the need for further experiments.

A generative design-based optimization model for multi-objective construction site layout planning

PurposeGiven the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.Design/methodology/approachA novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.FindingsThe results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.Practical implicationsThe findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.Originality/valueA new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

Development and calibration of a fast flow model for solid oxide cell stack internal manifolds

Due to the commercialization of solid oxide cells (SOC) progressing at an accelerated pace, computationally inexpensive SOC models adapted to the iterative nature of the engineering process are in increasing demand. Flow simulation in the stack is especially challenging in this regard because detailed computational fluid mechanic models are computationally demanding, while simplified models rely on pressure loss coefficients and friction factors not readily available in the literature. In this study, a computationally inexpensive algebraic model of an SOC stack internal manifold is developed and calibrated for laminar flow conditions. Thereby, pressure loss coefficients and Darcy friction factors are determined for a broad range of operating conditions through symbolic regression of Navier–Stokes flow simulation results of stacks of 20 to 40 cells. The derived Darcy friction factors for the inlet and outlet manifolds prove to be of particular importance, as they deviate strongly from the expressions assumed in similar modeling studies. The predictive power of the developed model is demonstrated by providing accurate predictions of the flow distribution in the stack, even outside of the calibration window.

FORECASTING SHORT-TIME LOAD DEMAND BY NON-LINEAR AUTOREGRESSIVE WITH EXTERNAL INPUTS

Accurate estimation of the load demand in each period could be a useful approach to implement necessary policies for better management in the load supply-consumption chain. Hourly load demand is a function of external parameters (such as calendar data) and internal parameters (load demand). Nonlinear Autoregressive with External Inputs (NARX) was selected because of the ability to use external and internal inputs. The model was trained and evaluated using the hourly load demand data for Shahrekord city in a four-year period. The novelties of this study include (i) identifying essential factors that have a significant effect on load demand and (ii) Reduce model inputs (parsimonious model). By analysis of variance (ANOVA), external factors were selected to increase the accuracy of the model in addition to simplicity. External inputs of the developed model included calendar and holiday data. Unlike other existing models, the number of inputs were reduced but the days classification was increased from two groups (working day and holiday) to three groups (working day, semi-holiday and holiday). These changes caused, in addition to increasing the accuracy of the model, the number of inputs to be reduced. The mean absolute percentage error average (MAPE) is 0.983%, which shows higher accuracy than similar models.

To end the eternal Wednesday afternoon: The search for the foundations of human existence in “The Last Gentleman” by Walker Percy

The article examines the philosophical issues in Walker Percy’s novel “The Last Gentleman”, revealed in the author’s modeling of the plot development and in creating character images with conscious reliance on the provisions of Kierkegaard’s and Marcel’s existentialist philosophy. The main problem in the book centers round the desire of a person to find his own way in a situation of existential choice, the search for faith as the significant basis for human existence. The protagonist, Will Barrett, is put by the author into the situation of absolute freedom and is presented with the choice of various conceptual models of life, personified in the images of the people surrounding him: his father’s stoic ethics, Rita’s secular humanism, Val’s uncompromising religious faith, Sutter’s desperate nihilism. The turning point in Will’s life comes with his falling in love with Kitty: it changes his way of thinking, laying the foundation for the subsequent discovery of God. The article discloses the symbolic meaning of the character’s way in the novel: from a crowded metropolis to the open wilderness of southwestern deserts, from feeling lost to the discovery of faith, from loneliness to the search for the significant Other. The dynamic movement becomes the most important element in the novel, the character’s actual travelling through the country acquires the features of a spiritual journey to truth. Despite the absence of Will’s crucial inner transformation at the end of the novel, the author, in accordance with Marcel’s philosophical concept, gives paramount importance to the journey itself, regarding it as the main event that gives meaning to human life.

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength.

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash-Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott's index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model's performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

Development of lightweight intrusion model in Industrial Internet of Things using deep learning technique

Nowadays, the IDS is being used in conjunction with the IIoT system to reduce the security risk, but on the other hand, the false rate of the IDS is very high. Therefore, in this work, a pre-training method, making use of both a deep neural network and a deep auto-encoder, has been proposed for the quick prediction of assaults with increased accuracy and a reduced false rate. The replicas were expanded using hyperparameter optimization (HPO) techniques. The proposed model delivers an alternative to deep learning construction replicas through an HPO procedure incorporating the Archimedes optimization algorithm. This optimization technique can be used to determine the hyperparameter value and the ideal categorical hyperparameter combination for improved detection performance. The DS2OS dataset is used alongside numerous other indicators to evaluate the efficacy of the developed model. The various existing techniques of assault detection have also been considered to show the effectiveness of the proposed model. Through the comparative evaluation of the outcomes, it is shown that the developed model provides better performance than the other existing models. Eventually, it is discovered that the suggested security paradigm is successful in fending off a variety of internal and external threats.

Investigation of Debye temperature and temperature-dependent EXAFS cumulants of Zn, Zr, α-Ti, Ru and Hf metals

In this work, the anharmonic Einstein model is developed to determine the Debye temperature and investigate the temperature effects on the extended x-ray absorption fine structure (EXAFS) cumulants of hexagonal close-packed (hcp) metals. We have derived the analytical expressions of the anharmonic effective potential, the effective force constant, the Debye temperature and the first four EXAFS cumulants as a function of axial ratio e = c/a. Numerical calculations have been conducted for hcp Zn, Zr, α-Ti, Ru and Hf metals up to temperature 800 K. Our findings indicate that the anharmonicity of thermal lattice vibrations significantly influences the EXAFS cumulants, particularly at high temperatures. Ru atoms have the strongest coupling force causing a phenomenon that Ru lattice shows a smaller thermal disorder, and Zn has a greater thermal disorder. Additionally, we highlight the significant contributions of thermal disorder to the mean-square relative displacement at high temperatures due to thermal lattice vibrations. Moreover, our Debye temperatures derived from the developed model align reasonably well with those reported in previous studies.

Looking at Sustainability More Fundamentally: Quest for a Holistic Worldview

An in-depth analysis of earlier work for sustainable development has revealed that well-intentioned and rigorous efforts globally towards achieving sustainability have at best achieved marginal results. The problem appears to be certain inherent contradictions within the contemporary paradigm of development. The article explores these unaddressed contradictions and argues that the present model of development is fundamentally incapable of resolving the prevalent problems of contemporary times. Hence, a strong need is felt to relook at sustainability more fundamentally and work towards a paradigm shift. Such a shift in the paradigm of development would put emphasis on sustainable consumption—a shift from sense gratification to actual human happiness and from treating nature as a resource for consumption to appreciating the inherent balance based on interconnectedness in nature.

Amílcar Cabral, Colonial Soil and the Politics of Insubmission

This article discusses the concept of ‘insubmission’. This concept is the cornerstone of Amílcar Cabral’s critical theory. Introduced in his early agronomic writings, it refers to the human species’ refusal to submit to the nature of which we are always a part. The context is the anticolonial critique of traditional European humanism. Insubmission is Cabral’s response to the dehumanizing effects of colonialism and the environmental impact of anthropocentric extractivism that accompanies it. As a linchpin in Cabral’s theoretical framework, insubmission serves to structure and impart meaning to other concepts. Notably, it provides fresh insights into the multifaceted concept of ‘resistance’. Cabral underscores the imperative of combating dehumanization through physical fortitude (physical and armed resistance), intellectual resilience (cultural resistance), and institutional strength (political resistance). Additionally, it emphasizes the necessity of averting environmental catastrophes through a socio-economic development model (economic resistance) underpinned by a resolute ethical commitment to responsible soil conservation practices.

Will the Low-carbon City Pilot Policy Achieve Regional Emission Reduction?

In order to address climate change and promote green and low-carbon development, the Chinese government has implemented a pilot policy for low-carbon cities since 2010 and has continuously expanded the scope of the pilots. Taking the second batch of low-carbon pilot policies as an example, this paper uses difference in difference method to study the impact of low-carbon pilot policies on reducing urban carbon emission intensity based on the panel data of prefecture level cities from 2006 to 2019. It is found that the construction of low-carbon cities has a positive effect on reducing carbon emissions,and this conclusion is still valid under the robustness test. Based on this conclusion,this paper argues that China should further promote low-carbon pilot policies and actively explore the development model of low-carbon cities, especially to improve the livable and green urban environment.